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from cluster_experiments.inference.analysis_plan import *

+ + +
+ + +
+ + + + +
+ + + + + + + + + +
+ + + +

+ +AnalysisPlan + + + +

+ +
+ +

A class used to represent an Analysis Plan with a list of hypothesis tests and a list of variants. +All the hypothesis tests in the same analysis plan will be analysed with the same dataframe, which will need to be passed in the analyze() method.

+

Attributes

+

tests : List[HypothesisTest] + A list of HypothesisTest instances +variants : List[Variant] + A list of Variant instances +variant_col : str + name of the column with the experiment groups +alpha : float + significance level used to construct confidence intervals

+ +
+ Source code in cluster_experiments/inference/analysis_plan.py +
class AnalysisPlan:
+    """
+    A class used to represent an Analysis Plan with a list of hypothesis tests and a list of variants.
+    All the hypothesis tests in the same analysis plan will be analysed with the same dataframe, which will need to be passed in the analyze() method.
+
+    Attributes
+    ----------
+    tests : List[HypothesisTest]
+        A list of HypothesisTest instances
+    variants : List[Variant]
+        A list of Variant instances
+    variant_col : str
+        name of the column with the experiment groups
+    alpha : float
+        significance level used to construct confidence intervals
+    """
+
+    def __init__(
+        self,
+        tests: List[HypothesisTest],
+        variants: List[Variant],
+        variant_col: str = "treatment",
+        alpha: float = 0.05,
+    ):
+        """
+        Parameters
+        ----------
+        tests : List[HypothesisTest]
+            A list of HypothesisTest instances
+        variants : List[Variant]
+            A list of Variant instances
+        variant_col : str
+            The name of the column containing the variant names.
+        alpha : float
+            significance level used to construct confidence intervals
+        """
+
+        self.tests = tests
+        self.variants = variants
+        self.variant_col = variant_col
+        self.alpha = alpha
+
+        self._validate_inputs()
+
+    def _validate_inputs(self):
+        """
+        Validates the inputs for the AnalysisPlan class.
+
+        Raises
+        ------
+        TypeError
+            If tests is not a list of HypothesisTest instances or if variants is not a list of Variant instances.
+        ValueError
+            If tests or variants are empty lists.
+        """
+        if not isinstance(self.tests, list) or not all(
+            isinstance(test, HypothesisTest) for test in self.tests
+        ):
+            raise TypeError("Tests must be a list of HypothesisTest instances")
+        if not isinstance(self.variants, list) or not all(
+            isinstance(variant, Variant) for variant in self.variants
+        ):
+            raise TypeError("Variants must be a list of Variant instances")
+        if not isinstance(self.variant_col, str):
+            raise TypeError("Variant_col must be a string")
+        if not self.tests:
+            raise ValueError("Tests list cannot be empty")
+        if not self.variants:
+            raise ValueError("Variants list cannot be empty")
+
+    def analyze(
+        self,
+        exp_data: pd.DataFrame,
+        pre_exp_data: Optional[pd.DataFrame] = None,
+        verbose: bool = False,
+    ) -> AnalysisPlanResults:
+        """
+        Method to run the experiment analysis.
+        """
+
+        # Validate input data at the beginning
+        self._validate_data(exp_data, pre_exp_data)
+
+        analysis_results = AnalysisPlanResults()
+
+        for test in self.tests:
+            exp_data = test.add_covariates(exp_data, pre_exp_data)
+
+            for treatment_variant in self.treatment_variants:
+                for dimension in test.dimensions:
+                    for dimension_value in dimension.iterate_dimension_values():
+
+                        if verbose:
+                            logger.info(
+                                f"Metric: {test.metric.alias}, "
+                                f"Treatment: {treatment_variant.name}, "
+                                f"Dimension: {dimension.name}, "
+                                f"Value: {dimension_value}"
+                            )
+
+                        test_results = test.get_test_results(
+                            exp_data=exp_data,
+                            control_variant=self.control_variant,
+                            treatment_variant=treatment_variant,
+                            variant_col=self.variant_col,
+                            dimension=dimension,
+                            dimension_value=dimension_value,
+                            alpha=self.alpha,
+                        )
+
+                        analysis_results = analysis_results + test_results
+
+        return analysis_results
+
+    def _validate_data(
+        self, exp_data: pd.DataFrame, pre_exp_data: Optional[pd.DataFrame] = None
+    ):
+        """
+        Validates the input dataframes for the analyze method.
+
+        Parameters
+        ----------
+        exp_data : pd.DataFrame
+            The experimental data
+        pre_exp_data : Optional[pd.DataFrame]
+            The pre-experimental data (optional)
+
+        Raises
+        ------
+        ValueError
+            If exp_data is not a DataFrame or is empty
+            If pre_exp_data is provided and is not a DataFrame or is empty
+        """
+        if not isinstance(exp_data, pd.DataFrame):
+            raise ValueError("exp_data must be a pandas DataFrame")
+        if exp_data.empty:
+            raise ValueError("exp_data cannot be empty")
+        if pre_exp_data is not None:
+            if not isinstance(pre_exp_data, pd.DataFrame):
+                raise ValueError("pre_exp_data must be a pandas DataFrame if provided")
+            if pre_exp_data.empty:
+                raise ValueError("pre_exp_data cannot be empty if provided")
+
+    @property
+    def control_variant(self) -> Variant:
+        """
+        Returns the control variant from the list of variants. Raises an error if no control variant is found.
+
+        Returns
+        -------
+        Variant
+            The control variant
+
+        Raises
+        ------
+        ValueError
+            If no control variant is found
+        """
+        for variant in self.variants:
+            if variant.is_control:
+                return variant
+        raise ValueError("No control variant found")
+
+    @property
+    def treatment_variants(self) -> List[Variant]:
+        """
+        Returns the treatment variants from the list of variants. Raises an error if no treatment variants are found.
+
+        Returns
+        -------
+        List[Variant]
+            A list of treatment variants
+
+        Raises
+        ------
+        ValueError
+            If no treatment variants are found
+        """
+        treatments = [variant for variant in self.variants if not variant.is_control]
+        if not treatments:
+            raise ValueError("No treatment variants found")
+        return treatments
+
+    @classmethod
+    def from_metrics(
+        cls,
+        metrics: List[Metric],
+        variants: List[Variant],
+        variant_col: str = "treatment",
+        alpha: float = 0.05,
+        dimensions: Optional[List[Dimension]] = None,
+        analysis_type: str = "default",
+        analysis_config: Optional[Dict[str, Any]] = None,
+        custom_analysis_type_mapper: Optional[Dict[str, ExperimentAnalysis]] = None,
+    ) -> "AnalysisPlan":
+        """
+        Creates a simplified AnalysisPlan instance from a list of metrics. It will create HypothesisTest objects under the hood.
+        This shortcut does not support cupac, and uses the same dimensions, analysis type and analysis config for all metrics.
+
+        Parameters
+        ----------
+        metrics : List[Metric]
+            A list of Metric instances
+        variants : List[Variant]
+            A list of Variant instances
+        variant_col : str
+            The name of the column containing the variant names.
+        alpha : float
+            Significance level used to construct confidence intervals
+        dimensions : Optional[List[Dimension]]
+            A list of Dimension instances (optional)
+        analysis_type : str
+            The type of analysis to be conducted (default: "default")
+        analysis_config : Optional[Dict[str, Any]]
+            A dictionary containing analysis configuration options (optional)
+        custom_analysis_type_mapper : Optional[Dict[str, ExperimentAnalysis]]
+            An optional dictionary mapping the names of custom analysis types to the corresponding ExperimentAnalysis classes
+
+        Returns
+        -------
+        AnalysisPlan
+            An instance of AnalysisPlan
+        """
+        tests = [
+            HypothesisTest(
+                metric=metric,
+                dimensions=dimensions or [],
+                analysis_type=analysis_type,
+                analysis_config=analysis_config or {},
+                custom_analysis_type_mapper=custom_analysis_type_mapper or {},
+            )
+            for metric in metrics
+        ]
+
+        return cls(
+            tests=tests,
+            variants=variants,
+            variant_col=variant_col,
+            alpha=alpha,
+        )
+
+
+ + + +
+ + + + + + +
+ + + +

+control_variant: Variant + + + property + readonly + + +

+ +
+ +

Returns the control variant from the list of variants. Raises an error if no control variant is found.

+
Returns
+

Variant + The control variant

+
Raises
+

ValueError + If no control variant is found

+
+ +
+ + + +
+ + + +

+treatment_variants: List[cluster_experiments.inference.variant.Variant] + + + property + readonly + + +

+ +
+ +

Returns the treatment variants from the list of variants. Raises an error if no treatment variants are found.

+
Returns
+

List[Variant] + A list of treatment variants

+
Raises
+

ValueError + If no treatment variants are found

+
+ +
+ + + + + + +
+ + + +

+__init__(self, tests, variants, variant_col='treatment', alpha=0.05) + + + special + + +

+ +
+ +
Parameters
+

tests : List[HypothesisTest] + A list of HypothesisTest instances +variants : List[Variant] + A list of Variant instances +variant_col : str + The name of the column containing the variant names. +alpha : float + significance level used to construct confidence intervals

+ +
+ Source code in cluster_experiments/inference/analysis_plan.py +
def __init__(
+    self,
+    tests: List[HypothesisTest],
+    variants: List[Variant],
+    variant_col: str = "treatment",
+    alpha: float = 0.05,
+):
+    """
+    Parameters
+    ----------
+    tests : List[HypothesisTest]
+        A list of HypothesisTest instances
+    variants : List[Variant]
+        A list of Variant instances
+    variant_col : str
+        The name of the column containing the variant names.
+    alpha : float
+        significance level used to construct confidence intervals
+    """
+
+    self.tests = tests
+    self.variants = variants
+    self.variant_col = variant_col
+    self.alpha = alpha
+
+    self._validate_inputs()
+
+
+
+ +
+ + + +
+ + + +

+analyze(self, exp_data, pre_exp_data=None, verbose=False) + + +

+ +
+ +

Method to run the experiment analysis.

+ +
+ Source code in cluster_experiments/inference/analysis_plan.py +
def analyze(
+    self,
+    exp_data: pd.DataFrame,
+    pre_exp_data: Optional[pd.DataFrame] = None,
+    verbose: bool = False,
+) -> AnalysisPlanResults:
+    """
+    Method to run the experiment analysis.
+    """
+
+    # Validate input data at the beginning
+    self._validate_data(exp_data, pre_exp_data)
+
+    analysis_results = AnalysisPlanResults()
+
+    for test in self.tests:
+        exp_data = test.add_covariates(exp_data, pre_exp_data)
+
+        for treatment_variant in self.treatment_variants:
+            for dimension in test.dimensions:
+                for dimension_value in dimension.iterate_dimension_values():
+
+                    if verbose:
+                        logger.info(
+                            f"Metric: {test.metric.alias}, "
+                            f"Treatment: {treatment_variant.name}, "
+                            f"Dimension: {dimension.name}, "
+                            f"Value: {dimension_value}"
+                        )
+
+                    test_results = test.get_test_results(
+                        exp_data=exp_data,
+                        control_variant=self.control_variant,
+                        treatment_variant=treatment_variant,
+                        variant_col=self.variant_col,
+                        dimension=dimension,
+                        dimension_value=dimension_value,
+                        alpha=self.alpha,
+                    )
+
+                    analysis_results = analysis_results + test_results
+
+    return analysis_results
+
+
+
+ +
+ + + +
+ + + +

+from_metrics(metrics, variants, variant_col='treatment', alpha=0.05, dimensions=None, analysis_type='default', analysis_config=None, custom_analysis_type_mapper=None) + + + classmethod + + +

+ +
+ +

Creates a simplified AnalysisPlan instance from a list of metrics. It will create HypothesisTest objects under the hood. +This shortcut does not support cupac, and uses the same dimensions, analysis type and analysis config for all metrics.

+
Parameters
+

metrics : List[Metric] + A list of Metric instances +variants : List[Variant] + A list of Variant instances +variant_col : str + The name of the column containing the variant names. +alpha : float + Significance level used to construct confidence intervals +dimensions : Optional[List[Dimension]] + A list of Dimension instances (optional) +analysis_type : str + The type of analysis to be conducted (default: "default") +analysis_config : Optional[Dict[str, Any]] + A dictionary containing analysis configuration options (optional) +custom_analysis_type_mapper : Optional[Dict[str, ExperimentAnalysis]] + An optional dictionary mapping the names of custom analysis types to the corresponding ExperimentAnalysis classes

+
Returns
+

AnalysisPlan + An instance of AnalysisPlan

+ +
+ Source code in cluster_experiments/inference/analysis_plan.py +
@classmethod
+def from_metrics(
+    cls,
+    metrics: List[Metric],
+    variants: List[Variant],
+    variant_col: str = "treatment",
+    alpha: float = 0.05,
+    dimensions: Optional[List[Dimension]] = None,
+    analysis_type: str = "default",
+    analysis_config: Optional[Dict[str, Any]] = None,
+    custom_analysis_type_mapper: Optional[Dict[str, ExperimentAnalysis]] = None,
+) -> "AnalysisPlan":
+    """
+    Creates a simplified AnalysisPlan instance from a list of metrics. It will create HypothesisTest objects under the hood.
+    This shortcut does not support cupac, and uses the same dimensions, analysis type and analysis config for all metrics.
+
+    Parameters
+    ----------
+    metrics : List[Metric]
+        A list of Metric instances
+    variants : List[Variant]
+        A list of Variant instances
+    variant_col : str
+        The name of the column containing the variant names.
+    alpha : float
+        Significance level used to construct confidence intervals
+    dimensions : Optional[List[Dimension]]
+        A list of Dimension instances (optional)
+    analysis_type : str
+        The type of analysis to be conducted (default: "default")
+    analysis_config : Optional[Dict[str, Any]]
+        A dictionary containing analysis configuration options (optional)
+    custom_analysis_type_mapper : Optional[Dict[str, ExperimentAnalysis]]
+        An optional dictionary mapping the names of custom analysis types to the corresponding ExperimentAnalysis classes
+
+    Returns
+    -------
+    AnalysisPlan
+        An instance of AnalysisPlan
+    """
+    tests = [
+        HypothesisTest(
+            metric=metric,
+            dimensions=dimensions or [],
+            analysis_type=analysis_type,
+            analysis_config=analysis_config or {},
+            custom_analysis_type_mapper=custom_analysis_type_mapper or {},
+        )
+        for metric in metrics
+    ]
+
+    return cls(
+        tests=tests,
+        variants=variants,
+        variant_col=variant_col,
+        alpha=alpha,
+    )
+
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+ + + + + + + + + \ No newline at end of file diff --git a/api/analysis_results.html b/api/analysis_results.html new file mode 100644 index 00000000..27e4381d --- /dev/null +++ b/api/analysis_results.html @@ -0,0 +1,1056 @@ + + + + + + + + + + + + + + + + + + + + + + `from cluster_experiments.inference.analysis_results import *` - Cluster Experiments Docs + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
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from cluster_experiments.inference.analysis_results import *

+ + +
+ + +
+ + + + +
+ + + + + + + +
+ + + +

+ +AnalysisPlanResults + + + + dataclass + + +

+ +
+ +

A dataclass used to represent the results of the experiment analysis.

+

Attributes

+

metric_alias : List[str] + The alias of the metric used in the test +control_variant_name : List[str] + The name of the control variant +treatment_variant_name : List[str] + The name of the treatment variant +control_variant_mean : List[float] + The mean value of the control variant +treatment_variant_mean : List[float] + The mean value of the treatment variant +analysis_type : List[str] + The type of analysis performed +ate : List[float] + The average treatment effect +ate_ci_lower : List[float] + The lower bound of the confidence interval for the ATE +ate_ci_upper : List[float] + The upper bound of the confidence interval for the ATE +p_value : List[float] + The p-value of the test +std_error : List[float] + The standard error of the test +dimension_name : List[str] + The name of the dimension +dimension_value : List[str] + The value of the dimension +!!! alpha "List[float]" + The significance level of the test

+ +
+ Source code in cluster_experiments/inference/analysis_results.py +
class AnalysisPlanResults:
+    """
+    A dataclass used to represent the results of the experiment analysis.
+
+    Attributes
+    ----------
+    metric_alias : List[str]
+        The alias of the metric used in the test
+    control_variant_name : List[str]
+        The name of the control variant
+    treatment_variant_name : List[str]
+        The name of the treatment variant
+    control_variant_mean : List[float]
+        The mean value of the control variant
+    treatment_variant_mean : List[float]
+        The mean value of the treatment variant
+    analysis_type : List[str]
+        The type of analysis performed
+    ate : List[float]
+        The average treatment effect
+    ate_ci_lower : List[float]
+        The lower bound of the confidence interval for the ATE
+    ate_ci_upper : List[float]
+        The upper bound of the confidence interval for the ATE
+    p_value : List[float]
+        The p-value of the test
+    std_error : List[float]
+        The standard error of the test
+    dimension_name : List[str]
+        The name of the dimension
+    dimension_value : List[str]
+        The value of the dimension
+    alpha: List[float]
+        The significance level of the test
+    """
+
+    metric_alias: List[str] = field(default_factory=lambda: [])
+    control_variant_name: List[str] = field(default_factory=lambda: [])
+    treatment_variant_name: List[str] = field(default_factory=lambda: [])
+    control_variant_mean: List[float] = field(default_factory=lambda: [])
+    treatment_variant_mean: List[float] = field(default_factory=lambda: [])
+    analysis_type: List[str] = field(default_factory=lambda: [])
+    ate: List[float] = field(default_factory=lambda: [])
+    ate_ci_lower: List[float] = field(default_factory=lambda: [])
+    ate_ci_upper: List[float] = field(default_factory=lambda: [])
+    p_value: List[float] = field(default_factory=lambda: [])
+    std_error: List[float] = field(default_factory=lambda: [])
+    dimension_name: List[str] = field(default_factory=lambda: [])
+    dimension_value: List[str] = field(default_factory=lambda: [])
+    alpha: List[float] = field(default_factory=lambda: [])
+
+    def __add__(self, other):
+        if not isinstance(other, AnalysisPlanResults):
+            return NotImplemented
+
+        return AnalysisPlanResults(
+            metric_alias=self.metric_alias + other.metric_alias,
+            control_variant_name=self.control_variant_name + other.control_variant_name,
+            treatment_variant_name=self.treatment_variant_name
+            + other.treatment_variant_name,
+            control_variant_mean=self.control_variant_mean + other.control_variant_mean,
+            treatment_variant_mean=self.treatment_variant_mean
+            + other.treatment_variant_mean,
+            analysis_type=self.analysis_type + other.analysis_type,
+            ate=self.ate + other.ate,
+            ate_ci_lower=self.ate_ci_lower + other.ate_ci_lower,
+            ate_ci_upper=self.ate_ci_upper + other.ate_ci_upper,
+            p_value=self.p_value + other.p_value,
+            std_error=self.std_error + other.std_error,
+            dimension_name=self.dimension_name + other.dimension_name,
+            dimension_value=self.dimension_value + other.dimension_value,
+            alpha=self.alpha + other.alpha,
+        )
+
+    def to_dataframe(self):
+        return pd.DataFrame(asdict(self))
+
+
+ + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ +
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+ + + + + + + + + \ No newline at end of file diff --git a/api/cupac_model.html b/api/cupac_model.html new file mode 100644 index 00000000..4bd32522 --- /dev/null +++ b/api/cupac_model.html @@ -0,0 +1,1653 @@ + + + + + + + + + + + + + + + + + + + + + + Pre experiment outcome model - Cluster Experiments Docs + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
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from cluster_experiments.cupac import *

+ + +
+ + +
+ + + + +
+ + + + + + + +
+ + + +

+ +CupacHandler + + + +

+ +
+ +

CupacHandler class. It handles operations related to the cupac model.

+

Its main goal is to call the add_covariates method, where it will add the ouptut from the cupac model, +and this should be used as covariates in the regression method for the hypothesis test.

+ +
+ Source code in cluster_experiments/cupac.py +
class CupacHandler:
+    """
+    CupacHandler class. It handles operations related to the cupac model.
+
+    Its main goal is to call the add_covariates method, where it will add the ouptut from the cupac model,
+    and this should be used as covariates in the regression method for the hypothesis test.
+    """
+
+    def __init__(
+        self,
+        cupac_model: Optional[BaseEstimator] = None,
+        target_col: str = "target",
+        features_cupac_model: Optional[List[str]] = None,
+        cache_fit: bool = True,
+    ):
+        self.cupac_model: BaseEstimator = cupac_model or EmptyRegressor()
+        self.target_col = target_col
+        self.cupac_outcome_name = f"estimate_{target_col}"
+        self.features_cupac_model: List[str] = features_cupac_model or []
+        self.is_cupac = not isinstance(self.cupac_model, EmptyRegressor)
+        self.cache_fit = cache_fit
+
+    def _prep_data_cupac(
+        self, df: pd.DataFrame, pre_experiment_df: pd.DataFrame
+    ) -> Tuple[pd.DataFrame, pd.DataFrame, pd.Series]:
+        """Prepares data for training and prediction"""
+        df = df.copy()
+        pre_experiment_df = pre_experiment_df.copy()
+        df_predict = df.drop(columns=[self.target_col])
+        # Split data into X and y
+        pre_experiment_x = pre_experiment_df.drop(columns=[self.target_col])
+        pre_experiment_y = pre_experiment_df[self.target_col]
+
+        # Keep only cupac features
+        if self.features_cupac_model:
+            pre_experiment_x = pre_experiment_x[self.features_cupac_model]
+            df_predict = df_predict[self.features_cupac_model]
+
+        return df_predict, pre_experiment_x, pre_experiment_y
+
+    def add_covariates(
+        self, df: pd.DataFrame, pre_experiment_df: Optional[pd.DataFrame] = None
+    ) -> pd.DataFrame:
+        """
+        Train model to predict outcome variable (based on pre-experiment data)
+        and  add the prediction to the experiment dataframe. Only do this if
+        we use cupac
+        Args:
+            pre_experiment_df: Dataframe with pre-experiment data.
+            df: Dataframe with outcome and treatment variables.
+        """
+        self.check_cupac_inputs(pre_experiment_df)
+
+        # Early return if no need to add covariates
+        if not self.need_covariates(pre_experiment_df):
+            return df
+
+        df = df.copy()
+        pre_experiment_df = pre_experiment_df.copy()
+        df_predict, pre_experiment_x, pre_experiment_y = self._prep_data_cupac(
+            df=df, pre_experiment_df=pre_experiment_df
+        )
+
+        # Fit model if it has not been fitted before
+        self._fit_cupac_model(pre_experiment_x, pre_experiment_y)
+
+        # Predict
+        estimated_target = self._predict_cupac_model(df_predict)
+
+        # Add cupac outcome name to df
+        df[self.cupac_outcome_name] = estimated_target
+        return df
+
+    def _fit_cupac_model(
+        self, pre_experiment_x: pd.DataFrame, pre_experiment_y: pd.Series
+    ):
+        """Fits the cupac model.
+        Caches the fitted model in the object, so we only fit it once.
+        We can disable this by setting cache_fit to False.
+        """
+        if not self.cache_fit:
+            self.cupac_model.fit(pre_experiment_x, pre_experiment_y)
+            return
+
+        try:
+            check_is_fitted(self.cupac_model)
+        except NotFittedError:
+            self.cupac_model.fit(pre_experiment_x, pre_experiment_y)
+
+    def _predict_cupac_model(self, df_predict: pd.DataFrame) -> ArrayLike:
+        """Predicts the cupac model"""
+        if hasattr(self.cupac_model, "predict_proba"):
+            return self.cupac_model.predict_proba(df_predict)[:, 1]
+        if hasattr(self.cupac_model, "predict"):
+            return self.cupac_model.predict(df_predict)
+        raise ValueError("cupac_model should have predict or predict_proba method.")
+
+    def need_covariates(self, pre_experiment_df: Optional[pd.DataFrame] = None) -> bool:
+        return pre_experiment_df is not None and self.is_cupac
+
+    def check_cupac_inputs(self, pre_experiment_df: Optional[pd.DataFrame] = None):
+        if self.is_cupac and pre_experiment_df is None:
+            raise ValueError("If cupac is used, pre_experiment_df should be provided.")
+
+        if not self.is_cupac and pre_experiment_df is not None:
+            raise ValueError(
+                "If cupac is not used, pre_experiment_df should not be provided - "
+                "remove pre_experiment_df argument or set cupac_model to not None."
+            )
+
+
+ + + +
+ + + + + + + + + + +
+ + + +

+add_covariates(self, df, pre_experiment_df=None) + + +

+ +
+ +

Train model to predict outcome variable (based on pre-experiment data) +and add the prediction to the experiment dataframe. Only do this if +we use cupac

+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
pre_experiment_dfOptional[pandas.core.frame.DataFrame]

Dataframe with pre-experiment data.

None
dfDataFrame

Dataframe with outcome and treatment variables.

required
+
+ Source code in cluster_experiments/cupac.py +
def add_covariates(
+    self, df: pd.DataFrame, pre_experiment_df: Optional[pd.DataFrame] = None
+) -> pd.DataFrame:
+    """
+    Train model to predict outcome variable (based on pre-experiment data)
+    and  add the prediction to the experiment dataframe. Only do this if
+    we use cupac
+    Args:
+        pre_experiment_df: Dataframe with pre-experiment data.
+        df: Dataframe with outcome and treatment variables.
+    """
+    self.check_cupac_inputs(pre_experiment_df)
+
+    # Early return if no need to add covariates
+    if not self.need_covariates(pre_experiment_df):
+        return df
+
+    df = df.copy()
+    pre_experiment_df = pre_experiment_df.copy()
+    df_predict, pre_experiment_x, pre_experiment_y = self._prep_data_cupac(
+        df=df, pre_experiment_df=pre_experiment_df
+    )
+
+    # Fit model if it has not been fitted before
+    self._fit_cupac_model(pre_experiment_x, pre_experiment_y)
+
+    # Predict
+    estimated_target = self._predict_cupac_model(df_predict)
+
+    # Add cupac outcome name to df
+    df[self.cupac_outcome_name] = estimated_target
+    return df
+
+
+
+ +
+ + + + + + + +
+ +
+ +
+ + + +
+ + + +

+ +EmptyRegressor (BaseEstimator) + + + + +

+ +
+ +

Empty regressor class. It does not do anything, used to glue the code of other estimators and PowerAnalysis

+

Each Regressor should have: +- fit method: Uses pre experiment data to fit some kind of model to be used as a covariate and reduce variance. +- predict method: Uses the fitted model to add the covariate on the experiment data.

+

It can add aggregates of the target in older data as a covariate, or a model (cupac) to predict the target.

+ +
+ Source code in cluster_experiments/cupac.py +
class EmptyRegressor(BaseEstimator):
+    """
+    Empty regressor class. It does not do anything, used to glue the code of other estimators and PowerAnalysis
+
+    Each Regressor should have:
+    - fit method: Uses pre experiment data to fit some kind of model to be used as a covariate and reduce variance.
+    - predict method: Uses the fitted model to add the covariate on the experiment data.
+
+    It can add aggregates of the target in older data as a covariate, or a model (cupac) to predict the target.
+    """
+
+    @classmethod
+    def from_config(cls, config):
+        return cls()
+
+
+ + + +
+ + + + + + + + + + + + +
+ +
+ +
+ + + +
+ + + +

+ +TargetAggregation (BaseEstimator) + + + + +

+ +
+ +

Adds average of target using pre-experiment data

+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
agg_colstr

Column to group by to aggregate target

required
target_colstr

Column to aggregate

'target'
smoothing_factorint

Smoothing factor for the smoothed mean

20

Usage: +

import pandas as pd
+from cluster_experiments.cupac import TargetAggregation
+
+df = pd.DataFrame({"agg_col": ["a", "a", "b", "b", "c", "c"], "target_col": [1, 2, 3, 4, 5, 6]})
+new_df = pd.DataFrame({"agg_col": ["a", "a", "b", "b", "c", "c"]})
+target_agg = TargetAggregation("agg_col", "target_col")
+target_agg.fit(df.drop(columns="target_col"), df["target_col"])
+df_with_target_agg = target_agg.predict(new_df)
+print(df_with_target_agg)
+

+ +
+ Source code in cluster_experiments/cupac.py +
class TargetAggregation(BaseEstimator):
+    """
+    Adds average of target using pre-experiment data
+
+    Args:
+        agg_col: Column to group by to aggregate target
+        target_col: Column to aggregate
+        smoothing_factor: Smoothing factor for the smoothed mean
+    Usage:
+    ```python
+    import pandas as pd
+    from cluster_experiments.cupac import TargetAggregation
+
+    df = pd.DataFrame({"agg_col": ["a", "a", "b", "b", "c", "c"], "target_col": [1, 2, 3, 4, 5, 6]})
+    new_df = pd.DataFrame({"agg_col": ["a", "a", "b", "b", "c", "c"]})
+    target_agg = TargetAggregation("agg_col", "target_col")
+    target_agg.fit(df.drop(columns="target_col"), df["target_col"])
+    df_with_target_agg = target_agg.predict(new_df)
+    print(df_with_target_agg)
+    ```
+    """
+
+    def __init__(
+        self,
+        agg_col: str,
+        target_col: str = "target",
+        smoothing_factor: int = 20,
+    ):
+        self.agg_col = agg_col
+        self.target_col = target_col
+        self.smoothing_factor = smoothing_factor
+        self.is_empty = False
+        self.mean_target_col = f"{self.target_col}_mean"
+        self.smooth_mean_target_col = f"{self.target_col}_smooth_mean"
+        self.pre_experiment_agg_df = pd.DataFrame()
+
+    def _get_pre_experiment_mean(self, pre_experiment_df: pd.DataFrame) -> float:
+        return pre_experiment_df[self.target_col].mean()
+
+    def fit(self, X: pd.DataFrame, y: pd.Series) -> "TargetAggregation":
+        """Fits "target encoder" model to pre-experiment data"""
+        pre_experiment_df = X.copy()
+        pre_experiment_df[self.target_col] = y
+
+        self.pre_experiment_mean = self._get_pre_experiment_mean(pre_experiment_df)
+        self.pre_experiment_agg_df = (
+            pre_experiment_df.assign(count=1)
+            .groupby(self.agg_col, as_index=False)
+            .agg({self.target_col: "sum", "count": "sum"})
+            .assign(
+                **{
+                    self.mean_target_col: lambda x: x[self.target_col] / x["count"],
+                    self.smooth_mean_target_col: lambda x: (
+                        x[self.target_col]
+                        + self.smoothing_factor * self.pre_experiment_mean
+                    )
+                    / (x["count"] + self.smoothing_factor),
+                }
+            )
+            .drop(columns=["count", self.target_col])
+        )
+        return self
+
+    def predict(self, X: pd.DataFrame) -> ArrayLike:
+        """Adds average target of pre-experiment data to experiment data"""
+        return (
+            X.merge(self.pre_experiment_agg_df, how="left", on=self.agg_col)[
+                self.smooth_mean_target_col
+            ]
+            .fillna(self.pre_experiment_mean)
+            .values
+        )
+
+    @classmethod
+    def from_config(cls, config):
+        """Creates TargetAggregation from PowerConfig"""
+        return cls(
+            agg_col=config.agg_col,
+            target_col=config.target_col,
+            smoothing_factor=config.smoothing_factor,
+        )
+
+
+ + + +
+ + + + + + + + + + +
+ + + +

+fit(self, X, y) + + +

+ +
+ +

Fits "target encoder" model to pre-experiment data

+ +
+ Source code in cluster_experiments/cupac.py +
def fit(self, X: pd.DataFrame, y: pd.Series) -> "TargetAggregation":
+    """Fits "target encoder" model to pre-experiment data"""
+    pre_experiment_df = X.copy()
+    pre_experiment_df[self.target_col] = y
+
+    self.pre_experiment_mean = self._get_pre_experiment_mean(pre_experiment_df)
+    self.pre_experiment_agg_df = (
+        pre_experiment_df.assign(count=1)
+        .groupby(self.agg_col, as_index=False)
+        .agg({self.target_col: "sum", "count": "sum"})
+        .assign(
+            **{
+                self.mean_target_col: lambda x: x[self.target_col] / x["count"],
+                self.smooth_mean_target_col: lambda x: (
+                    x[self.target_col]
+                    + self.smoothing_factor * self.pre_experiment_mean
+                )
+                / (x["count"] + self.smoothing_factor),
+            }
+        )
+        .drop(columns=["count", self.target_col])
+    )
+    return self
+
+
+
+ +
+ + + +
+ + + +

+from_config(config) + + + classmethod + + +

+ +
+ +

Creates TargetAggregation from PowerConfig

+ +
+ Source code in cluster_experiments/cupac.py +
@classmethod
+def from_config(cls, config):
+    """Creates TargetAggregation from PowerConfig"""
+    return cls(
+        agg_col=config.agg_col,
+        target_col=config.target_col,
+        smoothing_factor=config.smoothing_factor,
+    )
+
+
+
+ +
+ + + +
+ + + +

+predict(self, X) + + +

+ +
+ +

Adds average target of pre-experiment data to experiment data

+ +
+ Source code in cluster_experiments/cupac.py +
def predict(self, X: pd.DataFrame) -> ArrayLike:
+    """Adds average target of pre-experiment data to experiment data"""
+    return (
+        X.merge(self.pre_experiment_agg_df, how="left", on=self.agg_col)[
+            self.smooth_mean_target_col
+        ]
+        .fillna(self.pre_experiment_mean)
+        .values
+    )
+
+
+
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+ +
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+ + + + + + + + + \ No newline at end of file diff --git a/api/dimension.html b/api/dimension.html new file mode 100644 index 00000000..d59bc456 --- /dev/null +++ b/api/dimension.html @@ -0,0 +1,1344 @@ + + + + + + + + + + + + + + + + + + + + + + Dimension - Cluster Experiments Docs + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + +
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from cluster_experiments.inference.dimension import *

+ + +
+ + +
+ + + + +
+ + + + + + + +
+ + + +

+ +DefaultDimension (Dimension) + + + + + dataclass + + +

+ +
+ +

A class used to represent a Dimension with a default value representing total, i.e. no slicing.

+ +
+ Source code in cluster_experiments/inference/dimension.py +
class DefaultDimension(Dimension):
+    """
+    A class used to represent a Dimension with a default value representing total, i.e. no slicing.
+    """
+
+    def __init__(self):
+        super().__init__(name="__total_dimension", values=["total"])
+
+
+ + + +
+ + + + + + + + + + +
+ + + +

+__init__(self) + + + special + + +

+ +
+ +

Initialize self. See help(type(self)) for accurate signature.

+ +
+ Source code in cluster_experiments/inference/dimension.py +
def __init__(self):
+    super().__init__(name="__total_dimension", values=["total"])
+
+
+
+ +
+ + + + + + +
+ +
+ +
+ + + +
+ + + +

+ +Dimension + + + + dataclass + + +

+ +
+ +

A class used to represent a Dimension with a name and values.

+

Attributes

+

name : str + The name of the dimension +values : List[str] + A list of strings representing the possible values of the dimension

+ +
+ Source code in cluster_experiments/inference/dimension.py +
class Dimension:
+    """
+    A class used to represent a Dimension with a name and values.
+
+    Attributes
+    ----------
+    name : str
+        The name of the dimension
+    values : List[str]
+        A list of strings representing the possible values of the dimension
+    """
+
+    name: str
+    values: List[str]
+
+    def __post_init__(self):
+        """
+        Validates the inputs after initialization.
+        """
+        self._validate_inputs()
+
+    def _validate_inputs(self):
+        """
+        Validates the inputs for the Dimension class.
+
+        Raises
+        ------
+        TypeError
+            If the name is not a string or if values is not a list of strings.
+        """
+        if not isinstance(self.name, str):
+            raise TypeError("Dimension name must be a string")
+        if not isinstance(self.values, list) or not all(
+            isinstance(val, str) for val in self.values
+        ):
+            raise TypeError("Dimension values must be a list of strings")
+
+    def iterate_dimension_values(self):
+        """
+        A generator method to yield name and values from the dimension.
+
+        Yields
+        ------
+        Any
+            A unique value from the dimension.
+        """
+        seen = set()
+        for value in self.values:
+            if value not in seen:
+                seen.add(value)
+                yield value
+
+
+ + + +
+ + + + + + + + + + + + + +
+ + + +

+__post_init__(self) + + + special + + +

+ +
+ +

Validates the inputs after initialization.

+ +
+ Source code in cluster_experiments/inference/dimension.py +
def __post_init__(self):
+    """
+    Validates the inputs after initialization.
+    """
+    self._validate_inputs()
+
+
+
+ +
+ + + + +
+ + + +

+iterate_dimension_values(self) + + +

+ +
+ +

A generator method to yield name and values from the dimension.

+
Yields
+

Any + A unique value from the dimension.

+ +
+ Source code in cluster_experiments/inference/dimension.py +
def iterate_dimension_values(self):
+    """
+    A generator method to yield name and values from the dimension.
+
+    Yields
+    ------
+    Any
+        A unique value from the dimension.
+    """
+    seen = set()
+    for value in self.values:
+        if value not in seen:
+            seen.add(value)
+            yield value
+
+
+
+ +
+ + + + + +
+ +
+ +
+ + + + + + + +
+ +
+ +
+ + + + + + +
+
+ + +
+ +
+ + + +
+
+
+
+ + + + + + + + + \ No newline at end of file diff --git a/api/experiment_analysis.html b/api/experiment_analysis.html new file mode 100644 index 00000000..9adecc0c --- /dev/null +++ b/api/experiment_analysis.html @@ -0,0 +1,6431 @@ + + + + + + + + + + + + + + + + + + + + + + Experiment analysis methods - Cluster Experiments Docs + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + +
+ + +
+ +
+ + + + + + +
+
+ + + +
+
+
+ + + + +
+
+
+ + + +
+
+
+ + + +
+
+
+ + + +
+
+ + + + + + + + + +

from cluster_experiments.experiment_analysis import *

+ + +
+ + +
+ + + + +
+ + + + + + + +
+ + + +

+ +ClusteredOLSAnalysis (ExperimentAnalysis) + + + + +

+ +
+ +

Class to run OLS clustered analysis

+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
cluster_colsList[str]

list of columns to use as clusters

required
target_colstr

name of the column containing the variable to measure

'target'
treatment_colstr

name of the column containing the treatment variable

'treatment'
treatmentstr

name of the treatment to use as the treated group

'B'
covariatesOptional[List[str]]

list of columns to use as covariates

None
hypothesisstr

one of "two-sided", "less", "greater" indicating the alternative hypothesis

'two-sided'

Usage:

+
from cluster_experiments.experiment_analysis import ClusteredOLSAnalysis
+import pandas as pd
+
+df = pd.DataFrame({
+    'x': [1, 2, 3, 0, 0, 1, 2, 0],
+    'treatment': ["A"] * 2 + ["B"] * 2 + ["A"] * 2 + ["B"] * 2,
+    'cluster': [1, 1, 2, 2, 3, 3, 4, 4],
+})
+
+ClusteredOLSAnalysis(
+    cluster_cols=['cluster'],
+    target_col='x',
+).get_pvalue(df)
+
+ +
+ Source code in cluster_experiments/experiment_analysis.py +
class ClusteredOLSAnalysis(ExperimentAnalysis):
+    """
+    Class to run OLS clustered analysis
+
+    Arguments:
+        cluster_cols: list of columns to use as clusters
+        target_col: name of the column containing the variable to measure
+        treatment_col: name of the column containing the treatment variable
+        treatment: name of the treatment to use as the treated group
+        covariates: list of columns to use as covariates
+        hypothesis: one of "two-sided", "less", "greater" indicating the alternative hypothesis
+
+    Usage:
+
+    ```python
+    from cluster_experiments.experiment_analysis import ClusteredOLSAnalysis
+    import pandas as pd
+
+    df = pd.DataFrame({
+        'x': [1, 2, 3, 0, 0, 1, 2, 0],
+        'treatment': ["A"] * 2 + ["B"] * 2 + ["A"] * 2 + ["B"] * 2,
+        'cluster': [1, 1, 2, 2, 3, 3, 4, 4],
+    })
+
+    ClusteredOLSAnalysis(
+        cluster_cols=['cluster'],
+        target_col='x',
+    ).get_pvalue(df)
+    ```
+    """
+
+    def __init__(
+        self,
+        cluster_cols: List[str],
+        target_col: str = "target",
+        treatment_col: str = "treatment",
+        treatment: str = "B",
+        covariates: Optional[List[str]] = None,
+        hypothesis: str = "two-sided",
+    ):
+        super().__init__(
+            target_col=target_col,
+            treatment_col=treatment_col,
+            cluster_cols=cluster_cols,
+            treatment=treatment,
+            covariates=covariates,
+            hypothesis=hypothesis,
+        )
+        self.regressors = [self.treatment_col] + self.covariates
+        self.formula = f"{self.target_col} ~ {' + '.join(self.regressors)}"
+        self.cov_type = "cluster"
+
+    def fit_ols_clustered(self, df: pd.DataFrame):
+        """Returns the fitted OLS model"""
+        return sm.OLS.from_formula(self.formula, data=df,).fit(
+            cov_type=self.cov_type,
+            cov_kwds={"groups": self._get_cluster_column(df)},
+        )
+
+    def analysis_pvalue(self, df: pd.DataFrame, verbose: bool = False) -> float:
+        """Returns the p-value of the analysis
+        Arguments:
+            df: dataframe containing the data to analyze
+            verbose (Optional): bool, prints the regression summary if True
+        """
+        results_ols = self.fit_ols_clustered(df)
+        if verbose:
+            print(results_ols.summary())
+
+        p_value = self.pvalue_based_on_hypothesis(results_ols)
+        return p_value
+
+    def analysis_point_estimate(self, df: pd.DataFrame, verbose: bool = False) -> float:
+        """Returns the point estimate of the analysis
+        Arguments:
+            df: dataframe containing the data to analyze
+            verbose (Optional): bool, prints the regression summary if True
+        """
+        results_ols = self.fit_ols_clustered(df)
+        return results_ols.params[self.treatment_col]
+
+    def analysis_standard_error(self, df: pd.DataFrame, verbose: bool = False) -> float:
+        """Returns the standard error of the analysis
+        Arguments:
+            df: dataframe containing the data to analyze
+            verbose (Optional): bool, prints the regression summary if True
+        """
+        results_ols = self.fit_ols_clustered(df)
+        return results_ols.bse[self.treatment_col]
+
+    def analysis_confidence_interval(
+        self, df: pd.DataFrame, alpha: float, verbose: bool = False
+    ) -> ConfidenceInterval:
+        """Returns the confidence interval of the analysis
+        Arguments:
+            df: dataframe containing the data to analyze
+            alpha: significance level
+            verbose (Optional): bool, prints the regression summary if True
+        """
+        results_ols = self.fit_ols_clustered(df)
+        # Extract the confidence interval for the treatment column
+        conf_int_df = results_ols.conf_int(alpha=alpha)
+        lower_bound, upper_bound = conf_int_df.loc[self.treatment_col]
+
+        if verbose:
+            print(results_ols.summary())
+
+        # Return the confidence interval
+        return ConfidenceInterval(lower=lower_bound, upper=upper_bound, alpha=alpha)
+
+    def analysis_inference_results(
+        self, df: pd.DataFrame, alpha: float, verbose: bool = False
+    ) -> InferenceResults:
+        """Returns the inference results of the analysis
+        Arguments:
+            df: dataframe containing the data to analyze
+            alpha: significance level
+            verbose (Optional): bool, prints the regression summary if True
+        """
+        results_ols = self.fit_ols_clustered(df)
+
+        std_error = results_ols.bse[self.treatment_col]
+        ate = results_ols.params[self.treatment_col]
+        p_value = self.pvalue_based_on_hypothesis(results_ols)
+
+        # Extract the confidence interval for the treatment column
+        conf_int_df = results_ols.conf_int(alpha=alpha)
+        lower_bound, upper_bound = conf_int_df.loc[self.treatment_col]
+
+        if verbose:
+            print(results_ols.summary())
+
+        # Return the confidence interval
+        return InferenceResults(
+            ate=ate,
+            p_value=p_value,
+            std_error=std_error,
+            conf_int=ConfidenceInterval(
+                lower=lower_bound, upper=upper_bound, alpha=alpha
+            ),
+        )
+
+
+ + + +
+ + + + + + + + + + +
+ + + +

+analysis_confidence_interval(self, df, alpha, verbose=False) + + +

+ +
+ +

Returns the confidence interval of the analysis

+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
dfDataFrame

dataframe containing the data to analyze

required
alphafloat

significance level

required
verboseOptional

bool, prints the regression summary if True

False
+
+ Source code in cluster_experiments/experiment_analysis.py +
def analysis_confidence_interval(
+    self, df: pd.DataFrame, alpha: float, verbose: bool = False
+) -> ConfidenceInterval:
+    """Returns the confidence interval of the analysis
+    Arguments:
+        df: dataframe containing the data to analyze
+        alpha: significance level
+        verbose (Optional): bool, prints the regression summary if True
+    """
+    results_ols = self.fit_ols_clustered(df)
+    # Extract the confidence interval for the treatment column
+    conf_int_df = results_ols.conf_int(alpha=alpha)
+    lower_bound, upper_bound = conf_int_df.loc[self.treatment_col]
+
+    if verbose:
+        print(results_ols.summary())
+
+    # Return the confidence interval
+    return ConfidenceInterval(lower=lower_bound, upper=upper_bound, alpha=alpha)
+
+
+
+ +
+ + + +
+ + + +

+analysis_inference_results(self, df, alpha, verbose=False) + + +

+ +
+ +

Returns the inference results of the analysis

+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
dfDataFrame

dataframe containing the data to analyze

required
alphafloat

significance level

required
verboseOptional

bool, prints the regression summary if True

False
+
+ Source code in cluster_experiments/experiment_analysis.py +
def analysis_inference_results(
+    self, df: pd.DataFrame, alpha: float, verbose: bool = False
+) -> InferenceResults:
+    """Returns the inference results of the analysis
+    Arguments:
+        df: dataframe containing the data to analyze
+        alpha: significance level
+        verbose (Optional): bool, prints the regression summary if True
+    """
+    results_ols = self.fit_ols_clustered(df)
+
+    std_error = results_ols.bse[self.treatment_col]
+    ate = results_ols.params[self.treatment_col]
+    p_value = self.pvalue_based_on_hypothesis(results_ols)
+
+    # Extract the confidence interval for the treatment column
+    conf_int_df = results_ols.conf_int(alpha=alpha)
+    lower_bound, upper_bound = conf_int_df.loc[self.treatment_col]
+
+    if verbose:
+        print(results_ols.summary())
+
+    # Return the confidence interval
+    return InferenceResults(
+        ate=ate,
+        p_value=p_value,
+        std_error=std_error,
+        conf_int=ConfidenceInterval(
+            lower=lower_bound, upper=upper_bound, alpha=alpha
+        ),
+    )
+
+
+
+ +
+ + + +
+ + + +

+analysis_point_estimate(self, df, verbose=False) + + +

+ +
+ +

Returns the point estimate of the analysis

+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
dfDataFrame

dataframe containing the data to analyze

required
verboseOptional

bool, prints the regression summary if True

False
+
+ Source code in cluster_experiments/experiment_analysis.py +
def analysis_point_estimate(self, df: pd.DataFrame, verbose: bool = False) -> float:
+    """Returns the point estimate of the analysis
+    Arguments:
+        df: dataframe containing the data to analyze
+        verbose (Optional): bool, prints the regression summary if True
+    """
+    results_ols = self.fit_ols_clustered(df)
+    return results_ols.params[self.treatment_col]
+
+
+
+ +
+ + + +
+ + + +

+analysis_pvalue(self, df, verbose=False) + + +

+ +
+ +

Returns the p-value of the analysis

+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
dfDataFrame

dataframe containing the data to analyze

required
verboseOptional

bool, prints the regression summary if True

False
+
+ Source code in cluster_experiments/experiment_analysis.py +
def analysis_pvalue(self, df: pd.DataFrame, verbose: bool = False) -> float:
+    """Returns the p-value of the analysis
+    Arguments:
+        df: dataframe containing the data to analyze
+        verbose (Optional): bool, prints the regression summary if True
+    """
+    results_ols = self.fit_ols_clustered(df)
+    if verbose:
+        print(results_ols.summary())
+
+    p_value = self.pvalue_based_on_hypothesis(results_ols)
+    return p_value
+
+
+
+ +
+ + + +
+ + + +

+analysis_standard_error(self, df, verbose=False) + + +

+ +
+ +

Returns the standard error of the analysis

+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
dfDataFrame

dataframe containing the data to analyze

required
verboseOptional

bool, prints the regression summary if True

False
+
+ Source code in cluster_experiments/experiment_analysis.py +
def analysis_standard_error(self, df: pd.DataFrame, verbose: bool = False) -> float:
+    """Returns the standard error of the analysis
+    Arguments:
+        df: dataframe containing the data to analyze
+        verbose (Optional): bool, prints the regression summary if True
+    """
+    results_ols = self.fit_ols_clustered(df)
+    return results_ols.bse[self.treatment_col]
+
+
+
+ +
+ + + +
+ + + +

+fit_ols_clustered(self, df) + + +

+ +
+ +

Returns the fitted OLS model

+ +
+ Source code in cluster_experiments/experiment_analysis.py +
def fit_ols_clustered(self, df: pd.DataFrame):
+    """Returns the fitted OLS model"""
+    return sm.OLS.from_formula(self.formula, data=df,).fit(
+        cov_type=self.cov_type,
+        cov_kwds={"groups": self._get_cluster_column(df)},
+    )
+
+
+
+ +
+ + + + + +
+ +
+ +
+ + + +
+ + + +

+ +ConfidenceInterval + + + + dataclass + + +

+ +
+ +

Class to define the structure of a confidence interval.

+ +
+ Source code in cluster_experiments/experiment_analysis.py +
class ConfidenceInterval:
+    """
+    Class to define the structure of a confidence interval.
+    """
+
+    lower: float
+    upper: float
+    alpha: float
+
+
+ + + +
+ + + + + + + + + + + + + + + + + +
+ +
+ +
+ + + +
+ + + +

+ +ExperimentAnalysis (ABC) + + + + +

+ +
+ +

Abstract class to run the analysis of a given experiment

+

In order to create your own ExperimentAnalysis, +you should create a derived class that implements the analysis_pvalue method.

+

It can also be used as a component of the PowerAnalysis class.

+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
cluster_colsList[str]

list of columns to use as clusters

required
target_colstr

name of the column containing the variable to measure

'target'
treatment_colstr

name of the column containing the treatment variable

'treatment'
treatmentstr

name of the treatment to use as the treated group

'B'
covariatesOptional[List[str]]

list of columns to use as covariates

None
hypothesisstr

one of "two-sided", "less", "greater" indicating the alternative hypothesis

'two-sided'
+
+ Source code in cluster_experiments/experiment_analysis.py +
class ExperimentAnalysis(ABC):
+    """
+    Abstract class to run the analysis of a given experiment
+
+    In order to create your own ExperimentAnalysis,
+    you should create a derived class that implements the analysis_pvalue method.
+
+    It can also be used as a component of the PowerAnalysis class.
+
+    Arguments:
+        cluster_cols: list of columns to use as clusters
+        target_col: name of the column containing the variable to measure
+        treatment_col: name of the column containing the treatment variable
+        treatment: name of the treatment to use as the treated group
+        covariates: list of columns to use as covariates
+        hypothesis: one of "two-sided", "less", "greater" indicating the alternative hypothesis
+
+    """
+
+    def __init__(
+        self,
+        cluster_cols: List[str],
+        target_col: str = "target",
+        treatment_col: str = "treatment",
+        treatment: str = "B",
+        covariates: Optional[List[str]] = None,
+        hypothesis: str = "two-sided",
+    ):
+        self.target_col = target_col
+        self.treatment = treatment
+        self.treatment_col = treatment_col
+        self.cluster_cols = cluster_cols
+        self.covariates = covariates or []
+        self.hypothesis = hypothesis
+
+    def _get_cluster_column(self, df: pd.DataFrame) -> pd.Series:
+        """Paste all strings of cluster_cols in one single column"""
+        df = df.copy()
+        return df[self.cluster_cols].astype(str).sum(axis=1)
+
+    def _create_binary_treatment(self, df: pd.DataFrame) -> pd.DataFrame:
+        """Transforms treatment column into 0 - 1 column"""
+        df = df.copy()
+        df[self.treatment_col] = (df[self.treatment_col] == self.treatment).astype(int)
+        return df
+
+    @abstractmethod
+    def analysis_pvalue(
+        self,
+        df: pd.DataFrame,
+        verbose: bool = False,
+    ) -> float:
+        """
+        Returns the p-value of the analysis. Expects treatment to be 0-1 variable
+        Arguments:
+            df: dataframe containing the data to analyze
+            verbose (Optional): bool, prints the regression summary if True
+        """
+
+    def analysis_point_estimate(
+        self,
+        df: pd.DataFrame,
+        verbose: bool = False,
+    ) -> float:
+        """
+        Returns the point estimate of the analysis. Expects treatment to be 0-1 variable
+        Arguments:
+            df: dataframe containing the data to analyze
+            verbose (Optional): bool, prints the regression summary if True
+        """
+        raise NotImplementedError("Point estimate not implemented for this analysis")
+
+    def analysis_standard_error(
+        self,
+        df: pd.DataFrame,
+        verbose: bool = False,
+    ) -> float:
+        """
+        Returns the standard error of the analysis. Expects treatment to be 0-1 variable
+        Arguments:
+            df: dataframe containing the data to analyze
+            verbose (Optional): bool, prints the regression summary if True
+        """
+        raise NotImplementedError("Standard error not implemented for this analysis")
+
+    def analysis_confidence_interval(
+        self,
+        df: pd.DataFrame,
+        alpha: float,
+        verbose: bool = False,
+    ) -> ConfidenceInterval:
+        """
+        Returns the confidence interval of the analysis. Expects treatment to be 0-1 variable
+        Arguments:
+            df: dataframe containing the data to analyze
+            alpha: significance level
+            verbose (Optional): bool, prints the regression summary if True
+        """
+        raise NotImplementedError(
+            "Confidence Interval not implemented for this analysis"
+        )
+
+    def analysis_inference_results(
+        self,
+        df: pd.DataFrame,
+        alpha: float,
+        verbose: bool = False,
+    ) -> InferenceResults:
+        """
+        Returns the InferenceResults object of the analysis. Expects treatment to be 0-1 variable
+        Arguments:
+            df: dataframe containing the data to analyze
+            alpha: significance level
+            verbose (Optional): bool, prints the regression summary if True
+        """
+        raise NotImplementedError(
+            "Inference results are not implemented for this analysis"
+        )
+
+    def _data_checks(self, df: pd.DataFrame) -> None:
+        """Checks that the data is correct"""
+        if df[self.target_col].isnull().any():
+            raise ValueError(
+                f"There are null values in outcome column {self.treatment_col}"
+            )
+
+        if not is_numeric_dtype(df[self.target_col]):
+            raise ValueError(
+                f"Outcome column {self.target_col} should be numeric and not {df[self.target_col].dtype}"
+            )
+
+    def get_pvalue(self, df: pd.DataFrame) -> float:
+        """Returns the p-value of the analysis
+
+        Arguments:
+            df: dataframe containing the data to analyze
+        """
+        df = df.copy()
+        df = self._create_binary_treatment(df)
+        self._data_checks(df=df)
+        return self.analysis_pvalue(df)
+
+    def get_point_estimate(self, df: pd.DataFrame) -> float:
+        """Returns the point estimate of the analysis
+
+        Arguments:
+            df: dataframe containing the data to analyze
+        """
+        df = df.copy()
+        df = self._create_binary_treatment(df)
+        self._data_checks(df=df)
+        return self.analysis_point_estimate(df)
+
+    def get_standard_error(self, df: pd.DataFrame) -> float:
+        """Returns the standard error of the analysis
+
+        Arguments:
+            df: dataframe containing the data to analyze
+        """
+        df = df.copy()
+        df = self._create_binary_treatment(df)
+        self._data_checks(df=df)
+        return self.analysis_standard_error(df)
+
+    def get_confidence_interval(
+        self, df: pd.DataFrame, alpha: float
+    ) -> ConfidenceInterval:
+        """Returns the confidence interval of the analysis
+
+        Arguments:
+            df: dataframe containing the data to analyze
+            alpha: significance level
+        """
+        df = df.copy()
+        df = self._create_binary_treatment(df)
+        self._data_checks(df=df)
+        return self.analysis_confidence_interval(df, alpha)
+
+    def get_inference_results(self, df: pd.DataFrame, alpha: float) -> InferenceResults:
+        """Returns the inference results of the analysis
+
+        Arguments:
+            df: dataframe containing the data to analyze
+            alpha: significance level
+        """
+        df = df.copy()
+        df = self._create_binary_treatment(df)
+        self._data_checks(df=df)
+        return self.analysis_inference_results(df, alpha)
+
+    def pvalue_based_on_hypothesis(
+        self, model_result
+    ) -> float:  # todo add typehint statsmodels result
+        """Returns the p-value of the analysis
+        Arguments:
+            model_result: statsmodels result object
+            verbose (Optional): bool, prints the regression summary if True
+
+        """
+        treatment_effect = model_result.params[self.treatment_col]
+        p_value = model_result.pvalues[self.treatment_col]
+
+        if HypothesisEntries(self.hypothesis) == HypothesisEntries.LESS:
+            return p_value / 2 if treatment_effect <= 0 else 1 - p_value / 2
+        if HypothesisEntries(self.hypothesis) == HypothesisEntries.GREATER:
+            return p_value / 2 if treatment_effect >= 0 else 1 - p_value / 2
+        if HypothesisEntries(self.hypothesis) == HypothesisEntries.TWO_SIDED:
+            return p_value
+        raise ValueError(f"{self.hypothesis} is not a valid HypothesisEntries")
+
+    def _split_pre_experiment_df(self, df: pd.DataFrame):
+        raise NotImplementedError(
+            "This method should be implemented in the child class"
+        )
+
+    @classmethod
+    def from_config(cls, config):
+        """Creates an ExperimentAnalysis object from a PowerConfig object"""
+        return cls(
+            cluster_cols=config.cluster_cols,
+            target_col=config.target_col,
+            treatment_col=config.treatment_col,
+            treatment=config.treatment,
+            covariates=config.covariates,
+            hypothesis=config.hypothesis,
+        )
+
+
+ + + +
+ + + + + + + + + + +
+ + + +

+analysis_confidence_interval(self, df, alpha, verbose=False) + + +

+ +
+ +

Returns the confidence interval of the analysis. Expects treatment to be 0-1 variable

+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
dfDataFrame

dataframe containing the data to analyze

required
alphafloat

significance level

required
verboseOptional

bool, prints the regression summary if True

False
+
+ Source code in cluster_experiments/experiment_analysis.py +
def analysis_confidence_interval(
+    self,
+    df: pd.DataFrame,
+    alpha: float,
+    verbose: bool = False,
+) -> ConfidenceInterval:
+    """
+    Returns the confidence interval of the analysis. Expects treatment to be 0-1 variable
+    Arguments:
+        df: dataframe containing the data to analyze
+        alpha: significance level
+        verbose (Optional): bool, prints the regression summary if True
+    """
+    raise NotImplementedError(
+        "Confidence Interval not implemented for this analysis"
+    )
+
+
+
+ +
+ + + +
+ + + +

+analysis_inference_results(self, df, alpha, verbose=False) + + +

+ +
+ +

Returns the InferenceResults object of the analysis. Expects treatment to be 0-1 variable

+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
dfDataFrame

dataframe containing the data to analyze

required
alphafloat

significance level

required
verboseOptional

bool, prints the regression summary if True

False
+
+ Source code in cluster_experiments/experiment_analysis.py +
def analysis_inference_results(
+    self,
+    df: pd.DataFrame,
+    alpha: float,
+    verbose: bool = False,
+) -> InferenceResults:
+    """
+    Returns the InferenceResults object of the analysis. Expects treatment to be 0-1 variable
+    Arguments:
+        df: dataframe containing the data to analyze
+        alpha: significance level
+        verbose (Optional): bool, prints the regression summary if True
+    """
+    raise NotImplementedError(
+        "Inference results are not implemented for this analysis"
+    )
+
+
+
+ +
+ + + +
+ + + +

+analysis_point_estimate(self, df, verbose=False) + + +

+ +
+ +

Returns the point estimate of the analysis. Expects treatment to be 0-1 variable

+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
dfDataFrame

dataframe containing the data to analyze

required
verboseOptional

bool, prints the regression summary if True

False
+
+ Source code in cluster_experiments/experiment_analysis.py +
def analysis_point_estimate(
+    self,
+    df: pd.DataFrame,
+    verbose: bool = False,
+) -> float:
+    """
+    Returns the point estimate of the analysis. Expects treatment to be 0-1 variable
+    Arguments:
+        df: dataframe containing the data to analyze
+        verbose (Optional): bool, prints the regression summary if True
+    """
+    raise NotImplementedError("Point estimate not implemented for this analysis")
+
+
+
+ +
+ + + +
+ + + +

+analysis_pvalue(self, df, verbose=False) + + +

+ +
+ +

Returns the p-value of the analysis. Expects treatment to be 0-1 variable

+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
dfDataFrame

dataframe containing the data to analyze

required
verboseOptional

bool, prints the regression summary if True

False
+
+ Source code in cluster_experiments/experiment_analysis.py +
@abstractmethod
+def analysis_pvalue(
+    self,
+    df: pd.DataFrame,
+    verbose: bool = False,
+) -> float:
+    """
+    Returns the p-value of the analysis. Expects treatment to be 0-1 variable
+    Arguments:
+        df: dataframe containing the data to analyze
+        verbose (Optional): bool, prints the regression summary if True
+    """
+
+
+
+ +
+ + + +
+ + + +

+analysis_standard_error(self, df, verbose=False) + + +

+ +
+ +

Returns the standard error of the analysis. Expects treatment to be 0-1 variable

+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
dfDataFrame

dataframe containing the data to analyze

required
verboseOptional

bool, prints the regression summary if True

False
+
+ Source code in cluster_experiments/experiment_analysis.py +
def analysis_standard_error(
+    self,
+    df: pd.DataFrame,
+    verbose: bool = False,
+) -> float:
+    """
+    Returns the standard error of the analysis. Expects treatment to be 0-1 variable
+    Arguments:
+        df: dataframe containing the data to analyze
+        verbose (Optional): bool, prints the regression summary if True
+    """
+    raise NotImplementedError("Standard error not implemented for this analysis")
+
+
+
+ +
+ + + +
+ + + +

+from_config(config) + + + classmethod + + +

+ +
+ +

Creates an ExperimentAnalysis object from a PowerConfig object

+ +
+ Source code in cluster_experiments/experiment_analysis.py +
@classmethod
+def from_config(cls, config):
+    """Creates an ExperimentAnalysis object from a PowerConfig object"""
+    return cls(
+        cluster_cols=config.cluster_cols,
+        target_col=config.target_col,
+        treatment_col=config.treatment_col,
+        treatment=config.treatment,
+        covariates=config.covariates,
+        hypothesis=config.hypothesis,
+    )
+
+
+
+ +
+ + + +
+ + + +

+get_confidence_interval(self, df, alpha) + + +

+ +
+ +

Returns the confidence interval of the analysis

+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
dfDataFrame

dataframe containing the data to analyze

required
alphafloat

significance level

required
+
+ Source code in cluster_experiments/experiment_analysis.py +
def get_confidence_interval(
+    self, df: pd.DataFrame, alpha: float
+) -> ConfidenceInterval:
+    """Returns the confidence interval of the analysis
+
+    Arguments:
+        df: dataframe containing the data to analyze
+        alpha: significance level
+    """
+    df = df.copy()
+    df = self._create_binary_treatment(df)
+    self._data_checks(df=df)
+    return self.analysis_confidence_interval(df, alpha)
+
+
+
+ +
+ + + +
+ + + +

+get_inference_results(self, df, alpha) + + +

+ +
+ +

Returns the inference results of the analysis

+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
dfDataFrame

dataframe containing the data to analyze

required
alphafloat

significance level

required
+
+ Source code in cluster_experiments/experiment_analysis.py +
def get_inference_results(self, df: pd.DataFrame, alpha: float) -> InferenceResults:
+    """Returns the inference results of the analysis
+
+    Arguments:
+        df: dataframe containing the data to analyze
+        alpha: significance level
+    """
+    df = df.copy()
+    df = self._create_binary_treatment(df)
+    self._data_checks(df=df)
+    return self.analysis_inference_results(df, alpha)
+
+
+
+ +
+ + + +
+ + + +

+get_point_estimate(self, df) + + +

+ +
+ +

Returns the point estimate of the analysis

+ +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
dfDataFrame

dataframe containing the data to analyze

required
+
+ Source code in cluster_experiments/experiment_analysis.py +
def get_point_estimate(self, df: pd.DataFrame) -> float:
+    """Returns the point estimate of the analysis
+
+    Arguments:
+        df: dataframe containing the data to analyze
+    """
+    df = df.copy()
+    df = self._create_binary_treatment(df)
+    self._data_checks(df=df)
+    return self.analysis_point_estimate(df)
+
+
+
+ +
+ + + +
+ + + +

+get_pvalue(self, df) + + +

+ +
+ +

Returns the p-value of the analysis

+ +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
dfDataFrame

dataframe containing the data to analyze

required
+
+ Source code in cluster_experiments/experiment_analysis.py +
def get_pvalue(self, df: pd.DataFrame) -> float:
+    """Returns the p-value of the analysis
+
+    Arguments:
+        df: dataframe containing the data to analyze
+    """
+    df = df.copy()
+    df = self._create_binary_treatment(df)
+    self._data_checks(df=df)
+    return self.analysis_pvalue(df)
+
+
+
+ +
+ + + +
+ + + +

+get_standard_error(self, df) + + +

+ +
+ +

Returns the standard error of the analysis

+ +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
dfDataFrame

dataframe containing the data to analyze

required
+
+ Source code in cluster_experiments/experiment_analysis.py +
def get_standard_error(self, df: pd.DataFrame) -> float:
+    """Returns the standard error of the analysis
+
+    Arguments:
+        df: dataframe containing the data to analyze
+    """
+    df = df.copy()
+    df = self._create_binary_treatment(df)
+    self._data_checks(df=df)
+    return self.analysis_standard_error(df)
+
+
+
+ +
+ + + +
+ + + +

+pvalue_based_on_hypothesis(self, model_result) + + +

+ +
+ +

Returns the p-value of the analysis

+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
model_result

statsmodels result object

required
verboseOptional

bool, prints the regression summary if True

required
+
+ Source code in cluster_experiments/experiment_analysis.py +
def pvalue_based_on_hypothesis(
+    self, model_result
+) -> float:  # todo add typehint statsmodels result
+    """Returns the p-value of the analysis
+    Arguments:
+        model_result: statsmodels result object
+        verbose (Optional): bool, prints the regression summary if True
+
+    """
+    treatment_effect = model_result.params[self.treatment_col]
+    p_value = model_result.pvalues[self.treatment_col]
+
+    if HypothesisEntries(self.hypothesis) == HypothesisEntries.LESS:
+        return p_value / 2 if treatment_effect <= 0 else 1 - p_value / 2
+    if HypothesisEntries(self.hypothesis) == HypothesisEntries.GREATER:
+        return p_value / 2 if treatment_effect >= 0 else 1 - p_value / 2
+    if HypothesisEntries(self.hypothesis) == HypothesisEntries.TWO_SIDED:
+        return p_value
+    raise ValueError(f"{self.hypothesis} is not a valid HypothesisEntries")
+
+
+
+ +
+ + + + + +
+ +
+ +
+ + + +
+ + + +

+ +GeeExperimentAnalysis (ExperimentAnalysis) + + + + +

+ +
+ +

Class to run GEE clustered analysis

+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
cluster_colsList[str]

list of columns to use as clusters

required
target_colstr

name of the column containing the variable to measure

'target'
treatment_colstr

name of the column containing the treatment variable

'treatment'
treatmentstr

name of the treatment to use as the treated group

'B'
covariatesOptional[List[str]]

list of columns to use as covariates

None
hypothesisstr

one of "two-sided", "less", "greater" indicating the alternative hypothesis

'two-sided'

Usage:

+
from cluster_experiments.experiment_analysis import GeeExperimentAnalysis
+import pandas as pd
+
+df = pd.DataFrame({
+    'x': [1, 2, 3, 0, 0, 1],
+    'treatment': ["A"] * 3 + ["B"] * 3,
+    'cluster': [1] * 6,
+})
+
+GeeExperimentAnalysis(
+    cluster_cols=['cluster'],
+    target_col='x',
+).get_pvalue(df)
+
+ +
+ Source code in cluster_experiments/experiment_analysis.py +
class GeeExperimentAnalysis(ExperimentAnalysis):
+    """
+    Class to run GEE clustered analysis
+
+    Arguments:
+        cluster_cols: list of columns to use as clusters
+        target_col: name of the column containing the variable to measure
+        treatment_col: name of the column containing the treatment variable
+        treatment: name of the treatment to use as the treated group
+        covariates: list of columns to use as covariates
+        hypothesis: one of "two-sided", "less", "greater" indicating the alternative hypothesis
+
+    Usage:
+
+    ```python
+    from cluster_experiments.experiment_analysis import GeeExperimentAnalysis
+    import pandas as pd
+
+    df = pd.DataFrame({
+        'x': [1, 2, 3, 0, 0, 1],
+        'treatment': ["A"] * 3 + ["B"] * 3,
+        'cluster': [1] * 6,
+    })
+
+    GeeExperimentAnalysis(
+        cluster_cols=['cluster'],
+        target_col='x',
+    ).get_pvalue(df)
+    ```
+    """
+
+    def __init__(
+        self,
+        cluster_cols: List[str],
+        target_col: str = "target",
+        treatment_col: str = "treatment",
+        treatment: str = "B",
+        covariates: Optional[List[str]] = None,
+        hypothesis: str = "two-sided",
+    ):
+        super().__init__(
+            target_col=target_col,
+            treatment_col=treatment_col,
+            cluster_cols=cluster_cols,
+            treatment=treatment,
+            covariates=covariates,
+            hypothesis=hypothesis,
+        )
+        self.regressors = [self.treatment_col] + self.covariates
+        self.formula = f"{self.target_col} ~ {' + '.join(self.regressors)}"
+        self.fam = sm.families.Gaussian()
+        self.va = sm.cov_struct.Exchangeable()
+
+    def fit_gee(self, df: pd.DataFrame) -> sm.GEE:
+        """Returns the fitted GEE model"""
+        return sm.GEE.from_formula(
+            self.formula,
+            data=df,
+            groups=self._get_cluster_column(df),
+            family=self.fam,
+            cov_struct=self.va,
+        ).fit()
+
+    def analysis_pvalue(self, df: pd.DataFrame, verbose: bool = False) -> float:
+        """Returns the p-value of the analysis
+        Arguments:
+            df: dataframe containing the data to analyze
+            verbose (Optional): bool, prints the regression summary if True
+        """
+        results_gee = self.fit_gee(df)
+        if verbose:
+            print(results_gee.summary())
+
+        p_value = self.pvalue_based_on_hypothesis(results_gee)
+        return p_value
+
+    def analysis_point_estimate(self, df: pd.DataFrame, verbose: bool = False) -> float:
+        """Returns the point estimate of the analysis
+        Arguments:
+            df: dataframe containing the data to analyze
+            verbose (Optional): bool, prints the regression summary if True
+        """
+        results_gee = self.fit_gee(df)
+        return results_gee.params[self.treatment_col]
+
+    def analysis_standard_error(self, df: pd.DataFrame, verbose: bool = False) -> float:
+        """Returns the standard error of the analysis
+        Arguments:
+            df: dataframe containing the data to analyze
+            verbose (Optional): bool, prints the regression summary if True
+        """
+        results_gee = self.fit_gee(df)
+        return results_gee.bse[self.treatment_col]
+
+    def analysis_confidence_interval(
+        self, df: pd.DataFrame, alpha: float, verbose: bool = False
+    ) -> ConfidenceInterval:
+        """Returns the confidence interval of the analysis
+        Arguments:
+            df: dataframe containing the data to analyze
+            alpha: significance level
+            verbose (Optional): bool, prints the regression summary if True
+        """
+        results_gee = self.fit_gee(df)
+        # Extract the confidence interval for the treatment column
+        conf_int_df = results_gee.conf_int(alpha=alpha)
+        lower_bound, upper_bound = conf_int_df.loc[self.treatment_col]
+
+        if verbose:
+            print(results_gee.summary())
+
+        # Return the confidence interval
+        return ConfidenceInterval(lower=lower_bound, upper=upper_bound, alpha=alpha)
+
+    def analysis_inference_results(
+        self, df: pd.DataFrame, alpha: float, verbose: bool = False
+    ) -> InferenceResults:
+        """Returns the inference results of the analysis
+        Arguments:
+            df: dataframe containing the data to analyze
+            alpha: significance level
+            verbose (Optional): bool, prints the regression summary if True
+        """
+        results_gee = self.fit_gee(df)
+
+        std_error = results_gee.bse[self.treatment_col]
+        ate = results_gee.params[self.treatment_col]
+        p_value = self.pvalue_based_on_hypothesis(results_gee)
+
+        # Extract the confidence interval for the treatment column
+        conf_int_df = results_gee.conf_int(alpha=alpha)
+        lower_bound, upper_bound = conf_int_df.loc[self.treatment_col]
+
+        if verbose:
+            print(results_gee.summary())
+
+        # Return the confidence interval
+        return InferenceResults(
+            ate=ate,
+            p_value=p_value,
+            std_error=std_error,
+            conf_int=ConfidenceInterval(
+                lower=lower_bound, upper=upper_bound, alpha=alpha
+            ),
+        )
+
+
+ + + +
+ + + + + + + + + + +
+ + + +

+analysis_confidence_interval(self, df, alpha, verbose=False) + + +

+ +
+ +

Returns the confidence interval of the analysis

+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
dfDataFrame

dataframe containing the data to analyze

required
alphafloat

significance level

required
verboseOptional

bool, prints the regression summary if True

False
+
+ Source code in cluster_experiments/experiment_analysis.py +
def analysis_confidence_interval(
+    self, df: pd.DataFrame, alpha: float, verbose: bool = False
+) -> ConfidenceInterval:
+    """Returns the confidence interval of the analysis
+    Arguments:
+        df: dataframe containing the data to analyze
+        alpha: significance level
+        verbose (Optional): bool, prints the regression summary if True
+    """
+    results_gee = self.fit_gee(df)
+    # Extract the confidence interval for the treatment column
+    conf_int_df = results_gee.conf_int(alpha=alpha)
+    lower_bound, upper_bound = conf_int_df.loc[self.treatment_col]
+
+    if verbose:
+        print(results_gee.summary())
+
+    # Return the confidence interval
+    return ConfidenceInterval(lower=lower_bound, upper=upper_bound, alpha=alpha)
+
+
+
+ +
+ + + +
+ + + +

+analysis_inference_results(self, df, alpha, verbose=False) + + +

+ +
+ +

Returns the inference results of the analysis

+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
dfDataFrame

dataframe containing the data to analyze

required
alphafloat

significance level

required
verboseOptional

bool, prints the regression summary if True

False
+
+ Source code in cluster_experiments/experiment_analysis.py +
def analysis_inference_results(
+    self, df: pd.DataFrame, alpha: float, verbose: bool = False
+) -> InferenceResults:
+    """Returns the inference results of the analysis
+    Arguments:
+        df: dataframe containing the data to analyze
+        alpha: significance level
+        verbose (Optional): bool, prints the regression summary if True
+    """
+    results_gee = self.fit_gee(df)
+
+    std_error = results_gee.bse[self.treatment_col]
+    ate = results_gee.params[self.treatment_col]
+    p_value = self.pvalue_based_on_hypothesis(results_gee)
+
+    # Extract the confidence interval for the treatment column
+    conf_int_df = results_gee.conf_int(alpha=alpha)
+    lower_bound, upper_bound = conf_int_df.loc[self.treatment_col]
+
+    if verbose:
+        print(results_gee.summary())
+
+    # Return the confidence interval
+    return InferenceResults(
+        ate=ate,
+        p_value=p_value,
+        std_error=std_error,
+        conf_int=ConfidenceInterval(
+            lower=lower_bound, upper=upper_bound, alpha=alpha
+        ),
+    )
+
+
+
+ +
+ + + +
+ + + +

+analysis_point_estimate(self, df, verbose=False) + + +

+ +
+ +

Returns the point estimate of the analysis

+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
dfDataFrame

dataframe containing the data to analyze

required
verboseOptional

bool, prints the regression summary if True

False
+
+ Source code in cluster_experiments/experiment_analysis.py +
def analysis_point_estimate(self, df: pd.DataFrame, verbose: bool = False) -> float:
+    """Returns the point estimate of the analysis
+    Arguments:
+        df: dataframe containing the data to analyze
+        verbose (Optional): bool, prints the regression summary if True
+    """
+    results_gee = self.fit_gee(df)
+    return results_gee.params[self.treatment_col]
+
+
+
+ +
+ + + +
+ + + +

+analysis_pvalue(self, df, verbose=False) + + +

+ +
+ +

Returns the p-value of the analysis

+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
dfDataFrame

dataframe containing the data to analyze

required
verboseOptional

bool, prints the regression summary if True

False
+
+ Source code in cluster_experiments/experiment_analysis.py +
def analysis_pvalue(self, df: pd.DataFrame, verbose: bool = False) -> float:
+    """Returns the p-value of the analysis
+    Arguments:
+        df: dataframe containing the data to analyze
+        verbose (Optional): bool, prints the regression summary if True
+    """
+    results_gee = self.fit_gee(df)
+    if verbose:
+        print(results_gee.summary())
+
+    p_value = self.pvalue_based_on_hypothesis(results_gee)
+    return p_value
+
+
+
+ +
+ + + +
+ + + +

+analysis_standard_error(self, df, verbose=False) + + +

+ +
+ +

Returns the standard error of the analysis

+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
dfDataFrame

dataframe containing the data to analyze

required
verboseOptional

bool, prints the regression summary if True

False
+
+ Source code in cluster_experiments/experiment_analysis.py +
def analysis_standard_error(self, df: pd.DataFrame, verbose: bool = False) -> float:
+    """Returns the standard error of the analysis
+    Arguments:
+        df: dataframe containing the data to analyze
+        verbose (Optional): bool, prints the regression summary if True
+    """
+    results_gee = self.fit_gee(df)
+    return results_gee.bse[self.treatment_col]
+
+
+
+ +
+ + + +
+ + + +

+fit_gee(self, df) + + +

+ +
+ +

Returns the fitted GEE model

+ +
+ Source code in cluster_experiments/experiment_analysis.py +
def fit_gee(self, df: pd.DataFrame) -> sm.GEE:
+    """Returns the fitted GEE model"""
+    return sm.GEE.from_formula(
+        self.formula,
+        data=df,
+        groups=self._get_cluster_column(df),
+        family=self.fam,
+        cov_struct=self.va,
+    ).fit()
+
+
+
+ +
+ + + + + +
+ +
+ +
+ + + +
+ + + +

+ +InferenceResults + + + + dataclass + + +

+ +
+ +

Class to define the structure of complete statistical analysis results.

+ +
+ Source code in cluster_experiments/experiment_analysis.py +
class InferenceResults:
+    """
+    Class to define the structure of complete statistical analysis results.
+    """
+
+    ate: float
+    p_value: float
+    std_error: float
+    conf_int: ConfidenceInterval
+
+
+ + + +
+ + + + + + + + + + + + + + + + + + +
+ +
+ +
+ + + +
+ + + +

+ +MLMExperimentAnalysis (ExperimentAnalysis) + + + + +

+ +
+ +

Class to run Mixed Linear Models clustered analysis

+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
cluster_colsList[str]

list of columns to use as clusters

required
target_colstr

name of the column containing the variable to measure

'target'
treatment_colstr

name of the column containing the treatment variable

'treatment'
treatmentstr

name of the treatment to use as the treated group

'B'
covariatesOptional[List[str]]

list of columns to use as covariates

None
hypothesisstr

one of "two-sided", "less", "greater" indicating the alternative hypothesis

'two-sided'

Usage:

+
from cluster_experiments.experiment_analysis import MLMExperimentAnalysis
+import pandas as pd
+
+df = pd.DataFrame({
+    'x': [1, 2, 3, 0, 0, 1],
+    'treatment': ["A"] * 3 + ["B"] * 3,
+    'cluster': [1] * 6,
+})
+
+MLMExperimentAnalysis(
+    cluster_cols=['cluster'],
+    target_col='x',
+).get_pvalue(df)
+
+ +
+ Source code in cluster_experiments/experiment_analysis.py +
class MLMExperimentAnalysis(ExperimentAnalysis):
+    """
+    Class to run Mixed Linear Models clustered analysis
+
+    Arguments:
+        cluster_cols: list of columns to use as clusters
+        target_col: name of the column containing the variable to measure
+        treatment_col: name of the column containing the treatment variable
+        treatment: name of the treatment to use as the treated group
+        covariates: list of columns to use as covariates
+        hypothesis: one of "two-sided", "less", "greater" indicating the alternative hypothesis
+
+    Usage:
+
+    ```python
+    from cluster_experiments.experiment_analysis import MLMExperimentAnalysis
+    import pandas as pd
+
+    df = pd.DataFrame({
+        'x': [1, 2, 3, 0, 0, 1],
+        'treatment': ["A"] * 3 + ["B"] * 3,
+        'cluster': [1] * 6,
+    })
+
+    MLMExperimentAnalysis(
+        cluster_cols=['cluster'],
+        target_col='x',
+    ).get_pvalue(df)
+    ```
+    """
+
+    def __init__(
+        self,
+        cluster_cols: List[str],
+        target_col: str = "target",
+        treatment_col: str = "treatment",
+        treatment: str = "B",
+        covariates: Optional[List[str]] = None,
+        hypothesis: str = "two-sided",
+    ):
+        super().__init__(
+            target_col=target_col,
+            treatment_col=treatment_col,
+            cluster_cols=cluster_cols,
+            treatment=treatment,
+            covariates=covariates,
+            hypothesis=hypothesis,
+        )
+        self.regressors = [self.treatment_col] + self.covariates
+        self.formula = f"{self.target_col} ~ {' + '.join(self.regressors)}"
+
+        self.re_formula = None
+        self.vc_formula = None
+
+    def fit_mlm(self, df: pd.DataFrame) -> sm.MixedLM:
+        """Returns the fitted MLM model"""
+        return sm.MixedLM.from_formula(
+            formula=self.formula,
+            data=df,
+            groups=self._get_cluster_column(df),
+            re_formula=self.re_formula,
+            vc_formula=self.vc_formula,
+        ).fit()
+
+    def analysis_pvalue(self, df: pd.DataFrame, verbose: bool = False) -> float:
+        """Returns the p-value of the analysis
+        Arguments:
+            df: dataframe containing the data to analyze
+            verbose (Optional): bool, prints the regression summary if True
+        """
+        results_mlm = self.fit_mlm(df)
+        if verbose:
+            print(results_mlm.summary())
+
+        p_value = self.pvalue_based_on_hypothesis(results_mlm)
+        return p_value
+
+    def analysis_point_estimate(self, df: pd.DataFrame, verbose: bool = False) -> float:
+        """Returns the point estimate of the analysis
+        Arguments:
+            df: dataframe containing the data to analyze
+            verbose (Optional): bool, prints the regression summary if True
+        """
+        results_mlm = self.fit_mlm(df)
+        return results_mlm.params[self.treatment_col]
+
+    def analysis_standard_error(self, df: pd.DataFrame, verbose: bool = False) -> float:
+        """Returns the standard error of the analysis
+        Arguments:
+            df: dataframe containing the data to analyze
+            verbose (Optional): bool, prints the regression summary if True
+        """
+        results_mlm = self.fit_mlm(df)
+        return results_mlm.bse[self.treatment_col]
+
+
+ + + +
+ + + + + + + + + + +
+ + + +

+analysis_point_estimate(self, df, verbose=False) + + +

+ +
+ +

Returns the point estimate of the analysis

+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
dfDataFrame

dataframe containing the data to analyze

required
verboseOptional

bool, prints the regression summary if True

False
+
+ Source code in cluster_experiments/experiment_analysis.py +
def analysis_point_estimate(self, df: pd.DataFrame, verbose: bool = False) -> float:
+    """Returns the point estimate of the analysis
+    Arguments:
+        df: dataframe containing the data to analyze
+        verbose (Optional): bool, prints the regression summary if True
+    """
+    results_mlm = self.fit_mlm(df)
+    return results_mlm.params[self.treatment_col]
+
+
+
+ +
+ + + +
+ + + +

+analysis_pvalue(self, df, verbose=False) + + +

+ +
+ +

Returns the p-value of the analysis

+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
dfDataFrame

dataframe containing the data to analyze

required
verboseOptional

bool, prints the regression summary if True

False
+
+ Source code in cluster_experiments/experiment_analysis.py +
def analysis_pvalue(self, df: pd.DataFrame, verbose: bool = False) -> float:
+    """Returns the p-value of the analysis
+    Arguments:
+        df: dataframe containing the data to analyze
+        verbose (Optional): bool, prints the regression summary if True
+    """
+    results_mlm = self.fit_mlm(df)
+    if verbose:
+        print(results_mlm.summary())
+
+    p_value = self.pvalue_based_on_hypothesis(results_mlm)
+    return p_value
+
+
+
+ +
+ + + +
+ + + +

+analysis_standard_error(self, df, verbose=False) + + +

+ +
+ +

Returns the standard error of the analysis

+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
dfDataFrame

dataframe containing the data to analyze

required
verboseOptional

bool, prints the regression summary if True

False
+
+ Source code in cluster_experiments/experiment_analysis.py +
def analysis_standard_error(self, df: pd.DataFrame, verbose: bool = False) -> float:
+    """Returns the standard error of the analysis
+    Arguments:
+        df: dataframe containing the data to analyze
+        verbose (Optional): bool, prints the regression summary if True
+    """
+    results_mlm = self.fit_mlm(df)
+    return results_mlm.bse[self.treatment_col]
+
+
+
+ +
+ + + +
+ + + +

+fit_mlm(self, df) + + +

+ +
+ +

Returns the fitted MLM model

+ +
+ Source code in cluster_experiments/experiment_analysis.py +
def fit_mlm(self, df: pd.DataFrame) -> sm.MixedLM:
+    """Returns the fitted MLM model"""
+    return sm.MixedLM.from_formula(
+        formula=self.formula,
+        data=df,
+        groups=self._get_cluster_column(df),
+        re_formula=self.re_formula,
+        vc_formula=self.vc_formula,
+    ).fit()
+
+
+
+ +
+ + + + + +
+ +
+ +
+ + + +
+ + + +

+ +OLSAnalysis (ExperimentAnalysis) + + + + +

+ +
+ +

Class to run OLS analysis

+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
target_colstr

name of the column containing the variable to measure

'target'
treatment_colstr

name of the column containing the treatment variable

'treatment'
treatmentstr

name of the treatment to use as the treated group

'B'
covariatesOptional[List[str]]

list of columns to use as covariates

None
hypothesisstr

one of "two-sided", "less", "greater" indicating the alternative hypothesis

'two-sided'

Usage:

+
from cluster_experiments.experiment_analysis import OLSAnalysis
+import pandas as pd
+
+df = pd.DataFrame({
+    'x': [1, 2, 3, 0, 0, 1],
+    'treatment': ["A"] * 3 + ["B"] * 3,
+})
+
+OLSAnalysis(
+    target_col='x',
+).get_pvalue(df)
+
+ +
+ Source code in cluster_experiments/experiment_analysis.py +
class OLSAnalysis(ExperimentAnalysis):
+    """
+    Class to run OLS analysis
+
+    Arguments:
+        target_col: name of the column containing the variable to measure
+        treatment_col: name of the column containing the treatment variable
+        treatment: name of the treatment to use as the treated group
+        covariates: list of columns to use as covariates
+        hypothesis: one of "two-sided", "less", "greater" indicating the alternative hypothesis
+
+    Usage:
+
+    ```python
+    from cluster_experiments.experiment_analysis import OLSAnalysis
+    import pandas as pd
+
+    df = pd.DataFrame({
+        'x': [1, 2, 3, 0, 0, 1],
+        'treatment': ["A"] * 3 + ["B"] * 3,
+    })
+
+    OLSAnalysis(
+        target_col='x',
+    ).get_pvalue(df)
+    ```
+    """
+
+    def __init__(
+        self,
+        target_col: str = "target",
+        treatment_col: str = "treatment",
+        treatment: str = "B",
+        covariates: Optional[List[str]] = None,
+        hypothesis: str = "two-sided",
+    ):
+        self.target_col = target_col
+        self.treatment = treatment
+        self.treatment_col = treatment_col
+        self.covariates = covariates or []
+        self.regressors = [self.treatment_col] + self.covariates
+        self.formula = f"{self.target_col} ~ {' + '.join(self.regressors)}"
+        self.hypothesis = hypothesis
+
+    def fit_ols(self, df: pd.DataFrame):
+        """Returns the fitted OLS model"""
+        return sm.OLS.from_formula(self.formula, data=df).fit()
+
+    def analysis_pvalue(self, df: pd.DataFrame, verbose: bool = False) -> float:
+        """Returns the p-value of the analysis
+        Arguments:
+            df: dataframe containing the data to analyze
+            verbose (Optional): bool, prints the regression summary if True
+        """
+        results_ols = self.fit_ols(df=df)
+        if verbose:
+            print(results_ols.summary())
+
+        p_value = self.pvalue_based_on_hypothesis(results_ols)
+        return p_value
+
+    def analysis_point_estimate(self, df: pd.DataFrame, verbose: bool = False) -> float:
+        """Returns the point estimate of the analysis
+        Arguments:
+            df: dataframe containing the data to analyze
+            verbose (Optional): bool, prints the regression summary if True
+        """
+        results_ols = self.fit_ols(df=df)
+        return results_ols.params[self.treatment_col]
+
+    def analysis_standard_error(self, df: pd.DataFrame, verbose: bool = False) -> float:
+        """Returns the standard error of the analysis
+        Arguments:
+            df: dataframe containing the data to analyze
+            verbose (Optional): bool, prints the regression summary if True
+        """
+        results_ols = self.fit_ols(df=df)
+        return results_ols.bse[self.treatment_col]
+
+    def analysis_confidence_interval(
+        self, df: pd.DataFrame, alpha: float, verbose: bool = False
+    ) -> ConfidenceInterval:
+        """Returns the confidence interval of the analysis
+        Arguments:
+            df: dataframe containing the data to analyze
+            alpha: significance level
+            verbose (Optional): bool, prints the regression summary if True
+        """
+        results_ols = self.fit_ols(df)
+        # Extract the confidence interval for the treatment column
+        conf_int_df = results_ols.conf_int(alpha=alpha)
+        lower_bound, upper_bound = conf_int_df.loc[self.treatment_col]
+
+        if verbose:
+            print(results_ols.summary())
+
+        # Return the confidence interval
+        return ConfidenceInterval(lower=lower_bound, upper=upper_bound, alpha=alpha)
+
+    def analysis_inference_results(
+        self, df: pd.DataFrame, alpha: float, verbose: bool = False
+    ) -> InferenceResults:
+        """Returns the inference results of the analysis
+        Arguments:
+            df: dataframe containing the data to analyze
+            alpha: significance level
+            verbose (Optional): bool, prints the regression summary if True
+        """
+        results_ols = self.fit_ols(df)
+
+        std_error = results_ols.bse[self.treatment_col]
+        ate = results_ols.params[self.treatment_col]
+        p_value = self.pvalue_based_on_hypothesis(results_ols)
+
+        # Extract the confidence interval for the treatment column
+        conf_int_df = results_ols.conf_int(alpha=alpha)
+        lower_bound, upper_bound = conf_int_df.loc[self.treatment_col]
+
+        if verbose:
+            print(results_ols.summary())
+
+        # Return the confidence interval
+        return InferenceResults(
+            ate=ate,
+            p_value=p_value,
+            std_error=std_error,
+            conf_int=ConfidenceInterval(
+                lower=lower_bound, upper=upper_bound, alpha=alpha
+            ),
+        )
+
+    @classmethod
+    def from_config(cls, config):
+        """Creates an OLSAnalysis object from a PowerConfig object"""
+        return cls(
+            target_col=config.target_col,
+            treatment_col=config.treatment_col,
+            treatment=config.treatment,
+            covariates=config.covariates,
+            hypothesis=config.hypothesis,
+        )
+
+
+ + + +
+ + + + + + + + + + +
+ + + +

+analysis_confidence_interval(self, df, alpha, verbose=False) + + +

+ +
+ +

Returns the confidence interval of the analysis

+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
dfDataFrame

dataframe containing the data to analyze

required
alphafloat

significance level

required
verboseOptional

bool, prints the regression summary if True

False
+
+ Source code in cluster_experiments/experiment_analysis.py +
def analysis_confidence_interval(
+    self, df: pd.DataFrame, alpha: float, verbose: bool = False
+) -> ConfidenceInterval:
+    """Returns the confidence interval of the analysis
+    Arguments:
+        df: dataframe containing the data to analyze
+        alpha: significance level
+        verbose (Optional): bool, prints the regression summary if True
+    """
+    results_ols = self.fit_ols(df)
+    # Extract the confidence interval for the treatment column
+    conf_int_df = results_ols.conf_int(alpha=alpha)
+    lower_bound, upper_bound = conf_int_df.loc[self.treatment_col]
+
+    if verbose:
+        print(results_ols.summary())
+
+    # Return the confidence interval
+    return ConfidenceInterval(lower=lower_bound, upper=upper_bound, alpha=alpha)
+
+
+
+ +
+ + + +
+ + + +

+analysis_inference_results(self, df, alpha, verbose=False) + + +

+ +
+ +

Returns the inference results of the analysis

+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
dfDataFrame

dataframe containing the data to analyze

required
alphafloat

significance level

required
verboseOptional

bool, prints the regression summary if True

False
+
+ Source code in cluster_experiments/experiment_analysis.py +
def analysis_inference_results(
+    self, df: pd.DataFrame, alpha: float, verbose: bool = False
+) -> InferenceResults:
+    """Returns the inference results of the analysis
+    Arguments:
+        df: dataframe containing the data to analyze
+        alpha: significance level
+        verbose (Optional): bool, prints the regression summary if True
+    """
+    results_ols = self.fit_ols(df)
+
+    std_error = results_ols.bse[self.treatment_col]
+    ate = results_ols.params[self.treatment_col]
+    p_value = self.pvalue_based_on_hypothesis(results_ols)
+
+    # Extract the confidence interval for the treatment column
+    conf_int_df = results_ols.conf_int(alpha=alpha)
+    lower_bound, upper_bound = conf_int_df.loc[self.treatment_col]
+
+    if verbose:
+        print(results_ols.summary())
+
+    # Return the confidence interval
+    return InferenceResults(
+        ate=ate,
+        p_value=p_value,
+        std_error=std_error,
+        conf_int=ConfidenceInterval(
+            lower=lower_bound, upper=upper_bound, alpha=alpha
+        ),
+    )
+
+
+
+ +
+ + + +
+ + + +

+analysis_point_estimate(self, df, verbose=False) + + +

+ +
+ +

Returns the point estimate of the analysis

+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
dfDataFrame

dataframe containing the data to analyze

required
verboseOptional

bool, prints the regression summary if True

False
+
+ Source code in cluster_experiments/experiment_analysis.py +
def analysis_point_estimate(self, df: pd.DataFrame, verbose: bool = False) -> float:
+    """Returns the point estimate of the analysis
+    Arguments:
+        df: dataframe containing the data to analyze
+        verbose (Optional): bool, prints the regression summary if True
+    """
+    results_ols = self.fit_ols(df=df)
+    return results_ols.params[self.treatment_col]
+
+
+
+ +
+ + + +
+ + + +

+analysis_pvalue(self, df, verbose=False) + + +

+ +
+ +

Returns the p-value of the analysis

+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
dfDataFrame

dataframe containing the data to analyze

required
verboseOptional

bool, prints the regression summary if True

False
+
+ Source code in cluster_experiments/experiment_analysis.py +
def analysis_pvalue(self, df: pd.DataFrame, verbose: bool = False) -> float:
+    """Returns the p-value of the analysis
+    Arguments:
+        df: dataframe containing the data to analyze
+        verbose (Optional): bool, prints the regression summary if True
+    """
+    results_ols = self.fit_ols(df=df)
+    if verbose:
+        print(results_ols.summary())
+
+    p_value = self.pvalue_based_on_hypothesis(results_ols)
+    return p_value
+
+
+
+ +
+ + + +
+ + + +

+analysis_standard_error(self, df, verbose=False) + + +

+ +
+ +

Returns the standard error of the analysis

+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
dfDataFrame

dataframe containing the data to analyze

required
verboseOptional

bool, prints the regression summary if True

False
+
+ Source code in cluster_experiments/experiment_analysis.py +
def analysis_standard_error(self, df: pd.DataFrame, verbose: bool = False) -> float:
+    """Returns the standard error of the analysis
+    Arguments:
+        df: dataframe containing the data to analyze
+        verbose (Optional): bool, prints the regression summary if True
+    """
+    results_ols = self.fit_ols(df=df)
+    return results_ols.bse[self.treatment_col]
+
+
+
+ +
+ + + +
+ + + +

+fit_ols(self, df) + + +

+ +
+ +

Returns the fitted OLS model

+ +
+ Source code in cluster_experiments/experiment_analysis.py +
def fit_ols(self, df: pd.DataFrame):
+    """Returns the fitted OLS model"""
+    return sm.OLS.from_formula(self.formula, data=df).fit()
+
+
+
+ +
+ + + +
+ + + +

+from_config(config) + + + classmethod + + +

+ +
+ +

Creates an OLSAnalysis object from a PowerConfig object

+ +
+ Source code in cluster_experiments/experiment_analysis.py +
@classmethod
+def from_config(cls, config):
+    """Creates an OLSAnalysis object from a PowerConfig object"""
+    return cls(
+        target_col=config.target_col,
+        treatment_col=config.treatment_col,
+        treatment=config.treatment,
+        covariates=config.covariates,
+        hypothesis=config.hypothesis,
+    )
+
+
+
+ +
+ + + + + +
+ +
+ +
+ + + +
+ + + +

+ +PairedTTestClusteredAnalysis (ExperimentAnalysis) + + + + +

+ +
+ +

Class to run paired T-test analysis on aggregated data

+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
cluster_colsList[str]

list of columns to use as clusters

required
target_colstr

name of the column containing the variable to measure

'target'
treatment_colstr

name of the column containing the treatment variable

'treatment'
treatmentstr

name of the treatment to use as the treated group

'B'
strata_colsList[str]

list of index columns for paired t test. Should be a subset or equal to cluster_cols

required
hypothesisstr

one of "two-sided", "less", "greater" indicating the alternative hypothesis

'two-sided'

Usage:

+
from cluster_experiments.experiment_analysis import PairedTTestClusteredAnalysis
+import pandas as pd
+
+df = pd.DataFrame({
+    'x': [1, 2, 3, 4, 0, 0, 1, 1],
+    'treatment': ["A", "B", "A", "B"] * 2,
+    'cluster': [1, 2, 3, 4, 1, 2, 3, 4],
+})
+
+PairedTTestClusteredAnalysis(
+    cluster_cols=['cluster'],
+    strata_cols=['cluster'],
+    target_col='x',
+).get_pvalue(df)
+
+ +
+ Source code in cluster_experiments/experiment_analysis.py +
class PairedTTestClusteredAnalysis(ExperimentAnalysis):
+    """
+    Class to run paired T-test analysis on aggregated data
+
+    Arguments:
+        cluster_cols: list of columns to use as clusters
+        target_col: name of the column containing the variable to measure
+        treatment_col: name of the column containing the treatment variable
+        treatment: name of the treatment to use as the treated group
+        strata_cols: list of index columns for paired t test. Should be a subset or equal to cluster_cols
+        hypothesis: one of "two-sided", "less", "greater" indicating the alternative hypothesis
+
+    Usage:
+
+    ```python
+    from cluster_experiments.experiment_analysis import PairedTTestClusteredAnalysis
+    import pandas as pd
+
+    df = pd.DataFrame({
+        'x': [1, 2, 3, 4, 0, 0, 1, 1],
+        'treatment': ["A", "B", "A", "B"] * 2,
+        'cluster': [1, 2, 3, 4, 1, 2, 3, 4],
+    })
+
+    PairedTTestClusteredAnalysis(
+        cluster_cols=['cluster'],
+        strata_cols=['cluster'],
+        target_col='x',
+    ).get_pvalue(df)
+    ```
+    """
+
+    def __init__(
+        self,
+        cluster_cols: List[str],
+        strata_cols: List[str],
+        target_col: str = "target",
+        treatment_col: str = "treatment",
+        treatment: str = "B",
+        hypothesis: str = "two-sided",
+    ):
+        self.strata_cols = strata_cols
+        self.target_col = target_col
+        self.treatment = treatment
+        self.treatment_col = treatment_col
+        self.cluster_cols = cluster_cols
+        self.hypothesis = hypothesis
+
+    def _preprocessing(self, df: pd.DataFrame, verbose: bool = False) -> pd.DataFrame:
+        df_grouped = df.groupby(
+            self.cluster_cols + [self.treatment_col], as_index=False
+        )[self.target_col].mean()
+
+        n_control = df_grouped[self.treatment_col].value_counts()[0]
+        n_treatment = df_grouped[self.treatment_col].value_counts()[1]
+
+        if n_control != n_treatment:
+            logging.warning(
+                f"groups don't have same number of observations, {n_treatment =} and  {n_control =}"
+            )
+
+        assert all(
+            [x in self.cluster_cols for x in self.strata_cols]
+        ), f"strata should be a subset or equal to cluster_cols ({self.cluster_cols = }, {self.strata_cols = })"
+
+        df_pivot = df_grouped.pivot_table(
+            columns=self.treatment_col,
+            index=self.strata_cols,
+            values=self.target_col,
+        )
+
+        if df_pivot.isna().sum().sum() > 0:
+            logging.warning(
+                f"There are missing pairs for some clusters, removing the lonely ones: {df_pivot[df_pivot.isna().any(axis=1)].to_dict()}"
+            )
+
+        if verbose:
+            print(f"performing paired t test in this data \n {df_pivot} \n")
+
+        df_pivot = df_pivot.dropna()
+
+        return df_pivot
+
+    def analysis_pvalue(self, df: pd.DataFrame, verbose: bool = False) -> float:
+        """Returns the p-value of the analysis
+        Arguments:
+            df: dataframe containing the data to analyze
+            verbose (Optional): bool, prints the extra info if True
+        """
+        assert (
+            type(self.cluster_cols) is list
+        ), "cluster_cols needs to be a list of strings (even with one element)"
+        assert (
+            type(self.strata_cols) is list
+        ), "strata_cols needs to be a list of strings (even with one element)"
+
+        df_pivot = self._preprocessing(df=df)
+
+        t_test_results = ttest_rel(
+            df_pivot.iloc[:, 0], df_pivot.iloc[:, 1], alternative=self.hypothesis
+        )
+
+        if verbose:
+            print(f"paired t test results: \n {t_test_results} \n")
+
+        return t_test_results.pvalue
+
+    @classmethod
+    def from_config(cls, config):
+        """Creates a PairedTTestClusteredAnalysis object from a PowerConfig object"""
+        return cls(
+            cluster_cols=config.cluster_cols,
+            target_col=config.target_col,
+            treatment_col=config.treatment_col,
+            treatment=config.treatment,
+            strata_cols=config.strata_cols,
+            hypothesis=config.hypothesis,
+        )
+
+
+ + + +
+ + + + + + + + + + +
+ + + +

+analysis_pvalue(self, df, verbose=False) + + +

+ +
+ +

Returns the p-value of the analysis

+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
dfDataFrame

dataframe containing the data to analyze

required
verboseOptional

bool, prints the extra info if True

False
+
+ Source code in cluster_experiments/experiment_analysis.py +
def analysis_pvalue(self, df: pd.DataFrame, verbose: bool = False) -> float:
+    """Returns the p-value of the analysis
+    Arguments:
+        df: dataframe containing the data to analyze
+        verbose (Optional): bool, prints the extra info if True
+    """
+    assert (
+        type(self.cluster_cols) is list
+    ), "cluster_cols needs to be a list of strings (even with one element)"
+    assert (
+        type(self.strata_cols) is list
+    ), "strata_cols needs to be a list of strings (even with one element)"
+
+    df_pivot = self._preprocessing(df=df)
+
+    t_test_results = ttest_rel(
+        df_pivot.iloc[:, 0], df_pivot.iloc[:, 1], alternative=self.hypothesis
+    )
+
+    if verbose:
+        print(f"paired t test results: \n {t_test_results} \n")
+
+    return t_test_results.pvalue
+
+
+
+ +
+ + + +
+ + + +

+from_config(config) + + + classmethod + + +

+ +
+ +

Creates a PairedTTestClusteredAnalysis object from a PowerConfig object

+ +
+ Source code in cluster_experiments/experiment_analysis.py +
@classmethod
+def from_config(cls, config):
+    """Creates a PairedTTestClusteredAnalysis object from a PowerConfig object"""
+    return cls(
+        cluster_cols=config.cluster_cols,
+        target_col=config.target_col,
+        treatment_col=config.treatment_col,
+        treatment=config.treatment,
+        strata_cols=config.strata_cols,
+        hypothesis=config.hypothesis,
+    )
+
+
+
+ +
+ + + + + +
+ +
+ +
+ + + +
+ + + +

+ +SyntheticControlAnalysis (ExperimentAnalysis) + + + + +

+ +
+ +

Class to run Synthetic control analysis. It expects only one treatment cluster.

+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
target_colstr

The name of the column containing the variable to measure.

'target'
treatment_colstr

The name of the column containing the treatment variable.

'treatment'
treatmentstr

The name of the treatment to use as the treated group.

'B'
cluster_colslist

A list of columns to use as clusters.

required
hypothesisstr

One of "two-sided", "less", "greater" indicating the hypothesis.

'two-sided'
time_colstr

The name of the column containing the time data.

'date'
intervention_datestr

The date when the intervention occurred.

required

Usage:

+
from cluster_experiments.experiment_analysis import SyntheticControlAnalysis
+import pandas as pd
+import numpy as np
+from itertools import product
+
+dates = pd.date_range("2022-01-01", "2022-01-31", freq="d")
+
+users = [f"User {i}" for i in range(10)]
+
+# Create a combination of each date with each user
+combinations = list(product(users, dates))
+
+target_values = np.random.normal(0, 1, size=len(combinations))
+
+df = pd.DataFrame(combinations, columns=["user", "date"])
+df["target"] = target_values
+
+df["treatment"] = "A"
+df.loc[(df["user"] == "User 5"), "treatment"] = "B"
+
+SyntheticControlAnalysis(
+    cluster_cols=["user"], time_col="date", intervention_date="2022-01-15"
+).get_pvalue(df)
+
+ +
+ Source code in cluster_experiments/experiment_analysis.py +
class SyntheticControlAnalysis(ExperimentAnalysis):
+    """
+    Class to run Synthetic control analysis. It expects only one treatment cluster.
+
+    Arguments:
+
+        target_col (str): The name of the column containing the variable to measure.
+        treatment_col (str): The name of the column containing the treatment variable.
+        treatment (str): The name of the treatment to use as the treated group.
+        cluster_cols (list): A list of columns to use as clusters.
+        hypothesis (str): One of "two-sided", "less", "greater" indicating the hypothesis.
+        time_col (str): The name of the column containing the time data.
+        intervention_date (str): The date when the intervention occurred.
+    Usage:
+
+    ```python
+    from cluster_experiments.experiment_analysis import SyntheticControlAnalysis
+    import pandas as pd
+    import numpy as np
+    from itertools import product
+
+    dates = pd.date_range("2022-01-01", "2022-01-31", freq="d")
+
+    users = [f"User {i}" for i in range(10)]
+
+    # Create a combination of each date with each user
+    combinations = list(product(users, dates))
+
+    target_values = np.random.normal(0, 1, size=len(combinations))
+
+    df = pd.DataFrame(combinations, columns=["user", "date"])
+    df["target"] = target_values
+
+    df["treatment"] = "A"
+    df.loc[(df["user"] == "User 5"), "treatment"] = "B"
+
+    SyntheticControlAnalysis(
+        cluster_cols=["user"], time_col="date", intervention_date="2022-01-15"
+    ).get_pvalue(df)
+
+    ```
+    """
+
+    def __init__(
+        self,
+        intervention_date: str,
+        cluster_cols: List[str],
+        target_col: str = "target",
+        treatment_col: str = "treatment",
+        treatment: str = "B",
+        hypothesis: str = "two-sided",
+        time_col: str = "date",
+    ):
+        super().__init__(
+            treatment=treatment,
+            target_col=target_col,
+            treatment_col=treatment_col,
+            hypothesis=hypothesis,
+            cluster_cols=cluster_cols,
+        )
+
+        self.time_col = time_col
+        self.intervention_date = intervention_date
+
+        if time_col in cluster_cols:
+            raise ValueError("time columns should not be in cluster columns")
+
+    def _fit(self, pre_experiment_df: pd.DataFrame, verbose: bool) -> np.ndarray:
+        """Returns the weight of each donor"""
+
+        if not any(pre_experiment_df[self.treatment_col] == 1):
+            raise ValueError("No treatment unit found in the data.")
+
+        X = (
+            pre_experiment_df.query(f"{self.treatment_col} == 0")
+            .pivot(index=self.cluster_cols, columns=self.time_col)[self.target_col]
+            .T
+        )
+
+        y = (
+            pre_experiment_df.query(f"{self.treatment_col} == 1")
+            .pivot(index=self.cluster_cols, columns=self.time_col)[self.target_col]
+            .T.iloc[:, 0]
+        )
+
+        weights = get_w(X, y, verbose)
+
+        return weights
+
+    def _predict(
+        self, df: pd.DataFrame, weights: np.ndarray, treatment_cluster: str
+    ) -> pd.DataFrame:
+        """
+        This method adds a column with the synthetic results and filter only the treatment unit.
+
+        First, it calculates the weights of each donor in the control group using the `fit_synthetic` method.
+        It then uses these weights to create a synthetic control group that closely matches the treatment unit before the intervention.
+        The synthetic control group is added to the treatment unit in the dataframe.
+        """
+        synthetic = (
+            df[self._get_cluster_column(df) != treatment_cluster]
+            .pivot(index=self.time_col, columns=self.cluster_cols)[self.target_col]
+            .values.dot(weights)
+        )
+
+        # add synthetic to treatment cluster
+        return df[self._get_cluster_column(df) == treatment_cluster].assign(
+            synthetic=synthetic
+        )
+
+    def fit_predict_synthetic(
+        self,
+        df: pd.DataFrame,
+        pre_experiment_df: pd.DataFrame,
+        treatment_cluster: str,
+        verbose: bool = False,
+    ) -> pd.DataFrame:
+        """
+        Fit the synthetic control model and predict the results for the treatment cluster.
+        Args:
+            df: The dataframe containing the data after the intervention.
+            pre_experiment_df: The dataframe containing the data before the intervention.
+            treatment_cluster: The name of the treatment cluster.
+            verbose: If True, print the status of the optimization of weights.
+
+        Returns:
+            The dataframe with the synthetic results added to the treatment cluster.
+        """
+        weights = self._fit(pre_experiment_df=pre_experiment_df, verbose=verbose)
+
+        prediction = self._predict(
+            df=df, weights=weights, treatment_cluster=treatment_cluster
+        )
+        return prediction
+
+    def pvalue_based_on_hypothesis(
+        self, ate: np.float64, avg_effects: Dict[str, float]
+    ) -> float:
+        """
+        Returns the p-value of the analysis.
+        1. Count how many times the average effect is greater than the real treatment unit
+        2. Average it with the number of units. The result is the p-value using Fisher permutation exact test.
+        """
+
+        avg_effects = list(avg_effects.values())
+
+        if HypothesisEntries(self.hypothesis) == HypothesisEntries.LESS:
+            return np.mean(avg_effects < ate)
+        if HypothesisEntries(self.hypothesis) == HypothesisEntries.GREATER:
+            return np.mean(avg_effects > ate)
+        if HypothesisEntries(self.hypothesis) == HypothesisEntries.TWO_SIDED:
+            avg_effects = np.abs(avg_effects)
+            return np.mean(avg_effects > ate)
+
+        raise ValueError(f"{self.hypothesis} is not a valid HypothesisEntries")
+
+    def _get_treatment_cluster(self, df: pd.DataFrame) -> str:
+        """Returns the first treatment cluster. The current implementation of Synthetic Control only accepts one treatment cluster.
+        This will be left inside Synthetic class because it doesn't apply for other analyses"""
+        treatment_df = df[df[self.treatment_col] == 1]
+        treatment_cluster = self._get_cluster_column(treatment_df).unique()[0]
+        return treatment_cluster
+
+    def analysis_pvalue(self, df: pd.DataFrame, verbose: bool = False) -> float:
+        """
+        Returns the p-value of the analysis.
+        1. Calculate the average effect after intervention for each unit.
+        2. Count how many times the average effect is greater than the real treatment unit
+        3. Average it with the number of units. The result is the p-value using Fisher permutation test
+        """
+
+        clusters = self._get_cluster_column(df).unique()
+        treatment_cluster = self._get_treatment_cluster(df)
+
+        synthetic_donors = {
+            cluster: self.analysis_point_estimate(
+                treatment_cluster=cluster,
+                df=df,
+                verbose=verbose,
+            )
+            for cluster in clusters
+        }
+
+        ate = synthetic_donors[treatment_cluster]
+        synthetic_donors.pop(treatment_cluster)
+
+        return self.pvalue_based_on_hypothesis(ate=ate, avg_effects=synthetic_donors)
+
+    def analysis_point_estimate(
+        self,
+        df: pd.DataFrame,
+        treatment_cluster: Optional[str] = None,
+        verbose: bool = False,
+    ):
+        """
+        Calculate the point estimate for the treatment effect for a specified cluster by averaging across the time windows.
+        """
+        df, pre_experiment_df = self._split_pre_experiment_df(df)
+
+        if treatment_cluster is None:
+            treatment_cluster = self._get_treatment_cluster(df)
+
+        df = self.fit_predict_synthetic(
+            df, pre_experiment_df, treatment_cluster, verbose=verbose
+        )
+
+        df["effect"] = df[self.target_col] - df["synthetic"]
+        avg_effect = df["effect"].mean()
+        return avg_effect
+
+    def _split_pre_experiment_df(self, df: pd.DataFrame):
+        """Split the dataframe into pre-experiment and experiment dataframes"""
+        pre_experiment_df = df[(df[self.time_col] <= self.intervention_date)]
+        df = df[(df[self.time_col] > self.intervention_date)]
+        return df, pre_experiment_df
+
+
+ + + +
+ + + + + + + + + + +
+ + + +

+analysis_point_estimate(self, df, treatment_cluster=None, verbose=False) + + +

+ +
+ +

Calculate the point estimate for the treatment effect for a specified cluster by averaging across the time windows.

+ +
+ Source code in cluster_experiments/experiment_analysis.py +
def analysis_point_estimate(
+    self,
+    df: pd.DataFrame,
+    treatment_cluster: Optional[str] = None,
+    verbose: bool = False,
+):
+    """
+    Calculate the point estimate for the treatment effect for a specified cluster by averaging across the time windows.
+    """
+    df, pre_experiment_df = self._split_pre_experiment_df(df)
+
+    if treatment_cluster is None:
+        treatment_cluster = self._get_treatment_cluster(df)
+
+    df = self.fit_predict_synthetic(
+        df, pre_experiment_df, treatment_cluster, verbose=verbose
+    )
+
+    df["effect"] = df[self.target_col] - df["synthetic"]
+    avg_effect = df["effect"].mean()
+    return avg_effect
+
+
+
+ +
+ + + +
+ + + +

+analysis_pvalue(self, df, verbose=False) + + +

+ +
+ +

Returns the p-value of the analysis. +1. Calculate the average effect after intervention for each unit. +2. Count how many times the average effect is greater than the real treatment unit +3. Average it with the number of units. The result is the p-value using Fisher permutation test

+ +
+ Source code in cluster_experiments/experiment_analysis.py +
def analysis_pvalue(self, df: pd.DataFrame, verbose: bool = False) -> float:
+    """
+    Returns the p-value of the analysis.
+    1. Calculate the average effect after intervention for each unit.
+    2. Count how many times the average effect is greater than the real treatment unit
+    3. Average it with the number of units. The result is the p-value using Fisher permutation test
+    """
+
+    clusters = self._get_cluster_column(df).unique()
+    treatment_cluster = self._get_treatment_cluster(df)
+
+    synthetic_donors = {
+        cluster: self.analysis_point_estimate(
+            treatment_cluster=cluster,
+            df=df,
+            verbose=verbose,
+        )
+        for cluster in clusters
+    }
+
+    ate = synthetic_donors[treatment_cluster]
+    synthetic_donors.pop(treatment_cluster)
+
+    return self.pvalue_based_on_hypothesis(ate=ate, avg_effects=synthetic_donors)
+
+
+
+ +
+ + + +
+ + + +

+fit_predict_synthetic(self, df, pre_experiment_df, treatment_cluster, verbose=False) + + +

+ +
+ +

Fit the synthetic control model and predict the results for the treatment cluster.

+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
dfDataFrame

The dataframe containing the data after the intervention.

required
pre_experiment_dfDataFrame

The dataframe containing the data before the intervention.

required
treatment_clusterstr

The name of the treatment cluster.

required
verbosebool

If True, print the status of the optimization of weights.

False
+

Returns:

+ + + + + + + + + + + + + +
TypeDescription
DataFrame

The dataframe with the synthetic results added to the treatment cluster.

+
+ Source code in cluster_experiments/experiment_analysis.py +
def fit_predict_synthetic(
+    self,
+    df: pd.DataFrame,
+    pre_experiment_df: pd.DataFrame,
+    treatment_cluster: str,
+    verbose: bool = False,
+) -> pd.DataFrame:
+    """
+    Fit the synthetic control model and predict the results for the treatment cluster.
+    Args:
+        df: The dataframe containing the data after the intervention.
+        pre_experiment_df: The dataframe containing the data before the intervention.
+        treatment_cluster: The name of the treatment cluster.
+        verbose: If True, print the status of the optimization of weights.
+
+    Returns:
+        The dataframe with the synthetic results added to the treatment cluster.
+    """
+    weights = self._fit(pre_experiment_df=pre_experiment_df, verbose=verbose)
+
+    prediction = self._predict(
+        df=df, weights=weights, treatment_cluster=treatment_cluster
+    )
+    return prediction
+
+
+
+ +
+ + + +
+ + + +

+pvalue_based_on_hypothesis(self, ate, avg_effects) + + +

+ +
+ +

Returns the p-value of the analysis. +1. Count how many times the average effect is greater than the real treatment unit +2. Average it with the number of units. The result is the p-value using Fisher permutation exact test.

+ +
+ Source code in cluster_experiments/experiment_analysis.py +
def pvalue_based_on_hypothesis(
+    self, ate: np.float64, avg_effects: Dict[str, float]
+) -> float:
+    """
+    Returns the p-value of the analysis.
+    1. Count how many times the average effect is greater than the real treatment unit
+    2. Average it with the number of units. The result is the p-value using Fisher permutation exact test.
+    """
+
+    avg_effects = list(avg_effects.values())
+
+    if HypothesisEntries(self.hypothesis) == HypothesisEntries.LESS:
+        return np.mean(avg_effects < ate)
+    if HypothesisEntries(self.hypothesis) == HypothesisEntries.GREATER:
+        return np.mean(avg_effects > ate)
+    if HypothesisEntries(self.hypothesis) == HypothesisEntries.TWO_SIDED:
+        avg_effects = np.abs(avg_effects)
+        return np.mean(avg_effects > ate)
+
+    raise ValueError(f"{self.hypothesis} is not a valid HypothesisEntries")
+
+
+
+ +
+ + + + + +
+ +
+ +
+ + + +
+ + + +

+ +TTestClusteredAnalysis (ExperimentAnalysis) + + + + +

+ +
+ +

Class to run T-test analysis on aggregated data

+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
cluster_colsList[str]

list of columns to use as clusters

required
target_colstr

name of the column containing the variable to measure

'target'
treatment_colstr

name of the column containing the treatment variable

'treatment'
treatmentstr

name of the treatment to use as the treated group

'B'
hypothesisstr

one of "two-sided", "less", "greater" indicating the alternative hypothesis

'two-sided'

Usage:

+
from cluster_experiments.experiment_analysis import TTestClusteredAnalysis
+import pandas as pd
+
+df = pd.DataFrame({
+    'x': [1, 2, 3, 4, 0, 0, 1, 1],
+    'treatment': ["A", "B", "A", "B"] * 2,
+    'cluster': [1, 2, 3, 4, 1, 2, 3, 4],
+})
+
+TTestClusteredAnalysis(
+    cluster_cols=['cluster'],
+    target_col='x',
+).get_pvalue(df)
+
+ +
+ Source code in cluster_experiments/experiment_analysis.py +
class TTestClusteredAnalysis(ExperimentAnalysis):
+    """
+    Class to run T-test analysis on aggregated data
+
+    Arguments:
+        cluster_cols: list of columns to use as clusters
+        target_col: name of the column containing the variable to measure
+        treatment_col: name of the column containing the treatment variable
+        treatment: name of the treatment to use as the treated group
+        hypothesis: one of "two-sided", "less", "greater" indicating the alternative hypothesis
+
+    Usage:
+
+    ```python
+    from cluster_experiments.experiment_analysis import TTestClusteredAnalysis
+    import pandas as pd
+
+    df = pd.DataFrame({
+        'x': [1, 2, 3, 4, 0, 0, 1, 1],
+        'treatment': ["A", "B", "A", "B"] * 2,
+        'cluster': [1, 2, 3, 4, 1, 2, 3, 4],
+    })
+
+    TTestClusteredAnalysis(
+        cluster_cols=['cluster'],
+        target_col='x',
+    ).get_pvalue(df)
+    ```
+    """
+
+    def __init__(
+        self,
+        cluster_cols: List[str],
+        target_col: str = "target",
+        treatment_col: str = "treatment",
+        treatment: str = "B",
+        hypothesis: str = "two-sided",
+    ):
+        self.target_col = target_col
+        self.treatment = treatment
+        self.treatment_col = treatment_col
+        self.cluster_cols = cluster_cols
+        self.hypothesis = hypothesis
+
+    def analysis_pvalue(self, df: pd.DataFrame, verbose: bool = False) -> float:
+        """Returns the p-value of the analysis
+        Arguments:
+            df: dataframe containing the data to analyze
+            verbose (Optional): bool, prints the regression summary if True
+        """
+
+        df_grouped = df.groupby(
+            self.cluster_cols + [self.treatment_col], as_index=False
+        )[self.target_col].mean()
+
+        treatment_data = df_grouped.query(f"{self.treatment_col} == 1")[self.target_col]
+        control_data = df_grouped.query(f"{self.treatment_col} == 0")[self.target_col]
+        assert len(treatment_data), "treatment data should have more than 1 cluster"
+        assert len(control_data), "control data should have more than 1 cluster"
+        t_test_results = ttest_ind(
+            treatment_data, control_data, equal_var=False, alternative=self.hypothesis
+        )
+        return t_test_results.pvalue
+
+    @classmethod
+    def from_config(cls, config):
+        """Creates a TTestClusteredAnalysis object from a PowerConfig object"""
+        return cls(
+            cluster_cols=config.cluster_cols,
+            target_col=config.target_col,
+            treatment_col=config.treatment_col,
+            treatment=config.treatment,
+            hypothesis=config.hypothesis,
+        )
+
+
+ + + +
+ + + + + + + + + + +
+ + + +

+analysis_pvalue(self, df, verbose=False) + + +

+ +
+ +

Returns the p-value of the analysis

+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
dfDataFrame

dataframe containing the data to analyze

required
verboseOptional

bool, prints the regression summary if True

False
+
+ Source code in cluster_experiments/experiment_analysis.py +
def analysis_pvalue(self, df: pd.DataFrame, verbose: bool = False) -> float:
+    """Returns the p-value of the analysis
+    Arguments:
+        df: dataframe containing the data to analyze
+        verbose (Optional): bool, prints the regression summary if True
+    """
+
+    df_grouped = df.groupby(
+        self.cluster_cols + [self.treatment_col], as_index=False
+    )[self.target_col].mean()
+
+    treatment_data = df_grouped.query(f"{self.treatment_col} == 1")[self.target_col]
+    control_data = df_grouped.query(f"{self.treatment_col} == 0")[self.target_col]
+    assert len(treatment_data), "treatment data should have more than 1 cluster"
+    assert len(control_data), "control data should have more than 1 cluster"
+    t_test_results = ttest_ind(
+        treatment_data, control_data, equal_var=False, alternative=self.hypothesis
+    )
+    return t_test_results.pvalue
+
+
+
+ +
+ + + +
+ + + +

+from_config(config) + + + classmethod + + +

+ +
+ +

Creates a TTestClusteredAnalysis object from a PowerConfig object

+ +
+ Source code in cluster_experiments/experiment_analysis.py +
@classmethod
+def from_config(cls, config):
+    """Creates a TTestClusteredAnalysis object from a PowerConfig object"""
+    return cls(
+        cluster_cols=config.cluster_cols,
+        target_col=config.target_col,
+        treatment_col=config.treatment_col,
+        treatment=config.treatment,
+        hypothesis=config.hypothesis,
+    )
+
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+ + + + Skip to content + + +
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from cluster_experiments.inference.hypothesis_test import *

+ + +
+ + +
+ + + + +
+ + + + + + + +
+ + + +

+ +HypothesisTest + + + +

+ +
+ +

A class used to represent a Hypothesis Test with a metric, analysis, optional analysis configuration, and optional dimensions.

+

Attributes

+

metric : Metric + An instance of the Metric class +analysis_type : str + string mapping to an ExperimentAnalysis class. Must be either in the built-in analysis_mapping or in the custom_analysis_type_mapper if provided. +analysis_config : Optional[dict] + An optional dictionary representing the configuration for the analysis +dimensions : Optional[List[Dimension]] + An optional list of Dimension instances +cupac_config : Optional[dict] + An optional dictionary representing the configuration for the cupac model +custom_analysis_type_mapper : Optional[Dict[str, ExperimentAnalysis]] + An optional dictionary mapping the names of custom analysis types to the corresponding ExperimentAnalysis classes

+ +
+ Source code in cluster_experiments/inference/hypothesis_test.py +
class HypothesisTest:
+    """
+    A class used to represent a Hypothesis Test with a metric, analysis, optional analysis configuration, and optional dimensions.
+
+    Attributes
+    ----------
+    metric : Metric
+        An instance of the Metric class
+    analysis_type : str
+        string mapping to an ExperimentAnalysis class. Must be either in the built-in analysis_mapping or in the custom_analysis_type_mapper if provided.
+    analysis_config : Optional[dict]
+        An optional dictionary representing the configuration for the analysis
+    dimensions : Optional[List[Dimension]]
+        An optional list of Dimension instances
+    cupac_config : Optional[dict]
+        An optional dictionary representing the configuration for the cupac model
+    custom_analysis_type_mapper : Optional[Dict[str, ExperimentAnalysis]]
+        An optional dictionary mapping the names of custom analysis types to the corresponding ExperimentAnalysis classes
+    """
+
+    def __init__(
+        self,
+        metric: Metric,
+        analysis_type: str,
+        analysis_config: Optional[dict] = None,
+        dimensions: Optional[List[Dimension]] = None,
+        cupac_config: Optional[dict] = None,
+        custom_analysis_type_mapper: Optional[Dict[str, ExperimentAnalysis]] = None,
+    ):
+        """
+        Parameters
+        ----------
+        metric : Metric
+            An instance of the Metric class
+        analysis_type : str
+            string mapping to an ExperimentAnalysis class. Must be either in the built-in analysis_mapping or in the custom_analysis_type_mapper if provided.
+        analysis_config : Optional[dict]
+            An optional dictionary representing the configuration for the analysis
+        dimensions : Optional[List[Dimension]]
+            An optional list of Dimension instances
+        cupac_config : Optional[dict]
+            An optional dictionary representing the configuration for the cupac model
+        custom_analysis_type_mapper : Optional[Dict[str, ExperimentAnalysis]]
+            An optional dictionary mapping the names of custom analysis types to the corresponding ExperimentAnalysis classes
+        """
+        self._validate_inputs(
+            metric,
+            analysis_type,
+            analysis_config,
+            dimensions,
+            cupac_config,
+            custom_analysis_type_mapper,
+        )
+        self.metric = metric
+        self.analysis_type = analysis_type
+        self.analysis_config = analysis_config or {}
+        self.dimensions = [DefaultDimension()] + (dimensions or [])
+        self.cupac_config = cupac_config or {}
+        self.custom_analysis_type_mapper = custom_analysis_type_mapper or {}
+
+        self.analysis_type_mapper = self.custom_analysis_type_mapper or analysis_mapping
+        self.analysis_class = self.analysis_type_mapper[self.analysis_type]
+        self.is_cupac = bool(cupac_config)
+        self.cupac_handler = (
+            CupacHandler(**self.cupac_config) if self.is_cupac else None
+        )
+        self.cupac_covariate_col = (
+            self.cupac_handler.cupac_outcome_name if self.is_cupac else None
+        )
+
+        self.new_analysis_config = None
+        self.experiment_analysis = None
+
+    @staticmethod
+    def _validate_inputs(
+        metric: Metric,
+        analysis_type: str,
+        analysis_config: Optional[dict],
+        dimensions: Optional[List[Dimension]],
+        cupac_config: Optional[dict] = None,
+        custom_analysis_type_mapper: Optional[Dict[str, ExperimentAnalysis]] = None,
+    ):
+        """
+        Validates the inputs for the HypothesisTest class.
+
+        Parameters
+        ----------
+        metric : Metric
+            An instance of the Metric class
+        analysis_type : str
+            string mapper to an ExperimentAnalysis
+        analysis_config : Optional[dict]
+            An optional dictionary representing the configuration for the analysis
+        dimensions : Optional[List[Dimension]]
+            An optional list of Dimension instances
+        cupac_config : Optional[dict]
+            An optional dictionary representing the configuration for the cupac model
+        custom_analysis_type_mapper : Optional[dict[str, ExperimentAnalysis]]
+            An optional dictionary mapping the names of custom analysis types to the corresponding ExperimentAnalysis classes
+        """
+        # Check if metric is a valid Metric instance
+        if not isinstance(metric, Metric):
+            raise TypeError("Metric must be an instance of Metric")
+
+        # Check if analysis_type is a string
+        if not isinstance(analysis_type, str):
+            raise TypeError("Analysis must be a string")
+
+        # Check if analysis_config is a dictionary when provided
+        if analysis_config is not None and not isinstance(analysis_config, dict):
+            raise TypeError("analysis_config must be a dictionary if provided")
+
+        # Check if cupac_config is a dictionary when provided
+        if cupac_config is not None and not isinstance(cupac_config, dict):
+            raise TypeError("cupac_config must be a dictionary if provided")
+
+        # Check if dimensions is a list of Dimension instances when provided
+        if dimensions is not None and (
+            not isinstance(dimensions, list)
+            or not all(isinstance(dim, Dimension) for dim in dimensions)
+        ):
+            raise TypeError(
+                "Dimensions must be a list of Dimension instances if provided"
+            )
+
+        # Validate custom_analysis_type_mapper if provided
+        if custom_analysis_type_mapper:
+            # Ensure it's a dictionary
+            if not isinstance(custom_analysis_type_mapper, dict):
+                raise TypeError(
+                    "custom_analysis_type_mapper must be a dictionary if provided"
+                )
+
+            # Ensure all keys are strings and values are ExperimentAnalysis classes
+            for key, value in custom_analysis_type_mapper.items():
+                if not isinstance(key, str):
+                    raise TypeError(
+                        f"Key '{key}' in custom_analysis_type_mapper must be a string"
+                    )
+                if not issubclass(value, ExperimentAnalysis):
+                    raise TypeError(
+                        f"Value '{value}' for key '{key}' in custom_analysis_type_mapper must be a subclass of ExperimentAnalysis"
+                    )
+
+            # Ensure the analysis_type is in the custom mapper if a custom mapper is provided
+            if analysis_type not in custom_analysis_type_mapper:
+                raise ValueError(
+                    f"Analysis type '{analysis_type}' not found in the provided custom_analysis_type_mapper"
+                )
+
+        # If no custom_analysis_type_mapper, check if analysis_type exists in the default mapping
+        elif analysis_type not in analysis_mapping:
+            raise ValueError(
+                f"Analysis type '{analysis_type}' not found in analysis_mapping"
+            )
+
+    def get_inference_results(self, df: pd.DataFrame, alpha: float) -> InferenceResults:
+        """
+        Performs inference analysis on the provided DataFrame using the analysis class.
+
+        Parameters
+        ----------
+        df : pd.DataFrame
+            The dataframe containing the data for analysis.
+        alpha : float
+            The significance level to be used in the inference analysis.
+
+        Returns
+        -------
+        InferenceResults
+            The results containing the statistics of the inference procedure.
+        """
+
+        self.experiment_analysis = self.analysis_class(**self.new_analysis_config)
+        inference_results = self.experiment_analysis.get_inference_results(
+            df=df, alpha=alpha
+        )
+
+        return inference_results
+
+    def _prepare_analysis_config(
+        self, target_col: str, treatment_col: str, treatment: str
+    ) -> None:
+        """
+        Extends the analysis_config provided by the user, by adding or overriding the following keys:
+        - target_col
+        - treatment_col
+        - treatment
+
+        Also handles cupac covariate.
+
+        Returns
+        -------
+        dict
+            The prepared analysis configuration, ready to be ingested by the experiment analysis class
+        """
+        new_analysis_config = copy.deepcopy(self.analysis_config)
+
+        new_analysis_config["target_col"] = target_col
+        new_analysis_config["treatment_col"] = treatment_col
+        new_analysis_config["treatment"] = treatment
+
+        covariates = new_analysis_config.get("covariates", [])
+
+        if self.cupac_covariate_col and self.cupac_covariate_col not in covariates:
+            raise ValueError(
+                f"You provided a cupac configuration but did not provide the cupac covariate called {self.cupac_covariate_col} in the analysis_config"
+            )
+
+        self.new_analysis_config = new_analysis_config
+
+    @staticmethod
+    def prepare_data(
+        data: pd.DataFrame,
+        variant_col: str,
+        treatment_variant: Variant,
+        control_variant: Variant,
+        dimension_name: str,
+        dimension_value: str,
+    ) -> pd.DataFrame:
+        """
+        Prepares the data for the experiment analysis pipeline
+        """
+        prepared_df = data.copy()
+
+        prepared_df = prepared_df.assign(__total_dimension="total")
+
+        prepared_df = prepared_df.query(
+            f"{variant_col}.isin(['{treatment_variant.name}','{control_variant.name}'])"
+        ).query(f"{dimension_name} == '{dimension_value}'")
+
+        return prepared_df
+
+    def add_covariates(
+        self, exp_data: pd.DataFrame, pre_exp_data: pd.DataFrame
+    ) -> pd.DataFrame:
+        """
+        If the test is a cupac test, adds the covariates to the experimental data.
+        """
+        if self.is_cupac:
+            exp_data = self.cupac_handler.add_covariates(
+                df=exp_data, pre_experiment_df=pre_exp_data
+            )
+
+        return exp_data
+
+    def get_test_results(
+        self,
+        control_variant: Variant,
+        treatment_variant: Variant,
+        variant_col: str,
+        exp_data: pd.DataFrame,
+        dimension: Dimension,
+        dimension_value: str,
+        alpha: float,
+    ) -> AnalysisPlanResults:
+        """
+        Performs the hypothesis test on the provided data, for the given dimension value.
+
+        Parameters
+        ----------
+        control_variant : Variant
+            The control variant
+        treatment_variant : Variant
+            The treatment variant
+        variant_col : str
+            The column name representing the variant
+        exp_data : pd.DataFrame
+            The dataframe containing the data for analysis.
+        dimension : Dimension
+            The dimension instance
+        dimension_value : str
+            The value of the dimension
+        alpha : float
+            The significance level to be used in the inference analysis.
+
+        Returns
+        -------
+        AnalysisPlanResults
+            The results of the hypothesis test
+        """
+        self._prepare_analysis_config(
+            target_col=self.metric.target_column,
+            treatment_col=variant_col,
+            treatment=treatment_variant.name,
+        )
+
+        prepared_df = self.prepare_data(
+            data=exp_data,
+            variant_col=variant_col,
+            treatment_variant=treatment_variant,
+            control_variant=control_variant,
+            dimension_name=dimension.name,
+            dimension_value=dimension_value,
+        )
+
+        inference_results = self.get_inference_results(df=prepared_df, alpha=alpha)
+
+        control_variant_mean = self.metric.get_mean(
+            prepared_df.query(f"{variant_col}=='{control_variant.name}'")
+        )
+        treatment_variant_mean = self.metric.get_mean(
+            prepared_df.query(f"{variant_col}=='{treatment_variant.name}'")
+        )
+
+        test_results = AnalysisPlanResults(
+            metric_alias=[self.metric.alias],
+            control_variant_name=[control_variant.name],
+            treatment_variant_name=[treatment_variant.name],
+            control_variant_mean=[control_variant_mean],
+            treatment_variant_mean=[treatment_variant_mean],
+            analysis_type=[self.analysis_type],
+            ate=[inference_results.ate],
+            ate_ci_lower=[inference_results.conf_int.lower],
+            ate_ci_upper=[inference_results.conf_int.upper],
+            p_value=[inference_results.p_value],
+            std_error=[inference_results.std_error],
+            dimension_name=[dimension.name],
+            dimension_value=[dimension_value],
+            alpha=[alpha],
+        )
+
+        return test_results
+
+
+ + + +
+ + + + + + + + + +
+ + + +

+__init__(self, metric, analysis_type, analysis_config=None, dimensions=None, cupac_config=None, custom_analysis_type_mapper=None) + + + special + + +

+ +
+ +
Parameters
+

metric : Metric + An instance of the Metric class +analysis_type : str + string mapping to an ExperimentAnalysis class. Must be either in the built-in analysis_mapping or in the custom_analysis_type_mapper if provided. +analysis_config : Optional[dict] + An optional dictionary representing the configuration for the analysis +dimensions : Optional[List[Dimension]] + An optional list of Dimension instances +cupac_config : Optional[dict] + An optional dictionary representing the configuration for the cupac model +custom_analysis_type_mapper : Optional[Dict[str, ExperimentAnalysis]] + An optional dictionary mapping the names of custom analysis types to the corresponding ExperimentAnalysis classes

+ +
+ Source code in cluster_experiments/inference/hypothesis_test.py +
def __init__(
+    self,
+    metric: Metric,
+    analysis_type: str,
+    analysis_config: Optional[dict] = None,
+    dimensions: Optional[List[Dimension]] = None,
+    cupac_config: Optional[dict] = None,
+    custom_analysis_type_mapper: Optional[Dict[str, ExperimentAnalysis]] = None,
+):
+    """
+    Parameters
+    ----------
+    metric : Metric
+        An instance of the Metric class
+    analysis_type : str
+        string mapping to an ExperimentAnalysis class. Must be either in the built-in analysis_mapping or in the custom_analysis_type_mapper if provided.
+    analysis_config : Optional[dict]
+        An optional dictionary representing the configuration for the analysis
+    dimensions : Optional[List[Dimension]]
+        An optional list of Dimension instances
+    cupac_config : Optional[dict]
+        An optional dictionary representing the configuration for the cupac model
+    custom_analysis_type_mapper : Optional[Dict[str, ExperimentAnalysis]]
+        An optional dictionary mapping the names of custom analysis types to the corresponding ExperimentAnalysis classes
+    """
+    self._validate_inputs(
+        metric,
+        analysis_type,
+        analysis_config,
+        dimensions,
+        cupac_config,
+        custom_analysis_type_mapper,
+    )
+    self.metric = metric
+    self.analysis_type = analysis_type
+    self.analysis_config = analysis_config or {}
+    self.dimensions = [DefaultDimension()] + (dimensions or [])
+    self.cupac_config = cupac_config or {}
+    self.custom_analysis_type_mapper = custom_analysis_type_mapper or {}
+
+    self.analysis_type_mapper = self.custom_analysis_type_mapper or analysis_mapping
+    self.analysis_class = self.analysis_type_mapper[self.analysis_type]
+    self.is_cupac = bool(cupac_config)
+    self.cupac_handler = (
+        CupacHandler(**self.cupac_config) if self.is_cupac else None
+    )
+    self.cupac_covariate_col = (
+        self.cupac_handler.cupac_outcome_name if self.is_cupac else None
+    )
+
+    self.new_analysis_config = None
+    self.experiment_analysis = None
+
+
+
+ +
+ + + +
+ + + +

+add_covariates(self, exp_data, pre_exp_data) + + +

+ +
+ +

If the test is a cupac test, adds the covariates to the experimental data.

+ +
+ Source code in cluster_experiments/inference/hypothesis_test.py +
def add_covariates(
+    self, exp_data: pd.DataFrame, pre_exp_data: pd.DataFrame
+) -> pd.DataFrame:
+    """
+    If the test is a cupac test, adds the covariates to the experimental data.
+    """
+    if self.is_cupac:
+        exp_data = self.cupac_handler.add_covariates(
+            df=exp_data, pre_experiment_df=pre_exp_data
+        )
+
+    return exp_data
+
+
+
+ +
+ + + +
+ + + +

+get_inference_results(self, df, alpha) + + +

+ +
+ +

Performs inference analysis on the provided DataFrame using the analysis class.

+
Parameters
+

df : pd.DataFrame + The dataframe containing the data for analysis. +alpha : float + The significance level to be used in the inference analysis.

+
Returns
+

InferenceResults + The results containing the statistics of the inference procedure.

+ +
+ Source code in cluster_experiments/inference/hypothesis_test.py +
def get_inference_results(self, df: pd.DataFrame, alpha: float) -> InferenceResults:
+    """
+    Performs inference analysis on the provided DataFrame using the analysis class.
+
+    Parameters
+    ----------
+    df : pd.DataFrame
+        The dataframe containing the data for analysis.
+    alpha : float
+        The significance level to be used in the inference analysis.
+
+    Returns
+    -------
+    InferenceResults
+        The results containing the statistics of the inference procedure.
+    """
+
+    self.experiment_analysis = self.analysis_class(**self.new_analysis_config)
+    inference_results = self.experiment_analysis.get_inference_results(
+        df=df, alpha=alpha
+    )
+
+    return inference_results
+
+
+
+ +
+ + + +
+ + + +

+get_test_results(self, control_variant, treatment_variant, variant_col, exp_data, dimension, dimension_value, alpha) + + +

+ +
+ +

Performs the hypothesis test on the provided data, for the given dimension value.

+
Parameters
+

control_variant : Variant + The control variant +treatment_variant : Variant + The treatment variant +variant_col : str + The column name representing the variant +exp_data : pd.DataFrame + The dataframe containing the data for analysis. +dimension : Dimension + The dimension instance +dimension_value : str + The value of the dimension +alpha : float + The significance level to be used in the inference analysis.

+
Returns
+

AnalysisPlanResults + The results of the hypothesis test

+ +
+ Source code in cluster_experiments/inference/hypothesis_test.py +
def get_test_results(
+    self,
+    control_variant: Variant,
+    treatment_variant: Variant,
+    variant_col: str,
+    exp_data: pd.DataFrame,
+    dimension: Dimension,
+    dimension_value: str,
+    alpha: float,
+) -> AnalysisPlanResults:
+    """
+    Performs the hypothesis test on the provided data, for the given dimension value.
+
+    Parameters
+    ----------
+    control_variant : Variant
+        The control variant
+    treatment_variant : Variant
+        The treatment variant
+    variant_col : str
+        The column name representing the variant
+    exp_data : pd.DataFrame
+        The dataframe containing the data for analysis.
+    dimension : Dimension
+        The dimension instance
+    dimension_value : str
+        The value of the dimension
+    alpha : float
+        The significance level to be used in the inference analysis.
+
+    Returns
+    -------
+    AnalysisPlanResults
+        The results of the hypothesis test
+    """
+    self._prepare_analysis_config(
+        target_col=self.metric.target_column,
+        treatment_col=variant_col,
+        treatment=treatment_variant.name,
+    )
+
+    prepared_df = self.prepare_data(
+        data=exp_data,
+        variant_col=variant_col,
+        treatment_variant=treatment_variant,
+        control_variant=control_variant,
+        dimension_name=dimension.name,
+        dimension_value=dimension_value,
+    )
+
+    inference_results = self.get_inference_results(df=prepared_df, alpha=alpha)
+
+    control_variant_mean = self.metric.get_mean(
+        prepared_df.query(f"{variant_col}=='{control_variant.name}'")
+    )
+    treatment_variant_mean = self.metric.get_mean(
+        prepared_df.query(f"{variant_col}=='{treatment_variant.name}'")
+    )
+
+    test_results = AnalysisPlanResults(
+        metric_alias=[self.metric.alias],
+        control_variant_name=[control_variant.name],
+        treatment_variant_name=[treatment_variant.name],
+        control_variant_mean=[control_variant_mean],
+        treatment_variant_mean=[treatment_variant_mean],
+        analysis_type=[self.analysis_type],
+        ate=[inference_results.ate],
+        ate_ci_lower=[inference_results.conf_int.lower],
+        ate_ci_upper=[inference_results.conf_int.upper],
+        p_value=[inference_results.p_value],
+        std_error=[inference_results.std_error],
+        dimension_name=[dimension.name],
+        dimension_value=[dimension_value],
+        alpha=[alpha],
+    )
+
+    return test_results
+
+
+
+ +
+ + + +
+ + + +

+prepare_data(data, variant_col, treatment_variant, control_variant, dimension_name, dimension_value) + + + staticmethod + + +

+ +
+ +

Prepares the data for the experiment analysis pipeline

+ +
+ Source code in cluster_experiments/inference/hypothesis_test.py +
@staticmethod
+def prepare_data(
+    data: pd.DataFrame,
+    variant_col: str,
+    treatment_variant: Variant,
+    control_variant: Variant,
+    dimension_name: str,
+    dimension_value: str,
+) -> pd.DataFrame:
+    """
+    Prepares the data for the experiment analysis pipeline
+    """
+    prepared_df = data.copy()
+
+    prepared_df = prepared_df.assign(__total_dimension="total")
+
+    prepared_df = prepared_df.query(
+        f"{variant_col}.isin(['{treatment_variant.name}','{control_variant.name}'])"
+    ).query(f"{dimension_name} == '{dimension_value}'")
+
+    return prepared_df
+
+
+
+ +
+ + + + + +
+ +
+ +
+ + + + + + + +
+ +
+ +
+ + + + + + +
+
+ + +
+ +
+ + + +
+
+
+
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from cluster_experiments.inference.metric import *

+ + +
+ + +
+ + + + +
+ + + + + + + +
+ + + +

+ +Metric (ABC) + + + + +

+ +
+ +

An abstract base class used to represent a Metric with an alias.

+

Attributes

+

alias : str + A string representing the alias of the metric

+ +
+ Source code in cluster_experiments/inference/metric.py +
class Metric(ABC):
+    """
+    An abstract base class used to represent a Metric with an alias.
+
+    Attributes
+    ----------
+    alias : str
+        A string representing the alias of the metric
+    """
+
+    def __init__(self, alias: str):
+        """
+        Parameters
+        ----------
+        alias : str
+            The alias of the metric
+        """
+        self.alias = alias
+        self._validate_alias()
+
+    def _validate_alias(self):
+        """
+        Validates the alias input for the Metric class.
+
+        Raises
+        ------
+        TypeError
+            If the alias is not a string
+        """
+        if not isinstance(self.alias, str):
+            raise TypeError("Metric alias must be a string")
+
+    @property
+    @abstractmethod
+    def target_column(self) -> str:
+        """
+        Abstract property to return the target column to feed the experiment analysis class, from the metric definition.
+
+        Returns
+        -------
+        str
+            The target column name
+        """
+        pass
+
+    @abstractmethod
+    def get_mean(self, df: pd.DataFrame) -> float:
+        """
+        Abstract method to return the mean value of the metric, given a dataframe.
+
+        Returns
+        -------
+        float
+            The mean value of the metric
+        """
+        pass
+
+
+ + + +
+ + + + + + +
+ + + +

+target_column: str + + + property + readonly + + +

+ +
+ +

Abstract property to return the target column to feed the experiment analysis class, from the metric definition.

+
Returns
+

str + The target column name

+
+ +
+ + + + + + +
+ + + +

+__init__(self, alias) + + + special + + +

+ +
+ +
Parameters
+

alias : str + The alias of the metric

+ +
+ Source code in cluster_experiments/inference/metric.py +
def __init__(self, alias: str):
+    """
+    Parameters
+    ----------
+    alias : str
+        The alias of the metric
+    """
+    self.alias = alias
+    self._validate_alias()
+
+
+
+ +
+ + + +
+ + + +

+get_mean(self, df) + + +

+ +
+ +

Abstract method to return the mean value of the metric, given a dataframe.

+
Returns
+

float + The mean value of the metric

+ +
+ Source code in cluster_experiments/inference/metric.py +
@abstractmethod
+def get_mean(self, df: pd.DataFrame) -> float:
+    """
+    Abstract method to return the mean value of the metric, given a dataframe.
+
+    Returns
+    -------
+    float
+        The mean value of the metric
+    """
+    pass
+
+
+
+ +
+ + + + + +
+ +
+ +
+ + + +
+ + + +

+ +RatioMetric (Metric) + + + + +

+ +
+ +

A class used to represent a Ratio Metric with an alias, a numerator name, and a denominator name. +To be used when the metric is defined at a lower level than the data used for the analysis.

+

Example

+

In a clustered experiment the participants were randomised based on their country of residence. +The metric of interest is the salary of each participant. If the dataset fed into the analysis is at country-level, +then a RatioMetric must be used: the numerator would be the sum of all salaries in the country, +the denominator would be the number of participants in the country.

+

Attributes

+

alias : str + A string representing the alias of the metric +numerator_name : str + A string representing the numerator name of the metric +denominator_name : str + A string representing the denominator name of the metric

+ +
+ Source code in cluster_experiments/inference/metric.py +
class RatioMetric(Metric):
+    """
+    A class used to represent a Ratio Metric with an alias, a numerator name, and a denominator name.
+    To be used when the metric is defined at a lower level than the data used for the analysis.
+
+    Example
+    ----------
+    In a clustered experiment the participants were randomised based on their country of residence.
+    The metric of interest is the salary of each participant. If the dataset fed into the analysis is at country-level,
+    then a RatioMetric must be used: the numerator would be the sum of all salaries in the country,
+    the denominator would be the number of participants in the country.
+
+    Attributes
+    ----------
+    alias : str
+        A string representing the alias of the metric
+    numerator_name : str
+        A string representing the numerator name of the metric
+    denominator_name : str
+        A string representing the denominator name of the metric
+    """
+
+    def __init__(self, alias: str, numerator_name: str, denominator_name: str):
+        """
+        Parameters
+        ----------
+        alias : str
+            The alias of the metric
+        numerator_name : str
+            The numerator name of the metric
+        denominator_name : str
+            The denominator name of the metric
+        """
+        super().__init__(alias)
+        self.numerator_name = numerator_name
+        self.denominator_name = denominator_name
+        self._validate_names()
+
+    def _validate_names(self):
+        """
+        Validates the numerator and denominator names input for the RatioMetric class.
+
+        Raises
+        ------
+        TypeError
+            If the numerator or denominator names are not strings
+        """
+        if not isinstance(self.numerator_name, str) or not isinstance(
+            self.denominator_name, str
+        ):
+            raise TypeError("RatioMetric names must be strings")
+
+    @property
+    def target_column(self) -> str:
+        """
+        Returns the target column for the RatioMetric.
+
+        Returns
+        -------
+        str
+            The numerator name of the metric
+        """
+        return self.numerator_name
+
+    def get_mean(self, df: pd.DataFrame) -> float:
+        """
+        Returns the mean value of the metric, given a dataframe.
+
+        Returns
+        -------
+        float
+            The mean value of the metric
+        """
+        return df[self.numerator_name].mean() / df[self.denominator_name].mean()
+
+
+ + + +
+ + + + + + +
+ + + +

+target_column: str + + + property + readonly + + +

+ +
+ +

Returns the target column for the RatioMetric.

+
Returns
+

str + The numerator name of the metric

+
+ +
+ + + + + + +
+ + + +

+__init__(self, alias, numerator_name, denominator_name) + + + special + + +

+ +
+ +
Parameters
+

alias : str + The alias of the metric +numerator_name : str + The numerator name of the metric +denominator_name : str + The denominator name of the metric

+ +
+ Source code in cluster_experiments/inference/metric.py +
def __init__(self, alias: str, numerator_name: str, denominator_name: str):
+    """
+    Parameters
+    ----------
+    alias : str
+        The alias of the metric
+    numerator_name : str
+        The numerator name of the metric
+    denominator_name : str
+        The denominator name of the metric
+    """
+    super().__init__(alias)
+    self.numerator_name = numerator_name
+    self.denominator_name = denominator_name
+    self._validate_names()
+
+
+
+ +
+ + + +
+ + + +

+get_mean(self, df) + + +

+ +
+ +

Returns the mean value of the metric, given a dataframe.

+
Returns
+

float + The mean value of the metric

+ +
+ Source code in cluster_experiments/inference/metric.py +
def get_mean(self, df: pd.DataFrame) -> float:
+    """
+    Returns the mean value of the metric, given a dataframe.
+
+    Returns
+    -------
+    float
+        The mean value of the metric
+    """
+    return df[self.numerator_name].mean() / df[self.denominator_name].mean()
+
+
+
+ +
+ + + + + +
+ +
+ +
+ + + +
+ + + +

+ +SimpleMetric (Metric) + + + + +

+ +
+ +

A class used to represent a Simple Metric with an alias and a name. +To be used when the metric is defined at the same level of the data used for the analysis.

+

Example

+

In a clustered experiment the participants were randomised based on their country of residence. +The metric of interest is the salary of each participant. If the dataset fed into the analysis is at participant-level, +then a SimpleMetric must be used. However, if the dataset fed into the analysis is at country-level, then a RatioMetric must be used.

+

Attributes

+

alias : str + A string representing the alias of the metric +name : str + A string representing the name of the metric

+ +
+ Source code in cluster_experiments/inference/metric.py +
class SimpleMetric(Metric):
+    """
+    A class used to represent a Simple Metric with an alias and a name.
+    To be used when the metric is defined at the same level of the data used for the analysis.
+
+    Example
+    ----------
+    In a clustered experiment the participants were randomised based on their country of residence.
+    The metric of interest is the salary of each participant. If the dataset fed into the analysis is at participant-level,
+    then a SimpleMetric must be used. However, if the dataset fed into the analysis is at country-level, then a RatioMetric must be used.
+
+    Attributes
+    ----------
+    alias : str
+        A string representing the alias of the metric
+    name : str
+        A string representing the name of the metric
+    """
+
+    def __init__(self, alias: str, name: str):
+        """
+        Parameters
+        ----------
+        alias : str
+            The alias of the metric
+        name : str
+            The name of the metric
+        """
+        super().__init__(alias)
+        self.name = name
+        self._validate_name()
+
+    def _validate_name(self):
+        """
+        Validates the name input for the SimpleMetric class.
+
+        Raises
+        ------
+        TypeError
+            If the name is not a string
+        """
+        if not isinstance(self.name, str):
+            raise TypeError("SimpleMetric name must be a string")
+
+    @property
+    def target_column(self) -> str:
+        """
+        Returns the target column for the SimpleMetric.
+
+        Returns
+        -------
+        str
+            The name of the metric
+        """
+        return self.name
+
+    def get_mean(self, df: pd.DataFrame) -> float:
+        """
+        Returns the mean value of the metric, given a dataframe.
+
+        Returns
+        -------
+        float
+            The mean value of the metric
+        """
+        return df[self.name].mean()
+
+
+ + + +
+ + + + + + +
+ + + +

+target_column: str + + + property + readonly + + +

+ +
+ +

Returns the target column for the SimpleMetric.

+
Returns
+

str + The name of the metric

+
+ +
+ + + + + + +
+ + + +

+__init__(self, alias, name) + + + special + + +

+ +
+ +
Parameters
+

alias : str + The alias of the metric +name : str + The name of the metric

+ +
+ Source code in cluster_experiments/inference/metric.py +
def __init__(self, alias: str, name: str):
+    """
+    Parameters
+    ----------
+    alias : str
+        The alias of the metric
+    name : str
+        The name of the metric
+    """
+    super().__init__(alias)
+    self.name = name
+    self._validate_name()
+
+
+
+ +
+ + + +
+ + + +

+get_mean(self, df) + + +

+ +
+ +

Returns the mean value of the metric, given a dataframe.

+
Returns
+

float + The mean value of the metric

+ +
+ Source code in cluster_experiments/inference/metric.py +
def get_mean(self, df: pd.DataFrame) -> float:
+    """
+    Returns the mean value of the metric, given a dataframe.
+
+    Returns
+    -------
+    float
+        The mean value of the metric
+    """
+    return df[self.name].mean()
+
+
+
+ +
+ + + + + +
+ +
+ +
+ + + + + + + +
+ +
+ +
+ + + + + + +
+
+ + +
+ +
+ + + +
+
+
+
+ + + + + + + + + \ No newline at end of file diff --git a/api/perturbator.html b/api/perturbator.html new file mode 100644 index 00000000..172dfc20 --- /dev/null +++ b/api/perturbator.html @@ -0,0 +1,3586 @@ + + + + + + + + + + + + + + + + + + + + + + Perturbators - Cluster Experiments Docs + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
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+ + + +
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+ + + + + + + + + +

from cluster_experiments.perturbator import *

+ + +
+ + +
+ + + + +
+ + + + + + + +
+ + + +

+ +BetaRelativePerturbator (NormalPerturbator, RelativePositivePerturbator) + + + + +

+ +
+ +

A stochastic Perturbator for continuous targets that applies a sampled +effect from a scaled Beta distribution. It applies the effect multiplicatively.

+

The sampled effect is defined for values in the specified range +(range_min, range_max). It's recommended to set -1<range_min<0 and +range_max>0 in a "symmetric way" around 0, such that +log(1 + range_min) = -log(1 + range_max). +This ensures to have an "symmetric range" of perturbations that relatively +decrease the target as perturbations that relatively increase the target. +By "symmetry" of relative effects we mean that for an effect c > 0, an +increase of the target t via t*(1 + c) is "symmetric" to a decrease of t +via t/(1 + c). For example, an increase of 5x (i.e. by +400%, corresponding +to c_inc=4) is "symmetric" to a decrease of 5x (i.e. a decrease of -80%, +corresponding to c_dec = -0.8). In this case, 1 + c_dec = 1/(1 + c_inc), so +the relative effects c_inc and c_dec are "symmetric" in the sense that they +are inverse to each other.

+

The number of samples with 0 as target remains unchanged.

+

The stochastic effect is sampled from a beta distribution with parameters +mean and variance, which is linearly scaled to the range +(range_min, range_max). +If variance is not provided, the variance is abs(mean).

+
target -> target * (1 + effect), where effect ~ Beta(a, b)
+
+

The common beta parameters are derived from the mean and scale parameters, +combined with linear transformations to ensure the support in the given +range. The resulting beta parameters are scaled by abs(mu) to narrow the +beta distribution around the mean.

+
mu_transformed <- (mu - range_min) / (range_max - range_min)
+scale_transformed <- (scale - range_min) / (range_max - range_min)
+a <- mu_transformed / (scale_transformed * scale_transformed)
+b <- (1-mu_transformed) / (scale_transformed * scale_transformed)
+effect_transformed ~ beta(a/abs(mu), b/abs(mu))
+effect = effect_transformed * (range_max - range_min) + range_min
+
+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
average_effectOptional[float]

the average effect of the treatment. Defaults to None.

None
target_colstr

name of the target_col to use as the outcome. Defaults to "target".

'target'
treatment_colstr

the name of the column that contains the treatment. Defaults to "treatment".

'treatment'
treatmentstr

name of the treatment to use as the treated group. Defaults to "B".

'B'
scaleOptional[float]

the scale of the effect distribution. Defaults to None. +If not provided, the variance of the beta distribution is abs(mean).

None
range_minfloat

the minimum value of the target range, must be >-1. +Defaults to -0.8, which allows for up to 5x decreases of the target.

None
range_maxfloat

the maximum value of the target range. +Defaults to 4, which allows for up to 5x increases of the target.

None
reduce_varianceOptional[bool]

if True and if abs(average_effect)<1, we reduce +the variance of the beta distribution by multiplying the beta parameters by 1/abs(average_effect). +Defaults to None, which is equivalent to True.

None
+
+ Source code in cluster_experiments/perturbator.py +
class BetaRelativePerturbator(NormalPerturbator, RelativePositivePerturbator):
+    """
+    A stochastic Perturbator for continuous targets that applies a  sampled
+    effect from a scaled Beta distribution. It applies the effect multiplicatively.
+
+    The sampled effect is defined for values in the specified range
+    (range_min, range_max). It's recommended to set -1<range_min<0 and
+    range_max>0 in a "symmetric way" around 0, such that
+    log(1 + range_min) = -log(1 + range_max).
+    This ensures to have an "symmetric range" of perturbations that relatively
+    decrease the target as perturbations that relatively increase the target.
+    By "symmetry" of relative effects we mean that for an effect c > 0, an
+    increase of the target t via t*(1 + c) is "symmetric" to a decrease of t
+    via t/(1 + c). For example, an increase of 5x (i.e. by +400%, corresponding
+    to c_inc=4) is "symmetric" to a decrease of 5x (i.e. a decrease of -80%,
+    corresponding to c_dec = -0.8). In this case, 1 + c_dec = 1/(1 + c_inc), so
+    the relative effects c_inc and c_dec are "symmetric" in the sense that they
+    are inverse to each other.
+
+    The number of samples with 0 as target remains unchanged.
+
+    The stochastic effect is sampled from a beta distribution with parameters
+    mean and variance, which is linearly scaled to the range
+    (range_min, range_max).
+    If variance is not provided, the variance is abs(mean).
+
+    ```
+    target -> target * (1 + effect), where effect ~ Beta(a, b)
+    ```
+
+    The common beta parameters are derived from the mean and scale parameters,
+    combined with linear transformations to ensure the support in the given
+    range. The resulting beta parameters are scaled by abs(mu) to narrow the
+    beta distribution around the mean.
+
+    ```
+    mu_transformed <- (mu - range_min) / (range_max - range_min)
+    scale_transformed <- (scale - range_min) / (range_max - range_min)
+    a <- mu_transformed / (scale_transformed * scale_transformed)
+    b <- (1-mu_transformed) / (scale_transformed * scale_transformed)
+    effect_transformed ~ beta(a/abs(mu), b/abs(mu))
+    effect = effect_transformed * (range_max - range_min) + range_min
+    ```
+
+    Arguments:
+        average_effect (Optional[float], optional): the average effect of the treatment. Defaults to None.
+        target_col (str, optional): name of the target_col to use as the outcome. Defaults to "target".
+        treatment_col (str, optional): the name of the column that contains the treatment. Defaults to "treatment".
+        treatment (str, optional): name of the treatment to use as the treated group. Defaults to "B".
+        scale (Optional[float], optional): the scale of the effect distribution. Defaults to None.
+            If not provided, the variance of the beta distribution is abs(mean).
+        range_min (float, optional): the minimum value of the target range, must be >-1.
+            Defaults to -0.8, which allows for up to 5x decreases of the target.
+        range_max (float, optional): the maximum value of the target range.
+            Defaults to 4, which allows for up to 5x increases of the target.
+        reduce_variance (Optional[bool], optional): if True and if abs(average_effect)<1, we reduce
+            the variance of the beta distribution by multiplying the beta parameters by 1/abs(average_effect).
+            Defaults to None, which is equivalent to True.
+    """
+
+    def __init__(
+        self,
+        average_effect: Optional[float] = None,
+        target_col: str = "target",
+        treatment_col: str = "treatment",
+        treatment: str = "B",
+        scale: Optional[float] = None,
+        range_min: Optional[float] = None,
+        range_max: Optional[float] = None,
+        reduce_variance: Optional[bool] = None,
+    ):
+        self._check_range(range_min, range_max)
+        super().__init__(average_effect, target_col, treatment_col, treatment, scale)
+        self._range_min = range_min or -0.8
+        self._range_max = range_max or 4
+        self._reduce_variance = reduce_variance or True
+
+    def perturbate(
+        self, df: pd.DataFrame, average_effect: Optional[float] = None
+    ) -> pd.DataFrame:
+        """
+        Usage:
+        ```python
+        from cluster_experiments.perturbator import BetaRelativePerturbator
+        import pandas as pd
+        df = pd.DataFrame({"target": [1, 2, 3], "treatment": ["A", "B", "A"]})
+        perturbator = BetaRelativePerturbator(range_min = -0.5, range_max = 2)
+        # Increase target metric by 20% on average
+        perturbator.perturbate(df, average_effect=0.2)
+        ```
+        """
+        df = df.copy().reset_index(drop=True)
+        average_effect = self.get_average_effect(average_effect)
+        self.check_relative_effect_bounds(average_effect)
+        scale = self.get_scale(average_effect)
+        self.check_relative_effect_bounds(scale)
+        n = self.get_number_of_treated(df)
+        sampled_effect = self._sample_scaled_beta_effect(average_effect, scale, n)
+        df = self.apply_multiplicative_effect(df, sampled_effect)
+        return df
+
+    @staticmethod
+    def _check_range(range_min: float, range_max: float):
+        if range_min < -1:
+            raise ValueError(f"range_min needs to be greater than -1, got {range_min}")
+        if range_min >= range_max:
+            raise ValueError(
+                f"range_min needs to be smaller than range_max, got "
+                f"{range_min = } and {range_max = }"
+            )
+
+    def check_relative_effect_bounds(self, average_effect: float) -> None:
+        self.check_average_effect_greater_than(average_effect, x=self._range_min)
+        self.check_average_effect_smaller_than(average_effect, x=self._range_max)
+
+    def check_average_effect_greater_than(
+        self, average_effect: float, x: float
+    ) -> Optional[NoReturn]:
+        if average_effect <= x:
+            raise ValueError(
+                f"Simulated effect needs to be greater than range_min={x}, got {average_effect}"
+            )
+
+    def check_average_effect_smaller_than(
+        self, average_effect: float, x: float
+    ) -> Optional[NoReturn]:
+        if average_effect >= x:
+            raise ValueError(
+                f"Simulated effect needs to be smaller than range_max={x}, got {average_effect}"
+            )
+
+    def _reduce_variance_beta_params(
+        self, average_effect: float, a: float, b: float
+    ) -> Tuple[float, float]:
+        """
+        Multiplying the parameters of the beta distribution with a factor >1
+        reduces variance
+        """
+        if abs(average_effect) < 1:
+            a *= 1 / abs(average_effect)
+            b *= 1 / abs(average_effect)
+        return a, b
+
+    def _sample_scaled_beta_effect(
+        self, average_effect: float, scale: float, n: int
+    ) -> np.ndarray:
+        average_effect_inv_transf = self._inv_transform_to_range(average_effect)
+        scale_inv_transf = self._inv_transform_to_range(scale)
+        a = average_effect_inv_transf / (scale_inv_transf * scale_inv_transf)
+        b = (1 - average_effect_inv_transf) / (scale_inv_transf * scale_inv_transf)
+
+        if self._reduce_variance:
+            a, b = self._reduce_variance_beta_params(average_effect, a, b)
+        beta = np.random.beta(a, b, n)
+
+        return self._transform_to_range(beta)
+
+    def _transform_to_range(self, x: Union[float, np.ndarray]):
+        return x * (self._range_max - self._range_min) + self._range_min
+
+    def _inv_transform_to_range(self, x: Union[float, np.ndarray]):
+        return (x - self._range_min) / (self._range_max - self._range_min)
+
+    @classmethod
+    def from_config(cls, config):
+        """Creates a Perturbator object from a PowerConfig object"""
+        return cls(
+            average_effect=config.average_effect,
+            target_col=config.target_col,
+            treatment_col=config.treatment_col,
+            treatment=config.treatment,
+            scale=config.scale,
+            range_min=config.range_min,
+            range_max=config.range_max,
+        )
+
+
+ + + +
+ + + + + + + + + + + + + +
+ + + +

+from_config(config) + + + classmethod + + +

+ +
+ +

Creates a Perturbator object from a PowerConfig object

+ +
+ Source code in cluster_experiments/perturbator.py +
@classmethod
+def from_config(cls, config):
+    """Creates a Perturbator object from a PowerConfig object"""
+    return cls(
+        average_effect=config.average_effect,
+        target_col=config.target_col,
+        treatment_col=config.treatment_col,
+        treatment=config.treatment,
+        scale=config.scale,
+        range_min=config.range_min,
+        range_max=config.range_max,
+    )
+
+
+
+ +
+ + + +
+ + + +

+perturbate(self, df, average_effect=None) + + +

+ +
+ +

Usage: +

from cluster_experiments.perturbator import BetaRelativePerturbator
+import pandas as pd
+df = pd.DataFrame({"target": [1, 2, 3], "treatment": ["A", "B", "A"]})
+perturbator = BetaRelativePerturbator(range_min = -0.5, range_max = 2)
+# Increase target metric by 20% on average
+perturbator.perturbate(df, average_effect=0.2)
+

+ +
+ Source code in cluster_experiments/perturbator.py +
def perturbate(
+    self, df: pd.DataFrame, average_effect: Optional[float] = None
+) -> pd.DataFrame:
+    """
+    Usage:
+    ```python
+    from cluster_experiments.perturbator import BetaRelativePerturbator
+    import pandas as pd
+    df = pd.DataFrame({"target": [1, 2, 3], "treatment": ["A", "B", "A"]})
+    perturbator = BetaRelativePerturbator(range_min = -0.5, range_max = 2)
+    # Increase target metric by 20% on average
+    perturbator.perturbate(df, average_effect=0.2)
+    ```
+    """
+    df = df.copy().reset_index(drop=True)
+    average_effect = self.get_average_effect(average_effect)
+    self.check_relative_effect_bounds(average_effect)
+    scale = self.get_scale(average_effect)
+    self.check_relative_effect_bounds(scale)
+    n = self.get_number_of_treated(df)
+    sampled_effect = self._sample_scaled_beta_effect(average_effect, scale, n)
+    df = self.apply_multiplicative_effect(df, sampled_effect)
+    return df
+
+
+
+ +
+ + + + + +
+ +
+ +
+ + + +
+ + + +

+ +BetaRelativePositivePerturbator (NormalPerturbator, RelativePositivePerturbator) + + + + +

+ +
+ +

A stochastic Perturbator for continuous, positively-defined targets that applies a +sampled effect from the Beta distribution. It applies the effect multiplicatively.

+

WARNING: the average effect is only defined for values between 0 and 1 (not +included). Therefore, it only increments the target for the treated samples.

+

The number of samples with 0 as target remains unchanged.

+

The stochastic effect is sampled from a beta distribution with parameters mean and +variance. If variance is not provided, the variance is abs(mean). Hence, the effect +is bounded by 0 and 1.

+
target -> target * (1 + effect), where effect ~ Beta(a, b); a, b > 0
+                                   and target > 0 for all samples
+
+

The common beta parameters are derived from the mean and scale parameters (see +how below). That's why the average effect is only defined for values between 0 +and 1, otherwise one of the beta parameters would be negative or zero:

+

a <- mu / (scale * scale)
+b <- (1-mu) / (scale * scale)
+effect ~ beta(a, b)
+
+source: https://stackoverflow.com/a/51143208

+

Example: a mean = 0.2 and variance = 0.1, give a = 20 and b = 80 +Plot: https://www.wolframalpha.com/input?i=plot+distribution+of+beta%2820%2C+80%29

+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
average_effectOptional[float]

the average effect of the treatment. Defaults to None.

required
target_colstr

name of the target_col to use as the outcome. Defaults to "target".

required
treatment_colstr

the name of the column that contains the treatment. Defaults to "treatment".

required
treatmentstr

name of the treatment to use as the treated group. Defaults to "B".

required
scaleOptional[float]

the scale of the effect distribution. Defaults to None.

required
+
+ Source code in cluster_experiments/perturbator.py +
class BetaRelativePositivePerturbator(NormalPerturbator, RelativePositivePerturbator):
+    """
+    A stochastic Perturbator for continuous, positively-defined targets that applies a
+    sampled effect from the Beta distribution. It applies the effect multiplicatively.
+
+    *WARNING*: the average effect is only defined for values between 0 and 1 (not
+    included). Therefore, it only increments the target for the treated samples.
+
+    The number of samples with 0 as target remains unchanged.
+
+    The stochastic effect is sampled from a beta distribution with parameters mean and
+    variance. If variance is not provided, the variance is abs(mean). Hence, the effect
+    is bounded by 0 and 1.
+
+    ```
+    target -> target * (1 + effect), where effect ~ Beta(a, b); a, b > 0
+                                       and target > 0 for all samples
+    ```
+
+    The common beta parameters are derived from the mean and scale parameters (see
+    how below). That's why the average effect is only defined for values between 0
+    and 1, otherwise one of the beta parameters would be negative or zero:
+
+    ```
+    a <- mu / (scale * scale)
+    b <- (1-mu) / (scale * scale)
+    effect ~ beta(a, b)
+    ```
+    source: https://stackoverflow.com/a/51143208
+
+    Example: a mean = 0.2 and variance = 0.1, give a = 20 and b = 80
+    Plot: https://www.wolframalpha.com/input?i=plot+distribution+of+beta%2820%2C+80%29
+
+    Arguments:
+        average_effect (Optional[float], optional): the average effect of the treatment. Defaults to None.
+        target_col (str, optional): name of the target_col to use as the outcome. Defaults to "target".
+        treatment_col (str, optional): the name of the column that contains the treatment. Defaults to "treatment".
+        treatment (str, optional): name of the treatment to use as the treated group. Defaults to "B".
+        scale (Optional[float], optional): the scale of the effect distribution. Defaults to None.
+    """
+
+    def perturbate(
+        self, df: pd.DataFrame, average_effect: Optional[float] = None
+    ) -> pd.DataFrame:
+        """
+        Usage:
+        ```python
+        from cluster_experiments.perturbator import BetaRelativePositivePerturbator
+        import pandas as pd
+        df = pd.DataFrame({"target": [1, 2, 3], "treatment": ["A", "B", "A"]})
+        perturbator = BetaRelativePositivePerturbator()
+        # Increase target metric by 20%
+        perturbator.perturbate(df, average_effect=0.2)
+        # returns pd.DataFrame({"target": [1, 3, 3], "treatment": ["A", "B", "A"]})
+        ```
+        """
+        df = df.copy().reset_index(drop=True)
+        average_effect = self.get_average_effect(average_effect)
+        self.check_beta_positive_effect(df, average_effect)
+        scale = self.get_scale(average_effect)
+        n = self.get_number_of_treated(df)
+        sampled_effect = self._sample_beta_effect(average_effect, scale, n)
+        df = self.apply_multiplicative_effect(df, sampled_effect)
+        return df
+
+    def check_beta_positive_effect(self, df, average_effect):
+        self.check_average_effect_greater_than(average_effect, x=0)
+        self.check_average_effect_less_than(average_effect, x=1)
+        self.check_target_is_not_negative(df)
+        self.check_target_is_not_constant_zero(df, average_effect)
+
+    def check_average_effect_less_than(
+        self, average_effect: float, x: float
+    ) -> Optional[NoReturn]:
+        if average_effect >= x:
+            raise ValueError(
+                f"Simulated effect needs to be less than {x*100:.0f}%, got "
+                f"{average_effect*100:.1f}%"
+            )
+
+    def _sample_beta_effect(
+        self, average_effect: float, scale: float, n: int
+    ) -> np.ndarray:
+        a = average_effect / (scale * scale)
+        b = (1 - average_effect) / (scale * scale)
+        return np.random.beta(a, b, n)
+
+
+ + + +
+ + + + + + + + + + + +
+ + + +

+perturbate(self, df, average_effect=None) + + +

+ +
+ +

Usage: +

from cluster_experiments.perturbator import BetaRelativePositivePerturbator
+import pandas as pd
+df = pd.DataFrame({"target": [1, 2, 3], "treatment": ["A", "B", "A"]})
+perturbator = BetaRelativePositivePerturbator()
+# Increase target metric by 20%
+perturbator.perturbate(df, average_effect=0.2)
+# returns pd.DataFrame({"target": [1, 3, 3], "treatment": ["A", "B", "A"]})
+

+ +
+ Source code in cluster_experiments/perturbator.py +
def perturbate(
+    self, df: pd.DataFrame, average_effect: Optional[float] = None
+) -> pd.DataFrame:
+    """
+    Usage:
+    ```python
+    from cluster_experiments.perturbator import BetaRelativePositivePerturbator
+    import pandas as pd
+    df = pd.DataFrame({"target": [1, 2, 3], "treatment": ["A", "B", "A"]})
+    perturbator = BetaRelativePositivePerturbator()
+    # Increase target metric by 20%
+    perturbator.perturbate(df, average_effect=0.2)
+    # returns pd.DataFrame({"target": [1, 3, 3], "treatment": ["A", "B", "A"]})
+    ```
+    """
+    df = df.copy().reset_index(drop=True)
+    average_effect = self.get_average_effect(average_effect)
+    self.check_beta_positive_effect(df, average_effect)
+    scale = self.get_scale(average_effect)
+    n = self.get_number_of_treated(df)
+    sampled_effect = self._sample_beta_effect(average_effect, scale, n)
+    df = self.apply_multiplicative_effect(df, sampled_effect)
+    return df
+
+
+
+ +
+ + + + + +
+ +
+ +
+ + + +
+ + + +

+ +BinaryPerturbator (Perturbator) + + + + +

+ +
+ +

BinaryPerturbator is a Perturbator that adds is used to deal with binary outcome variables. +It randomly selects some treated instances and flips their outcome from 0 to 1 or 1 to 0, depending on the effect being positive or negative

+ +
+ Source code in cluster_experiments/perturbator.py +
class BinaryPerturbator(Perturbator):
+    """
+    BinaryPerturbator is a Perturbator that adds is used to deal with binary outcome variables.
+    It randomly selects some treated instances and flips their outcome from 0 to 1 or 1 to 0, depending on the effect being positive or negative
+    """
+
+    def _sample_max(self, df: pd.DataFrame, n: int) -> pd.DataFrame:
+        """Like sample without replacement,
+        but if you are to sample more than 100% of the data,
+        it just returns the whole dataframe."""
+        if n >= len(df):
+            return df
+        return df.sample(n=n)
+
+    def _data_checks(self, df: pd.DataFrame, average_effect: float) -> None:
+        """Check that outcome is indeed binary, and average effect is in (-1, 1)"""
+
+        if set(df[self.target_col].unique()) - {0, 1}:
+            raise ValueError(
+                f"Target column must be binary, found {set(df[self.target_col].unique())}"
+            )
+
+        if average_effect > 1 or average_effect < -1:
+            raise ValueError(
+                f"Average effect must be in (-1, 1), found {average_effect}"
+            )
+
+    def perturbate(
+        self, df: pd.DataFrame, average_effect: Optional[float] = None
+    ) -> pd.DataFrame:
+        """
+        Usage:
+
+        ```python
+        from cluster_experiments.perturbator import BinaryPerturbator
+        import pandas as pd
+        df = pd.DataFrame({"target": [1, 0, 1], "treatment": ["A", "B", "A"]})
+        perturbator = BinaryPerturbator()
+        perturbator.perturbate(df, average_effect=0.1)
+        ```
+        """
+
+        df = df.copy().reset_index(drop=True)
+        average_effect = self.get_average_effect(average_effect)
+
+        self._data_checks(df, average_effect)
+
+        from_target, to_target = 1, 0
+        if average_effect > 0:
+            from_target, to_target = 0, 1
+
+        n_transformed = abs(int(average_effect * len(df.query(self.treated_query))))
+        idx = list(
+            # Sample of negative cases in group B
+            df.query(f"{self.target_col} == {from_target} & {self.treated_query}")
+            .pipe(self._sample_max, n=n_transformed)
+            .index.drop_duplicates()
+        )
+        df.loc[idx, self.target_col] = to_target
+        return df
+
+
+ + + +
+ + + + + + + + + +
+ + + +

+perturbate(self, df, average_effect=None) + + +

+ +
+ +

Usage:

+
from cluster_experiments.perturbator import BinaryPerturbator
+import pandas as pd
+df = pd.DataFrame({"target": [1, 0, 1], "treatment": ["A", "B", "A"]})
+perturbator = BinaryPerturbator()
+perturbator.perturbate(df, average_effect=0.1)
+
+ +
+ Source code in cluster_experiments/perturbator.py +
def perturbate(
+    self, df: pd.DataFrame, average_effect: Optional[float] = None
+) -> pd.DataFrame:
+    """
+    Usage:
+
+    ```python
+    from cluster_experiments.perturbator import BinaryPerturbator
+    import pandas as pd
+    df = pd.DataFrame({"target": [1, 0, 1], "treatment": ["A", "B", "A"]})
+    perturbator = BinaryPerturbator()
+    perturbator.perturbate(df, average_effect=0.1)
+    ```
+    """
+
+    df = df.copy().reset_index(drop=True)
+    average_effect = self.get_average_effect(average_effect)
+
+    self._data_checks(df, average_effect)
+
+    from_target, to_target = 1, 0
+    if average_effect > 0:
+        from_target, to_target = 0, 1
+
+    n_transformed = abs(int(average_effect * len(df.query(self.treated_query))))
+    idx = list(
+        # Sample of negative cases in group B
+        df.query(f"{self.target_col} == {from_target} & {self.treated_query}")
+        .pipe(self._sample_max, n=n_transformed)
+        .index.drop_duplicates()
+    )
+    df.loc[idx, self.target_col] = to_target
+    return df
+
+
+
+ +
+ + + + + +
+ +
+ +
+ + + +
+ + + +

+ +ConstantPerturbator (Perturbator) + + + + +

+ +
+ +

ConstantPerturbator is a Perturbator that adds a constant effect to the target column of the treated instances.

+ +
+ Source code in cluster_experiments/perturbator.py +
class ConstantPerturbator(Perturbator):
+    """
+    ConstantPerturbator is a Perturbator that adds a constant effect to the target column of the treated instances.
+    """
+
+    def perturbate(
+        self, df: pd.DataFrame, average_effect: Optional[float] = None
+    ) -> pd.DataFrame:
+        """
+        Usage:
+
+        ```python
+        from cluster_experiments.perturbator import ConstantPerturbator
+        import pandas as pd
+        df = pd.DataFrame({"target": [1, 2, 3], "treatment": ["A", "B", "A"]})
+        perturbator = ConstantPerturbator()
+        perturbator.perturbate(df, average_effect=1)
+        ```
+        """
+        df = df.copy().reset_index(drop=True)
+        average_effect = self.get_average_effect(average_effect)
+        df = self.apply_additive_effect(df, average_effect)
+        return df
+
+    def apply_additive_effect(
+        self, df: pd.DataFrame, effect: Union[float, np.ndarray]
+    ) -> pd.DataFrame:
+        df.loc[df[self.treatment_col] == self.treatment, self.target_col] += effect
+        return df
+
+
+ + + +
+ + + + + + + + + + +
+ + + +

+perturbate(self, df, average_effect=None) + + +

+ +
+ +

Usage:

+
from cluster_experiments.perturbator import ConstantPerturbator
+import pandas as pd
+df = pd.DataFrame({"target": [1, 2, 3], "treatment": ["A", "B", "A"]})
+perturbator = ConstantPerturbator()
+perturbator.perturbate(df, average_effect=1)
+
+ +
+ Source code in cluster_experiments/perturbator.py +
def perturbate(
+    self, df: pd.DataFrame, average_effect: Optional[float] = None
+) -> pd.DataFrame:
+    """
+    Usage:
+
+    ```python
+    from cluster_experiments.perturbator import ConstantPerturbator
+    import pandas as pd
+    df = pd.DataFrame({"target": [1, 2, 3], "treatment": ["A", "B", "A"]})
+    perturbator = ConstantPerturbator()
+    perturbator.perturbate(df, average_effect=1)
+    ```
+    """
+    df = df.copy().reset_index(drop=True)
+    average_effect = self.get_average_effect(average_effect)
+    df = self.apply_additive_effect(df, average_effect)
+    return df
+
+
+
+ +
+ + + + + +
+ +
+ +
+ + + +
+ + + +

+ +NormalPerturbator (ConstantPerturbator) + + + + +

+ +
+ +

The NormalPerturbator class implements a perturbator that adds a stochastic effect +to the target column of the treated instances. The stochastic effect is sampled from a +normal distribution with mean average_effect and variance scale. If scale is not +provided, the variance is abs(average_effect). If scale is provided, a +value not much bigger than the average_effect is suggested.

+
target -> target + effect, where effect ~ Normal(average_effect, scale)
+
+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
average_effectOptional[float]

the average effect of the treatment. Defaults to None.

None
target_colstr

name of the target_col to use as the outcome. Defaults to "target".

'target'
treatment_colstr

the name of the column that contains the treatment. Defaults to "treatment".

'treatment'
treatmentstr

name of the treatment to use as the treated group. Defaults to "B".

'B'
scaleOptional[float]

the scale of the effect distribution. Defaults to None.

None
+
+ Source code in cluster_experiments/perturbator.py +
class NormalPerturbator(ConstantPerturbator):
+    """The NormalPerturbator class implements a perturbator that adds a stochastic effect
+    to the target column of the treated instances. The stochastic effect is sampled from a
+    normal distribution with mean average_effect and variance scale. If scale is not
+    provided, the variance is abs(average_effect). If scale is provided, a
+    value not much bigger than the average_effect is suggested.
+
+    ```
+    target -> target + effect, where effect ~ Normal(average_effect, scale)
+    ```
+
+    Arguments:
+        average_effect (Optional[float], optional): the average effect of the treatment. Defaults to None.
+        target_col (str, optional): name of the target_col to use as the outcome. Defaults to "target".
+        treatment_col (str, optional): the name of the column that contains the treatment. Defaults to "treatment".
+        treatment (str, optional): name of the treatment to use as the treated group. Defaults to "B".
+        scale (Optional[float], optional): the scale of the effect distribution. Defaults to None.
+    """
+
+    def __init__(
+        self,
+        average_effect: Optional[float] = None,
+        target_col: str = "target",
+        treatment_col: str = "treatment",
+        treatment: str = "B",
+        scale: Optional[float] = None,
+    ):
+        super().__init__(average_effect, target_col, treatment_col, treatment)
+        self._scale = scale
+
+    def perturbate(
+        self, df: pd.DataFrame, average_effect: Optional[float] = None
+    ) -> pd.DataFrame:
+        """Perturbate with a normal effect with mean average_effect and
+        std abs(average_effect).
+
+        Arguments:
+            df (pd.DataFrame): the dataframe to perturbate.
+            average_effect (Optional[float], optional): the average effect. Defaults to None.
+
+        Returns:
+            pd.DataFrame: the perturbated dataframe.
+
+        Usage:
+
+        ```python
+        from cluster_experiments.perturbator import NormalPerturbator
+        import pandas as pd
+        df = pd.DataFrame({"target": [1, 2, 3], "treatment": ["A", "B", "A"]})
+        perturbator = NormalPerturbator()
+        perturbator.perturbate(df, average_effect=1)
+        ```
+        """
+        df = df.copy().reset_index(drop=True)
+        average_effect = self.get_average_effect(average_effect)
+        scale = self.get_scale(average_effect)
+        n = self.get_number_of_treated(df)
+        sampled_effect = self._sample_normal_effect(average_effect, scale, n)
+        df = self.apply_additive_effect(df, sampled_effect)
+        return df
+
+    def get_scale(self, average_effect: float) -> float:
+        """Get the scale of the normal distribution. If scale is not provided, the
+        variance is abs(average_effect). Raises a ValueError if scale is not positive.
+        """
+        scale = abs(average_effect) if self._scale is None else self._scale
+        if scale <= 0:
+            raise ValueError(f"scale must be positive, got {scale}")
+        return scale
+
+    def get_number_of_treated(self, df: pd.DataFrame) -> int:
+        """Get the number of treated instances in the dataframe"""
+        return (df[self.treatment_col] == self.treatment).sum()
+
+    def _sample_normal_effect(
+        self, average_effect: float, scale: float, n: int
+    ) -> np.ndarray:
+        return np.random.normal(average_effect, scale, n)
+
+    @classmethod
+    def from_config(cls, config):
+        """Creates a Perturbator object from a PowerConfig object"""
+        return cls(
+            average_effect=config.average_effect,
+            target_col=config.target_col,
+            treatment_col=config.treatment_col,
+            treatment=config.treatment,
+            scale=config.scale,
+        )
+
+
+ + + +
+ + + + + + + + + + +
+ + + +

+from_config(config) + + + classmethod + + +

+ +
+ +

Creates a Perturbator object from a PowerConfig object

+ +
+ Source code in cluster_experiments/perturbator.py +
@classmethod
+def from_config(cls, config):
+    """Creates a Perturbator object from a PowerConfig object"""
+    return cls(
+        average_effect=config.average_effect,
+        target_col=config.target_col,
+        treatment_col=config.treatment_col,
+        treatment=config.treatment,
+        scale=config.scale,
+    )
+
+
+
+ +
+ + + +
+ + + +

+get_number_of_treated(self, df) + + +

+ +
+ +

Get the number of treated instances in the dataframe

+ +
+ Source code in cluster_experiments/perturbator.py +
def get_number_of_treated(self, df: pd.DataFrame) -> int:
+    """Get the number of treated instances in the dataframe"""
+    return (df[self.treatment_col] == self.treatment).sum()
+
+
+
+ +
+ + + +
+ + + +

+get_scale(self, average_effect) + + +

+ +
+ +

Get the scale of the normal distribution. If scale is not provided, the +variance is abs(average_effect). Raises a ValueError if scale is not positive.

+ +
+ Source code in cluster_experiments/perturbator.py +
def get_scale(self, average_effect: float) -> float:
+    """Get the scale of the normal distribution. If scale is not provided, the
+    variance is abs(average_effect). Raises a ValueError if scale is not positive.
+    """
+    scale = abs(average_effect) if self._scale is None else self._scale
+    if scale <= 0:
+        raise ValueError(f"scale must be positive, got {scale}")
+    return scale
+
+
+
+ +
+ + + +
+ + + +

+perturbate(self, df, average_effect=None) + + +

+ +
+ +

Perturbate with a normal effect with mean average_effect and +std abs(average_effect).

+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
dfpd.DataFrame

the dataframe to perturbate.

required
average_effectOptional[float]

the average effect. Defaults to None.

None
+

Returns:

+ + + + + + + + + + + + + +
TypeDescription
pd.DataFrame

the perturbated dataframe.

Usage:

+
from cluster_experiments.perturbator import NormalPerturbator
+import pandas as pd
+df = pd.DataFrame({"target": [1, 2, 3], "treatment": ["A", "B", "A"]})
+perturbator = NormalPerturbator()
+perturbator.perturbate(df, average_effect=1)
+
+ +
+ Source code in cluster_experiments/perturbator.py +
def perturbate(
+    self, df: pd.DataFrame, average_effect: Optional[float] = None
+) -> pd.DataFrame:
+    """Perturbate with a normal effect with mean average_effect and
+    std abs(average_effect).
+
+    Arguments:
+        df (pd.DataFrame): the dataframe to perturbate.
+        average_effect (Optional[float], optional): the average effect. Defaults to None.
+
+    Returns:
+        pd.DataFrame: the perturbated dataframe.
+
+    Usage:
+
+    ```python
+    from cluster_experiments.perturbator import NormalPerturbator
+    import pandas as pd
+    df = pd.DataFrame({"target": [1, 2, 3], "treatment": ["A", "B", "A"]})
+    perturbator = NormalPerturbator()
+    perturbator.perturbate(df, average_effect=1)
+    ```
+    """
+    df = df.copy().reset_index(drop=True)
+    average_effect = self.get_average_effect(average_effect)
+    scale = self.get_scale(average_effect)
+    n = self.get_number_of_treated(df)
+    sampled_effect = self._sample_normal_effect(average_effect, scale, n)
+    df = self.apply_additive_effect(df, sampled_effect)
+    return df
+
+
+
+ +
+ + + + + +
+ +
+ +
+ + + +
+ + + +

+ +Perturbator (ABC) + + + + +

+ +
+ +

Abstract perturbator. Perturbators are used to simulate a fictitious effect when running a power analysis.

+

The idea is that, when running a power analysis, we split our instances according to a RandomSplitter, and the +instances that got the treatment, are perturbated with a fictional effect via the Perturbator.

+

In order to create your own perturbator, you should create a derived class that implements the perturbate method. +The perturbate method should add the average effect in the desired way and return the dataframe with the extra average effect, +without affecting the initial dataframe. Keep in mind to use df = df.copy() in the first line of the perturbate method.

+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
average_effectOptional[float]

The average effect of the treatment

None
target_colstr

name of the target_col to use as the outcome

'target'
treatment_colstr

The name of the column that contains the treatment

'treatment'
treatmentstr

name of the treatment to use as the treated group

'B'
+
+ Source code in cluster_experiments/perturbator.py +
class Perturbator(ABC):
+    """
+    Abstract perturbator. Perturbators are used to simulate a fictitious effect when running a power analysis.
+
+    The idea is that, when running a power analysis, we split our instances according to a RandomSplitter, and the
+    instances that got the treatment, are perturbated with a fictional effect via the Perturbator.
+
+    In order to create your own perturbator, you should create a derived class that implements the perturbate method.
+    The perturbate method should add the average effect in the desired way and return the dataframe with the extra average effect,
+    without affecting the initial dataframe. Keep in mind to use `df = df.copy()` in the first line of the perturbate method.
+
+    Arguments:
+        average_effect: The average effect of the treatment
+        target_col: name of the target_col to use as the outcome
+        treatment_col: The name of the column that contains the treatment
+        treatment: name of the treatment to use as the treated group
+
+    """
+
+    def __init__(
+        self,
+        average_effect: Optional[float] = None,
+        target_col: str = "target",
+        treatment_col: str = "treatment",
+        treatment: str = "B",
+    ):
+        self.average_effect = average_effect
+        self.target_col = target_col
+        self.treatment_col = treatment_col
+        self.treatment = treatment
+        self.treated_query = f"{self.treatment_col} == '{self.treatment}'"
+
+    def get_average_effect(self, average_effect: Optional[float] = None) -> float:
+        average_effect = (
+            average_effect if average_effect is not None else self.average_effect
+        )
+        if average_effect is None:
+            raise ValueError(
+                "average_effect must be provided, either in the constructor or in the method call"
+            )
+        return average_effect
+
+    @abstractmethod
+    def perturbate(
+        self, df: pd.DataFrame, average_effect: Optional[float] = None
+    ) -> pd.DataFrame:
+        """Method to perturbate a dataframe"""
+
+    @classmethod
+    def from_config(cls, config):
+        """Creates a Perturbator object from a PowerConfig object"""
+        return cls(
+            average_effect=config.average_effect,
+            target_col=config.target_col,
+            treatment_col=config.treatment_col,
+            treatment=config.treatment,
+        )
+
+
+ + + +
+ + + + + + + + + + +
+ + + +

+from_config(config) + + + classmethod + + +

+ +
+ +

Creates a Perturbator object from a PowerConfig object

+ +
+ Source code in cluster_experiments/perturbator.py +
@classmethod
+def from_config(cls, config):
+    """Creates a Perturbator object from a PowerConfig object"""
+    return cls(
+        average_effect=config.average_effect,
+        target_col=config.target_col,
+        treatment_col=config.treatment_col,
+        treatment=config.treatment,
+    )
+
+
+
+ +
+ + + + +
+ + + +

+perturbate(self, df, average_effect=None) + + +

+ +
+ +

Method to perturbate a dataframe

+ +
+ Source code in cluster_experiments/perturbator.py +
@abstractmethod
+def perturbate(
+    self, df: pd.DataFrame, average_effect: Optional[float] = None
+) -> pd.DataFrame:
+    """Method to perturbate a dataframe"""
+
+
+
+ +
+ + + + + +
+ +
+ +
+ + + +
+ + + +

+ +RelativePositivePerturbator (Perturbator) + + + + +

+ +
+ +

A Perturbator for continuous, positively-defined targets +applies a simulated effect multiplicatively for the treated samples, ie. +proportional to the target value for each sample. The number of samples with 0 +as target remains unchanged.

+
target -> target * (1 + average_effect), where -1 < average_effect < inf
+                                           and target > 0 for all samples
+
+ +
+ Source code in cluster_experiments/perturbator.py +
class RelativePositivePerturbator(Perturbator):
+    """
+    A Perturbator for continuous, positively-defined targets
+    applies a simulated effect multiplicatively for the treated samples, ie.
+    proportional to the target value for each sample. The number of samples with 0
+    as target remains unchanged.
+
+    ```
+    target -> target * (1 + average_effect), where -1 < average_effect < inf
+                                               and target > 0 for all samples
+    ```
+    """
+
+    def perturbate(
+        self, df: pd.DataFrame, average_effect: Optional[float] = None
+    ) -> pd.DataFrame:
+        """
+        Usage:
+        ```python
+        from cluster_experiments.perturbator import RelativePositivePerturbator
+        import pandas as pd
+        df = pd.DataFrame({"target": [1, 2, 3], "treatment": ["A", "B", "A"]})
+        perturbator = RelativePositivePerturbator()
+        # Increase target metric by 50%
+        perturbator.perturbate(df, average_effect=0.5)
+        # returns pd.DataFrame({"target": [1, 3, 3], "treatment": ["A", "B", "A"]})
+        ```
+        """
+        df = df.copy().reset_index(drop=True)
+        average_effect = self.get_average_effect(average_effect)
+        self.check_relative_positive_effect(df, average_effect)
+        df = self.apply_multiplicative_effect(df, average_effect)
+        return df
+
+    def check_relative_positive_effect(
+        self, df: pd.DataFrame, average_effect: float
+    ) -> None:
+        self.check_average_effect_greater_than(average_effect, x=-1)
+        self.check_target_is_not_negative(df)
+        self.check_target_is_not_constant_zero(df, average_effect)
+
+    def check_target_is_not_constant_zero(
+        self, df: pd.DataFrame, average_effect: float
+    ) -> Optional[NoReturn]:
+        treatment_zeros = (
+            (df[self.treatment_col] != self.treatment) | (df[self.target_col] == 0)
+        ).mean()
+        if 1.0 == treatment_zeros:
+            raise ValueError(
+                f"All treatment samples have {self.target_col} = 0, relative effect "
+                f"{average_effect} will have no effect"
+            )
+
+    def check_target_is_not_negative(self, df: pd.DataFrame) -> Optional[NoReturn]:
+        if any(df[self.target_col] < 0):
+            raise ValueError(
+                f"All {self.target_col} values need to be positive or 0, "
+                f"got {df[self.target_col].min()}"
+            )
+
+    def check_average_effect_greater_than(
+        self, average_effect: float, x: float
+    ) -> Optional[NoReturn]:
+        if average_effect <= x:
+            raise ValueError(
+                f"Simulated effect needs to be greater than {x*100:.0f}%, got "
+                f"{average_effect*100:.1f}%"
+            )
+
+    def apply_multiplicative_effect(
+        self, df: pd.DataFrame, effect: Union[float, np.ndarray]
+    ) -> pd.DataFrame:
+        df.loc[df[self.treatment_col] == self.treatment, self.target_col] *= 1 + effect
+        return df
+
+
+ + + +
+ + + + + + + + + + + + + + +
+ + + +

+perturbate(self, df, average_effect=None) + + +

+ +
+ +

Usage: +

from cluster_experiments.perturbator import RelativePositivePerturbator
+import pandas as pd
+df = pd.DataFrame({"target": [1, 2, 3], "treatment": ["A", "B", "A"]})
+perturbator = RelativePositivePerturbator()
+# Increase target metric by 50%
+perturbator.perturbate(df, average_effect=0.5)
+# returns pd.DataFrame({"target": [1, 3, 3], "treatment": ["A", "B", "A"]})
+

+ +
+ Source code in cluster_experiments/perturbator.py +
def perturbate(
+    self, df: pd.DataFrame, average_effect: Optional[float] = None
+) -> pd.DataFrame:
+    """
+    Usage:
+    ```python
+    from cluster_experiments.perturbator import RelativePositivePerturbator
+    import pandas as pd
+    df = pd.DataFrame({"target": [1, 2, 3], "treatment": ["A", "B", "A"]})
+    perturbator = RelativePositivePerturbator()
+    # Increase target metric by 50%
+    perturbator.perturbate(df, average_effect=0.5)
+    # returns pd.DataFrame({"target": [1, 3, 3], "treatment": ["A", "B", "A"]})
+    ```
+    """
+    df = df.copy().reset_index(drop=True)
+    average_effect = self.get_average_effect(average_effect)
+    self.check_relative_positive_effect(df, average_effect)
+    df = self.apply_multiplicative_effect(df, average_effect)
+    return df
+
+
+
+ +
+ + + + + +
+ +
+ +
+ + + +
+ + + +

+ +SegmentedBetaRelativePerturbator (BetaRelativePositivePerturbator) + + + + +

+ +
+ +

A stochastic Perturbator for continuous targets that applies a sampled +effect from the Beta distribution. It applies the effect multiplicatively +and based on given segments. +For each segment, the average segment effect is sampled from a beta +distribution with support in (0, 1). Within each segment, the individual +effects are sampled from a beta distribution with mean equal to the segment +average effect and support in (range_min, range_max).

+

The number of samples with 0 as target remains unchanged.

+

For additional details and recommendations on the parameters, see the +documentation for the BetaRelativePerturbator class.

+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
average_effectOptional[float]

the average effect of the treatment. Defaults to None.

None
target_colstr

name of the target_col to use as the outcome. Defaults to "target".

'target'
treatment_colstr

the name of the column that contains the treatment. Defaults to "treatment".

'treatment'
treatmentstr

name of the treatment to use as the treated group. Defaults to "B".

'B'
scaleOptional[float]

the scale of the effect distribution. Defaults to None.

None
range_minfloat

the minimum value of the target range, must be >-1. +Defaults to -0.8, which allows for up to 5x decreases of the target.

None
range_maxfloat

the maximum value of the target range. +Defaults to 4, which allows for up to 5x increases of the target.

None
segment_colsOptional[List[str]]

the columns to use for segmenting. Defaults to None.

required
+
+ Source code in cluster_experiments/perturbator.py +
class SegmentedBetaRelativePerturbator(BetaRelativePositivePerturbator):
+    """
+    A stochastic Perturbator for continuous targets that applies a sampled
+    effect from the Beta distribution. It applies the effect multiplicatively
+    and based on given segments.
+    For each segment, the average segment effect is sampled from a beta
+    distribution with support in (0, 1). Within each segment, the individual
+    effects are sampled from a beta distribution with mean equal to the segment
+    average effect and support in (range_min, range_max).
+
+    The number of samples with 0 as target remains unchanged.
+
+    For additional details and recommendations on the parameters, see the
+    documentation for the `BetaRelativePerturbator` class.
+
+    Arguments:
+        average_effect (Optional[float], optional): the average effect of the treatment. Defaults to None.
+        target_col (str, optional): name of the target_col to use as the outcome. Defaults to "target".
+        treatment_col (str, optional): the name of the column that contains the treatment. Defaults to "treatment".
+        treatment (str, optional): name of the treatment to use as the treated group. Defaults to "B".
+        scale (Optional[float], optional): the scale of the effect distribution. Defaults to None.
+        range_min (float, optional): the minimum value of the target range, must be >-1.
+            Defaults to -0.8, which allows for up to 5x decreases of the target.
+        range_max (float, optional): the maximum value of the target range.
+            Defaults to 4, which allows for up to 5x increases of the target.
+        segment_cols (Optional[List[str]], optional): the columns to use for segmenting. Defaults to None.
+    """
+
+    def __init__(
+        self,
+        segment_cols: List[str],
+        average_effect: Optional[float] = None,
+        target_col: str = "target",
+        treatment_col: str = "treatment",
+        treatment: str = "B",
+        scale: Optional[float] = None,
+        range_min: Optional[float] = None,
+        range_max: Optional[float] = None,
+    ):
+        super().__init__(average_effect, target_col, treatment_col, treatment, scale)
+        self._range_min = range_min or -0.8
+        self._range_max = range_max or 4
+        self._segment_cols = segment_cols
+        self.segment_col = self._get_segment_col_name(segment_cols)
+
+    @staticmethod
+    def _get_segment_col_name(segment_cols: List[str]):
+        if not isinstance(segment_cols, list):
+            raise ValueError(
+                f"segment_cols must be of type List[str], got type {type(segment_cols)}"
+            )
+        return "_cluster_" + "_".join(segment_cols)
+
+    def _set_segment_col_values(self, df: pd.DataFrame):
+        if self.segment_col in df.columns:
+            raise ValueError(
+                f"Cannot use {self.segment_col=} as perturbator clustering "
+                f"column, as it already exists in the input dataframe!"
+            )
+        return df.copy().assign(
+            **{self.segment_col: df[self._segment_cols].astype(str).sum(axis=1)}
+        )
+
+    def get_cluster_perturbator_fixed_params(
+        self, average_effect: Optional[float] = None
+    ) -> Dict[str, Any]:
+        average_effect = self.get_average_effect(average_effect)
+        self.check_average_effect_greater_than(average_effect, x=0)
+        self.check_average_effect_less_than(average_effect, x=1)
+        scale = self.get_scale(average_effect)
+        return {
+            "average_effect": average_effect,
+            "scale": scale,
+        }
+
+    def get_cluster_perturbator(self, **kwargs) -> Perturbator:
+        sampled_effect = self._sample_beta_effect(
+            kwargs["average_effect"], kwargs["scale"], 1
+        )
+        cluster_perturbator = BetaRelativePerturbator(
+            average_effect=sampled_effect,
+            target_col=self.target_col,
+            treatment_col=self.treatment_col,
+            treatment=self.treatment,
+            range_min=self._range_min,
+            range_max=self._range_max,
+        )
+        return cluster_perturbator
+
+    def perturbate(
+        self,
+        df: pd.DataFrame,
+        average_effect: Optional[float] = None,
+    ) -> pd.DataFrame:
+        df = df.copy().reset_index(drop=True)
+        df = self._set_segment_col_values(df)
+
+        cluster_perturbator_params = self.get_cluster_perturbator_fixed_params(
+            average_effect
+        )
+        df_perturbed = pd.concat(
+            [
+                self.get_cluster_perturbator(**cluster_perturbator_params).perturbate(
+                    df=df[df[self.segment_col] == cluster].copy()
+                )
+                for cluster in df[self.segment_col].unique()
+            ]
+        )
+        return df_perturbed.drop(columns=self.segment_col).reset_index(drop=True)
+
+    @classmethod
+    def from_config(cls, config):
+        """Creates a Perturbator object from a PowerConfig object"""
+        return cls(
+            average_effect=config.average_effect,
+            target_col=config.target_col,
+            treatment_col=config.treatment_col,
+            treatment=config.treatment,
+            scale=config.scale,
+            range_min=config.range_min,
+            range_max=config.range_max,
+            segment_cols=config.segment_cols,
+        )
+
+
+ + + +
+ + + + + + + + + + +
+ + + +

+from_config(config) + + + classmethod + + +

+ +
+ +

Creates a Perturbator object from a PowerConfig object

+ +
+ Source code in cluster_experiments/perturbator.py +
@classmethod
+def from_config(cls, config):
+    """Creates a Perturbator object from a PowerConfig object"""
+    return cls(
+        average_effect=config.average_effect,
+        target_col=config.target_col,
+        treatment_col=config.treatment_col,
+        treatment=config.treatment,
+        scale=config.scale,
+        range_min=config.range_min,
+        range_max=config.range_max,
+        segment_cols=config.segment_cols,
+    )
+
+
+
+ +
+ + + + + +
+ + + +

+perturbate(self, df, average_effect=None) + + +

+ +
+ +

Usage: +

from cluster_experiments.perturbator import BetaRelativePositivePerturbator
+import pandas as pd
+df = pd.DataFrame({"target": [1, 2, 3], "treatment": ["A", "B", "A"]})
+perturbator = BetaRelativePositivePerturbator()
+# Increase target metric by 20%
+perturbator.perturbate(df, average_effect=0.2)
+# returns pd.DataFrame({"target": [1, 3, 3], "treatment": ["A", "B", "A"]})
+

+ +
+ Source code in cluster_experiments/perturbator.py +
def perturbate(
+    self,
+    df: pd.DataFrame,
+    average_effect: Optional[float] = None,
+) -> pd.DataFrame:
+    df = df.copy().reset_index(drop=True)
+    df = self._set_segment_col_values(df)
+
+    cluster_perturbator_params = self.get_cluster_perturbator_fixed_params(
+        average_effect
+    )
+    df_perturbed = pd.concat(
+        [
+            self.get_cluster_perturbator(**cluster_perturbator_params).perturbate(
+                df=df[df[self.segment_col] == cluster].copy()
+            )
+            for cluster in df[self.segment_col].unique()
+        ]
+    )
+    return df_perturbed.drop(columns=self.segment_col).reset_index(drop=True)
+
+
+
+ +
+ + + + + +
+ +
+ +
+ + + +
+ + + +

+ +UniformPerturbator (Perturbator) + + + + +

+ +
+ +

UniformPerturbator is a Perturbator that adds a constant effect to the target column of the treated instances.

+ +
+ Source code in cluster_experiments/perturbator.py +
class UniformPerturbator(Perturbator):
+    """
+    UniformPerturbator is a Perturbator that adds a constant effect to the target column of the treated instances.
+    """
+
+    def __init__(
+        self,
+        average_effect: Optional[float] = None,
+        target_col: str = "target",
+        treatment_col: str = "treatment",
+        treatment: str = "B",
+    ):
+        super().__init__(average_effect, target_col, treatment_col, treatment)
+        logging.warning(
+            "UniformPerturbator is deprecated and will be removed in future versions. "
+            "Use ConstantPerturbator instead."
+        )
+
+    def perturbate(
+        self, df: pd.DataFrame, average_effect: Optional[float] = None
+    ) -> pd.DataFrame:
+        """
+        Usage:
+
+        ```python
+        from cluster_experiments.perturbator import UniformPerturbator
+        import pandas as pd
+        df = pd.DataFrame({"target": [1, 2, 3], "treatment": ["A", "B", "A"]})
+        perturbator = UniformPerturbator()
+        perturbator.perturbate(df, average_effect=1)
+        ```
+        """
+        df = df.copy().reset_index(drop=True)
+        average_effect = self.get_average_effect(average_effect)
+        df = self.apply_additive_effect(df, average_effect)
+        return df
+
+    def apply_additive_effect(
+        self, df: pd.DataFrame, effect: Union[float, np.ndarray]
+    ) -> pd.DataFrame:
+        df.loc[df[self.treatment_col] == self.treatment, self.target_col] += effect
+        return df
+
+
+ + + +
+ + + + + + + + + + + +
+ + + +

+perturbate(self, df, average_effect=None) + + +

+ +
+ +

Usage:

+
from cluster_experiments.perturbator import UniformPerturbator
+import pandas as pd
+df = pd.DataFrame({"target": [1, 2, 3], "treatment": ["A", "B", "A"]})
+perturbator = UniformPerturbator()
+perturbator.perturbate(df, average_effect=1)
+
+ +
+ Source code in cluster_experiments/perturbator.py +
def perturbate(
+    self, df: pd.DataFrame, average_effect: Optional[float] = None
+) -> pd.DataFrame:
+    """
+    Usage:
+
+    ```python
+    from cluster_experiments.perturbator import UniformPerturbator
+    import pandas as pd
+    df = pd.DataFrame({"target": [1, 2, 3], "treatment": ["A", "B", "A"]})
+    perturbator = UniformPerturbator()
+    perturbator.perturbate(df, average_effect=1)
+    ```
+    """
+    df = df.copy().reset_index(drop=True)
+    average_effect = self.get_average_effect(average_effect)
+    df = self.apply_additive_effect(df, average_effect)
+    return df
+
+
+
+ +
+ + + + + +
+ +
+ +
+ + + + + + + +
+ +
+ +
+ + + + + + +
+
+ + +
+ +
+ + + +
+
+
+
+ + + + + + + + + \ No newline at end of file diff --git a/api/power_analysis.html b/api/power_analysis.html new file mode 100644 index 00000000..28ee36da --- /dev/null +++ b/api/power_analysis.html @@ -0,0 +1,4238 @@ + + + + + + + + + + + + + + + + + + + + + + Power analysis - Cluster Experiments Docs + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
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+ +
+ + + + + + +
+
+ + + +
+
+
+ + + + +
+
+
+ + + + + + + +
+
+ + + + + + + + + +

from cluster_experiments.power_analysis import *

+ + +
+ + +
+ + + + +
+ + + + + + + +
+ + + +

+ +NormalPowerAnalysis + + + +

+ +
+ +

Class used to run Power analysis, using the central limit theorem to estimate power based on standard errors of the estimator, +and the fact that the coefficients of a regression are normally distributed. +It does so by running simulations. In each simulation: +1. Assign treatment to dataframe randomly +2. Add pre-experiment data if needed +3. Get standard error from analysis

+

Finally it returns the power of the analysis by counting how many times the effect was detected.

+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
splitterRandomSplitter

RandomSplitter class to randomly assign treatment to dataframe.

required
analysisExperimentAnalysis

ExperimentAnalysis class to use for analysis.

required
cupac_modelOptional[sklearn.base.BaseEstimator]

Sklearn estimator class to add pre-experiment data to dataframe. If None, no pre-experiment data will be added.

None
target_colstr

Name of the column with the outcome variable.

'target'
treatment_colstr

Name of the column with the treatment variable.

'treatment'
treatmentstr

value of treatment_col considered to be treatment (not control)

'B'
controlstr

value of treatment_col considered to be control (not treatment)

'A'
n_simulationsint

Number of simulations to run.

100
alphafloat

Significance level.

0.05
features_cupac_modelOptional[List[str]]

Covariates to be used in cupac model

None
seedOptional[int]

Optional. Seed to use for the splitter.

None

Usage: +

from datetime import date
+
+import numpy as np
+import pandas as pd
+from cluster_experiments.experiment_analysis import GeeExperimentAnalysis
+from cluster_experiments.power_analysis import NormalPowerAnalysis
+from cluster_experiments.random_splitter import ClusteredSplitter
+
+N = 1_000
+users = [f"User {i}" for i in range(1000)]
+clusters = [f"Cluster {i}" for i in range(100)]
+dates = [f"{date(2022, 1, i):%Y-%m-%d}" for i in range(1, 32)]
+df = pd.DataFrame(
+    {
+        "cluster": np.random.choice(clusters, size=N),
+        "target": np.random.normal(0, 1, size=N),
+        "user": np.random.choice(users, size=N),
+        "date": np.random.choice(dates, size=N),
+    }
+)
+
+experiment_dates = [f"{date(2022, 1, i):%Y-%m-%d}" for i in range(15, 32)]
+sw = ClusteredSplitter(
+    cluster_cols=["cluster", "date"],
+)
+
+analysis = GeeExperimentAnalysis(
+    cluster_cols=["cluster", "date"],
+)
+
+pw = NormalPowerAnalysis(
+    splitter=sw, analysis=analysis, n_simulations=50
+)
+
+power = pw.power_analysis(df, average_effect=0.1)
+print(f"{power = }")
+

+ +
+ Source code in cluster_experiments/power_analysis.py +
class NormalPowerAnalysis:
+    """
+    Class used to run Power analysis, using the central limit theorem to estimate power based on standard errors of the estimator,
+    and the fact that the coefficients of a regression are normally distributed.
+    It does so by running simulations. In each simulation:
+    1. Assign treatment to dataframe randomly
+    2. Add pre-experiment data if needed
+    3. Get standard error from analysis
+
+    Finally it returns the power of the analysis by counting how many times the effect was detected.
+
+    Args:
+        splitter: RandomSplitter class to randomly assign treatment to dataframe.
+        analysis: ExperimentAnalysis class to use for analysis.
+        cupac_model: Sklearn estimator class to add pre-experiment data to dataframe. If None, no pre-experiment data will be added.
+        target_col: Name of the column with the outcome variable.
+        treatment_col: Name of the column with the treatment variable.
+        treatment: value of treatment_col considered to be treatment (not control)
+        control: value of treatment_col considered to be control (not treatment)
+        n_simulations: Number of simulations to run.
+        alpha: Significance level.
+        features_cupac_model: Covariates to be used in cupac model
+        seed: Optional. Seed to use for the splitter.
+
+    Usage:
+    ```python
+    from datetime import date
+
+    import numpy as np
+    import pandas as pd
+    from cluster_experiments.experiment_analysis import GeeExperimentAnalysis
+    from cluster_experiments.power_analysis import NormalPowerAnalysis
+    from cluster_experiments.random_splitter import ClusteredSplitter
+
+    N = 1_000
+    users = [f"User {i}" for i in range(1000)]
+    clusters = [f"Cluster {i}" for i in range(100)]
+    dates = [f"{date(2022, 1, i):%Y-%m-%d}" for i in range(1, 32)]
+    df = pd.DataFrame(
+        {
+            "cluster": np.random.choice(clusters, size=N),
+            "target": np.random.normal(0, 1, size=N),
+            "user": np.random.choice(users, size=N),
+            "date": np.random.choice(dates, size=N),
+        }
+    )
+
+    experiment_dates = [f"{date(2022, 1, i):%Y-%m-%d}" for i in range(15, 32)]
+    sw = ClusteredSplitter(
+        cluster_cols=["cluster", "date"],
+    )
+
+    analysis = GeeExperimentAnalysis(
+        cluster_cols=["cluster", "date"],
+    )
+
+    pw = NormalPowerAnalysis(
+        splitter=sw, analysis=analysis, n_simulations=50
+    )
+
+    power = pw.power_analysis(df, average_effect=0.1)
+    print(f"{power = }")
+    ```
+    """
+
+    def __init__(
+        self,
+        splitter: RandomSplitter,
+        analysis: ExperimentAnalysis,
+        cupac_model: Optional[BaseEstimator] = None,
+        target_col: str = "target",
+        treatment_col: str = "treatment",
+        treatment: str = "B",
+        control: str = "A",
+        n_simulations: int = 100,
+        alpha: float = 0.05,
+        features_cupac_model: Optional[List[str]] = None,
+        seed: Optional[int] = None,
+        hypothesis: str = "two-sided",
+        time_col: Optional[str] = None,
+    ):
+        self.splitter = splitter
+        self.analysis = analysis
+        self.n_simulations = n_simulations
+        self.target_col = target_col
+        self.treatment = treatment
+        self.control = control
+        self.treatment_col = treatment_col
+        self.alpha = alpha
+        self.hypothesis = hypothesis
+        self.time_col = time_col
+
+        self.cupac_handler = CupacHandler(
+            cupac_model=cupac_model,
+            target_col=target_col,
+            features_cupac_model=features_cupac_model,
+        )
+        if seed is not None:
+            random.seed(seed)  # seed for splitter
+            np.random.seed(seed)  # numpy seed
+            # may need to seed other stochasticity sources if added
+
+        self.check_inputs()
+
+    def _split(self, df: pd.DataFrame) -> pd.DataFrame:
+        """
+        Split dataframe.
+        Args:
+            df: Dataframe with outcome variable
+        """
+        treatment_df = self.splitter.assign_treatment_df(df)
+        self.log_nulls(treatment_df)
+        treatment_df = treatment_df.query(
+            f"{self.treatment_col}.notnull()", engine="python"
+        ).query(
+            f"{self.treatment_col}.isin(['{self.treatment}', '{self.control}'])",
+            engine="python",
+        )
+        return treatment_df
+
+    def _get_standard_error(
+        self,
+        df: pd.DataFrame,
+        n_simulations: int,
+        verbose: bool,
+    ) -> Generator[float, None, None]:
+        for _ in tqdm(range(n_simulations), disable=not verbose):
+            split_df = self._split(df)
+            yield self.analysis.get_standard_error(split_df)
+
+    def _normal_power_calculation(
+        self, alpha: float, std_error: float, average_effect: float
+    ) -> float:
+        """Returns the power of the analysis using the normal distribution.
+        Arguments:
+            alpha: significance level
+            std_error: standard error of the analysis
+            average_effect: effect size of the analysis
+        """
+        if HypothesisEntries(self.analysis.hypothesis) == HypothesisEntries.LESS:
+            z_alpha = norm.ppf(alpha)
+            return float(norm.cdf(z_alpha - average_effect / std_error))
+
+        if HypothesisEntries(self.analysis.hypothesis) == HypothesisEntries.GREATER:
+            z_alpha = norm.ppf(1 - alpha)
+            return 1 - float(norm.cdf(z_alpha - average_effect / std_error))
+
+        if HypothesisEntries(self.analysis.hypothesis) == HypothesisEntries.TWO_SIDED:
+            z_alpha = norm.ppf(1 - alpha / 2)
+            norm_cdf_right = norm.cdf(z_alpha - average_effect / std_error)
+            norm_cdf_left = norm.cdf(-z_alpha - average_effect / std_error)
+            return float(norm_cdf_left + (1 - norm_cdf_right))
+
+        raise ValueError(f"{self.analysis.hypothesis} is not a valid HypothesisEntries")
+
+    def _normal_mde_calculation(
+        self, alpha: float, std_error: float, power: float
+    ) -> float:
+        """
+        Returns the minimum detectable effect of the analysis using the normal distribution.
+        Args:
+            alpha: Significance level.
+            std_error: Standard error of the analysis.
+            power: Power of the analysis.
+        """
+        if HypothesisEntries(self.analysis.hypothesis) == HypothesisEntries.LESS:
+            z_alpha = norm.ppf(alpha)
+            z_beta = norm.ppf(1 - power)
+        elif HypothesisEntries(self.analysis.hypothesis) == HypothesisEntries.GREATER:
+            z_alpha = norm.ppf(1 - alpha)
+            z_beta = norm.ppf(power)
+        elif HypothesisEntries(self.analysis.hypothesis) == HypothesisEntries.TWO_SIDED:
+            # we are neglecting norm_cdf_left
+            z_alpha = norm.ppf(1 - alpha / 2)
+            z_beta = norm.ppf(power)
+        else:
+            raise ValueError(
+                f"{self.analysis.hypothesis} is not a valid HypothesisEntries"
+            )
+
+        return float(z_alpha + z_beta) * std_error
+
+    def mde_power_line(
+        self,
+        df: pd.DataFrame,
+        pre_experiment_df: Optional[pd.DataFrame] = None,
+        verbose: bool = False,
+        powers: Iterable[float] = (),
+        n_simulations: Optional[int] = None,
+        alpha: Optional[float] = None,
+    ) -> Dict[float, float]:
+        """
+        Returns the minimum detectable effect of the analysis.
+
+        Args:
+            df: Dataframe with outcome and treatment variables.
+            pre_experiment_df: Dataframe with pre-experiment data.
+            verbose: Whether to show progress bar.
+            power: Power of the analysis.
+            n_simulations: Number of simulations to run.
+            alpha: Significance level.
+        """
+        alpha = self.alpha if alpha is None else alpha
+        std_error = self._get_average_standard_error(
+            df=df,
+            pre_experiment_df=pre_experiment_df,
+            verbose=verbose,
+            n_simulations=n_simulations,
+        )
+        return {
+            power: self._normal_mde_calculation(
+                alpha=alpha, std_error=std_error, power=power
+            )
+            for power in powers
+        }
+
+    def mde(
+        self,
+        df: pd.DataFrame,
+        pre_experiment_df: Optional[pd.DataFrame] = None,
+        verbose: bool = False,
+        power: float = 0.8,
+        n_simulations: Optional[int] = None,
+        alpha: Optional[float] = None,
+    ) -> float:
+        """
+        Returns the minimum detectable effect of the analysis.
+
+        Args:
+            df: Dataframe with outcome and treatment variables.
+            pre_experiment_df: Dataframe with pre-experiment data.
+            verbose: Whether to show progress bar.
+            power: Power of the analysis.
+            n_simulations: Number of simulations to run.
+            alpha: Significance level.
+        """
+        return self.mde_power_line(
+            df=df,
+            pre_experiment_df=pre_experiment_df,
+            verbose=verbose,
+            powers=[power],
+            n_simulations=n_simulations,
+            alpha=alpha,
+        )[power]
+
+    def _get_average_standard_error(
+        self,
+        df: pd.DataFrame,
+        pre_experiment_df: Optional[pd.DataFrame] = None,
+        verbose: bool = False,
+        n_simulations: Optional[int] = None,
+    ) -> float:
+        """
+        Gets standard error to be used in normal power calculation.
+
+        Args:
+            df: Dataframe with outcome and treatment variables.
+            pre_experiment_df: Dataframe with pre-experiment data.
+            verbose: Whether to show progress bar.
+            average_effects: Average effects to test.
+            n_simulations: Number of simulations to run.
+            alpha: Significance level.
+        """
+        n_simulations = self.n_simulations if n_simulations is None else n_simulations
+
+        df = df.copy()
+        df = self.cupac_handler.add_covariates(df, pre_experiment_df)
+
+        std_errors = list(self._get_standard_error(df, n_simulations, verbose))
+        std_error_mean = float(np.mean(std_errors))
+
+        return std_error_mean
+
+    def run_average_standard_error(
+        self,
+        df: pd.DataFrame,
+        pre_experiment_df: Optional[pd.DataFrame] = None,
+        verbose: bool = False,
+        n_simulations: Optional[int] = None,
+        experiment_length: Iterable[int] = (),
+    ) -> Generator[Tuple[float, int], None, None]:
+        """
+        Run power analysis by simulation, using standard errors from the analysis.
+
+        Args:
+            df: Dataframe with outcome and treatment variables.
+            pre_experiment_df: Dataframe with pre-experiment data.
+            verbose: Whether to show progress bar.
+            n_simulations: Number of simulations to run.
+            experiment_length: Length of the experiment in days.
+        """
+        n_simulations = self.n_simulations if n_simulations is None else n_simulations
+
+        for n_days in experiment_length:
+            df_time = df.copy()
+            experiment_start = df_time[self.time_col].min()
+            df_time = df_time.loc[
+                df_time[self.time_col] < experiment_start + pd.Timedelta(days=n_days)
+            ]
+            std_error_mean = self._get_average_standard_error(
+                df=df_time,
+                pre_experiment_df=pre_experiment_df,
+                verbose=verbose,
+                n_simulations=n_simulations,
+            )
+            yield std_error_mean, n_days
+
+    def power_time_line(
+        self,
+        df: pd.DataFrame,
+        pre_experiment_df: Optional[pd.DataFrame] = None,
+        verbose: bool = False,
+        average_effects: Iterable[float] = (),
+        experiment_length: Iterable[int] = (),
+        n_simulations: Optional[int] = None,
+        alpha: Optional[float] = None,
+    ) -> List[Dict]:
+        """
+        Run power analysis by simulation, using standard errors from the analysis.
+
+        Args:
+            df: Dataframe with outcome and treatment variables.
+            pre_experiment_df: Dataframe with pre-experiment data.
+            verbose: Whether to show progress bar.
+            average_effects: Average effects to test.
+            experiment_length: Length of the experiment in days.
+            n_simulations: Number of simulations to run.
+            alpha: Significance level.
+        """
+        alpha = self.alpha if alpha is None else alpha
+
+        results = []
+        for std_error_mean, n_days in self.run_average_standard_error(
+            df=df,
+            pre_experiment_df=pre_experiment_df,
+            verbose=verbose,
+            n_simulations=n_simulations,
+            experiment_length=experiment_length,
+        ):
+            for effect in average_effects:
+                power = self._normal_power_calculation(
+                    alpha=alpha, std_error=std_error_mean, average_effect=effect
+                )
+                results.append(
+                    {"effect": effect, "power": power, "experiment_length": n_days}
+                )
+
+        return results
+
+    def mde_time_line(
+        self,
+        df: pd.DataFrame,
+        pre_experiment_df: Optional[pd.DataFrame] = None,
+        verbose: bool = False,
+        powers: Iterable[float] = (),
+        experiment_length: Iterable[int] = (),
+        n_simulations: Optional[int] = None,
+        alpha: Optional[float] = None,
+    ) -> List[Dict]:
+        alpha = self.alpha if alpha is None else alpha
+
+        results = []
+        for std_error_mean, n_days in self.run_average_standard_error(
+            df=df,
+            pre_experiment_df=pre_experiment_df,
+            verbose=verbose,
+            n_simulations=n_simulations,
+            experiment_length=experiment_length,
+        ):
+            for power in powers:
+                mde = self._normal_mde_calculation(
+                    alpha=alpha, std_error=std_error_mean, power=power
+                )
+                results.append(
+                    {"power": power, "mde": mde, "experiment_length": n_days}
+                )
+        return results
+
+    def power_line(
+        self,
+        df: pd.DataFrame,
+        pre_experiment_df: Optional[pd.DataFrame] = None,
+        verbose: bool = False,
+        average_effects: Iterable[float] = (),
+        n_simulations: Optional[int] = None,
+        alpha: Optional[float] = None,
+    ) -> Dict[float, float]:
+        """
+        Run power analysis by simulation, using standard errors from the analysis.
+        Args:
+            df: Dataframe with outcome and treatment variables.
+            pre_experiment_df: Dataframe with pre-experiment data.
+            verbose: Whether to show progress bar.
+            average_effects: Average effects to test.
+            n_simulations: Number of simulations to run.
+            alpha: Significance level.
+        """
+        alpha = self.alpha if alpha is None else alpha
+
+        std_error_mean = self._get_average_standard_error(
+            df=df,
+            pre_experiment_df=pre_experiment_df,
+            verbose=verbose,
+            n_simulations=n_simulations,
+        )
+
+        return {
+            effect: self._normal_power_calculation(
+                alpha=alpha, std_error=std_error_mean, average_effect=effect
+            )
+            for effect in average_effects
+        }
+
+    def power_analysis(
+        self,
+        df: pd.DataFrame,
+        pre_experiment_df: Optional[pd.DataFrame] = None,
+        verbose: bool = False,
+        average_effect: float = 0.0,
+        n_simulations: Optional[int] = None,
+        alpha: Optional[float] = None,
+    ) -> float:
+        """
+        Run power analysis by simulation, using standard errors from the analysis.
+        Args:
+            df: Dataframe with outcome and treatment variables.
+            pre_experiment_df: Dataframe with pre-experiment data.
+            verbose: Whether to show progress bar.
+            average_effect: Average effect of treatment.
+            n_simulations: Number of simulations to run.
+            alpha: Significance level.
+        """
+        return self.power_line(
+            df=df,
+            pre_experiment_df=pre_experiment_df,
+            verbose=verbose,
+            average_effects=[average_effect],
+            n_simulations=n_simulations,
+            alpha=alpha,
+        )[average_effect]
+
+    def log_nulls(self, df: pd.DataFrame) -> None:
+        """Warns about dropping nulls in treatment column"""
+        n_nulls = len(df.query(f"{self.treatment_col}.isnull()", engine="python"))
+        if n_nulls > 0:
+            logging.warning(
+                f"There are {n_nulls} null values in treatment, dropping them"
+            )
+
+    @classmethod
+    def from_dict(cls, config_dict: dict) -> "NormalPowerAnalysis":
+        """Constructs PowerAnalysis from dictionary"""
+        config = PowerConfig(**config_dict)
+        return cls.from_config(config)
+
+    @classmethod
+    def from_config(cls, config: PowerConfig) -> "NormalPowerAnalysis":
+        """Constructs PowerAnalysis from PowerConfig"""
+        splitter_cls = _get_mapping_key(splitter_mapping, config.splitter)
+        analysis_cls = _get_mapping_key(analysis_mapping, config.analysis)
+        cupac_cls = _get_mapping_key(cupac_model_mapping, config.cupac_model)
+        return cls(
+            splitter=splitter_cls.from_config(config),
+            analysis=analysis_cls.from_config(config),
+            cupac_model=cupac_cls.from_config(config),
+            target_col=config.target_col,
+            treatment_col=config.treatment_col,
+            treatment=config.treatment,
+            control=config.control,
+            n_simulations=config.n_simulations,
+            alpha=config.alpha,
+            features_cupac_model=config.features_cupac_model,
+            seed=config.seed,
+            hypothesis=config.hypothesis,
+            time_col=config.time_col,
+        )
+
+    def check_treatment_col(self):
+        """Checks consistency of treatment column"""
+        assert (
+            self.analysis.treatment_col == self.treatment_col
+        ), f"treatment_col in analysis ({self.analysis.treatment_col}) must be the same as treatment_col in PowerAnalysis ({self.treatment_col})"
+
+        assert (
+            self.analysis.treatment_col == self.splitter.treatment_col
+        ), f"treatment_col in analysis ({self.analysis.treatment_col}) must be the same as treatment_col in splitter ({self.splitter.treatment_col})"
+
+    def check_target_col(self):
+        assert (
+            self.analysis.target_col == self.target_col
+        ), f"target_col in analysis ({self.analysis.target_col}) must be the same as target_col in PowerAnalysis ({self.target_col})"
+
+    def check_treatment(self):
+        assert (
+            self.analysis.treatment == self.treatment
+        ), f"treatment in analysis ({self.analysis.treatment}) must be the same as treatment in PowerAnalysis ({self.treatment})"
+
+        assert (
+            self.analysis.treatment in self.splitter.treatments
+        ), f"treatment in analysis ({self.analysis.treatment}) must be in treatments in splitter ({self.splitter.treatments})"
+
+        assert (
+            self.control in self.splitter.treatments
+        ), f"control in power analysis ({self.control}) must be in treatments in splitter ({self.splitter.treatments})"
+
+    def check_covariates(self):
+        if hasattr(self.analysis, "covariates"):
+            cupac_in_covariates = (
+                self.cupac_handler.cupac_outcome_name in self.analysis.covariates
+            )
+
+            assert cupac_in_covariates or not self.cupac_handler.is_cupac, (
+                f"covariates in analysis must contain {self.cupac_handler.cupac_outcome_name} if cupac_model is not None. "
+                f"If you want to use cupac_model, you must add the cupac outcome to the covariates of the analysis "
+                f"You may want to do covariates=['{self.cupac_handler.cupac_outcome_name}'] in your analysis method or your config"
+            )
+
+            if hasattr(self.splitter, "cluster_cols"):
+                if set(self.analysis.covariates).intersection(
+                    set(self.splitter.cluster_cols)
+                ):
+                    logging.warning(
+                        f"covariates in analysis ({self.analysis.covariates}) are also cluster_cols in splitter ({self.splitter.cluster_cols}). "
+                        f"Be specially careful when using switchback splitters, since the time splitter column is being overriden"
+                    )
+
+    def check_clusters(self):
+        has_analysis_clusters = hasattr(self.analysis, "cluster_cols")
+        has_splitter_clusters = hasattr(self.splitter, "cluster_cols")
+        not_cluster_cols_cond = not has_analysis_clusters or not has_splitter_clusters
+        assert (
+            not_cluster_cols_cond
+            or self.analysis.cluster_cols == self.splitter.cluster_cols
+        ), f"cluster_cols in analysis ({self.analysis.cluster_cols}) must be the same as cluster_cols in splitter ({self.splitter.cluster_cols})"
+
+        assert (
+            has_splitter_clusters
+            or not has_analysis_clusters
+            or not self.analysis.cluster_cols
+            or isinstance(self.splitter, RepeatedSampler)
+        ), "analysis has cluster_cols but splitter does not."
+
+        assert (
+            has_analysis_clusters
+            or not has_splitter_clusters
+            or not self.splitter.cluster_cols
+        ), "splitter has cluster_cols but analysis does not."
+
+        has_time_col = hasattr(self.splitter, "time_col")
+        assert not (
+            has_time_col
+            and has_splitter_clusters
+            and self.splitter.time_col not in self.splitter.cluster_cols
+        ), "in switchback splitters, time_col must be in cluster_cols"
+
+    def check_inputs(self):
+        self.check_covariates()
+        self.check_treatment_col()
+        self.check_target_col()
+        self.check_treatment()
+        self.check_clusters()
+
+
+ + + +
+ + + + + + + + + + + + + + + +
+ + + +

+check_treatment_col(self) + + +

+ +
+ +

Checks consistency of treatment column

+ +
+ Source code in cluster_experiments/power_analysis.py +
def check_treatment_col(self):
+    """Checks consistency of treatment column"""
+    assert (
+        self.analysis.treatment_col == self.treatment_col
+    ), f"treatment_col in analysis ({self.analysis.treatment_col}) must be the same as treatment_col in PowerAnalysis ({self.treatment_col})"
+
+    assert (
+        self.analysis.treatment_col == self.splitter.treatment_col
+    ), f"treatment_col in analysis ({self.analysis.treatment_col}) must be the same as treatment_col in splitter ({self.splitter.treatment_col})"
+
+
+
+ +
+ + + +
+ + + +

+from_config(config) + + + classmethod + + +

+ +
+ +

Constructs PowerAnalysis from PowerConfig

+ +
+ Source code in cluster_experiments/power_analysis.py +
@classmethod
+def from_config(cls, config: PowerConfig) -> "NormalPowerAnalysis":
+    """Constructs PowerAnalysis from PowerConfig"""
+    splitter_cls = _get_mapping_key(splitter_mapping, config.splitter)
+    analysis_cls = _get_mapping_key(analysis_mapping, config.analysis)
+    cupac_cls = _get_mapping_key(cupac_model_mapping, config.cupac_model)
+    return cls(
+        splitter=splitter_cls.from_config(config),
+        analysis=analysis_cls.from_config(config),
+        cupac_model=cupac_cls.from_config(config),
+        target_col=config.target_col,
+        treatment_col=config.treatment_col,
+        treatment=config.treatment,
+        control=config.control,
+        n_simulations=config.n_simulations,
+        alpha=config.alpha,
+        features_cupac_model=config.features_cupac_model,
+        seed=config.seed,
+        hypothesis=config.hypothesis,
+        time_col=config.time_col,
+    )
+
+
+
+ +
+ + + +
+ + + +

+from_dict(config_dict) + + + classmethod + + +

+ +
+ +

Constructs PowerAnalysis from dictionary

+ +
+ Source code in cluster_experiments/power_analysis.py +
@classmethod
+def from_dict(cls, config_dict: dict) -> "NormalPowerAnalysis":
+    """Constructs PowerAnalysis from dictionary"""
+    config = PowerConfig(**config_dict)
+    return cls.from_config(config)
+
+
+
+ +
+ + + +
+ + + +

+log_nulls(self, df) + + +

+ +
+ +

Warns about dropping nulls in treatment column

+ +
+ Source code in cluster_experiments/power_analysis.py +
def log_nulls(self, df: pd.DataFrame) -> None:
+    """Warns about dropping nulls in treatment column"""
+    n_nulls = len(df.query(f"{self.treatment_col}.isnull()", engine="python"))
+    if n_nulls > 0:
+        logging.warning(
+            f"There are {n_nulls} null values in treatment, dropping them"
+        )
+
+
+
+ +
+ + + +
+ + + +

+mde(self, df, pre_experiment_df=None, verbose=False, power=0.8, n_simulations=None, alpha=None) + + +

+ +
+ +

Returns the minimum detectable effect of the analysis.

+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
dfDataFrame

Dataframe with outcome and treatment variables.

required
pre_experiment_dfOptional[pandas.core.frame.DataFrame]

Dataframe with pre-experiment data.

None
verbosebool

Whether to show progress bar.

False
powerfloat

Power of the analysis.

0.8
n_simulationsOptional[int]

Number of simulations to run.

None
alphaOptional[float]

Significance level.

None
+
+ Source code in cluster_experiments/power_analysis.py +
def mde(
+    self,
+    df: pd.DataFrame,
+    pre_experiment_df: Optional[pd.DataFrame] = None,
+    verbose: bool = False,
+    power: float = 0.8,
+    n_simulations: Optional[int] = None,
+    alpha: Optional[float] = None,
+) -> float:
+    """
+    Returns the minimum detectable effect of the analysis.
+
+    Args:
+        df: Dataframe with outcome and treatment variables.
+        pre_experiment_df: Dataframe with pre-experiment data.
+        verbose: Whether to show progress bar.
+        power: Power of the analysis.
+        n_simulations: Number of simulations to run.
+        alpha: Significance level.
+    """
+    return self.mde_power_line(
+        df=df,
+        pre_experiment_df=pre_experiment_df,
+        verbose=verbose,
+        powers=[power],
+        n_simulations=n_simulations,
+        alpha=alpha,
+    )[power]
+
+
+
+ +
+ + + +
+ + + +

+mde_power_line(self, df, pre_experiment_df=None, verbose=False, powers=(), n_simulations=None, alpha=None) + + +

+ +
+ +

Returns the minimum detectable effect of the analysis.

+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
dfDataFrame

Dataframe with outcome and treatment variables.

required
pre_experiment_dfOptional[pandas.core.frame.DataFrame]

Dataframe with pre-experiment data.

None
verbosebool

Whether to show progress bar.

False
power

Power of the analysis.

required
n_simulationsOptional[int]

Number of simulations to run.

None
alphaOptional[float]

Significance level.

None
+
+ Source code in cluster_experiments/power_analysis.py +
def mde_power_line(
+    self,
+    df: pd.DataFrame,
+    pre_experiment_df: Optional[pd.DataFrame] = None,
+    verbose: bool = False,
+    powers: Iterable[float] = (),
+    n_simulations: Optional[int] = None,
+    alpha: Optional[float] = None,
+) -> Dict[float, float]:
+    """
+    Returns the minimum detectable effect of the analysis.
+
+    Args:
+        df: Dataframe with outcome and treatment variables.
+        pre_experiment_df: Dataframe with pre-experiment data.
+        verbose: Whether to show progress bar.
+        power: Power of the analysis.
+        n_simulations: Number of simulations to run.
+        alpha: Significance level.
+    """
+    alpha = self.alpha if alpha is None else alpha
+    std_error = self._get_average_standard_error(
+        df=df,
+        pre_experiment_df=pre_experiment_df,
+        verbose=verbose,
+        n_simulations=n_simulations,
+    )
+    return {
+        power: self._normal_mde_calculation(
+            alpha=alpha, std_error=std_error, power=power
+        )
+        for power in powers
+    }
+
+
+
+ +
+ + + + +
+ + + +

+power_analysis(self, df, pre_experiment_df=None, verbose=False, average_effect=0.0, n_simulations=None, alpha=None) + + +

+ +
+ +

Run power analysis by simulation, using standard errors from the analysis.

+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
dfDataFrame

Dataframe with outcome and treatment variables.

required
pre_experiment_dfOptional[pandas.core.frame.DataFrame]

Dataframe with pre-experiment data.

None
verbosebool

Whether to show progress bar.

False
average_effectfloat

Average effect of treatment.

0.0
n_simulationsOptional[int]

Number of simulations to run.

None
alphaOptional[float]

Significance level.

None
+
+ Source code in cluster_experiments/power_analysis.py +
def power_analysis(
+    self,
+    df: pd.DataFrame,
+    pre_experiment_df: Optional[pd.DataFrame] = None,
+    verbose: bool = False,
+    average_effect: float = 0.0,
+    n_simulations: Optional[int] = None,
+    alpha: Optional[float] = None,
+) -> float:
+    """
+    Run power analysis by simulation, using standard errors from the analysis.
+    Args:
+        df: Dataframe with outcome and treatment variables.
+        pre_experiment_df: Dataframe with pre-experiment data.
+        verbose: Whether to show progress bar.
+        average_effect: Average effect of treatment.
+        n_simulations: Number of simulations to run.
+        alpha: Significance level.
+    """
+    return self.power_line(
+        df=df,
+        pre_experiment_df=pre_experiment_df,
+        verbose=verbose,
+        average_effects=[average_effect],
+        n_simulations=n_simulations,
+        alpha=alpha,
+    )[average_effect]
+
+
+
+ +
+ + + +
+ + + +

+power_line(self, df, pre_experiment_df=None, verbose=False, average_effects=(), n_simulations=None, alpha=None) + + +

+ +
+ +

Run power analysis by simulation, using standard errors from the analysis.

+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
dfDataFrame

Dataframe with outcome and treatment variables.

required
pre_experiment_dfOptional[pandas.core.frame.DataFrame]

Dataframe with pre-experiment data.

None
verbosebool

Whether to show progress bar.

False
average_effectsIterable[float]

Average effects to test.

()
n_simulationsOptional[int]

Number of simulations to run.

None
alphaOptional[float]

Significance level.

None
+
+ Source code in cluster_experiments/power_analysis.py +
def power_line(
+    self,
+    df: pd.DataFrame,
+    pre_experiment_df: Optional[pd.DataFrame] = None,
+    verbose: bool = False,
+    average_effects: Iterable[float] = (),
+    n_simulations: Optional[int] = None,
+    alpha: Optional[float] = None,
+) -> Dict[float, float]:
+    """
+    Run power analysis by simulation, using standard errors from the analysis.
+    Args:
+        df: Dataframe with outcome and treatment variables.
+        pre_experiment_df: Dataframe with pre-experiment data.
+        verbose: Whether to show progress bar.
+        average_effects: Average effects to test.
+        n_simulations: Number of simulations to run.
+        alpha: Significance level.
+    """
+    alpha = self.alpha if alpha is None else alpha
+
+    std_error_mean = self._get_average_standard_error(
+        df=df,
+        pre_experiment_df=pre_experiment_df,
+        verbose=verbose,
+        n_simulations=n_simulations,
+    )
+
+    return {
+        effect: self._normal_power_calculation(
+            alpha=alpha, std_error=std_error_mean, average_effect=effect
+        )
+        for effect in average_effects
+    }
+
+
+
+ +
+ + + +
+ + + +

+power_time_line(self, df, pre_experiment_df=None, verbose=False, average_effects=(), experiment_length=(), n_simulations=None, alpha=None) + + +

+ +
+ +

Run power analysis by simulation, using standard errors from the analysis.

+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
dfDataFrame

Dataframe with outcome and treatment variables.

required
pre_experiment_dfOptional[pandas.core.frame.DataFrame]

Dataframe with pre-experiment data.

None
verbosebool

Whether to show progress bar.

False
average_effectsIterable[float]

Average effects to test.

()
experiment_lengthIterable[int]

Length of the experiment in days.

()
n_simulationsOptional[int]

Number of simulations to run.

None
alphaOptional[float]

Significance level.

None
+
+ Source code in cluster_experiments/power_analysis.py +
def power_time_line(
+    self,
+    df: pd.DataFrame,
+    pre_experiment_df: Optional[pd.DataFrame] = None,
+    verbose: bool = False,
+    average_effects: Iterable[float] = (),
+    experiment_length: Iterable[int] = (),
+    n_simulations: Optional[int] = None,
+    alpha: Optional[float] = None,
+) -> List[Dict]:
+    """
+    Run power analysis by simulation, using standard errors from the analysis.
+
+    Args:
+        df: Dataframe with outcome and treatment variables.
+        pre_experiment_df: Dataframe with pre-experiment data.
+        verbose: Whether to show progress bar.
+        average_effects: Average effects to test.
+        experiment_length: Length of the experiment in days.
+        n_simulations: Number of simulations to run.
+        alpha: Significance level.
+    """
+    alpha = self.alpha if alpha is None else alpha
+
+    results = []
+    for std_error_mean, n_days in self.run_average_standard_error(
+        df=df,
+        pre_experiment_df=pre_experiment_df,
+        verbose=verbose,
+        n_simulations=n_simulations,
+        experiment_length=experiment_length,
+    ):
+        for effect in average_effects:
+            power = self._normal_power_calculation(
+                alpha=alpha, std_error=std_error_mean, average_effect=effect
+            )
+            results.append(
+                {"effect": effect, "power": power, "experiment_length": n_days}
+            )
+
+    return results
+
+
+
+ +
+ + + +
+ + + +

+run_average_standard_error(self, df, pre_experiment_df=None, verbose=False, n_simulations=None, experiment_length=()) + + +

+ +
+ +

Run power analysis by simulation, using standard errors from the analysis.

+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
dfDataFrame

Dataframe with outcome and treatment variables.

required
pre_experiment_dfOptional[pandas.core.frame.DataFrame]

Dataframe with pre-experiment data.

None
verbosebool

Whether to show progress bar.

False
n_simulationsOptional[int]

Number of simulations to run.

None
experiment_lengthIterable[int]

Length of the experiment in days.

()
+
+ Source code in cluster_experiments/power_analysis.py +
def run_average_standard_error(
+    self,
+    df: pd.DataFrame,
+    pre_experiment_df: Optional[pd.DataFrame] = None,
+    verbose: bool = False,
+    n_simulations: Optional[int] = None,
+    experiment_length: Iterable[int] = (),
+) -> Generator[Tuple[float, int], None, None]:
+    """
+    Run power analysis by simulation, using standard errors from the analysis.
+
+    Args:
+        df: Dataframe with outcome and treatment variables.
+        pre_experiment_df: Dataframe with pre-experiment data.
+        verbose: Whether to show progress bar.
+        n_simulations: Number of simulations to run.
+        experiment_length: Length of the experiment in days.
+    """
+    n_simulations = self.n_simulations if n_simulations is None else n_simulations
+
+    for n_days in experiment_length:
+        df_time = df.copy()
+        experiment_start = df_time[self.time_col].min()
+        df_time = df_time.loc[
+            df_time[self.time_col] < experiment_start + pd.Timedelta(days=n_days)
+        ]
+        std_error_mean = self._get_average_standard_error(
+            df=df_time,
+            pre_experiment_df=pre_experiment_df,
+            verbose=verbose,
+            n_simulations=n_simulations,
+        )
+        yield std_error_mean, n_days
+
+
+
+ +
+ + + + + +
+ +
+ +
+ + + +
+ + + +

+ +PowerAnalysis + + + +

+ +
+ +

Class used to run Power analysis. It does so by running simulations. In each simulation: +1. Assign treatment to dataframe randomly +2. Perturbate dataframe +3. Add pre-experiment data if needed +4. Run analysis

+

Finally it returns the power of the analysis by counting how many times the effect was detected.

+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
perturbatorPerturbator

Perturbator class to perturbate dataframe with treatment assigned.

required
splitterRandomSplitter

RandomSplitter class to randomly assign treatment to dataframe.

required
analysisExperimentAnalysis

ExperimentAnalysis class to use for analysis.

required
cupac_modelOptional[sklearn.base.BaseEstimator]

Sklearn estimator class to add pre-experiment data to dataframe. If None, no pre-experiment data will be added.

None
target_colstr

Name of the column with the outcome variable.

'target'
treatment_colstr

Name of the column with the treatment variable.

'treatment'
treatmentstr

value of treatment_col considered to be treatment (not control)

'B'
controlstr

value of treatment_col considered to be control (not treatment)

'A'
n_simulationsint

Number of simulations to run.

100
alphafloat

Significance level.

0.05
features_cupac_modelOptional[List[str]]

Covariates to be used in cupac model

None
seedOptional[int]

Optional. Seed to use for the splitter.

None

Usage: +

from datetime import date
+
+import numpy as np
+import pandas as pd
+from cluster_experiments.experiment_analysis import GeeExperimentAnalysis
+from cluster_experiments.perturbator import ConstantPerturbator
+from cluster_experiments.power_analysis import PowerAnalysis
+from cluster_experiments.random_splitter import ClusteredSplitter
+
+N = 1_000
+users = [f"User {i}" for i in range(1000)]
+clusters = [f"Cluster {i}" for i in range(100)]
+dates = [f"{date(2022, 1, i):%Y-%m-%d}" for i in range(1, 32)]
+df = pd.DataFrame(
+    {
+        "cluster": np.random.choice(clusters, size=N),
+        "target": np.random.normal(0, 1, size=N),
+        "user": np.random.choice(users, size=N),
+        "date": np.random.choice(dates, size=N),
+    }
+)
+
+experiment_dates = [f"{date(2022, 1, i):%Y-%m-%d}" for i in range(15, 32)]
+sw = ClusteredSplitter(
+    cluster_cols=["cluster", "date"],
+)
+
+perturbator = ConstantPerturbator()
+
+analysis = GeeExperimentAnalysis(
+    cluster_cols=["cluster", "date"],
+)
+
+pw = PowerAnalysis(
+    perturbator=perturbator, splitter=sw, analysis=analysis, n_simulations=50
+)
+
+power = pw.power_analysis(df, average_effect=0.1)
+print(f"{power = }")
+

+ +
+ Source code in cluster_experiments/power_analysis.py +
class PowerAnalysis:
+    """
+    Class used to run Power analysis. It does so by running simulations. In each simulation:
+    1. Assign treatment to dataframe randomly
+    2. Perturbate dataframe
+    3. Add pre-experiment data if needed
+    4. Run analysis
+
+    Finally it returns the power of the analysis by counting how many times the effect was detected.
+
+    Args:
+        perturbator: Perturbator class to perturbate dataframe with treatment assigned.
+        splitter: RandomSplitter class to randomly assign treatment to dataframe.
+        analysis: ExperimentAnalysis class to use for analysis.
+        cupac_model: Sklearn estimator class to add pre-experiment data to dataframe. If None, no pre-experiment data will be added.
+        target_col: Name of the column with the outcome variable.
+        treatment_col: Name of the column with the treatment variable.
+        treatment: value of treatment_col considered to be treatment (not control)
+        control: value of treatment_col considered to be control (not treatment)
+        n_simulations: Number of simulations to run.
+        alpha: Significance level.
+        features_cupac_model: Covariates to be used in cupac model
+        seed: Optional. Seed to use for the splitter.
+
+    Usage:
+    ```python
+    from datetime import date
+
+    import numpy as np
+    import pandas as pd
+    from cluster_experiments.experiment_analysis import GeeExperimentAnalysis
+    from cluster_experiments.perturbator import ConstantPerturbator
+    from cluster_experiments.power_analysis import PowerAnalysis
+    from cluster_experiments.random_splitter import ClusteredSplitter
+
+    N = 1_000
+    users = [f"User {i}" for i in range(1000)]
+    clusters = [f"Cluster {i}" for i in range(100)]
+    dates = [f"{date(2022, 1, i):%Y-%m-%d}" for i in range(1, 32)]
+    df = pd.DataFrame(
+        {
+            "cluster": np.random.choice(clusters, size=N),
+            "target": np.random.normal(0, 1, size=N),
+            "user": np.random.choice(users, size=N),
+            "date": np.random.choice(dates, size=N),
+        }
+    )
+
+    experiment_dates = [f"{date(2022, 1, i):%Y-%m-%d}" for i in range(15, 32)]
+    sw = ClusteredSplitter(
+        cluster_cols=["cluster", "date"],
+    )
+
+    perturbator = ConstantPerturbator()
+
+    analysis = GeeExperimentAnalysis(
+        cluster_cols=["cluster", "date"],
+    )
+
+    pw = PowerAnalysis(
+        perturbator=perturbator, splitter=sw, analysis=analysis, n_simulations=50
+    )
+
+    power = pw.power_analysis(df, average_effect=0.1)
+    print(f"{power = }")
+    ```
+    """
+
+    def __init__(
+        self,
+        perturbator: Perturbator,
+        splitter: RandomSplitter,
+        analysis: ExperimentAnalysis,
+        cupac_model: Optional[BaseEstimator] = None,
+        target_col: str = "target",
+        treatment_col: str = "treatment",
+        treatment: str = "B",
+        control: str = "A",
+        n_simulations: int = 100,
+        alpha: float = 0.05,
+        features_cupac_model: Optional[List[str]] = None,
+        seed: Optional[int] = None,
+        hypothesis: str = "two-sided",
+    ):
+        self.perturbator = perturbator
+        self.splitter = splitter
+        self.analysis = analysis
+        self.n_simulations = n_simulations
+        self.target_col = target_col
+        self.treatment = treatment
+        self.control = control
+        self.treatment_col = treatment_col
+        self.alpha = alpha
+        self.hypothesis = hypothesis
+
+        self.cupac_handler = CupacHandler(
+            cupac_model=cupac_model,
+            target_col=target_col,
+            features_cupac_model=features_cupac_model,
+        )
+        if seed is not None:
+            random.seed(seed)  # seed for splitter
+            np.random.seed(seed)  # seed for the binary perturbator
+            # may need to seed other stochasticity sources if added
+
+        self.check_inputs()
+
+    def _simulate_perturbed_df(
+        self,
+        df: pd.DataFrame,
+        pre_experiment_df: Optional[pd.DataFrame] = None,
+        verbose: bool = False,
+        average_effect: Optional[float] = None,
+        n_simulations: int = 100,
+    ) -> Generator[pd.DataFrame, None, None]:
+        """Yields splitted + perturbated dataframe for each iteration of the simulation."""
+        df = df.copy()
+        df = self.cupac_handler.add_covariates(df, pre_experiment_df)
+
+        for _ in tqdm(range(n_simulations), disable=not verbose):
+            yield self._split_and_perturbate(df, average_effect)
+
+    def simulate_pvalue(
+        self,
+        df: pd.DataFrame,
+        pre_experiment_df: Optional[pd.DataFrame] = None,
+        verbose: bool = False,
+        average_effect: Optional[float] = None,
+        n_simulations: int = 100,
+    ) -> Generator[float, None, None]:
+        """
+        Yields p-values for each iteration of the simulation.
+        In general, this is to be used in power_analysis method. However,
+        if you're interested in the distribution of p-values, you can use this method to generate them.
+        Args:
+            df: Dataframe with outcome variable.
+            pre_experiment_df: Dataframe with pre-experiment data.
+            verbose: Whether to show progress bar.
+            average_effect: Average effect of treatment. If None, it will use the perturbator average effect.
+            n_simulations: Number of simulations to run.
+        """
+        for perturbed_df in self._simulate_perturbed_df(
+            df,
+            pre_experiment_df=pre_experiment_df,
+            verbose=verbose,
+            average_effect=average_effect,
+            n_simulations=n_simulations,
+        ):
+            yield self.analysis.get_pvalue(perturbed_df)
+
+    def running_power_analysis(
+        self,
+        df: pd.DataFrame,
+        pre_experiment_df: Optional[pd.DataFrame] = None,
+        verbose: bool = False,
+        average_effect: Optional[float] = None,
+        n_simulations: int = 100,
+    ) -> Generator[float, None, None]:
+        """
+        Yields running power for each iteration of the simulation.
+        if you're interested in getting the power at each iteration, you can use this method to generate them.
+        Args:
+            df: Dataframe with outcome variable.
+            pre_experiment_df: Dataframe with pre-experiment data.
+            verbose: Whether to show progress bar.
+            average_effect: Average effect of treatment. If None, it will use the perturbator average effect.
+            n_simulations: Number of simulations to run.
+        """
+        n_rejected = 0
+        for i, perturbed_df in enumerate(
+            self._simulate_perturbed_df(
+                df,
+                pre_experiment_df=pre_experiment_df,
+                verbose=verbose,
+                average_effect=average_effect,
+                n_simulations=n_simulations,
+            )
+        ):
+            p_value = self.analysis.get_pvalue(perturbed_df)
+            n_rejected += int(p_value < self.alpha)
+            yield n_rejected / (i + 1)
+
+    def simulate_point_estimate(
+        self,
+        df: pd.DataFrame,
+        pre_experiment_df: Optional[pd.DataFrame] = None,
+        verbose: bool = False,
+        average_effect: Optional[float] = None,
+        n_simulations: int = 100,
+    ) -> Generator[float, None, None]:
+        """
+        Yields point estimates for each iteration of the simulation.
+        In general, this is to be used in power_analysis method. However,
+        if you're interested in the distribution of point estimates, you can use this method to generate them.
+
+        This is an experimental feature and it might change in the future.
+
+        Args:
+            df: Dataframe with outcome and treatment variables.
+            pre_experiment_df: Dataframe with pre-experiment data.
+            verbose: Whether to show progress bar.
+            average_effect: Average effect of treatment. If None, it will use the perturbator average effect.
+            n_simulations: Number of simulations to run.
+        """
+        for perturbed_df in self._simulate_perturbed_df(
+            df,
+            pre_experiment_df=pre_experiment_df,
+            verbose=verbose,
+            average_effect=average_effect,
+            n_simulations=n_simulations,
+        ):
+            yield self.analysis.get_point_estimate(perturbed_df)
+
+    def power_analysis(
+        self,
+        df: pd.DataFrame,
+        pre_experiment_df: Optional[pd.DataFrame] = None,
+        verbose: bool = False,
+        average_effect: Optional[float] = None,
+        n_simulations: Optional[int] = None,
+        alpha: Optional[float] = None,
+        n_jobs: int = 1,
+    ) -> float:
+        """
+        Run power analysis by simulation
+        Args:
+            df: Dataframe with outcome and treatment variables.
+            pre_experiment_df: Dataframe with pre-experiment data.
+            verbose: Whether to show progress bar.
+            average_effect: Average effect of treatment. If None, it will use the perturbator average effect.
+            n_simulations: Number of simulations to run.
+            alpha: Significance level.
+            n_jobs: Number of jobs to run in parallel. If 1, it will run in serial.
+        """
+        n_simulations = self.n_simulations if n_simulations is None else n_simulations
+        alpha = self.alpha if alpha is None else alpha
+
+        df = df.copy()
+        df = self.cupac_handler.add_covariates(df, pre_experiment_df)
+
+        if n_jobs == 1:
+            return self._non_parallel_loop(
+                df, average_effect, n_simulations, alpha, verbose
+            )
+        elif n_jobs > 1 or n_jobs == -1:
+            return self._parallel_loop(
+                df, average_effect, n_simulations, alpha, verbose, n_jobs
+            )
+        else:
+            raise ValueError("n_jobs must be greater than 0, or -1.")
+
+    def _split(self, df: pd.DataFrame) -> pd.DataFrame:
+        """
+        Split dataframe.
+        Args:
+            df: Dataframe with outcome variable
+        """
+        treatment_df = self.splitter.assign_treatment_df(df)
+        self.log_nulls(treatment_df)
+        treatment_df = treatment_df.query(
+            f"{self.treatment_col}.notnull()", engine="python"
+        ).query(
+            f"{self.treatment_col}.isin(['{self.treatment}', '{self.control}'])",
+            engine="python",
+        )
+
+        return treatment_df
+
+    def _perturbate(
+        self, treatment_df: pd.DataFrame, average_effect: Optional[float]
+    ) -> pd.DataFrame:
+        """
+        Perturbate dataframe using perturbator.
+        Args:
+            df: Dataframe with outcome variable
+            average_effect: Average effect of treatment. If None, it will use the perturbator average effect.
+        """
+
+        perturbed_df = self.perturbator.perturbate(
+            treatment_df, average_effect=average_effect
+        )
+        return perturbed_df
+
+    def _split_and_perturbate(
+        self, df: pd.DataFrame, average_effect: Optional[float]
+    ) -> pd.DataFrame:
+        treatment_df = self._split(df)
+        perturbed_df = self._perturbate(
+            treatment_df=treatment_df, average_effect=average_effect
+        )
+        return perturbed_df
+
+    def _run_simulation(self, args: Tuple[pd.DataFrame, Optional[float]]) -> float:
+        df, average_effect = args
+        perturbed_df = self._split_and_perturbate(df, average_effect)
+        return self.analysis.get_pvalue(perturbed_df)
+
+    def _non_parallel_loop(
+        self,
+        df: pd.DataFrame,
+        average_effect: Optional[float],
+        n_simulations: int,
+        alpha: float,
+        verbose: bool,
+    ) -> float:
+        """
+        Run power analysis by simulation in serial
+        Args:
+            df: Dataframe with outcome and treatment variables.
+            average_effect: Average effect of treatment. If None, it will use the perturbator average effect.
+            n_simulations: Number of simulations to run.
+            alpha: Significance level.
+        """
+        n_detected_mde = 0
+        for _ in tqdm(range(n_simulations), disable=not verbose):
+            p_value = self._run_simulation((df, average_effect))
+            if verbose:
+                print(f"p_value of simulation run: {p_value:.3f}")
+            n_detected_mde += p_value < alpha
+
+        return n_detected_mde / n_simulations
+
+    def _parallel_loop(
+        self,
+        df: pd.DataFrame,
+        average_effect: Optional[float],
+        n_simulations: int,
+        alpha: float,
+        verbose: bool,
+        n_jobs: int,
+    ) -> float:
+        """
+        Run power analysis by simulation in parallel
+        Args:
+            df: Dataframe with outcome and treatment variables.
+            average_effect: Average effect of treatment. If None, it will use the perturbator average effect.
+            n_simulations: Number of simulations to run.
+            alpha: Significance level.
+            n_jobs: Number of jobs to run in parallel.
+        """
+        from multiprocessing import Pool, cpu_count
+
+        n_jobs = n_jobs if n_jobs != -1 else cpu_count()
+
+        n_detected_mde = 0
+        with Pool(processes=n_jobs) as pool:
+            args = [(df, average_effect) for _ in range(n_simulations)]
+            results = pool.imap_unordered(self._run_simulation, args)
+            for p_value in tqdm(results, total=n_simulations, disable=not verbose):
+                n_detected_mde += p_value < alpha
+
+        return n_detected_mde / n_simulations
+
+    def power_line(
+        self,
+        df: pd.DataFrame,
+        pre_experiment_df: Optional[pd.DataFrame] = None,
+        verbose: bool = False,
+        average_effects: Iterable[float] = (),
+        n_simulations: Optional[int] = None,
+        alpha: Optional[float] = None,
+        n_jobs: int = 1,
+    ) -> Dict[float, float]:
+        """Runs power analysis with multiple average effects
+
+        Args:
+            df: Dataframe with outcome and treatment variables.
+            pre_experiment_df: Dataframe with pre-experiment data.
+            verbose: Whether to show progress bar.
+            average_effects: Average effects to test.
+            n_simulations: Number of simulations to run.
+            alpha: Significance level.
+            n_jobs: Number of jobs to run in parallel.
+
+        Returns:
+            Dictionary with average effects as keys and power as values.
+        """
+        return {
+            effect: self.power_analysis(
+                df=df,
+                pre_experiment_df=pre_experiment_df,
+                verbose=verbose,
+                average_effect=effect,
+                n_simulations=n_simulations,
+                alpha=alpha,
+                n_jobs=n_jobs,
+            )
+            for effect in tqdm(
+                list(average_effects), disable=not verbose, desc="Effects loop"
+            )
+        }
+
+    def log_nulls(self, df: pd.DataFrame) -> None:
+        """Warns about dropping nulls in treatment column"""
+        n_nulls = len(df.query(f"{self.treatment_col}.isnull()", engine="python"))
+        if n_nulls > 0:
+            logging.warning(
+                f"There are {n_nulls} null values in treatment, dropping them"
+            )
+
+    @classmethod
+    def from_dict(cls, config_dict: dict) -> "PowerAnalysis":
+        """Constructs PowerAnalysis from dictionary"""
+        config = PowerConfig(**config_dict)
+        return cls.from_config(config)
+
+    @classmethod
+    def from_config(cls, config: PowerConfig) -> "PowerAnalysis":
+        """Constructs PowerAnalysis from PowerConfig"""
+        perturbator_cls = _get_mapping_key(perturbator_mapping, config.perturbator)
+        splitter_cls = _get_mapping_key(splitter_mapping, config.splitter)
+        analysis_cls = _get_mapping_key(analysis_mapping, config.analysis)
+        cupac_cls = _get_mapping_key(cupac_model_mapping, config.cupac_model)
+        return cls(
+            perturbator=perturbator_cls.from_config(config),
+            splitter=splitter_cls.from_config(config),
+            analysis=analysis_cls.from_config(config),
+            cupac_model=cupac_cls.from_config(config),
+            target_col=config.target_col,
+            treatment_col=config.treatment_col,
+            treatment=config.treatment,
+            control=config.control,
+            n_simulations=config.n_simulations,
+            alpha=config.alpha,
+            features_cupac_model=config.features_cupac_model,
+            seed=config.seed,
+            hypothesis=config.hypothesis,
+        )
+
+    def check_treatment_col(self):
+        """Checks consistency of treatment column"""
+        assert (
+            self.analysis.treatment_col == self.perturbator.treatment_col
+        ), f"treatment_col in analysis ({self.analysis.treatment_col}) must be the same as treatment_col in perturbator ({self.perturbator.treatment_col})"
+
+        assert (
+            self.analysis.treatment_col == self.treatment_col
+        ), f"treatment_col in analysis ({self.analysis.treatment_col}) must be the same as treatment_col in PowerAnalysis ({self.treatment_col})"
+
+        assert (
+            self.analysis.treatment_col == self.splitter.treatment_col
+        ), f"treatment_col in analysis ({self.analysis.treatment_col}) must be the same as treatment_col in splitter ({self.splitter.treatment_col})"
+
+    def check_target_col(self):
+        assert (
+            self.analysis.target_col == self.perturbator.target_col
+        ), f"target_col in analysis ({self.analysis.target_col}) must be the same as target_col in perturbator ({self.perturbator.target_col})"
+
+        assert (
+            self.analysis.target_col == self.target_col
+        ), f"target_col in analysis ({self.analysis.target_col}) must be the same as target_col in PowerAnalysis ({self.target_col})"
+
+    def check_treatment(self):
+        assert (
+            self.analysis.treatment == self.perturbator.treatment
+        ), f"treatment in analysis ({self.analysis.treatment}) must be the same as treatment in perturbator ({self.perturbator.treatment})"
+
+        assert (
+            self.analysis.treatment == self.treatment
+        ), f"treatment in analysis ({self.analysis.treatment}) must be the same as treatment in PowerAnalysis ({self.treatment})"
+
+        assert (
+            self.analysis.treatment in self.splitter.treatments
+        ), f"treatment in analysis ({self.analysis.treatment}) must be in treatments in splitter ({self.splitter.treatments})"
+
+        assert (
+            self.control in self.splitter.treatments
+        ), f"control in power analysis ({self.control}) must be in treatments in splitter ({self.splitter.treatments})"
+
+    def check_covariates(self):
+        if hasattr(self.analysis, "covariates"):
+            cupac_in_covariates = (
+                self.cupac_handler.cupac_outcome_name in self.analysis.covariates
+            )
+
+            assert cupac_in_covariates or not self.cupac_handler.is_cupac, (
+                f"covariates in analysis must contain {self.cupac_handler.cupac_outcome_name} if cupac_model is not None. "
+                f"If you want to use cupac_model, you must add the cupac outcome to the covariates of the analysis "
+                f"You may want to do covariates=['{self.cupac_handler.cupac_outcome_name}'] in your analysis method or your config"
+            )
+
+            if hasattr(self.splitter, "cluster_cols"):
+                if set(self.analysis.covariates).intersection(
+                    set(self.splitter.cluster_cols)
+                ):
+                    logging.warning(
+                        f"covariates in analysis ({self.analysis.covariates}) are also cluster_cols in splitter ({self.splitter.cluster_cols}). "
+                        f"Be specially careful when using switchback splitters, since the time splitter column is being overriden"
+                    )
+
+    def check_clusters(self):
+        has_analysis_clusters = hasattr(self.analysis, "cluster_cols")
+        has_splitter_clusters = hasattr(self.splitter, "cluster_cols")
+        not_cluster_cols_cond = not has_analysis_clusters or not has_splitter_clusters
+        assert (
+            not_cluster_cols_cond
+            or self.analysis.cluster_cols == self.splitter.cluster_cols
+        ), f"cluster_cols in analysis ({self.analysis.cluster_cols}) must be the same as cluster_cols in splitter ({self.splitter.cluster_cols})"
+
+        assert (
+            has_splitter_clusters
+            or not has_analysis_clusters
+            or not self.analysis.cluster_cols
+            or isinstance(self.splitter, RepeatedSampler)
+        ), "analysis has cluster_cols but splitter does not."
+
+        assert (
+            has_analysis_clusters
+            or not has_splitter_clusters
+            or not self.splitter.cluster_cols
+        ), "splitter has cluster_cols but analysis does not."
+
+        has_time_col = hasattr(self.splitter, "time_col")
+        assert not (
+            has_time_col
+            and has_splitter_clusters
+            and self.splitter.time_col not in self.splitter.cluster_cols
+        ), "in switchback splitters, time_col must be in cluster_cols"
+
+    def check_inputs(self):
+        self.check_covariates()
+        self.check_treatment_col()
+        self.check_target_col()
+        self.check_treatment()
+        self.check_clusters()
+
+
+ + + +
+ + + + + + + + + + + + + + + +
+ + + +

+check_treatment_col(self) + + +

+ +
+ +

Checks consistency of treatment column

+ +
+ Source code in cluster_experiments/power_analysis.py +
def check_treatment_col(self):
+    """Checks consistency of treatment column"""
+    assert (
+        self.analysis.treatment_col == self.perturbator.treatment_col
+    ), f"treatment_col in analysis ({self.analysis.treatment_col}) must be the same as treatment_col in perturbator ({self.perturbator.treatment_col})"
+
+    assert (
+        self.analysis.treatment_col == self.treatment_col
+    ), f"treatment_col in analysis ({self.analysis.treatment_col}) must be the same as treatment_col in PowerAnalysis ({self.treatment_col})"
+
+    assert (
+        self.analysis.treatment_col == self.splitter.treatment_col
+    ), f"treatment_col in analysis ({self.analysis.treatment_col}) must be the same as treatment_col in splitter ({self.splitter.treatment_col})"
+
+
+
+ +
+ + + +
+ + + +

+from_config(config) + + + classmethod + + +

+ +
+ +

Constructs PowerAnalysis from PowerConfig

+ +
+ Source code in cluster_experiments/power_analysis.py +
@classmethod
+def from_config(cls, config: PowerConfig) -> "PowerAnalysis":
+    """Constructs PowerAnalysis from PowerConfig"""
+    perturbator_cls = _get_mapping_key(perturbator_mapping, config.perturbator)
+    splitter_cls = _get_mapping_key(splitter_mapping, config.splitter)
+    analysis_cls = _get_mapping_key(analysis_mapping, config.analysis)
+    cupac_cls = _get_mapping_key(cupac_model_mapping, config.cupac_model)
+    return cls(
+        perturbator=perturbator_cls.from_config(config),
+        splitter=splitter_cls.from_config(config),
+        analysis=analysis_cls.from_config(config),
+        cupac_model=cupac_cls.from_config(config),
+        target_col=config.target_col,
+        treatment_col=config.treatment_col,
+        treatment=config.treatment,
+        control=config.control,
+        n_simulations=config.n_simulations,
+        alpha=config.alpha,
+        features_cupac_model=config.features_cupac_model,
+        seed=config.seed,
+        hypothesis=config.hypothesis,
+    )
+
+
+
+ +
+ + + +
+ + + +

+from_dict(config_dict) + + + classmethod + + +

+ +
+ +

Constructs PowerAnalysis from dictionary

+ +
+ Source code in cluster_experiments/power_analysis.py +
@classmethod
+def from_dict(cls, config_dict: dict) -> "PowerAnalysis":
+    """Constructs PowerAnalysis from dictionary"""
+    config = PowerConfig(**config_dict)
+    return cls.from_config(config)
+
+
+
+ +
+ + + +
+ + + +

+log_nulls(self, df) + + +

+ +
+ +

Warns about dropping nulls in treatment column

+ +
+ Source code in cluster_experiments/power_analysis.py +
def log_nulls(self, df: pd.DataFrame) -> None:
+    """Warns about dropping nulls in treatment column"""
+    n_nulls = len(df.query(f"{self.treatment_col}.isnull()", engine="python"))
+    if n_nulls > 0:
+        logging.warning(
+            f"There are {n_nulls} null values in treatment, dropping them"
+        )
+
+
+
+ +
+ + + +
+ + + +

+power_analysis(self, df, pre_experiment_df=None, verbose=False, average_effect=None, n_simulations=None, alpha=None, n_jobs=1) + + +

+ +
+ +

Run power analysis by simulation

+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
dfDataFrame

Dataframe with outcome and treatment variables.

required
pre_experiment_dfOptional[pandas.core.frame.DataFrame]

Dataframe with pre-experiment data.

None
verbosebool

Whether to show progress bar.

False
average_effectOptional[float]

Average effect of treatment. If None, it will use the perturbator average effect.

None
n_simulationsOptional[int]

Number of simulations to run.

None
alphaOptional[float]

Significance level.

None
n_jobsint

Number of jobs to run in parallel. If 1, it will run in serial.

1
+
+ Source code in cluster_experiments/power_analysis.py +
def power_analysis(
+    self,
+    df: pd.DataFrame,
+    pre_experiment_df: Optional[pd.DataFrame] = None,
+    verbose: bool = False,
+    average_effect: Optional[float] = None,
+    n_simulations: Optional[int] = None,
+    alpha: Optional[float] = None,
+    n_jobs: int = 1,
+) -> float:
+    """
+    Run power analysis by simulation
+    Args:
+        df: Dataframe with outcome and treatment variables.
+        pre_experiment_df: Dataframe with pre-experiment data.
+        verbose: Whether to show progress bar.
+        average_effect: Average effect of treatment. If None, it will use the perturbator average effect.
+        n_simulations: Number of simulations to run.
+        alpha: Significance level.
+        n_jobs: Number of jobs to run in parallel. If 1, it will run in serial.
+    """
+    n_simulations = self.n_simulations if n_simulations is None else n_simulations
+    alpha = self.alpha if alpha is None else alpha
+
+    df = df.copy()
+    df = self.cupac_handler.add_covariates(df, pre_experiment_df)
+
+    if n_jobs == 1:
+        return self._non_parallel_loop(
+            df, average_effect, n_simulations, alpha, verbose
+        )
+    elif n_jobs > 1 or n_jobs == -1:
+        return self._parallel_loop(
+            df, average_effect, n_simulations, alpha, verbose, n_jobs
+        )
+    else:
+        raise ValueError("n_jobs must be greater than 0, or -1.")
+
+
+
+ +
+ + + +
+ + + +

+power_line(self, df, pre_experiment_df=None, verbose=False, average_effects=(), n_simulations=None, alpha=None, n_jobs=1) + + +

+ +
+ +

Runs power analysis with multiple average effects

+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
dfDataFrame

Dataframe with outcome and treatment variables.

required
pre_experiment_dfOptional[pandas.core.frame.DataFrame]

Dataframe with pre-experiment data.

None
verbosebool

Whether to show progress bar.

False
average_effectsIterable[float]

Average effects to test.

()
n_simulationsOptional[int]

Number of simulations to run.

None
alphaOptional[float]

Significance level.

None
n_jobsint

Number of jobs to run in parallel.

1
+

Returns:

+ + + + + + + + + + + + + +
TypeDescription
Dict[float, float]

Dictionary with average effects as keys and power as values.

+
+ Source code in cluster_experiments/power_analysis.py +
def power_line(
+    self,
+    df: pd.DataFrame,
+    pre_experiment_df: Optional[pd.DataFrame] = None,
+    verbose: bool = False,
+    average_effects: Iterable[float] = (),
+    n_simulations: Optional[int] = None,
+    alpha: Optional[float] = None,
+    n_jobs: int = 1,
+) -> Dict[float, float]:
+    """Runs power analysis with multiple average effects
+
+    Args:
+        df: Dataframe with outcome and treatment variables.
+        pre_experiment_df: Dataframe with pre-experiment data.
+        verbose: Whether to show progress bar.
+        average_effects: Average effects to test.
+        n_simulations: Number of simulations to run.
+        alpha: Significance level.
+        n_jobs: Number of jobs to run in parallel.
+
+    Returns:
+        Dictionary with average effects as keys and power as values.
+    """
+    return {
+        effect: self.power_analysis(
+            df=df,
+            pre_experiment_df=pre_experiment_df,
+            verbose=verbose,
+            average_effect=effect,
+            n_simulations=n_simulations,
+            alpha=alpha,
+            n_jobs=n_jobs,
+        )
+        for effect in tqdm(
+            list(average_effects), disable=not verbose, desc="Effects loop"
+        )
+    }
+
+
+
+ +
+ + + +
+ + + +

+running_power_analysis(self, df, pre_experiment_df=None, verbose=False, average_effect=None, n_simulations=100) + + +

+ +
+ +

Yields running power for each iteration of the simulation. +if you're interested in getting the power at each iteration, you can use this method to generate them.

+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
dfDataFrame

Dataframe with outcome variable.

required
pre_experiment_dfOptional[pandas.core.frame.DataFrame]

Dataframe with pre-experiment data.

None
verbosebool

Whether to show progress bar.

False
average_effectOptional[float]

Average effect of treatment. If None, it will use the perturbator average effect.

None
n_simulationsint

Number of simulations to run.

100
+
+ Source code in cluster_experiments/power_analysis.py +
def running_power_analysis(
+    self,
+    df: pd.DataFrame,
+    pre_experiment_df: Optional[pd.DataFrame] = None,
+    verbose: bool = False,
+    average_effect: Optional[float] = None,
+    n_simulations: int = 100,
+) -> Generator[float, None, None]:
+    """
+    Yields running power for each iteration of the simulation.
+    if you're interested in getting the power at each iteration, you can use this method to generate them.
+    Args:
+        df: Dataframe with outcome variable.
+        pre_experiment_df: Dataframe with pre-experiment data.
+        verbose: Whether to show progress bar.
+        average_effect: Average effect of treatment. If None, it will use the perturbator average effect.
+        n_simulations: Number of simulations to run.
+    """
+    n_rejected = 0
+    for i, perturbed_df in enumerate(
+        self._simulate_perturbed_df(
+            df,
+            pre_experiment_df=pre_experiment_df,
+            verbose=verbose,
+            average_effect=average_effect,
+            n_simulations=n_simulations,
+        )
+    ):
+        p_value = self.analysis.get_pvalue(perturbed_df)
+        n_rejected += int(p_value < self.alpha)
+        yield n_rejected / (i + 1)
+
+
+
+ +
+ + + +
+ + + +

+simulate_point_estimate(self, df, pre_experiment_df=None, verbose=False, average_effect=None, n_simulations=100) + + +

+ +
+ +

Yields point estimates for each iteration of the simulation. +In general, this is to be used in power_analysis method. However, +if you're interested in the distribution of point estimates, you can use this method to generate them.

+

This is an experimental feature and it might change in the future.

+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
dfDataFrame

Dataframe with outcome and treatment variables.

required
pre_experiment_dfOptional[pandas.core.frame.DataFrame]

Dataframe with pre-experiment data.

None
verbosebool

Whether to show progress bar.

False
average_effectOptional[float]

Average effect of treatment. If None, it will use the perturbator average effect.

None
n_simulationsint

Number of simulations to run.

100
+
+ Source code in cluster_experiments/power_analysis.py +
def simulate_point_estimate(
+    self,
+    df: pd.DataFrame,
+    pre_experiment_df: Optional[pd.DataFrame] = None,
+    verbose: bool = False,
+    average_effect: Optional[float] = None,
+    n_simulations: int = 100,
+) -> Generator[float, None, None]:
+    """
+    Yields point estimates for each iteration of the simulation.
+    In general, this is to be used in power_analysis method. However,
+    if you're interested in the distribution of point estimates, you can use this method to generate them.
+
+    This is an experimental feature and it might change in the future.
+
+    Args:
+        df: Dataframe with outcome and treatment variables.
+        pre_experiment_df: Dataframe with pre-experiment data.
+        verbose: Whether to show progress bar.
+        average_effect: Average effect of treatment. If None, it will use the perturbator average effect.
+        n_simulations: Number of simulations to run.
+    """
+    for perturbed_df in self._simulate_perturbed_df(
+        df,
+        pre_experiment_df=pre_experiment_df,
+        verbose=verbose,
+        average_effect=average_effect,
+        n_simulations=n_simulations,
+    ):
+        yield self.analysis.get_point_estimate(perturbed_df)
+
+
+
+ +
+ + + +
+ + + +

+simulate_pvalue(self, df, pre_experiment_df=None, verbose=False, average_effect=None, n_simulations=100) + + +

+ +
+ +

Yields p-values for each iteration of the simulation. +In general, this is to be used in power_analysis method. However, +if you're interested in the distribution of p-values, you can use this method to generate them.

+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
dfDataFrame

Dataframe with outcome variable.

required
pre_experiment_dfOptional[pandas.core.frame.DataFrame]

Dataframe with pre-experiment data.

None
verbosebool

Whether to show progress bar.

False
average_effectOptional[float]

Average effect of treatment. If None, it will use the perturbator average effect.

None
n_simulationsint

Number of simulations to run.

100
+
+ Source code in cluster_experiments/power_analysis.py +
def simulate_pvalue(
+    self,
+    df: pd.DataFrame,
+    pre_experiment_df: Optional[pd.DataFrame] = None,
+    verbose: bool = False,
+    average_effect: Optional[float] = None,
+    n_simulations: int = 100,
+) -> Generator[float, None, None]:
+    """
+    Yields p-values for each iteration of the simulation.
+    In general, this is to be used in power_analysis method. However,
+    if you're interested in the distribution of p-values, you can use this method to generate them.
+    Args:
+        df: Dataframe with outcome variable.
+        pre_experiment_df: Dataframe with pre-experiment data.
+        verbose: Whether to show progress bar.
+        average_effect: Average effect of treatment. If None, it will use the perturbator average effect.
+        n_simulations: Number of simulations to run.
+    """
+    for perturbed_df in self._simulate_perturbed_df(
+        df,
+        pre_experiment_df=pre_experiment_df,
+        verbose=verbose,
+        average_effect=average_effect,
+        n_simulations=n_simulations,
+    ):
+        yield self.analysis.get_pvalue(perturbed_df)
+
+
+
+ +
+ + + + + +
+ +
+ +
+ + + +
+ + + +

+ +PowerAnalysisWithPreExperimentData (PowerAnalysis) + + + + +

+ +
+ +

This is intended to work mainly for diff-in-diff or synthetic control-like estimators, and NOT for cases of CUPED/CUPAC. +Same as PowerAnalysis, but allowing a perturbation only at experiment period and keeping pre-experiment df intact. +Using this class, the pre experiment df is also available when the class is instantiated.

+ +
+ Source code in cluster_experiments/power_analysis.py +
class PowerAnalysisWithPreExperimentData(PowerAnalysis):
+    """
+    This is intended to work mainly for diff-in-diff or synthetic control-like estimators, and NOT for cases of CUPED/CUPAC.
+    Same as PowerAnalysis, but allowing a perturbation only at experiment period and keeping pre-experiment df intact.
+    Using this class, the pre experiment df is also available when the class is instantiated.
+    """
+
+    def _perturbate(
+        self, treatment_df: pd.DataFrame, average_effect: Optional[float]
+    ) -> pd.DataFrame:
+        if not hasattr(self.analysis, "_split_pre_experiment_df"):
+            raise AttributeError(
+                "The PowerAnalysisWithPreExperimentData is intended to work mainly for diff-in-diff or synthetic control-like estimators."
+                "For other cases use the PowerAnalysis"
+            )
+
+        df, pre_experiment_df = self.analysis._split_pre_experiment_df(treatment_df)
+
+        perturbed_df = self.perturbator.perturbate(df, average_effect=average_effect)
+
+        return pd.concat([perturbed_df, pre_experiment_df])
+
+
+ + +
+ +
+ + + + + + + +
+ +
+ +
+ + + + + + +
+
+ + +
+ +
+ + + +
+
+
+
+ + + + + + + + + \ No newline at end of file diff --git a/api/power_config.html b/api/power_config.html new file mode 100644 index 00000000..a9dc18d5 --- /dev/null +++ b/api/power_config.html @@ -0,0 +1,1421 @@ + + + + + + + + + + + + + + + + + + + + + + Power config - Cluster Experiments Docs + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + +
+ + +
+ +
+ + + + + + +
+
+ + + +
+
+
+ + + + +
+
+
+ + + +
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+ + + +
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+ + + +
+
+ + + + + + + + + +

from cluster_experiments.power_config import *

+ + +
+ + +
+ + + + +
+ + + + + + + + + + + + +
+ + + +

+ +PowerConfig + + + + dataclass + + +

+ +
+ +

Dataclass to create a power analysis from.

+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
splitterstr

Splitter object to use

required
perturbatorstr

Perturbator object to use, defaults to "" for normal power analysis

''
analysisstr

ExperimentAnalysis object to use

required
washoverstr

Washover object to use, defaults to ""

''
cupac_modelstr

CUPAC model to use

''
n_simulationsint

number of simulations to run

100
cluster_colsOptional[List[str]]

list of columns to use as clusters

None
target_colstr

column to use as target

'target'
treatment_colstr

column to use as treatment

'treatment'
treatmentstr

what value of treatment_col should be considered as treatment

'B'
controlstr

what value of treatment_col should be considered as control

'A'
strata_colsOptional[List[str]]

columns to stratify with

None
splitter_weightsOptional[List[float]]

weights to use for the splitter, should have the same length as treatments, each weight should correspond to an element in treatments

None
switch_frequencyOptional[str]

how often to switch treatments

None
time_colOptional[str]

column to use as time in switchback splitter

None
washover_time_deltaUnion[datetime.timedelta, int]

optional, int indicating the washover time in minutes or datetime.timedelta object

None
covariatesOptional[List[str]]

list of columns to use as covariates

None
average_effectOptional[float]

average effect to use in the perturbator

None
scaleOptional[float]

scale to use in stochastic perturbators

None
range_minOptional[float]

minimum value of the target range for relative beta perturbator, must be >-1

None
range_maxOptional[float]

maximum value of the target range for relative beta perturbator

None
reduce_varianceOptional[bool]

whether to reduce variance in the BetaRelative perturbator

None
segment_colsOptional[List[str]]

list of segmentation columns for segmented perturbator

None
treatmentsOptional[List[str]]

list of treatments to use

None
alphafloat

alpha value to use in the power analysis

0.05
agg_colstr

column to use for aggregation in the CUPAC model

''
smoothing_factorfloat

smoothing value to use in the CUPAC model

20
features_cupac_modelOptional[List[str]]

list of features to use in the CUPAC model

None
seedOptional[int]

seed to make the power analysis reproducible

None

Usage:

+
from cluster_experiments.power_config import PowerConfig
+from cluster_experiments.power_analysis import PowerAnalysis, NormalPowerAnalysis
+
+p = PowerConfig(
+    analysis="gee",
+    splitter="clustered_balance",
+    perturbator="constant",
+    cluster_cols=["city"],
+    n_simulations=100,
+    alpha=0.05,
+)
+power_analysis = PowerAnalysis.from_config(p)
+
+normal_power_analysis = NormalPowerAnalysis.from_config(p)
+
+ +
+ Source code in cluster_experiments/power_config.py +
class PowerConfig:
+    """
+    Dataclass to create a power analysis from.
+
+    Arguments:
+        splitter: Splitter object to use
+        perturbator: Perturbator object to use, defaults to "" for normal power analysis
+        analysis: ExperimentAnalysis object to use
+        washover: Washover object to use, defaults to ""
+        cupac_model: CUPAC model to use
+        n_simulations: number of simulations to run
+        cluster_cols: list of columns to use as clusters
+        target_col: column to use as target
+        treatment_col: column to use as treatment
+        treatment: what value of treatment_col should be considered as treatment
+        control: what value of treatment_col should be considered as control
+        strata_cols: columns to stratify with
+        splitter_weights: weights to use for the splitter, should have the same length as treatments, each weight should correspond to an element in treatments
+        switch_frequency: how often to switch treatments
+        time_col: column to use as time in switchback splitter
+        washover_time_delta: optional, int indicating the washover time in minutes or datetime.timedelta object
+        covariates: list of columns to use as covariates
+        average_effect: average effect to use in the perturbator
+        scale: scale to use in stochastic perturbators
+        range_min: minimum value of the target range for relative beta perturbator, must be >-1
+        range_max: maximum value of the target range for relative beta perturbator
+        reduce_variance: whether to reduce variance in the BetaRelative perturbator
+        segment_cols: list of segmentation columns for segmented perturbator
+        treatments: list of treatments to use
+        alpha: alpha value to use in the power analysis
+        agg_col: column to use for aggregation in the CUPAC model
+        smoothing_factor: smoothing value to use in the CUPAC model
+        features_cupac_model: list of features to use in the CUPAC model
+        seed: seed to make the power analysis reproducible
+
+    Usage:
+
+    ```python
+    from cluster_experiments.power_config import PowerConfig
+    from cluster_experiments.power_analysis import PowerAnalysis, NormalPowerAnalysis
+
+    p = PowerConfig(
+        analysis="gee",
+        splitter="clustered_balance",
+        perturbator="constant",
+        cluster_cols=["city"],
+        n_simulations=100,
+        alpha=0.05,
+    )
+    power_analysis = PowerAnalysis.from_config(p)
+
+    normal_power_analysis = NormalPowerAnalysis.from_config(p)
+    ```
+    """
+
+    # mappings
+    splitter: str
+    analysis: str
+    perturbator: str = ""
+    washover: str = ""
+
+    # Needed
+    cluster_cols: Optional[List[str]] = None
+
+    # optional mappings
+    cupac_model: str = ""
+
+    # Shared
+    target_col: str = "target"
+    treatment_col: str = "treatment"
+    treatment: str = "B"
+
+    # Perturbator
+    average_effect: Optional[float] = None
+    scale: Optional[float] = None
+    range_min: Optional[float] = None
+    range_max: Optional[float] = None
+    reduce_variance: Optional[bool] = None
+    segment_cols: Optional[List[str]] = None
+
+    # Splitter
+    treatments: Optional[List[str]] = None
+    strata_cols: Optional[List[str]] = None
+    splitter_weights: Optional[List[float]] = None
+    switch_frequency: Optional[str] = None
+    # Switchback
+    time_col: Optional[str] = None
+    washover_time_delta: Optional[Union[datetime.timedelta, int]] = None
+
+    # Analysis
+    covariates: Optional[List[str]] = None
+    hypothesis: str = "two-sided"
+
+    # Power analysis
+    n_simulations: int = 100
+    alpha: float = 0.05
+    control: str = "A"
+
+    # Cupac
+    agg_col: str = ""
+    smoothing_factor: float = 20
+    features_cupac_model: Optional[List[str]] = None
+
+    seed: Optional[int] = None
+
+    def __post_init__(self):
+        if "switchback" not in self.splitter:
+            if self._are_different(self.switch_frequency, None):
+                self._set_and_log("switch_frequency", None, "splitter")
+            if self._are_different(self.washover_time_delta, None):
+                self._set_and_log("washover_time_delta", None, "splitter")
+            if self._are_different(self.washover, ""):
+                self._set_and_log("washover", "", "splitter")
+            # an exception is made when we have no perturbator (normal power analysis)
+            if self._are_different(self.time_col, None) and self.perturbator != "":
+                self._set_and_log("time_col", None, "splitter")
+
+        if self.perturbator not in {"normal", "beta_relative_positive"}:
+            if self._are_different(self.scale, None):
+                self._set_and_log("scale", None, "perturbator")
+
+        if self.perturbator not in {"beta_relative", "segmented_beta_relative"}:
+            if self._are_different(self.range_min, None):
+                self._set_and_log("range_min", None, "perturbator")
+            if self._are_different(self.range_max, None):
+                self._set_and_log("range_max", None, "perturbator")
+            if self._are_different(self.reduce_variance, None):
+                self._set_and_log("reduce_variance", None, "perturbator")
+
+        if self.perturbator not in {"segmented_beta_relative"}:
+            if self._are_different(self.segment_cols, None):
+                self._set_and_log("segment_cols", None, "perturbator")
+
+        if "stratified" not in self.splitter and "paired_ttest" not in self.analysis:
+            if self._are_different(self.strata_cols, None):
+                self._set_and_log("strata_cols", None, "splitter")
+
+        if "stratified" in self.splitter or "balanced" in self.splitter:
+            if self._are_different(self.splitter_weights, None):
+                self._set_and_log("splitter_weights", None, "splitter")
+
+        if self.cupac_model != "mean_cupac_model":
+            if self._are_different(self.agg_col, ""):
+                self._set_and_log("agg_col", "", "cupac_model")
+            if self._are_different(self.smoothing_factor, 20):
+                self._set_and_log("smoothing_factor", 20, "cupac_model")
+        # for now, features_cupac_model are not used
+        if self._are_different(self.features_cupac_model, None):
+            self._set_and_log("features_cupac_model", None, "cupac_model")
+
+        if "ttest" in self.analysis:
+            if self._are_different(self.covariates, None):
+                self._set_and_log("covariates", None, "analysis")
+
+        if "segmented" in self.perturbator:
+            self._raise_error_if_missing("segment_cols", "perturbator")
+
+    def _are_different(self, arg1, arg2) -> bool:
+        return arg1 != arg2
+
+    def _set_and_log(self, attr, value, other_attr):
+        logging.warning(
+            f"{attr} = {getattr(self, attr)} has no effect with "
+            f"{other_attr} = {getattr(self, other_attr)}. "
+            f"Overriding {attr} to {value}."
+        )
+        setattr(self, attr, value)
+
+    def _raise_error_if_missing(self, attr, other_attr):
+        if getattr(self, attr) is None:
+            raise MissingArgumentError(
+                f"{attr} is required when using "
+                f"{other_attr} = {getattr(self, other_attr)}."
+            )
+
+
+ + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ +
+ + + + + + +
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+ + +
+ +
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+
+ + + + + + + + + \ No newline at end of file diff --git a/api/random_splitter.html b/api/random_splitter.html new file mode 100644 index 00000000..c2c6f7f2 --- /dev/null +++ b/api/random_splitter.html @@ -0,0 +1,3613 @@ + + + + + + + + + + + + + + + + + + + + + + Splitter - Cluster Experiments Docs + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + +
+ + +
+ +
+ + + + + + +
+
+ + + +
+
+
+ + + + +
+
+
+ + + + + + + +
+
+ + + + + + + + + +

from cluster_experiments.random_splitter import *

+ + +
+ + +
+ + + + +
+ + + + + + + +
+ + + +

+ +BalancedClusteredSplitter (ClusteredSplitter) + + + + +

+ +
+ +

Like ClusteredSplitter, but ensures that treatments are balanced among clusters. That is, if we have +25 clusters and 2 treatments, 13 clusters should have treatment A and 12 clusters should have treatment B.

+ +
+ Source code in cluster_experiments/random_splitter.py +
class BalancedClusteredSplitter(ClusteredSplitter):
+    """Like ClusteredSplitter, but ensures that treatments are balanced among clusters. That is, if we have
+    25 clusters and 2 treatments, 13 clusters should have treatment A and 12 clusters should have treatment B."""
+
+    def sample_treatment(
+        self,
+        cluster_df: pd.DataFrame,
+    ) -> List[str]:
+        """
+        Samples treatments for each cluster
+
+        Arguments:
+            cluster_df: dataframe to assign treatments to
+        """
+        n_clusters = len(cluster_df)
+        n_treatments = len(self.treatments)
+        n_per_treatment = n_clusters // n_treatments
+        n_extra = n_clusters % n_treatments
+        treatments = []
+        for i in range(n_treatments):
+            treatments += [self.treatments[i]] * (n_per_treatment + (i < n_extra))
+        random.shuffle(treatments)
+        return treatments
+
+
+ + + +
+ + + + + + + + + +
+ + + +

+sample_treatment(self, cluster_df) + + +

+ +
+ +

Samples treatments for each cluster

+ +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
cluster_dfDataFrame

dataframe to assign treatments to

required
+
+ Source code in cluster_experiments/random_splitter.py +
def sample_treatment(
+    self,
+    cluster_df: pd.DataFrame,
+) -> List[str]:
+    """
+    Samples treatments for each cluster
+
+    Arguments:
+        cluster_df: dataframe to assign treatments to
+    """
+    n_clusters = len(cluster_df)
+    n_treatments = len(self.treatments)
+    n_per_treatment = n_clusters // n_treatments
+    n_extra = n_clusters % n_treatments
+    treatments = []
+    for i in range(n_treatments):
+        treatments += [self.treatments[i]] * (n_per_treatment + (i < n_extra))
+    random.shuffle(treatments)
+    return treatments
+
+
+
+ +
+ + + + + +
+ +
+ +
+ + + +
+ + + +

+ +BalancedSwitchbackSplitter (BalancedClusteredSplitter, SwitchbackSplitter) + + + + +

+ +
+ +

Like SwitchbackSplitter, but ensures that treatments are balanced among clusters. That is, if we have +25 clusters and 2 treatments, 13 clusters should have treatment A and 12 clusters should have treatment B.

+ +
+ Source code in cluster_experiments/random_splitter.py +
class BalancedSwitchbackSplitter(BalancedClusteredSplitter, SwitchbackSplitter):
+    """
+    Like SwitchbackSplitter, but ensures that treatments are balanced among clusters. That is, if we have
+    25 clusters and 2 treatments, 13 clusters should have treatment A and 12 clusters should have treatment B.
+    """
+
+    pass
+
+
+ + +
+ +
+ + + +
+ + + +

+ +ClusteredSplitter (RandomSplitter) + + + + +

+ +
+ +

Splits randomly using clusters

+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
cluster_colsList[str]

List of columns to use as clusters

required
treatmentsOptional[List[str]]

list of treatments

None
treatment_colstr

Name of the column with the treatment variable.

'treatment'
splitter_weightsOptional[List[float]]

weights to use for the splitter, should have the same length as treatments, each weight should correspond to an element in treatments

None

Usage: +

import pandas as pd
+from cluster_experiments.random_splitter import ClusteredSplitter
+splitter = ClusteredSplitter(cluster_cols=["city"])
+df = pd.DataFrame({"city": ["A", "B", "C"]})
+df = splitter.assign_treatment_df(df)
+print(df)
+

+ +
+ Source code in cluster_experiments/random_splitter.py +
class ClusteredSplitter(RandomSplitter):
+    """
+    Splits randomly using clusters
+
+    Arguments:
+        cluster_cols: List of columns to use as clusters
+        treatments: list of treatments
+        treatment_col: Name of the column with the treatment variable.
+        splitter_weights: weights to use for the splitter, should have the same length as treatments, each weight should correspond to an element in treatments
+
+    Usage:
+    ```python
+    import pandas as pd
+    from cluster_experiments.random_splitter import ClusteredSplitter
+    splitter = ClusteredSplitter(cluster_cols=["city"])
+    df = pd.DataFrame({"city": ["A", "B", "C"]})
+    df = splitter.assign_treatment_df(df)
+    print(df)
+    ```
+    """
+
+    def __init__(
+        self,
+        cluster_cols: List[str],
+        treatments: Optional[List[str]] = None,
+        treatment_col: str = "treatment",
+        splitter_weights: Optional[List[float]] = None,
+    ) -> None:
+        self.treatments = treatments or ["A", "B"]
+        self.cluster_cols = cluster_cols
+        self.treatment_col = treatment_col
+        self.splitter_weights = splitter_weights
+
+    def assign_treatment_df(
+        self,
+        df: pd.DataFrame,
+    ) -> pd.DataFrame:
+        """
+        Takes a df, randomizes treatments and adds the treatment column to the dataframe
+
+        Arguments:
+            df: dataframe to assign treatments to
+        """
+        df = df.copy()
+
+        # raise error if any nulls in cluster_cols
+        if df[self.cluster_cols].isnull().values.any():
+            raise ValueError(
+                f"Null values found in cluster_cols: {self.cluster_cols}. "
+                "Please remove nulls before running the splitter."
+            )
+
+        clusters_df = df.loc[:, self.cluster_cols].drop_duplicates()
+        clusters_df[self.treatment_col] = self.sample_treatment(clusters_df)
+        df = df.merge(clusters_df, on=self.cluster_cols, how="left")
+        return df
+
+    def sample_treatment(
+        self,
+        cluster_df: pd.DataFrame,
+    ) -> List[str]:
+        """
+        Samples treatments for each cluster
+
+        Arguments:
+            cluster_df: dataframe to assign treatments to
+        """
+        return random.choices(
+            self.treatments, k=len(cluster_df), weights=self.splitter_weights
+        )
+
+
+ + + +
+ + + + + + + + + + +
+ + + +

+assign_treatment_df(self, df) + + +

+ +
+ +

Takes a df, randomizes treatments and adds the treatment column to the dataframe

+ +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
dfDataFrame

dataframe to assign treatments to

required
+
+ Source code in cluster_experiments/random_splitter.py +
def assign_treatment_df(
+    self,
+    df: pd.DataFrame,
+) -> pd.DataFrame:
+    """
+    Takes a df, randomizes treatments and adds the treatment column to the dataframe
+
+    Arguments:
+        df: dataframe to assign treatments to
+    """
+    df = df.copy()
+
+    # raise error if any nulls in cluster_cols
+    if df[self.cluster_cols].isnull().values.any():
+        raise ValueError(
+            f"Null values found in cluster_cols: {self.cluster_cols}. "
+            "Please remove nulls before running the splitter."
+        )
+
+    clusters_df = df.loc[:, self.cluster_cols].drop_duplicates()
+    clusters_df[self.treatment_col] = self.sample_treatment(clusters_df)
+    df = df.merge(clusters_df, on=self.cluster_cols, how="left")
+    return df
+
+
+
+ +
+ + + +
+ + + +

+sample_treatment(self, cluster_df) + + +

+ +
+ +

Samples treatments for each cluster

+ +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
cluster_dfDataFrame

dataframe to assign treatments to

required
+
+ Source code in cluster_experiments/random_splitter.py +
def sample_treatment(
+    self,
+    cluster_df: pd.DataFrame,
+) -> List[str]:
+    """
+    Samples treatments for each cluster
+
+    Arguments:
+        cluster_df: dataframe to assign treatments to
+    """
+    return random.choices(
+        self.treatments, k=len(cluster_df), weights=self.splitter_weights
+    )
+
+
+
+ +
+ + + + + +
+ +
+ +
+ + + +
+ + + +

+ +FixedSizeClusteredSplitter (ClusteredSplitter) + + + + +

+ +
+ +

This class represents a splitter that splits clusters into treatment groups with a predefined number of +treatment clusters. This is particularly useful for synthetic control analysis, where we only want 1 cluster ( +unit) to be in treatment group and the rest in control The cluster that receives treatment remains random.

+ +

Attributes:

+ + + + + + + + + + + + + + + + + + + + +
NameTypeDescription
cluster_colsList[str]

List of columns to use as clusters.

n_treatment_clustersint

The predefined number of treatment clusters.

+
+ Source code in cluster_experiments/random_splitter.py +
class FixedSizeClusteredSplitter(ClusteredSplitter):
+    """
+    This class  represents a splitter that splits clusters into treatment groups with a predefined number of
+    treatment clusters. This is particularly useful for synthetic control analysis, where we only want 1 cluster (
+    unit) to be in treatment group and the rest in control The cluster that receives treatment remains random.
+
+    Attributes:
+        cluster_cols (List[str]): List of columns to use as clusters.
+        n_treatment_clusters (int): The predefined number of treatment clusters.
+
+    """
+
+    def __init__(self, cluster_cols: List[str], n_treatment_clusters: int):
+        super().__init__(cluster_cols=cluster_cols)
+        self.n_treatment_clusters = n_treatment_clusters
+
+    def sample_treatment(
+        self,
+        cluster_df: pd.DataFrame,
+    ) -> List[str]:
+        """
+        Samples treatments for each cluster.
+
+        Args:
+            cluster_df (pd.DataFrame): Dataframe to assign treatments to.
+
+        Returns:
+            List[str]: A list of treatments for each cluster.
+        """
+        n_control_treatment = [
+            len(cluster_df) - self.n_treatment_clusters,
+            self.n_treatment_clusters,
+        ]
+
+        sample_treatment = [
+            treatment
+            for treatment, count in zip(self.treatments, n_control_treatment)
+            for _ in range(count)
+        ]
+        random.shuffle(sample_treatment)
+        return sample_treatment
+
+
+ + + +
+ + + + + + + + + + +
+ + + +

+sample_treatment(self, cluster_df) + + +

+ +
+ +

Samples treatments for each cluster.

+ +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
cluster_dfpd.DataFrame

Dataframe to assign treatments to.

required
+

Returns:

+ + + + + + + + + + + + + +
TypeDescription
List[str]

A list of treatments for each cluster.

+
+ Source code in cluster_experiments/random_splitter.py +
def sample_treatment(
+    self,
+    cluster_df: pd.DataFrame,
+) -> List[str]:
+    """
+    Samples treatments for each cluster.
+
+    Args:
+        cluster_df (pd.DataFrame): Dataframe to assign treatments to.
+
+    Returns:
+        List[str]: A list of treatments for each cluster.
+    """
+    n_control_treatment = [
+        len(cluster_df) - self.n_treatment_clusters,
+        self.n_treatment_clusters,
+    ]
+
+    sample_treatment = [
+        treatment
+        for treatment, count in zip(self.treatments, n_control_treatment)
+        for _ in range(count)
+    ]
+    random.shuffle(sample_treatment)
+    return sample_treatment
+
+
+
+ +
+ + + + + +
+ +
+ +
+ + + +
+ + + +

+ +NonClusteredSplitter (RandomSplitter) + + + + +

+ +
+ +

Splits randomly without clusters

+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
treatmentsOptional[List[str]]

list of treatments

None
treatment_colstr

Name of the column with the treatment variable.

'treatment'

Usage: +

import pandas as pd
+from cluster_experiments.random_splitter import NonClusteredSplitter
+splitter = NonClusteredSplitter(
+    treatments=["A", "B"],
+)
+df = pd.DataFrame({"city": ["A", "B", "C"]})
+df = splitter.assign_treatment_df(df)
+print(df)
+

+ +
+ Source code in cluster_experiments/random_splitter.py +
class NonClusteredSplitter(RandomSplitter):
+    """
+    Splits randomly without clusters
+
+    Arguments:
+        treatments: list of treatments
+        treatment_col: Name of the column with the treatment variable.
+
+    Usage:
+    ```python
+    import pandas as pd
+    from cluster_experiments.random_splitter import NonClusteredSplitter
+    splitter = NonClusteredSplitter(
+        treatments=["A", "B"],
+    )
+    df = pd.DataFrame({"city": ["A", "B", "C"]})
+    df = splitter.assign_treatment_df(df)
+    print(df)
+    ```
+    """
+
+    def __init__(
+        self,
+        treatments: Optional[List[str]] = None,
+        treatment_col: str = "treatment",
+        splitter_weights: Optional[List[float]] = None,
+    ) -> None:
+        self.treatments = treatments or ["A", "B"]
+        self.treatment_col = treatment_col
+        self.splitter_weights = splitter_weights
+
+    def assign_treatment_df(
+        self,
+        df: pd.DataFrame,
+    ) -> pd.DataFrame:
+        """
+        Takes a df, randomizes treatments and adds the treatment column to the dataframe
+
+        Arguments:
+            df: dataframe to assign treatments to
+        """
+        df = df.copy()
+        df[self.treatment_col] = random.choices(
+            self.treatments, k=len(df), weights=self.splitter_weights
+        )
+        return df
+
+    @classmethod
+    def from_config(cls, config):
+        """Creates a NonClusteredSplitter from a PowerConfig"""
+        return cls(
+            treatments=config.treatments,
+            treatment_col=config.treatment_col,
+            splitter_weights=config.splitter_weights,
+        )
+
+
+ + + +
+ + + + + + + + + + +
+ + + +

+assign_treatment_df(self, df) + + +

+ +
+ +

Takes a df, randomizes treatments and adds the treatment column to the dataframe

+ +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
dfDataFrame

dataframe to assign treatments to

required
+
+ Source code in cluster_experiments/random_splitter.py +
def assign_treatment_df(
+    self,
+    df: pd.DataFrame,
+) -> pd.DataFrame:
+    """
+    Takes a df, randomizes treatments and adds the treatment column to the dataframe
+
+    Arguments:
+        df: dataframe to assign treatments to
+    """
+    df = df.copy()
+    df[self.treatment_col] = random.choices(
+        self.treatments, k=len(df), weights=self.splitter_weights
+    )
+    return df
+
+
+
+ +
+ + + +
+ + + +

+from_config(config) + + + classmethod + + +

+ +
+ +

Creates a NonClusteredSplitter from a PowerConfig

+ +
+ Source code in cluster_experiments/random_splitter.py +
@classmethod
+def from_config(cls, config):
+    """Creates a NonClusteredSplitter from a PowerConfig"""
+    return cls(
+        treatments=config.treatments,
+        treatment_col=config.treatment_col,
+        splitter_weights=config.splitter_weights,
+    )
+
+
+
+ +
+ + + + + +
+ +
+ +
+ + + +
+ + + +

+ +RandomSplitter (ABC) + + + + +

+ +
+ +

Abstract class to split instances in a switchback or clustered way. It can be used to create a calendar/split of clusters +or to run a power analysis.

+

In order to create your own RandomSplitter, you should write your own assign_treatment_df method, that takes a dataframe as an input and returns the same dataframe with the treatment_col column.

+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
cluster_colsOptional[List[str]]

List of columns to use as clusters

None
treatmentsOptional[List[str]]

list of treatments

None
treatment_colstr

Name of the column with the treatment variable.

'treatment'
splitter_weightsOptional[List[float]]

weights to use for the splitter, should have the same length as treatments, each weight should correspond to an element in treatments

None
+
+ Source code in cluster_experiments/random_splitter.py +
class RandomSplitter(ABC):
+    """
+    Abstract class to split instances in a switchback or clustered way. It can be used to create a calendar/split of clusters
+    or to run a power analysis.
+
+    In order to create your own RandomSplitter, you should write your own assign_treatment_df method, that takes a dataframe as an input and returns the same dataframe with the treatment_col column.
+
+    Arguments:
+        cluster_cols: List of columns to use as clusters
+        treatments: list of treatments
+        treatment_col: Name of the column with the treatment variable.
+        splitter_weights: weights to use for the splitter, should have the same length as treatments, each weight should correspond to an element in treatments
+
+    """
+
+    def __init__(
+        self,
+        cluster_cols: Optional[List[str]] = None,
+        treatments: Optional[List[str]] = None,
+        treatment_col: str = "treatment",
+        splitter_weights: Optional[List[float]] = None,
+    ) -> None:
+        self.treatments = treatments or ["A", "B"]
+        self.cluster_cols = cluster_cols or []
+        self.treatment_col = treatment_col
+        self.splitter_weights = splitter_weights
+
+    @abstractmethod
+    def assign_treatment_df(
+        self,
+        df: pd.DataFrame,
+    ) -> pd.DataFrame:
+        """
+        Takes a df, randomizes treatments and adds the treatment column to the dataframe
+
+        Arguments:
+            df: dataframe to assign treatments to
+        """
+
+    @classmethod
+    def from_config(cls, config):
+        """Creates a RandomSplitter from a PowerConfig"""
+        return cls(
+            treatments=config.treatments,
+            cluster_cols=config.cluster_cols,
+            treatment_col=config.treatment_col,
+            splitter_weights=config.splitter_weights,
+        )
+
+
+ + + +
+ + + + + + + + + + +
+ + + +

+assign_treatment_df(self, df) + + +

+ +
+ +

Takes a df, randomizes treatments and adds the treatment column to the dataframe

+ +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
dfDataFrame

dataframe to assign treatments to

required
+
+ Source code in cluster_experiments/random_splitter.py +
@abstractmethod
+def assign_treatment_df(
+    self,
+    df: pd.DataFrame,
+) -> pd.DataFrame:
+    """
+    Takes a df, randomizes treatments and adds the treatment column to the dataframe
+
+    Arguments:
+        df: dataframe to assign treatments to
+    """
+
+
+
+ +
+ + + +
+ + + +

+from_config(config) + + + classmethod + + +

+ +
+ +

Creates a RandomSplitter from a PowerConfig

+ +
+ Source code in cluster_experiments/random_splitter.py +
@classmethod
+def from_config(cls, config):
+    """Creates a RandomSplitter from a PowerConfig"""
+    return cls(
+        treatments=config.treatments,
+        cluster_cols=config.cluster_cols,
+        treatment_col=config.treatment_col,
+        splitter_weights=config.splitter_weights,
+    )
+
+
+
+ +
+ + + + + +
+ +
+ +
+ + + +
+ + + +

+ +RepeatedSampler (RandomSplitter) + + + + +

+ +
+ +

Doesn't actually split the data, but repeatedly samples (i.e. duplicates) all rows for all treatments. +This is useful for backtesting, where we assume to have access to all counterfactuals.

+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
treatmentsOptional[List[str]]

list of treatments

None
treatment_colstr

Name of the column with the treatment variable.

'treatment'

Usage: +

import pandas as pd
+from cluster_experiments.random_splitter import RepeatedSampler
+splitter = RepeatedSampler(
+    treatments=["A", "B"],
+)
+df = pd.DataFrame({"city": ["A", "B", "C"]})
+df = splitter.assign_treatment_df(df)
+print(df)
+

+ +
+ Source code in cluster_experiments/random_splitter.py +
class RepeatedSampler(RandomSplitter):
+    """
+    Doesn't actually split the data, but repeatedly samples (i.e. duplicates) all rows for all treatments.
+    This is useful for backtesting, where we assume to have access to all counterfactuals.
+
+    Arguments:
+        treatments: list of treatments
+        treatment_col: Name of the column with the treatment variable.
+
+    Usage:
+    ```python
+    import pandas as pd
+    from cluster_experiments.random_splitter import RepeatedSampler
+    splitter = RepeatedSampler(
+        treatments=["A", "B"],
+    )
+    df = pd.DataFrame({"city": ["A", "B", "C"]})
+    df = splitter.assign_treatment_df(df)
+    print(df)
+    ```
+    """
+
+    def __init__(
+        self,
+        treatments: Optional[List[str]] = None,
+        treatment_col: str = "treatment",
+    ) -> None:
+        self.treatments = treatments or ["A", "B"]
+        self.treatment_col = treatment_col
+
+    def assign_treatment_df(
+        self,
+        df: pd.DataFrame,
+    ) -> pd.DataFrame:
+        df = df.copy()
+
+        dfs = []
+        for treatment in self.treatments:
+            df_treat = df.copy().assign(**{self.treatment_col: treatment})
+            dfs.append(df_treat)
+
+        return pd.concat(dfs).reset_index(drop=True)
+
+    @classmethod
+    def from_config(cls, config):
+        """Creates a RepeatedSampler from a PowerConfig"""
+        return cls(
+            treatments=config.treatments,
+            treatment_col=config.treatment_col,
+        )
+
+
+ + + +
+ + + + + + + + + + +
+ + + +

+assign_treatment_df(self, df) + + +

+ +
+ +

Takes a df, randomizes treatments and adds the treatment column to the dataframe

+ +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
dfDataFrame

dataframe to assign treatments to

required
+
+ Source code in cluster_experiments/random_splitter.py +
def assign_treatment_df(
+    self,
+    df: pd.DataFrame,
+) -> pd.DataFrame:
+    df = df.copy()
+
+    dfs = []
+    for treatment in self.treatments:
+        df_treat = df.copy().assign(**{self.treatment_col: treatment})
+        dfs.append(df_treat)
+
+    return pd.concat(dfs).reset_index(drop=True)
+
+
+
+ +
+ + + +
+ + + +

+from_config(config) + + + classmethod + + +

+ +
+ +

Creates a RepeatedSampler from a PowerConfig

+ +
+ Source code in cluster_experiments/random_splitter.py +
@classmethod
+def from_config(cls, config):
+    """Creates a RepeatedSampler from a PowerConfig"""
+    return cls(
+        treatments=config.treatments,
+        treatment_col=config.treatment_col,
+    )
+
+
+
+ +
+ + + + + +
+ +
+ +
+ + + +
+ + + +

+ +StratifiedClusteredSplitter (RandomSplitter) + + + + +

+ +
+ +

Splits randomly with clusters, ensuring a balanced allocation of treatment groups across clusters and strata. +To be used, for example, when having days as clusters and days of the week as stratus. This splitter will make sure +that we won't have all Sundays in treatment and no Sundays in control.

+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
cluster_colsOptional[List[str]]

List of columns to use as clusters

None
treatmentsOptional[List[str]]

list of treatments

None
treatment_colstr

Name of the column with the treatment variable.

'treatment'
strata_colsOptional[List[str]]

List of columns to use as strata

None

Usage: +

import pandas as pd
+from cluster_experiments.random_splitter import StratifiedClusteredSplitter
+splitter = StratifiedClusteredSplitter(cluster_cols=["city"],strata_cols=["country"])
+df = pd.DataFrame({"city": ["A", "B", "C","D"], "country":["C1","C2","C2","C1"]})
+df = splitter.assign_treatment_df(df)
+print(df)
+

+ +
+ Source code in cluster_experiments/random_splitter.py +
class StratifiedClusteredSplitter(RandomSplitter):
+    """
+    Splits randomly with clusters, ensuring a balanced allocation of treatment groups across clusters and strata.
+    To be used, for example, when having days as clusters and days of the week as stratus. This splitter will make sure
+    that we won't have all Sundays in treatment and no Sundays in control.
+
+    Arguments:
+        cluster_cols: List of columns to use as clusters
+        treatments: list of treatments
+        treatment_col: Name of the column with the treatment variable.
+        strata_cols: List of columns to use as strata
+
+    Usage:
+    ```python
+    import pandas as pd
+    from cluster_experiments.random_splitter import StratifiedClusteredSplitter
+    splitter = StratifiedClusteredSplitter(cluster_cols=["city"],strata_cols=["country"])
+    df = pd.DataFrame({"city": ["A", "B", "C","D"], "country":["C1","C2","C2","C1"]})
+    df = splitter.assign_treatment_df(df)
+    print(df)
+    ```
+    """
+
+    def __init__(
+        self,
+        cluster_cols: Optional[List[str]] = None,
+        treatments: Optional[List[str]] = None,
+        treatment_col: str = "treatment",
+        strata_cols: Optional[List[str]] = None,
+    ) -> None:
+        super().__init__(
+            cluster_cols=cluster_cols,
+            treatments=treatments,
+            treatment_col=treatment_col,
+        )
+        if not strata_cols or strata_cols == [""]:
+            raise ValueError(
+                f"Splitter {self.__class__.__name__} requires strata_cols,"
+                f" got {strata_cols = }"
+            )
+        self.strata_cols = strata_cols
+
+    def assign_treatment_df(self, df: pd.DataFrame) -> pd.DataFrame:
+        df = df.copy()
+        df_unique_shuffled = (
+            df.loc[:, list(set(self.cluster_cols + self.strata_cols))]
+            .drop_duplicates()
+            .sample(frac=1)
+            .reset_index(drop=True)
+        )
+
+        # check that, for a given cluster, there is only 1 strata
+        for strata_col in self.strata_cols:
+            if (
+                df_unique_shuffled.groupby(self.cluster_cols)[strata_col]
+                .nunique()
+                .max()
+                > 1
+            ):
+                raise ValueError(
+                    f"There are multiple values in {strata_col} for the same cluster item \n"
+                    "You cannot stratify on this column",
+                )
+
+        # random shuffling
+        random_sorted_treatments = list(np.random.permutation(self.treatments))
+
+        df_unique_shuffled[self.treatment_col] = (
+            df_unique_shuffled.groupby(self.strata_cols, as_index=False)
+            .cumcount()
+            .mod(len(random_sorted_treatments))
+            .map(dict(enumerate(random_sorted_treatments)))
+        )
+
+        df = df.merge(
+            df_unique_shuffled, on=self.cluster_cols + self.strata_cols, how="left"
+        )
+
+        return df
+
+    @classmethod
+    def from_config(cls, config):
+        """Creates a StratifiedClusteredSplitter from a PowerConfig"""
+        return cls(
+            treatments=config.treatments,
+            cluster_cols=config.cluster_cols,
+            strata_cols=config.strata_cols,
+            treatment_col=config.treatment_col,
+        )
+
+
+ + + +
+ + + + + + + + + + +
+ + + +

+assign_treatment_df(self, df) + + +

+ +
+ +

Takes a df, randomizes treatments and adds the treatment column to the dataframe

+ +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
dfDataFrame

dataframe to assign treatments to

required
+
+ Source code in cluster_experiments/random_splitter.py +
def assign_treatment_df(self, df: pd.DataFrame) -> pd.DataFrame:
+    df = df.copy()
+    df_unique_shuffled = (
+        df.loc[:, list(set(self.cluster_cols + self.strata_cols))]
+        .drop_duplicates()
+        .sample(frac=1)
+        .reset_index(drop=True)
+    )
+
+    # check that, for a given cluster, there is only 1 strata
+    for strata_col in self.strata_cols:
+        if (
+            df_unique_shuffled.groupby(self.cluster_cols)[strata_col]
+            .nunique()
+            .max()
+            > 1
+        ):
+            raise ValueError(
+                f"There are multiple values in {strata_col} for the same cluster item \n"
+                "You cannot stratify on this column",
+            )
+
+    # random shuffling
+    random_sorted_treatments = list(np.random.permutation(self.treatments))
+
+    df_unique_shuffled[self.treatment_col] = (
+        df_unique_shuffled.groupby(self.strata_cols, as_index=False)
+        .cumcount()
+        .mod(len(random_sorted_treatments))
+        .map(dict(enumerate(random_sorted_treatments)))
+    )
+
+    df = df.merge(
+        df_unique_shuffled, on=self.cluster_cols + self.strata_cols, how="left"
+    )
+
+    return df
+
+
+
+ +
+ + + +
+ + + +

+from_config(config) + + + classmethod + + +

+ +
+ +

Creates a StratifiedClusteredSplitter from a PowerConfig

+ +
+ Source code in cluster_experiments/random_splitter.py +
@classmethod
+def from_config(cls, config):
+    """Creates a StratifiedClusteredSplitter from a PowerConfig"""
+    return cls(
+        treatments=config.treatments,
+        cluster_cols=config.cluster_cols,
+        strata_cols=config.strata_cols,
+        treatment_col=config.treatment_col,
+    )
+
+
+
+ +
+ + + + + +
+ +
+ +
+ + + +
+ + + +

+ +StratifiedSwitchbackSplitter (StratifiedClusteredSplitter, SwitchbackSplitter) + + + + +

+ +
+ +

Splits randomly with clusters, ensuring a balanced allocation of treatment groups across clusters and strata. +To be used, for example, when having days as clusters and days of the week as stratus. This splitter will make sure +that we won't have all Sundays in treatment and no Sundays in control.

+

It can be created using the time_col and switch_frequency arguments, just like the SwitchbackSplitter.

+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
time_colstr

Name of the column with the time variable.

'date'
switch_frequencystr

Frequency of the switchback. Must be a string (e.g. "1D")

'1D'
cluster_colsOptional[List[str]]

List of columns to use as clusters

None
treatmentsOptional[List[str]]

list of treatments

None
treatment_colstr

Name of the column with the treatment variable.

'treatment'
splitter_weightsOptional[List[float]]

List of weights for the treatments. If None, all treatments will have the same weight.

None
strata_colsOptional[List[str]]

List of columns to use as strata

None

Usage: +

import pandas as pd
+from cluster_experiments.random_splitter import StratifiedSwitchbackSplitter
+splitter = StratifiedSwitchbackSplitter(time_col="date",switch_frequency="1D",strata_cols=["country"], cluster_cols=["country", "date"])
+df = pd.DataFrame({"date": ["2020-01-01", "2020-01-02", "2020-01-03","2020-01-04"], "country":["C1","C2","C2","C1"]})
+df = splitter.assign_treatment_df(df)
+print(df)
+

+ +
+ Source code in cluster_experiments/random_splitter.py +
class StratifiedSwitchbackSplitter(StratifiedClusteredSplitter, SwitchbackSplitter):
+    """
+    Splits randomly with clusters, ensuring a balanced allocation of treatment groups across clusters and strata.
+    To be used, for example, when having days as clusters and days of the week as stratus. This splitter will make sure
+    that we won't have all Sundays in treatment and no Sundays in control.
+
+    It can be created using the time_col and switch_frequency arguments, just like the SwitchbackSplitter.
+
+    Arguments:
+        time_col: Name of the column with the time variable.
+        switch_frequency: Frequency of the switchback. Must be a string (e.g. "1D")
+        cluster_cols: List of columns to use as clusters
+        treatments: list of treatments
+        treatment_col: Name of the column with the treatment variable.
+        splitter_weights: List of weights for the treatments. If None, all treatments will have the same weight.
+        strata_cols: List of columns to use as strata
+
+    Usage:
+    ```python
+    import pandas as pd
+    from cluster_experiments.random_splitter import StratifiedSwitchbackSplitter
+    splitter = StratifiedSwitchbackSplitter(time_col="date",switch_frequency="1D",strata_cols=["country"], cluster_cols=["country", "date"])
+    df = pd.DataFrame({"date": ["2020-01-01", "2020-01-02", "2020-01-03","2020-01-04"], "country":["C1","C2","C2","C1"]})
+    df = splitter.assign_treatment_df(df)
+    print(df)
+    ```
+    """
+
+    def __init__(
+        self,
+        time_col: str = "date",
+        switch_frequency: str = "1D",
+        cluster_cols: Optional[List[str]] = None,
+        treatments: Optional[List[str]] = None,
+        treatment_col: str = "treatment",
+        splitter_weights: Optional[List[float]] = None,
+        washover: Optional[Washover] = None,
+        strata_cols: Optional[List[str]] = None,
+    ) -> None:
+        # Inherit init from SwitchbackSplitter
+        SwitchbackSplitter.__init__(
+            self,
+            time_col=time_col,
+            switch_frequency=switch_frequency,
+            cluster_cols=cluster_cols,
+            treatments=treatments,
+            treatment_col=treatment_col,
+            splitter_weights=splitter_weights,
+            washover=washover,
+        )
+        self.strata_cols = strata_cols or ["strata"]
+
+    def assign_treatment_df(self, df: pd.DataFrame) -> pd.DataFrame:
+        df = df.copy()
+        df = self._prepare_switchback_df(df)
+        df = StratifiedClusteredSplitter.assign_treatment_df(self, df)
+        return self.washover.washover(
+            df=df,
+            treatment_col=self.treatment_col,
+            truncated_time_col=self.time_col,
+            cluster_cols=self.cluster_cols,
+        )
+
+    @classmethod
+    def from_config(cls, config) -> "StratifiedSwitchbackSplitter":
+        """Creates a StratifiedSwitchbackSplitter from a PowerConfig"""
+        washover_cls = _get_mapping_key(washover_mapping, config.washover)
+        return cls(
+            treatments=config.treatments,
+            cluster_cols=config.cluster_cols,
+            strata_cols=config.strata_cols,
+            treatment_col=config.treatment_col,
+            time_col=config.time_col,
+            switch_frequency=config.switch_frequency,
+            splitter_weights=config.splitter_weights,
+            washover=washover_cls.from_config(config),
+        )
+
+
+ + + +
+ + + + + + + + + + +
+ + + +

+assign_treatment_df(self, df) + + +

+ +
+ +

Creates the switchback column, adds it to cluster_cols and then calls ClusteredSplitter assign_treatment_df

+ +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
dfDataFrame

dataframe to assign treatments to

required
+
+ Source code in cluster_experiments/random_splitter.py +
def assign_treatment_df(self, df: pd.DataFrame) -> pd.DataFrame:
+    df = df.copy()
+    df = self._prepare_switchback_df(df)
+    df = StratifiedClusteredSplitter.assign_treatment_df(self, df)
+    return self.washover.washover(
+        df=df,
+        treatment_col=self.treatment_col,
+        truncated_time_col=self.time_col,
+        cluster_cols=self.cluster_cols,
+    )
+
+
+
+ +
+ + + +
+ + + +

+from_config(config) + + + classmethod + + +

+ +
+ +

Creates a StratifiedSwitchbackSplitter from a PowerConfig

+ +
+ Source code in cluster_experiments/random_splitter.py +
@classmethod
+def from_config(cls, config) -> "StratifiedSwitchbackSplitter":
+    """Creates a StratifiedSwitchbackSplitter from a PowerConfig"""
+    washover_cls = _get_mapping_key(washover_mapping, config.washover)
+    return cls(
+        treatments=config.treatments,
+        cluster_cols=config.cluster_cols,
+        strata_cols=config.strata_cols,
+        treatment_col=config.treatment_col,
+        time_col=config.time_col,
+        switch_frequency=config.switch_frequency,
+        splitter_weights=config.splitter_weights,
+        washover=washover_cls.from_config(config),
+    )
+
+
+
+ +
+ + + + + +
+ +
+ +
+ + + +
+ + + +

+ +SwitchbackSplitter (ClusteredSplitter) + + + + +

+ +
+ +

Splits randomly using clusters and time column

+

It is a clustered splitter but one of the cluster columns is obtained by truncating the time column to the switch frequency.

+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
time_colOptional[str]

Name of the column with the time variable.

None
switch_frequencyOptional[str]

Frequency to switch treatments. Uses pandas frequency aliases (https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases)

None
cluster_colsOptional[List[str]]

List of columns to use as clusters

None
treatmentsOptional[List[str]]

list of treatments

None
treatment_colstr

Name of the column with the treatment variable.

'treatment'
splitter_weightsOptional[List[float]]

weights to use for the splitter, should have the same length as treatments, each weight should correspond to an element in treatments

None

Usage: +

import pandas as pd
+from cluster_experiments.random_splitter import SwitchbackSplitter
+splitter = SwitchbackSplitter(time_col="date", switch_frequency="1D", cluster_cols=["date"])
+df = pd.DataFrame({"date": pd.date_range("2020-01-01", "2020-01-03")})
+df = splitter.assign_treatment_df(df)
+print(df)
+

+ +
+ Source code in cluster_experiments/random_splitter.py +
class SwitchbackSplitter(ClusteredSplitter):
+    """
+    Splits randomly using clusters and time column
+
+    It is a clustered splitter but one of the cluster columns is obtained by truncating the time column to the switch frequency.
+
+    Arguments:
+        time_col: Name of the column with the time variable.
+        switch_frequency: Frequency to switch treatments. Uses pandas frequency aliases (https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases)
+        cluster_cols: List of columns to use as clusters
+        treatments: list of treatments
+        treatment_col: Name of the column with the treatment variable.
+        splitter_weights: weights to use for the splitter, should have the same length as treatments, each weight should correspond to an element in treatments
+
+    Usage:
+    ```python
+    import pandas as pd
+    from cluster_experiments.random_splitter import SwitchbackSplitter
+    splitter = SwitchbackSplitter(time_col="date", switch_frequency="1D", cluster_cols=["date"])
+    df = pd.DataFrame({"date": pd.date_range("2020-01-01", "2020-01-03")})
+    df = splitter.assign_treatment_df(df)
+    print(df)
+    ```
+    """
+
+    def __init__(
+        self,
+        time_col: Optional[str] = None,
+        switch_frequency: Optional[str] = None,
+        cluster_cols: Optional[List[str]] = None,
+        treatments: Optional[List[str]] = None,
+        treatment_col: str = "treatment",
+        splitter_weights: Optional[List[float]] = None,
+        washover: Optional[Washover] = None,
+    ) -> None:
+        self.time_col = time_col or "date"
+        self.switch_frequency = switch_frequency or "1D"
+        self.cluster_cols = cluster_cols or []
+        self.treatments = treatments or ["A", "B"]
+        self.treatment_col = treatment_col
+        self.splitter_weights = splitter_weights
+        self.washover = washover or EmptyWashover()
+        self._check_clusters()
+
+    def _check_clusters(self):
+        """Check if time_col is in cluster_cols"""
+        assert (
+            self.time_col in self.cluster_cols
+        ), "in switchback splitters, time_col must be in cluster_cols"
+
+    def _get_time_col_cluster(self, df: pd.DataFrame) -> pd.Series:
+        df = df.copy()
+        df[self.time_col] = pd.to_datetime(df[self.time_col])
+        # Given the switch frequency, truncate the time column to the switch frequency
+        # Using pandas frequency aliases: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases
+        if "W" in self.switch_frequency or "M" in self.switch_frequency:
+            return df[self.time_col].dt.to_period(self.switch_frequency).dt.start_time
+        return df[self.time_col].dt.floor(self.switch_frequency)
+
+    def _prepare_switchback_df(self, df: pd.DataFrame) -> pd.DataFrame:
+        df = df.copy()
+        # Build time_col switchback column
+        # Overwriting column, this is the worst! If we use the column as a covariate, we're screwed. Needs improvement
+        df[_original_time_column(self.time_col)] = df[self.time_col]
+        df[self.time_col] = self._get_time_col_cluster(df)
+        return df
+
+    def assign_treatment_df(
+        self,
+        df: pd.DataFrame,
+    ) -> pd.DataFrame:
+        """
+        Creates the switchback column, adds it to cluster_cols and then calls ClusteredSplitter assign_treatment_df
+
+        Arguments:
+            df: dataframe to assign treatments to
+        """
+        df = df.copy()
+        df = self._prepare_switchback_df(df)
+        df = super().assign_treatment_df(df)
+        df = self.washover.washover(
+            df,
+            truncated_time_col=self.time_col,
+            treatment_col=self.treatment_col,
+            cluster_cols=self.cluster_cols,
+        )
+        return df
+
+    @classmethod
+    def from_config(cls, config) -> "SwitchbackSplitter":
+        washover_cls = _get_mapping_key(washover_mapping, config.washover)
+        return cls(
+            time_col=config.time_col,
+            switch_frequency=config.switch_frequency,
+            cluster_cols=config.cluster_cols,
+            treatments=config.treatments,
+            treatment_col=config.treatment_col,
+            splitter_weights=config.splitter_weights,
+            washover=washover_cls.from_config(config),
+        )
+
+
+ + + +
+ + + + + + + + + + +
+ + + +

+assign_treatment_df(self, df) + + +

+ +
+ +

Creates the switchback column, adds it to cluster_cols and then calls ClusteredSplitter assign_treatment_df

+ +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
dfDataFrame

dataframe to assign treatments to

required
+
+ Source code in cluster_experiments/random_splitter.py +
def assign_treatment_df(
+    self,
+    df: pd.DataFrame,
+) -> pd.DataFrame:
+    """
+    Creates the switchback column, adds it to cluster_cols and then calls ClusteredSplitter assign_treatment_df
+
+    Arguments:
+        df: dataframe to assign treatments to
+    """
+    df = df.copy()
+    df = self._prepare_switchback_df(df)
+    df = super().assign_treatment_df(df)
+    df = self.washover.washover(
+        df,
+        truncated_time_col=self.time_col,
+        treatment_col=self.treatment_col,
+        cluster_cols=self.cluster_cols,
+    )
+    return df
+
+
+
+ +
+ + + +
+ + + +

+from_config(config) + + + classmethod + + +

+ +
+ +

Creates a RandomSplitter from a PowerConfig

+ +
+ Source code in cluster_experiments/random_splitter.py +
@classmethod
+def from_config(cls, config) -> "SwitchbackSplitter":
+    washover_cls = _get_mapping_key(washover_mapping, config.washover)
+    return cls(
+        time_col=config.time_col,
+        switch_frequency=config.switch_frequency,
+        cluster_cols=config.cluster_cols,
+        treatments=config.treatments,
+        treatment_col=config.treatment_col,
+        splitter_weights=config.splitter_weights,
+        washover=washover_cls.from_config(config),
+    )
+
+
+
+ +
+ + + + + +
+ +
+ +
+ + + + + + + +
+ +
+ +
+ + + + + + +
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+ + +
+ +
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+
+
+
+ + + + + + + + + \ No newline at end of file diff --git a/api/variant.html b/api/variant.html new file mode 100644 index 00000000..c8dbddb3 --- /dev/null +++ b/api/variant.html @@ -0,0 +1,1121 @@ + + + + + + + + + + + + + + + + + + + + + + Variant - Cluster Experiments Docs + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
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+ + + + + + + + + +

from cluster_experiments.inference.variant import *

+ + +
+ + +
+ + + + +
+ + + + + + + +
+ + + +

+ +Variant + + + + dataclass + + +

+ +
+ +

A class used to represent a Variant with a name and a control flag.

+

Attributes

+

name : str + The name of the variant +is_control : bool + A boolean indicating if the variant is a control variant

+ +
+ Source code in cluster_experiments/inference/variant.py +
class Variant:
+    """
+    A class used to represent a Variant with a name and a control flag.
+
+    Attributes
+    ----------
+    name : str
+        The name of the variant
+    is_control : bool
+        A boolean indicating if the variant is a control variant
+    """
+
+    name: str
+    is_control: bool
+
+    def __post_init__(self):
+        """
+        Validates the inputs after initialization.
+        """
+        self._validate_inputs()
+
+    def _validate_inputs(self):
+        """
+        Validates the inputs for the Variant class.
+
+        Raises
+        ------
+        TypeError
+            If the name is not a string or if is_control is not a boolean.
+        """
+        if not isinstance(self.name, str):
+            raise TypeError("Variant name must be a string")
+        if not isinstance(self.is_control, bool):
+            raise TypeError("Variant is_control must be a boolean")
+
+
+ + + +
+ + + + + + + + + + + + + +
+ + + +

+__post_init__(self) + + + special + + +

+ +
+ +

Validates the inputs after initialization.

+ +
+ Source code in cluster_experiments/inference/variant.py +
def __post_init__(self):
+    """
+    Validates the inputs after initialization.
+    """
+    self._validate_inputs()
+
+
+
+ +
+ + + + + + +
+ +
+ +
+ + + + + + + +
+ +
+ +
+ + + + + + +
+
+ + +
+ +
+ + + +
+
+
+
+ + + + + + + + + \ No newline at end of file diff --git a/api/washover.html b/api/washover.html new file mode 100644 index 00000000..5bcc454d --- /dev/null +++ b/api/washover.html @@ -0,0 +1,1866 @@ + + + + + + + + + + + + + + + + + + + + + + Washover - Cluster Experiments Docs + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + +
+ + +
+ +
+ + + + + + +
+
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+
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+ + + +
+
+ + + + + + + + + +

from cluster_experiments.washover import *

+ + +
+ + +
+ + + + +
+ + + + + + + + +
+ + + +

+ +ConstantWashover (Washover) + + + + +

+ +
+ +

Constant washover - we drop all rows in the washover period when +there is a switch where the treatment is different.

+ +
+ Source code in cluster_experiments/washover.py +
class ConstantWashover(Washover):
+    """Constant washover - we drop all rows in the washover period when
+    there is a switch where the treatment is different."""
+
+    def __init__(self, washover_time_delta: datetime.timedelta):
+        self.washover_time_delta = washover_time_delta
+
+    def washover(
+        self,
+        df: pd.DataFrame,
+        truncated_time_col: str,
+        treatment_col: str,
+        cluster_cols: List[str],
+        original_time_col: Optional[str] = None,
+    ) -> pd.DataFrame:
+        """Constant washover - we drop all rows in the washover period when
+        there is a switch where the treatment is different.
+
+        Args:
+            df (pd.DataFrame): Input dataframe.
+            truncated_time_col (str): Name of the truncated time column.
+            treatment_col (str): Name of the treatment column.
+            cluster_cols (List[str]): List of clusters of experiment.
+            original_time_col (Optional[str], optional): Name of the original time column.
+
+        Returns:
+            pd.DataFrame: Same dataframe as input without the rows in the washover period.
+
+        Usage:
+        ```python
+        import numpy as np
+        import pandas as pd
+        from datetime import datetime, timedelta
+
+        from cluster_experiments import ConstantWashover
+
+        np.random.seed(42)
+
+        num_rows = 10
+
+        def random_timestamp(start_time, end_time):
+            time_delta = end_time - start_time
+            random_seconds = np.random.randint(0, time_delta.total_seconds())
+            return start_time + timedelta(seconds=random_seconds)
+
+        def generate_data(start_time, end_time, treatment):
+            data = {
+                'order_id': np.random.randint(10**9, 10**10, size=num_rows),
+                'city_code': 'VAL',
+                'activation_time_local': [random_timestamp(start_time, end_time) for _ in range(num_rows)],
+                'bin_start_time_local': start_time,
+                'treatment': treatment
+            }
+            return pd.DataFrame(data)
+
+        start_times = [datetime(2024, 1, 22, 9, 0), datetime(2024, 1, 22, 11, 0),
+                    datetime(2024, 1, 22, 13, 0), datetime(2024, 1, 22, 15, 0)]
+
+        treatments = ['control', 'variation', 'variation', 'control']
+
+        dataframes = [generate_data(start, start + timedelta(hours=2), treatment) for start, treatment in zip(start_times, treatments)]
+
+        df = pd.concat(dataframes).sort_values(by='activation_time_local').reset_index(drop=True)
+
+        ## Define washover with 30 min duration
+        washover = ConstantWashover(washover_time_delta=timedelta(minutes=30))
+
+        ## Apply washover to the dataframe, the orders with activation time within the first 30 minutes after every change in the treatment column, clustering by city and 2h time bin, will be dropped
+        df_analysis_washover = washover.washover(
+            df=df,
+            truncated_time_col='bin_start_time_local',
+            treatment_col='treatment',
+            cluster_cols=['city_code','bin_start_time_local'],
+            original_time_col='activation_time_local',
+        )
+        ```
+        """
+        # Set original time column
+        original_time_col = (
+            original_time_col
+            if original_time_col
+            else _original_time_column(truncated_time_col)
+        )
+
+        # Validate columns
+        self._validate_columns(df, truncated_time_col, cluster_cols, original_time_col)
+
+        # Cluster columns that do not involve time
+        non_time_cols = list(set(cluster_cols) - set([truncated_time_col]))
+        # For each cluster, we need to check if treatment has changed wrt last time
+        df_agg = df.sort_values([original_time_col]).copy()
+        df_agg = df_agg.drop_duplicates(subset=cluster_cols + [treatment_col])
+
+        if non_time_cols:
+            df_agg["__changed"] = (
+                df_agg.groupby(non_time_cols)[treatment_col].shift(1)
+                != df_agg[treatment_col]
+            )
+        else:
+            df_agg["__changed"] = (
+                df_agg[treatment_col].shift(1) != df_agg[treatment_col]
+            )
+        df_agg = df_agg.loc[:, cluster_cols + ["__changed"]]
+        return (
+            df.merge(df_agg, on=cluster_cols, how="inner")
+            .assign(
+                __time_since_switch=lambda x: x[original_time_col].astype(
+                    "datetime64[ns]"
+                )
+                - x[truncated_time_col].astype("datetime64[ns]"),
+                __after_washover=lambda x: x["__time_since_switch"]
+                > self.washover_time_delta,
+            )
+            # add not changed in query
+            .query("__after_washover or not __changed")
+            .drop(columns=["__time_since_switch", "__after_washover", "__changed"])
+        )
+
+    @classmethod
+    def from_config(cls, config) -> "Washover":
+        if not config.washover_time_delta:
+            raise ValueError(
+                f"Washover time delta must be specified for ConstantWashover, while it is {config.washover_time_delta = }"
+            )
+
+        washover_time_delta = config.washover_time_delta
+        if isinstance(washover_time_delta, int):
+            washover_time_delta = datetime.timedelta(minutes=config.washover_time_delta)
+        return cls(washover_time_delta=washover_time_delta)
+
+
+ + + +
+ + + + + + + + + + + +
+ + + +

+washover(self, df, truncated_time_col, treatment_col, cluster_cols, original_time_col=None) + + +

+ +
+ +

Constant washover - we drop all rows in the washover period when +there is a switch where the treatment is different.

+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
dfpd.DataFrame

Input dataframe.

required
truncated_time_colstr

Name of the truncated time column.

required
treatment_colstr

Name of the treatment column.

required
cluster_colsList[str]

List of clusters of experiment.

required
original_time_colOptional[str]

Name of the original time column.

None
+

Returns:

+ + + + + + + + + + + + + +
TypeDescription
pd.DataFrame

Same dataframe as input without the rows in the washover period.

Usage: +

import numpy as np
+import pandas as pd
+from datetime import datetime, timedelta
+
+from cluster_experiments import ConstantWashover
+
+np.random.seed(42)
+
+num_rows = 10
+
+def random_timestamp(start_time, end_time):
+    time_delta = end_time - start_time
+    random_seconds = np.random.randint(0, time_delta.total_seconds())
+    return start_time + timedelta(seconds=random_seconds)
+
+def generate_data(start_time, end_time, treatment):
+    data = {
+        'order_id': np.random.randint(10**9, 10**10, size=num_rows),
+        'city_code': 'VAL',
+        'activation_time_local': [random_timestamp(start_time, end_time) for _ in range(num_rows)],
+        'bin_start_time_local': start_time,
+        'treatment': treatment
+    }
+    return pd.DataFrame(data)
+
+start_times = [datetime(2024, 1, 22, 9, 0), datetime(2024, 1, 22, 11, 0),
+            datetime(2024, 1, 22, 13, 0), datetime(2024, 1, 22, 15, 0)]
+
+treatments = ['control', 'variation', 'variation', 'control']
+
+dataframes = [generate_data(start, start + timedelta(hours=2), treatment) for start, treatment in zip(start_times, treatments)]
+
+df = pd.concat(dataframes).sort_values(by='activation_time_local').reset_index(drop=True)
+
+## Define washover with 30 min duration
+washover = ConstantWashover(washover_time_delta=timedelta(minutes=30))
+
+## Apply washover to the dataframe, the orders with activation time within the first 30 minutes after every change in the treatment column, clustering by city and 2h time bin, will be dropped
+df_analysis_washover = washover.washover(
+    df=df,
+    truncated_time_col='bin_start_time_local',
+    treatment_col='treatment',
+    cluster_cols=['city_code','bin_start_time_local'],
+    original_time_col='activation_time_local',
+)
+

+ +
+ Source code in cluster_experiments/washover.py +
def washover(
+    self,
+    df: pd.DataFrame,
+    truncated_time_col: str,
+    treatment_col: str,
+    cluster_cols: List[str],
+    original_time_col: Optional[str] = None,
+) -> pd.DataFrame:
+    """Constant washover - we drop all rows in the washover period when
+    there is a switch where the treatment is different.
+
+    Args:
+        df (pd.DataFrame): Input dataframe.
+        truncated_time_col (str): Name of the truncated time column.
+        treatment_col (str): Name of the treatment column.
+        cluster_cols (List[str]): List of clusters of experiment.
+        original_time_col (Optional[str], optional): Name of the original time column.
+
+    Returns:
+        pd.DataFrame: Same dataframe as input without the rows in the washover period.
+
+    Usage:
+    ```python
+    import numpy as np
+    import pandas as pd
+    from datetime import datetime, timedelta
+
+    from cluster_experiments import ConstantWashover
+
+    np.random.seed(42)
+
+    num_rows = 10
+
+    def random_timestamp(start_time, end_time):
+        time_delta = end_time - start_time
+        random_seconds = np.random.randint(0, time_delta.total_seconds())
+        return start_time + timedelta(seconds=random_seconds)
+
+    def generate_data(start_time, end_time, treatment):
+        data = {
+            'order_id': np.random.randint(10**9, 10**10, size=num_rows),
+            'city_code': 'VAL',
+            'activation_time_local': [random_timestamp(start_time, end_time) for _ in range(num_rows)],
+            'bin_start_time_local': start_time,
+            'treatment': treatment
+        }
+        return pd.DataFrame(data)
+
+    start_times = [datetime(2024, 1, 22, 9, 0), datetime(2024, 1, 22, 11, 0),
+                datetime(2024, 1, 22, 13, 0), datetime(2024, 1, 22, 15, 0)]
+
+    treatments = ['control', 'variation', 'variation', 'control']
+
+    dataframes = [generate_data(start, start + timedelta(hours=2), treatment) for start, treatment in zip(start_times, treatments)]
+
+    df = pd.concat(dataframes).sort_values(by='activation_time_local').reset_index(drop=True)
+
+    ## Define washover with 30 min duration
+    washover = ConstantWashover(washover_time_delta=timedelta(minutes=30))
+
+    ## Apply washover to the dataframe, the orders with activation time within the first 30 minutes after every change in the treatment column, clustering by city and 2h time bin, will be dropped
+    df_analysis_washover = washover.washover(
+        df=df,
+        truncated_time_col='bin_start_time_local',
+        treatment_col='treatment',
+        cluster_cols=['city_code','bin_start_time_local'],
+        original_time_col='activation_time_local',
+    )
+    ```
+    """
+    # Set original time column
+    original_time_col = (
+        original_time_col
+        if original_time_col
+        else _original_time_column(truncated_time_col)
+    )
+
+    # Validate columns
+    self._validate_columns(df, truncated_time_col, cluster_cols, original_time_col)
+
+    # Cluster columns that do not involve time
+    non_time_cols = list(set(cluster_cols) - set([truncated_time_col]))
+    # For each cluster, we need to check if treatment has changed wrt last time
+    df_agg = df.sort_values([original_time_col]).copy()
+    df_agg = df_agg.drop_duplicates(subset=cluster_cols + [treatment_col])
+
+    if non_time_cols:
+        df_agg["__changed"] = (
+            df_agg.groupby(non_time_cols)[treatment_col].shift(1)
+            != df_agg[treatment_col]
+        )
+    else:
+        df_agg["__changed"] = (
+            df_agg[treatment_col].shift(1) != df_agg[treatment_col]
+        )
+    df_agg = df_agg.loc[:, cluster_cols + ["__changed"]]
+    return (
+        df.merge(df_agg, on=cluster_cols, how="inner")
+        .assign(
+            __time_since_switch=lambda x: x[original_time_col].astype(
+                "datetime64[ns]"
+            )
+            - x[truncated_time_col].astype("datetime64[ns]"),
+            __after_washover=lambda x: x["__time_since_switch"]
+            > self.washover_time_delta,
+        )
+        # add not changed in query
+        .query("__after_washover or not __changed")
+        .drop(columns=["__time_since_switch", "__after_washover", "__changed"])
+    )
+
+
+
+ +
+ + + + + +
+ +
+ +
+ + + +
+ + + +

+ +EmptyWashover (Washover) + + + + +

+ +
+ +

No washover - assumes no spill-over effects from one treatment to another.

+ +
+ Source code in cluster_experiments/washover.py +
class EmptyWashover(Washover):
+    """No washover - assumes no spill-over effects from one treatment to another."""
+
+    def washover(
+        self,
+        df: pd.DataFrame,
+        truncated_time_col: str,
+        treatment_col: str,
+        cluster_cols: List[str],
+        original_time_col: Optional[str] = None,
+    ) -> pd.DataFrame:
+        """No washover - returns the same dataframe as input.
+
+        Args:
+            df (pd.DataFrame): Input dataframe.
+            truncated_time_col (str): Name of the truncated time column.
+            treatment_col (str): Name of the treatment column.
+            cluster_cols (List[str]): List of clusters of experiment.
+            original_time_col (Optional[str], optional): Name of the original time column.
+
+        Returns:
+            pd.DataFrame: Same dataframe as input.
+
+        Usage:
+        ```python
+        from cluster_experiments import SwitchbackSplitter
+        from cluster_experiments import EmptyWashover
+
+        washover = EmptyWashover()
+
+        n = 10
+        df = pd.DataFrame(
+            {
+                # Random time each minute in 2022-01-01, length 10
+                "time": pd.date_range("2022-01-01", "2022-01-02", freq="1min")[
+                    np.random.randint(24 * 60, size=n)
+                ],
+                "city": random.choices(["TGN", "NYC", "LON", "REU"], k=n),
+            }
+        )
+
+
+        splitter = SwitchbackSplitter(
+            washover=washover,
+            time_col="time",
+            cluster_cols=["city", "time"],
+            treatment_col="treatment",
+            switch_frequency="30T",
+        )
+
+        out_df = splitter.assign_treatment_df(df=washover_split_df)
+        ```
+        """
+        return df
+
+
+ + + +
+ + + + + + + + + +
+ + + +

+washover(self, df, truncated_time_col, treatment_col, cluster_cols, original_time_col=None) + + +

+ +
+ +

No washover - returns the same dataframe as input.

+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
dfpd.DataFrame

Input dataframe.

required
truncated_time_colstr

Name of the truncated time column.

required
treatment_colstr

Name of the treatment column.

required
cluster_colsList[str]

List of clusters of experiment.

required
original_time_colOptional[str]

Name of the original time column.

None
+

Returns:

+ + + + + + + + + + + + + +
TypeDescription
pd.DataFrame

Same dataframe as input.

Usage: +

from cluster_experiments import SwitchbackSplitter
+from cluster_experiments import EmptyWashover
+
+washover = EmptyWashover()
+
+n = 10
+df = pd.DataFrame(
+    {
+        # Random time each minute in 2022-01-01, length 10
+        "time": pd.date_range("2022-01-01", "2022-01-02", freq="1min")[
+            np.random.randint(24 * 60, size=n)
+        ],
+        "city": random.choices(["TGN", "NYC", "LON", "REU"], k=n),
+    }
+)
+
+
+splitter = SwitchbackSplitter(
+    washover=washover,
+    time_col="time",
+    cluster_cols=["city", "time"],
+    treatment_col="treatment",
+    switch_frequency="30T",
+)
+
+out_df = splitter.assign_treatment_df(df=washover_split_df)
+

+ +
+ Source code in cluster_experiments/washover.py +
def washover(
+    self,
+    df: pd.DataFrame,
+    truncated_time_col: str,
+    treatment_col: str,
+    cluster_cols: List[str],
+    original_time_col: Optional[str] = None,
+) -> pd.DataFrame:
+    """No washover - returns the same dataframe as input.
+
+    Args:
+        df (pd.DataFrame): Input dataframe.
+        truncated_time_col (str): Name of the truncated time column.
+        treatment_col (str): Name of the treatment column.
+        cluster_cols (List[str]): List of clusters of experiment.
+        original_time_col (Optional[str], optional): Name of the original time column.
+
+    Returns:
+        pd.DataFrame: Same dataframe as input.
+
+    Usage:
+    ```python
+    from cluster_experiments import SwitchbackSplitter
+    from cluster_experiments import EmptyWashover
+
+    washover = EmptyWashover()
+
+    n = 10
+    df = pd.DataFrame(
+        {
+            # Random time each minute in 2022-01-01, length 10
+            "time": pd.date_range("2022-01-01", "2022-01-02", freq="1min")[
+                np.random.randint(24 * 60, size=n)
+            ],
+            "city": random.choices(["TGN", "NYC", "LON", "REU"], k=n),
+        }
+    )
+
+
+    splitter = SwitchbackSplitter(
+        washover=washover,
+        time_col="time",
+        cluster_cols=["city", "time"],
+        treatment_col="treatment",
+        switch_frequency="30T",
+    )
+
+    out_df = splitter.assign_treatment_df(df=washover_split_df)
+    ```
+    """
+    return df
+
+
+
+ +
+ + + + + +
+ +
+ +
+ + + +
+ + + +

+ +Washover (ABC) + + + + +

+ +
+ +

Abstract class to model washovers in the switchback splitter.

+ +
+ Source code in cluster_experiments/washover.py +
class Washover(ABC):
+    """Abstract class to model washovers in the switchback splitter."""
+
+    def _validate_columns(
+        self,
+        df: pd.DataFrame,
+        truncated_time_col: str,
+        cluster_cols: List[str],
+        original_time_col: str,
+    ):
+        """Validate that all the columns required for the washover are present in the dataframe.
+
+        Args:
+            df (pd.DataFrame): Input dataframe.
+            truncated_time_col (str): Name of the truncated time column.
+            cluster_cols (List[str]): List of clusters of experiment.
+            original_time_col (str): Name of the original time column.
+
+        Returns:
+            None: This method does not return any data; it only performs validation.
+
+        """
+        if original_time_col not in df.columns:
+            raise ValueError(
+                f"{original_time_col = } is not in the dataframe columns and/or not specified as an input."
+            )
+        if truncated_time_col not in cluster_cols:
+            raise ValueError(f"{truncated_time_col = } is not in the cluster columns.")
+        for col in cluster_cols:
+            if col not in df.columns:
+                raise ValueError(f"{col = } cluster is not in the dataframe columns.")
+
+    @abstractmethod
+    def washover(
+        self,
+        df: pd.DataFrame,
+        truncated_time_col: str,
+        treatment_col: str,
+        cluster_cols: List[str],
+        original_time_col: Optional[str] = None,
+    ) -> pd.DataFrame:
+        """Abstract method to add washvover to the dataframe."""
+
+    @classmethod
+    def from_config(cls, config) -> "Washover":
+        return cls()
+
+
+ + + +
+ + + + + + + + + + +
+ + + +

+washover(self, df, truncated_time_col, treatment_col, cluster_cols, original_time_col=None) + + +

+ +
+ +

Abstract method to add washvover to the dataframe.

+ +
+ Source code in cluster_experiments/washover.py +
@abstractmethod
+def washover(
+    self,
+    df: pd.DataFrame,
+    truncated_time_col: str,
+    treatment_col: str,
+    cluster_cols: List[str],
+    original_time_col: Optional[str] = None,
+) -> pd.DataFrame:
+    """Abstract method to add washvover to the dataframe."""
+
+
+
+ +
+ + + + + +
+ +
+ +
+ + + + + + + +
+ +
+ +
+ + + + + + +
+
+ + +
+ +
+ + + +
+
+
+
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t=this.parentNode;if(t){e.length===0&&t.removeChild(this);for(let r=e.length-1;r>=0;r--){let n=e[r];typeof n!="object"?n=document.createTextNode(n):n.parentNode&&n.parentNode.removeChild(n),r?t.insertBefore(this.previousSibling,n):t.replaceChild(n,this)}}}));document.documentElement.classList.remove("no-js");document.documentElement.classList.add("js");var tt=go(),vr=ko(),gt=jo(),on=Ro(),_e=qo(),gr=Kr("(min-width: 960px)"),Ki=Kr("(min-width: 1220px)"),Qi=Fo(),Yi=le(),Bi=document.forms.namedItem("search")?(__search==null?void 0:__search.index)||je(new URL("search/search_index.json",Yi.base)):Se,an=new S;Ei({alert$:an});Z("navigation.instant")&&Si({document$:tt,location$:vr,viewport$:_e});var qi;((qi=Yi.version)==null?void 0:qi.provider)==="mike"&&Li({document$:tt});A(vr,gt).pipe(ke(125)).subscribe(()=>{Ke("drawer",!1),Ke("search",!1)});on.pipe(x(({mode:e})=>e==="global")).subscribe(e=>{switch(e.type){case"p":case",":let t=pe("[href][rel=prev]");typeof t!="undefined"&&t.click();break;case"n":case".":let r=pe("[href][rel=next]");typeof r!="undefined"&&r.click();break}});Vi({document$:tt,tablet$:gr});Ni({document$:tt});zi({viewport$:_e,tablet$:gr});var Qe=bi(Oe("header"),{viewport$:_e}),br=tt.pipe(m(()=>Oe("main")),w(e=>yi(e,{viewport$:_e,header$:Qe})),X(1)),Ws=A(...ne("consent").map(e=>Yo(e,{target$:gt})),...ne("dialog").map(e=>hi(e,{alert$:an})),...ne("header").map(e=>vi(e,{viewport$:_e,header$:Qe,main$:br})),...ne("palette").map(e=>xi(e)),...ne("search").map(e=>Hi(e,{index$:Bi,keyboard$:on})),...ne("source").map(e=>Fi(e))),Vs=P(()=>A(...ne("announce").map(e=>Qo(e)),...ne("content").map(e=>di(e,{target$:gt,print$:Qi})),...ne("content").map(e=>Z("search.highlight")?Pi(e,{index$:Bi,location$:vr}):H),...ne("header-title").map(e=>gi(e,{viewport$:_e,header$:Qe})),...ne("sidebar").map(e=>e.getAttribute("data-md-type")==="navigation"?Qr(Ki,()=>nn(e,{viewport$:_e,header$:Qe,main$:br})):Qr(gr,()=>nn(e,{viewport$:_e,header$:Qe,main$:br}))),...ne("tabs").map(e=>Ui(e,{viewport$:_e,header$:Qe})),...ne("toc").map(e=>Di(e,{viewport$:_e,header$:Qe,target$:gt})),...ne("top").map(e=>Wi(e,{viewport$:_e,header$:Qe,main$:br,target$:gt})))),Gi=tt.pipe(w(()=>Vs),et(Ws),X(1));Gi.subscribe();window.document$=tt;window.location$=vr;window.target$=gt;window.keyboard$=on;window.viewport$=_e;window.tablet$=gr;window.screen$=Ki;window.print$=Qi;window.alert$=an;window.component$=Gi;})(); 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"sourcesContent": ["(function (global, factory) {\n typeof exports === 'object' && typeof module !== 'undefined' ? factory() :\n typeof define === 'function' && define.amd ? define(factory) :\n (factory());\n}(this, (function () { 'use strict';\n\n /**\n * Applies the :focus-visible polyfill at the given scope.\n * A scope in this case is either the top-level Document or a Shadow Root.\n *\n * @param {(Document|ShadowRoot)} scope\n * @see https://github.com/WICG/focus-visible\n */\n function applyFocusVisiblePolyfill(scope) {\n var hadKeyboardEvent = true;\n var hadFocusVisibleRecently = false;\n var hadFocusVisibleRecentlyTimeout = null;\n\n var inputTypesAllowlist = {\n text: true,\n search: true,\n url: true,\n tel: true,\n email: true,\n password: true,\n number: true,\n date: true,\n month: true,\n week: true,\n time: true,\n datetime: true,\n 'datetime-local': true\n };\n\n /**\n * Helper function for legacy browsers and iframes which sometimes focus\n * elements like document, body, and non-interactive SVG.\n * @param {Element} el\n */\n function isValidFocusTarget(el) {\n if (\n el &&\n el !== document &&\n el.nodeName !== 'HTML' &&\n el.nodeName !== 'BODY' &&\n 'classList' in el &&\n 'contains' in el.classList\n ) {\n return true;\n }\n return false;\n }\n\n /**\n * Computes whether the given element should automatically trigger the\n * `focus-visible` class being added, i.e. whether it should always match\n * `:focus-visible` when focused.\n * @param {Element} el\n * @return {boolean}\n */\n function focusTriggersKeyboardModality(el) {\n var type = el.type;\n var tagName = el.tagName;\n\n if (tagName === 'INPUT' && inputTypesAllowlist[type] && !el.readOnly) {\n return true;\n }\n\n if (tagName === 'TEXTAREA' && !el.readOnly) {\n return true;\n }\n\n if (el.isContentEditable) {\n return true;\n }\n\n return false;\n }\n\n /**\n * Add the `focus-visible` class to the given element if it was not added by\n * the author.\n * @param {Element} el\n */\n function addFocusVisibleClass(el) {\n if (el.classList.contains('focus-visible')) {\n return;\n }\n el.classList.add('focus-visible');\n el.setAttribute('data-focus-visible-added', '');\n }\n\n /**\n * Remove the `focus-visible` class from the given element if it was not\n * originally added by the author.\n * @param {Element} el\n */\n function removeFocusVisibleClass(el) {\n if (!el.hasAttribute('data-focus-visible-added')) {\n return;\n }\n el.classList.remove('focus-visible');\n el.removeAttribute('data-focus-visible-added');\n }\n\n /**\n * If the most recent user interaction was via the keyboard;\n * and the key press did not include a meta, alt/option, or control key;\n * then the modality is keyboard. Otherwise, the modality is not keyboard.\n * Apply `focus-visible` to any current active element and keep track\n * of our keyboard modality state with `hadKeyboardEvent`.\n * @param {KeyboardEvent} e\n */\n function onKeyDown(e) {\n if (e.metaKey || e.altKey || e.ctrlKey) {\n return;\n }\n\n if (isValidFocusTarget(scope.activeElement)) {\n addFocusVisibleClass(scope.activeElement);\n }\n\n hadKeyboardEvent = true;\n }\n\n /**\n * If at any point a user clicks with a pointing device, ensure that we change\n * the modality away from keyboard.\n * This avoids the situation where a user presses a key on an already focused\n * element, and then clicks on a different element, focusing it with a\n * pointing device, while we still think we're in keyboard modality.\n * @param {Event} e\n */\n function onPointerDown(e) {\n hadKeyboardEvent = false;\n }\n\n /**\n * On `focus`, add the `focus-visible` class to the target if:\n * - the target received focus as a result of keyboard navigation, or\n * - the event target is an element that will likely require interaction\n * via the keyboard (e.g. a text box)\n * @param {Event} e\n */\n function onFocus(e) {\n // Prevent IE from focusing the document or HTML element.\n if (!isValidFocusTarget(e.target)) {\n return;\n }\n\n if (hadKeyboardEvent || focusTriggersKeyboardModality(e.target)) {\n addFocusVisibleClass(e.target);\n }\n }\n\n /**\n * On `blur`, remove the `focus-visible` class from the target.\n * @param {Event} e\n */\n function onBlur(e) {\n if (!isValidFocusTarget(e.target)) {\n return;\n }\n\n if (\n e.target.classList.contains('focus-visible') ||\n e.target.hasAttribute('data-focus-visible-added')\n ) {\n // To detect a tab/window switch, we look for a blur event followed\n // rapidly by a visibility change.\n // If we don't see a visibility change within 100ms, it's probably a\n // regular focus change.\n hadFocusVisibleRecently = true;\n window.clearTimeout(hadFocusVisibleRecentlyTimeout);\n hadFocusVisibleRecentlyTimeout = window.setTimeout(function() {\n hadFocusVisibleRecently = false;\n }, 100);\n removeFocusVisibleClass(e.target);\n }\n }\n\n /**\n * If the user changes tabs, keep track of whether or not the previously\n * focused element had .focus-visible.\n * @param {Event} e\n */\n function onVisibilityChange(e) {\n if (document.visibilityState === 'hidden') {\n // If the tab becomes active again, the browser will handle calling focus\n // on the element (Safari actually calls it twice).\n // If this tab change caused a blur on an element with focus-visible,\n // re-apply the class when the user switches back to the tab.\n if (hadFocusVisibleRecently) {\n hadKeyboardEvent = true;\n }\n addInitialPointerMoveListeners();\n }\n }\n\n /**\n * Add a group of listeners to detect usage of any pointing devices.\n * These listeners will be added when the polyfill first loads, and anytime\n * the window is blurred, so that they are active when the window regains\n * focus.\n */\n function addInitialPointerMoveListeners() {\n document.addEventListener('mousemove', onInitialPointerMove);\n document.addEventListener('mousedown', onInitialPointerMove);\n document.addEventListener('mouseup', onInitialPointerMove);\n document.addEventListener('pointermove', onInitialPointerMove);\n document.addEventListener('pointerdown', onInitialPointerMove);\n document.addEventListener('pointerup', onInitialPointerMove);\n document.addEventListener('touchmove', onInitialPointerMove);\n document.addEventListener('touchstart', onInitialPointerMove);\n document.addEventListener('touchend', onInitialPointerMove);\n }\n\n function removeInitialPointerMoveListeners() {\n document.removeEventListener('mousemove', onInitialPointerMove);\n document.removeEventListener('mousedown', onInitialPointerMove);\n document.removeEventListener('mouseup', onInitialPointerMove);\n document.removeEventListener('pointermove', onInitialPointerMove);\n document.removeEventListener('pointerdown', onInitialPointerMove);\n document.removeEventListener('pointerup', onInitialPointerMove);\n document.removeEventListener('touchmove', onInitialPointerMove);\n document.removeEventListener('touchstart', onInitialPointerMove);\n document.removeEventListener('touchend', onInitialPointerMove);\n }\n\n /**\n * When the polfyill first loads, assume the user is in keyboard modality.\n * If any event is received from a pointing device (e.g. mouse, pointer,\n * touch), turn off keyboard modality.\n * This accounts for situations where focus enters the page from the URL bar.\n * @param {Event} e\n */\n function onInitialPointerMove(e) {\n // Work around a Safari quirk that fires a mousemove on whenever the\n // window blurs, even if you're tabbing out of the page. \u00AF\\_(\u30C4)_/\u00AF\n if (e.target.nodeName && e.target.nodeName.toLowerCase() === 'html') {\n return;\n }\n\n hadKeyboardEvent = false;\n removeInitialPointerMoveListeners();\n }\n\n // For some kinds of state, we are interested in changes at the global scope\n // only. For example, global pointer input, global key presses and global\n // visibility change should affect the state at every scope:\n document.addEventListener('keydown', onKeyDown, true);\n document.addEventListener('mousedown', onPointerDown, true);\n document.addEventListener('pointerdown', onPointerDown, true);\n document.addEventListener('touchstart', onPointerDown, true);\n document.addEventListener('visibilitychange', onVisibilityChange, true);\n\n addInitialPointerMoveListeners();\n\n // For focus and blur, we specifically care about state changes in the local\n // scope. This is because focus / blur events that originate from within a\n // shadow root are not re-dispatched from the host element if it was already\n // the active element in its own scope:\n scope.addEventListener('focus', onFocus, true);\n scope.addEventListener('blur', onBlur, true);\n\n // We detect that a node is a ShadowRoot by ensuring that it is a\n // DocumentFragment and also has a host property. This check covers native\n // implementation and polyfill implementation transparently. If we only cared\n // about the native implementation, we could just check if the scope was\n // an instance of a ShadowRoot.\n if (scope.nodeType === Node.DOCUMENT_FRAGMENT_NODE && scope.host) {\n // Since a ShadowRoot is a special kind of DocumentFragment, it does not\n // have a root element to add a class to. So, we add this attribute to the\n // host element instead:\n scope.host.setAttribute('data-js-focus-visible', '');\n } else if (scope.nodeType === Node.DOCUMENT_NODE) {\n document.documentElement.classList.add('js-focus-visible');\n document.documentElement.setAttribute('data-js-focus-visible', '');\n }\n }\n\n // It is important to wrap all references to global window and document in\n // these checks to support server-side rendering use cases\n // @see https://github.com/WICG/focus-visible/issues/199\n if (typeof window !== 'undefined' && typeof document !== 'undefined') {\n // Make the polyfill helper globally available. This can be used as a signal\n // to interested libraries that wish to coordinate with the polyfill for e.g.,\n // applying the polyfill to a shadow root:\n window.applyFocusVisiblePolyfill = applyFocusVisiblePolyfill;\n\n // Notify interested libraries of the polyfill's presence, in case the\n // polyfill was loaded lazily:\n var event;\n\n try {\n event = new CustomEvent('focus-visible-polyfill-ready');\n } catch (error) {\n // IE11 does not support using CustomEvent as a constructor directly:\n event = document.createEvent('CustomEvent');\n event.initCustomEvent('focus-visible-polyfill-ready', false, false, {});\n }\n\n window.dispatchEvent(event);\n }\n\n if (typeof document !== 'undefined') {\n // Apply the polyfill to the global document, so that no JavaScript\n // coordination is required to use the polyfill in the top-level document:\n applyFocusVisiblePolyfill(document);\n }\n\n})));\n", "(function(global) {\r\n /**\r\n * Polyfill URLSearchParams\r\n *\r\n * Inspired from : https://github.com/WebReflection/url-search-params/blob/master/src/url-search-params.js\r\n */\r\n\r\n var checkIfIteratorIsSupported = function() {\r\n try {\r\n return !!Symbol.iterator;\r\n } catch (error) {\r\n return false;\r\n }\r\n };\r\n\r\n\r\n var iteratorSupported = checkIfIteratorIsSupported();\r\n\r\n var createIterator = function(items) {\r\n var iterator = {\r\n next: function() {\r\n var value = items.shift();\r\n return { done: value === void 0, value: value };\r\n }\r\n };\r\n\r\n if (iteratorSupported) {\r\n iterator[Symbol.iterator] = function() {\r\n return iterator;\r\n };\r\n }\r\n\r\n return iterator;\r\n };\r\n\r\n /**\r\n * Search param name and values should be encoded according to https://url.spec.whatwg.org/#urlencoded-serializing\r\n * encodeURIComponent() produces the same result except encoding spaces as `%20` instead of `+`.\r\n */\r\n var serializeParam = function(value) {\r\n return encodeURIComponent(value).replace(/%20/g, '+');\r\n };\r\n\r\n var deserializeParam = function(value) {\r\n return decodeURIComponent(String(value).replace(/\\+/g, ' '));\r\n };\r\n\r\n var polyfillURLSearchParams = function() {\r\n\r\n var URLSearchParams = function(searchString) {\r\n Object.defineProperty(this, '_entries', { writable: true, value: {} });\r\n var typeofSearchString = typeof searchString;\r\n\r\n if (typeofSearchString === 'undefined') {\r\n // do nothing\r\n } else if (typeofSearchString === 'string') {\r\n if (searchString !== '') {\r\n this._fromString(searchString);\r\n }\r\n } else if (searchString instanceof URLSearchParams) {\r\n var _this = this;\r\n searchString.forEach(function(value, name) {\r\n _this.append(name, value);\r\n });\r\n } else if ((searchString !== null) && (typeofSearchString === 'object')) {\r\n if (Object.prototype.toString.call(searchString) === '[object Array]') {\r\n for (var i = 0; i < searchString.length; i++) {\r\n var entry = searchString[i];\r\n if ((Object.prototype.toString.call(entry) === '[object Array]') || (entry.length !== 2)) {\r\n this.append(entry[0], entry[1]);\r\n } else {\r\n throw new TypeError('Expected [string, any] as entry at index ' + i + ' of URLSearchParams\\'s input');\r\n }\r\n }\r\n } else {\r\n for (var key in searchString) {\r\n if (searchString.hasOwnProperty(key)) {\r\n this.append(key, searchString[key]);\r\n }\r\n }\r\n }\r\n } else {\r\n throw new TypeError('Unsupported input\\'s type for URLSearchParams');\r\n }\r\n };\r\n\r\n var proto = URLSearchParams.prototype;\r\n\r\n proto.append = function(name, value) {\r\n if (name in this._entries) {\r\n this._entries[name].push(String(value));\r\n } else {\r\n this._entries[name] = [String(value)];\r\n }\r\n };\r\n\r\n proto.delete = function(name) {\r\n delete this._entries[name];\r\n };\r\n\r\n proto.get = function(name) {\r\n return (name in this._entries) ? this._entries[name][0] : null;\r\n };\r\n\r\n proto.getAll = function(name) {\r\n return (name in this._entries) ? this._entries[name].slice(0) : [];\r\n };\r\n\r\n proto.has = function(name) {\r\n return (name in this._entries);\r\n };\r\n\r\n proto.set = function(name, value) {\r\n this._entries[name] = [String(value)];\r\n };\r\n\r\n proto.forEach = function(callback, thisArg) {\r\n var entries;\r\n for (var name in this._entries) {\r\n if (this._entries.hasOwnProperty(name)) {\r\n entries = this._entries[name];\r\n for (var i = 0; i < entries.length; i++) {\r\n callback.call(thisArg, entries[i], name, this);\r\n }\r\n }\r\n }\r\n };\r\n\r\n proto.keys = function() {\r\n var items = [];\r\n this.forEach(function(value, name) {\r\n items.push(name);\r\n });\r\n return createIterator(items);\r\n };\r\n\r\n proto.values = function() {\r\n var items = [];\r\n this.forEach(function(value) {\r\n items.push(value);\r\n });\r\n return createIterator(items);\r\n };\r\n\r\n proto.entries = function() {\r\n var items = [];\r\n this.forEach(function(value, name) {\r\n items.push([name, value]);\r\n });\r\n return createIterator(items);\r\n };\r\n\r\n if (iteratorSupported) {\r\n proto[Symbol.iterator] = proto.entries;\r\n }\r\n\r\n proto.toString = function() {\r\n var searchArray = [];\r\n this.forEach(function(value, name) {\r\n searchArray.push(serializeParam(name) + '=' + serializeParam(value));\r\n });\r\n return searchArray.join('&');\r\n };\r\n\r\n\r\n global.URLSearchParams = URLSearchParams;\r\n };\r\n\r\n var checkIfURLSearchParamsSupported = function() {\r\n try {\r\n var URLSearchParams = global.URLSearchParams;\r\n\r\n return (\r\n (new URLSearchParams('?a=1').toString() === 'a=1') &&\r\n (typeof URLSearchParams.prototype.set === 'function') &&\r\n (typeof URLSearchParams.prototype.entries === 'function')\r\n );\r\n } catch (e) {\r\n return false;\r\n }\r\n };\r\n\r\n if (!checkIfURLSearchParamsSupported()) {\r\n polyfillURLSearchParams();\r\n }\r\n\r\n var proto = global.URLSearchParams.prototype;\r\n\r\n if (typeof proto.sort !== 'function') {\r\n proto.sort = function() {\r\n var _this = this;\r\n var items = [];\r\n this.forEach(function(value, name) {\r\n items.push([name, value]);\r\n if (!_this._entries) {\r\n _this.delete(name);\r\n }\r\n });\r\n items.sort(function(a, b) {\r\n if (a[0] < b[0]) {\r\n return -1;\r\n } else if (a[0] > b[0]) {\r\n return +1;\r\n } else {\r\n return 0;\r\n }\r\n });\r\n if (_this._entries) { // force reset because IE keeps keys index\r\n _this._entries = {};\r\n }\r\n for (var i = 0; i < items.length; i++) {\r\n this.append(items[i][0], items[i][1]);\r\n }\r\n };\r\n }\r\n\r\n if (typeof proto._fromString !== 'function') {\r\n Object.defineProperty(proto, '_fromString', {\r\n enumerable: false,\r\n configurable: false,\r\n writable: false,\r\n value: function(searchString) {\r\n if (this._entries) {\r\n this._entries = {};\r\n } else {\r\n var keys = [];\r\n this.forEach(function(value, name) {\r\n keys.push(name);\r\n });\r\n for (var i = 0; i < keys.length; i++) {\r\n this.delete(keys[i]);\r\n }\r\n }\r\n\r\n searchString = searchString.replace(/^\\?/, '');\r\n var attributes = searchString.split('&');\r\n var attribute;\r\n for (var i = 0; i < attributes.length; i++) {\r\n attribute = attributes[i].split('=');\r\n this.append(\r\n deserializeParam(attribute[0]),\r\n (attribute.length > 1) ? deserializeParam(attribute[1]) : ''\r\n );\r\n }\r\n }\r\n });\r\n }\r\n\r\n // HTMLAnchorElement\r\n\r\n})(\r\n (typeof global !== 'undefined') ? global\r\n : ((typeof window !== 'undefined') ? window\r\n : ((typeof self !== 'undefined') ? self : this))\r\n);\r\n\r\n(function(global) {\r\n /**\r\n * Polyfill URL\r\n *\r\n * Inspired from : https://github.com/arv/DOM-URL-Polyfill/blob/master/src/url.js\r\n */\r\n\r\n var checkIfURLIsSupported = function() {\r\n try {\r\n var u = new global.URL('b', 'http://a');\r\n u.pathname = 'c d';\r\n return (u.href === 'http://a/c%20d') && u.searchParams;\r\n } catch (e) {\r\n return false;\r\n }\r\n };\r\n\r\n\r\n var polyfillURL = function() {\r\n var _URL = global.URL;\r\n\r\n var URL = function(url, base) {\r\n if (typeof url !== 'string') url = String(url);\r\n if (base && typeof base !== 'string') base = String(base);\r\n\r\n // Only create another document if the base is different from current location.\r\n var doc = document, baseElement;\r\n if (base && (global.location === void 0 || base !== global.location.href)) {\r\n base = base.toLowerCase();\r\n doc = document.implementation.createHTMLDocument('');\r\n baseElement = doc.createElement('base');\r\n baseElement.href = base;\r\n doc.head.appendChild(baseElement);\r\n try {\r\n if (baseElement.href.indexOf(base) !== 0) throw new Error(baseElement.href);\r\n } catch (err) {\r\n throw new Error('URL unable to set base ' + base + ' due to ' + err);\r\n }\r\n }\r\n\r\n var anchorElement = doc.createElement('a');\r\n anchorElement.href = url;\r\n if (baseElement) {\r\n doc.body.appendChild(anchorElement);\r\n anchorElement.href = anchorElement.href; // force href to refresh\r\n }\r\n\r\n var inputElement = doc.createElement('input');\r\n inputElement.type = 'url';\r\n inputElement.value = url;\r\n\r\n if (anchorElement.protocol === ':' || !/:/.test(anchorElement.href) || (!inputElement.checkValidity() && !base)) {\r\n throw new TypeError('Invalid URL');\r\n }\r\n\r\n Object.defineProperty(this, '_anchorElement', {\r\n value: anchorElement\r\n });\r\n\r\n\r\n // create a linked searchParams which reflect its changes on URL\r\n var searchParams = new global.URLSearchParams(this.search);\r\n var enableSearchUpdate = true;\r\n var enableSearchParamsUpdate = true;\r\n var _this = this;\r\n ['append', 'delete', 'set'].forEach(function(methodName) {\r\n var method = searchParams[methodName];\r\n searchParams[methodName] = function() {\r\n method.apply(searchParams, arguments);\r\n if (enableSearchUpdate) {\r\n enableSearchParamsUpdate = false;\r\n _this.search = searchParams.toString();\r\n enableSearchParamsUpdate = true;\r\n }\r\n };\r\n });\r\n\r\n Object.defineProperty(this, 'searchParams', {\r\n value: searchParams,\r\n enumerable: true\r\n });\r\n\r\n var search = void 0;\r\n Object.defineProperty(this, '_updateSearchParams', {\r\n enumerable: false,\r\n configurable: false,\r\n writable: false,\r\n value: function() {\r\n if (this.search !== search) {\r\n search = this.search;\r\n if (enableSearchParamsUpdate) {\r\n enableSearchUpdate = false;\r\n this.searchParams._fromString(this.search);\r\n enableSearchUpdate = true;\r\n }\r\n }\r\n }\r\n });\r\n };\r\n\r\n var proto = URL.prototype;\r\n\r\n var linkURLWithAnchorAttribute = function(attributeName) {\r\n Object.defineProperty(proto, attributeName, {\r\n get: function() {\r\n return this._anchorElement[attributeName];\r\n },\r\n set: function(value) {\r\n this._anchorElement[attributeName] = value;\r\n },\r\n enumerable: true\r\n });\r\n };\r\n\r\n ['hash', 'host', 'hostname', 'port', 'protocol']\r\n .forEach(function(attributeName) {\r\n linkURLWithAnchorAttribute(attributeName);\r\n });\r\n\r\n Object.defineProperty(proto, 'search', {\r\n get: function() {\r\n return this._anchorElement['search'];\r\n },\r\n set: function(value) {\r\n this._anchorElement['search'] = value;\r\n this._updateSearchParams();\r\n },\r\n enumerable: true\r\n });\r\n\r\n Object.defineProperties(proto, {\r\n\r\n 'toString': {\r\n get: function() {\r\n var _this = this;\r\n return function() {\r\n return _this.href;\r\n };\r\n }\r\n },\r\n\r\n 'href': {\r\n get: function() {\r\n return this._anchorElement.href.replace(/\\?$/, '');\r\n },\r\n set: function(value) {\r\n this._anchorElement.href = value;\r\n this._updateSearchParams();\r\n },\r\n enumerable: true\r\n },\r\n\r\n 'pathname': {\r\n get: function() {\r\n return this._anchorElement.pathname.replace(/(^\\/?)/, '/');\r\n },\r\n set: function(value) {\r\n this._anchorElement.pathname = value;\r\n },\r\n enumerable: true\r\n },\r\n\r\n 'origin': {\r\n get: function() {\r\n // get expected port from protocol\r\n var expectedPort = { 'http:': 80, 'https:': 443, 'ftp:': 21 }[this._anchorElement.protocol];\r\n // add port to origin if, expected port is different than actual port\r\n // and it is not empty f.e http://foo:8080\r\n // 8080 != 80 && 8080 != ''\r\n var addPortToOrigin = this._anchorElement.port != expectedPort &&\r\n this._anchorElement.port !== '';\r\n\r\n return this._anchorElement.protocol +\r\n '//' +\r\n this._anchorElement.hostname +\r\n (addPortToOrigin ? (':' + this._anchorElement.port) : '');\r\n },\r\n enumerable: true\r\n },\r\n\r\n 'password': { // TODO\r\n get: function() {\r\n return '';\r\n },\r\n set: function(value) {\r\n },\r\n enumerable: true\r\n },\r\n\r\n 'username': { // TODO\r\n get: function() {\r\n return '';\r\n },\r\n set: function(value) {\r\n },\r\n enumerable: true\r\n },\r\n });\r\n\r\n URL.createObjectURL = function(blob) {\r\n return _URL.createObjectURL.apply(_URL, arguments);\r\n };\r\n\r\n URL.revokeObjectURL = function(url) {\r\n return _URL.revokeObjectURL.apply(_URL, arguments);\r\n };\r\n\r\n global.URL = URL;\r\n\r\n };\r\n\r\n if (!checkIfURLIsSupported()) {\r\n polyfillURL();\r\n }\r\n\r\n if ((global.location !== void 0) && !('origin' in global.location)) {\r\n var getOrigin = function() {\r\n return global.location.protocol + '//' + global.location.hostname + (global.location.port ? 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IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT,\r\nINDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM\r\nLOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR\r\nOTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR\r\nPERFORMANCE OF THIS SOFTWARE.\r\n***************************************************************************** */\r\n/* global global, define, System, Reflect, Promise */\r\nvar __extends;\r\nvar __assign;\r\nvar __rest;\r\nvar __decorate;\r\nvar __param;\r\nvar __metadata;\r\nvar __awaiter;\r\nvar __generator;\r\nvar __exportStar;\r\nvar __values;\r\nvar __read;\r\nvar __spread;\r\nvar __spreadArrays;\r\nvar __spreadArray;\r\nvar __await;\r\nvar __asyncGenerator;\r\nvar __asyncDelegator;\r\nvar __asyncValues;\r\nvar __makeTemplateObject;\r\nvar __importStar;\r\nvar __importDefault;\r\nvar __classPrivateFieldGet;\r\nvar __classPrivateFieldSet;\r\nvar __createBinding;\r\n(function (factory) {\r\n var root = typeof global === \"object\" ? global : typeof self === \"object\" ? self : typeof this === \"object\" ? this : {};\r\n if (typeof define === \"function\" && define.amd) {\r\n define(\"tslib\", [\"exports\"], function (exports) { factory(createExporter(root, createExporter(exports))); });\r\n }\r\n else if (typeof module === \"object\" && typeof module.exports === \"object\") {\r\n factory(createExporter(root, createExporter(module.exports)));\r\n }\r\n else {\r\n factory(createExporter(root));\r\n }\r\n function createExporter(exports, previous) {\r\n if (exports !== root) {\r\n if (typeof Object.create === \"function\") {\r\n Object.defineProperty(exports, \"__esModule\", { value: true });\r\n }\r\n else {\r\n exports.__esModule = true;\r\n }\r\n }\r\n return function (id, v) { return exports[id] = previous ? previous(id, v) : v; };\r\n }\r\n})\r\n(function (exporter) {\r\n var extendStatics = Object.setPrototypeOf ||\r\n ({ __proto__: [] } instanceof Array && function (d, b) { d.__proto__ = b; }) ||\r\n function (d, b) { for (var p in b) if (Object.prototype.hasOwnProperty.call(b, p)) d[p] = b[p]; };\r\n\r\n __extends = function (d, b) {\r\n if (typeof b !== \"function\" && b !== null)\r\n throw new TypeError(\"Class extends value \" + String(b) + \" is not a constructor or null\");\r\n extendStatics(d, b);\r\n function __() { this.constructor = d; }\r\n d.prototype = b === null ? 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'right' : 'left'] = '-9999px'; // Move element to the same position vertically\n\n var yPosition = window.pageYOffset || document.documentElement.scrollTop;\n fakeElement.style.top = \"\".concat(yPosition, \"px\");\n fakeElement.setAttribute('readonly', '');\n fakeElement.value = value;\n return fakeElement;\n}\n;// CONCATENATED MODULE: ./src/actions/copy.js\n\n\n\n/**\n * Create fake copy action wrapper using a fake element.\n * @param {String} target\n * @param {Object} options\n * @return {String}\n */\n\nvar fakeCopyAction = function fakeCopyAction(value, options) {\n var fakeElement = createFakeElement(value);\n options.container.appendChild(fakeElement);\n var selectedText = select_default()(fakeElement);\n command('copy');\n fakeElement.remove();\n return selectedText;\n};\n/**\n * Copy action wrapper.\n * @param {String|HTMLElement} target\n * @param {Object} options\n * @return {String}\n */\n\n\nvar ClipboardActionCopy = function ClipboardActionCopy(target) {\n var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {\n container: document.body\n };\n var selectedText = '';\n\n if (typeof target === 'string') {\n selectedText = fakeCopyAction(target, options);\n } else if (target instanceof HTMLInputElement && !['text', 'search', 'url', 'tel', 'password'].includes(target === null || target === void 0 ? void 0 : target.type)) {\n // If input type doesn't support `setSelectionRange`. Simulate it. https://developer.mozilla.org/en-US/docs/Web/API/HTMLInputElement/setSelectionRange\n selectedText = fakeCopyAction(target.value, options);\n } else {\n selectedText = select_default()(target);\n command('copy');\n }\n\n return selectedText;\n};\n\n/* harmony default export */ var actions_copy = (ClipboardActionCopy);\n;// CONCATENATED MODULE: ./src/actions/default.js\nfunction _typeof(obj) { \"@babel/helpers - typeof\"; if (typeof Symbol === \"function\" && typeof Symbol.iterator === \"symbol\") { _typeof = function _typeof(obj) { return typeof obj; }; } else { _typeof = function _typeof(obj) { return obj && typeof Symbol === \"function\" && obj.constructor === Symbol && obj !== Symbol.prototype ? \"symbol\" : typeof obj; }; } return _typeof(obj); }\n\n\n\n/**\n * Inner function which performs selection from either `text` or `target`\n * properties and then executes copy or cut operations.\n * @param {Object} options\n */\n\nvar ClipboardActionDefault = function ClipboardActionDefault() {\n var options = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : {};\n // Defines base properties passed from constructor.\n var _options$action = options.action,\n action = _options$action === void 0 ? 'copy' : _options$action,\n container = options.container,\n target = options.target,\n text = options.text; // Sets the `action` to be performed which can be either 'copy' or 'cut'.\n\n if (action !== 'copy' && action !== 'cut') {\n throw new Error('Invalid \"action\" value, use either \"copy\" or \"cut\"');\n } // Sets the `target` property using an element that will be have its content copied.\n\n\n if (target !== undefined) {\n if (target && _typeof(target) === 'object' && target.nodeType === 1) {\n if (action === 'copy' && target.hasAttribute('disabled')) {\n throw new Error('Invalid \"target\" attribute. Please use \"readonly\" instead of \"disabled\" attribute');\n }\n\n if (action === 'cut' && (target.hasAttribute('readonly') || target.hasAttribute('disabled'))) {\n throw new Error('Invalid \"target\" attribute. You can\\'t cut text from elements with \"readonly\" or \"disabled\" attributes');\n }\n } else {\n throw new Error('Invalid \"target\" value, use a valid Element');\n }\n } // Define selection strategy based on `text` property.\n\n\n if (text) {\n return actions_copy(text, {\n container: container\n });\n } // Defines which selection strategy based on `target` property.\n\n\n if (target) {\n return action === 'cut' ? actions_cut(target) : actions_copy(target, {\n container: container\n });\n }\n};\n\n/* harmony default export */ var actions_default = (ClipboardActionDefault);\n;// CONCATENATED MODULE: ./src/clipboard.js\nfunction clipboard_typeof(obj) { \"@babel/helpers - typeof\"; if (typeof Symbol === \"function\" && typeof Symbol.iterator === \"symbol\") { clipboard_typeof = function _typeof(obj) { return typeof obj; }; } else { clipboard_typeof = function _typeof(obj) { return obj && typeof Symbol === \"function\" && obj.constructor === Symbol && obj !== Symbol.prototype ? \"symbol\" : typeof obj; }; } return clipboard_typeof(obj); }\n\nfunction _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError(\"Cannot call a class as a function\"); } }\n\nfunction _defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if (\"value\" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } }\n\nfunction _createClass(Constructor, protoProps, staticProps) { if (protoProps) _defineProperties(Constructor.prototype, protoProps); if (staticProps) _defineProperties(Constructor, staticProps); return Constructor; }\n\nfunction _inherits(subClass, superClass) { if (typeof superClass !== \"function\" && superClass !== null) { throw new TypeError(\"Super expression must either be null or a function\"); } subClass.prototype = Object.create(superClass && superClass.prototype, { constructor: { value: subClass, writable: true, configurable: true } }); if (superClass) _setPrototypeOf(subClass, superClass); }\n\nfunction _setPrototypeOf(o, p) { _setPrototypeOf = Object.setPrototypeOf || function _setPrototypeOf(o, p) { o.__proto__ = p; return o; }; return _setPrototypeOf(o, p); }\n\nfunction _createSuper(Derived) { var hasNativeReflectConstruct = _isNativeReflectConstruct(); return function _createSuperInternal() { var Super = _getPrototypeOf(Derived), result; if (hasNativeReflectConstruct) { var NewTarget = _getPrototypeOf(this).constructor; result = Reflect.construct(Super, arguments, NewTarget); } else { result = Super.apply(this, arguments); } return _possibleConstructorReturn(this, result); }; }\n\nfunction _possibleConstructorReturn(self, call) { if (call && (clipboard_typeof(call) === \"object\" || typeof call === \"function\")) { return call; } return _assertThisInitialized(self); }\n\nfunction _assertThisInitialized(self) { if (self === void 0) { throw new ReferenceError(\"this hasn't been initialised - super() hasn't been called\"); } return self; }\n\nfunction _isNativeReflectConstruct() { if (typeof Reflect === \"undefined\" || !Reflect.construct) return false; if (Reflect.construct.sham) return false; if (typeof Proxy === \"function\") return true; try { Date.prototype.toString.call(Reflect.construct(Date, [], function () {})); return true; } catch (e) { return false; } }\n\nfunction _getPrototypeOf(o) { _getPrototypeOf = Object.setPrototypeOf ? Object.getPrototypeOf : function _getPrototypeOf(o) { return o.__proto__ || Object.getPrototypeOf(o); }; return _getPrototypeOf(o); }\n\n\n\n\n\n\n/**\n * Helper function to retrieve attribute value.\n * @param {String} suffix\n * @param {Element} element\n */\n\nfunction getAttributeValue(suffix, element) {\n var attribute = \"data-clipboard-\".concat(suffix);\n\n if (!element.hasAttribute(attribute)) {\n return;\n }\n\n return element.getAttribute(attribute);\n}\n/**\n * Base class which takes one or more elements, adds event listeners to them,\n * and instantiates a new `ClipboardAction` on each click.\n */\n\n\nvar Clipboard = /*#__PURE__*/function (_Emitter) {\n _inherits(Clipboard, _Emitter);\n\n var _super = _createSuper(Clipboard);\n\n /**\n * @param {String|HTMLElement|HTMLCollection|NodeList} trigger\n * @param {Object} options\n */\n function Clipboard(trigger, options) {\n var _this;\n\n _classCallCheck(this, Clipboard);\n\n _this = _super.call(this);\n\n _this.resolveOptions(options);\n\n _this.listenClick(trigger);\n\n return _this;\n }\n /**\n * Defines if attributes would be resolved using internal setter functions\n * or custom functions that were passed in the constructor.\n * @param {Object} options\n */\n\n\n _createClass(Clipboard, [{\n key: \"resolveOptions\",\n value: function resolveOptions() {\n var options = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : {};\n this.action = typeof options.action === 'function' ? options.action : this.defaultAction;\n this.target = typeof options.target === 'function' ? options.target : this.defaultTarget;\n this.text = typeof options.text === 'function' ? options.text : this.defaultText;\n this.container = clipboard_typeof(options.container) === 'object' ? options.container : document.body;\n }\n /**\n * Adds a click event listener to the passed trigger.\n * @param {String|HTMLElement|HTMLCollection|NodeList} trigger\n */\n\n }, {\n key: \"listenClick\",\n value: function listenClick(trigger) {\n var _this2 = this;\n\n this.listener = listen_default()(trigger, 'click', function (e) {\n return _this2.onClick(e);\n });\n }\n /**\n * Defines a new `ClipboardAction` on each click event.\n * @param {Event} e\n */\n\n }, {\n key: \"onClick\",\n value: function onClick(e) {\n var trigger = e.delegateTarget || e.currentTarget;\n var action = this.action(trigger) || 'copy';\n var text = actions_default({\n action: action,\n container: this.container,\n target: this.target(trigger),\n text: this.text(trigger)\n }); // Fires an event based on the copy operation result.\n\n this.emit(text ? 'success' : 'error', {\n action: action,\n text: text,\n trigger: trigger,\n clearSelection: function clearSelection() {\n if (trigger) {\n trigger.focus();\n }\n\n window.getSelection().removeAllRanges();\n }\n });\n }\n /**\n * Default `action` lookup function.\n * @param {Element} trigger\n */\n\n }, {\n key: \"defaultAction\",\n value: function defaultAction(trigger) {\n return getAttributeValue('action', trigger);\n }\n /**\n * Default `target` lookup function.\n * @param {Element} trigger\n */\n\n }, {\n key: \"defaultTarget\",\n value: function defaultTarget(trigger) {\n var selector = getAttributeValue('target', trigger);\n\n if (selector) {\n return document.querySelector(selector);\n }\n }\n /**\n * Allow fire programmatically a copy action\n * @param {String|HTMLElement} target\n * @param {Object} options\n * @returns Text copied.\n */\n\n }, {\n key: \"defaultText\",\n\n /**\n * Default `text` lookup function.\n * @param {Element} trigger\n */\n value: function defaultText(trigger) {\n return getAttributeValue('text', trigger);\n }\n /**\n * Destroy lifecycle.\n */\n\n }, {\n key: \"destroy\",\n value: function destroy() {\n this.listener.destroy();\n }\n }], [{\n key: \"copy\",\n value: function copy(target) {\n var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {\n container: document.body\n };\n return actions_copy(target, options);\n }\n /**\n * Allow fire programmatically a cut action\n * @param {String|HTMLElement} target\n * @returns Text cutted.\n */\n\n }, {\n key: \"cut\",\n value: function cut(target) {\n return actions_cut(target);\n }\n /**\n * Returns the support of the given action, or all actions if no action is\n * given.\n * @param {String} [action]\n */\n\n }, {\n key: \"isSupported\",\n value: function isSupported() {\n var action = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : ['copy', 'cut'];\n var actions = typeof action === 'string' ? [action] : action;\n var support = !!document.queryCommandSupported;\n actions.forEach(function (action) {\n support = support && !!document.queryCommandSupported(action);\n });\n return support;\n }\n }]);\n\n return Clipboard;\n}((tiny_emitter_default()));\n\n/* harmony default export */ var clipboard = (Clipboard);\n\n/***/ }),\n\n/***/ 828:\n/***/ (function(module) {\n\nvar DOCUMENT_NODE_TYPE = 9;\n\n/**\n * A polyfill for Element.matches()\n */\nif (typeof Element !== 'undefined' && !Element.prototype.matches) {\n var proto = Element.prototype;\n\n proto.matches = proto.matchesSelector ||\n proto.mozMatchesSelector ||\n proto.msMatchesSelector ||\n proto.oMatchesSelector ||\n proto.webkitMatchesSelector;\n}\n\n/**\n * Finds the closest parent that matches a selector.\n *\n * @param {Element} element\n * @param {String} selector\n * @return {Function}\n */\nfunction closest (element, selector) {\n while (element && element.nodeType !== DOCUMENT_NODE_TYPE) {\n if (typeof element.matches === 'function' &&\n element.matches(selector)) {\n return element;\n }\n element = element.parentNode;\n }\n}\n\nmodule.exports = closest;\n\n\n/***/ }),\n\n/***/ 438:\n/***/ (function(module, __unused_webpack_exports, __webpack_require__) {\n\nvar closest = __webpack_require__(828);\n\n/**\n * Delegates event to a selector.\n *\n * @param {Element} element\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @param {Boolean} useCapture\n * @return {Object}\n */\nfunction _delegate(element, selector, type, callback, useCapture) {\n var listenerFn = listener.apply(this, arguments);\n\n element.addEventListener(type, listenerFn, useCapture);\n\n return {\n destroy: function() {\n element.removeEventListener(type, listenerFn, useCapture);\n }\n }\n}\n\n/**\n * Delegates event to a selector.\n *\n * @param {Element|String|Array} [elements]\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @param {Boolean} useCapture\n * @return {Object}\n */\nfunction delegate(elements, selector, type, callback, useCapture) {\n // Handle the regular Element usage\n if (typeof elements.addEventListener === 'function') {\n return _delegate.apply(null, arguments);\n }\n\n // Handle Element-less usage, it defaults to global delegation\n if (typeof type === 'function') {\n // Use `document` as the first parameter, then apply arguments\n // This is a short way to .unshift `arguments` without running into deoptimizations\n return _delegate.bind(null, document).apply(null, arguments);\n }\n\n // Handle Selector-based usage\n if (typeof elements === 'string') {\n elements = document.querySelectorAll(elements);\n }\n\n // Handle Array-like based usage\n return Array.prototype.map.call(elements, function (element) {\n return _delegate(element, selector, type, callback, useCapture);\n });\n}\n\n/**\n * Finds closest match and invokes callback.\n *\n * @param {Element} element\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @return {Function}\n */\nfunction listener(element, selector, type, callback) {\n return function(e) {\n e.delegateTarget = closest(e.target, selector);\n\n if (e.delegateTarget) {\n callback.call(element, e);\n }\n }\n}\n\nmodule.exports = delegate;\n\n\n/***/ }),\n\n/***/ 879:\n/***/ (function(__unused_webpack_module, exports) {\n\n/**\n * Check if argument is a HTML element.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.node = function(value) {\n return value !== undefined\n && value instanceof HTMLElement\n && value.nodeType === 1;\n};\n\n/**\n * Check if argument is a list of HTML elements.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.nodeList = function(value) {\n var type = Object.prototype.toString.call(value);\n\n return value !== undefined\n && (type === '[object NodeList]' || type === '[object HTMLCollection]')\n && ('length' in value)\n && (value.length === 0 || exports.node(value[0]));\n};\n\n/**\n * Check if argument is a string.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.string = function(value) {\n return typeof value === 'string'\n || value instanceof String;\n};\n\n/**\n * Check if argument is a function.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.fn = function(value) {\n var type = Object.prototype.toString.call(value);\n\n return type === '[object Function]';\n};\n\n\n/***/ }),\n\n/***/ 370:\n/***/ (function(module, __unused_webpack_exports, __webpack_require__) {\n\nvar is = __webpack_require__(879);\nvar delegate = __webpack_require__(438);\n\n/**\n * Validates all params and calls the right\n * listener function based on its target type.\n *\n * @param {String|HTMLElement|HTMLCollection|NodeList} target\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listen(target, type, callback) {\n if (!target && !type && !callback) {\n throw new Error('Missing required arguments');\n }\n\n if (!is.string(type)) {\n throw new TypeError('Second argument must be a String');\n }\n\n if (!is.fn(callback)) {\n throw new TypeError('Third argument must be a Function');\n }\n\n if (is.node(target)) {\n return listenNode(target, type, callback);\n }\n else if (is.nodeList(target)) {\n return listenNodeList(target, type, callback);\n }\n else if (is.string(target)) {\n return listenSelector(target, type, callback);\n }\n else {\n throw new TypeError('First argument must be a String, HTMLElement, HTMLCollection, or NodeList');\n }\n}\n\n/**\n * Adds an event listener to a HTML element\n * and returns a remove listener function.\n *\n * @param {HTMLElement} node\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listenNode(node, type, callback) {\n node.addEventListener(type, callback);\n\n return {\n destroy: function() {\n node.removeEventListener(type, callback);\n }\n }\n}\n\n/**\n * Add an event listener to a list of HTML elements\n * and returns a remove listener function.\n *\n * @param {NodeList|HTMLCollection} nodeList\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listenNodeList(nodeList, type, callback) {\n Array.prototype.forEach.call(nodeList, function(node) {\n node.addEventListener(type, callback);\n });\n\n return {\n destroy: function() {\n Array.prototype.forEach.call(nodeList, function(node) {\n node.removeEventListener(type, callback);\n });\n }\n }\n}\n\n/**\n * Add an event listener to a selector\n * and returns a remove listener function.\n *\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listenSelector(selector, type, callback) {\n return delegate(document.body, selector, type, callback);\n}\n\nmodule.exports = listen;\n\n\n/***/ }),\n\n/***/ 817:\n/***/ (function(module) {\n\nfunction select(element) {\n var selectedText;\n\n if (element.nodeName === 'SELECT') {\n element.focus();\n\n selectedText = element.value;\n }\n else if (element.nodeName === 'INPUT' || element.nodeName === 'TEXTAREA') {\n var isReadOnly = element.hasAttribute('readonly');\n\n if (!isReadOnly) {\n element.setAttribute('readonly', '');\n }\n\n element.select();\n element.setSelectionRange(0, element.value.length);\n\n if (!isReadOnly) {\n element.removeAttribute('readonly');\n }\n\n selectedText = element.value;\n }\n else {\n if (element.hasAttribute('contenteditable')) {\n element.focus();\n }\n\n var selection = window.getSelection();\n var range = document.createRange();\n\n range.selectNodeContents(element);\n selection.removeAllRanges();\n selection.addRange(range);\n\n selectedText = selection.toString();\n }\n\n return selectedText;\n}\n\nmodule.exports = select;\n\n\n/***/ }),\n\n/***/ 279:\n/***/ (function(module) {\n\nfunction E () {\n // Keep this empty so it's easier to inherit from\n // (via https://github.com/lipsmack from https://github.com/scottcorgan/tiny-emitter/issues/3)\n}\n\nE.prototype = {\n on: function (name, callback, ctx) {\n var e = this.e || (this.e = {});\n\n (e[name] || (e[name] = [])).push({\n fn: callback,\n ctx: ctx\n });\n\n return this;\n },\n\n once: function (name, callback, ctx) {\n var self = this;\n function listener () {\n self.off(name, listener);\n callback.apply(ctx, arguments);\n };\n\n listener._ = callback\n return this.on(name, listener, ctx);\n },\n\n emit: function (name) {\n var data = [].slice.call(arguments, 1);\n var evtArr = ((this.e || (this.e = {}))[name] || []).slice();\n var i = 0;\n var len = evtArr.length;\n\n for (i; i < len; i++) {\n evtArr[i].fn.apply(evtArr[i].ctx, data);\n }\n\n return this;\n },\n\n off: function (name, callback) {\n var e = this.e || (this.e = {});\n var evts = e[name];\n var liveEvents = [];\n\n if (evts && callback) {\n for (var i = 0, len = evts.length; i < len; i++) {\n if (evts[i].fn !== callback && evts[i].fn._ !== callback)\n liveEvents.push(evts[i]);\n }\n }\n\n // Remove event from queue to prevent memory leak\n // Suggested by https://github.com/lazd\n // Ref: https://github.com/scottcorgan/tiny-emitter/commit/c6ebfaa9bc973b33d110a84a307742b7cf94c953#commitcomment-5024910\n\n (liveEvents.length)\n ? e[name] = liveEvents\n : delete e[name];\n\n return this;\n }\n};\n\nmodule.exports = E;\nmodule.exports.TinyEmitter = E;\n\n\n/***/ })\n\n/******/ \t});\n/************************************************************************/\n/******/ \t// The module cache\n/******/ \tvar __webpack_module_cache__ = {};\n/******/ \t\n/******/ \t// The require function\n/******/ \tfunction __webpack_require__(moduleId) {\n/******/ \t\t// Check if module is in cache\n/******/ \t\tif(__webpack_module_cache__[moduleId]) {\n/******/ \t\t\treturn __webpack_module_cache__[moduleId].exports;\n/******/ \t\t}\n/******/ \t\t// Create a new module (and put it into the cache)\n/******/ \t\tvar module = __webpack_module_cache__[moduleId] = {\n/******/ \t\t\t// no module.id needed\n/******/ \t\t\t// no module.loaded needed\n/******/ \t\t\texports: {}\n/******/ \t\t};\n/******/ \t\n/******/ \t\t// Execute the module function\n/******/ \t\t__webpack_modules__[moduleId](module, module.exports, __webpack_require__);\n/******/ \t\n/******/ \t\t// Return the exports of the module\n/******/ \t\treturn module.exports;\n/******/ \t}\n/******/ \t\n/************************************************************************/\n/******/ \t/* webpack/runtime/compat get default export */\n/******/ \t!function() {\n/******/ \t\t// getDefaultExport function for compatibility with non-harmony modules\n/******/ \t\t__webpack_require__.n = function(module) {\n/******/ \t\t\tvar getter = module && module.__esModule ?\n/******/ \t\t\t\tfunction() { return module['default']; } :\n/******/ \t\t\t\tfunction() { return module; };\n/******/ \t\t\t__webpack_require__.d(getter, { a: getter });\n/******/ \t\t\treturn getter;\n/******/ \t\t};\n/******/ \t}();\n/******/ \t\n/******/ \t/* webpack/runtime/define property getters */\n/******/ \t!function() {\n/******/ \t\t// define getter functions for harmony exports\n/******/ \t\t__webpack_require__.d = function(exports, definition) {\n/******/ \t\t\tfor(var key in definition) {\n/******/ \t\t\t\tif(__webpack_require__.o(definition, key) && !__webpack_require__.o(exports, key)) {\n/******/ \t\t\t\t\tObject.defineProperty(exports, key, { enumerable: true, get: definition[key] });\n/******/ \t\t\t\t}\n/******/ \t\t\t}\n/******/ \t\t};\n/******/ \t}();\n/******/ \t\n/******/ \t/* webpack/runtime/hasOwnProperty shorthand */\n/******/ \t!function() {\n/******/ \t\t__webpack_require__.o = function(obj, prop) { return Object.prototype.hasOwnProperty.call(obj, prop); }\n/******/ \t}();\n/******/ \t\n/************************************************************************/\n/******/ \t// module exports must be returned from runtime so entry inlining is disabled\n/******/ \t// startup\n/******/ \t// Load entry module and return exports\n/******/ \treturn __webpack_require__(686);\n/******/ })()\n.default;\n});", "/*!\n * escape-html\n * Copyright(c) 2012-2013 TJ Holowaychuk\n * Copyright(c) 2015 Andreas Lubbe\n * Copyright(c) 2015 Tiancheng \"Timothy\" Gu\n * MIT Licensed\n */\n\n'use strict';\n\n/**\n * Module variables.\n * @private\n */\n\nvar matchHtmlRegExp = /[\"'&<>]/;\n\n/**\n * Module exports.\n * @public\n */\n\nmodule.exports = escapeHtml;\n\n/**\n * Escape special characters in the given string of html.\n *\n * @param {string} string The string to escape for inserting into HTML\n * @return {string}\n * @public\n */\n\nfunction escapeHtml(string) {\n var str = '' + string;\n var match = matchHtmlRegExp.exec(str);\n\n if (!match) {\n return str;\n }\n\n var escape;\n var html = '';\n var index = 0;\n var lastIndex = 0;\n\n for (index = match.index; index < str.length; index++) {\n switch (str.charCodeAt(index)) {\n case 34: // \"\n escape = '"';\n break;\n case 38: // &\n escape = '&';\n break;\n case 39: // '\n escape = ''';\n break;\n case 60: // <\n escape = '<';\n break;\n case 62: // >\n escape = '>';\n break;\n default:\n continue;\n }\n\n if (lastIndex !== index) {\n html += str.substring(lastIndex, index);\n }\n\n lastIndex = index + 1;\n html += escape;\n }\n\n return lastIndex !== index\n ? html + str.substring(lastIndex, index)\n : html;\n}\n", "Array.prototype.flat||Object.defineProperty(Array.prototype,\"flat\",{configurable:!0,value:function r(){var t=isNaN(arguments[0])?1:Number(arguments[0]);return t?Array.prototype.reduce.call(this,function(a,e){return Array.isArray(e)?a.push.apply(a,r.call(e,t-1)):a.push(e),a},[]):Array.prototype.slice.call(this)},writable:!0}),Array.prototype.flatMap||Object.defineProperty(Array.prototype,\"flatMap\",{configurable:!0,value:function(r){return Array.prototype.map.apply(this,arguments).flat()},writable:!0})\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport \"array-flat-polyfill\"\nimport \"focus-visible\"\nimport \"unfetch/polyfill\"\nimport \"url-polyfill\"\n\nimport {\n EMPTY,\n NEVER,\n Subject,\n defer,\n delay,\n filter,\n map,\n merge,\n mergeWith,\n shareReplay,\n switchMap\n} from \"rxjs\"\n\nimport { configuration, feature } from \"./_\"\nimport {\n at,\n getOptionalElement,\n requestJSON,\n setToggle,\n watchDocument,\n watchKeyboard,\n watchLocation,\n watchLocationTarget,\n watchMedia,\n watchPrint,\n watchViewport\n} from \"./browser\"\nimport {\n getComponentElement,\n getComponentElements,\n mountAnnounce,\n mountBackToTop,\n mountConsent,\n mountContent,\n mountDialog,\n mountHeader,\n mountHeaderTitle,\n mountPalette,\n mountSearch,\n mountSearchHiglight,\n mountSidebar,\n mountSource,\n mountTableOfContents,\n mountTabs,\n watchHeader,\n watchMain\n} from \"./components\"\nimport {\n SearchIndex,\n setupClipboardJS,\n setupInstantLoading,\n setupVersionSelector\n} from \"./integrations\"\nimport {\n patchIndeterminate,\n patchScrollfix,\n patchScrolllock\n} from \"./patches\"\nimport \"./polyfills\"\n\n/* ----------------------------------------------------------------------------\n * Application\n * ------------------------------------------------------------------------- */\n\n/* Yay, JavaScript is available */\ndocument.documentElement.classList.remove(\"no-js\")\ndocument.documentElement.classList.add(\"js\")\n\n/* Set up navigation observables and subjects */\nconst document$ = watchDocument()\nconst location$ = watchLocation()\nconst target$ = watchLocationTarget()\nconst keyboard$ = watchKeyboard()\n\n/* Set up media observables */\nconst viewport$ = watchViewport()\nconst tablet$ = watchMedia(\"(min-width: 960px)\")\nconst screen$ = watchMedia(\"(min-width: 1220px)\")\nconst print$ = watchPrint()\n\n/* Retrieve search index, if search is enabled */\nconst config = configuration()\nconst index$ = document.forms.namedItem(\"search\")\n ? __search?.index || requestJSON(\n new URL(\"search/search_index.json\", config.base)\n )\n : NEVER\n\n/* Set up Clipboard.js integration */\nconst alert$ = new Subject()\nsetupClipboardJS({ alert$ })\n\n/* Set up instant loading, if enabled */\nif (feature(\"navigation.instant\"))\n setupInstantLoading({ document$, location$, viewport$ })\n\n/* Set up version selector */\nif (config.version?.provider === \"mike\")\n setupVersionSelector({ document$ })\n\n/* Always close drawer and search on navigation */\nmerge(location$, target$)\n .pipe(\n delay(125)\n )\n .subscribe(() => {\n setToggle(\"drawer\", false)\n setToggle(\"search\", false)\n })\n\n/* Set up global keyboard handlers */\nkeyboard$\n .pipe(\n filter(({ mode }) => mode === \"global\")\n )\n .subscribe(key => {\n switch (key.type) {\n\n /* Go to previous page */\n case \"p\":\n case \",\":\n const prev = getOptionalElement(\"[href][rel=prev]\")\n if (typeof prev !== \"undefined\")\n prev.click()\n break\n\n /* Go to next page */\n case \"n\":\n case \".\":\n const next = getOptionalElement(\"[href][rel=next]\")\n if (typeof next !== \"undefined\")\n next.click()\n break\n }\n })\n\n/* Set up patches */\npatchIndeterminate({ document$, tablet$ })\npatchScrollfix({ document$ })\npatchScrolllock({ viewport$, tablet$ })\n\n/* Set up header and main area observable */\nconst header$ = watchHeader(getComponentElement(\"header\"), { viewport$ })\nconst main$ = document$\n .pipe(\n map(() => getComponentElement(\"main\")),\n switchMap(el => watchMain(el, { viewport$, header$ })),\n shareReplay(1)\n )\n\n/* Set up control component observables */\nconst control$ = merge(\n\n /* Consent */\n ...getComponentElements(\"consent\")\n .map(el => mountConsent(el, { target$ })),\n\n /* Dialog */\n ...getComponentElements(\"dialog\")\n .map(el => mountDialog(el, { alert$ })),\n\n /* Header */\n ...getComponentElements(\"header\")\n .map(el => mountHeader(el, { viewport$, header$, main$ })),\n\n /* Color palette */\n ...getComponentElements(\"palette\")\n .map(el => mountPalette(el)),\n\n /* Search */\n ...getComponentElements(\"search\")\n .map(el => mountSearch(el, { index$, keyboard$ })),\n\n /* Repository information */\n ...getComponentElements(\"source\")\n .map(el => mountSource(el))\n)\n\n/* Set up content component observables */\nconst content$ = defer(() => merge(\n\n /* Announcement bar */\n ...getComponentElements(\"announce\")\n .map(el => mountAnnounce(el)),\n\n /* Content */\n ...getComponentElements(\"content\")\n .map(el => mountContent(el, { target$, print$ })),\n\n /* Search highlighting */\n ...getComponentElements(\"content\")\n .map(el => feature(\"search.highlight\")\n ? mountSearchHiglight(el, { index$, location$ })\n : EMPTY\n ),\n\n /* Header title */\n ...getComponentElements(\"header-title\")\n .map(el => mountHeaderTitle(el, { viewport$, header$ })),\n\n /* Sidebar */\n ...getComponentElements(\"sidebar\")\n .map(el => el.getAttribute(\"data-md-type\") === \"navigation\"\n ? at(screen$, () => mountSidebar(el, { viewport$, header$, main$ }))\n : at(tablet$, () => mountSidebar(el, { viewport$, header$, main$ }))\n ),\n\n /* Navigation tabs */\n ...getComponentElements(\"tabs\")\n .map(el => mountTabs(el, { viewport$, header$ })),\n\n /* Table of contents */\n ...getComponentElements(\"toc\")\n .map(el => mountTableOfContents(el, { viewport$, header$, target$ })),\n\n /* Back-to-top button */\n ...getComponentElements(\"top\")\n .map(el => mountBackToTop(el, { viewport$, header$, main$, target$ }))\n))\n\n/* Set up component observables */\nconst component$ = document$\n .pipe(\n switchMap(() => content$),\n mergeWith(control$),\n shareReplay(1)\n )\n\n/* Subscribe to all components */\ncomponent$.subscribe()\n\n/* ----------------------------------------------------------------------------\n * Exports\n * ------------------------------------------------------------------------- */\n\nwindow.document$ = document$ /* Document observable */\nwindow.location$ = location$ /* Location subject */\nwindow.target$ = target$ /* Location target observable */\nwindow.keyboard$ = keyboard$ /* Keyboard observable */\nwindow.viewport$ = viewport$ /* Viewport observable */\nwindow.tablet$ = tablet$ /* Media tablet observable */\nwindow.screen$ = screen$ /* Media screen observable */\nwindow.print$ = print$ /* Media print observable */\nwindow.alert$ = alert$ /* Alert subject */\nwindow.component$ = component$ /* Component observable */\n", "self.fetch||(self.fetch=function(e,n){return n=n||{},new Promise(function(t,s){var r=new XMLHttpRequest,o=[],u=[],i={},a=function(){return{ok:2==(r.status/100|0),statusText:r.statusText,status:r.status,url:r.responseURL,text:function(){return Promise.resolve(r.responseText)},json:function(){return Promise.resolve(r.responseText).then(JSON.parse)},blob:function(){return Promise.resolve(new Blob([r.response]))},clone:a,headers:{keys:function(){return o},entries:function(){return u},get:function(e){return i[e.toLowerCase()]},has:function(e){return e.toLowerCase()in i}}}};for(var c in r.open(n.method||\"get\",e,!0),r.onload=function(){r.getAllResponseHeaders().replace(/^(.*?):[^\\S\\n]*([\\s\\S]*?)$/gm,function(e,n,t){o.push(n=n.toLowerCase()),u.push([n,t]),i[n]=i[n]?i[n]+\",\"+t:t}),t(a())},r.onerror=s,r.withCredentials=\"include\"==n.credentials,n.headers)r.setRequestHeader(c,n.headers[c]);r.send(n.body||null)})});\n", "import tslib from '../tslib.js';\r\nconst {\r\n __extends,\r\n __assign,\r\n __rest,\r\n __decorate,\r\n __param,\r\n __metadata,\r\n __awaiter,\r\n __generator,\r\n __exportStar,\r\n __createBinding,\r\n __values,\r\n __read,\r\n __spread,\r\n __spreadArrays,\r\n __spreadArray,\r\n __await,\r\n __asyncGenerator,\r\n __asyncDelegator,\r\n __asyncValues,\r\n __makeTemplateObject,\r\n __importStar,\r\n __importDefault,\r\n __classPrivateFieldGet,\r\n __classPrivateFieldSet,\r\n} = tslib;\r\nexport {\r\n __extends,\r\n __assign,\r\n __rest,\r\n __decorate,\r\n __param,\r\n __metadata,\r\n __awaiter,\r\n __generator,\r\n __exportStar,\r\n __createBinding,\r\n __values,\r\n __read,\r\n __spread,\r\n __spreadArrays,\r\n __spreadArray,\r\n __await,\r\n __asyncGenerator,\r\n __asyncDelegator,\r\n __asyncValues,\r\n __makeTemplateObject,\r\n __importStar,\r\n __importDefault,\r\n __classPrivateFieldGet,\r\n __classPrivateFieldSet,\r\n};\r\n", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n ReplaySubject,\n Subject,\n fromEvent\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch document\n *\n * Documents are implemented as subjects, so all downstream observables are\n * automatically updated when a new document is emitted.\n *\n * @returns Document subject\n */\nexport function watchDocument(): Subject {\n const document$ = new ReplaySubject(1)\n fromEvent(document, \"DOMContentLoaded\", { once: true })\n .subscribe(() => document$.next(document))\n\n /* Return document */\n return document$\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve all elements matching the query selector\n *\n * @template T - Element type\n *\n * @param selector - Query selector\n * @param node - Node of reference\n *\n * @returns Elements\n */\nexport function getElements(\n selector: T, node?: ParentNode\n): HTMLElementTagNameMap[T][]\n\nexport function getElements(\n selector: string, node?: ParentNode\n): T[]\n\nexport function getElements(\n selector: string, node: ParentNode = document\n): T[] {\n return Array.from(node.querySelectorAll(selector))\n}\n\n/**\n * Retrieve an element matching a query selector or throw a reference error\n *\n * Note that this function assumes that the element is present. If unsure if an\n * element is existent, use the `getOptionalElement` function instead.\n *\n * @template T - Element type\n *\n * @param selector - Query selector\n * @param node - Node of reference\n *\n * @returns Element\n */\nexport function getElement(\n selector: T, node?: ParentNode\n): HTMLElementTagNameMap[T]\n\nexport function getElement(\n selector: string, node?: ParentNode\n): T\n\nexport function getElement(\n selector: string, node: ParentNode = document\n): T {\n const el = getOptionalElement(selector, node)\n if (typeof el === \"undefined\")\n throw new ReferenceError(\n `Missing element: expected \"${selector}\" to be present`\n )\n\n /* Return element */\n return el\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Retrieve an optional element matching the query selector\n *\n * @template T - Element type\n *\n * @param selector - Query selector\n * @param node - Node of reference\n *\n * @returns Element or nothing\n */\nexport function getOptionalElement(\n selector: T, node?: ParentNode\n): HTMLElementTagNameMap[T] | undefined\n\nexport function getOptionalElement(\n selector: string, node?: ParentNode\n): T | undefined\n\nexport function getOptionalElement(\n selector: string, node: ParentNode = document\n): T | undefined {\n return node.querySelector(selector) || undefined\n}\n\n/**\n * Retrieve the currently active element\n *\n * @returns Element or nothing\n */\nexport function getActiveElement(): HTMLElement | undefined {\n return document.activeElement instanceof HTMLElement\n ? document.activeElement || undefined\n : undefined\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n debounceTime,\n distinctUntilChanged,\n fromEvent,\n map,\n merge,\n startWith\n} from \"rxjs\"\n\nimport { getActiveElement } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch element focus\n *\n * Previously, this function used `focus` and `blur` events to determine whether\n * an element is focused, but this doesn't work if there are focusable elements\n * within the elements itself. A better solutions are `focusin` and `focusout`\n * events, which bubble up the tree and allow for more fine-grained control.\n *\n * `debounceTime` is necessary, because when a focus change happens inside an\n * element, the observable would first emit `false` and then `true` again.\n *\n * @param el - Element\n *\n * @returns Element focus observable\n */\nexport function watchElementFocus(\n el: HTMLElement\n): Observable {\n return merge(\n fromEvent(document.body, \"focusin\"),\n fromEvent(document.body, \"focusout\")\n )\n .pipe(\n debounceTime(1),\n map(() => {\n const active = getActiveElement()\n return typeof active !== \"undefined\"\n ? el.contains(active)\n : false\n }),\n startWith(el === getActiveElement()),\n distinctUntilChanged()\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n animationFrameScheduler,\n auditTime,\n fromEvent,\n map,\n merge,\n startWith\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Element offset\n */\nexport interface ElementOffset {\n x: number /* Horizontal offset */\n y: number /* Vertical offset */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve element offset\n *\n * @param el - Element\n *\n * @returns Element offset\n */\nexport function getElementOffset(\n el: HTMLElement\n): ElementOffset {\n return {\n x: el.offsetLeft,\n y: el.offsetTop\n }\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch element offset\n *\n * @param el - Element\n *\n * @returns Element offset observable\n */\nexport function watchElementOffset(\n el: HTMLElement\n): Observable {\n return merge(\n fromEvent(window, \"load\"),\n fromEvent(window, \"resize\")\n )\n .pipe(\n auditTime(0, animationFrameScheduler),\n map(() => getElementOffset(el)),\n startWith(getElementOffset(el))\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n animationFrameScheduler,\n auditTime,\n fromEvent,\n map,\n merge,\n startWith\n} from \"rxjs\"\n\nimport { ElementOffset } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve element content offset (= scroll offset)\n *\n * @param el - Element\n *\n * @returns Element content offset\n */\nexport function getElementContentOffset(\n el: HTMLElement\n): ElementOffset {\n return {\n x: el.scrollLeft,\n y: el.scrollTop\n }\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch element content offset\n *\n * @param el - Element\n *\n * @returns Element content offset observable\n */\nexport function watchElementContentOffset(\n el: HTMLElement\n): Observable {\n return merge(\n fromEvent(el, \"scroll\"),\n fromEvent(window, \"resize\")\n )\n .pipe(\n auditTime(0, animationFrameScheduler),\n map(() => getElementContentOffset(el)),\n startWith(getElementContentOffset(el))\n )\n}\n", "/**\r\n * A collection of shims that provide minimal functionality of the ES6 collections.\r\n *\r\n * These implementations are not meant to be used outside of the ResizeObserver\r\n * modules as they cover only a limited range of use cases.\r\n */\r\n/* eslint-disable require-jsdoc, valid-jsdoc */\r\nvar MapShim = (function () {\r\n if (typeof Map !== 'undefined') {\r\n return Map;\r\n }\r\n /**\r\n * Returns index in provided array that matches the specified key.\r\n *\r\n * @param {Array} arr\r\n * @param {*} key\r\n * @returns {number}\r\n */\r\n function getIndex(arr, key) {\r\n var result = -1;\r\n arr.some(function (entry, index) {\r\n if (entry[0] === key) {\r\n result = index;\r\n return true;\r\n }\r\n return false;\r\n });\r\n return result;\r\n }\r\n return /** @class */ (function () {\r\n function class_1() {\r\n this.__entries__ = [];\r\n }\r\n Object.defineProperty(class_1.prototype, \"size\", {\r\n /**\r\n * @returns {boolean}\r\n */\r\n get: function () {\r\n return this.__entries__.length;\r\n },\r\n enumerable: true,\r\n configurable: true\r\n });\r\n /**\r\n * @param {*} key\r\n * @returns {*}\r\n */\r\n class_1.prototype.get = function (key) {\r\n var index = getIndex(this.__entries__, key);\r\n var entry = this.__entries__[index];\r\n return entry && entry[1];\r\n };\r\n /**\r\n * @param {*} key\r\n * @param {*} value\r\n * @returns {void}\r\n */\r\n class_1.prototype.set = function (key, value) {\r\n var index = getIndex(this.__entries__, key);\r\n if (~index) {\r\n this.__entries__[index][1] = value;\r\n }\r\n else {\r\n this.__entries__.push([key, value]);\r\n }\r\n };\r\n /**\r\n * @param {*} key\r\n * @returns {void}\r\n */\r\n class_1.prototype.delete = function (key) {\r\n var entries = this.__entries__;\r\n var index = getIndex(entries, key);\r\n if (~index) {\r\n entries.splice(index, 1);\r\n }\r\n };\r\n /**\r\n * @param {*} key\r\n * @returns {void}\r\n */\r\n class_1.prototype.has = function (key) {\r\n return !!~getIndex(this.__entries__, key);\r\n };\r\n /**\r\n * @returns {void}\r\n */\r\n class_1.prototype.clear = function () {\r\n this.__entries__.splice(0);\r\n };\r\n /**\r\n * @param {Function} callback\r\n * @param {*} [ctx=null]\r\n * @returns {void}\r\n */\r\n class_1.prototype.forEach = function (callback, ctx) {\r\n if (ctx === void 0) { ctx = null; }\r\n for (var _i = 0, _a = this.__entries__; _i < _a.length; _i++) {\r\n var entry = _a[_i];\r\n callback.call(ctx, entry[1], entry[0]);\r\n }\r\n };\r\n return class_1;\r\n }());\r\n})();\n\n/**\r\n * Detects whether window and document objects are available in current environment.\r\n */\r\nvar isBrowser = typeof window !== 'undefined' && typeof document !== 'undefined' && window.document === document;\n\n// Returns global object of a current environment.\r\nvar global$1 = (function () {\r\n if (typeof global !== 'undefined' && global.Math === Math) {\r\n return global;\r\n }\r\n if (typeof self !== 'undefined' && self.Math === Math) {\r\n return self;\r\n }\r\n if (typeof window !== 'undefined' && window.Math === Math) {\r\n return window;\r\n }\r\n // eslint-disable-next-line no-new-func\r\n return Function('return this')();\r\n})();\n\n/**\r\n * A shim for the requestAnimationFrame which falls back to the setTimeout if\r\n * first one is not supported.\r\n *\r\n * @returns {number} Requests' identifier.\r\n */\r\nvar requestAnimationFrame$1 = (function () {\r\n if (typeof requestAnimationFrame === 'function') {\r\n // It's required to use a bounded function because IE sometimes throws\r\n // an \"Invalid calling object\" error if rAF is invoked without the global\r\n // object on the left hand side.\r\n return requestAnimationFrame.bind(global$1);\r\n }\r\n return function (callback) { return setTimeout(function () { return callback(Date.now()); }, 1000 / 60); };\r\n})();\n\n// Defines minimum timeout before adding a trailing call.\r\nvar trailingTimeout = 2;\r\n/**\r\n * Creates a wrapper function which ensures that provided callback will be\r\n * invoked only once during the specified delay period.\r\n *\r\n * @param {Function} callback - Function to be invoked after the delay period.\r\n * @param {number} delay - Delay after which to invoke callback.\r\n * @returns {Function}\r\n */\r\nfunction throttle (callback, delay) {\r\n var leadingCall = false, trailingCall = false, lastCallTime = 0;\r\n /**\r\n * Invokes the original callback function and schedules new invocation if\r\n * the \"proxy\" was called during current request.\r\n *\r\n * @returns {void}\r\n */\r\n function resolvePending() {\r\n if (leadingCall) {\r\n leadingCall = false;\r\n callback();\r\n }\r\n if (trailingCall) {\r\n proxy();\r\n }\r\n }\r\n /**\r\n * Callback invoked after the specified delay. It will further postpone\r\n * invocation of the original function delegating it to the\r\n * requestAnimationFrame.\r\n *\r\n * @returns {void}\r\n */\r\n function timeoutCallback() {\r\n requestAnimationFrame$1(resolvePending);\r\n }\r\n /**\r\n * Schedules invocation of the original function.\r\n *\r\n * @returns {void}\r\n */\r\n function proxy() {\r\n var timeStamp = Date.now();\r\n if (leadingCall) {\r\n // Reject immediately following calls.\r\n if (timeStamp - lastCallTime < trailingTimeout) {\r\n return;\r\n }\r\n // Schedule new call to be in invoked when the pending one is resolved.\r\n // This is important for \"transitions\" which never actually start\r\n // immediately so there is a chance that we might miss one if change\r\n // happens amids the pending invocation.\r\n trailingCall = true;\r\n }\r\n else {\r\n leadingCall = true;\r\n trailingCall = false;\r\n setTimeout(timeoutCallback, delay);\r\n }\r\n lastCallTime = timeStamp;\r\n }\r\n return proxy;\r\n}\n\n// Minimum delay before invoking the update of observers.\r\nvar REFRESH_DELAY = 20;\r\n// A list of substrings of CSS properties used to find transition events that\r\n// might affect dimensions of observed elements.\r\nvar transitionKeys = ['top', 'right', 'bottom', 'left', 'width', 'height', 'size', 'weight'];\r\n// Check if MutationObserver is available.\r\nvar mutationObserverSupported = typeof MutationObserver !== 'undefined';\r\n/**\r\n * Singleton controller class which handles updates of ResizeObserver instances.\r\n */\r\nvar ResizeObserverController = /** @class */ (function () {\r\n /**\r\n * Creates a new instance of ResizeObserverController.\r\n *\r\n * @private\r\n */\r\n function ResizeObserverController() {\r\n /**\r\n * Indicates whether DOM listeners have been added.\r\n *\r\n * @private {boolean}\r\n */\r\n this.connected_ = false;\r\n /**\r\n * Tells that controller has subscribed for Mutation Events.\r\n *\r\n * @private {boolean}\r\n */\r\n this.mutationEventsAdded_ = false;\r\n /**\r\n * Keeps reference to the instance of MutationObserver.\r\n *\r\n * @private {MutationObserver}\r\n */\r\n this.mutationsObserver_ = null;\r\n /**\r\n * A list of connected observers.\r\n *\r\n * @private {Array}\r\n */\r\n this.observers_ = [];\r\n this.onTransitionEnd_ = this.onTransitionEnd_.bind(this);\r\n this.refresh = throttle(this.refresh.bind(this), REFRESH_DELAY);\r\n }\r\n /**\r\n * Adds observer to observers list.\r\n *\r\n * @param {ResizeObserverSPI} observer - Observer to be added.\r\n * @returns {void}\r\n */\r\n ResizeObserverController.prototype.addObserver = function (observer) {\r\n if (!~this.observers_.indexOf(observer)) {\r\n this.observers_.push(observer);\r\n }\r\n // Add listeners if they haven't been added yet.\r\n if (!this.connected_) {\r\n this.connect_();\r\n }\r\n };\r\n /**\r\n * Removes observer from observers list.\r\n *\r\n * @param {ResizeObserverSPI} observer - Observer to be removed.\r\n * @returns {void}\r\n */\r\n ResizeObserverController.prototype.removeObserver = function (observer) {\r\n var observers = this.observers_;\r\n var index = observers.indexOf(observer);\r\n // Remove observer if it's present in registry.\r\n if (~index) {\r\n observers.splice(index, 1);\r\n }\r\n // Remove listeners if controller has no connected observers.\r\n if (!observers.length && this.connected_) {\r\n this.disconnect_();\r\n }\r\n };\r\n /**\r\n * Invokes the update of observers. It will continue running updates insofar\r\n * it detects changes.\r\n *\r\n * @returns {void}\r\n */\r\n ResizeObserverController.prototype.refresh = function () {\r\n var changesDetected = this.updateObservers_();\r\n // Continue running updates if changes have been detected as there might\r\n // be future ones caused by CSS transitions.\r\n if (changesDetected) {\r\n this.refresh();\r\n }\r\n };\r\n /**\r\n * Updates every observer from observers list and notifies them of queued\r\n * entries.\r\n *\r\n * @private\r\n * @returns {boolean} Returns \"true\" if any observer has detected changes in\r\n * dimensions of it's elements.\r\n */\r\n ResizeObserverController.prototype.updateObservers_ = function () {\r\n // Collect observers that have active observations.\r\n var activeObservers = this.observers_.filter(function (observer) {\r\n return observer.gatherActive(), observer.hasActive();\r\n });\r\n // Deliver notifications in a separate cycle in order to avoid any\r\n // collisions between observers, e.g. when multiple instances of\r\n // ResizeObserver are tracking the same element and the callback of one\r\n // of them changes content dimensions of the observed target. Sometimes\r\n // this may result in notifications being blocked for the rest of observers.\r\n activeObservers.forEach(function (observer) { return observer.broadcastActive(); });\r\n return activeObservers.length > 0;\r\n };\r\n /**\r\n * Initializes DOM listeners.\r\n *\r\n * @private\r\n * @returns {void}\r\n */\r\n ResizeObserverController.prototype.connect_ = function () {\r\n // Do nothing if running in a non-browser environment or if listeners\r\n // have been already added.\r\n if (!isBrowser || this.connected_) {\r\n return;\r\n }\r\n // Subscription to the \"Transitionend\" event is used as a workaround for\r\n // delayed transitions. This way it's possible to capture at least the\r\n // final state of an element.\r\n document.addEventListener('transitionend', this.onTransitionEnd_);\r\n window.addEventListener('resize', this.refresh);\r\n if (mutationObserverSupported) {\r\n this.mutationsObserver_ = new MutationObserver(this.refresh);\r\n this.mutationsObserver_.observe(document, {\r\n attributes: true,\r\n childList: true,\r\n characterData: true,\r\n subtree: true\r\n });\r\n }\r\n else {\r\n document.addEventListener('DOMSubtreeModified', this.refresh);\r\n this.mutationEventsAdded_ = true;\r\n }\r\n this.connected_ = true;\r\n };\r\n /**\r\n * Removes DOM listeners.\r\n *\r\n * @private\r\n * @returns {void}\r\n */\r\n ResizeObserverController.prototype.disconnect_ = function () {\r\n // Do nothing if running in a non-browser environment or if listeners\r\n // have been already removed.\r\n if (!isBrowser || !this.connected_) {\r\n return;\r\n }\r\n document.removeEventListener('transitionend', this.onTransitionEnd_);\r\n window.removeEventListener('resize', this.refresh);\r\n if (this.mutationsObserver_) {\r\n this.mutationsObserver_.disconnect();\r\n }\r\n if (this.mutationEventsAdded_) {\r\n document.removeEventListener('DOMSubtreeModified', this.refresh);\r\n }\r\n this.mutationsObserver_ = null;\r\n this.mutationEventsAdded_ = false;\r\n this.connected_ = false;\r\n };\r\n /**\r\n * \"Transitionend\" event handler.\r\n *\r\n * @private\r\n * @param {TransitionEvent} event\r\n * @returns {void}\r\n */\r\n ResizeObserverController.prototype.onTransitionEnd_ = function (_a) {\r\n var _b = _a.propertyName, propertyName = _b === void 0 ? '' : _b;\r\n // Detect whether transition may affect dimensions of an element.\r\n var isReflowProperty = transitionKeys.some(function (key) {\r\n return !!~propertyName.indexOf(key);\r\n });\r\n if (isReflowProperty) {\r\n this.refresh();\r\n }\r\n };\r\n /**\r\n * Returns instance of the ResizeObserverController.\r\n *\r\n * @returns {ResizeObserverController}\r\n */\r\n ResizeObserverController.getInstance = function () {\r\n if (!this.instance_) {\r\n this.instance_ = new ResizeObserverController();\r\n }\r\n return this.instance_;\r\n };\r\n /**\r\n * Holds reference to the controller's instance.\r\n *\r\n * @private {ResizeObserverController}\r\n */\r\n ResizeObserverController.instance_ = null;\r\n return ResizeObserverController;\r\n}());\n\n/**\r\n * Defines non-writable/enumerable properties of the provided target object.\r\n *\r\n * @param {Object} target - Object for which to define properties.\r\n * @param {Object} props - Properties to be defined.\r\n * @returns {Object} Target object.\r\n */\r\nvar defineConfigurable = (function (target, props) {\r\n for (var _i = 0, _a = Object.keys(props); _i < _a.length; _i++) {\r\n var key = _a[_i];\r\n Object.defineProperty(target, key, {\r\n value: props[key],\r\n enumerable: false,\r\n writable: false,\r\n configurable: true\r\n });\r\n }\r\n return target;\r\n});\n\n/**\r\n * Returns the global object associated with provided element.\r\n *\r\n * @param {Object} target\r\n * @returns {Object}\r\n */\r\nvar getWindowOf = (function (target) {\r\n // Assume that the element is an instance of Node, which means that it\r\n // has the \"ownerDocument\" property from which we can retrieve a\r\n // corresponding global object.\r\n var ownerGlobal = target && target.ownerDocument && target.ownerDocument.defaultView;\r\n // Return the local global object if it's not possible extract one from\r\n // provided element.\r\n return ownerGlobal || global$1;\r\n});\n\n// Placeholder of an empty content rectangle.\r\nvar emptyRect = createRectInit(0, 0, 0, 0);\r\n/**\r\n * Converts provided string to a number.\r\n *\r\n * @param {number|string} value\r\n * @returns {number}\r\n */\r\nfunction toFloat(value) {\r\n return parseFloat(value) || 0;\r\n}\r\n/**\r\n * Extracts borders size from provided styles.\r\n *\r\n * @param {CSSStyleDeclaration} styles\r\n * @param {...string} positions - Borders positions (top, right, ...)\r\n * @returns {number}\r\n */\r\nfunction getBordersSize(styles) {\r\n var positions = [];\r\n for (var _i = 1; _i < arguments.length; _i++) {\r\n positions[_i - 1] = arguments[_i];\r\n }\r\n return positions.reduce(function (size, position) {\r\n var value = styles['border-' + position + '-width'];\r\n return size + toFloat(value);\r\n }, 0);\r\n}\r\n/**\r\n * Extracts paddings sizes from provided styles.\r\n *\r\n * @param {CSSStyleDeclaration} styles\r\n * @returns {Object} Paddings box.\r\n */\r\nfunction getPaddings(styles) {\r\n var positions = ['top', 'right', 'bottom', 'left'];\r\n var paddings = {};\r\n for (var _i = 0, positions_1 = positions; _i < positions_1.length; _i++) {\r\n var position = positions_1[_i];\r\n var value = styles['padding-' + position];\r\n paddings[position] = toFloat(value);\r\n }\r\n return paddings;\r\n}\r\n/**\r\n * Calculates content rectangle of provided SVG element.\r\n *\r\n * @param {SVGGraphicsElement} target - Element content rectangle of which needs\r\n * to be calculated.\r\n * @returns {DOMRectInit}\r\n */\r\nfunction getSVGContentRect(target) {\r\n var bbox = target.getBBox();\r\n return createRectInit(0, 0, bbox.width, bbox.height);\r\n}\r\n/**\r\n * Calculates content rectangle of provided HTMLElement.\r\n *\r\n * @param {HTMLElement} target - Element for which to calculate the content rectangle.\r\n * @returns {DOMRectInit}\r\n */\r\nfunction getHTMLElementContentRect(target) {\r\n // Client width & height properties can't be\r\n // used exclusively as they provide rounded values.\r\n var clientWidth = target.clientWidth, clientHeight = target.clientHeight;\r\n // By this condition we can catch all non-replaced inline, hidden and\r\n // detached elements. Though elements with width & height properties less\r\n // than 0.5 will be discarded as well.\r\n //\r\n // Without it we would need to implement separate methods for each of\r\n // those cases and it's not possible to perform a precise and performance\r\n // effective test for hidden elements. E.g. even jQuery's ':visible' filter\r\n // gives wrong results for elements with width & height less than 0.5.\r\n if (!clientWidth && !clientHeight) {\r\n return emptyRect;\r\n }\r\n var styles = getWindowOf(target).getComputedStyle(target);\r\n var paddings = getPaddings(styles);\r\n var horizPad = paddings.left + paddings.right;\r\n var vertPad = paddings.top + paddings.bottom;\r\n // Computed styles of width & height are being used because they are the\r\n // only dimensions available to JS that contain non-rounded values. It could\r\n // be possible to utilize the getBoundingClientRect if only it's data wasn't\r\n // affected by CSS transformations let alone paddings, borders and scroll bars.\r\n var width = toFloat(styles.width), height = toFloat(styles.height);\r\n // Width & height include paddings and borders when the 'border-box' box\r\n // model is applied (except for IE).\r\n if (styles.boxSizing === 'border-box') {\r\n // Following conditions are required to handle Internet Explorer which\r\n // doesn't include paddings and borders to computed CSS dimensions.\r\n //\r\n // We can say that if CSS dimensions + paddings are equal to the \"client\"\r\n // properties then it's either IE, and thus we don't need to subtract\r\n // anything, or an element merely doesn't have paddings/borders styles.\r\n if (Math.round(width + horizPad) !== clientWidth) {\r\n width -= getBordersSize(styles, 'left', 'right') + horizPad;\r\n }\r\n if (Math.round(height + vertPad) !== clientHeight) {\r\n height -= getBordersSize(styles, 'top', 'bottom') + vertPad;\r\n }\r\n }\r\n // Following steps can't be applied to the document's root element as its\r\n // client[Width/Height] properties represent viewport area of the window.\r\n // Besides, it's as well not necessary as the itself neither has\r\n // rendered scroll bars nor it can be clipped.\r\n if (!isDocumentElement(target)) {\r\n // In some browsers (only in Firefox, actually) CSS width & height\r\n // include scroll bars size which can be removed at this step as scroll\r\n // bars are the only difference between rounded dimensions + paddings\r\n // and \"client\" properties, though that is not always true in Chrome.\r\n var vertScrollbar = Math.round(width + horizPad) - clientWidth;\r\n var horizScrollbar = Math.round(height + vertPad) - clientHeight;\r\n // Chrome has a rather weird rounding of \"client\" properties.\r\n // E.g. for an element with content width of 314.2px it sometimes gives\r\n // the client width of 315px and for the width of 314.7px it may give\r\n // 314px. And it doesn't happen all the time. So just ignore this delta\r\n // as a non-relevant.\r\n if (Math.abs(vertScrollbar) !== 1) {\r\n width -= vertScrollbar;\r\n }\r\n if (Math.abs(horizScrollbar) !== 1) {\r\n height -= horizScrollbar;\r\n }\r\n }\r\n return createRectInit(paddings.left, paddings.top, width, height);\r\n}\r\n/**\r\n * Checks whether provided element is an instance of the SVGGraphicsElement.\r\n *\r\n * @param {Element} target - Element to be checked.\r\n * @returns {boolean}\r\n */\r\nvar isSVGGraphicsElement = (function () {\r\n // Some browsers, namely IE and Edge, don't have the SVGGraphicsElement\r\n // interface.\r\n if (typeof SVGGraphicsElement !== 'undefined') {\r\n return function (target) { return target instanceof getWindowOf(target).SVGGraphicsElement; };\r\n }\r\n // If it's so, then check that element is at least an instance of the\r\n // SVGElement and that it has the \"getBBox\" method.\r\n // eslint-disable-next-line no-extra-parens\r\n return function (target) { return (target instanceof getWindowOf(target).SVGElement &&\r\n typeof target.getBBox === 'function'); };\r\n})();\r\n/**\r\n * Checks whether provided element is a document element ().\r\n *\r\n * @param {Element} target - Element to be checked.\r\n * @returns {boolean}\r\n */\r\nfunction isDocumentElement(target) {\r\n return target === getWindowOf(target).document.documentElement;\r\n}\r\n/**\r\n * Calculates an appropriate content rectangle for provided html or svg element.\r\n *\r\n * @param {Element} target - Element content rectangle of which needs to be calculated.\r\n * @returns {DOMRectInit}\r\n */\r\nfunction getContentRect(target) {\r\n if (!isBrowser) {\r\n return emptyRect;\r\n }\r\n if (isSVGGraphicsElement(target)) {\r\n return getSVGContentRect(target);\r\n }\r\n return getHTMLElementContentRect(target);\r\n}\r\n/**\r\n * Creates rectangle with an interface of the DOMRectReadOnly.\r\n * Spec: https://drafts.fxtf.org/geometry/#domrectreadonly\r\n *\r\n * @param {DOMRectInit} rectInit - Object with rectangle's x/y coordinates and dimensions.\r\n * @returns {DOMRectReadOnly}\r\n */\r\nfunction createReadOnlyRect(_a) {\r\n var x = _a.x, y = _a.y, width = _a.width, height = _a.height;\r\n // If DOMRectReadOnly is available use it as a prototype for the rectangle.\r\n var Constr = typeof DOMRectReadOnly !== 'undefined' ? DOMRectReadOnly : Object;\r\n var rect = Object.create(Constr.prototype);\r\n // Rectangle's properties are not writable and non-enumerable.\r\n defineConfigurable(rect, {\r\n x: x, y: y, width: width, height: height,\r\n top: y,\r\n right: x + width,\r\n bottom: height + y,\r\n left: x\r\n });\r\n return rect;\r\n}\r\n/**\r\n * Creates DOMRectInit object based on the provided dimensions and the x/y coordinates.\r\n * Spec: https://drafts.fxtf.org/geometry/#dictdef-domrectinit\r\n *\r\n * @param {number} x - X coordinate.\r\n * @param {number} y - Y coordinate.\r\n * @param {number} width - Rectangle's width.\r\n * @param {number} height - Rectangle's height.\r\n * @returns {DOMRectInit}\r\n */\r\nfunction createRectInit(x, y, width, height) {\r\n return { x: x, y: y, width: width, height: height };\r\n}\n\n/**\r\n * Class that is responsible for computations of the content rectangle of\r\n * provided DOM element and for keeping track of it's changes.\r\n */\r\nvar ResizeObservation = /** @class */ (function () {\r\n /**\r\n * Creates an instance of ResizeObservation.\r\n *\r\n * @param {Element} target - Element to be observed.\r\n */\r\n function ResizeObservation(target) {\r\n /**\r\n * Broadcasted width of content rectangle.\r\n *\r\n * @type {number}\r\n */\r\n this.broadcastWidth = 0;\r\n /**\r\n * Broadcasted height of content rectangle.\r\n *\r\n * @type {number}\r\n */\r\n this.broadcastHeight = 0;\r\n /**\r\n * Reference to the last observed content rectangle.\r\n *\r\n * @private {DOMRectInit}\r\n */\r\n this.contentRect_ = createRectInit(0, 0, 0, 0);\r\n this.target = target;\r\n }\r\n /**\r\n * Updates content rectangle and tells whether it's width or height properties\r\n * have changed since the last broadcast.\r\n *\r\n * @returns {boolean}\r\n */\r\n ResizeObservation.prototype.isActive = function () {\r\n var rect = getContentRect(this.target);\r\n this.contentRect_ = rect;\r\n return (rect.width !== this.broadcastWidth ||\r\n rect.height !== this.broadcastHeight);\r\n };\r\n /**\r\n * Updates 'broadcastWidth' and 'broadcastHeight' properties with a data\r\n * from the corresponding properties of the last observed content rectangle.\r\n *\r\n * @returns {DOMRectInit} Last observed content rectangle.\r\n */\r\n ResizeObservation.prototype.broadcastRect = function () {\r\n var rect = this.contentRect_;\r\n this.broadcastWidth = rect.width;\r\n this.broadcastHeight = rect.height;\r\n return rect;\r\n };\r\n return ResizeObservation;\r\n}());\n\nvar ResizeObserverEntry = /** @class */ (function () {\r\n /**\r\n * Creates an instance of ResizeObserverEntry.\r\n *\r\n * @param {Element} target - Element that is being observed.\r\n * @param {DOMRectInit} rectInit - Data of the element's content rectangle.\r\n */\r\n function ResizeObserverEntry(target, rectInit) {\r\n var contentRect = createReadOnlyRect(rectInit);\r\n // According to the specification following properties are not writable\r\n // and are also not enumerable in the native implementation.\r\n //\r\n // Property accessors are not being used as they'd require to define a\r\n // private WeakMap storage which may cause memory leaks in browsers that\r\n // don't support this type of collections.\r\n defineConfigurable(this, { target: target, contentRect: contentRect });\r\n }\r\n return ResizeObserverEntry;\r\n}());\n\nvar ResizeObserverSPI = /** @class */ (function () {\r\n /**\r\n * Creates a new instance of ResizeObserver.\r\n *\r\n * @param {ResizeObserverCallback} callback - Callback function that is invoked\r\n * when one of the observed elements changes it's content dimensions.\r\n * @param {ResizeObserverController} controller - Controller instance which\r\n * is responsible for the updates of observer.\r\n * @param {ResizeObserver} callbackCtx - Reference to the public\r\n * ResizeObserver instance which will be passed to callback function.\r\n */\r\n function ResizeObserverSPI(callback, controller, callbackCtx) {\r\n /**\r\n * Collection of resize observations that have detected changes in dimensions\r\n * of elements.\r\n *\r\n * @private {Array}\r\n */\r\n this.activeObservations_ = [];\r\n /**\r\n * Registry of the ResizeObservation instances.\r\n *\r\n * @private {Map}\r\n */\r\n this.observations_ = new MapShim();\r\n if (typeof callback !== 'function') {\r\n throw new TypeError('The callback provided as parameter 1 is not a function.');\r\n }\r\n this.callback_ = callback;\r\n this.controller_ = controller;\r\n this.callbackCtx_ = callbackCtx;\r\n }\r\n /**\r\n * Starts observing provided element.\r\n *\r\n * @param {Element} target - Element to be observed.\r\n * @returns {void}\r\n */\r\n ResizeObserverSPI.prototype.observe = function (target) {\r\n if (!arguments.length) {\r\n throw new TypeError('1 argument required, but only 0 present.');\r\n }\r\n // Do nothing if current environment doesn't have the Element interface.\r\n if (typeof Element === 'undefined' || !(Element instanceof Object)) {\r\n return;\r\n }\r\n if (!(target instanceof getWindowOf(target).Element)) {\r\n throw new TypeError('parameter 1 is not of type \"Element\".');\r\n }\r\n var observations = this.observations_;\r\n // Do nothing if element is already being observed.\r\n if (observations.has(target)) {\r\n return;\r\n }\r\n observations.set(target, new ResizeObservation(target));\r\n this.controller_.addObserver(this);\r\n // Force the update of observations.\r\n this.controller_.refresh();\r\n };\r\n /**\r\n * Stops observing provided element.\r\n *\r\n * @param {Element} target - Element to stop observing.\r\n * @returns {void}\r\n */\r\n ResizeObserverSPI.prototype.unobserve = function (target) {\r\n if (!arguments.length) {\r\n throw new TypeError('1 argument required, but only 0 present.');\r\n }\r\n // Do nothing if current environment doesn't have the Element interface.\r\n if (typeof Element === 'undefined' || !(Element instanceof Object)) {\r\n return;\r\n }\r\n if (!(target instanceof getWindowOf(target).Element)) {\r\n throw new TypeError('parameter 1 is not of type \"Element\".');\r\n }\r\n var observations = this.observations_;\r\n // Do nothing if element is not being observed.\r\n if (!observations.has(target)) {\r\n return;\r\n }\r\n observations.delete(target);\r\n if (!observations.size) {\r\n this.controller_.removeObserver(this);\r\n }\r\n };\r\n /**\r\n * Stops observing all elements.\r\n *\r\n * @returns {void}\r\n */\r\n ResizeObserverSPI.prototype.disconnect = function () {\r\n this.clearActive();\r\n this.observations_.clear();\r\n this.controller_.removeObserver(this);\r\n };\r\n /**\r\n * Collects observation instances the associated element of which has changed\r\n * it's content rectangle.\r\n *\r\n * @returns {void}\r\n */\r\n ResizeObserverSPI.prototype.gatherActive = function () {\r\n var _this = this;\r\n this.clearActive();\r\n this.observations_.forEach(function (observation) {\r\n if (observation.isActive()) {\r\n _this.activeObservations_.push(observation);\r\n }\r\n });\r\n };\r\n /**\r\n * Invokes initial callback function with a list of ResizeObserverEntry\r\n * instances collected from active resize observations.\r\n *\r\n * @returns {void}\r\n */\r\n ResizeObserverSPI.prototype.broadcastActive = function () {\r\n // Do nothing if observer doesn't have active observations.\r\n if (!this.hasActive()) {\r\n return;\r\n }\r\n var ctx = this.callbackCtx_;\r\n // Create ResizeObserverEntry instance for every active observation.\r\n var entries = this.activeObservations_.map(function (observation) {\r\n return new ResizeObserverEntry(observation.target, observation.broadcastRect());\r\n });\r\n this.callback_.call(ctx, entries, ctx);\r\n this.clearActive();\r\n };\r\n /**\r\n * Clears the collection of active observations.\r\n *\r\n * @returns {void}\r\n */\r\n ResizeObserverSPI.prototype.clearActive = function () {\r\n this.activeObservations_.splice(0);\r\n };\r\n /**\r\n * Tells whether observer has active observations.\r\n *\r\n * @returns {boolean}\r\n */\r\n ResizeObserverSPI.prototype.hasActive = function () {\r\n return this.activeObservations_.length > 0;\r\n };\r\n return ResizeObserverSPI;\r\n}());\n\n// Registry of internal observers. If WeakMap is not available use current shim\r\n// for the Map collection as it has all required methods and because WeakMap\r\n// can't be fully polyfilled anyway.\r\nvar observers = typeof WeakMap !== 'undefined' ? new WeakMap() : new MapShim();\r\n/**\r\n * ResizeObserver API. Encapsulates the ResizeObserver SPI implementation\r\n * exposing only those methods and properties that are defined in the spec.\r\n */\r\nvar ResizeObserver = /** @class */ (function () {\r\n /**\r\n * Creates a new instance of ResizeObserver.\r\n *\r\n * @param {ResizeObserverCallback} callback - Callback that is invoked when\r\n * dimensions of the observed elements change.\r\n */\r\n function ResizeObserver(callback) {\r\n if (!(this instanceof ResizeObserver)) {\r\n throw new TypeError('Cannot call a class as a function.');\r\n }\r\n if (!arguments.length) {\r\n throw new TypeError('1 argument required, but only 0 present.');\r\n }\r\n var controller = ResizeObserverController.getInstance();\r\n var observer = new ResizeObserverSPI(callback, controller, this);\r\n observers.set(this, observer);\r\n }\r\n return ResizeObserver;\r\n}());\r\n// Expose public methods of ResizeObserver.\r\n[\r\n 'observe',\r\n 'unobserve',\r\n 'disconnect'\r\n].forEach(function (method) {\r\n ResizeObserver.prototype[method] = function () {\r\n var _a;\r\n return (_a = observers.get(this))[method].apply(_a, arguments);\r\n };\r\n});\n\nvar index = (function () {\r\n // Export existing implementation if available.\r\n if (typeof global$1.ResizeObserver !== 'undefined') {\r\n return global$1.ResizeObserver;\r\n }\r\n return ResizeObserver;\r\n})();\n\nexport default index;\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport ResizeObserver from \"resize-observer-polyfill\"\nimport {\n NEVER,\n Observable,\n Subject,\n defer,\n filter,\n finalize,\n map,\n merge,\n of,\n shareReplay,\n startWith,\n switchMap,\n tap\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Element offset\n */\nexport interface ElementSize {\n width: number /* Element width */\n height: number /* Element height */\n}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Resize observer entry subject\n */\nconst entry$ = new Subject()\n\n/**\n * Resize observer observable\n *\n * This observable will create a `ResizeObserver` on the first subscription\n * and will automatically terminate it when there are no more subscribers.\n * It's quite important to centralize observation in a single `ResizeObserver`,\n * as the performance difference can be quite dramatic, as the link shows.\n *\n * @see https://bit.ly/3iIYfEm - Google Groups on performance\n */\nconst observer$ = defer(() => of(\n new ResizeObserver(entries => {\n for (const entry of entries)\n entry$.next(entry)\n })\n))\n .pipe(\n switchMap(observer => merge(NEVER, of(observer))\n .pipe(\n finalize(() => observer.disconnect())\n )\n ),\n shareReplay(1)\n )\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve element size\n *\n * @param el - Element\n *\n * @returns Element size\n */\nexport function getElementSize(\n el: HTMLElement\n): ElementSize {\n return {\n width: el.offsetWidth,\n height: el.offsetHeight\n }\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch element size\n *\n * This function returns an observable that subscribes to a single internal\n * instance of `ResizeObserver` upon subscription, and emit resize events until\n * termination. Note that this function should not be called with the same\n * element twice, as the first unsubscription will terminate observation.\n *\n * Sadly, we can't use the `DOMRect` objects returned by the observer, because\n * we need the emitted values to be consistent with `getElementSize`, which will\n * return the used values (rounded) and not actual values (unrounded). Thus, we\n * use the `offset*` properties. See the linked GitHub issue.\n *\n * @see https://bit.ly/3m0k3he - GitHub issue\n *\n * @param el - Element\n *\n * @returns Element size observable\n */\nexport function watchElementSize(\n el: HTMLElement\n): Observable {\n return observer$\n .pipe(\n tap(observer => observer.observe(el)),\n switchMap(observer => entry$\n .pipe(\n filter(({ target }) => target === el),\n finalize(() => observer.unobserve(el)),\n map(() => getElementSize(el))\n )\n ),\n startWith(getElementSize(el))\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { ElementSize } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve element content size (= scroll width and height)\n *\n * @param el - Element\n *\n * @returns Element content size\n */\nexport function getElementContentSize(\n el: HTMLElement\n): ElementSize {\n return {\n width: el.scrollWidth,\n height: el.scrollHeight\n }\n}\n\n/**\n * Retrieve the overflowing container of an element, if any\n *\n * @param el - Element\n *\n * @returns Overflowing container or nothing\n */\nexport function getElementContainer(\n el: HTMLElement\n): HTMLElement | undefined {\n let parent = el.parentElement\n while (parent)\n if (\n el.scrollWidth <= parent.scrollWidth &&\n el.scrollHeight <= parent.scrollHeight\n )\n parent = (el = parent).parentElement\n else\n break\n\n /* Return overflowing container */\n return parent ? el : undefined\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n NEVER,\n Observable,\n Subject,\n defer,\n distinctUntilChanged,\n filter,\n finalize,\n map,\n merge,\n of,\n shareReplay,\n switchMap,\n tap\n} from \"rxjs\"\n\nimport {\n getElementContentSize,\n getElementSize,\n watchElementContentOffset\n} from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Intersection observer entry subject\n */\nconst entry$ = new Subject()\n\n/**\n * Intersection observer observable\n *\n * This observable will create an `IntersectionObserver` on first subscription\n * and will automatically terminate it when there are no more subscribers.\n *\n * @see https://bit.ly/3iIYfEm - Google Groups on performance\n */\nconst observer$ = defer(() => of(\n new IntersectionObserver(entries => {\n for (const entry of entries)\n entry$.next(entry)\n }, {\n threshold: 0\n })\n))\n .pipe(\n switchMap(observer => merge(NEVER, of(observer))\n .pipe(\n finalize(() => observer.disconnect())\n )\n ),\n shareReplay(1)\n )\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch element visibility\n *\n * @param el - Element\n *\n * @returns Element visibility observable\n */\nexport function watchElementVisibility(\n el: HTMLElement\n): Observable {\n return observer$\n .pipe(\n tap(observer => observer.observe(el)),\n switchMap(observer => entry$\n .pipe(\n filter(({ target }) => target === el),\n finalize(() => observer.unobserve(el)),\n map(({ isIntersecting }) => isIntersecting)\n )\n )\n )\n}\n\n/**\n * Watch element boundary\n *\n * This function returns an observable which emits whether the bottom content\n * boundary (= scroll offset) of an element is within a certain threshold.\n *\n * @param el - Element\n * @param threshold - Threshold\n *\n * @returns Element boundary observable\n */\nexport function watchElementBoundary(\n el: HTMLElement, threshold = 16\n): Observable {\n return watchElementContentOffset(el)\n .pipe(\n map(({ y }) => {\n const visible = getElementSize(el)\n const content = getElementContentSize(el)\n return y >= (\n content.height - visible.height - threshold\n )\n }),\n distinctUntilChanged()\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n fromEvent,\n map,\n startWith\n} from \"rxjs\"\n\nimport { getElement } from \"../element\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Toggle\n */\nexport type Toggle =\n | \"drawer\" /* Toggle for drawer */\n | \"search\" /* Toggle for search */\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Toggle map\n */\nconst toggles: Record = {\n drawer: getElement(\"[data-md-toggle=drawer]\"),\n search: getElement(\"[data-md-toggle=search]\")\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve the value of a toggle\n *\n * @param name - Toggle\n *\n * @returns Toggle value\n */\nexport function getToggle(name: Toggle): boolean {\n return toggles[name].checked\n}\n\n/**\n * Set toggle\n *\n * Simulating a click event seems to be the most cross-browser compatible way\n * of changing the value while also emitting a `change` event. Before, Material\n * used `CustomEvent` to programmatically change the value of a toggle, but this\n * is a much simpler and cleaner solution which doesn't require a polyfill.\n *\n * @param name - Toggle\n * @param value - Toggle value\n */\nexport function setToggle(name: Toggle, value: boolean): void {\n if (toggles[name].checked !== value)\n toggles[name].click()\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch toggle\n *\n * @param name - Toggle\n *\n * @returns Toggle value observable\n */\nexport function watchToggle(name: Toggle): Observable {\n const el = toggles[name]\n return fromEvent(el, \"change\")\n .pipe(\n map(() => el.checked),\n startWith(el.checked)\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n filter,\n fromEvent,\n map,\n share\n} from \"rxjs\"\n\nimport { getActiveElement } from \"../element\"\nimport { getToggle } from \"../toggle\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Keyboard mode\n */\nexport type KeyboardMode =\n | \"global\" /* Global */\n | \"search\" /* Search is open */\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Keyboard\n */\nexport interface Keyboard {\n mode: KeyboardMode /* Keyboard mode */\n type: string /* Key type */\n claim(): void /* Key claim */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Check whether an element may receive keyboard input\n *\n * @param el - Element\n * @param type - Key type\n *\n * @returns Test result\n */\nfunction isSusceptibleToKeyboard(\n el: HTMLElement, type: string\n): boolean {\n switch (el.constructor) {\n\n /* Input elements */\n case HTMLInputElement:\n /* @ts-expect-error - omit unnecessary type cast */\n if (el.type === \"radio\")\n return /^Arrow/.test(type)\n else\n return true\n\n /* Select element and textarea */\n case HTMLSelectElement:\n case HTMLTextAreaElement:\n return true\n\n /* Everything else */\n default:\n return el.isContentEditable\n }\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch keyboard\n *\n * @returns Keyboard observable\n */\nexport function watchKeyboard(): Observable {\n return fromEvent(window, \"keydown\")\n .pipe(\n filter(ev => !(ev.metaKey || ev.ctrlKey)),\n map(ev => ({\n mode: getToggle(\"search\") ? \"search\" : \"global\",\n type: ev.key,\n claim() {\n ev.preventDefault()\n ev.stopPropagation()\n }\n } as Keyboard)),\n filter(({ mode, type }) => {\n if (mode === \"global\") {\n const active = getActiveElement()\n if (typeof active !== \"undefined\")\n return !isSusceptibleToKeyboard(active, type)\n }\n return true\n }),\n share()\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { Subject } from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve location\n *\n * This function returns a `URL` object (and not `Location`) to normalize the\n * typings across the application. Furthermore, locations need to be tracked\n * without setting them and `Location` is a singleton which represents the\n * current location.\n *\n * @returns URL\n */\nexport function getLocation(): URL {\n return new URL(location.href)\n}\n\n/**\n * Set location\n *\n * @param url - URL to change to\n */\nexport function setLocation(url: URL): void {\n location.href = url.href\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch location\n *\n * @returns Location subject\n */\nexport function watchLocation(): Subject {\n return new Subject()\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { JSX as JSXInternal } from \"preact\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * HTML attributes\n */\ntype Attributes =\n & JSXInternal.HTMLAttributes\n & JSXInternal.SVGAttributes\n & Record\n\n/**\n * Child element\n */\ntype Child =\n | HTMLElement\n | Text\n | string\n | number\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Append a child node to an element\n *\n * @param el - Element\n * @param child - Child node(s)\n */\nfunction appendChild(el: HTMLElement, child: Child | Child[]): void {\n\n /* Handle primitive types (including raw HTML) */\n if (typeof child === \"string\" || typeof child === \"number\") {\n el.innerHTML += child.toString()\n\n /* Handle nodes */\n } else if (child instanceof Node) {\n el.appendChild(child)\n\n /* Handle nested children */\n } else if (Array.isArray(child)) {\n for (const node of child)\n appendChild(el, node)\n }\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * JSX factory\n *\n * @template T - Element type\n *\n * @param tag - HTML tag\n * @param attributes - HTML attributes\n * @param children - Child elements\n *\n * @returns Element\n */\nexport function h(\n tag: T, attributes?: Attributes | null, ...children: Child[]\n): HTMLElementTagNameMap[T]\n\nexport function h(\n tag: string, attributes?: Attributes | null, ...children: Child[]\n): T\n\nexport function h(\n tag: string, attributes?: Attributes | null, ...children: Child[]\n): T {\n const el = document.createElement(tag)\n\n /* Set attributes, if any */\n if (attributes)\n for (const attr of Object.keys(attributes)) {\n if (typeof attributes[attr] === \"undefined\")\n continue\n\n /* Set default attribute or boolean */\n if (typeof attributes[attr] !== \"boolean\")\n el.setAttribute(attr, attributes[attr])\n else\n el.setAttribute(attr, \"\")\n }\n\n /* Append child nodes */\n for (const child of children)\n appendChild(el, child)\n\n /* Return element */\n return el as T\n}\n\n/* ----------------------------------------------------------------------------\n * Namespace\n * ------------------------------------------------------------------------- */\n\nexport declare namespace h {\n namespace JSX {\n type Element = HTMLElement\n type IntrinsicElements = JSXInternal.IntrinsicElements\n }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Truncate a string after the given number of characters\n *\n * This is not a very reasonable approach, since the summaries kind of suck.\n * It would be better to create something more intelligent, highlighting the\n * search occurrences and making a better summary out of it, but this note was\n * written three years ago, so who knows if we'll ever fix it.\n *\n * @param value - Value to be truncated\n * @param n - Number of characters\n *\n * @returns Truncated value\n */\nexport function truncate(value: string, n: number): string {\n let i = n\n if (value.length > i) {\n while (value[i] !== \" \" && --i > 0) { /* keep eating */ }\n return `${value.substring(0, i)}...`\n }\n return value\n}\n\n/**\n * Round a number for display with repository facts\n *\n * This is a reverse-engineered version of GitHub's weird rounding algorithm\n * for stars, forks and all other numbers. While all numbers below `1,000` are\n * returned as-is, bigger numbers are converted to fixed numbers:\n *\n * - `1,049` => `1k`\n * - `1,050` => `1.1k`\n * - `1,949` => `1.9k`\n * - `1,950` => `2k`\n *\n * @param value - Original value\n *\n * @returns Rounded value\n */\nexport function round(value: number): string {\n if (value > 999) {\n const digits = +((value - 950) % 1000 > 99)\n return `${((value + 0.000001) / 1000).toFixed(digits)}k`\n } else {\n return value.toString()\n }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n filter,\n fromEvent,\n map,\n shareReplay,\n startWith\n} from \"rxjs\"\n\nimport { getOptionalElement } from \"~/browser\"\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve location hash\n *\n * @returns Location hash\n */\nexport function getLocationHash(): string {\n return location.hash.substring(1)\n}\n\n/**\n * Set location hash\n *\n * Setting a new fragment identifier via `location.hash` will have no effect\n * if the value doesn't change. When a new fragment identifier is set, we want\n * the browser to target the respective element at all times, which is why we\n * use this dirty little trick.\n *\n * @param hash - Location hash\n */\nexport function setLocationHash(hash: string): void {\n const el = h(\"a\", { href: hash })\n el.addEventListener(\"click\", ev => ev.stopPropagation())\n el.click()\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch location hash\n *\n * @returns Location hash observable\n */\nexport function watchLocationHash(): Observable {\n return fromEvent(window, \"hashchange\")\n .pipe(\n map(getLocationHash),\n startWith(getLocationHash()),\n filter(hash => hash.length > 0),\n shareReplay(1)\n )\n}\n\n/**\n * Watch location target\n *\n * @returns Location target observable\n */\nexport function watchLocationTarget(): Observable {\n return watchLocationHash()\n .pipe(\n map(id => getOptionalElement(`[id=\"${id}\"]`)!),\n filter(el => typeof el !== \"undefined\")\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n EMPTY,\n Observable,\n fromEvent,\n fromEventPattern,\n map,\n merge,\n startWith,\n switchMap\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch media query\n *\n * Note that although `MediaQueryList.addListener` is deprecated we have to\n * use it, because it's the only way to ensure proper downward compatibility.\n *\n * @see https://bit.ly/3dUBH2m - GitHub issue\n *\n * @param query - Media query\n *\n * @returns Media observable\n */\nexport function watchMedia(query: string): Observable {\n const media = matchMedia(query)\n return fromEventPattern(next => (\n media.addListener(() => next(media.matches))\n ))\n .pipe(\n startWith(media.matches)\n )\n}\n\n/**\n * Watch print mode\n *\n * @returns Print observable\n */\nexport function watchPrint(): Observable {\n const media = matchMedia(\"print\")\n return merge(\n fromEvent(window, \"beforeprint\").pipe(map(() => true)),\n fromEvent(window, \"afterprint\").pipe(map(() => false))\n )\n .pipe(\n startWith(media.matches)\n )\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Toggle an observable with a media observable\n *\n * @template T - Data type\n *\n * @param query$ - Media observable\n * @param factory - Observable factory\n *\n * @returns Toggled observable\n */\nexport function at(\n query$: Observable, factory: () => Observable\n): Observable {\n return query$\n .pipe(\n switchMap(active => active ? factory() : EMPTY)\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n EMPTY,\n Observable,\n catchError,\n from,\n map,\n of,\n shareReplay,\n switchMap,\n throwError\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch the given URL\n *\n * If the request fails (e.g. when dispatched from `file://` locations), the\n * observable will complete without emitting a value.\n *\n * @param url - Request URL\n * @param options - Options\n *\n * @returns Response observable\n */\nexport function request(\n url: URL | string, options: RequestInit = { credentials: \"same-origin\" }\n): Observable {\n return from(fetch(`${url}`, options))\n .pipe(\n catchError(() => EMPTY),\n switchMap(res => res.status !== 200\n ? throwError(() => new Error(res.statusText))\n : of(res)\n )\n )\n}\n\n/**\n * Fetch JSON from the given URL\n *\n * @template T - Data type\n *\n * @param url - Request URL\n * @param options - Options\n *\n * @returns Data observable\n */\nexport function requestJSON(\n url: URL | string, options?: RequestInit\n): Observable {\n return request(url, options)\n .pipe(\n switchMap(res => res.json()),\n shareReplay(1)\n )\n}\n\n/**\n * Fetch XML from the given URL\n *\n * @param url - Request URL\n * @param options - Options\n *\n * @returns Data observable\n */\nexport function requestXML(\n url: URL | string, options?: RequestInit\n): Observable {\n const dom = new DOMParser()\n return request(url, options)\n .pipe(\n switchMap(res => res.text()),\n map(res => dom.parseFromString(res, \"text/xml\")),\n shareReplay(1)\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n defer,\n finalize,\n fromEvent,\n map,\n merge,\n switchMap,\n take,\n throwError\n} from \"rxjs\"\n\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Create and load a `script` element\n *\n * This function returns an observable that will emit when the script was\n * successfully loaded, or throw an error if it didn't.\n *\n * @param src - Script URL\n *\n * @returns Script observable\n */\nexport function watchScript(src: string): Observable {\n const script = h(\"script\", { src })\n return defer(() => {\n document.head.appendChild(script)\n return merge(\n fromEvent(script, \"load\"),\n fromEvent(script, \"error\")\n .pipe(\n switchMap(() => (\n throwError(() => new ReferenceError(`Invalid script: ${src}`))\n ))\n )\n )\n .pipe(\n map(() => undefined),\n finalize(() => document.head.removeChild(script)),\n take(1)\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n fromEvent,\n map,\n merge,\n startWith\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Viewport offset\n */\nexport interface ViewportOffset {\n x: number /* Horizontal offset */\n y: number /* Vertical offset */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve viewport offset\n *\n * On iOS Safari, viewport offset can be negative due to overflow scrolling.\n * As this may induce strange behaviors downstream, we'll just limit it to 0.\n *\n * @returns Viewport offset\n */\nexport function getViewportOffset(): ViewportOffset {\n return {\n x: Math.max(0, scrollX),\n y: Math.max(0, scrollY)\n }\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch viewport offset\n *\n * @returns Viewport offset observable\n */\nexport function watchViewportOffset(): Observable {\n return merge(\n fromEvent(window, \"scroll\", { passive: true }),\n fromEvent(window, \"resize\", { passive: true })\n )\n .pipe(\n map(getViewportOffset),\n startWith(getViewportOffset())\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n fromEvent,\n map,\n startWith\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Viewport size\n */\nexport interface ViewportSize {\n width: number /* Viewport width */\n height: number /* Viewport height */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve viewport size\n *\n * @returns Viewport size\n */\nexport function getViewportSize(): ViewportSize {\n return {\n width: innerWidth,\n height: innerHeight\n }\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch viewport size\n *\n * @returns Viewport size observable\n */\nexport function watchViewportSize(): Observable {\n return fromEvent(window, \"resize\", { passive: true })\n .pipe(\n map(getViewportSize),\n startWith(getViewportSize())\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n combineLatest,\n map,\n shareReplay\n} from \"rxjs\"\n\nimport {\n ViewportOffset,\n watchViewportOffset\n} from \"../offset\"\nimport {\n ViewportSize,\n watchViewportSize\n} from \"../size\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Viewport\n */\nexport interface Viewport {\n offset: ViewportOffset /* Viewport offset */\n size: ViewportSize /* Viewport size */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch viewport\n *\n * @returns Viewport observable\n */\nexport function watchViewport(): Observable {\n return combineLatest([\n watchViewportOffset(),\n watchViewportSize()\n ])\n .pipe(\n map(([offset, size]) => ({ offset, size })),\n shareReplay(1)\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n combineLatest,\n distinctUntilKeyChanged,\n map\n} from \"rxjs\"\n\nimport { Header } from \"~/components\"\n\nimport { getElementOffset } from \"../../element\"\nimport { Viewport } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n viewport$: Observable /* Viewport observable */\n header$: Observable
/* Header observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch viewport relative to element\n *\n * @param el - Element\n * @param options - Options\n *\n * @returns Viewport observable\n */\nexport function watchViewportAt(\n el: HTMLElement, { viewport$, header$ }: WatchOptions\n): Observable {\n const size$ = viewport$\n .pipe(\n distinctUntilKeyChanged(\"size\")\n )\n\n /* Compute element offset */\n const offset$ = combineLatest([size$, header$])\n .pipe(\n map(() => getElementOffset(el))\n )\n\n /* Compute relative viewport, return hot observable */\n return combineLatest([header$, viewport$, offset$])\n .pipe(\n map(([{ height }, { offset, size }, { x, y }]) => ({\n offset: {\n x: offset.x - x,\n y: offset.y - y + height\n },\n size\n }))\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n fromEvent,\n map,\n share,\n switchMap,\n tap,\n throttle\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Worker message\n */\nexport interface WorkerMessage {\n type: unknown /* Message type */\n data?: unknown /* Message data */\n}\n\n/**\n * Worker handler\n *\n * @template T - Message type\n */\nexport interface WorkerHandler<\n T extends WorkerMessage\n> {\n tx$: Subject /* Message transmission subject */\n rx$: Observable /* Message receive observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n *\n * @template T - Worker message type\n */\ninterface WatchOptions {\n tx$: Observable /* Message transmission observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch a web worker\n *\n * This function returns an observable that sends all values emitted by the\n * message observable to the web worker. Web worker communication is expected\n * to be bidirectional (request-response) and synchronous. Messages that are\n * emitted during a pending request are throttled, the last one is emitted.\n *\n * @param worker - Web worker\n * @param options - Options\n *\n * @returns Worker message observable\n */\nexport function watchWorker(\n worker: Worker, { tx$ }: WatchOptions\n): Observable {\n\n /* Intercept messages from worker-like objects */\n const rx$ = fromEvent(worker, \"message\")\n .pipe(\n map(({ data }) => data as T)\n )\n\n /* Send and receive messages, return hot observable */\n return tx$\n .pipe(\n throttle(() => rx$, { leading: true, trailing: true }),\n tap(message => worker.postMessage(message)),\n switchMap(() => rx$),\n share()\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { getElement, getLocation } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Feature flag\n */\nexport type Flag =\n | \"announce.dismiss\" /* Dismissable announcement bar */\n | \"content.code.annotate\" /* Code annotations */\n | \"content.lazy\" /* Lazy content elements */\n | \"content.tabs.link\" /* Link content tabs */\n | \"header.autohide\" /* Hide header */\n | \"navigation.expand\" /* Automatic expansion */\n | \"navigation.indexes\" /* Section pages */\n | \"navigation.instant\" /* Instant loading */\n | \"navigation.sections\" /* Section navigation */\n | \"navigation.tabs\" /* Tabs navigation */\n | \"navigation.tabs.sticky\" /* Tabs navigation (sticky) */\n | \"navigation.top\" /* Back-to-top button */\n | \"navigation.tracking\" /* Anchor tracking */\n | \"search.highlight\" /* Search highlighting */\n | \"search.share\" /* Search sharing */\n | \"search.suggest\" /* Search suggestions */\n | \"toc.follow\" /* Following table of contents */\n | \"toc.integrate\" /* Integrated table of contents */\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Translation\n */\nexport type Translation =\n | \"clipboard.copy\" /* Copy to clipboard */\n | \"clipboard.copied\" /* Copied to clipboard */\n | \"search.config.lang\" /* Search language */\n | \"search.config.pipeline\" /* Search pipeline */\n | \"search.config.separator\" /* Search separator */\n | \"search.placeholder\" /* Search */\n | \"search.result.placeholder\" /* Type to start searching */\n | \"search.result.none\" /* No matching documents */\n | \"search.result.one\" /* 1 matching document */\n | \"search.result.other\" /* # matching documents */\n | \"search.result.more.one\" /* 1 more on this page */\n | \"search.result.more.other\" /* # more on this page */\n | \"search.result.term.missing\" /* Missing */\n | \"select.version.title\" /* Version selector */\n\n/**\n * Translations\n */\nexport type Translations = Record\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Versioning\n */\nexport interface Versioning {\n provider: \"mike\" /* Version provider */\n default?: string /* Default version */\n}\n\n/**\n * Configuration\n */\nexport interface Config {\n base: string /* Base URL */\n features: Flag[] /* Feature flags */\n translations: Translations /* Translations */\n search: string /* Search worker URL */\n tags?: Record /* Tags mapping */\n version?: Versioning /* Versioning */\n}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve global configuration and make base URL absolute\n */\nconst script = getElement(\"#__config\")\nconst config: Config = JSON.parse(script.textContent!)\nconfig.base = `${new URL(config.base, getLocation())}`\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve global configuration\n *\n * @returns Global configuration\n */\nexport function configuration(): Config {\n return config\n}\n\n/**\n * Check whether a feature flag is enabled\n *\n * @param flag - Feature flag\n *\n * @returns Test result\n */\nexport function feature(flag: Flag): boolean {\n return config.features.includes(flag)\n}\n\n/**\n * Retrieve the translation for the given key\n *\n * @param key - Key to be translated\n * @param value - Positional value, if any\n *\n * @returns Translation\n */\nexport function translation(\n key: Translation, value?: string | number\n): string {\n return typeof value !== \"undefined\"\n ? config.translations[key].replace(\"#\", value.toString())\n : config.translations[key]\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { getElement, getElements } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Component type\n */\nexport type ComponentType =\n | \"announce\" /* Announcement bar */\n | \"container\" /* Container */\n | \"consent\" /* Consent */\n | \"content\" /* Content */\n | \"dialog\" /* Dialog */\n | \"header\" /* Header */\n | \"header-title\" /* Header title */\n | \"header-topic\" /* Header topic */\n | \"main\" /* Main area */\n | \"outdated\" /* Version warning */\n | \"palette\" /* Color palette */\n | \"search\" /* Search */\n | \"search-query\" /* Search input */\n | \"search-result\" /* Search results */\n | \"search-share\" /* Search sharing */\n | \"search-suggest\" /* Search suggestions */\n | \"sidebar\" /* Sidebar */\n | \"skip\" /* Skip link */\n | \"source\" /* Repository information */\n | \"tabs\" /* Navigation tabs */\n | \"toc\" /* Table of contents */\n | \"top\" /* Back-to-top button */\n\n/**\n * Component\n *\n * @template T - Component type\n * @template U - Reference type\n */\nexport type Component<\n T extends {} = {},\n U extends HTMLElement = HTMLElement\n> =\n T & {\n ref: U /* Component reference */\n }\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Component type map\n */\ninterface ComponentTypeMap {\n \"announce\": HTMLElement /* Announcement bar */\n \"container\": HTMLElement /* Container */\n \"consent\": HTMLElement /* Consent */\n \"content\": HTMLElement /* Content */\n \"dialog\": HTMLElement /* Dialog */\n \"header\": HTMLElement /* Header */\n \"header-title\": HTMLElement /* Header title */\n \"header-topic\": HTMLElement /* Header topic */\n \"main\": HTMLElement /* Main area */\n \"outdated\": HTMLElement /* Version warning */\n \"palette\": HTMLElement /* Color palette */\n \"search\": HTMLElement /* Search */\n \"search-query\": HTMLInputElement /* Search input */\n \"search-result\": HTMLElement /* Search results */\n \"search-share\": HTMLAnchorElement /* Search sharing */\n \"search-suggest\": HTMLElement /* Search suggestions */\n \"sidebar\": HTMLElement /* Sidebar */\n \"skip\": HTMLAnchorElement /* Skip link */\n \"source\": HTMLAnchorElement /* Repository information */\n \"tabs\": HTMLElement /* Navigation tabs */\n \"toc\": HTMLElement /* Table of contents */\n \"top\": HTMLAnchorElement /* Back-to-top button */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve the element for a given component or throw a reference error\n *\n * @template T - Component type\n *\n * @param type - Component type\n * @param node - Node of reference\n *\n * @returns Element\n */\nexport function getComponentElement(\n type: T, node: ParentNode = document\n): ComponentTypeMap[T] {\n return getElement(`[data-md-component=${type}]`, node)\n}\n\n/**\n * Retrieve all elements for a given component\n *\n * @template T - Component type\n *\n * @param type - Component type\n * @param node - Node of reference\n *\n * @returns Elements\n */\nexport function getComponentElements(\n type: T, node: ParentNode = document\n): ComponentTypeMap[T][] {\n return getElements(`[data-md-component=${type}]`, node)\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n EMPTY,\n Observable,\n Subject,\n defer,\n finalize,\n fromEvent,\n map,\n startWith,\n tap\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport { getElement } from \"~/browser\"\n\nimport { Component } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Announcement bar\n */\nexport interface Announce {\n hash: number /* Content hash */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch announcement bar\n *\n * @param el - Announcement bar element\n *\n * @returns Announcement bar observable\n */\nexport function watchAnnounce(\n el: HTMLElement\n): Observable {\n const button = getElement(\".md-typeset > :first-child\", el)\n return fromEvent(button, \"click\", { once: true })\n .pipe(\n map(() => getElement(\".md-typeset\", el)),\n map(content => ({ hash: __md_hash(content.innerHTML) }))\n )\n}\n\n/**\n * Mount announcement bar\n *\n * @param el - Announcement bar element\n *\n * @returns Announcement bar component observable\n */\nexport function mountAnnounce(\n el: HTMLElement\n): Observable> {\n if (!feature(\"announce.dismiss\") || !el.childElementCount)\n return EMPTY\n\n /* Mount component on subscription */\n return defer(() => {\n const push$ = new Subject()\n push$\n .pipe(\n startWith({ hash: __md_get(\"__announce\") })\n )\n .subscribe(({ hash }) => {\n if (hash && hash === (__md_get(\"__announce\") ?? hash)) {\n el.hidden = true\n\n /* Persist preference in local storage */\n __md_set(\"__announce\", hash)\n }\n })\n\n /* Create and return component */\n return watchAnnounce(el)\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n finalize,\n map,\n tap\n} from \"rxjs\"\n\nimport { Component } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Consent\n */\nexport interface Consent {\n hidden: boolean /* Consent is hidden */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n target$: Observable /* Target observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n target$: Observable /* Target observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch consent\n *\n * @param el - Consent element\n * @param options - Options\n *\n * @returns Consent observable\n */\nexport function watchConsent(\n el: HTMLElement, { target$ }: WatchOptions\n): Observable {\n return target$\n .pipe(\n map(target => ({ hidden: target !== el }))\n )\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Mount consent\n *\n * @param el - Consent element\n * @param options - Options\n *\n * @returns Consent component observable\n */\nexport function mountConsent(\n el: HTMLElement, options: MountOptions\n): Observable> {\n const internal$ = new Subject()\n internal$.subscribe(({ hidden }) => {\n el.hidden = hidden\n })\n\n /* Create and return component */\n return watchConsent(el, options)\n .pipe(\n tap(state => internal$.next(state)),\n finalize(() => internal$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport ClipboardJS from \"clipboard\"\nimport {\n EMPTY,\n Observable,\n Subject,\n defer,\n distinctUntilChanged,\n distinctUntilKeyChanged,\n filter,\n finalize,\n map,\n mergeWith,\n switchMap,\n take,\n tap\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport {\n getElementContentSize,\n watchElementSize,\n watchElementVisibility\n} from \"~/browser\"\nimport { renderClipboardButton } from \"~/templates\"\n\nimport { Component } from \"../../../_\"\nimport {\n Annotation,\n mountAnnotationList\n} from \"../../annotation\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Code block\n */\nexport interface CodeBlock {\n scrollable: boolean /* Code block overflows */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n target$: Observable /* Location target observable */\n print$: Observable /* Media print observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Global sequence number for code blocks\n */\nlet sequence = 0\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Find candidate list element directly following a code block\n *\n * @param el - Code block element\n *\n * @returns List element or nothing\n */\nfunction findCandidateList(el: HTMLElement): HTMLElement | undefined {\n if (el.nextElementSibling) {\n const sibling = el.nextElementSibling as HTMLElement\n if (sibling.tagName === \"OL\")\n return sibling\n\n /* Skip empty paragraphs - see https://bit.ly/3r4ZJ2O */\n else if (sibling.tagName === \"P\" && !sibling.children.length)\n return findCandidateList(sibling)\n }\n\n /* Everything else */\n return undefined\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch code block\n *\n * This function monitors size changes of the viewport, as well as switches of\n * content tabs with embedded code blocks, as both may trigger overflow.\n *\n * @param el - Code block element\n *\n * @returns Code block observable\n */\nexport function watchCodeBlock(\n el: HTMLElement\n): Observable {\n return watchElementSize(el)\n .pipe(\n map(({ width }) => {\n const content = getElementContentSize(el)\n return {\n scrollable: content.width > width\n }\n }),\n distinctUntilKeyChanged(\"scrollable\")\n )\n}\n\n/**\n * Mount code block\n *\n * This function ensures that an overflowing code block is focusable through\n * keyboard, so it can be scrolled without a mouse to improve on accessibility.\n * Furthermore, if code annotations are enabled, they are mounted if and only\n * if the code block is currently visible, e.g., not in a hidden content tab.\n *\n * Note that code blocks may be mounted eagerly or lazily. If they're mounted\n * lazily (on first visibility), code annotation anchor links will not work,\n * as they are evaluated on initial page load, and code annotations in general\n * might feel a little bumpier.\n *\n * @param el - Code block element\n * @param options - Options\n *\n * @returns Code block and annotation component observable\n */\nexport function mountCodeBlock(\n el: HTMLElement, options: MountOptions\n): Observable> {\n const { matches: hover } = matchMedia(\"(hover)\")\n\n /* Defer mounting of code block - see https://bit.ly/3vHVoVD */\n const factory$ = defer(() => {\n const push$ = new Subject()\n push$.subscribe(({ scrollable }) => {\n if (scrollable && hover)\n el.setAttribute(\"tabindex\", \"0\")\n else\n el.removeAttribute(\"tabindex\")\n })\n\n /* Render button for Clipboard.js integration */\n if (ClipboardJS.isSupported()) {\n const parent = el.closest(\"pre\")!\n parent.id = `__code_${++sequence}`\n parent.insertBefore(\n renderClipboardButton(parent.id),\n el\n )\n }\n\n /* Handle code annotations */\n const container = el.closest(\".highlight\")\n if (container instanceof HTMLElement) {\n const list = findCandidateList(container)\n\n /* Mount code annotations, if enabled */\n if (typeof list !== \"undefined\" && (\n container.classList.contains(\"annotate\") ||\n feature(\"content.code.annotate\")\n )) {\n const annotations$ = mountAnnotationList(list, el, options)\n\n /* Create and return component */\n return watchCodeBlock(el)\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state })),\n mergeWith(\n watchElementSize(container)\n .pipe(\n map(({ width, height }) => width && height),\n distinctUntilChanged(),\n switchMap(active => active ? annotations$ : EMPTY)\n )\n )\n )\n }\n }\n\n /* Create and return component */\n return watchCodeBlock(el)\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n })\n\n /* Mount code block lazily */\n if (feature(\"content.lazy\"))\n return watchElementVisibility(el)\n .pipe(\n filter(visible => visible),\n take(1),\n switchMap(() => factory$)\n )\n\n /* Mount code block */\n return factory$\n}\n", "/*\n * Copyright (c) 2016-2021 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a tooltip\n *\n * @param id - Tooltip identifier\n *\n * @returns Element\n */\nexport function renderTooltip(id?: string): HTMLElement {\n return (\n
\n
\n
\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { h } from \"~/utilities\"\n\nimport { renderTooltip } from \"../tooltip\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render an annotation\n *\n * @param id - Annotation identifier\n * @param prefix - Tooltip identifier prefix\n *\n * @returns Element\n */\nexport function renderAnnotation(\n id: string | number, prefix?: string\n): HTMLElement {\n prefix = prefix ? `${prefix}_annotation_${id}` : undefined\n\n /* Render tooltip with anchor, if given */\n if (prefix) {\n const anchor = prefix ? `#${prefix}` : undefined\n return (\n \n )\n } else {\n return (\n \n )\n }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { translation } from \"~/_\"\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a 'copy-to-clipboard' button\n *\n * @param id - Unique identifier\n *\n * @returns Element\n */\nexport function renderClipboardButton(id: string): HTMLElement {\n return (\n code`}\n >\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { ComponentChild } from \"preact\"\n\nimport { configuration, feature, translation } from \"~/_\"\nimport {\n SearchDocument,\n SearchMetadata,\n SearchResultItem\n} from \"~/integrations/search\"\nimport { h, truncate } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Render flag\n */\nconst enum Flag {\n TEASER = 1, /* Render teaser */\n PARENT = 2 /* Render as parent */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper function\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a search document\n *\n * @param document - Search document\n * @param flag - Render flags\n *\n * @returns Element\n */\nfunction renderSearchDocument(\n document: SearchDocument & SearchMetadata, flag: Flag\n): HTMLElement {\n const parent = flag & Flag.PARENT\n const teaser = flag & Flag.TEASER\n\n /* Render missing query terms */\n const missing = Object.keys(document.terms)\n .filter(key => !document.terms[key])\n .reduce((list, key) => [\n ...list, {key}, \" \"\n ], [])\n .slice(0, -1)\n\n /* Assemble query string for highlighting */\n const url = new URL(document.location)\n if (feature(\"search.highlight\"))\n url.searchParams.set(\"h\", Object.entries(document.terms)\n .filter(([, match]) => match)\n .reduce((highlight, [value]) => `${highlight} ${value}`.trim(), \"\")\n )\n\n /* Render article or section, depending on flags */\n const { tags } = configuration()\n return (\n \n \n {parent > 0 &&
}\n

{document.title}

\n {teaser > 0 && document.text.length > 0 &&\n

\n {truncate(document.text, 320)}\n

\n }\n {document.tags && document.tags.map(tag => {\n const id = tag.replace(/<[^>]+>/g, \"\")\n const type = tags\n ? id in tags\n ? `md-tag-icon md-tag-icon--${tags[id]}`\n : \"md-tag-icon\"\n : \"\"\n return (\n {tag}\n )\n })}\n {teaser > 0 && missing.length > 0 &&\n

\n {translation(\"search.result.term.missing\")}: {...missing}\n

\n }\n \n
\n )\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a search result\n *\n * @param result - Search result\n *\n * @returns Element\n */\nexport function renderSearchResultItem(\n result: SearchResultItem\n): HTMLElement {\n const threshold = result[0].score\n const docs = [...result]\n\n /* Find and extract parent article */\n const parent = docs.findIndex(doc => !doc.location.includes(\"#\"))\n const [article] = docs.splice(parent, 1)\n\n /* Determine last index above threshold */\n let index = docs.findIndex(doc => doc.score < threshold)\n if (index === -1)\n index = docs.length\n\n /* Partition sections */\n const best = docs.slice(0, index)\n const more = docs.slice(index)\n\n /* Render children */\n const children = [\n renderSearchDocument(article, Flag.PARENT | +(!parent && index === 0)),\n ...best.map(section => renderSearchDocument(section, Flag.TEASER)),\n ...more.length ? [\n
\n \n {more.length > 0 && more.length === 1\n ? translation(\"search.result.more.one\")\n : translation(\"search.result.more.other\", more.length)\n }\n \n {...more.map(section => renderSearchDocument(section, Flag.TEASER))}\n
\n ] : []\n ]\n\n /* Render search result */\n return (\n
  • \n {children}\n
  • \n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { SourceFacts } from \"~/components\"\nimport { h, round } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render repository facts\n *\n * @param facts - Repository facts\n *\n * @returns Element\n */\nexport function renderSourceFacts(facts: SourceFacts): HTMLElement {\n return (\n
      \n {Object.entries(facts).map(([key, value]) => (\n
    • \n {typeof value === \"number\" ? round(value) : value}\n
    • \n ))}\n
    \n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Tabbed control type\n */\ntype TabbedControlType =\n | \"prev\"\n | \"next\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render control for content tabs\n *\n * @param type - Control type\n *\n * @returns Element\n */\nexport function renderTabbedControl(\n type: TabbedControlType\n): HTMLElement {\n const classes = `tabbed-control tabbed-control--${type}`\n return (\n \n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a table inside a wrapper to improve scrolling on mobile\n *\n * @param table - Table element\n *\n * @returns Element\n */\nexport function renderTable(table: HTMLElement): HTMLElement {\n return (\n
    \n
    \n {table}\n
    \n
    \n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { configuration, translation } from \"~/_\"\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Version\n */\nexport interface Version {\n version: string /* Version identifier */\n title: string /* Version title */\n aliases: string[] /* Version aliases */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a version\n *\n * @param version - Version\n *\n * @returns Element\n */\nfunction renderVersion(version: Version): HTMLElement {\n const config = configuration()\n\n /* Ensure trailing slash - see https://bit.ly/3rL5u3f */\n const url = new URL(`../${version.version}/`, config.base)\n return (\n
  • \n \n {version.title}\n \n
  • \n )\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a version selector\n *\n * @param versions - Versions\n * @param active - Active version\n *\n * @returns Element\n */\nexport function renderVersionSelector(\n versions: Version[], active: Version\n): HTMLElement {\n return (\n
    \n \n {active.title}\n \n
      \n {versions.map(renderVersion)}\n
    \n
    \n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n animationFrameScheduler,\n auditTime,\n combineLatest,\n debounceTime,\n defer,\n delay,\n filter,\n finalize,\n fromEvent,\n map,\n merge,\n switchMap,\n take,\n takeLast,\n takeUntil,\n tap,\n throttleTime,\n withLatestFrom\n} from \"rxjs\"\n\nimport {\n ElementOffset,\n getActiveElement,\n getElementSize,\n watchElementContentOffset,\n watchElementFocus,\n watchElementOffset,\n watchElementVisibility\n} from \"~/browser\"\n\nimport { Component } from \"../../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Annotation\n */\nexport interface Annotation {\n active: boolean /* Annotation is active */\n offset: ElementOffset /* Annotation offset */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n target$: Observable /* Location target observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch annotation\n *\n * @param el - Annotation element\n * @param container - Containing element\n *\n * @returns Annotation observable\n */\nexport function watchAnnotation(\n el: HTMLElement, container: HTMLElement\n): Observable {\n const offset$ = defer(() => combineLatest([\n watchElementOffset(el),\n watchElementContentOffset(container)\n ]))\n .pipe(\n map(([{ x, y }, scroll]): ElementOffset => {\n const { width, height } = getElementSize(el)\n return ({\n x: x - scroll.x + width / 2,\n y: y - scroll.y + height / 2\n })\n })\n )\n\n /* Actively watch annotation on focus */\n return watchElementFocus(el)\n .pipe(\n switchMap(active => offset$\n .pipe(\n map(offset => ({ active, offset })),\n take(+!active || Infinity)\n )\n )\n )\n}\n\n/**\n * Mount annotation\n *\n * @param el - Annotation element\n * @param container - Containing element\n * @param options - Options\n *\n * @returns Annotation component observable\n */\nexport function mountAnnotation(\n el: HTMLElement, container: HTMLElement, { target$ }: MountOptions\n): Observable> {\n const [tooltip, index] = Array.from(el.children)\n\n /* Mount component on subscription */\n return defer(() => {\n const push$ = new Subject()\n const done$ = push$.pipe(takeLast(1))\n push$.subscribe({\n\n /* Handle emission */\n next({ offset }) {\n el.style.setProperty(\"--md-tooltip-x\", `${offset.x}px`)\n el.style.setProperty(\"--md-tooltip-y\", `${offset.y}px`)\n },\n\n /* Handle complete */\n complete() {\n el.style.removeProperty(\"--md-tooltip-x\")\n el.style.removeProperty(\"--md-tooltip-y\")\n }\n })\n\n /* Start animation only when annotation is visible */\n watchElementVisibility(el)\n .pipe(\n takeUntil(done$)\n )\n .subscribe(visible => {\n el.toggleAttribute(\"data-md-visible\", visible)\n })\n\n /* Toggle tooltip presence to mitigate empty lines when copying */\n merge(\n push$.pipe(filter(({ active }) => active)),\n push$.pipe(debounceTime(250), filter(({ active }) => !active))\n )\n .subscribe({\n\n /* Handle emission */\n next({ active }) {\n if (active)\n el.prepend(tooltip)\n else\n tooltip.remove()\n },\n\n /* Handle complete */\n complete() {\n el.prepend(tooltip)\n }\n })\n\n /* Toggle tooltip visibility */\n push$\n .pipe(\n auditTime(16, animationFrameScheduler)\n )\n .subscribe(({ active }) => {\n tooltip.classList.toggle(\"md-tooltip--active\", active)\n })\n\n /* Track relative origin of tooltip */\n push$\n .pipe(\n throttleTime(125, animationFrameScheduler),\n filter(() => !!el.offsetParent),\n map(() => el.offsetParent!.getBoundingClientRect()),\n map(({ x }) => x)\n )\n .subscribe({\n\n /* Handle emission */\n next(origin) {\n if (origin)\n el.style.setProperty(\"--md-tooltip-0\", `${-origin}px`)\n else\n el.style.removeProperty(\"--md-tooltip-0\")\n },\n\n /* Handle complete */\n complete() {\n el.style.removeProperty(\"--md-tooltip-0\")\n }\n })\n\n /* Allow to copy link without scrolling to anchor */\n fromEvent(index, \"click\")\n .pipe(\n takeUntil(done$),\n filter(ev => !(ev.metaKey || ev.ctrlKey))\n )\n .subscribe(ev => ev.preventDefault())\n\n /* Allow to open link in new tab or blur on close */\n fromEvent(index, \"mousedown\")\n .pipe(\n takeUntil(done$),\n withLatestFrom(push$)\n )\n .subscribe(([ev, { active }]) => {\n\n /* Open in new tab */\n if (ev.button !== 0 || ev.metaKey || ev.ctrlKey) {\n ev.preventDefault()\n\n /* Close annotation */\n } else if (active) {\n ev.preventDefault()\n\n /* Focus parent annotation, if any */\n const parent = el.parentElement!.closest(\".md-annotation\")\n if (parent instanceof HTMLElement)\n parent.focus()\n else\n getActiveElement()?.blur()\n }\n })\n\n /* Open and focus annotation on location target */\n target$\n .pipe(\n takeUntil(done$),\n filter(target => target === tooltip),\n delay(125)\n )\n .subscribe(() => el.focus())\n\n /* Create and return component */\n return watchAnnotation(el, container)\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n EMPTY,\n Observable,\n Subject,\n defer,\n finalize,\n merge,\n share,\n takeLast,\n takeUntil\n} from \"rxjs\"\n\nimport {\n getElement,\n getElements,\n getOptionalElement\n} from \"~/browser\"\nimport { renderAnnotation } from \"~/templates\"\n\nimport { Component } from \"../../../_\"\nimport {\n Annotation,\n mountAnnotation\n} from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n target$: Observable /* Location target observable */\n print$: Observable /* Media print observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Find all annotation markers in the given code block\n *\n * @param container - Containing element\n *\n * @returns Annotation markers\n */\nfunction findAnnotationMarkers(container: HTMLElement): Text[] {\n const markers: Text[] = []\n for (const el of getElements(\".c, .c1, .cm\", container)) {\n const nodes: Text[] = []\n\n /* Find all text nodes in current element */\n const it = document.createNodeIterator(el, NodeFilter.SHOW_TEXT)\n for (let node = it.nextNode(); node; node = it.nextNode())\n nodes.push(node as Text)\n\n /* Find all markers in each text node */\n for (let text of nodes) {\n let match: RegExpExecArray | null\n\n /* Split text at marker and add to list */\n while ((match = /(\\(\\d+\\))(!)?/.exec(text.textContent!))) {\n const [, id, force] = match\n if (typeof force === \"undefined\") {\n const marker = text.splitText(match.index)\n text = marker.splitText(id.length)\n markers.push(marker)\n\n /* Replace entire text with marker */\n } else {\n text.textContent = id\n markers.push(text)\n break\n }\n }\n }\n }\n return markers\n}\n\n/**\n * Swap the child nodes of two elements\n *\n * @param source - Source element\n * @param target - Target element\n */\nfunction swap(source: HTMLElement, target: HTMLElement): void {\n target.append(...Array.from(source.childNodes))\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount annotation list\n *\n * This function analyzes the containing code block and checks for markers\n * referring to elements in the given annotation list. If no markers are found,\n * the list is left untouched. Otherwise, list elements are rendered as\n * annotations inside the code block.\n *\n * @param el - Annotation list element\n * @param container - Containing element\n * @param options - Options\n *\n * @returns Annotation component observable\n */\nexport function mountAnnotationList(\n el: HTMLElement, container: HTMLElement, { target$, print$ }: MountOptions\n): Observable> {\n\n /* Compute prefix for tooltip anchors */\n const parent = container.closest(\"[id]\")\n const prefix = parent?.id\n\n /* Find and replace all markers with empty annotations */\n const annotations = new Map()\n for (const marker of findAnnotationMarkers(container)) {\n const [, id] = marker.textContent!.match(/\\((\\d+)\\)/)!\n if (getOptionalElement(`li:nth-child(${id})`, el)) {\n annotations.set(id, renderAnnotation(id, prefix))\n marker.replaceWith(annotations.get(id)!)\n }\n }\n\n /* Keep list if there are no annotations to render */\n if (annotations.size === 0)\n return EMPTY\n\n /* Mount component on subscription */\n return defer(() => {\n const done$ = new Subject()\n\n /* Retrieve container pairs for swapping */\n const pairs: [HTMLElement, HTMLElement][] = []\n for (const [id, annotation] of annotations)\n pairs.push([\n getElement(\".md-typeset\", annotation),\n getElement(`li:nth-child(${id})`, el)\n ])\n\n /* Handle print mode - see https://bit.ly/3rgPdpt */\n print$\n .pipe(\n takeUntil(done$.pipe(takeLast(1)))\n )\n .subscribe(active => {\n el.hidden = !active\n\n /* Show annotations in code block or list (print) */\n for (const [inner, child] of pairs)\n if (!active)\n swap(child, inner)\n else\n swap(inner, child)\n })\n\n /* Create and return component */\n return merge(...[...annotations]\n .map(([, annotation]) => (\n mountAnnotation(annotation, container, { target$ })\n ))\n )\n .pipe(\n finalize(() => done$.complete()),\n share()\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n map,\n of,\n shareReplay,\n tap\n} from \"rxjs\"\n\nimport { watchScript } from \"~/browser\"\nimport { h } from \"~/utilities\"\n\nimport { Component } from \"../../../_\"\n\nimport themeCSS from \"./index.css\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mermaid diagram\n */\nexport interface Mermaid {}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Mermaid instance observable\n */\nlet mermaid$: Observable\n\n/**\n * Global sequence number for diagrams\n */\nlet sequence = 0\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch Mermaid script\n *\n * @returns Mermaid scripts observable\n */\nfunction fetchScripts(): Observable {\n return typeof mermaid === \"undefined\" || mermaid instanceof Element\n ? watchScript(\"https://unpkg.com/mermaid@9.0.1/dist/mermaid.min.js\")\n : of(undefined)\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount Mermaid diagram\n *\n * @param el - Code block element\n *\n * @returns Mermaid diagram component observable\n */\nexport function mountMermaid(\n el: HTMLElement\n): Observable> {\n el.classList.remove(\"mermaid\") // Hack: mitigate https://bit.ly/3CiN6Du\n mermaid$ ||= fetchScripts()\n .pipe(\n tap(() => mermaid.initialize({\n startOnLoad: false,\n themeCSS\n })),\n map(() => undefined),\n shareReplay(1)\n )\n\n /* Render diagram */\n mermaid$.subscribe(() => {\n el.classList.add(\"mermaid\") // Hack: mitigate https://bit.ly/3CiN6Du\n const id = `__mermaid_${sequence++}`\n const host = h(\"div\", { class: \"mermaid\" })\n mermaid.mermaidAPI.render(id, el.textContent, (svg: string) => {\n\n /* Create a shadow root and inject diagram */\n const shadow = host.attachShadow({ mode: \"closed\" })\n shadow.innerHTML = svg\n\n /* Replace code block with diagram */\n el.replaceWith(host)\n })\n })\n\n /* Create and return component */\n return mermaid$\n .pipe(\n map(() => ({ ref: el }))\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n defer,\n filter,\n finalize,\n map,\n merge,\n tap\n} from \"rxjs\"\n\nimport { Component } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Details\n */\nexport interface Details {\n action: \"open\" | \"close\" /* Details state */\n reveal?: boolean /* Details is revealed */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n target$: Observable /* Location target observable */\n print$: Observable /* Media print observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n target$: Observable /* Location target observable */\n print$: Observable /* Media print observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch details\n *\n * @param el - Details element\n * @param options - Options\n *\n * @returns Details observable\n */\nexport function watchDetails(\n el: HTMLDetailsElement, { target$, print$ }: WatchOptions\n): Observable
    {\n let open = true\n return merge(\n\n /* Open and focus details on location target */\n target$\n .pipe(\n map(target => target.closest(\"details:not([open])\")!),\n filter(details => el === details),\n map(() => ({\n action: \"open\", reveal: true\n }) as Details)\n ),\n\n /* Open details on print and close afterwards */\n print$\n .pipe(\n filter(active => active || !open),\n tap(() => open = el.open),\n map(active => ({\n action: active ? \"open\" : \"close\"\n }) as Details)\n )\n )\n}\n\n/**\n * Mount details\n *\n * This function ensures that `details` tags are opened on anchor jumps and\n * prior to printing, so the whole content of the page is visible.\n *\n * @param el - Details element\n * @param options - Options\n *\n * @returns Details component observable\n */\nexport function mountDetails(\n el: HTMLDetailsElement, options: MountOptions\n): Observable> {\n return defer(() => {\n const push$ = new Subject
    ()\n push$.subscribe(({ action, reveal }) => {\n el.toggleAttribute(\"open\", action === \"open\")\n if (reveal)\n el.scrollIntoView()\n })\n\n /* Create and return component */\n return watchDetails(el, options)\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { Observable, of } from \"rxjs\"\n\nimport { renderTable } from \"~/templates\"\nimport { h } from \"~/utilities\"\n\nimport { Component } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Data table\n */\nexport interface DataTable {}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Sentinel for replacement\n */\nconst sentinel = h(\"table\")\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount data table\n *\n * This function wraps a data table in another scrollable container, so it can\n * be smoothly scrolled on smaller screen sizes and won't break the layout.\n *\n * @param el - Data table element\n *\n * @returns Data table component observable\n */\nexport function mountDataTable(\n el: HTMLElement\n): Observable> {\n el.replaceWith(sentinel)\n sentinel.replaceWith(renderTable(el))\n\n /* Create and return component */\n return of({ ref: el })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n animationFrameScheduler,\n asyncScheduler,\n auditTime,\n combineLatest,\n defer,\n finalize,\n fromEvent,\n map,\n merge,\n skip,\n startWith,\n subscribeOn,\n takeLast,\n takeUntil,\n tap\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport {\n getElement,\n getElementContentOffset,\n getElementContentSize,\n getElementOffset,\n getElementSize,\n getElements,\n watchElementContentOffset,\n watchElementSize\n} from \"~/browser\"\nimport { renderTabbedControl } from \"~/templates\"\n\nimport { Component } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Content tabs\n */\nexport interface ContentTabs {\n active: HTMLLabelElement /* Active tab label */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch content tabs\n *\n * @param el - Content tabs element\n *\n * @returns Content tabs observable\n */\nexport function watchContentTabs(\n el: HTMLElement\n): Observable {\n const inputs = getElements(\":scope > input\", el)\n const initial = inputs.find(input => input.checked) || inputs[0]\n return merge(...inputs.map(input => fromEvent(input, \"change\")\n .pipe(\n map(() => getElement(`label[for=\"${input.id}\"]`))\n )\n ))\n .pipe(\n startWith(getElement(`label[for=\"${initial.id}\"]`)),\n map(active => ({ active }))\n )\n}\n\n/**\n * Mount content tabs\n *\n * This function scrolls the active tab into view. While this functionality is\n * provided by browsers as part of `scrollInfoView`, browsers will always also\n * scroll the vertical axis, which we do not want. Thus, we decided to provide\n * this functionality ourselves.\n *\n * @param el - Content tabs element\n *\n * @returns Content tabs component observable\n */\nexport function mountContentTabs(\n el: HTMLElement\n): Observable> {\n\n /* Render content tab previous button for pagination */\n const prev = renderTabbedControl(\"prev\")\n el.append(prev)\n\n /* Render content tab next button for pagination */\n const next = renderTabbedControl(\"next\")\n el.append(next)\n\n /* Mount component on subscription */\n const container = getElement(\".tabbed-labels\", el)\n return defer(() => {\n const push$ = new Subject()\n const done$ = push$.pipe(takeLast(1))\n combineLatest([push$, watchElementSize(el)])\n .pipe(\n auditTime(1, animationFrameScheduler),\n takeUntil(done$)\n )\n .subscribe({\n\n /* Handle emission */\n next([{ active }, size]) {\n const offset = getElementOffset(active)\n const { width } = getElementSize(active)\n\n /* Set tab indicator offset and width */\n el.style.setProperty(\"--md-indicator-x\", `${offset.x}px`)\n el.style.setProperty(\"--md-indicator-width\", `${width}px`)\n\n /* Scroll container to active content tab */\n const content = getElementContentOffset(container)\n if (\n offset.x < content.x ||\n offset.x + width > content.x + size.width\n )\n container.scrollTo({\n left: Math.max(0, offset.x - 16),\n behavior: \"smooth\"\n })\n },\n\n /* Handle complete */\n complete() {\n el.style.removeProperty(\"--md-indicator-x\")\n el.style.removeProperty(\"--md-indicator-width\")\n }\n })\n\n /* Hide content tab buttons on borders */\n combineLatest([\n watchElementContentOffset(container),\n watchElementSize(container)\n ])\n .pipe(\n takeUntil(done$)\n )\n .subscribe(([offset, size]) => {\n const content = getElementContentSize(container)\n prev.hidden = offset.x < 16\n next.hidden = offset.x > content.width - size.width - 16\n })\n\n /* Paginate content tab container on click */\n merge(\n fromEvent(prev, \"click\").pipe(map(() => -1)),\n fromEvent(next, \"click\").pipe(map(() => +1))\n )\n .pipe(\n takeUntil(done$)\n )\n .subscribe(direction => {\n const { width } = getElementSize(container)\n container.scrollBy({\n left: width * direction,\n behavior: \"smooth\"\n })\n })\n\n /* Set up linking of content tabs, if enabled */\n if (feature(\"content.tabs.link\"))\n push$.pipe(skip(1))\n .subscribe(({ active }) => {\n const tab = active.innerText.trim()\n for (const set of getElements(\"[data-tabs]\"))\n for (const input of getElements(\n \":scope > input\", set\n )) {\n const label = getElement(`label[for=\"${input.id}\"]`)\n if (label.innerText.trim() === tab) {\n input.click()\n break\n }\n }\n\n /* Persist active tabs in local storage */\n const tabs = __md_get(\"__tabs\") || []\n __md_set(\"__tabs\", [...new Set([tab, ...tabs])])\n })\n\n /* Create and return component */\n return watchContentTabs(el)\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n })\n .pipe(\n subscribeOn(asyncScheduler)\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { Observable, merge } from \"rxjs\"\n\nimport { getElements } from \"~/browser\"\n\nimport { Component } from \"../../_\"\nimport { Annotation } from \"../annotation\"\nimport {\n CodeBlock,\n Mermaid,\n mountCodeBlock,\n mountMermaid\n} from \"../code\"\nimport {\n Details,\n mountDetails\n} from \"../details\"\nimport {\n DataTable,\n mountDataTable\n} from \"../table\"\nimport {\n ContentTabs,\n mountContentTabs\n} from \"../tabs\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Content\n */\nexport type Content =\n | Annotation\n | ContentTabs\n | CodeBlock\n | Mermaid\n | DataTable\n | Details\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n target$: Observable /* Location target observable */\n print$: Observable /* Media print observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount content\n *\n * This function mounts all components that are found in the content of the\n * actual article, including code blocks, data tables and details.\n *\n * @param el - Content element\n * @param options - Options\n *\n * @returns Content component observable\n */\nexport function mountContent(\n el: HTMLElement, { target$, print$ }: MountOptions\n): Observable> {\n return merge(\n\n /* Code blocks */\n ...getElements(\"pre:not(.mermaid) > code\", el)\n .map(child => mountCodeBlock(child, { target$, print$ })),\n\n /* Mermaid diagrams */\n ...getElements(\"pre.mermaid\", el)\n .map(child => mountMermaid(child)),\n\n /* Data tables */\n ...getElements(\"table:not([class])\", el)\n .map(child => mountDataTable(child)),\n\n /* Details */\n ...getElements(\"details\", el)\n .map(child => mountDetails(child, { target$, print$ })),\n\n /* Content tabs */\n ...getElements(\"[data-tabs]\", el)\n .map(child => mountContentTabs(child))\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n defer,\n delay,\n finalize,\n map,\n merge,\n of,\n switchMap,\n tap\n} from \"rxjs\"\n\nimport { getElement } from \"~/browser\"\n\nimport { Component } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Dialog\n */\nexport interface Dialog {\n message: string /* Dialog message */\n active: boolean /* Dialog is active */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n alert$: Subject /* Alert subject */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n alert$: Subject /* Alert subject */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch dialog\n *\n * @param _el - Dialog element\n * @param options - Options\n *\n * @returns Dialog observable\n */\nexport function watchDialog(\n _el: HTMLElement, { alert$ }: WatchOptions\n): Observable {\n return alert$\n .pipe(\n switchMap(message => merge(\n of(true),\n of(false).pipe(delay(2000))\n )\n .pipe(\n map(active => ({ message, active }))\n )\n )\n )\n}\n\n/**\n * Mount dialog\n *\n * This function reveals the dialog in the right corner when a new alert is\n * emitted through the subject that is passed as part of the options.\n *\n * @param el - Dialog element\n * @param options - Options\n *\n * @returns Dialog component observable\n */\nexport function mountDialog(\n el: HTMLElement, options: MountOptions\n): Observable> {\n const inner = getElement(\".md-typeset\", el)\n return defer(() => {\n const push$ = new Subject()\n push$.subscribe(({ message, active }) => {\n el.classList.toggle(\"md-dialog--active\", active)\n inner.textContent = message\n })\n\n /* Create and return component */\n return watchDialog(el, options)\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n bufferCount,\n combineLatest,\n combineLatestWith,\n defer,\n distinctUntilChanged,\n distinctUntilKeyChanged,\n filter,\n map,\n of,\n shareReplay,\n startWith,\n switchMap,\n takeLast,\n takeUntil\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport {\n Viewport,\n watchElementSize,\n watchToggle\n} from \"~/browser\"\n\nimport { Component } from \"../../_\"\nimport { Main } from \"../../main\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Header\n */\nexport interface Header {\n height: number /* Header visible height */\n hidden: boolean /* Header is hidden */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n viewport$: Observable /* Viewport observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n viewport$: Observable /* Viewport observable */\n header$: Observable
    /* Header observable */\n main$: Observable
    /* Main area observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Compute whether the header is hidden\n *\n * If the user scrolls past a certain threshold, the header can be hidden when\n * scrolling down, and shown when scrolling up.\n *\n * @param options - Options\n *\n * @returns Toggle observable\n */\nfunction isHidden({ viewport$ }: WatchOptions): Observable {\n if (!feature(\"header.autohide\"))\n return of(false)\n\n /* Compute direction and turning point */\n const direction$ = viewport$\n .pipe(\n map(({ offset: { y } }) => y),\n bufferCount(2, 1),\n map(([a, b]) => [a < b, b] as const),\n distinctUntilKeyChanged(0)\n )\n\n /* Compute whether header should be hidden */\n const hidden$ = combineLatest([viewport$, direction$])\n .pipe(\n filter(([{ offset }, [, y]]) => Math.abs(y - offset.y) > 100),\n map(([, [direction]]) => direction),\n distinctUntilChanged()\n )\n\n /* Compute threshold for hiding */\n const search$ = watchToggle(\"search\")\n return combineLatest([viewport$, search$])\n .pipe(\n map(([{ offset }, search]) => offset.y > 400 && !search),\n distinctUntilChanged(),\n switchMap(active => active ? hidden$ : of(false)),\n startWith(false)\n )\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch header\n *\n * @param el - Header element\n * @param options - Options\n *\n * @returns Header observable\n */\nexport function watchHeader(\n el: HTMLElement, options: WatchOptions\n): Observable
    {\n return defer(() => combineLatest([\n watchElementSize(el),\n isHidden(options)\n ]))\n .pipe(\n map(([{ height }, hidden]) => ({\n height,\n hidden\n })),\n distinctUntilChanged((a, b) => (\n a.height === b.height &&\n a.hidden === b.hidden\n )),\n shareReplay(1)\n )\n}\n\n/**\n * Mount header\n *\n * This function manages the different states of the header, i.e. whether it's\n * hidden or rendered with a shadow. This depends heavily on the main area.\n *\n * @param el - Header element\n * @param options - Options\n *\n * @returns Header component observable\n */\nexport function mountHeader(\n el: HTMLElement, { header$, main$ }: MountOptions\n): Observable> {\n return defer(() => {\n const push$ = new Subject
    ()\n const done$ = push$.pipe(takeLast(1))\n push$\n .pipe(\n distinctUntilKeyChanged(\"active\"),\n combineLatestWith(header$)\n )\n .subscribe(([{ active }, { hidden }]) => {\n el.classList.toggle(\"md-header--shadow\", active && !hidden)\n el.hidden = hidden\n })\n\n /* Link to main area */\n main$.subscribe(push$)\n\n /* Create and return component */\n return header$\n .pipe(\n takeUntil(done$),\n map(state => ({ ref: el, ...state }))\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n EMPTY,\n Observable,\n Subject,\n defer,\n distinctUntilKeyChanged,\n finalize,\n map,\n tap\n} from \"rxjs\"\n\nimport {\n Viewport,\n getElementSize,\n getOptionalElement,\n watchViewportAt\n} from \"~/browser\"\n\nimport { Component } from \"../../_\"\nimport { Header } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Header\n */\nexport interface HeaderTitle {\n active: boolean /* Header title is active */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n viewport$: Observable /* Viewport observable */\n header$: Observable
    /* Header observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n viewport$: Observable /* Viewport observable */\n header$: Observable
    /* Header observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch header title\n *\n * @param el - Heading element\n * @param options - Options\n *\n * @returns Header title observable\n */\nexport function watchHeaderTitle(\n el: HTMLElement, { viewport$, header$ }: WatchOptions\n): Observable {\n return watchViewportAt(el, { viewport$, header$ })\n .pipe(\n map(({ offset: { y } }) => {\n const { height } = getElementSize(el)\n return {\n active: y >= height\n }\n }),\n distinctUntilKeyChanged(\"active\")\n )\n}\n\n/**\n * Mount header title\n *\n * This function swaps the header title from the site title to the title of the\n * current page when the user scrolls past the first headline.\n *\n * @param el - Header title element\n * @param options - Options\n *\n * @returns Header title component observable\n */\nexport function mountHeaderTitle(\n el: HTMLElement, options: MountOptions\n): Observable> {\n return defer(() => {\n const push$ = new Subject()\n push$.subscribe(({ active }) => {\n el.classList.toggle(\"md-header__title--active\", active)\n })\n\n /* Obtain headline, if any */\n const heading = getOptionalElement(\"article h1\")\n if (typeof heading === \"undefined\")\n return EMPTY\n\n /* Create and return component */\n return watchHeaderTitle(heading, options)\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n combineLatest,\n distinctUntilChanged,\n distinctUntilKeyChanged,\n map,\n switchMap\n} from \"rxjs\"\n\nimport {\n Viewport,\n watchElementSize\n} from \"~/browser\"\n\nimport { Header } from \"../header\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Main area\n */\nexport interface Main {\n offset: number /* Main area top offset */\n height: number /* Main area visible height */\n active: boolean /* Main area is active */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n viewport$: Observable /* Viewport observable */\n header$: Observable
    /* Header observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch main area\n *\n * This function returns an observable that computes the visual parameters of\n * the main area which depends on the viewport vertical offset and height, as\n * well as the height of the header element, if the header is fixed.\n *\n * @param el - Main area element\n * @param options - Options\n *\n * @returns Main area observable\n */\nexport function watchMain(\n el: HTMLElement, { viewport$, header$ }: WatchOptions\n): Observable
    {\n\n /* Compute necessary adjustment for header */\n const adjust$ = header$\n .pipe(\n map(({ height }) => height),\n distinctUntilChanged()\n )\n\n /* Compute the main area's top and bottom borders */\n const border$ = adjust$\n .pipe(\n switchMap(() => watchElementSize(el)\n .pipe(\n map(({ height }) => ({\n top: el.offsetTop,\n bottom: el.offsetTop + height\n })),\n distinctUntilKeyChanged(\"bottom\")\n )\n )\n )\n\n /* Compute the main area's offset, visible height and if we scrolled past */\n return combineLatest([adjust$, border$, viewport$])\n .pipe(\n map(([header, { top, bottom }, { offset: { y }, size: { height } }]) => {\n height = Math.max(0, height\n - Math.max(0, top - y, header)\n - Math.max(0, height + y - bottom)\n )\n return {\n offset: top - header,\n height,\n active: top - header <= y\n }\n }),\n distinctUntilChanged((a, b) => (\n a.offset === b.offset &&\n a.height === b.height &&\n a.active === b.active\n ))\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n asyncScheduler,\n defer,\n finalize,\n fromEvent,\n map,\n mergeMap,\n observeOn,\n of,\n shareReplay,\n startWith,\n tap\n} from \"rxjs\"\n\nimport { getElements } from \"~/browser\"\n\nimport { Component } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Palette colors\n */\nexport interface PaletteColor {\n scheme?: string /* Color scheme */\n primary?: string /* Primary color */\n accent?: string /* Accent color */\n}\n\n/**\n * Palette\n */\nexport interface Palette {\n index: number /* Palette index */\n color: PaletteColor /* Palette colors */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch color palette\n *\n * @param inputs - Color palette element\n *\n * @returns Color palette observable\n */\nexport function watchPalette(\n inputs: HTMLInputElement[]\n): Observable {\n const current = __md_get(\"__palette\") || {\n index: inputs.findIndex(input => matchMedia(\n input.getAttribute(\"data-md-color-media\")!\n ).matches)\n }\n\n /* Emit changes in color palette */\n return of(...inputs)\n .pipe(\n mergeMap(input => fromEvent(input, \"change\")\n .pipe(\n map(() => input)\n )\n ),\n startWith(inputs[Math.max(0, current.index)]),\n map(input => ({\n index: inputs.indexOf(input),\n color: {\n scheme: input.getAttribute(\"data-md-color-scheme\"),\n primary: input.getAttribute(\"data-md-color-primary\"),\n accent: input.getAttribute(\"data-md-color-accent\")\n }\n } as Palette)),\n shareReplay(1)\n )\n}\n\n/**\n * Mount color palette\n *\n * @param el - Color palette element\n *\n * @returns Color palette component observable\n */\nexport function mountPalette(\n el: HTMLElement\n): Observable> {\n return defer(() => {\n const push$ = new Subject()\n push$.subscribe(palette => {\n document.body.setAttribute(\"data-md-color-switching\", \"\")\n\n /* Set color palette */\n for (const [key, value] of Object.entries(palette.color))\n document.body.setAttribute(`data-md-color-${key}`, value)\n\n /* Toggle visibility */\n for (let index = 0; index < inputs.length; index++) {\n const label = inputs[index].nextElementSibling\n if (label instanceof HTMLElement)\n label.hidden = palette.index !== index\n }\n\n /* Persist preference in local storage */\n __md_set(\"__palette\", palette)\n })\n\n /* Revert transition durations after color switch */\n push$.pipe(observeOn(asyncScheduler))\n .subscribe(() => {\n document.body.removeAttribute(\"data-md-color-switching\")\n })\n\n /* Create and return component */\n const inputs = getElements(\"input\", el)\n return watchPalette(inputs)\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport ClipboardJS from \"clipboard\"\nimport {\n Observable,\n Subject,\n map,\n tap\n} from \"rxjs\"\n\nimport { translation } from \"~/_\"\nimport { getElement } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Setup options\n */\ninterface SetupOptions {\n alert$: Subject /* Alert subject */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Extract text to copy\n *\n * @param el - HTML element\n *\n * @returns Extracted text\n */\nfunction extract(el: HTMLElement): string {\n el.setAttribute(\"data-md-copying\", \"\")\n const text = el.innerText\n el.removeAttribute(\"data-md-copying\")\n return text\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Set up Clipboard.js integration\n *\n * @param options - Options\n */\nexport function setupClipboardJS(\n { alert$ }: SetupOptions\n): void {\n if (ClipboardJS.isSupported()) {\n new Observable(subscriber => {\n new ClipboardJS(\"[data-clipboard-target], [data-clipboard-text]\", {\n text: el => (\n el.getAttribute(\"data-clipboard-text\")! ||\n extract(getElement(\n el.getAttribute(\"data-clipboard-target\")!\n ))\n )\n })\n .on(\"success\", ev => subscriber.next(ev))\n })\n .pipe(\n tap(ev => {\n const trigger = ev.trigger as HTMLElement\n trigger.focus()\n }),\n map(() => translation(\"clipboard.copied\"))\n )\n .subscribe(alert$)\n }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n EMPTY,\n Observable,\n catchError,\n defaultIfEmpty,\n map,\n of,\n tap\n} from \"rxjs\"\n\nimport { configuration } from \"~/_\"\nimport { getElements, requestXML } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Sitemap, i.e. a list of URLs\n */\nexport type Sitemap = string[]\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Preprocess a list of URLs\n *\n * This function replaces the `site_url` in the sitemap with the actual base\n * URL, to allow instant loading to work in occasions like Netlify previews.\n *\n * @param urls - URLs\n *\n * @returns URL path parts\n */\nfunction preprocess(urls: Sitemap): Sitemap {\n if (urls.length < 2)\n return [\"\"]\n\n /* Take the first two URLs and remove everything after the last slash */\n const [root, next] = [...urls]\n .sort((a, b) => a.length - b.length)\n .map(url => url.replace(/[^/]+$/, \"\"))\n\n /* Compute common prefix */\n let index = 0\n if (root === next)\n index = root.length\n else\n while (root.charCodeAt(index) === next.charCodeAt(index))\n index++\n\n /* Remove common prefix and return in original order */\n return urls.map(url => url.replace(root.slice(0, index), \"\"))\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch the sitemap for the given base URL\n *\n * @param base - Base URL\n *\n * @returns Sitemap observable\n */\nexport function fetchSitemap(base?: URL): Observable {\n const cached = __md_get(\"__sitemap\", sessionStorage, base)\n if (cached) {\n return of(cached)\n } else {\n const config = configuration()\n return requestXML(new URL(\"sitemap.xml\", base || config.base))\n .pipe(\n map(sitemap => preprocess(getElements(\"loc\", sitemap)\n .map(node => node.textContent!)\n )),\n catchError(() => EMPTY), // @todo refactor instant loading\n defaultIfEmpty([]),\n tap(sitemap => __md_set(\"__sitemap\", sitemap, sessionStorage, base))\n )\n }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n EMPTY,\n NEVER,\n Observable,\n Subject,\n bufferCount,\n catchError,\n concatMap,\n debounceTime,\n distinctUntilChanged,\n distinctUntilKeyChanged,\n filter,\n fromEvent,\n map,\n merge,\n of,\n sample,\n share,\n skip,\n skipUntil,\n switchMap\n} from \"rxjs\"\n\nimport { configuration, feature } from \"~/_\"\nimport {\n Viewport,\n ViewportOffset,\n getElements,\n getOptionalElement,\n request,\n setLocation,\n setLocationHash\n} from \"~/browser\"\nimport { getComponentElement } from \"~/components\"\nimport { h } from \"~/utilities\"\n\nimport { fetchSitemap } from \"../sitemap\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * History state\n */\nexport interface HistoryState {\n url: URL /* State URL */\n offset?: ViewportOffset /* State viewport offset */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Setup options\n */\ninterface SetupOptions {\n document$: Subject /* Document subject */\n location$: Subject /* Location subject */\n viewport$: Observable /* Viewport observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Set up instant loading\n *\n * When fetching, theoretically, we could use `responseType: \"document\"`, but\n * since all MkDocs links are relative, we need to make sure that the current\n * location matches the document we just loaded. Otherwise any relative links\n * in the document could use the old location.\n *\n * This is the reason why we need to synchronize history events and the process\n * of fetching the document for navigation changes (except `popstate` events):\n *\n * 1. Fetch document via `XMLHTTPRequest`\n * 2. Set new location via `history.pushState`\n * 3. Parse and emit fetched document\n *\n * For `popstate` events, we must not use `history.pushState`, or the forward\n * history will be irreversibly overwritten. In case the request fails, the\n * location change is dispatched regularly.\n *\n * @param options - Options\n */\nexport function setupInstantLoading(\n { document$, location$, viewport$ }: SetupOptions\n): void {\n const config = configuration()\n if (location.protocol === \"file:\")\n return\n\n /* Disable automatic scroll restoration */\n if (\"scrollRestoration\" in history) {\n history.scrollRestoration = \"manual\"\n\n /* Hack: ensure that reloads restore viewport offset */\n fromEvent(window, \"beforeunload\")\n .subscribe(() => {\n history.scrollRestoration = \"auto\"\n })\n }\n\n /* Hack: ensure absolute favicon link to omit 404s when switching */\n const favicon = getOptionalElement(\"link[rel=icon]\")\n if (typeof favicon !== \"undefined\")\n favicon.href = favicon.href\n\n /* Intercept internal navigation */\n const push$ = fetchSitemap()\n .pipe(\n map(paths => paths.map(path => `${new URL(path, config.base)}`)),\n switchMap(urls => fromEvent(document.body, \"click\")\n .pipe(\n filter(ev => !ev.metaKey && !ev.ctrlKey),\n switchMap(ev => {\n if (ev.target instanceof Element) {\n const el = ev.target.closest(\"a\")\n if (el && !el.target) {\n const url = new URL(el.href)\n\n /* Canonicalize URL */\n url.search = \"\"\n url.hash = \"\"\n\n /* Check if URL should be intercepted */\n if (\n url.pathname !== location.pathname &&\n urls.includes(url.toString())\n ) {\n ev.preventDefault()\n return of({\n url: new URL(el.href)\n })\n }\n }\n }\n return NEVER\n })\n )\n ),\n share()\n )\n\n /* Intercept history back and forward */\n const pop$ = fromEvent(window, \"popstate\")\n .pipe(\n filter(ev => ev.state !== null),\n map(ev => ({\n url: new URL(location.href),\n offset: ev.state\n })),\n share()\n )\n\n /* Emit location change */\n merge(push$, pop$)\n .pipe(\n distinctUntilChanged((a, b) => a.url.href === b.url.href),\n map(({ url }) => url)\n )\n .subscribe(location$)\n\n /* Fetch document via `XMLHTTPRequest` */\n const response$ = location$\n .pipe(\n distinctUntilKeyChanged(\"pathname\"),\n switchMap(url => request(url.href)\n .pipe(\n catchError(() => {\n setLocation(url)\n return NEVER\n })\n )\n ),\n share()\n )\n\n /* Set new location via `history.pushState` */\n push$\n .pipe(\n sample(response$)\n )\n .subscribe(({ url }) => {\n history.pushState({}, \"\", `${url}`)\n })\n\n /* Parse and emit fetched document */\n const dom = new DOMParser()\n response$\n .pipe(\n switchMap(res => res.text()),\n map(res => dom.parseFromString(res, \"text/html\"))\n )\n .subscribe(document$)\n\n /* Replace meta tags and components */\n document$\n .pipe(\n skip(1)\n )\n .subscribe(replacement => {\n for (const selector of [\n\n /* Meta tags */\n \"title\",\n \"link[rel=canonical]\",\n \"meta[name=author]\",\n \"meta[name=description]\",\n\n /* Components */\n \"[data-md-component=announce]\",\n \"[data-md-component=container]\",\n \"[data-md-component=header-topic]\",\n \"[data-md-component=outdated]\",\n \"[data-md-component=logo]\",\n \"[data-md-component=skip]\",\n ...feature(\"navigation.tabs.sticky\")\n ? [\"[data-md-component=tabs]\"]\n : []\n ]) {\n const source = getOptionalElement(selector)\n const target = getOptionalElement(selector, replacement)\n if (\n typeof source !== \"undefined\" &&\n typeof target !== \"undefined\"\n ) {\n source.replaceWith(target)\n }\n }\n })\n\n /* Re-evaluate scripts */\n document$\n .pipe(\n skip(1),\n map(() => getComponentElement(\"container\")),\n switchMap(el => getElements(\"script\", el)),\n concatMap(el => {\n const script = h(\"script\")\n if (el.src) {\n for (const name of el.getAttributeNames())\n script.setAttribute(name, el.getAttribute(name)!)\n el.replaceWith(script)\n\n /* Complete when script is loaded */\n return new Observable(observer => {\n script.onload = () => observer.complete()\n })\n\n /* Complete immediately */\n } else {\n script.textContent = el.textContent\n el.replaceWith(script)\n return EMPTY\n }\n })\n )\n .subscribe()\n\n /* Emit history state change */\n merge(push$, pop$)\n .pipe(\n sample(document$)\n )\n .subscribe(({ url, offset }) => {\n if (url.hash && !offset) {\n setLocationHash(url.hash)\n } else {\n window.scrollTo(0, offset?.y || 0)\n }\n })\n\n /* Debounce update of viewport offset */\n viewport$\n .pipe(\n skipUntil(push$),\n debounceTime(250),\n distinctUntilKeyChanged(\"offset\")\n )\n .subscribe(({ offset }) => {\n history.replaceState(offset, \"\")\n })\n\n /* Set viewport offset from history */\n merge(push$, pop$)\n .pipe(\n bufferCount(2, 1),\n filter(([a, b]) => a.url.pathname === b.url.pathname),\n map(([, state]) => state)\n )\n .subscribe(({ offset }) => {\n window.scrollTo(0, offset?.y || 0)\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport escapeHTML from \"escape-html\"\n\nimport { SearchIndexDocument } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search document\n */\nexport interface SearchDocument extends SearchIndexDocument {\n parent?: SearchIndexDocument /* Parent article */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Search document mapping\n */\nexport type SearchDocumentMap = Map\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Create a search document mapping\n *\n * @param docs - Search index documents\n *\n * @returns Search document map\n */\nexport function setupSearchDocumentMap(\n docs: SearchIndexDocument[]\n): SearchDocumentMap {\n const documents = new Map()\n const parents = new Set()\n for (const doc of docs) {\n const [path, hash] = doc.location.split(\"#\")\n\n /* Extract location, title and tags */\n const location = doc.location\n const title = doc.title\n const tags = doc.tags\n\n /* Escape and cleanup text */\n const text = escapeHTML(doc.text)\n .replace(/\\s+(?=[,.:;!?])/g, \"\")\n .replace(/\\s+/g, \" \")\n\n /* Handle section */\n if (hash) {\n const parent = documents.get(path)!\n\n /* Ignore first section, override article */\n if (!parents.has(parent)) {\n parent.title = doc.title\n parent.text = text\n\n /* Remember that we processed the article */\n parents.add(parent)\n\n /* Add subsequent section */\n } else {\n documents.set(location, {\n location,\n title,\n text,\n parent\n })\n }\n\n /* Add article */\n } else {\n documents.set(location, {\n location,\n title,\n text,\n ...tags && { tags }\n })\n }\n }\n return documents\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport escapeHTML from \"escape-html\"\n\nimport { SearchIndexConfig } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search highlight function\n *\n * @param value - Value\n *\n * @returns Highlighted value\n */\nexport type SearchHighlightFn = (value: string) => string\n\n/**\n * Search highlight factory function\n *\n * @param query - Query value\n *\n * @returns Search highlight function\n */\nexport type SearchHighlightFactoryFn = (query: string) => SearchHighlightFn\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Create a search highlighter\n *\n * @param config - Search index configuration\n * @param escape - Whether to escape HTML\n *\n * @returns Search highlight factory function\n */\nexport function setupSearchHighlighter(\n config: SearchIndexConfig, escape: boolean\n): SearchHighlightFactoryFn {\n const separator = new RegExp(config.separator, \"img\")\n const highlight = (_: unknown, data: string, term: string) => {\n return `${data}${term}`\n }\n\n /* Return factory function */\n return (query: string) => {\n query = query\n .replace(/[\\s*+\\-:~^]+/g, \" \")\n .trim()\n\n /* Create search term match expression */\n const match = new RegExp(`(^|${config.separator})(${\n query\n .replace(/[|\\\\{}()[\\]^$+*?.-]/g, \"\\\\$&\")\n .replace(separator, \"|\")\n })`, \"img\")\n\n /* Highlight string value */\n return value => (\n escape\n ? escapeHTML(value)\n : value\n )\n .replace(match, highlight)\n .replace(/<\\/mark>(\\s+)]*>/img, \"$1\")\n }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search transformation function\n *\n * @param value - Query value\n *\n * @returns Transformed query value\n */\nexport type SearchTransformFn = (value: string) => string\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Default transformation function\n *\n * 1. Search for terms in quotation marks and prepend a `+` modifier to denote\n * that the resulting document must contain all terms, converting the query\n * to an `AND` query (as opposed to the default `OR` behavior). While users\n * may expect terms enclosed in quotation marks to map to span queries, i.e.\n * for which order is important, Lunr.js doesn't support them, so the best\n * we can do is to convert the terms to an `AND` query.\n *\n * 2. Replace control characters which are not located at the beginning of the\n * query or preceded by white space, or are not followed by a non-whitespace\n * character or are at the end of the query string. Furthermore, filter\n * unmatched quotation marks.\n *\n * 3. Trim excess whitespace from left and right.\n *\n * @param query - Query value\n *\n * @returns Transformed query value\n */\nexport function defaultTransform(query: string): string {\n return query\n .split(/\"([^\"]+)\"/g) /* => 1 */\n .map((terms, index) => index & 1\n ? terms.replace(/^\\b|^(?![^\\x00-\\x7F]|$)|\\s+/g, \" +\")\n : terms\n )\n .join(\"\")\n .replace(/\"|(?:^|\\s+)[*+\\-:^~]+(?=\\s+|$)/g, \"\") /* => 2 */\n .trim() /* => 3 */\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A RTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { SearchIndex, SearchResult } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search message type\n */\nexport const enum SearchMessageType {\n SETUP, /* Search index setup */\n READY, /* Search index ready */\n QUERY, /* Search query */\n RESULT /* Search results */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Message containing the data necessary to setup the search index\n */\nexport interface SearchSetupMessage {\n type: SearchMessageType.SETUP /* Message type */\n data: SearchIndex /* Message data */\n}\n\n/**\n * Message indicating the search index is ready\n */\nexport interface SearchReadyMessage {\n type: SearchMessageType.READY /* Message type */\n}\n\n/**\n * Message containing a search query\n */\nexport interface SearchQueryMessage {\n type: SearchMessageType.QUERY /* Message type */\n data: string /* Message data */\n}\n\n/**\n * Message containing results for a search query\n */\nexport interface SearchResultMessage {\n type: SearchMessageType.RESULT /* Message type */\n data: SearchResult /* Message data */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Message exchanged with the search worker\n */\nexport type SearchMessage =\n | SearchSetupMessage\n | SearchReadyMessage\n | SearchQueryMessage\n | SearchResultMessage\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Type guard for search setup messages\n *\n * @param message - Search worker message\n *\n * @returns Test result\n */\nexport function isSearchSetupMessage(\n message: SearchMessage\n): message is SearchSetupMessage {\n return message.type === SearchMessageType.SETUP\n}\n\n/**\n * Type guard for search ready messages\n *\n * @param message - Search worker message\n *\n * @returns Test result\n */\nexport function isSearchReadyMessage(\n message: SearchMessage\n): message is SearchReadyMessage {\n return message.type === SearchMessageType.READY\n}\n\n/**\n * Type guard for search query messages\n *\n * @param message - Search worker message\n *\n * @returns Test result\n */\nexport function isSearchQueryMessage(\n message: SearchMessage\n): message is SearchQueryMessage {\n return message.type === SearchMessageType.QUERY\n}\n\n/**\n * Type guard for search result messages\n *\n * @param message - Search worker message\n *\n * @returns Test result\n */\nexport function isSearchResultMessage(\n message: SearchMessage\n): message is SearchResultMessage {\n return message.type === SearchMessageType.RESULT\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A RTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n ObservableInput,\n Subject,\n from,\n map,\n share\n} from \"rxjs\"\n\nimport { configuration, feature, translation } from \"~/_\"\nimport { WorkerHandler, watchWorker } from \"~/browser\"\n\nimport { SearchIndex } from \"../../_\"\nimport {\n SearchOptions,\n SearchPipeline\n} from \"../../options\"\nimport {\n SearchMessage,\n SearchMessageType,\n SearchSetupMessage,\n isSearchResultMessage\n} from \"../message\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search worker\n */\nexport type SearchWorker = WorkerHandler\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Set up search index\n *\n * @param data - Search index\n *\n * @returns Search index\n */\nfunction setupSearchIndex({ config, docs }: SearchIndex): SearchIndex {\n\n /* Override default language with value from translation */\n if (config.lang.length === 1 && config.lang[0] === \"en\")\n config.lang = [\n translation(\"search.config.lang\")\n ]\n\n /* Override default separator with value from translation */\n if (config.separator === \"[\\\\s\\\\-]+\")\n config.separator = translation(\"search.config.separator\")\n\n /* Set pipeline from translation */\n const pipeline = translation(\"search.config.pipeline\")\n .split(/\\s*,\\s*/)\n .filter(Boolean) as SearchPipeline\n\n /* Determine search options */\n const options: SearchOptions = {\n pipeline,\n suggestions: feature(\"search.suggest\")\n }\n\n /* Return search index after defaulting */\n return { config, docs, options }\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Set up search worker\n *\n * This function creates a web worker to set up and query the search index,\n * which is done using Lunr.js. The index must be passed as an observable to\n * enable hacks like _localsearch_ via search index embedding as JSON.\n *\n * @param url - Worker URL\n * @param index - Search index observable input\n *\n * @returns Search worker\n */\nexport function setupSearchWorker(\n url: string, index: ObservableInput\n): SearchWorker {\n const config = configuration()\n const worker = new Worker(url)\n\n /* Create communication channels and resolve relative links */\n const tx$ = new Subject()\n const rx$ = watchWorker(worker, { tx$ })\n .pipe(\n map(message => {\n if (isSearchResultMessage(message)) {\n for (const result of message.data.items)\n for (const document of result)\n document.location = `${new URL(document.location, config.base)}`\n }\n return message\n }),\n share()\n )\n\n /* Set up search index */\n from(index)\n .pipe(\n map(data => ({\n type: SearchMessageType.SETUP,\n data: setupSearchIndex(data)\n } as SearchSetupMessage))\n )\n .subscribe(tx$.next.bind(tx$))\n\n /* Return search worker */\n return { tx$, rx$ }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n EMPTY,\n Subject,\n catchError,\n combineLatest,\n filter,\n fromEvent,\n map,\n of,\n switchMap,\n withLatestFrom\n} from \"rxjs\"\n\nimport { configuration } from \"~/_\"\nimport {\n getElement,\n getLocation,\n requestJSON,\n setLocation\n} from \"~/browser\"\nimport { getComponentElements } from \"~/components\"\nimport {\n Version,\n renderVersionSelector\n} from \"~/templates\"\n\nimport { fetchSitemap } from \"../sitemap\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Setup options\n */\ninterface SetupOptions {\n document$: Subject /* Document subject */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Set up version selector\n *\n * @param options - Options\n */\nexport function setupVersionSelector(\n { document$ }: SetupOptions\n): void {\n const config = configuration()\n const versions$ = requestJSON(\n new URL(\"../versions.json\", config.base)\n )\n .pipe(\n catchError(() => EMPTY) // @todo refactor instant loading\n )\n\n /* Determine current version */\n const current$ = versions$\n .pipe(\n map(versions => {\n const [, current] = config.base.match(/([^/]+)\\/?$/)!\n return versions.find(({ version, aliases }) => (\n version === current || aliases.includes(current)\n )) || versions[0]\n })\n )\n\n /* Intercept inter-version navigation */\n versions$\n .pipe(\n map(versions => new Map(versions.map(version => [\n `${new URL(`../${version.version}/`, config.base)}`,\n version\n ]))),\n switchMap(urls => fromEvent(document.body, \"click\")\n .pipe(\n filter(ev => !ev.metaKey && !ev.ctrlKey),\n withLatestFrom(current$),\n switchMap(([ev, current]) => {\n if (ev.target instanceof Element) {\n const el = ev.target.closest(\"a\")\n if (el && !el.target && urls.has(el.href)) {\n const url = el.href\n // This is a temporary hack to detect if a version inside the\n // version selector or on another part of the site was clicked.\n // If we're inside the version selector, we definitely want to\n // find the same page, as we might have different deployments\n // due to aliases. However, if we're outside the version\n // selector, we must abort here, because we might otherwise\n // interfere with instant loading. We need to refactor this\n // at some point together with instant loading.\n //\n // See https://github.com/squidfunk/mkdocs-material/issues/4012\n if (!ev.target.closest(\".md-version\")) {\n const version = urls.get(url)!\n if (version === current)\n return EMPTY\n }\n ev.preventDefault()\n return of(url)\n }\n }\n return EMPTY\n }),\n switchMap(url => {\n const { version } = urls.get(url)!\n return fetchSitemap(new URL(url))\n .pipe(\n map(sitemap => {\n const location = getLocation()\n const path = location.href.replace(config.base, \"\")\n return sitemap.includes(path.split(\"#\")[0])\n ? new URL(`../${version}/${path}`, config.base)\n : new URL(url)\n })\n )\n })\n )\n )\n )\n .subscribe(url => setLocation(url))\n\n /* Render version selector and warning */\n combineLatest([versions$, current$])\n .subscribe(([versions, current]) => {\n const topic = getElement(\".md-header__topic\")\n topic.appendChild(renderVersionSelector(versions, current))\n })\n\n /* Integrate outdated version banner with instant loading */\n document$.pipe(switchMap(() => current$))\n .subscribe(current => {\n\n /* Check if version state was already determined */\n let outdated = __md_get(\"__outdated\", sessionStorage)\n if (outdated === null) {\n const latest = config.version?.default || \"latest\"\n outdated = !current.aliases.includes(latest)\n\n /* Persist version state in session storage */\n __md_set(\"__outdated\", outdated, sessionStorage)\n }\n\n /* Unhide outdated version banner */\n if (outdated)\n for (const warning of getComponentElements(\"outdated\"))\n warning.hidden = false\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n combineLatest,\n delay,\n distinctUntilChanged,\n distinctUntilKeyChanged,\n filter,\n finalize,\n fromEvent,\n map,\n merge,\n share,\n shareReplay,\n startWith,\n take,\n takeLast,\n takeUntil,\n tap\n} from \"rxjs\"\n\nimport { translation } from \"~/_\"\nimport {\n getLocation,\n setToggle,\n watchElementFocus,\n watchToggle\n} from \"~/browser\"\nimport {\n SearchMessageType,\n SearchQueryMessage,\n SearchWorker,\n defaultTransform,\n isSearchReadyMessage\n} from \"~/integrations\"\n\nimport { Component } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search query\n */\nexport interface SearchQuery {\n value: string /* Query value */\n focus: boolean /* Query focus */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch search query\n *\n * Note that the focus event which triggers re-reading the current query value\n * is delayed by `1ms` so the input's empty state is allowed to propagate.\n *\n * @param el - Search query element\n * @param worker - Search worker\n *\n * @returns Search query observable\n */\nexport function watchSearchQuery(\n el: HTMLInputElement, { rx$ }: SearchWorker\n): Observable {\n const fn = __search?.transform || defaultTransform\n\n /* Immediately show search dialog */\n const { searchParams } = getLocation()\n if (searchParams.has(\"q\"))\n setToggle(\"search\", true)\n\n /* Intercept query parameter (deep link) */\n const param$ = rx$\n .pipe(\n filter(isSearchReadyMessage),\n take(1),\n map(() => searchParams.get(\"q\") || \"\")\n )\n\n /* Remove query parameter when search is closed */\n watchToggle(\"search\")\n .pipe(\n filter(active => !active),\n take(1)\n )\n .subscribe(() => {\n const url = new URL(location.href)\n url.searchParams.delete(\"q\")\n history.replaceState({}, \"\", `${url}`)\n })\n\n /* Set query from parameter */\n param$.subscribe(value => { // TODO: not ideal - find a better way\n if (value) {\n el.value = value\n el.focus()\n }\n })\n\n /* Intercept focus and input events */\n const focus$ = watchElementFocus(el)\n const value$ = merge(\n fromEvent(el, \"keyup\"),\n fromEvent(el, \"focus\").pipe(delay(1)),\n param$\n )\n .pipe(\n map(() => fn(el.value)),\n startWith(\"\"),\n distinctUntilChanged(),\n )\n\n /* Combine into single observable */\n return combineLatest([value$, focus$])\n .pipe(\n map(([value, focus]) => ({ value, focus })),\n shareReplay(1)\n )\n}\n\n/**\n * Mount search query\n *\n * @param el - Search query element\n * @param worker - Search worker\n *\n * @returns Search query component observable\n */\nexport function mountSearchQuery(\n el: HTMLInputElement, { tx$, rx$ }: SearchWorker\n): Observable> {\n const push$ = new Subject()\n const done$ = push$.pipe(takeLast(1))\n\n /* Handle value changes */\n push$\n .pipe(\n distinctUntilKeyChanged(\"value\"),\n map(({ value }): SearchQueryMessage => ({\n type: SearchMessageType.QUERY,\n data: value\n }))\n )\n .subscribe(tx$.next.bind(tx$))\n\n /* Handle focus changes */\n push$\n .pipe(\n distinctUntilKeyChanged(\"focus\")\n )\n .subscribe(({ focus }) => {\n if (focus) {\n setToggle(\"search\", focus)\n el.placeholder = \"\"\n } else {\n el.placeholder = translation(\"search.placeholder\")\n }\n })\n\n /* Handle reset */\n fromEvent(el.form!, \"reset\")\n .pipe(\n takeUntil(done$)\n )\n .subscribe(() => el.focus())\n\n /* Create and return component */\n return watchSearchQuery(el, { tx$, rx$ })\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state })),\n share()\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n bufferCount,\n filter,\n finalize,\n map,\n merge,\n of,\n skipUntil,\n switchMap,\n take,\n tap,\n withLatestFrom,\n zipWith\n} from \"rxjs\"\n\nimport { translation } from \"~/_\"\nimport {\n getElement,\n watchElementBoundary\n} from \"~/browser\"\nimport {\n SearchResult,\n SearchWorker,\n isSearchReadyMessage,\n isSearchResultMessage\n} from \"~/integrations\"\nimport { renderSearchResultItem } from \"~/templates\"\nimport { round } from \"~/utilities\"\n\nimport { Component } from \"../../_\"\nimport { SearchQuery } from \"../query\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n query$: Observable /* Search query observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount search result list\n *\n * This function performs a lazy rendering of the search results, depending on\n * the vertical offset of the search result container.\n *\n * @param el - Search result list element\n * @param worker - Search worker\n * @param options - Options\n *\n * @returns Search result list component observable\n */\nexport function mountSearchResult(\n el: HTMLElement, { rx$ }: SearchWorker, { query$ }: MountOptions\n): Observable> {\n const push$ = new Subject()\n const boundary$ = watchElementBoundary(el.parentElement!)\n .pipe(\n filter(Boolean)\n )\n\n /* Retrieve nested components */\n const meta = getElement(\":scope > :first-child\", el)\n const list = getElement(\":scope > :last-child\", el)\n\n /* Wait until search is ready */\n const ready$ = rx$\n .pipe(\n filter(isSearchReadyMessage),\n take(1)\n )\n\n /* Update search result metadata */\n push$\n .pipe(\n withLatestFrom(query$),\n skipUntil(ready$)\n )\n .subscribe(([{ items }, { value }]) => {\n if (value) {\n switch (items.length) {\n\n /* No results */\n case 0:\n meta.textContent = translation(\"search.result.none\")\n break\n\n /* One result */\n case 1:\n meta.textContent = translation(\"search.result.one\")\n break\n\n /* Multiple result */\n default:\n meta.textContent = translation(\n \"search.result.other\",\n round(items.length)\n )\n }\n } else {\n meta.textContent = translation(\"search.result.placeholder\")\n }\n })\n\n /* Update search result list */\n push$\n .pipe(\n tap(() => list.innerHTML = \"\"),\n switchMap(({ items }) => merge(\n of(...items.slice(0, 10)),\n of(...items.slice(10))\n .pipe(\n bufferCount(4),\n zipWith(boundary$),\n switchMap(([chunk]) => chunk)\n )\n ))\n )\n .subscribe(result => list.appendChild(\n renderSearchResultItem(result)\n ))\n\n /* Filter search result message */\n const result$ = rx$\n .pipe(\n filter(isSearchResultMessage),\n map(({ data }) => data)\n )\n\n /* Create and return component */\n return result$\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n finalize,\n fromEvent,\n map,\n tap\n} from \"rxjs\"\n\nimport { getLocation } from \"~/browser\"\n\nimport { Component } from \"../../_\"\nimport { SearchQuery } from \"../query\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search sharing\n */\nexport interface SearchShare {\n url: URL /* Deep link for sharing */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n query$: Observable /* Search query observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n query$: Observable /* Search query observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount search sharing\n *\n * @param _el - Search sharing element\n * @param options - Options\n *\n * @returns Search sharing observable\n */\nexport function watchSearchShare(\n _el: HTMLElement, { query$ }: WatchOptions\n): Observable {\n return query$\n .pipe(\n map(({ value }) => {\n const url = getLocation()\n url.hash = \"\"\n url.searchParams.delete(\"h\")\n url.searchParams.set(\"q\", value)\n return { url }\n })\n )\n}\n\n/**\n * Mount search sharing\n *\n * @param el - Search sharing element\n * @param options - Options\n *\n * @returns Search sharing component observable\n */\nexport function mountSearchShare(\n el: HTMLAnchorElement, options: MountOptions\n): Observable> {\n const push$ = new Subject()\n push$.subscribe(({ url }) => {\n el.setAttribute(\"data-clipboard-text\", el.href)\n el.href = `${url}`\n })\n\n /* Prevent following of link */\n fromEvent(el, \"click\")\n .subscribe(ev => ev.preventDefault())\n\n /* Create and return component */\n return watchSearchShare(el, options)\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n asyncScheduler,\n combineLatestWith,\n distinctUntilChanged,\n filter,\n finalize,\n fromEvent,\n map,\n merge,\n observeOn,\n tap\n} from \"rxjs\"\n\nimport { Keyboard } from \"~/browser\"\nimport {\n SearchResult,\n SearchWorker,\n isSearchResultMessage\n} from \"~/integrations\"\n\nimport { Component, getComponentElement } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search suggestions\n */\nexport interface SearchSuggest {}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n keyboard$: Observable /* Keyboard observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount search suggestions\n *\n * This function will perform a lazy rendering of the search results, depending\n * on the vertical offset of the search result container.\n *\n * @param el - Search result list element\n * @param worker - Search worker\n * @param options - Options\n *\n * @returns Search result list component observable\n */\nexport function mountSearchSuggest(\n el: HTMLElement, { rx$ }: SearchWorker, { keyboard$ }: MountOptions\n): Observable> {\n const push$ = new Subject()\n\n /* Retrieve query component and track all changes */\n const query = getComponentElement(\"search-query\")\n const query$ = merge(\n fromEvent(query, \"keydown\"),\n fromEvent(query, \"focus\")\n )\n .pipe(\n observeOn(asyncScheduler),\n map(() => query.value),\n distinctUntilChanged(),\n )\n\n /* Update search suggestions */\n push$\n .pipe(\n combineLatestWith(query$),\n map(([{ suggestions }, value]) => {\n const words = value.split(/([\\s-]+)/)\n if (suggestions?.length && words[words.length - 1]) {\n const last = suggestions[suggestions.length - 1]\n if (last.startsWith(words[words.length - 1]))\n words[words.length - 1] = last\n } else {\n words.length = 0\n }\n return words\n })\n )\n .subscribe(words => el.innerHTML = words\n .join(\"\")\n .replace(/\\s/g, \" \")\n )\n\n /* Set up search keyboard handlers */\n keyboard$\n .pipe(\n filter(({ mode }) => mode === \"search\")\n )\n .subscribe(key => {\n switch (key.type) {\n\n /* Right arrow: accept current suggestion */\n case \"ArrowRight\":\n if (\n el.innerText.length &&\n query.selectionStart === query.value.length\n )\n query.value = el.innerText\n break\n }\n })\n\n /* Filter search result message */\n const result$ = rx$\n .pipe(\n filter(isSearchResultMessage),\n map(({ data }) => data)\n )\n\n /* Create and return component */\n return result$\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(() => ({ ref: el }))\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n NEVER,\n Observable,\n ObservableInput,\n filter,\n merge,\n mergeWith,\n sample,\n take\n} from \"rxjs\"\n\nimport { configuration } from \"~/_\"\nimport {\n Keyboard,\n getActiveElement,\n getElements,\n setToggle\n} from \"~/browser\"\nimport {\n SearchIndex,\n SearchResult,\n isSearchQueryMessage,\n isSearchReadyMessage,\n setupSearchWorker\n} from \"~/integrations\"\n\nimport {\n Component,\n getComponentElement,\n getComponentElements\n} from \"../../_\"\nimport {\n SearchQuery,\n mountSearchQuery\n} from \"../query\"\nimport { mountSearchResult } from \"../result\"\nimport {\n SearchShare,\n mountSearchShare\n} from \"../share\"\nimport {\n SearchSuggest,\n mountSearchSuggest\n} from \"../suggest\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search\n */\nexport type Search =\n | SearchQuery\n | SearchResult\n | SearchShare\n | SearchSuggest\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n index$: ObservableInput /* Search index observable */\n keyboard$: Observable /* Keyboard observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount search\n *\n * This function sets up the search functionality, including the underlying\n * web worker and all keyboard bindings.\n *\n * @param el - Search element\n * @param options - Options\n *\n * @returns Search component observable\n */\nexport function mountSearch(\n el: HTMLElement, { index$, keyboard$ }: MountOptions\n): Observable> {\n const config = configuration()\n try {\n const url = __search?.worker || config.search\n const worker = setupSearchWorker(url, index$)\n\n /* Retrieve query and result components */\n const query = getComponentElement(\"search-query\", el)\n const result = getComponentElement(\"search-result\", el)\n\n /* Re-emit query when search is ready */\n const { tx$, rx$ } = worker\n tx$\n .pipe(\n filter(isSearchQueryMessage),\n sample(rx$.pipe(filter(isSearchReadyMessage))),\n take(1)\n )\n .subscribe(tx$.next.bind(tx$))\n\n /* Set up search keyboard handlers */\n keyboard$\n .pipe(\n filter(({ mode }) => mode === \"search\")\n )\n .subscribe(key => {\n const active = getActiveElement()\n switch (key.type) {\n\n /* Enter: go to first (best) result */\n case \"Enter\":\n if (active === query) {\n const anchors = new Map()\n for (const anchor of getElements(\n \":first-child [href]\", result\n )) {\n const article = anchor.firstElementChild!\n anchors.set(anchor, parseFloat(\n article.getAttribute(\"data-md-score\")!\n ))\n }\n\n /* Go to result with highest score, if any */\n if (anchors.size) {\n const [[best]] = [...anchors].sort(([, a], [, b]) => b - a)\n best.click()\n }\n\n /* Otherwise omit form submission */\n key.claim()\n }\n break\n\n /* Escape or Tab: close search */\n case \"Escape\":\n case \"Tab\":\n setToggle(\"search\", false)\n query.blur()\n break\n\n /* Vertical arrows: select previous or next search result */\n case \"ArrowUp\":\n case \"ArrowDown\":\n if (typeof active === \"undefined\") {\n query.focus()\n } else {\n const els = [query, ...getElements(\n \":not(details) > [href], summary, details[open] [href]\",\n result\n )]\n const i = Math.max(0, (\n Math.max(0, els.indexOf(active)) + els.length + (\n key.type === \"ArrowUp\" ? -1 : +1\n )\n ) % els.length)\n els[i].focus()\n }\n\n /* Prevent scrolling of page */\n key.claim()\n break\n\n /* All other keys: hand to search query */\n default:\n if (query !== getActiveElement())\n query.focus()\n }\n })\n\n /* Set up global keyboard handlers */\n keyboard$\n .pipe(\n filter(({ mode }) => mode === \"global\"),\n )\n .subscribe(key => {\n switch (key.type) {\n\n /* Open search and select query */\n case \"f\":\n case \"s\":\n case \"/\":\n query.focus()\n query.select()\n\n /* Prevent scrolling of page */\n key.claim()\n break\n }\n })\n\n /* Create and return component */\n const query$ = mountSearchQuery(query, worker)\n const result$ = mountSearchResult(result, worker, { query$ })\n return merge(query$, result$)\n .pipe(\n mergeWith(\n\n /* Search sharing */\n ...getComponentElements(\"search-share\", el)\n .map(child => mountSearchShare(child, { query$ })),\n\n /* Search suggestions */\n ...getComponentElements(\"search-suggest\", el)\n .map(child => mountSearchSuggest(child, worker, { keyboard$ }))\n )\n )\n\n /* Gracefully handle broken search */\n } catch (err) {\n el.hidden = true\n return NEVER\n }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n ObservableInput,\n combineLatest,\n filter,\n map,\n startWith\n} from \"rxjs\"\n\nimport { getLocation } from \"~/browser\"\nimport {\n SearchIndex,\n setupSearchHighlighter\n} from \"~/integrations\"\nimport { h } from \"~/utilities\"\n\nimport { Component } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search highlighting\n */\nexport interface SearchHighlight {\n nodes: Map /* Map of replacements */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n index$: ObservableInput /* Search index observable */\n location$: Observable /* Location observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount search highlighting\n *\n * @param el - Content element\n * @param options - Options\n *\n * @returns Search highlighting component observable\n */\nexport function mountSearchHiglight(\n el: HTMLElement, { index$, location$ }: MountOptions\n): Observable> {\n return combineLatest([\n index$,\n location$\n .pipe(\n startWith(getLocation()),\n filter(url => !!url.searchParams.get(\"h\"))\n )\n ])\n .pipe(\n map(([index, url]) => setupSearchHighlighter(index.config, true)(\n url.searchParams.get(\"h\")!\n )),\n map(fn => {\n const nodes = new Map()\n\n /* Traverse text nodes and collect matches */\n const it = document.createNodeIterator(el, NodeFilter.SHOW_TEXT)\n for (let node = it.nextNode(); node; node = it.nextNode()) {\n if (node.parentElement?.offsetHeight) {\n const original = node.textContent!\n const replaced = fn(original)\n if (replaced.length > original.length)\n nodes.set(node as ChildNode, replaced)\n }\n }\n\n /* Replace original nodes with matches */\n for (const [node, text] of nodes) {\n const { childNodes } = h(\"span\", null, text)\n node.replaceWith(...Array.from(childNodes))\n }\n\n /* Return component */\n return { ref: el, nodes }\n })\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n animationFrameScheduler,\n auditTime,\n combineLatest,\n defer,\n distinctUntilChanged,\n finalize,\n map,\n observeOn,\n take,\n tap,\n withLatestFrom\n} from \"rxjs\"\n\nimport {\n Viewport,\n getElement,\n getElementContainer,\n getElementOffset,\n getElementSize,\n getElements\n} from \"~/browser\"\n\nimport { Component } from \"../_\"\nimport { Header } from \"../header\"\nimport { Main } from \"../main\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Sidebar\n */\nexport interface Sidebar {\n height: number /* Sidebar height */\n locked: boolean /* Sidebar is locked */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n viewport$: Observable /* Viewport observable */\n main$: Observable
    /* Main area observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n viewport$: Observable /* Viewport observable */\n header$: Observable
    /* Header observable */\n main$: Observable
    /* Main area observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch sidebar\n *\n * This function returns an observable that computes the visual parameters of\n * the sidebar which depends on the vertical viewport offset, as well as the\n * height of the main area. When the page is scrolled beyond the header, the\n * sidebar is locked and fills the remaining space.\n *\n * @param el - Sidebar element\n * @param options - Options\n *\n * @returns Sidebar observable\n */\nexport function watchSidebar(\n el: HTMLElement, { viewport$, main$ }: WatchOptions\n): Observable {\n const parent = el.parentElement!\n const adjust =\n parent.offsetTop -\n parent.parentElement!.offsetTop\n\n /* Compute the sidebar's available height and if it should be locked */\n return combineLatest([main$, viewport$])\n .pipe(\n map(([{ offset, height }, { offset: { y } }]) => {\n height = height\n + Math.min(adjust, Math.max(0, y - offset))\n - adjust\n return {\n height,\n locked: y >= offset + adjust\n }\n }),\n distinctUntilChanged((a, b) => (\n a.height === b.height &&\n a.locked === b.locked\n ))\n )\n}\n\n/**\n * Mount sidebar\n *\n * This function doesn't set the height of the actual sidebar, but of its first\n * child \u2013 the `.md-sidebar__scrollwrap` element in order to mitigiate jittery\n * sidebars when the footer is scrolled into view. At some point we switched\n * from `absolute` / `fixed` positioning to `sticky` positioning, significantly\n * reducing jitter in some browsers (respectively Firefox and Safari) when\n * scrolling from the top. However, top-aligned sticky positioning means that\n * the sidebar snaps to the bottom when the end of the container is reached.\n * This is what leads to the mentioned jitter, as the sidebar's height may be\n * updated too slowly.\n *\n * This behaviour can be mitigiated by setting the height of the sidebar to `0`\n * while preserving the padding, and the height on its first element.\n *\n * @param el - Sidebar element\n * @param options - Options\n *\n * @returns Sidebar component observable\n */\nexport function mountSidebar(\n el: HTMLElement, { header$, ...options }: MountOptions\n): Observable> {\n const inner = getElement(\".md-sidebar__scrollwrap\", el)\n const { y } = getElementOffset(inner)\n return defer(() => {\n const push$ = new Subject()\n push$\n .pipe(\n auditTime(0, animationFrameScheduler),\n withLatestFrom(header$)\n )\n .subscribe({\n\n /* Handle emission */\n next([{ height }, { height: offset }]) {\n inner.style.height = `${height - 2 * y}px`\n el.style.top = `${offset}px`\n },\n\n /* Handle complete */\n complete() {\n inner.style.height = \"\"\n el.style.top = \"\"\n }\n })\n\n /* Bring active item into view on initial load */\n push$\n .pipe(\n observeOn(animationFrameScheduler),\n take(1)\n )\n .subscribe(() => {\n for (const item of getElements(\".md-nav__link--active[href]\", el)) {\n const container = getElementContainer(item)\n if (typeof container !== \"undefined\") {\n const offset = item.offsetTop - container.offsetTop\n const { height } = getElementSize(container)\n container.scrollTo({\n top: offset - height / 2\n })\n }\n }\n })\n\n /* Create and return component */\n return watchSidebar(el, options)\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { Repo, User } from \"github-types\"\nimport {\n EMPTY,\n Observable,\n catchError,\n defaultIfEmpty,\n map,\n zip\n} from \"rxjs\"\n\nimport { requestJSON } from \"~/browser\"\n\nimport { SourceFacts } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * GitHub release (partial)\n */\ninterface Release {\n tag_name: string /* Tag name */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch GitHub repository facts\n *\n * @param user - GitHub user or organization\n * @param repo - GitHub repository\n *\n * @returns Repository facts observable\n */\nexport function fetchSourceFactsFromGitHub(\n user: string, repo?: string\n): Observable {\n if (typeof repo !== \"undefined\") {\n const url = `https://api.github.com/repos/${user}/${repo}`\n return zip(\n\n /* Fetch version */\n requestJSON(`${url}/releases/latest`)\n .pipe(\n catchError(() => EMPTY), // @todo refactor instant loading\n map(release => ({\n version: release.tag_name\n })),\n defaultIfEmpty({})\n ),\n\n /* Fetch stars and forks */\n requestJSON(url)\n .pipe(\n catchError(() => EMPTY), // @todo refactor instant loading\n map(info => ({\n stars: info.stargazers_count,\n forks: info.forks_count\n })),\n defaultIfEmpty({})\n )\n )\n .pipe(\n map(([release, info]) => ({ ...release, ...info }))\n )\n\n /* User or organization */\n } else {\n const url = `https://api.github.com/users/${user}`\n return requestJSON(url)\n .pipe(\n map(info => ({\n repositories: info.public_repos\n })),\n defaultIfEmpty({})\n )\n }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { ProjectSchema } from \"gitlab\"\nimport {\n EMPTY,\n Observable,\n catchError,\n defaultIfEmpty,\n map\n} from \"rxjs\"\n\nimport { requestJSON } from \"~/browser\"\n\nimport { SourceFacts } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch GitLab repository facts\n *\n * @param base - GitLab base\n * @param project - GitLab project\n *\n * @returns Repository facts observable\n */\nexport function fetchSourceFactsFromGitLab(\n base: string, project: string\n): Observable {\n const url = `https://${base}/api/v4/projects/${encodeURIComponent(project)}`\n return requestJSON(url)\n .pipe(\n catchError(() => EMPTY), // @todo refactor instant loading\n map(({ star_count, forks_count }) => ({\n stars: star_count,\n forks: forks_count\n })),\n defaultIfEmpty({})\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { EMPTY, Observable } from \"rxjs\"\n\nimport { fetchSourceFactsFromGitHub } from \"../github\"\nimport { fetchSourceFactsFromGitLab } from \"../gitlab\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Repository facts for repositories\n */\nexport interface RepositoryFacts {\n stars?: number /* Number of stars */\n forks?: number /* Number of forks */\n version?: string /* Latest version */\n}\n\n/**\n * Repository facts for organizations\n */\nexport interface OrganizationFacts {\n repositories?: number /* Number of repositories */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Repository facts\n */\nexport type SourceFacts =\n | RepositoryFacts\n | OrganizationFacts\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch repository facts\n *\n * @param url - Repository URL\n *\n * @returns Repository facts observable\n */\nexport function fetchSourceFacts(\n url: string\n): Observable {\n const [type] = url.match(/(git(?:hub|lab))/i) || []\n switch (type.toLowerCase()) {\n\n /* GitHub repository */\n case \"github\":\n const [, user, repo] = url.match(/^.+github\\.com\\/([^/]+)\\/?([^/]+)?/i)!\n return fetchSourceFactsFromGitHub(user, repo)\n\n /* GitLab repository */\n case \"gitlab\":\n const [, base, slug] = url.match(/^.+?([^/]*gitlab[^/]+)\\/(.+?)\\/?$/i)!\n return fetchSourceFactsFromGitLab(base, slug)\n\n /* Everything else */\n default:\n return EMPTY\n }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n EMPTY,\n Observable,\n Subject,\n catchError,\n defer,\n filter,\n finalize,\n map,\n of,\n shareReplay,\n tap\n} from \"rxjs\"\n\nimport { getElement } from \"~/browser\"\nimport { renderSourceFacts } from \"~/templates\"\n\nimport { Component } from \"../../_\"\nimport {\n SourceFacts,\n fetchSourceFacts\n} from \"../facts\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Repository information\n */\nexport interface Source {\n facts: SourceFacts /* Repository facts */\n}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Repository information observable\n */\nlet fetch$: Observable\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch repository information\n *\n * This function tries to read the repository facts from session storage, and\n * if unsuccessful, fetches them from the underlying provider.\n *\n * @param el - Repository information element\n *\n * @returns Repository information observable\n */\nexport function watchSource(\n el: HTMLAnchorElement\n): Observable {\n return fetch$ ||= defer(() => {\n const cached = __md_get(\"__source\", sessionStorage)\n if (cached)\n return of(cached)\n else\n return fetchSourceFacts(el.href)\n .pipe(\n tap(facts => __md_set(\"__source\", facts, sessionStorage))\n )\n })\n .pipe(\n catchError(() => EMPTY),\n filter(facts => Object.keys(facts).length > 0),\n map(facts => ({ facts })),\n shareReplay(1)\n )\n}\n\n/**\n * Mount repository information\n *\n * @param el - Repository information element\n *\n * @returns Repository information component observable\n */\nexport function mountSource(\n el: HTMLAnchorElement\n): Observable> {\n const inner = getElement(\":scope > :last-child\", el)\n return defer(() => {\n const push$ = new Subject()\n push$.subscribe(({ facts }) => {\n inner.appendChild(renderSourceFacts(facts))\n inner.classList.add(\"md-source__repository--active\")\n })\n\n /* Create and return component */\n return watchSource(el)\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n defer,\n distinctUntilKeyChanged,\n finalize,\n map,\n of,\n switchMap,\n tap\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport {\n Viewport,\n watchElementSize,\n watchViewportAt\n} from \"~/browser\"\n\nimport { Component } from \"../_\"\nimport { Header } from \"../header\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Navigation tabs\n */\nexport interface Tabs {\n hidden: boolean /* Navigation tabs are hidden */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n viewport$: Observable /* Viewport observable */\n header$: Observable
    /* Header observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n viewport$: Observable /* Viewport observable */\n header$: Observable
    /* Header observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch navigation tabs\n *\n * @param el - Navigation tabs element\n * @param options - Options\n *\n * @returns Navigation tabs observable\n */\nexport function watchTabs(\n el: HTMLElement, { viewport$, header$ }: WatchOptions\n): Observable {\n return watchElementSize(document.body)\n .pipe(\n switchMap(() => watchViewportAt(el, { header$, viewport$ })),\n map(({ offset: { y } }) => {\n return {\n hidden: y >= 10\n }\n }),\n distinctUntilKeyChanged(\"hidden\")\n )\n}\n\n/**\n * Mount navigation tabs\n *\n * This function hides the navigation tabs when scrolling past the threshold\n * and makes them reappear in a nice CSS animation when scrolling back up.\n *\n * @param el - Navigation tabs element\n * @param options - Options\n *\n * @returns Navigation tabs component observable\n */\nexport function mountTabs(\n el: HTMLElement, options: MountOptions\n): Observable> {\n return defer(() => {\n const push$ = new Subject()\n push$.subscribe({\n\n /* Handle emission */\n next({ hidden }) {\n el.hidden = hidden\n },\n\n /* Handle complete */\n complete() {\n el.hidden = false\n }\n })\n\n /* Create and return component */\n return (\n feature(\"navigation.tabs.sticky\")\n ? of({ hidden: false })\n : watchTabs(el, options)\n )\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n bufferCount,\n combineLatestWith,\n debounceTime,\n defer,\n distinctUntilChanged,\n distinctUntilKeyChanged,\n filter,\n finalize,\n map,\n merge,\n of,\n repeat,\n scan,\n share,\n skip,\n startWith,\n switchMap,\n takeLast,\n takeUntil,\n tap,\n withLatestFrom\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport {\n Viewport,\n getElement,\n getElementContainer,\n getElementSize,\n getElements,\n getLocation,\n getOptionalElement,\n watchElementSize\n} from \"~/browser\"\n\nimport {\n Component,\n getComponentElement\n} from \"../_\"\nimport { Header } from \"../header\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Table of contents\n */\nexport interface TableOfContents {\n prev: HTMLAnchorElement[][] /* Anchors (previous) */\n next: HTMLAnchorElement[][] /* Anchors (next) */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n viewport$: Observable /* Viewport observable */\n header$: Observable
    /* Header observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n viewport$: Observable /* Viewport observable */\n header$: Observable
    /* Header observable */\n target$: Observable /* Location target observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch table of contents\n *\n * This is effectively a scroll spy implementation which will account for the\n * fixed header and automatically re-calculate anchor offsets when the viewport\n * is resized. The returned observable will only emit if the table of contents\n * needs to be repainted.\n *\n * This implementation tracks an anchor element's entire path starting from its\n * level up to the top-most anchor element, e.g. `[h3, h2, h1]`. Although the\n * Material theme currently doesn't make use of this information, it enables\n * the styling of the entire hierarchy through customization.\n *\n * Note that the current anchor is the last item of the `prev` anchor list.\n *\n * @param el - Table of contents element\n * @param options - Options\n *\n * @returns Table of contents observable\n */\nexport function watchTableOfContents(\n el: HTMLElement, { viewport$, header$ }: WatchOptions\n): Observable {\n const table = new Map()\n\n /* Compute anchor-to-target mapping */\n const anchors = getElements(\"[href^=\\\\#]\", el)\n for (const anchor of anchors) {\n const id = decodeURIComponent(anchor.hash.substring(1))\n const target = getOptionalElement(`[id=\"${id}\"]`)\n if (typeof target !== \"undefined\")\n table.set(anchor, target)\n }\n\n /* Compute necessary adjustment for header */\n const adjust$ = header$\n .pipe(\n distinctUntilKeyChanged(\"height\"),\n map(({ height }) => {\n const main = getComponentElement(\"main\")\n const grid = getElement(\":scope > :first-child\", main)\n return height + 0.8 * (\n grid.offsetTop -\n main.offsetTop\n )\n }),\n share()\n )\n\n /* Compute partition of previous and next anchors */\n const partition$ = watchElementSize(document.body)\n .pipe(\n distinctUntilKeyChanged(\"height\"),\n\n /* Build index to map anchor paths to vertical offsets */\n switchMap(body => defer(() => {\n let path: HTMLAnchorElement[] = []\n return of([...table].reduce((index, [anchor, target]) => {\n while (path.length) {\n const last = table.get(path[path.length - 1])!\n if (last.tagName >= target.tagName) {\n path.pop()\n } else {\n break\n }\n }\n\n /* If the current anchor is hidden, continue with its parent */\n let offset = target.offsetTop\n while (!offset && target.parentElement) {\n target = target.parentElement\n offset = target.offsetTop\n }\n\n /* Map reversed anchor path to vertical offset */\n return index.set(\n [...path = [...path, anchor]].reverse(),\n offset\n )\n }, new Map()))\n })\n .pipe(\n\n /* Sort index by vertical offset (see https://bit.ly/30z6QSO) */\n map(index => new Map([...index].sort(([, a], [, b]) => a - b))),\n combineLatestWith(adjust$),\n\n /* Re-compute partition when viewport offset changes */\n switchMap(([index, adjust]) => viewport$\n .pipe(\n scan(([prev, next], { offset: { y }, size }) => {\n const last = y + size.height >= Math.floor(body.height)\n\n /* Look forward */\n while (next.length) {\n const [, offset] = next[0]\n if (offset - adjust < y || last) {\n prev = [...prev, next.shift()!]\n } else {\n break\n }\n }\n\n /* Look backward */\n while (prev.length) {\n const [, offset] = prev[prev.length - 1]\n if (offset - adjust >= y && !last) {\n next = [prev.pop()!, ...next]\n } else {\n break\n }\n }\n\n /* Return partition */\n return [prev, next]\n }, [[], [...index]]),\n distinctUntilChanged((a, b) => (\n a[0] === b[0] &&\n a[1] === b[1]\n ))\n )\n )\n )\n )\n )\n\n /* Compute and return anchor list migrations */\n return partition$\n .pipe(\n map(([prev, next]) => ({\n prev: prev.map(([path]) => path),\n next: next.map(([path]) => path)\n })),\n\n /* Extract anchor list migrations */\n startWith({ prev: [], next: [] }),\n bufferCount(2, 1),\n map(([a, b]) => {\n\n /* Moving down */\n if (a.prev.length < b.prev.length) {\n return {\n prev: b.prev.slice(Math.max(0, a.prev.length - 1), b.prev.length),\n next: []\n }\n\n /* Moving up */\n } else {\n return {\n prev: b.prev.slice(-1),\n next: b.next.slice(0, b.next.length - a.next.length)\n }\n }\n })\n )\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Mount table of contents\n *\n * @param el - Table of contents element\n * @param options - Options\n *\n * @returns Table of contents component observable\n */\nexport function mountTableOfContents(\n el: HTMLElement, { viewport$, header$, target$ }: MountOptions\n): Observable> {\n return defer(() => {\n const push$ = new Subject()\n const done$ = push$.pipe(takeLast(1))\n push$.subscribe(({ prev, next }) => {\n\n /* Look forward */\n for (const [anchor] of next) {\n anchor.classList.remove(\"md-nav__link--passed\")\n anchor.classList.remove(\"md-nav__link--active\")\n }\n\n /* Look backward */\n for (const [index, [anchor]] of prev.entries()) {\n anchor.classList.add(\"md-nav__link--passed\")\n anchor.classList.toggle(\n \"md-nav__link--active\",\n index === prev.length - 1\n )\n }\n })\n\n /* Set up following, if enabled */\n if (feature(\"toc.follow\")) {\n\n /* Toggle smooth scrolling only for anchor clicks */\n const smooth$ = merge(\n viewport$.pipe(debounceTime(1), map(() => undefined)),\n viewport$.pipe(debounceTime(250), map(() => \"smooth\" as const))\n )\n\n /* Bring active anchor into view */\n push$\n .pipe(\n filter(({ prev }) => prev.length > 0),\n withLatestFrom(smooth$)\n )\n .subscribe(([{ prev }, behavior]) => {\n const [anchor] = prev[prev.length - 1]\n if (anchor.offsetHeight) {\n\n /* Retrieve overflowing container and scroll */\n const container = getElementContainer(anchor)\n if (typeof container !== \"undefined\") {\n const offset = anchor.offsetTop - container.offsetTop\n const { height } = getElementSize(container)\n container.scrollTo({\n top: offset - height / 2,\n behavior\n })\n }\n }\n })\n }\n\n /* Set up anchor tracking, if enabled */\n if (feature(\"navigation.tracking\"))\n viewport$\n .pipe(\n takeUntil(done$),\n distinctUntilKeyChanged(\"offset\"),\n debounceTime(250),\n skip(1),\n takeUntil(target$.pipe(skip(1))),\n repeat({ delay: 250 }),\n withLatestFrom(push$)\n )\n .subscribe(([, { prev }]) => {\n const url = getLocation()\n\n /* Set hash fragment to active anchor */\n const anchor = prev[prev.length - 1]\n if (anchor && anchor.length) {\n const [active] = anchor\n const { hash } = new URL(active.href)\n if (url.hash !== hash) {\n url.hash = hash\n history.replaceState({}, \"\", `${url}`)\n }\n\n /* Reset anchor when at the top */\n } else {\n url.hash = \"\"\n history.replaceState({}, \"\", `${url}`)\n }\n })\n\n /* Create and return component */\n return watchTableOfContents(el, { viewport$, header$ })\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n bufferCount,\n combineLatest,\n distinctUntilChanged,\n distinctUntilKeyChanged,\n endWith,\n finalize,\n map,\n repeat,\n skip,\n takeLast,\n takeUntil,\n tap\n} from \"rxjs\"\n\nimport { Viewport } from \"~/browser\"\n\nimport { Component } from \"../_\"\nimport { Header } from \"../header\"\nimport { Main } from \"../main\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Back-to-top button\n */\nexport interface BackToTop {\n hidden: boolean /* Back-to-top button is hidden */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n viewport$: Observable /* Viewport observable */\n main$: Observable
    /* Main area observable */\n target$: Observable /* Location target observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n viewport$: Observable /* Viewport observable */\n header$: Observable
    /* Header observable */\n main$: Observable
    /* Main area observable */\n target$: Observable /* Location target observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch back-to-top\n *\n * @param _el - Back-to-top element\n * @param options - Options\n *\n * @returns Back-to-top observable\n */\nexport function watchBackToTop(\n _el: HTMLElement, { viewport$, main$, target$ }: WatchOptions\n): Observable {\n\n /* Compute direction */\n const direction$ = viewport$\n .pipe(\n map(({ offset: { y } }) => y),\n bufferCount(2, 1),\n map(([a, b]) => a > b && b > 0),\n distinctUntilChanged()\n )\n\n /* Compute whether main area is active */\n const active$ = main$\n .pipe(\n map(({ active }) => active)\n )\n\n /* Compute threshold for hiding */\n return combineLatest([active$, direction$])\n .pipe(\n map(([active, direction]) => !(active && direction)),\n distinctUntilChanged(),\n takeUntil(target$.pipe(skip(1))),\n endWith(true),\n repeat({ delay: 250 }),\n map(hidden => ({ hidden }))\n )\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Mount back-to-top\n *\n * @param el - Back-to-top element\n * @param options - Options\n *\n * @returns Back-to-top component observable\n */\nexport function mountBackToTop(\n el: HTMLElement, { viewport$, header$, main$, target$ }: MountOptions\n): Observable> {\n const push$ = new Subject()\n const done$ = push$.pipe(takeLast(1))\n push$.subscribe({\n\n /* Handle emission */\n next({ hidden }) {\n el.hidden = hidden\n if (hidden) {\n el.setAttribute(\"tabindex\", \"-1\")\n el.blur()\n } else {\n el.removeAttribute(\"tabindex\")\n }\n },\n\n /* Handle complete */\n complete() {\n el.style.top = \"\"\n el.hidden = true\n el.removeAttribute(\"tabindex\")\n }\n })\n\n /* Watch header height */\n header$\n .pipe(\n takeUntil(done$),\n distinctUntilKeyChanged(\"height\")\n )\n .subscribe(({ height }) => {\n el.style.top = `${height + 16}px`\n })\n\n /* Create and return component */\n return watchBackToTop(el, { viewport$, main$, target$ })\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n fromEvent,\n map,\n mergeMap,\n switchMap,\n takeWhile,\n tap,\n withLatestFrom\n} from \"rxjs\"\n\nimport { getElements } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch options\n */\ninterface PatchOptions {\n document$: Observable /* Document observable */\n tablet$: Observable /* Media tablet observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch indeterminate checkboxes\n *\n * This function replaces the indeterminate \"pseudo state\" with the actual\n * indeterminate state, which is used to keep navigation always expanded.\n *\n * @param options - Options\n */\nexport function patchIndeterminate(\n { document$, tablet$ }: PatchOptions\n): void {\n document$\n .pipe(\n switchMap(() => getElements(\n // @todo `data-md-state` is deprecated and removed in v9\n \".md-toggle--indeterminate, [data-md-state=indeterminate]\"\n )),\n tap(el => {\n el.indeterminate = true\n el.checked = false\n }),\n mergeMap(el => fromEvent(el, \"change\")\n .pipe(\n takeWhile(() => el.classList.contains(\"md-toggle--indeterminate\")),\n map(() => el)\n )\n ),\n withLatestFrom(tablet$)\n )\n .subscribe(([el, tablet]) => {\n el.classList.remove(\"md-toggle--indeterminate\")\n if (tablet)\n el.checked = false\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n filter,\n fromEvent,\n map,\n mergeMap,\n switchMap,\n tap\n} from \"rxjs\"\n\nimport { getElements } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch options\n */\ninterface PatchOptions {\n document$: Observable /* Document observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Check whether the given device is an Apple device\n *\n * @returns Test result\n */\nfunction isAppleDevice(): boolean {\n return /(iPad|iPhone|iPod)/.test(navigator.userAgent)\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch all elements with `data-md-scrollfix` attributes\n *\n * This is a year-old patch which ensures that overflow scrolling works at the\n * top and bottom of containers on iOS by ensuring a `1px` scroll offset upon\n * the start of a touch event.\n *\n * @see https://bit.ly/2SCtAOO - Original source\n *\n * @param options - Options\n */\nexport function patchScrollfix(\n { document$ }: PatchOptions\n): void {\n document$\n .pipe(\n switchMap(() => getElements(\"[data-md-scrollfix]\")),\n tap(el => el.removeAttribute(\"data-md-scrollfix\")),\n filter(isAppleDevice),\n mergeMap(el => fromEvent(el, \"touchstart\")\n .pipe(\n map(() => el)\n )\n )\n )\n .subscribe(el => {\n const top = el.scrollTop\n\n /* We're at the top of the container */\n if (top === 0) {\n el.scrollTop = 1\n\n /* We're at the bottom of the container */\n } else if (top + el.offsetHeight === el.scrollHeight) {\n el.scrollTop = top - 1\n }\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n combineLatest,\n delay,\n map,\n of,\n switchMap,\n withLatestFrom\n} from \"rxjs\"\n\nimport {\n Viewport,\n watchToggle\n} from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch options\n */\ninterface PatchOptions {\n viewport$: Observable /* Viewport observable */\n tablet$: Observable /* Media tablet observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch the document body to lock when search is open\n *\n * For mobile and tablet viewports, the search is rendered full screen, which\n * leads to scroll leaking when at the top or bottom of the search result. This\n * function locks the body when the search is in full screen mode, and restores\n * the scroll position when leaving.\n *\n * @param options - Options\n */\nexport function patchScrolllock(\n { viewport$, tablet$ }: PatchOptions\n): void {\n combineLatest([watchToggle(\"search\"), tablet$])\n .pipe(\n map(([active, tablet]) => active && !tablet),\n switchMap(active => of(active)\n .pipe(\n delay(active ? 400 : 100)\n )\n ),\n withLatestFrom(viewport$)\n )\n .subscribe(([active, { offset: { y }}]) => {\n if (active) {\n document.body.setAttribute(\"data-md-scrolllock\", \"\")\n document.body.style.top = `-${y}px`\n } else {\n const value = -1 * parseInt(document.body.style.top, 10)\n document.body.removeAttribute(\"data-md-scrolllock\")\n document.body.style.top = \"\"\n if (value)\n window.scrollTo(0, value)\n }\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Polyfills\n * ------------------------------------------------------------------------- */\n\n/* Polyfill `Object.entries` */\nif (!Object.entries)\n Object.entries = function (obj: object) {\n const data: [string, string][] = []\n for (const key of Object.keys(obj))\n // @ts-expect-error - ignore property access warning\n data.push([key, obj[key]])\n\n /* Return entries */\n return data\n }\n\n/* Polyfill `Object.values` */\nif (!Object.values)\n Object.values = function (obj: object) {\n const data: string[] = []\n for (const key of Object.keys(obj))\n // @ts-expect-error - ignore property access warning\n 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nu når og også om op os over på selv sig sin sine sit skal skulle som sådan thi til ud under var vi vil ville vor være været".split(" ")),e.Pipeline.registerFunction(e.da.stopWordFilter,"stopWordFilter-da")}}); \ No newline at end of file diff --git a/assets/javascripts/lunr/min/lunr.de.min.js b/assets/javascripts/lunr/min/lunr.de.min.js new file mode 100644 index 00000000..f3b5c108 --- /dev/null +++ b/assets/javascripts/lunr/min/lunr.de.min.js @@ -0,0 +1,18 @@ +/*! + * Lunr languages, `German` language + * https://github.com/MihaiValentin/lunr-languages + * + * Copyright 2014, Mihai Valentin + * http://www.mozilla.org/MPL/ + */ +/*! + * based on + * Snowball JavaScript Library v0.3 + * http://code.google.com/p/urim/ + * http://snowball.tartarus.org/ + * + * Copyright 2010, Oleg Mazko + * http://www.mozilla.org/MPL/ + */ + +!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 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andere anderem anderen anderer anderes anderm andern anderr anders auch auf aus bei bin bis bist da damit dann das dasselbe dazu daß dein deine deinem deinen deiner deines dem demselben den denn denselben der derer derselbe derselben des desselben dessen dich die dies diese dieselbe dieselben diesem diesen dieser dieses dir doch dort du durch ein eine einem einen einer eines einig einige einigem einigen einiger einiges einmal er es etwas euch euer eure eurem euren eurer eures für gegen gewesen hab habe haben hat hatte hatten hier hin hinter ich ihm ihn ihnen ihr ihre ihrem ihren ihrer ihres im in indem ins ist jede jedem jeden jeder jedes jene jenem jenen jener jenes jetzt kann kein keine keinem keinen keiner keines können könnte machen man manche manchem manchen mancher manches mein meine meinem meinen meiner meines mich mir mit muss musste nach nicht nichts noch nun nur ob oder ohne sehr sein seine seinem seinen seiner seines selbst sich sie sind so solche solchem solchen solcher solches soll sollte sondern sonst um und uns unse unsem unsen unser unses unter viel vom von vor war waren warst was weg weil weiter welche welchem welchen welcher welches wenn werde werden wie wieder will wir wird wirst wo wollen wollte während würde würden zu zum zur zwar zwischen über".split(" ")),e.Pipeline.registerFunction(e.de.stopWordFilter,"stopWordFilter-de")}}); \ No newline at end of file diff --git a/assets/javascripts/lunr/min/lunr.du.min.js b/assets/javascripts/lunr/min/lunr.du.min.js new file mode 100644 index 00000000..49a0f3f0 --- /dev/null +++ b/assets/javascripts/lunr/min/lunr.du.min.js @@ -0,0 +1,18 @@ +/*! + * Lunr languages, `Dutch` language + * https://github.com/MihaiValentin/lunr-languages + * + * Copyright 2014, Mihai Valentin + * http://www.mozilla.org/MPL/ + */ +/*! + * based on + * Snowball JavaScript Library v0.3 + * http://code.google.com/p/urim/ + * http://snowball.tartarus.org/ + * + * Copyright 2010, Oleg Mazko + * http://www.mozilla.org/MPL/ + */ + +!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. 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r("",-1,6),new r("á",0,1),new r("ä",0,1),new r("é",0,2),new r("ë",0,2),new r("í",0,3),new r("ï",0,3),new r("ó",0,4),new r("ö",0,4),new r("ú",0,5),new r("ü",0,5)],p=[new r("",-1,3),new r("I",0,2),new r("Y",0,1)],g=[new r("dd",-1,-1),new r("kk",-1,-1),new r("tt",-1,-1)],h=[new r("ene",-1,2),new r("se",-1,3),new r("en",-1,2),new r("heden",2,1),new r("s",-1,3)],k=[new r("end",-1,1),new r("ig",-1,2),new r("ing",-1,1),new r("lijk",-1,3),new r("baar",-1,4),new r("bar",-1,5)],v=[new r("aa",-1,-1),new r("ee",-1,-1),new r("oo",-1,-1),new r("uu",-1,-1)],q=[17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,128],j=[1,0,0,17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,128],z=[17,67,16,1,0,0,0,0,0,0,0,0,0,0,0,0,128],C=new i;this.setCurrent=function(e){C.setCurrent(e)},this.getCurrent=function(){return C.getCurrent()},this.stem=function(){var r=C.cursor;return e(),C.cursor=r,o(),C.limit_backward=r,C.cursor=C.limit,d(),C.cursor=C.limit_backward,s(),!0}};return function(e){return"function"==typeof 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keressünk keresztül ki kívül között közül legalább legyen lehet lehetett lenne lenni lesz lett maga magát majd majd meg mellett mely melyek mert mi mikor milyen minden mindenki mindent mindig mint mintha mit mivel miért most már más másik még míg nagy nagyobb nagyon ne nekem neki nem nincs néha néhány nélkül olyan ott pedig persze rá s saját sem semmi sok sokat sokkal szemben szerint szinte számára talán tehát teljes tovább továbbá több ugyanis utolsó után utána vagy vagyis vagyok valaki valami valamint való van vannak vele vissza viszont volna volt voltak voltam voltunk által általában át én éppen és így õ õk õket össze úgy új újabb újra".split(" ")),e.Pipeline.registerFunction(e.hu.stopWordFilter,"stopWordFilter-hu")}}); \ No newline at end of file diff --git a/assets/javascripts/lunr/min/lunr.it.min.js b/assets/javascripts/lunr/min/lunr.it.min.js new file mode 100644 index 00000000..344b6a3c --- /dev/null +++ b/assets/javascripts/lunr/min/lunr.it.min.js @@ -0,0 +1,18 @@ +/*! + * Lunr languages, `Italian` language + * https://github.com/MihaiValentin/lunr-languages + * + * Copyright 2014, Mihai Valentin + * http://www.mozilla.org/MPL/ + */ +/*! + * based on + * Snowball JavaScript Library v0.3 + * http://code.google.com/p/urim/ + * http://snowball.tartarus.org/ + * + * Copyright 2010, Oleg Mazko + * http://www.mozilla.org/MPL/ + */ + +!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. 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avevo avrai avranno avrebbe avrebbero avrei avremmo avremo avreste avresti avrete avrà avrò avuta avute avuti avuto c che chi ci coi col come con contro cui da dagl dagli dai dal dall dalla dalle dallo degl degli dei del dell della delle dello di dov dove e ebbe ebbero ebbi ed era erano eravamo eravate eri ero essendo faccia facciamo facciano facciate faccio facemmo facendo facesse facessero facessi facessimo faceste facesti faceva facevamo facevano facevate facevi facevo fai fanno farai faranno farebbe farebbero farei faremmo faremo fareste faresti farete farà farò fece fecero feci fosse fossero fossi fossimo foste fosti fu fui fummo furono gli ha hai hanno ho i il in io l la le lei li lo loro lui ma mi mia mie miei mio ne negl negli nei nel nell nella nelle nello noi non nostra nostre nostri nostro o per perché più quale quanta quante quanti quanto quella quelle quelli quello questa queste questi questo sarai saranno sarebbe sarebbero sarei saremmo saremo sareste saresti sarete sarà sarò se sei si sia siamo siano siate siete sono sta stai stando stanno starai staranno starebbe starebbero starei staremmo staremo stareste staresti starete starà starò stava stavamo stavano stavate stavi stavo stemmo stesse stessero stessi stessimo steste stesti stette stettero stetti stia stiamo stiano stiate sto su sua sue sugl sugli sui sul sull sulla sulle sullo suo suoi ti tra tu tua tue tuo tuoi tutti tutto un una uno vi voi vostra vostre vostri vostro è".split(" ")),e.Pipeline.registerFunction(e.it.stopWordFilter,"stopWordFilter-it")}}); \ No newline at end of file diff --git a/assets/javascripts/lunr/min/lunr.ja.min.js b/assets/javascripts/lunr/min/lunr.ja.min.js new file mode 100644 index 00000000..5f254ebe --- /dev/null +++ b/assets/javascripts/lunr/min/lunr.ja.min.js @@ -0,0 +1 @@ +!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is 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t(){for(;!C.in_grouping(q,97,232);){if(C.cursor>=C.limit)return!0;C.cursor++}for(;!C.out_grouping(q,97,232);){if(C.cursor>=C.limit)return!0;C.cursor++}return!1}function s(){for(var r;;)if(C.bra=C.cursor,r=C.find_among(p,3))switch(C.ket=C.cursor,r){case 1:C.slice_from("y");break;case 2:C.slice_from("i");break;case 3:if(C.cursor>=C.limit)return;C.cursor++}}function u(){return _<=C.cursor}function c(){return d<=C.cursor}function a(){var r=C.limit-C.cursor;C.find_among_b(g,3)&&(C.cursor=C.limit-r,C.ket=C.cursor,C.cursor>C.limit_backward&&(C.cursor--,C.bra=C.cursor,C.slice_del()))}function l(){var r;w=!1,C.ket=C.cursor,C.eq_s_b(1,"e")&&(C.bra=C.cursor,u()&&(r=C.limit-C.cursor,C.out_grouping_b(q,97,232)&&(C.cursor=C.limit-r,C.slice_del(),w=!0,a())))}function m(){var r;u()&&(r=C.limit-C.cursor,C.out_grouping_b(q,97,232)&&(C.cursor=C.limit-r,C.eq_s_b(3,"gem")||(C.cursor=C.limit-r,C.slice_del(),a())))}function f(){var 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i;this.setCurrent=function(r){C.setCurrent(r)},this.getCurrent=function(){return C.getCurrent()},this.stem=function(){var e=C.cursor;return r(),C.cursor=e,o(),C.limit_backward=e,C.cursor=C.limit,f(),C.cursor=C.limit_backward,s(),!0}};return function(r){return"function"==typeof r.update?r.update(function(r){return n.setCurrent(r),n.stem(),n.getCurrent()}):(n.setCurrent(r),n.stem(),n.getCurrent())}}(),r.Pipeline.registerFunction(r.nl.stemmer,"stemmer-nl"),r.nl.stopWordFilter=r.generateStopWordFilter(" aan al alles als altijd andere ben bij daar dan dat de der deze die dit doch doen door dus een eens en er ge geen geweest haar had heb hebben heeft hem het hier hij hoe hun iemand iets ik in is ja je kan kon kunnen maar me meer men met mij mijn moet na naar niet niets nog nu of om omdat onder ons ook op over reeds te tegen toch toen tot u uit uw van veel voor want waren was wat werd wezen wie wil worden wordt zal ze zelf zich zij zijn zo zonder zou".split(" 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dă ea ei el ele eram este eu eşti face fata fi fie fiecare fii fim fiu fiţi frumos fără graţie halbă iar ieri la le li lor lui lângă lîngă mai mea mei mele mereu meu mi mie mine mult multă mulţi mulţumesc mâine mîine mă ne nevoie nici nicăieri nimeni nimeri nimic nişte noastre noastră noi noroc nostru nouă noştri nu opt ori oricare orice oricine oricum oricând oricât oricînd oricît oriunde patra patru patrulea pe pentru peste pic poate pot prea prima primul prin puţin puţina puţină până pînă rog sa sale sau se spate spre sub sunt suntem sunteţi sută sînt sîntem sînteţi să săi său ta tale te timp tine toate toată tot totuşi toţi trei treia treilea tu tăi tău un una unde undeva unei uneia unele uneori unii unor unora unu unui unuia unul vi voastre voastră voi vostru vouă voştri vreme vreo vreun vă zece zero zi zice îi îl îmi împotriva în înainte înaintea încotro încât încît între întrucât întrucît îţi ăla ălea ăsta ăstea ăştia şapte şase şi ştiu ţi ţie".split(" ")),e.Pipeline.registerFunction(e.ro.stopWordFilter,"stopWordFilter-ro")}}); \ No newline at end of file diff --git a/assets/javascripts/lunr/min/lunr.ru.min.js b/assets/javascripts/lunr/min/lunr.ru.min.js new file mode 100644 index 00000000..186cc485 --- /dev/null +++ b/assets/javascripts/lunr/min/lunr.ru.min.js @@ -0,0 +1,18 @@ +/*! + * Lunr languages, `Russian` language + * https://github.com/MihaiValentin/lunr-languages + * + * Copyright 2014, Mihai Valentin + * http://www.mozilla.org/MPL/ + */ +/*! + * based on + * Snowball JavaScript Library v0.3 + * http://code.google.com/p/urim/ + * http://snowball.tartarus.org/ + * + * Copyright 2010, Oleg Mazko + * http://www.mozilla.org/MPL/ + */ + +!function(e,n){"function"==typeof define&&define.amd?define(n):"object"==typeof exports?module.exports=n():n()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. 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http://snowball.tartarus.org/ + * + * Copyright 2010, Oleg Mazko + * http://www.mozilla.org/MPL/ + */ + +!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. 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e;this.setCurrent=function(r){Dr.setCurrent(r)},this.getCurrent=function(){return Dr.getCurrent()},this.stem=function(){return!!($()&&(Dr.limit_backward=Dr.cursor,Dr.cursor=Dr.limit,J(),Dr.cursor=Dr.limit,nr&&(R(),Dr.cursor=Dr.limit_backward,er())))}};return function(r){return"function"==typeof r.update?r.update(function(r){return n.setCurrent(r),n.stem(),n.getCurrent()}):(n.setCurrent(r),n.stem(),n.getCurrent())}}(),r.Pipeline.registerFunction(r.tr.stemmer,"stemmer-tr"),r.tr.stopWordFilter=r.generateStopWordFilter("acaba altmış altı ama ancak arada aslında ayrıca bana bazı belki ben benden beni benim beri beş bile bin bir biri birkaç birkez birçok birşey birşeyi biz bizden bize bizi bizim bu buna bunda bundan bunlar bunları bunların bunu bunun burada böyle böylece da daha dahi de defa değil diye diğer doksan dokuz dolayı dolayısıyla dört edecek eden ederek edilecek ediliyor edilmesi ediyor elli en etmesi etti ettiği ettiğini eğer gibi göre halen hangi hatta hem henüz hep hepsi her 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b/assets/javascripts/lunr/min/lunr.vi.min.js new file mode 100644 index 00000000..22aed28c --- /dev/null +++ b/assets/javascripts/lunr/min/lunr.vi.min.js @@ -0,0 +1 @@ +!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. 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Register as an anonymous module. + define(factory) + } else if (typeof exports === 'object') { + /** + * Node. Does not work with strict CommonJS, but + * only CommonJS-like environments that support module.exports, + * like Node. + */ + module.exports = factory() + } else { + // Browser globals (root is window) + factory()(root.lunr); + } +}(this, function () { + /** + * Just return a value to define the module export. + * This example returns an object, but the module + * can return a function as the exported value. + */ + + return function(lunr) { + // TinySegmenter 0.1 -- Super compact Japanese tokenizer in Javascript + // (c) 2008 Taku Kudo + // TinySegmenter is freely distributable under the terms of a new BSD licence. + // For details, see http://chasen.org/~taku/software/TinySegmenter/LICENCE.txt + + function TinySegmenter() { + var patterns = { + "[一二三四五六七八九十百千万億兆]":"M", + "[一-龠々〆ヵヶ]":"H", + "[ぁ-ん]":"I", + "[ァ-ヴーア-ン゙ー]":"K", + "[a-zA-Za-zA-Z]":"A", + "[0-90-9]":"N" + } + this.chartype_ = []; + for (var i in patterns) { + var regexp = new RegExp(i); + this.chartype_.push([regexp, patterns[i]]); 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+ + return this; + } + TinySegmenter.prototype.ctype_ = function(str) { + for (var i in this.chartype_) { + if (str.match(this.chartype_[i][0])) { + return this.chartype_[i][1]; + } + } + return "O"; + } + + TinySegmenter.prototype.ts_ = function(v) { + if (v) { return v; } + return 0; + } + + TinySegmenter.prototype.segment = function(input) { + if (input == null || input == undefined || input == "") { + return []; + } + var result = []; + var seg = ["B3","B2","B1"]; + var ctype = ["O","O","O"]; + var o = input.split(""); + for (i = 0; i < o.length; ++i) { + seg.push(o[i]); + ctype.push(this.ctype_(o[i])) + } + seg.push("E1"); + seg.push("E2"); + seg.push("E3"); + ctype.push("O"); + ctype.push("O"); + ctype.push("O"); + var word = seg[3]; + var p1 = "U"; + var p2 = "U"; + var p3 = "U"; + for (var i = 4; i < seg.length - 3; ++i) { + var score = this.BIAS__; + var w1 = seg[i-3]; + var w2 = seg[i-2]; + var w3 = seg[i-1]; + var w4 = seg[i]; + var w5 = seg[i+1]; + var w6 = seg[i+2]; + var c1 = ctype[i-3]; + var c2 = ctype[i-2]; + var c3 = ctype[i-1]; + var c4 = ctype[i]; + var c5 = ctype[i+1]; + var c6 = ctype[i+2]; + score += this.ts_(this.UP1__[p1]); + score += this.ts_(this.UP2__[p2]); + score += this.ts_(this.UP3__[p3]); + score += this.ts_(this.BP1__[p1 + p2]); + score += this.ts_(this.BP2__[p2 + p3]); + score += this.ts_(this.UW1__[w1]); + score += this.ts_(this.UW2__[w2]); + score += this.ts_(this.UW3__[w3]); + score += this.ts_(this.UW4__[w4]); + score += this.ts_(this.UW5__[w5]); + score += this.ts_(this.UW6__[w6]); + score += this.ts_(this.BW1__[w2 + w3]); + score += this.ts_(this.BW2__[w3 + w4]); + score += this.ts_(this.BW3__[w4 + w5]); + score += this.ts_(this.TW1__[w1 + w2 + w3]); + score += this.ts_(this.TW2__[w2 + w3 + w4]); + score += this.ts_(this.TW3__[w3 + w4 + w5]); + score += this.ts_(this.TW4__[w4 + w5 + w6]); + score += this.ts_(this.UC1__[c1]); + score += this.ts_(this.UC2__[c2]); + score += this.ts_(this.UC3__[c3]); + score += this.ts_(this.UC4__[c4]); + score += this.ts_(this.UC5__[c5]); + score += this.ts_(this.UC6__[c6]); + score += this.ts_(this.BC1__[c2 + c3]); + score += this.ts_(this.BC2__[c3 + c4]); + score += this.ts_(this.BC3__[c4 + c5]); + score += this.ts_(this.TC1__[c1 + c2 + c3]); + score += this.ts_(this.TC2__[c2 + c3 + c4]); + score += this.ts_(this.TC3__[c3 + c4 + c5]); + score += this.ts_(this.TC4__[c4 + c5 + c6]); + // score += this.ts_(this.TC5__[c4 + c5 + c6]); + score += this.ts_(this.UQ1__[p1 + c1]); + score += this.ts_(this.UQ2__[p2 + c2]); + score += this.ts_(this.UQ3__[p3 + c3]); + score += this.ts_(this.BQ1__[p2 + c2 + c3]); + score += this.ts_(this.BQ2__[p2 + c3 + c4]); + score += this.ts_(this.BQ3__[p3 + c2 + c3]); + score += this.ts_(this.BQ4__[p3 + c3 + c4]); + score += this.ts_(this.TQ1__[p2 + c1 + c2 + c3]); + score += this.ts_(this.TQ2__[p2 + c2 + c3 + c4]); + score += this.ts_(this.TQ3__[p3 + c1 + c2 + c3]); + score += this.ts_(this.TQ4__[p3 + c2 + c3 + c4]); + var p = "O"; + if (score > 0) { + result.push(word); + word = ""; + p = "B"; + } + p1 = p2; + p2 = p3; + p3 = p; + word += seg[i]; + } + result.push(word); + + return result; + } + + lunr.TinySegmenter = TinySegmenter; + }; + +})); \ No newline at end of file diff --git a/assets/javascripts/lunr/wordcut.js b/assets/javascripts/lunr/wordcut.js new file mode 100644 index 00000000..146f4b44 --- /dev/null +++ b/assets/javascripts/lunr/wordcut.js @@ -0,0 +1,6708 @@ +(function(f){if(typeof exports==="object"&&typeof module!=="undefined"){module.exports=f()}else if(typeof define==="function"&&define.amd){define([],f)}else{var g;if(typeof window!=="undefined"){g=window}else if(typeof global!=="undefined"){g=global}else if(typeof self!=="undefined"){g=self}else{g=this}(g.lunr || (g.lunr = {})).wordcut = f()}})(function(){var define,module,exports;return (function e(t,n,r){function s(o,u){if(!n[o]){if(!t[o]){var a=typeof require=="function"&&require;if(!u&&a)return a(o,!0);if(i)return i(o,!0);var f=new Error("Cannot find module '"+o+"'");throw f.code="MODULE_NOT_FOUND",f}var l=n[o]={exports:{}};t[o][0].call(l.exports,function(e){var n=t[o][1][e];return s(n?n:e)},l,l.exports,e,t,n,r)}return n[o].exports}var i=typeof require=="function"&&require;for(var o=0;o 1; + }) + this.addWords(words, false) + } + if(finalize){ + this.finalizeDict(); + } + }, + + dictSeek: function (l, r, ch, strOffset, pos) { + var ans = null; + while (l <= r) { + var m = Math.floor((l + r) / 2), + dict_item = this.dict[m], + len = dict_item.length; + if (len <= strOffset) { + l = m + 1; + } else { + var ch_ = dict_item[strOffset]; + if (ch_ < ch) { + l = m + 1; + } else if (ch_ > ch) { + r = m - 1; + } else { + ans = m; + if (pos == LEFT) { + r = m - 1; + } else { + l = m + 1; + } + } + } + } + return ans; + }, + + isFinal: function (acceptor) { + return this.dict[acceptor.l].length == acceptor.strOffset; + }, + + createAcceptor: function () { + return { + l: 0, + r: this.dict.length - 1, + strOffset: 0, + isFinal: false, + dict: this, + transit: function (ch) { + return this.dict.transit(this, ch); + }, + isError: false, + tag: "DICT", + w: 1, + type: "DICT" + }; + }, + + transit: function (acceptor, ch) { + var l = this.dictSeek(acceptor.l, + acceptor.r, + ch, + acceptor.strOffset, + LEFT); + if (l !== null) { + var r = this.dictSeek(l, + acceptor.r, + ch, + acceptor.strOffset, + RIGHT); + acceptor.l = l; + acceptor.r = r; + acceptor.strOffset++; + acceptor.isFinal = this.isFinal(acceptor); + } else { + acceptor.isError = true; + } + return acceptor; + }, + + sortuniq: function(a){ + return a.sort().filter(function(item, pos, arr){ + return !pos || item != arr[pos - 1]; + }) + }, + + flatten: function(a){ + //[[1,2],[3]] -> [1,2,3] + return [].concat.apply([], a); + } +}; +module.exports = WordcutDict; + +}).call(this,"/dist/tmp") +},{"glob":16,"path":22}],3:[function(require,module,exports){ +var WordRule = { + createAcceptor: function(tag) { + if (tag["WORD_RULE"]) + return null; + + return {strOffset: 0, + isFinal: false, + transit: function(ch) { + var lch = ch.toLowerCase(); + if (lch >= "a" && lch <= "z") { + this.isFinal = true; + this.strOffset++; + } else { + this.isError = true; + } + return this; + }, + isError: false, + tag: "WORD_RULE", + type: "WORD_RULE", + w: 1}; + } +}; + +var NumberRule = { + createAcceptor: function(tag) { + if (tag["NUMBER_RULE"]) + return null; + + return {strOffset: 0, + isFinal: false, + transit: function(ch) { + if (ch >= "0" && ch <= "9") { + this.isFinal = true; + this.strOffset++; + } else { + this.isError = true; + } + return this; + }, + isError: false, + tag: "NUMBER_RULE", + type: "NUMBER_RULE", + w: 1}; + } +}; + +var SpaceRule = { + tag: "SPACE_RULE", + createAcceptor: function(tag) { + + if (tag["SPACE_RULE"]) + return null; + + return {strOffset: 0, + isFinal: false, + transit: function(ch) { + if (ch == " " || ch == "\t" || ch == "\r" || ch == "\n" || + ch == "\u00A0" || ch=="\u2003"//nbsp and emsp + ) { + this.isFinal = true; + this.strOffset++; + } else { + this.isError = true; + } + return this; + }, + isError: false, + tag: SpaceRule.tag, + w: 1, + type: "SPACE_RULE"}; + } +} + +var SingleSymbolRule = { + tag: "SINSYM", + createAcceptor: function(tag) { + return {strOffset: 0, + isFinal: false, + transit: function(ch) { + if (this.strOffset == 0 && ch.match(/^[\@\(\)\/\,\-\."`]$/)) { + this.isFinal = true; + this.strOffset++; + } else { + this.isError = true; + } + return this; + }, + isError: false, + tag: "SINSYM", + w: 1, + type: "SINSYM"}; + } +} + + +var LatinRules = [WordRule, SpaceRule, SingleSymbolRule, NumberRule]; + +module.exports = LatinRules; + +},{}],4:[function(require,module,exports){ +var _ = require("underscore") + , WordcutCore = require("./wordcut_core"); +var PathInfoBuilder = { + + /* + buildByPartAcceptors: function(path, acceptors, i) { + var + var genInfos = partAcceptors.reduce(function(genInfos, acceptor) { + + }, []); + + return genInfos; + } + */ + + buildByAcceptors: function(path, finalAcceptors, i) { + var self = this; + var infos = finalAcceptors.map(function(acceptor) { + var p = i - acceptor.strOffset + 1 + , _info = path[p]; + + var info = {p: p, + mw: _info.mw + (acceptor.mw === undefined ? 0 : acceptor.mw), + w: acceptor.w + _info.w, + unk: (acceptor.unk ? acceptor.unk : 0) + _info.unk, + type: acceptor.type}; + + if (acceptor.type == "PART") { + for(var j = p + 1; j <= i; j++) { + path[j].merge = p; + } + info.merge = p; + } + + return info; + }); + return infos.filter(function(info) { return info; }); + }, + + fallback: function(path, leftBoundary, text, i) { + var _info = path[leftBoundary]; + if (text[i].match(/[\u0E48-\u0E4E]/)) { + if (leftBoundary != 0) + leftBoundary = path[leftBoundary].p; + return {p: leftBoundary, + mw: 0, + w: 1 + _info.w, + unk: 1 + _info.unk, + type: "UNK"}; +/* } else if(leftBoundary > 0 && path[leftBoundary].type !== "UNK") { + leftBoundary = path[leftBoundary].p; + return {p: leftBoundary, + w: 1 + _info.w, + unk: 1 + _info.unk, + type: "UNK"}; */ + } else { + return {p: leftBoundary, + mw: _info.mw, + w: 1 + _info.w, + unk: 1 + _info.unk, + type: "UNK"}; + } + }, + + build: function(path, finalAcceptors, i, leftBoundary, text) { + var basicPathInfos = this.buildByAcceptors(path, finalAcceptors, i); + if (basicPathInfos.length > 0) { + return basicPathInfos; + } else { + return [this.fallback(path, leftBoundary, text, i)]; + } + } +}; + +module.exports = function() { + return _.clone(PathInfoBuilder); +} + +},{"./wordcut_core":8,"underscore":25}],5:[function(require,module,exports){ +var _ = require("underscore"); + + +var PathSelector = { + selectPath: function(paths) { + var path = paths.reduce(function(selectedPath, path) { + if (selectedPath == null) { + return path; + } else { + if (path.unk < selectedPath.unk) + return path; + if (path.unk == selectedPath.unk) { + if (path.mw < selectedPath.mw) + return path + if (path.mw == selectedPath.mw) { + if (path.w < selectedPath.w) + return path; + } + } + return selectedPath; + } + }, null); + return path; + }, + + createPath: function() { + return [{p:null, w:0, unk:0, type: "INIT", mw:0}]; + } +}; + +module.exports = function() { + return _.clone(PathSelector); +}; + +},{"underscore":25}],6:[function(require,module,exports){ +function isMatch(pat, offset, ch) { + if (pat.length <= offset) + return false; + var _ch = pat[offset]; + return _ch == ch || + (_ch.match(/[กข]/) && ch.match(/[ก-ฮ]/)) || + (_ch.match(/[มบ]/) && ch.match(/[ก-ฮ]/)) || + (_ch.match(/\u0E49/) && ch.match(/[\u0E48-\u0E4B]/)); +} + +var Rule0 = { + pat: "เหก็ม", + createAcceptor: function(tag) { + return {strOffset: 0, + isFinal: false, + transit: function(ch) { + if (isMatch(Rule0.pat, this.strOffset,ch)) { + this.isFinal = (this.strOffset + 1 == Rule0.pat.length); + this.strOffset++; + } else { + this.isError = true; + } + return this; + }, + isError: false, + tag: "THAI_RULE", + type: "THAI_RULE", + w: 1}; + } +}; + +var PartRule = { + createAcceptor: function(tag) { + return {strOffset: 0, + patterns: [ + "แก", "เก", "ก้", "กก์", "กา", "กี", "กิ", "กืก" + ], + isFinal: false, + transit: function(ch) { + var offset = this.strOffset; + this.patterns = this.patterns.filter(function(pat) { + return isMatch(pat, offset, ch); + }); + + if (this.patterns.length > 0) { + var len = 1 + offset; + this.isFinal = this.patterns.some(function(pat) { + return pat.length == len; + }); + this.strOffset++; + } else { + this.isError = true; + } + return this; + }, + isError: false, + tag: "PART", + type: "PART", + unk: 1, + w: 1}; + } +}; + +var ThaiRules = [Rule0, PartRule]; + +module.exports = ThaiRules; + +},{}],7:[function(require,module,exports){ +var sys = require("sys") + , WordcutDict = require("./dict") + , WordcutCore = require("./wordcut_core") + , PathInfoBuilder = require("./path_info_builder") + , PathSelector = require("./path_selector") + , Acceptors = require("./acceptors") + , latinRules = require("./latin_rules") + , thaiRules = require("./thai_rules") + , _ = require("underscore"); + + +var Wordcut = Object.create(WordcutCore); +Wordcut.defaultPathInfoBuilder = PathInfoBuilder; +Wordcut.defaultPathSelector = PathSelector; +Wordcut.defaultAcceptors = Acceptors; +Wordcut.defaultLatinRules = latinRules; +Wordcut.defaultThaiRules = thaiRules; +Wordcut.defaultDict = WordcutDict; + + +Wordcut.initNoDict = function(dict_path) { + var self = this; + self.pathInfoBuilder = new self.defaultPathInfoBuilder; + self.pathSelector = new self.defaultPathSelector; + self.acceptors = new self.defaultAcceptors; + self.defaultLatinRules.forEach(function(rule) { + self.acceptors.creators.push(rule); + }); + self.defaultThaiRules.forEach(function(rule) { + self.acceptors.creators.push(rule); + }); +}; + +Wordcut.init = function(dict_path, withDefault, additionalWords) { + withDefault = withDefault || false; + this.initNoDict(); + var dict = _.clone(this.defaultDict); + dict.init(dict_path, withDefault, additionalWords); + this.acceptors.creators.push(dict); +}; + +module.exports = Wordcut; + +},{"./acceptors":1,"./dict":2,"./latin_rules":3,"./path_info_builder":4,"./path_selector":5,"./thai_rules":6,"./wordcut_core":8,"sys":28,"underscore":25}],8:[function(require,module,exports){ +var WordcutCore = { + + buildPath: function(text) { + var self = this + , path = self.pathSelector.createPath() + , leftBoundary = 0; + self.acceptors.reset(); + for (var i = 0; i < text.length; i++) { + var ch = text[i]; + self.acceptors.transit(ch); + + var possiblePathInfos = self + .pathInfoBuilder + .build(path, + self.acceptors.getFinalAcceptors(), + i, + leftBoundary, + text); + var selectedPath = self.pathSelector.selectPath(possiblePathInfos) + + path.push(selectedPath); + if (selectedPath.type !== "UNK") { + leftBoundary = i; + } + } + return path; + }, + + pathToRanges: function(path) { + var e = path.length - 1 + , ranges = []; + + while (e > 0) { + var info = path[e] + , s = info.p; + + if (info.merge !== undefined && ranges.length > 0) { + var r = ranges[ranges.length - 1]; + r.s = info.merge; + s = r.s; + } else { + ranges.push({s:s, e:e}); + } + e = s; + } + return ranges.reverse(); + }, + + rangesToText: function(text, ranges, delimiter) { + return ranges.map(function(r) { + return text.substring(r.s, r.e); + }).join(delimiter); + }, + + cut: function(text, delimiter) { + var path = this.buildPath(text) + , ranges = this.pathToRanges(path); + return this + .rangesToText(text, ranges, + (delimiter === undefined ? "|" : delimiter)); + }, + + cutIntoRanges: function(text, noText) { + var path = this.buildPath(text) + , ranges = this.pathToRanges(path); + + if (!noText) { + ranges.forEach(function(r) { + r.text = text.substring(r.s, r.e); + }); + } + return ranges; + }, + + cutIntoArray: function(text) { + var path = this.buildPath(text) + , ranges = this.pathToRanges(path); + + return ranges.map(function(r) { + return text.substring(r.s, r.e) + }); + } +}; + +module.exports = WordcutCore; + +},{}],9:[function(require,module,exports){ +// http://wiki.commonjs.org/wiki/Unit_Testing/1.0 +// +// THIS IS NOT TESTED NOR LIKELY TO WORK OUTSIDE V8! +// +// Originally from narwhal.js (http://narwhaljs.org) +// Copyright (c) 2009 Thomas Robinson <280north.com> +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files (the 'Software'), to +// deal in the Software without restriction, including without limitation the +// rights to use, copy, modify, merge, publish, distribute, sublicense, and/or +// sell copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions: +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED 'AS IS', WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +// AUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN +// ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION +// WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + +// when used in node, this will actually load the util module we depend on +// versus loading the builtin util module as happens otherwise +// this is a bug in node module loading as far as I am concerned +var util = require('util/'); + +var pSlice = Array.prototype.slice; +var hasOwn = Object.prototype.hasOwnProperty; + +// 1. The assert module provides functions that throw +// AssertionError's when particular conditions are not met. The +// assert module must conform to the following interface. + +var assert = module.exports = ok; + +// 2. The AssertionError is defined in assert. +// new assert.AssertionError({ message: message, +// actual: actual, +// expected: expected }) + +assert.AssertionError = function AssertionError(options) { + this.name = 'AssertionError'; + this.actual = options.actual; + this.expected = options.expected; + this.operator = options.operator; + if (options.message) { + this.message = options.message; + this.generatedMessage = false; + } else { + this.message = getMessage(this); + this.generatedMessage = true; + } + var stackStartFunction = options.stackStartFunction || fail; + + if (Error.captureStackTrace) { + Error.captureStackTrace(this, stackStartFunction); + } + else { + // non v8 browsers so we can have a stacktrace + var err = new Error(); + if (err.stack) { + var out = err.stack; + + // try to strip useless frames + var fn_name = stackStartFunction.name; + var idx = out.indexOf('\n' + fn_name); + if (idx >= 0) { + // once we have located the function frame + // we need to strip out everything before it (and its line) + var next_line = out.indexOf('\n', idx + 1); + out = out.substring(next_line + 1); + } + + this.stack = out; + } + } +}; + +// assert.AssertionError instanceof Error +util.inherits(assert.AssertionError, Error); + +function replacer(key, value) { + if (util.isUndefined(value)) { + return '' + value; + } + if (util.isNumber(value) && !isFinite(value)) { + return value.toString(); + } + if (util.isFunction(value) || util.isRegExp(value)) { + return value.toString(); + } + return value; +} + +function truncate(s, n) { + if (util.isString(s)) { + return s.length < n ? s : s.slice(0, n); + } else { + return s; + } +} + +function getMessage(self) { + return truncate(JSON.stringify(self.actual, replacer), 128) + ' ' + + self.operator + ' ' + + truncate(JSON.stringify(self.expected, replacer), 128); +} + +// At present only the three keys mentioned above are used and +// understood by the spec. Implementations or sub modules can pass +// other keys to the AssertionError's constructor - they will be +// ignored. + +// 3. All of the following functions must throw an AssertionError +// when a corresponding condition is not met, with a message that +// may be undefined if not provided. All assertion methods provide +// both the actual and expected values to the assertion error for +// display purposes. + +function fail(actual, expected, message, operator, stackStartFunction) { + throw new assert.AssertionError({ + message: message, + actual: actual, + expected: expected, + operator: operator, + stackStartFunction: stackStartFunction + }); +} + +// EXTENSION! allows for well behaved errors defined elsewhere. +assert.fail = fail; + +// 4. Pure assertion tests whether a value is truthy, as determined +// by !!guard. +// assert.ok(guard, message_opt); +// This statement is equivalent to assert.equal(true, !!guard, +// message_opt);. To test strictly for the value true, use +// assert.strictEqual(true, guard, message_opt);. + +function ok(value, message) { + if (!value) fail(value, true, message, '==', assert.ok); +} +assert.ok = ok; + +// 5. The equality assertion tests shallow, coercive equality with +// ==. +// assert.equal(actual, expected, message_opt); + +assert.equal = function equal(actual, expected, message) { + if (actual != expected) fail(actual, expected, message, '==', assert.equal); +}; + +// 6. The non-equality assertion tests for whether two objects are not equal +// with != assert.notEqual(actual, expected, message_opt); + +assert.notEqual = function notEqual(actual, expected, message) { + if (actual == expected) { + fail(actual, expected, message, '!=', assert.notEqual); + } +}; + +// 7. The equivalence assertion tests a deep equality relation. +// assert.deepEqual(actual, expected, message_opt); + +assert.deepEqual = function deepEqual(actual, expected, message) { + if (!_deepEqual(actual, expected)) { + fail(actual, expected, message, 'deepEqual', assert.deepEqual); + } +}; + +function _deepEqual(actual, expected) { + // 7.1. All identical values are equivalent, as determined by ===. + if (actual === expected) { + return true; + + } else if (util.isBuffer(actual) && util.isBuffer(expected)) { + if (actual.length != expected.length) return false; + + for (var i = 0; i < actual.length; i++) { + if (actual[i] !== expected[i]) return false; + } + + return true; + + // 7.2. If the expected value is a Date object, the actual value is + // equivalent if it is also a Date object that refers to the same time. + } else if (util.isDate(actual) && util.isDate(expected)) { + return actual.getTime() === expected.getTime(); + + // 7.3 If the expected value is a RegExp object, the actual value is + // equivalent if it is also a RegExp object with the same source and + // properties (`global`, `multiline`, `lastIndex`, `ignoreCase`). + } else if (util.isRegExp(actual) && util.isRegExp(expected)) { + return actual.source === expected.source && + actual.global === expected.global && + actual.multiline === expected.multiline && + actual.lastIndex === expected.lastIndex && + actual.ignoreCase === expected.ignoreCase; + + // 7.4. Other pairs that do not both pass typeof value == 'object', + // equivalence is determined by ==. + } else if (!util.isObject(actual) && !util.isObject(expected)) { + return actual == expected; + + // 7.5 For all other Object pairs, including Array objects, equivalence is + // determined by having the same number of owned properties (as verified + // with Object.prototype.hasOwnProperty.call), the same set of keys + // (although not necessarily the same order), equivalent values for every + // corresponding key, and an identical 'prototype' property. Note: this + // accounts for both named and indexed properties on Arrays. + } else { + return objEquiv(actual, expected); + } +} + +function isArguments(object) { + return Object.prototype.toString.call(object) == '[object Arguments]'; +} + +function objEquiv(a, b) { + if (util.isNullOrUndefined(a) || util.isNullOrUndefined(b)) + return false; + // an identical 'prototype' property. + if (a.prototype !== b.prototype) return false; + // if one is a primitive, the other must be same + if (util.isPrimitive(a) || util.isPrimitive(b)) { + return a === b; + } + var aIsArgs = isArguments(a), + bIsArgs = isArguments(b); + if ((aIsArgs && !bIsArgs) || (!aIsArgs && bIsArgs)) + return false; + if (aIsArgs) { + a = pSlice.call(a); + b = pSlice.call(b); + return _deepEqual(a, b); + } + var ka = objectKeys(a), + kb = objectKeys(b), + key, i; + // having the same number of owned properties (keys incorporates + // hasOwnProperty) + if (ka.length != kb.length) + return false; + //the same set of keys (although not necessarily the same order), + ka.sort(); + kb.sort(); + //~~~cheap key test + for (i = ka.length - 1; i >= 0; i--) { + if (ka[i] != kb[i]) + return false; + } + //equivalent values for every corresponding key, and + //~~~possibly expensive deep test + for (i = ka.length - 1; i >= 0; i--) { + key = ka[i]; + if (!_deepEqual(a[key], b[key])) return false; + } + return true; +} + +// 8. The non-equivalence assertion tests for any deep inequality. +// assert.notDeepEqual(actual, expected, message_opt); + +assert.notDeepEqual = function notDeepEqual(actual, expected, message) { + if (_deepEqual(actual, expected)) { + fail(actual, expected, message, 'notDeepEqual', assert.notDeepEqual); + } +}; + +// 9. The strict equality assertion tests strict equality, as determined by ===. +// assert.strictEqual(actual, expected, message_opt); + +assert.strictEqual = function strictEqual(actual, expected, message) { + if (actual !== expected) { + fail(actual, expected, message, '===', assert.strictEqual); + } +}; + +// 10. The strict non-equality assertion tests for strict inequality, as +// determined by !==. assert.notStrictEqual(actual, expected, message_opt); + +assert.notStrictEqual = function notStrictEqual(actual, expected, message) { + if (actual === expected) { + fail(actual, expected, message, '!==', assert.notStrictEqual); + } +}; + +function expectedException(actual, expected) { + if (!actual || !expected) { + return false; + } + + if (Object.prototype.toString.call(expected) == '[object RegExp]') { + return expected.test(actual); + } else if (actual instanceof expected) { + return true; + } else if (expected.call({}, actual) === true) { + return true; + } + + return false; +} + +function _throws(shouldThrow, block, expected, message) { + var actual; + + if (util.isString(expected)) { + message = expected; + expected = null; + } + + try { + block(); + } catch (e) { + actual = e; + } + + message = (expected && expected.name ? ' (' + expected.name + ').' : '.') + + (message ? ' ' + message : '.'); + + if (shouldThrow && !actual) { + fail(actual, expected, 'Missing expected exception' + message); + } + + if (!shouldThrow && expectedException(actual, expected)) { + fail(actual, expected, 'Got unwanted exception' + message); + } + + if ((shouldThrow && actual && expected && + !expectedException(actual, expected)) || (!shouldThrow && actual)) { + throw actual; + } +} + +// 11. Expected to throw an error: +// assert.throws(block, Error_opt, message_opt); + +assert.throws = function(block, /*optional*/error, /*optional*/message) { + _throws.apply(this, [true].concat(pSlice.call(arguments))); +}; + +// EXTENSION! This is annoying to write outside this module. +assert.doesNotThrow = function(block, /*optional*/message) { + _throws.apply(this, [false].concat(pSlice.call(arguments))); +}; + +assert.ifError = function(err) { if (err) {throw err;}}; + +var objectKeys = Object.keys || function (obj) { + var keys = []; + for (var key in obj) { + if (hasOwn.call(obj, key)) keys.push(key); + } + return keys; +}; + +},{"util/":28}],10:[function(require,module,exports){ +'use strict'; +module.exports = balanced; +function balanced(a, b, str) { + if (a instanceof RegExp) a = maybeMatch(a, str); + if (b instanceof RegExp) b = maybeMatch(b, str); + + var r = range(a, b, str); + + return r && { + start: r[0], + end: r[1], + pre: str.slice(0, r[0]), + body: str.slice(r[0] + a.length, r[1]), + post: str.slice(r[1] + b.length) + }; +} + +function maybeMatch(reg, str) { + var m = str.match(reg); + return m ? m[0] : null; +} + +balanced.range = range; +function range(a, b, str) { + var begs, beg, left, right, result; + var ai = str.indexOf(a); + var bi = str.indexOf(b, ai + 1); + var i = ai; + + if (ai >= 0 && bi > 0) { + begs = []; + left = str.length; + + while (i >= 0 && !result) { + if (i == ai) { + begs.push(i); + ai = str.indexOf(a, i + 1); + } else if (begs.length == 1) { + result = [ begs.pop(), bi ]; + } else { + beg = begs.pop(); + if (beg < left) { + left = beg; + right = bi; + } + + bi = str.indexOf(b, i + 1); + } + + i = ai < bi && ai >= 0 ? ai : bi; + } + + if (begs.length) { + result = [ left, right ]; + } + } + + return result; +} + +},{}],11:[function(require,module,exports){ +var concatMap = require('concat-map'); +var balanced = require('balanced-match'); + +module.exports = expandTop; + +var escSlash = '\0SLASH'+Math.random()+'\0'; +var escOpen = '\0OPEN'+Math.random()+'\0'; +var escClose = '\0CLOSE'+Math.random()+'\0'; +var escComma = '\0COMMA'+Math.random()+'\0'; +var escPeriod = '\0PERIOD'+Math.random()+'\0'; + +function numeric(str) { + return parseInt(str, 10) == str + ? parseInt(str, 10) + : str.charCodeAt(0); +} + +function escapeBraces(str) { + return str.split('\\\\').join(escSlash) + .split('\\{').join(escOpen) + .split('\\}').join(escClose) + .split('\\,').join(escComma) + .split('\\.').join(escPeriod); +} + +function unescapeBraces(str) { + return str.split(escSlash).join('\\') + .split(escOpen).join('{') + .split(escClose).join('}') + .split(escComma).join(',') + .split(escPeriod).join('.'); +} + + +// Basically just str.split(","), but handling cases +// where we have nested braced sections, which should be +// treated as individual members, like {a,{b,c},d} +function parseCommaParts(str) { + if (!str) + return ['']; + + var parts = []; + var m = balanced('{', '}', str); + + if (!m) + return str.split(','); + + var pre = m.pre; + var body = m.body; + var post = m.post; + var p = pre.split(','); + + p[p.length-1] += '{' + body + '}'; + var postParts = parseCommaParts(post); + if (post.length) { + p[p.length-1] += postParts.shift(); + p.push.apply(p, postParts); + } + + parts.push.apply(parts, p); + + return parts; +} + +function expandTop(str) { + if (!str) + return []; + + // I don't know why Bash 4.3 does this, but it does. + // Anything starting with {} will have the first two bytes preserved + // but *only* at the top level, so {},a}b will not expand to anything, + // but a{},b}c will be expanded to [a}c,abc]. + // One could argue that this is a bug in Bash, but since the goal of + // this module is to match Bash's rules, we escape a leading {} + if (str.substr(0, 2) === '{}') { + str = '\\{\\}' + str.substr(2); + } + + return expand(escapeBraces(str), true).map(unescapeBraces); +} + +function identity(e) { + return e; +} + +function embrace(str) { + return '{' + str + '}'; +} +function isPadded(el) { + return /^-?0\d/.test(el); +} + +function lte(i, y) { + return i <= y; +} +function gte(i, y) { + return i >= y; +} + +function expand(str, isTop) { + var expansions = []; + + var m = balanced('{', '}', str); + if (!m || /\$$/.test(m.pre)) return [str]; + + var isNumericSequence = /^-?\d+\.\.-?\d+(?:\.\.-?\d+)?$/.test(m.body); + var isAlphaSequence = /^[a-zA-Z]\.\.[a-zA-Z](?:\.\.-?\d+)?$/.test(m.body); + var isSequence = isNumericSequence || isAlphaSequence; + var isOptions = m.body.indexOf(',') >= 0; + if (!isSequence && !isOptions) { + // {a},b} + if (m.post.match(/,.*\}/)) { + str = m.pre + '{' + m.body + escClose + m.post; + return expand(str); + } + return [str]; + } + + var n; + if (isSequence) { + n = m.body.split(/\.\./); + } else { + n = parseCommaParts(m.body); + if (n.length === 1) { + // x{{a,b}}y ==> x{a}y x{b}y + n = expand(n[0], false).map(embrace); + if (n.length === 1) { + var post = m.post.length + ? expand(m.post, false) + : ['']; + return post.map(function(p) { + return m.pre + n[0] + p; + }); + } + } + } + + // at this point, n is the parts, and we know it's not a comma set + // with a single entry. + + // no need to expand pre, since it is guaranteed to be free of brace-sets + var pre = m.pre; + var post = m.post.length + ? expand(m.post, false) + : ['']; + + var N; + + if (isSequence) { + var x = numeric(n[0]); + var y = numeric(n[1]); + var width = Math.max(n[0].length, n[1].length) + var incr = n.length == 3 + ? Math.abs(numeric(n[2])) + : 1; + var test = lte; + var reverse = y < x; + if (reverse) { + incr *= -1; + test = gte; + } + var pad = n.some(isPadded); + + N = []; + + for (var i = x; test(i, y); i += incr) { + var c; + if (isAlphaSequence) { + c = String.fromCharCode(i); + if (c === '\\') + c = ''; + } else { + c = String(i); + if (pad) { + var need = width - c.length; + if (need > 0) { + var z = new Array(need + 1).join('0'); + if (i < 0) + c = '-' + z + c.slice(1); + else + c = z + c; + } + } + } + N.push(c); + } + } else { + N = concatMap(n, function(el) { return expand(el, false) }); + } + + for (var j = 0; j < N.length; j++) { + for (var k = 0; k < post.length; k++) { + var expansion = pre + N[j] + post[k]; + if (!isTop || isSequence || expansion) + expansions.push(expansion); + } + } + + return expansions; +} + + +},{"balanced-match":10,"concat-map":13}],12:[function(require,module,exports){ + +},{}],13:[function(require,module,exports){ +module.exports = function (xs, fn) { + var res = []; + for (var i = 0; i < xs.length; i++) { + var x = fn(xs[i], i); + if (isArray(x)) res.push.apply(res, x); + else res.push(x); + } + return res; +}; + +var isArray = Array.isArray || function (xs) { + return Object.prototype.toString.call(xs) === '[object Array]'; +}; + +},{}],14:[function(require,module,exports){ +// Copyright Joyent, Inc. and other Node contributors. +// +// Permission is hereby granted, free of charge, to any person obtaining a +// copy of this software and associated documentation files (the +// "Software"), to deal in the Software without restriction, including +// without limitation the rights to use, copy, modify, merge, publish, +// distribute, sublicense, and/or sell copies of the Software, and to permit +// persons to whom the Software is furnished to do so, subject to the +// following conditions: +// +// The above copyright notice and this permission notice shall be included +// in all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS +// OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF +// MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN +// NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, +// DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR +// OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE +// USE OR OTHER DEALINGS IN THE SOFTWARE. + +function EventEmitter() { + this._events = this._events || {}; + this._maxListeners = this._maxListeners || undefined; +} +module.exports = EventEmitter; + +// Backwards-compat with node 0.10.x +EventEmitter.EventEmitter = EventEmitter; + +EventEmitter.prototype._events = undefined; +EventEmitter.prototype._maxListeners = undefined; + +// By default EventEmitters will print a warning if more than 10 listeners are +// added to it. This is a useful default which helps finding memory leaks. +EventEmitter.defaultMaxListeners = 10; + +// Obviously not all Emitters should be limited to 10. This function allows +// that to be increased. Set to zero for unlimited. +EventEmitter.prototype.setMaxListeners = function(n) { + if (!isNumber(n) || n < 0 || isNaN(n)) + throw TypeError('n must be a positive number'); + this._maxListeners = n; + return this; +}; + +EventEmitter.prototype.emit = function(type) { + var er, handler, len, args, i, listeners; + + if (!this._events) + this._events = {}; + + // If there is no 'error' event listener then throw. + if (type === 'error') { + if (!this._events.error || + (isObject(this._events.error) && !this._events.error.length)) { + er = arguments[1]; + if (er instanceof Error) { + throw er; // Unhandled 'error' event + } + throw TypeError('Uncaught, unspecified "error" event.'); + } + } + + handler = this._events[type]; + + if (isUndefined(handler)) + return false; + + if (isFunction(handler)) { + switch (arguments.length) { + // fast cases + case 1: + handler.call(this); + break; + case 2: + handler.call(this, arguments[1]); + break; + case 3: + handler.call(this, arguments[1], arguments[2]); + break; + // slower + default: + len = arguments.length; + args = new Array(len - 1); + for (i = 1; i < len; i++) + args[i - 1] = arguments[i]; + handler.apply(this, args); + } + } else if (isObject(handler)) { + len = arguments.length; + args = new Array(len - 1); + for (i = 1; i < len; i++) + args[i - 1] = arguments[i]; + + listeners = handler.slice(); + len = listeners.length; + for (i = 0; i < len; i++) + listeners[i].apply(this, args); + } + + return true; +}; + +EventEmitter.prototype.addListener = function(type, listener) { + var m; + + if (!isFunction(listener)) + throw TypeError('listener must be a function'); + + if (!this._events) + this._events = {}; + + // To avoid recursion in the case that type === "newListener"! Before + // adding it to the listeners, first emit "newListener". + if (this._events.newListener) + this.emit('newListener', type, + isFunction(listener.listener) ? + listener.listener : listener); + + if (!this._events[type]) + // Optimize the case of one listener. Don't need the extra array object. + this._events[type] = listener; + else if (isObject(this._events[type])) + // If we've already got an array, just append. + this._events[type].push(listener); + else + // Adding the second element, need to change to array. + this._events[type] = [this._events[type], listener]; + + // Check for listener leak + if (isObject(this._events[type]) && !this._events[type].warned) { + var m; + if (!isUndefined(this._maxListeners)) { + m = this._maxListeners; + } else { + m = EventEmitter.defaultMaxListeners; + } + + if (m && m > 0 && this._events[type].length > m) { + this._events[type].warned = true; + console.error('(node) warning: possible EventEmitter memory ' + + 'leak detected. %d listeners added. ' + + 'Use emitter.setMaxListeners() to increase limit.', + this._events[type].length); + if (typeof console.trace === 'function') { + // not supported in IE 10 + console.trace(); + } + } + } + + return this; +}; + +EventEmitter.prototype.on = EventEmitter.prototype.addListener; + +EventEmitter.prototype.once = function(type, listener) { + if (!isFunction(listener)) + throw TypeError('listener must be a function'); + + var fired = false; + + function g() { + this.removeListener(type, g); + + if (!fired) { + fired = true; + listener.apply(this, arguments); + } + } + + g.listener = listener; + this.on(type, g); + + return this; +}; + +// emits a 'removeListener' event iff the listener was removed +EventEmitter.prototype.removeListener = function(type, listener) { + var list, position, length, i; + + if (!isFunction(listener)) + throw TypeError('listener must be a function'); + + if (!this._events || !this._events[type]) + return this; + + list = this._events[type]; + length = list.length; + position = -1; + + if (list === listener || + (isFunction(list.listener) && list.listener === listener)) { + delete this._events[type]; + if (this._events.removeListener) + this.emit('removeListener', type, listener); + + } else if (isObject(list)) { + for (i = length; i-- > 0;) { + if (list[i] === listener || + (list[i].listener && list[i].listener === listener)) { + position = i; + break; + } + } + + if (position < 0) + return this; + + if (list.length === 1) { + list.length = 0; + delete this._events[type]; + } else { + list.splice(position, 1); + } + + if (this._events.removeListener) + this.emit('removeListener', type, listener); + } + + return this; +}; + +EventEmitter.prototype.removeAllListeners = function(type) { + var key, listeners; + + if (!this._events) + return this; + + // not listening for removeListener, no need to emit + if (!this._events.removeListener) { + if (arguments.length === 0) + this._events = {}; + else if (this._events[type]) + delete this._events[type]; + return this; + } + + // emit removeListener for all listeners on all events + if (arguments.length === 0) { + for (key in this._events) { + if (key === 'removeListener') continue; + this.removeAllListeners(key); + } + this.removeAllListeners('removeListener'); + this._events = {}; + return this; + } + + listeners = this._events[type]; + + if (isFunction(listeners)) { + this.removeListener(type, listeners); + } else { + // LIFO order + while (listeners.length) + this.removeListener(type, listeners[listeners.length - 1]); + } + delete this._events[type]; + + return this; +}; + +EventEmitter.prototype.listeners = function(type) { + var ret; + if (!this._events || !this._events[type]) + ret = []; + else if (isFunction(this._events[type])) + ret = [this._events[type]]; + else + ret = this._events[type].slice(); + return ret; +}; + +EventEmitter.listenerCount = function(emitter, type) { + var ret; + if (!emitter._events || !emitter._events[type]) + ret = 0; + else if (isFunction(emitter._events[type])) + ret = 1; + else + ret = emitter._events[type].length; + return ret; +}; + +function isFunction(arg) { + return typeof arg === 'function'; +} + +function isNumber(arg) { + return typeof arg === 'number'; +} + +function isObject(arg) { + return typeof arg === 'object' && arg !== null; +} + +function isUndefined(arg) { + return arg === void 0; +} + +},{}],15:[function(require,module,exports){ +(function (process){ +exports.alphasort = alphasort +exports.alphasorti = alphasorti +exports.setopts = setopts +exports.ownProp = ownProp +exports.makeAbs = makeAbs +exports.finish = finish +exports.mark = mark +exports.isIgnored = isIgnored +exports.childrenIgnored = childrenIgnored + +function ownProp (obj, field) { + return Object.prototype.hasOwnProperty.call(obj, field) +} + +var path = require("path") +var minimatch = require("minimatch") +var isAbsolute = require("path-is-absolute") +var Minimatch = minimatch.Minimatch + +function alphasorti (a, b) { + return a.toLowerCase().localeCompare(b.toLowerCase()) +} + +function alphasort (a, b) { + return a.localeCompare(b) +} + +function setupIgnores (self, options) { + self.ignore = options.ignore || [] + + if (!Array.isArray(self.ignore)) + self.ignore = [self.ignore] + + if (self.ignore.length) { + self.ignore = self.ignore.map(ignoreMap) + } +} + +function ignoreMap (pattern) { + var gmatcher = null + if (pattern.slice(-3) === '/**') { + var gpattern = pattern.replace(/(\/\*\*)+$/, '') + gmatcher = new Minimatch(gpattern) + } + + return { + matcher: new Minimatch(pattern), + gmatcher: gmatcher + } +} + +function setopts (self, pattern, options) { + if (!options) + options = {} + + // base-matching: just use globstar for that. + if (options.matchBase && -1 === pattern.indexOf("/")) { + if (options.noglobstar) { + throw new Error("base matching requires globstar") + } + pattern = "**/" + pattern + } + + self.silent = !!options.silent + self.pattern = pattern + self.strict = options.strict !== false + self.realpath = !!options.realpath + self.realpathCache = options.realpathCache || Object.create(null) + self.follow = !!options.follow + self.dot = !!options.dot + self.mark = !!options.mark + self.nodir = !!options.nodir + if (self.nodir) + self.mark = true + self.sync = !!options.sync + self.nounique = !!options.nounique + self.nonull = !!options.nonull + self.nosort = !!options.nosort + self.nocase = !!options.nocase + self.stat = !!options.stat + self.noprocess = !!options.noprocess + + self.maxLength = options.maxLength || Infinity + self.cache = options.cache || Object.create(null) + self.statCache = options.statCache || Object.create(null) + self.symlinks = options.symlinks || Object.create(null) + + setupIgnores(self, options) + + self.changedCwd = false + var cwd = process.cwd() + if (!ownProp(options, "cwd")) + self.cwd = cwd + else { + self.cwd = options.cwd + self.changedCwd = path.resolve(options.cwd) !== cwd + } + + self.root = options.root || path.resolve(self.cwd, "/") + self.root = path.resolve(self.root) + if (process.platform === "win32") + self.root = self.root.replace(/\\/g, "/") + + self.nomount = !!options.nomount + + // disable comments and negation unless the user explicitly + // passes in false as the option. + options.nonegate = options.nonegate === false ? false : true + options.nocomment = options.nocomment === false ? false : true + deprecationWarning(options) + + self.minimatch = new Minimatch(pattern, options) + self.options = self.minimatch.options +} + +// TODO(isaacs): remove entirely in v6 +// exported to reset in tests +exports.deprecationWarned +function deprecationWarning(options) { + if (!options.nonegate || !options.nocomment) { + if (process.noDeprecation !== true && !exports.deprecationWarned) { + var msg = 'glob WARNING: comments and negation will be disabled in v6' + if (process.throwDeprecation) + throw new Error(msg) + else if (process.traceDeprecation) + console.trace(msg) + else + console.error(msg) + + exports.deprecationWarned = true + } + } +} + +function finish (self) { + var nou = self.nounique + var all = nou ? [] : Object.create(null) + + for (var i = 0, l = self.matches.length; i < l; i ++) { + var matches = self.matches[i] + if (!matches || Object.keys(matches).length === 0) { + if (self.nonull) { + // do like the shell, and spit out the literal glob + var literal = self.minimatch.globSet[i] + if (nou) + all.push(literal) + else + all[literal] = true + } + } else { + // had matches + var m = Object.keys(matches) + if (nou) + all.push.apply(all, m) + else + m.forEach(function (m) { + all[m] = true + }) + } + } + + if (!nou) + all = Object.keys(all) + + if (!self.nosort) + all = all.sort(self.nocase ? alphasorti : alphasort) + + // at *some* point we statted all of these + if (self.mark) { + for (var i = 0; i < all.length; i++) { + all[i] = self._mark(all[i]) + } + if (self.nodir) { + all = all.filter(function (e) { + return !(/\/$/.test(e)) + }) + } + } + + if (self.ignore.length) + all = all.filter(function(m) { + return !isIgnored(self, m) + }) + + self.found = all +} + +function mark (self, p) { + var abs = makeAbs(self, p) + var c = self.cache[abs] + var m = p + if (c) { + var isDir = c === 'DIR' || Array.isArray(c) + var slash = p.slice(-1) === '/' + + if (isDir && !slash) + m += '/' + else if (!isDir && slash) + m = m.slice(0, -1) + + if (m !== p) { + var mabs = makeAbs(self, m) + self.statCache[mabs] = self.statCache[abs] + self.cache[mabs] = self.cache[abs] + } + } + + return m +} + +// lotta situps... +function makeAbs (self, f) { + var abs = f + if (f.charAt(0) === '/') { + abs = path.join(self.root, f) + } else if (isAbsolute(f) || f === '') { + abs = f + } else if (self.changedCwd) { + abs = path.resolve(self.cwd, f) + } else { + abs = path.resolve(f) + } + return abs +} + + +// Return true, if pattern ends with globstar '**', for the accompanying parent directory. +// Ex:- If node_modules/** is the pattern, add 'node_modules' to ignore list along with it's contents +function isIgnored (self, path) { + if (!self.ignore.length) + return false + + return self.ignore.some(function(item) { + return item.matcher.match(path) || !!(item.gmatcher && item.gmatcher.match(path)) + }) +} + +function childrenIgnored (self, path) { + if (!self.ignore.length) + return false + + return self.ignore.some(function(item) { + return !!(item.gmatcher && item.gmatcher.match(path)) + }) +} + +}).call(this,require('_process')) +},{"_process":24,"minimatch":20,"path":22,"path-is-absolute":23}],16:[function(require,module,exports){ +(function (process){ +// Approach: +// +// 1. Get the minimatch set +// 2. For each pattern in the set, PROCESS(pattern, false) +// 3. Store matches per-set, then uniq them +// +// PROCESS(pattern, inGlobStar) +// Get the first [n] items from pattern that are all strings +// Join these together. This is PREFIX. +// If there is no more remaining, then stat(PREFIX) and +// add to matches if it succeeds. END. +// +// If inGlobStar and PREFIX is symlink and points to dir +// set ENTRIES = [] +// else readdir(PREFIX) as ENTRIES +// If fail, END +// +// with ENTRIES +// If pattern[n] is GLOBSTAR +// // handle the case where the globstar match is empty +// // by pruning it out, and testing the resulting pattern +// PROCESS(pattern[0..n] + pattern[n+1 .. $], false) +// // handle other cases. +// for ENTRY in ENTRIES (not dotfiles) +// // attach globstar + tail onto the entry +// // Mark that this entry is a globstar match +// PROCESS(pattern[0..n] + ENTRY + pattern[n .. $], true) +// +// else // not globstar +// for ENTRY in ENTRIES (not dotfiles, unless pattern[n] is dot) +// Test ENTRY against pattern[n] +// If fails, continue +// If passes, PROCESS(pattern[0..n] + item + pattern[n+1 .. $]) +// +// Caveat: +// Cache all stats and readdirs results to minimize syscall. Since all +// we ever care about is existence and directory-ness, we can just keep +// `true` for files, and [children,...] for directories, or `false` for +// things that don't exist. + +module.exports = glob + +var fs = require('fs') +var minimatch = require('minimatch') +var Minimatch = minimatch.Minimatch +var inherits = require('inherits') +var EE = require('events').EventEmitter +var path = require('path') +var assert = require('assert') +var isAbsolute = require('path-is-absolute') +var globSync = require('./sync.js') +var common = require('./common.js') +var alphasort = common.alphasort +var alphasorti = common.alphasorti +var setopts = common.setopts +var ownProp = common.ownProp +var inflight = require('inflight') +var util = require('util') +var childrenIgnored = common.childrenIgnored +var isIgnored = common.isIgnored + +var once = require('once') + +function glob (pattern, options, cb) { + if (typeof options === 'function') cb = options, options = {} + if (!options) options = {} + + if (options.sync) { + if (cb) + throw new TypeError('callback provided to sync glob') + return globSync(pattern, options) + } + + return new Glob(pattern, options, cb) +} + +glob.sync = globSync +var GlobSync = glob.GlobSync = globSync.GlobSync + +// old api surface +glob.glob = glob + +glob.hasMagic = function (pattern, options_) { + var options = util._extend({}, options_) + options.noprocess = true + + var g = new Glob(pattern, options) + var set = g.minimatch.set + if (set.length > 1) + return true + + for (var j = 0; j < set[0].length; j++) { + if (typeof set[0][j] !== 'string') + return true + } + + return false +} + +glob.Glob = Glob +inherits(Glob, EE) +function Glob (pattern, options, cb) { + if (typeof options === 'function') { + cb = options + options = null + } + + if (options && options.sync) { + if (cb) + throw new TypeError('callback provided to sync glob') + return new GlobSync(pattern, options) + } + + if (!(this instanceof Glob)) + return new Glob(pattern, options, cb) + + setopts(this, pattern, options) + this._didRealPath = false + + // process each pattern in the minimatch set + var n = this.minimatch.set.length + + // The matches are stored as {: true,...} so that + // duplicates are automagically pruned. + // Later, we do an Object.keys() on these. + // Keep them as a list so we can fill in when nonull is set. + this.matches = new Array(n) + + if (typeof cb === 'function') { + cb = once(cb) + this.on('error', cb) + this.on('end', function (matches) { + cb(null, matches) + }) + } + + var self = this + var n = this.minimatch.set.length + this._processing = 0 + this.matches = new Array(n) + + this._emitQueue = [] + this._processQueue = [] + this.paused = false + + if (this.noprocess) + return this + + if (n === 0) + return done() + + for (var i = 0; i < n; i ++) { + this._process(this.minimatch.set[i], i, false, done) + } + + function done () { + --self._processing + if (self._processing <= 0) + self._finish() + } +} + +Glob.prototype._finish = function () { + assert(this instanceof Glob) + if (this.aborted) + return + + if (this.realpath && !this._didRealpath) + return this._realpath() + + common.finish(this) + this.emit('end', this.found) +} + +Glob.prototype._realpath = function () { + if (this._didRealpath) + return + + this._didRealpath = true + + var n = this.matches.length + if (n === 0) + return this._finish() + + var self = this + for (var i = 0; i < this.matches.length; i++) + this._realpathSet(i, next) + + function next () { + if (--n === 0) + self._finish() + } +} + +Glob.prototype._realpathSet = function (index, cb) { + var matchset = this.matches[index] + if (!matchset) + return cb() + + var found = Object.keys(matchset) + var self = this + var n = found.length + + if (n === 0) + return cb() + + var set = this.matches[index] = Object.create(null) + found.forEach(function (p, i) { + // If there's a problem with the stat, then it means that + // one or more of the links in the realpath couldn't be + // resolved. just return the abs value in that case. + p = self._makeAbs(p) + fs.realpath(p, self.realpathCache, function (er, real) { + if (!er) + set[real] = true + else if (er.syscall === 'stat') + set[p] = true + else + self.emit('error', er) // srsly wtf right here + + if (--n === 0) { + self.matches[index] = set + cb() + } + }) + }) +} + +Glob.prototype._mark = function (p) { + return common.mark(this, p) +} + +Glob.prototype._makeAbs = function (f) { + return common.makeAbs(this, f) +} + +Glob.prototype.abort = function () { + this.aborted = true + this.emit('abort') +} + +Glob.prototype.pause = function () { + if (!this.paused) { + this.paused = true + this.emit('pause') + } +} + +Glob.prototype.resume = function () { + if (this.paused) { + this.emit('resume') + this.paused = false + if (this._emitQueue.length) { + var eq = this._emitQueue.slice(0) + this._emitQueue.length = 0 + for (var i = 0; i < eq.length; i ++) { + var e = eq[i] + this._emitMatch(e[0], e[1]) + } + } + if (this._processQueue.length) { + var pq = this._processQueue.slice(0) + this._processQueue.length = 0 + for (var i = 0; i < pq.length; i ++) { + var p = pq[i] + this._processing-- + this._process(p[0], p[1], p[2], p[3]) + } + } + } +} + +Glob.prototype._process = function (pattern, index, inGlobStar, cb) { + assert(this instanceof Glob) + assert(typeof cb === 'function') + + if (this.aborted) + return + + this._processing++ + if (this.paused) { + this._processQueue.push([pattern, index, inGlobStar, cb]) + return + } + + //console.error('PROCESS %d', this._processing, pattern) + + // Get the first [n] parts of pattern that are all strings. + var n = 0 + while (typeof pattern[n] === 'string') { + n ++ + } + // now n is the index of the first one that is *not* a string. + + // see if there's anything else + var prefix + switch (n) { + // if not, then this is rather simple + case pattern.length: + this._processSimple(pattern.join('/'), index, cb) + return + + case 0: + // pattern *starts* with some non-trivial item. + // going to readdir(cwd), but not include the prefix in matches. + prefix = null + break + + default: + // pattern has some string bits in the front. + // whatever it starts with, whether that's 'absolute' like /foo/bar, + // or 'relative' like '../baz' + prefix = pattern.slice(0, n).join('/') + break + } + + var remain = pattern.slice(n) + + // get the list of entries. + var read + if (prefix === null) + read = '.' + else if (isAbsolute(prefix) || isAbsolute(pattern.join('/'))) { + if (!prefix || !isAbsolute(prefix)) + prefix = '/' + prefix + read = prefix + } else + read = prefix + + var abs = this._makeAbs(read) + + //if ignored, skip _processing + if (childrenIgnored(this, read)) + return cb() + + var isGlobStar = remain[0] === minimatch.GLOBSTAR + if (isGlobStar) + this._processGlobStar(prefix, read, abs, remain, index, inGlobStar, cb) + else + this._processReaddir(prefix, read, abs, remain, index, inGlobStar, cb) +} + +Glob.prototype._processReaddir = function (prefix, read, abs, remain, index, inGlobStar, cb) { + var self = this + this._readdir(abs, inGlobStar, function (er, entries) { + return self._processReaddir2(prefix, read, abs, remain, index, inGlobStar, entries, cb) + }) +} + +Glob.prototype._processReaddir2 = function (prefix, read, abs, remain, index, inGlobStar, entries, cb) { + + // if the abs isn't a dir, then nothing can match! + if (!entries) + return cb() + + // It will only match dot entries if it starts with a dot, or if + // dot is set. Stuff like @(.foo|.bar) isn't allowed. + var pn = remain[0] + var negate = !!this.minimatch.negate + var rawGlob = pn._glob + var dotOk = this.dot || rawGlob.charAt(0) === '.' + + var matchedEntries = [] + for (var i = 0; i < entries.length; i++) { + var e = entries[i] + if (e.charAt(0) !== '.' || dotOk) { + var m + if (negate && !prefix) { + m = !e.match(pn) + } else { + m = e.match(pn) + } + if (m) + matchedEntries.push(e) + } + } + + //console.error('prd2', prefix, entries, remain[0]._glob, matchedEntries) + + var len = matchedEntries.length + // If there are no matched entries, then nothing matches. + if (len === 0) + return cb() + + // if this is the last remaining pattern bit, then no need for + // an additional stat *unless* the user has specified mark or + // stat explicitly. We know they exist, since readdir returned + // them. + + if (remain.length === 1 && !this.mark && !this.stat) { + if (!this.matches[index]) + this.matches[index] = Object.create(null) + + for (var i = 0; i < len; i ++) { + var e = matchedEntries[i] + if (prefix) { + if (prefix !== '/') + e = prefix + '/' + e + else + e = prefix + e + } + + if (e.charAt(0) === '/' && !this.nomount) { + e = path.join(this.root, e) + } + this._emitMatch(index, e) + } + // This was the last one, and no stats were needed + return cb() + } + + // now test all matched entries as stand-ins for that part + // of the pattern. + remain.shift() + for (var i = 0; i < len; i ++) { + var e = matchedEntries[i] + var newPattern + if (prefix) { + if (prefix !== '/') + e = prefix + '/' + e + else + e = prefix + e + } + this._process([e].concat(remain), index, inGlobStar, cb) + } + cb() +} + +Glob.prototype._emitMatch = function (index, e) { + if (this.aborted) + return + + if (this.matches[index][e]) + return + + if (isIgnored(this, e)) + return + + if (this.paused) { + this._emitQueue.push([index, e]) + return + } + + var abs = this._makeAbs(e) + + if (this.nodir) { + var c = this.cache[abs] + if (c === 'DIR' || Array.isArray(c)) + return + } + + if (this.mark) + e = this._mark(e) + + this.matches[index][e] = true + + var st = this.statCache[abs] + if (st) + this.emit('stat', e, st) + + this.emit('match', e) +} + +Glob.prototype._readdirInGlobStar = function (abs, cb) { + if (this.aborted) + return + + // follow all symlinked directories forever + // just proceed as if this is a non-globstar situation + if (this.follow) + return this._readdir(abs, false, cb) + + var lstatkey = 'lstat\0' + abs + var self = this + var lstatcb = inflight(lstatkey, lstatcb_) + + if (lstatcb) + fs.lstat(abs, lstatcb) + + function lstatcb_ (er, lstat) { + if (er) + return cb() + + var isSym = lstat.isSymbolicLink() + self.symlinks[abs] = isSym + + // If it's not a symlink or a dir, then it's definitely a regular file. + // don't bother doing a readdir in that case. + if (!isSym && !lstat.isDirectory()) { + self.cache[abs] = 'FILE' + cb() + } else + self._readdir(abs, false, cb) + } +} + +Glob.prototype._readdir = function (abs, inGlobStar, cb) { + if (this.aborted) + return + + cb = inflight('readdir\0'+abs+'\0'+inGlobStar, cb) + if (!cb) + return + + //console.error('RD %j %j', +inGlobStar, abs) + if (inGlobStar && !ownProp(this.symlinks, abs)) + return this._readdirInGlobStar(abs, cb) + + if (ownProp(this.cache, abs)) { + var c = this.cache[abs] + if (!c || c === 'FILE') + return cb() + + if (Array.isArray(c)) + return cb(null, c) + } + + var self = this + fs.readdir(abs, readdirCb(this, abs, cb)) +} + +function readdirCb (self, abs, cb) { + return function (er, entries) { + if (er) + self._readdirError(abs, er, cb) + else + self._readdirEntries(abs, entries, cb) + } +} + +Glob.prototype._readdirEntries = function (abs, entries, cb) { + if (this.aborted) + return + + // if we haven't asked to stat everything, then just + // assume that everything in there exists, so we can avoid + // having to stat it a second time. + if (!this.mark && !this.stat) { + for (var i = 0; i < entries.length; i ++) { + var e = entries[i] + if (abs === '/') + e = abs + e + else + e = abs + '/' + e + this.cache[e] = true + } + } + + this.cache[abs] = entries + return cb(null, entries) +} + +Glob.prototype._readdirError = function (f, er, cb) { + if (this.aborted) + return + + // handle errors, and cache the information + switch (er.code) { + case 'ENOTSUP': // https://github.com/isaacs/node-glob/issues/205 + case 'ENOTDIR': // totally normal. means it *does* exist. + this.cache[this._makeAbs(f)] = 'FILE' + break + + case 'ENOENT': // not terribly unusual + case 'ELOOP': + case 'ENAMETOOLONG': + case 'UNKNOWN': + this.cache[this._makeAbs(f)] = false + break + + default: // some unusual error. Treat as failure. + this.cache[this._makeAbs(f)] = false + if (this.strict) { + this.emit('error', er) + // If the error is handled, then we abort + // if not, we threw out of here + this.abort() + } + if (!this.silent) + console.error('glob error', er) + break + } + + return cb() +} + +Glob.prototype._processGlobStar = function (prefix, read, abs, remain, index, inGlobStar, cb) { + var self = this + this._readdir(abs, inGlobStar, function (er, entries) { + self._processGlobStar2(prefix, read, abs, remain, index, inGlobStar, entries, cb) + }) +} + + +Glob.prototype._processGlobStar2 = function (prefix, read, abs, remain, index, inGlobStar, entries, cb) { + //console.error('pgs2', prefix, remain[0], entries) + + // no entries means not a dir, so it can never have matches + // foo.txt/** doesn't match foo.txt + if (!entries) + return cb() + + // test without the globstar, and with every child both below + // and replacing the globstar. + var remainWithoutGlobStar = remain.slice(1) + var gspref = prefix ? [ prefix ] : [] + var noGlobStar = gspref.concat(remainWithoutGlobStar) + + // the noGlobStar pattern exits the inGlobStar state + this._process(noGlobStar, index, false, cb) + + var isSym = this.symlinks[abs] + var len = entries.length + + // If it's a symlink, and we're in a globstar, then stop + if (isSym && inGlobStar) + return cb() + + for (var i = 0; i < len; i++) { + var e = entries[i] + if (e.charAt(0) === '.' && !this.dot) + continue + + // these two cases enter the inGlobStar state + var instead = gspref.concat(entries[i], remainWithoutGlobStar) + this._process(instead, index, true, cb) + + var below = gspref.concat(entries[i], remain) + this._process(below, index, true, cb) + } + + cb() +} + +Glob.prototype._processSimple = function (prefix, index, cb) { + // XXX review this. Shouldn't it be doing the mounting etc + // before doing stat? kinda weird? + var self = this + this._stat(prefix, function (er, exists) { + self._processSimple2(prefix, index, er, exists, cb) + }) +} +Glob.prototype._processSimple2 = function (prefix, index, er, exists, cb) { + + //console.error('ps2', prefix, exists) + + if (!this.matches[index]) + this.matches[index] = Object.create(null) + + // If it doesn't exist, then just mark the lack of results + if (!exists) + return cb() + + if (prefix && isAbsolute(prefix) && !this.nomount) { + var trail = /[\/\\]$/.test(prefix) + if (prefix.charAt(0) === '/') { + prefix = path.join(this.root, prefix) + } else { + prefix = path.resolve(this.root, prefix) + if (trail) + prefix += '/' + } + } + + if (process.platform === 'win32') + prefix = prefix.replace(/\\/g, '/') + + // Mark this as a match + this._emitMatch(index, prefix) + cb() +} + +// Returns either 'DIR', 'FILE', or false +Glob.prototype._stat = function (f, cb) { + var abs = this._makeAbs(f) + var needDir = f.slice(-1) === '/' + + if (f.length > this.maxLength) + return cb() + + if (!this.stat && ownProp(this.cache, abs)) { + var c = this.cache[abs] + + if (Array.isArray(c)) + c = 'DIR' + + // It exists, but maybe not how we need it + if (!needDir || c === 'DIR') + return cb(null, c) + + if (needDir && c === 'FILE') + return cb() + + // otherwise we have to stat, because maybe c=true + // if we know it exists, but not what it is. + } + + var exists + var stat = this.statCache[abs] + if (stat !== undefined) { + if (stat === false) + return cb(null, stat) + else { + var type = stat.isDirectory() ? 'DIR' : 'FILE' + if (needDir && type === 'FILE') + return cb() + else + return cb(null, type, stat) + } + } + + var self = this + var statcb = inflight('stat\0' + abs, lstatcb_) + if (statcb) + fs.lstat(abs, statcb) + + function lstatcb_ (er, lstat) { + if (lstat && lstat.isSymbolicLink()) { + // If it's a symlink, then treat it as the target, unless + // the target does not exist, then treat it as a file. + return fs.stat(abs, function (er, stat) { + if (er) + self._stat2(f, abs, null, lstat, cb) + else + self._stat2(f, abs, er, stat, cb) + }) + } else { + self._stat2(f, abs, er, lstat, cb) + } + } +} + +Glob.prototype._stat2 = function (f, abs, er, stat, cb) { + if (er) { + this.statCache[abs] = false + return cb() + } + + var needDir = f.slice(-1) === '/' + this.statCache[abs] = stat + + if (abs.slice(-1) === '/' && !stat.isDirectory()) + return cb(null, false, stat) + + var c = stat.isDirectory() ? 'DIR' : 'FILE' + this.cache[abs] = this.cache[abs] || c + + if (needDir && c !== 'DIR') + return cb() + + return cb(null, c, stat) +} + +}).call(this,require('_process')) +},{"./common.js":15,"./sync.js":17,"_process":24,"assert":9,"events":14,"fs":12,"inflight":18,"inherits":19,"minimatch":20,"once":21,"path":22,"path-is-absolute":23,"util":28}],17:[function(require,module,exports){ +(function (process){ +module.exports = globSync +globSync.GlobSync = GlobSync + +var fs = require('fs') +var minimatch = require('minimatch') +var Minimatch = minimatch.Minimatch +var Glob = require('./glob.js').Glob +var util = require('util') +var path = require('path') +var assert = require('assert') +var isAbsolute = require('path-is-absolute') +var common = require('./common.js') +var alphasort = common.alphasort +var alphasorti = common.alphasorti +var setopts = common.setopts +var ownProp = common.ownProp +var childrenIgnored = common.childrenIgnored + +function globSync (pattern, options) { + if (typeof options === 'function' || arguments.length === 3) + throw new TypeError('callback provided to sync glob\n'+ + 'See: https://github.com/isaacs/node-glob/issues/167') + + return new GlobSync(pattern, options).found +} + +function GlobSync (pattern, options) { + if (!pattern) + throw new Error('must provide pattern') + + if (typeof options === 'function' || arguments.length === 3) + throw new TypeError('callback provided to sync glob\n'+ + 'See: https://github.com/isaacs/node-glob/issues/167') + + if (!(this instanceof GlobSync)) + return new GlobSync(pattern, options) + + setopts(this, pattern, options) + + if (this.noprocess) + return this + + var n = this.minimatch.set.length + this.matches = new Array(n) + for (var i = 0; i < n; i ++) { + this._process(this.minimatch.set[i], i, false) + } + this._finish() +} + +GlobSync.prototype._finish = function () { + assert(this instanceof GlobSync) + if (this.realpath) { + var self = this + this.matches.forEach(function (matchset, index) { + var set = self.matches[index] = Object.create(null) + for (var p in matchset) { + try { + p = self._makeAbs(p) + var real = fs.realpathSync(p, self.realpathCache) + set[real] = true + } catch (er) { + if (er.syscall === 'stat') + set[self._makeAbs(p)] = true + else + throw er + } + } + }) + } + common.finish(this) +} + + +GlobSync.prototype._process = function (pattern, index, inGlobStar) { + assert(this instanceof GlobSync) + + // Get the first [n] parts of pattern that are all strings. + var n = 0 + while (typeof pattern[n] === 'string') { + n ++ + } + // now n is the index of the first one that is *not* a string. + + // See if there's anything else + var prefix + switch (n) { + // if not, then this is rather simple + case pattern.length: + this._processSimple(pattern.join('/'), index) + return + + case 0: + // pattern *starts* with some non-trivial item. + // going to readdir(cwd), but not include the prefix in matches. + prefix = null + break + + default: + // pattern has some string bits in the front. + // whatever it starts with, whether that's 'absolute' like /foo/bar, + // or 'relative' like '../baz' + prefix = pattern.slice(0, n).join('/') + break + } + + var remain = pattern.slice(n) + + // get the list of entries. + var read + if (prefix === null) + read = '.' + else if (isAbsolute(prefix) || isAbsolute(pattern.join('/'))) { + if (!prefix || !isAbsolute(prefix)) + prefix = '/' + prefix + read = prefix + } else + read = prefix + + var abs = this._makeAbs(read) + + //if ignored, skip processing + if (childrenIgnored(this, read)) + return + + var isGlobStar = remain[0] === minimatch.GLOBSTAR + if (isGlobStar) + this._processGlobStar(prefix, read, abs, remain, index, inGlobStar) + else + this._processReaddir(prefix, read, abs, remain, index, inGlobStar) +} + + +GlobSync.prototype._processReaddir = function (prefix, read, abs, remain, index, inGlobStar) { + var entries = this._readdir(abs, inGlobStar) + + // if the abs isn't a dir, then nothing can match! + if (!entries) + return + + // It will only match dot entries if it starts with a dot, or if + // dot is set. Stuff like @(.foo|.bar) isn't allowed. + var pn = remain[0] + var negate = !!this.minimatch.negate + var rawGlob = pn._glob + var dotOk = this.dot || rawGlob.charAt(0) === '.' + + var matchedEntries = [] + for (var i = 0; i < entries.length; i++) { + var e = entries[i] + if (e.charAt(0) !== '.' || dotOk) { + var m + if (negate && !prefix) { + m = !e.match(pn) + } else { + m = e.match(pn) + } + if (m) + matchedEntries.push(e) + } + } + + var len = matchedEntries.length + // If there are no matched entries, then nothing matches. + if (len === 0) + return + + // if this is the last remaining pattern bit, then no need for + // an additional stat *unless* the user has specified mark or + // stat explicitly. We know they exist, since readdir returned + // them. + + if (remain.length === 1 && !this.mark && !this.stat) { + if (!this.matches[index]) + this.matches[index] = Object.create(null) + + for (var i = 0; i < len; i ++) { + var e = matchedEntries[i] + if (prefix) { + if (prefix.slice(-1) !== '/') + e = prefix + '/' + e + else + e = prefix + e + } + + if (e.charAt(0) === '/' && !this.nomount) { + e = path.join(this.root, e) + } + this.matches[index][e] = true + } + // This was the last one, and no stats were needed + return + } + + // now test all matched entries as stand-ins for that part + // of the pattern. + remain.shift() + for (var i = 0; i < len; i ++) { + var e = matchedEntries[i] + var newPattern + if (prefix) + newPattern = [prefix, e] + else + newPattern = [e] + this._process(newPattern.concat(remain), index, inGlobStar) + } +} + + +GlobSync.prototype._emitMatch = function (index, e) { + var abs = this._makeAbs(e) + if (this.mark) + e = this._mark(e) + + if (this.matches[index][e]) + return + + if (this.nodir) { + var c = this.cache[this._makeAbs(e)] + if (c === 'DIR' || Array.isArray(c)) + return + } + + this.matches[index][e] = true + if (this.stat) + this._stat(e) +} + + +GlobSync.prototype._readdirInGlobStar = function (abs) { + // follow all symlinked directories forever + // just proceed as if this is a non-globstar situation + if (this.follow) + return this._readdir(abs, false) + + var entries + var lstat + var stat + try { + lstat = fs.lstatSync(abs) + } catch (er) { + // lstat failed, doesn't exist + return null + } + + var isSym = lstat.isSymbolicLink() + this.symlinks[abs] = isSym + + // If it's not a symlink or a dir, then it's definitely a regular file. + // don't bother doing a readdir in that case. + if (!isSym && !lstat.isDirectory()) + this.cache[abs] = 'FILE' + else + entries = this._readdir(abs, false) + + return entries +} + +GlobSync.prototype._readdir = function (abs, inGlobStar) { + var entries + + if (inGlobStar && !ownProp(this.symlinks, abs)) + return this._readdirInGlobStar(abs) + + if (ownProp(this.cache, abs)) { + var c = this.cache[abs] + if (!c || c === 'FILE') + return null + + if (Array.isArray(c)) + return c + } + + try { + return this._readdirEntries(abs, fs.readdirSync(abs)) + } catch (er) { + this._readdirError(abs, er) + return null + } +} + +GlobSync.prototype._readdirEntries = function (abs, entries) { + // if we haven't asked to stat everything, then just + // assume that everything in there exists, so we can avoid + // having to stat it a second time. + if (!this.mark && !this.stat) { + for (var i = 0; i < entries.length; i ++) { + var e = entries[i] + if (abs === '/') + e = abs + e + else + e = abs + '/' + e + this.cache[e] = true + } + } + + this.cache[abs] = entries + + // mark and cache dir-ness + return entries +} + +GlobSync.prototype._readdirError = function (f, er) { + // handle errors, and cache the information + switch (er.code) { + case 'ENOTSUP': // https://github.com/isaacs/node-glob/issues/205 + case 'ENOTDIR': // totally normal. means it *does* exist. + this.cache[this._makeAbs(f)] = 'FILE' + break + + case 'ENOENT': // not terribly unusual + case 'ELOOP': + case 'ENAMETOOLONG': + case 'UNKNOWN': + this.cache[this._makeAbs(f)] = false + break + + default: // some unusual error. Treat as failure. + this.cache[this._makeAbs(f)] = false + if (this.strict) + throw er + if (!this.silent) + console.error('glob error', er) + break + } +} + +GlobSync.prototype._processGlobStar = function (prefix, read, abs, remain, index, inGlobStar) { + + var entries = this._readdir(abs, inGlobStar) + + // no entries means not a dir, so it can never have matches + // foo.txt/** doesn't match foo.txt + if (!entries) + return + + // test without the globstar, and with every child both below + // and replacing the globstar. + var remainWithoutGlobStar = remain.slice(1) + var gspref = prefix ? [ prefix ] : [] + var noGlobStar = gspref.concat(remainWithoutGlobStar) + + // the noGlobStar pattern exits the inGlobStar state + this._process(noGlobStar, index, false) + + var len = entries.length + var isSym = this.symlinks[abs] + + // If it's a symlink, and we're in a globstar, then stop + if (isSym && inGlobStar) + return + + for (var i = 0; i < len; i++) { + var e = entries[i] + if (e.charAt(0) === '.' && !this.dot) + continue + + // these two cases enter the inGlobStar state + var instead = gspref.concat(entries[i], remainWithoutGlobStar) + this._process(instead, index, true) + + var below = gspref.concat(entries[i], remain) + this._process(below, index, true) + } +} + +GlobSync.prototype._processSimple = function (prefix, index) { + // XXX review this. Shouldn't it be doing the mounting etc + // before doing stat? kinda weird? + var exists = this._stat(prefix) + + if (!this.matches[index]) + this.matches[index] = Object.create(null) + + // If it doesn't exist, then just mark the lack of results + if (!exists) + return + + if (prefix && isAbsolute(prefix) && !this.nomount) { + var trail = /[\/\\]$/.test(prefix) + if (prefix.charAt(0) === '/') { + prefix = path.join(this.root, prefix) + } else { + prefix = path.resolve(this.root, prefix) + if (trail) + prefix += '/' + } + } + + if (process.platform === 'win32') + prefix = prefix.replace(/\\/g, '/') + + // Mark this as a match + this.matches[index][prefix] = true +} + +// Returns either 'DIR', 'FILE', or false +GlobSync.prototype._stat = function (f) { + var abs = this._makeAbs(f) + var needDir = f.slice(-1) === '/' + + if (f.length > this.maxLength) + return false + + if (!this.stat && ownProp(this.cache, abs)) { + var c = this.cache[abs] + + if (Array.isArray(c)) + c = 'DIR' + + // It exists, but maybe not how we need it + if (!needDir || c === 'DIR') + return c + + if (needDir && c === 'FILE') + return false + + // otherwise we have to stat, because maybe c=true + // if we know it exists, but not what it is. + } + + var exists + var stat = this.statCache[abs] + if (!stat) { + var lstat + try { + lstat = fs.lstatSync(abs) + } catch (er) { + return false + } + + if (lstat.isSymbolicLink()) { + try { + stat = fs.statSync(abs) + } catch (er) { + stat = lstat + } + } else { + stat = lstat + } + } + + this.statCache[abs] = stat + + var c = stat.isDirectory() ? 'DIR' : 'FILE' + this.cache[abs] = this.cache[abs] || c + + if (needDir && c !== 'DIR') + return false + + return c +} + +GlobSync.prototype._mark = function (p) { + return common.mark(this, p) +} + +GlobSync.prototype._makeAbs = function (f) { + return common.makeAbs(this, f) +} + +}).call(this,require('_process')) +},{"./common.js":15,"./glob.js":16,"_process":24,"assert":9,"fs":12,"minimatch":20,"path":22,"path-is-absolute":23,"util":28}],18:[function(require,module,exports){ +(function (process){ +var wrappy = require('wrappy') +var reqs = Object.create(null) +var once = require('once') + +module.exports = wrappy(inflight) + +function inflight (key, cb) { + if (reqs[key]) { + reqs[key].push(cb) + return null + } else { + reqs[key] = [cb] + return makeres(key) + } +} + +function makeres (key) { + return once(function RES () { + var cbs = reqs[key] + var len = cbs.length + var args = slice(arguments) + + // XXX It's somewhat ambiguous whether a new callback added in this + // pass should be queued for later execution if something in the + // list of callbacks throws, or if it should just be discarded. + // However, it's such an edge case that it hardly matters, and either + // choice is likely as surprising as the other. + // As it happens, we do go ahead and schedule it for later execution. + try { + for (var i = 0; i < len; i++) { + cbs[i].apply(null, args) + } + } finally { + if (cbs.length > len) { + // added more in the interim. + // de-zalgo, just in case, but don't call again. + cbs.splice(0, len) + process.nextTick(function () { + RES.apply(null, args) + }) + } else { + delete reqs[key] + } + } + }) +} + +function slice (args) { + var length = args.length + var array = [] + + for (var i = 0; i < length; i++) array[i] = args[i] + return array +} + +}).call(this,require('_process')) +},{"_process":24,"once":21,"wrappy":29}],19:[function(require,module,exports){ +if (typeof Object.create === 'function') { + // implementation from standard node.js 'util' module + module.exports = function inherits(ctor, superCtor) { + ctor.super_ = superCtor + ctor.prototype = Object.create(superCtor.prototype, { + constructor: { + value: ctor, + enumerable: false, + writable: true, + configurable: true + } + }); + }; +} else { + // old school shim for old browsers + module.exports = function inherits(ctor, superCtor) { + ctor.super_ = superCtor + var TempCtor = function () {} + TempCtor.prototype = superCtor.prototype + ctor.prototype = new TempCtor() + ctor.prototype.constructor = ctor + } +} + +},{}],20:[function(require,module,exports){ +module.exports = minimatch +minimatch.Minimatch = Minimatch + +var path = { sep: '/' } +try { + path = require('path') +} catch (er) {} + +var GLOBSTAR = minimatch.GLOBSTAR = Minimatch.GLOBSTAR = {} +var expand = require('brace-expansion') + +var plTypes = { + '!': { open: '(?:(?!(?:', close: '))[^/]*?)'}, + '?': { open: '(?:', close: ')?' }, + '+': { open: '(?:', close: ')+' }, + '*': { open: '(?:', close: ')*' }, + '@': { open: '(?:', close: ')' } +} + +// any single thing other than / +// don't need to escape / when using new RegExp() +var qmark = '[^/]' + +// * => any number of characters +var star = qmark + '*?' + +// ** when dots are allowed. Anything goes, except .. and . +// not (^ or / followed by one or two dots followed by $ or /), +// followed by anything, any number of times. +var twoStarDot = '(?:(?!(?:\\\/|^)(?:\\.{1,2})($|\\\/)).)*?' + +// not a ^ or / followed by a dot, +// followed by anything, any number of times. +var twoStarNoDot = '(?:(?!(?:\\\/|^)\\.).)*?' + +// characters that need to be escaped in RegExp. +var reSpecials = charSet('().*{}+?[]^$\\!') + +// "abc" -> { a:true, b:true, c:true } +function charSet (s) { + return s.split('').reduce(function (set, c) { + set[c] = true + return set + }, {}) +} + +// normalizes slashes. +var slashSplit = /\/+/ + +minimatch.filter = filter +function filter (pattern, options) { + options = options || {} + return function (p, i, list) { + return minimatch(p, pattern, options) + } +} + +function ext (a, b) { + a = a || {} + b = b || {} + var t = {} + Object.keys(b).forEach(function (k) { + t[k] = b[k] + }) + Object.keys(a).forEach(function (k) { + t[k] = a[k] + }) + return t +} + +minimatch.defaults = function (def) { + if (!def || !Object.keys(def).length) return minimatch + + var orig = minimatch + + var m = function minimatch (p, pattern, options) { + return orig.minimatch(p, pattern, ext(def, options)) + } + + m.Minimatch = function Minimatch (pattern, options) { + return new orig.Minimatch(pattern, ext(def, options)) + } + + return m +} + +Minimatch.defaults = function (def) { + if (!def || !Object.keys(def).length) return Minimatch + return minimatch.defaults(def).Minimatch +} + +function minimatch (p, pattern, options) { + if (typeof pattern !== 'string') { + throw new TypeError('glob pattern string required') + } + + if (!options) options = {} + + // shortcut: comments match nothing. + if (!options.nocomment && pattern.charAt(0) === '#') { + return false + } + + // "" only matches "" + if (pattern.trim() === '') return p === '' + + return new Minimatch(pattern, options).match(p) +} + +function Minimatch (pattern, options) { + if (!(this instanceof Minimatch)) { + return new Minimatch(pattern, options) + } + + if (typeof pattern !== 'string') { + throw new TypeError('glob pattern string required') + } + + if (!options) options = {} + pattern = pattern.trim() + + // windows support: need to use /, not \ + if (path.sep !== '/') { + pattern = pattern.split(path.sep).join('/') + } + + this.options = options + this.set = [] + this.pattern = pattern + this.regexp = null + this.negate = false + this.comment = false + this.empty = false + + // make the set of regexps etc. + this.make() +} + +Minimatch.prototype.debug = function () {} + +Minimatch.prototype.make = make +function make () { + // don't do it more than once. + if (this._made) return + + var pattern = this.pattern + var options = this.options + + // empty patterns and comments match nothing. + if (!options.nocomment && pattern.charAt(0) === '#') { + this.comment = true + return + } + if (!pattern) { + this.empty = true + return + } + + // step 1: figure out negation, etc. + this.parseNegate() + + // step 2: expand braces + var set = this.globSet = this.braceExpand() + + if (options.debug) this.debug = console.error + + this.debug(this.pattern, set) + + // step 3: now we have a set, so turn each one into a series of path-portion + // matching patterns. + // These will be regexps, except in the case of "**", which is + // set to the GLOBSTAR object for globstar behavior, + // and will not contain any / characters + set = this.globParts = set.map(function (s) { + return s.split(slashSplit) + }) + + this.debug(this.pattern, set) + + // glob --> regexps + set = set.map(function (s, si, set) { + return s.map(this.parse, this) + }, this) + + this.debug(this.pattern, set) + + // filter out everything that didn't compile properly. + set = set.filter(function (s) { + return s.indexOf(false) === -1 + }) + + this.debug(this.pattern, set) + + this.set = set +} + +Minimatch.prototype.parseNegate = parseNegate +function parseNegate () { + var pattern = this.pattern + var negate = false + var options = this.options + var negateOffset = 0 + + if (options.nonegate) return + + for (var i = 0, l = pattern.length + ; i < l && pattern.charAt(i) === '!' + ; i++) { + negate = !negate + negateOffset++ + } + + if (negateOffset) this.pattern = pattern.substr(negateOffset) + this.negate = negate +} + +// Brace expansion: +// a{b,c}d -> abd acd +// a{b,}c -> abc ac +// a{0..3}d -> a0d a1d a2d a3d +// a{b,c{d,e}f}g -> abg acdfg acefg +// a{b,c}d{e,f}g -> abdeg acdeg abdeg abdfg +// +// Invalid sets are not expanded. +// a{2..}b -> a{2..}b +// a{b}c -> a{b}c +minimatch.braceExpand = function (pattern, options) { + return braceExpand(pattern, options) +} + +Minimatch.prototype.braceExpand = braceExpand + +function braceExpand (pattern, options) { + if (!options) { + if (this instanceof Minimatch) { + options = this.options + } else { + options = {} + } + } + + pattern = typeof pattern === 'undefined' + ? this.pattern : pattern + + if (typeof pattern === 'undefined') { + throw new TypeError('undefined pattern') + } + + if (options.nobrace || + !pattern.match(/\{.*\}/)) { + // shortcut. no need to expand. + return [pattern] + } + + return expand(pattern) +} + +// parse a component of the expanded set. +// At this point, no pattern may contain "/" in it +// so we're going to return a 2d array, where each entry is the full +// pattern, split on '/', and then turned into a regular expression. +// A regexp is made at the end which joins each array with an +// escaped /, and another full one which joins each regexp with |. +// +// Following the lead of Bash 4.1, note that "**" only has special meaning +// when it is the *only* thing in a path portion. Otherwise, any series +// of * is equivalent to a single *. Globstar behavior is enabled by +// default, and can be disabled by setting options.noglobstar. +Minimatch.prototype.parse = parse +var SUBPARSE = {} +function parse (pattern, isSub) { + if (pattern.length > 1024 * 64) { + throw new TypeError('pattern is too long') + } + + var options = this.options + + // shortcuts + if (!options.noglobstar && pattern === '**') return GLOBSTAR + if (pattern === '') return '' + + var re = '' + var hasMagic = !!options.nocase + var escaping = false + // ? => one single character + var patternListStack = [] + var negativeLists = [] + var stateChar + var inClass = false + var reClassStart = -1 + var classStart = -1 + // . and .. never match anything that doesn't start with ., + // even when options.dot is set. + var patternStart = pattern.charAt(0) === '.' ? '' // anything + // not (start or / followed by . or .. followed by / or end) + : options.dot ? '(?!(?:^|\\\/)\\.{1,2}(?:$|\\\/))' + : '(?!\\.)' + var self = this + + function clearStateChar () { + if (stateChar) { + // we had some state-tracking character + // that wasn't consumed by this pass. + switch (stateChar) { + case '*': + re += star + hasMagic = true + break + case '?': + re += qmark + hasMagic = true + break + default: + re += '\\' + stateChar + break + } + self.debug('clearStateChar %j %j', stateChar, re) + stateChar = false + } + } + + for (var i = 0, len = pattern.length, c + ; (i < len) && (c = pattern.charAt(i)) + ; i++) { + this.debug('%s\t%s %s %j', pattern, i, re, c) + + // skip over any that are escaped. + if (escaping && reSpecials[c]) { + re += '\\' + c + escaping = false + continue + } + + switch (c) { + case '/': + // completely not allowed, even escaped. + // Should already be path-split by now. + return false + + case '\\': + clearStateChar() + escaping = true + continue + + // the various stateChar values + // for the "extglob" stuff. + case '?': + case '*': + case '+': + case '@': + case '!': + this.debug('%s\t%s %s %j <-- stateChar', pattern, i, re, c) + + // all of those are literals inside a class, except that + // the glob [!a] means [^a] in regexp + if (inClass) { + this.debug(' in class') + if (c === '!' && i === classStart + 1) c = '^' + re += c + continue + } + + // if we already have a stateChar, then it means + // that there was something like ** or +? in there. + // Handle the stateChar, then proceed with this one. + self.debug('call clearStateChar %j', stateChar) + clearStateChar() + stateChar = c + // if extglob is disabled, then +(asdf|foo) isn't a thing. + // just clear the statechar *now*, rather than even diving into + // the patternList stuff. + if (options.noext) clearStateChar() + continue + + case '(': + if (inClass) { + re += '(' + continue + } + + if (!stateChar) { + re += '\\(' + continue + } + + patternListStack.push({ + type: stateChar, + start: i - 1, + reStart: re.length, + open: plTypes[stateChar].open, + close: plTypes[stateChar].close + }) + // negation is (?:(?!js)[^/]*) + re += stateChar === '!' ? '(?:(?!(?:' : '(?:' + this.debug('plType %j %j', stateChar, re) + stateChar = false + continue + + case ')': + if (inClass || !patternListStack.length) { + re += '\\)' + continue + } + + clearStateChar() + hasMagic = true + var pl = patternListStack.pop() + // negation is (?:(?!js)[^/]*) + // The others are (?:) + re += pl.close + if (pl.type === '!') { + negativeLists.push(pl) + } + pl.reEnd = re.length + continue + + case '|': + if (inClass || !patternListStack.length || escaping) { + re += '\\|' + escaping = false + continue + } + + clearStateChar() + re += '|' + continue + + // these are mostly the same in regexp and glob + case '[': + // swallow any state-tracking char before the [ + clearStateChar() + + if (inClass) { + re += '\\' + c + continue + } + + inClass = true + classStart = i + reClassStart = re.length + re += c + continue + + case ']': + // a right bracket shall lose its special + // meaning and represent itself in + // a bracket expression if it occurs + // first in the list. -- POSIX.2 2.8.3.2 + if (i === classStart + 1 || !inClass) { + re += '\\' + c + escaping = false + continue + } + + // handle the case where we left a class open. + // "[z-a]" is valid, equivalent to "\[z-a\]" + if (inClass) { + // split where the last [ was, make sure we don't have + // an invalid re. if so, re-walk the contents of the + // would-be class to re-translate any characters that + // were passed through as-is + // TODO: It would probably be faster to determine this + // without a try/catch and a new RegExp, but it's tricky + // to do safely. For now, this is safe and works. + var cs = pattern.substring(classStart + 1, i) + try { + RegExp('[' + cs + ']') + } catch (er) { + // not a valid class! + var sp = this.parse(cs, SUBPARSE) + re = re.substr(0, reClassStart) + '\\[' + sp[0] + '\\]' + hasMagic = hasMagic || sp[1] + inClass = false + continue + } + } + + // finish up the class. + hasMagic = true + inClass = false + re += c + continue + + default: + // swallow any state char that wasn't consumed + clearStateChar() + + if (escaping) { + // no need + escaping = false + } else if (reSpecials[c] + && !(c === '^' && inClass)) { + re += '\\' + } + + re += c + + } // switch + } // for + + // handle the case where we left a class open. + // "[abc" is valid, equivalent to "\[abc" + if (inClass) { + // split where the last [ was, and escape it + // this is a huge pita. We now have to re-walk + // the contents of the would-be class to re-translate + // any characters that were passed through as-is + cs = pattern.substr(classStart + 1) + sp = this.parse(cs, SUBPARSE) + re = re.substr(0, reClassStart) + '\\[' + sp[0] + hasMagic = hasMagic || sp[1] + } + + // handle the case where we had a +( thing at the *end* + // of the pattern. + // each pattern list stack adds 3 chars, and we need to go through + // and escape any | chars that were passed through as-is for the regexp. + // Go through and escape them, taking care not to double-escape any + // | chars that were already escaped. + for (pl = patternListStack.pop(); pl; pl = patternListStack.pop()) { + var tail = re.slice(pl.reStart + pl.open.length) + this.debug('setting tail', re, pl) + // maybe some even number of \, then maybe 1 \, followed by a | + tail = tail.replace(/((?:\\{2}){0,64})(\\?)\|/g, function (_, $1, $2) { + if (!$2) { + // the | isn't already escaped, so escape it. + $2 = '\\' + } + + // need to escape all those slashes *again*, without escaping the + // one that we need for escaping the | character. As it works out, + // escaping an even number of slashes can be done by simply repeating + // it exactly after itself. That's why this trick works. + // + // I am sorry that you have to see this. + return $1 + $1 + $2 + '|' + }) + + this.debug('tail=%j\n %s', tail, tail, pl, re) + var t = pl.type === '*' ? star + : pl.type === '?' ? qmark + : '\\' + pl.type + + hasMagic = true + re = re.slice(0, pl.reStart) + t + '\\(' + tail + } + + // handle trailing things that only matter at the very end. + clearStateChar() + if (escaping) { + // trailing \\ + re += '\\\\' + } + + // only need to apply the nodot start if the re starts with + // something that could conceivably capture a dot + var addPatternStart = false + switch (re.charAt(0)) { + case '.': + case '[': + case '(': addPatternStart = true + } + + // Hack to work around lack of negative lookbehind in JS + // A pattern like: *.!(x).!(y|z) needs to ensure that a name + // like 'a.xyz.yz' doesn't match. So, the first negative + // lookahead, has to look ALL the way ahead, to the end of + // the pattern. + for (var n = negativeLists.length - 1; n > -1; n--) { + var nl = negativeLists[n] + + var nlBefore = re.slice(0, nl.reStart) + var nlFirst = re.slice(nl.reStart, nl.reEnd - 8) + var nlLast = re.slice(nl.reEnd - 8, nl.reEnd) + var nlAfter = re.slice(nl.reEnd) + + nlLast += nlAfter + + // Handle nested stuff like *(*.js|!(*.json)), where open parens + // mean that we should *not* include the ) in the bit that is considered + // "after" the negated section. + var openParensBefore = nlBefore.split('(').length - 1 + var cleanAfter = nlAfter + for (i = 0; i < openParensBefore; i++) { + cleanAfter = cleanAfter.replace(/\)[+*?]?/, '') + } + nlAfter = cleanAfter + + var dollar = '' + if (nlAfter === '' && isSub !== SUBPARSE) { + dollar = '$' + } + var newRe = nlBefore + nlFirst + nlAfter + dollar + nlLast + re = newRe + } + + // if the re is not "" at this point, then we need to make sure + // it doesn't match against an empty path part. + // Otherwise a/* will match a/, which it should not. + if (re !== '' && hasMagic) { + re = '(?=.)' + re + } + + if (addPatternStart) { + re = patternStart + re + } + + // parsing just a piece of a larger pattern. + if (isSub === SUBPARSE) { + return [re, hasMagic] + } + + // skip the regexp for non-magical patterns + // unescape anything in it, though, so that it'll be + // an exact match against a file etc. + if (!hasMagic) { + return globUnescape(pattern) + } + + var flags = options.nocase ? 'i' : '' + try { + var regExp = new RegExp('^' + re + '$', flags) + } catch (er) { + // If it was an invalid regular expression, then it can't match + // anything. This trick looks for a character after the end of + // the string, which is of course impossible, except in multi-line + // mode, but it's not a /m regex. + return new RegExp('$.') + } + + regExp._glob = pattern + regExp._src = re + + return regExp +} + +minimatch.makeRe = function (pattern, options) { + return new Minimatch(pattern, options || {}).makeRe() +} + +Minimatch.prototype.makeRe = makeRe +function makeRe () { + if (this.regexp || this.regexp === false) return this.regexp + + // at this point, this.set is a 2d array of partial + // pattern strings, or "**". + // + // It's better to use .match(). This function shouldn't + // be used, really, but it's pretty convenient sometimes, + // when you just want to work with a regex. + var set = this.set + + if (!set.length) { + this.regexp = false + return this.regexp + } + var options = this.options + + var twoStar = options.noglobstar ? star + : options.dot ? twoStarDot + : twoStarNoDot + var flags = options.nocase ? 'i' : '' + + var re = set.map(function (pattern) { + return pattern.map(function (p) { + return (p === GLOBSTAR) ? twoStar + : (typeof p === 'string') ? regExpEscape(p) + : p._src + }).join('\\\/') + }).join('|') + + // must match entire pattern + // ending in a * or ** will make it less strict. + re = '^(?:' + re + ')$' + + // can match anything, as long as it's not this. + if (this.negate) re = '^(?!' + re + ').*$' + + try { + this.regexp = new RegExp(re, flags) + } catch (ex) { + this.regexp = false + } + return this.regexp +} + +minimatch.match = function (list, pattern, options) { + options = options || {} + var mm = new Minimatch(pattern, options) + list = list.filter(function (f) { + return mm.match(f) + }) + if (mm.options.nonull && !list.length) { + list.push(pattern) + } + return list +} + +Minimatch.prototype.match = match +function match (f, partial) { + this.debug('match', f, this.pattern) + // short-circuit in the case of busted things. + // comments, etc. + if (this.comment) return false + if (this.empty) return f === '' + + if (f === '/' && partial) return true + + var options = this.options + + // windows: need to use /, not \ + if (path.sep !== '/') { + f = f.split(path.sep).join('/') + } + + // treat the test path as a set of pathparts. + f = f.split(slashSplit) + this.debug(this.pattern, 'split', f) + + // just ONE of the pattern sets in this.set needs to match + // in order for it to be valid. If negating, then just one + // match means that we have failed. + // Either way, return on the first hit. + + var set = this.set + this.debug(this.pattern, 'set', set) + + // Find the basename of the path by looking for the last non-empty segment + var filename + var i + for (i = f.length - 1; i >= 0; i--) { + filename = f[i] + if (filename) break + } + + for (i = 0; i < set.length; i++) { + var pattern = set[i] + var file = f + if (options.matchBase && pattern.length === 1) { + file = [filename] + } + var hit = this.matchOne(file, pattern, partial) + if (hit) { + if (options.flipNegate) return true + return !this.negate + } + } + + // didn't get any hits. this is success if it's a negative + // pattern, failure otherwise. + if (options.flipNegate) return false + return this.negate +} + +// set partial to true to test if, for example, +// "/a/b" matches the start of "/*/b/*/d" +// Partial means, if you run out of file before you run +// out of pattern, then that's fine, as long as all +// the parts match. +Minimatch.prototype.matchOne = function (file, pattern, partial) { + var options = this.options + + this.debug('matchOne', + { 'this': this, file: file, pattern: pattern }) + + this.debug('matchOne', file.length, pattern.length) + + for (var fi = 0, + pi = 0, + fl = file.length, + pl = pattern.length + ; (fi < fl) && (pi < pl) + ; fi++, pi++) { + this.debug('matchOne loop') + var p = pattern[pi] + var f = file[fi] + + this.debug(pattern, p, f) + + // should be impossible. + // some invalid regexp stuff in the set. + if (p === false) return false + + if (p === GLOBSTAR) { + this.debug('GLOBSTAR', [pattern, p, f]) + + // "**" + // a/**/b/**/c would match the following: + // a/b/x/y/z/c + // a/x/y/z/b/c + // a/b/x/b/x/c + // a/b/c + // To do this, take the rest of the pattern after + // the **, and see if it would match the file remainder. + // If so, return success. + // If not, the ** "swallows" a segment, and try again. + // This is recursively awful. + // + // a/**/b/**/c matching a/b/x/y/z/c + // - a matches a + // - doublestar + // - matchOne(b/x/y/z/c, b/**/c) + // - b matches b + // - doublestar + // - matchOne(x/y/z/c, c) -> no + // - matchOne(y/z/c, c) -> no + // - matchOne(z/c, c) -> no + // - matchOne(c, c) yes, hit + var fr = fi + var pr = pi + 1 + if (pr === pl) { + this.debug('** at the end') + // a ** at the end will just swallow the rest. + // We have found a match. + // however, it will not swallow /.x, unless + // options.dot is set. + // . and .. are *never* matched by **, for explosively + // exponential reasons. + for (; fi < fl; fi++) { + if (file[fi] === '.' || file[fi] === '..' || + (!options.dot && file[fi].charAt(0) === '.')) return false + } + return true + } + + // ok, let's see if we can swallow whatever we can. + while (fr < fl) { + var swallowee = file[fr] + + this.debug('\nglobstar while', file, fr, pattern, pr, swallowee) + + // XXX remove this slice. Just pass the start index. + if (this.matchOne(file.slice(fr), pattern.slice(pr), partial)) { + this.debug('globstar found match!', fr, fl, swallowee) + // found a match. + return true + } else { + // can't swallow "." or ".." ever. + // can only swallow ".foo" when explicitly asked. + if (swallowee === '.' || swallowee === '..' || + (!options.dot && swallowee.charAt(0) === '.')) { + this.debug('dot detected!', file, fr, pattern, pr) + break + } + + // ** swallows a segment, and continue. + this.debug('globstar swallow a segment, and continue') + fr++ + } + } + + // no match was found. + // However, in partial mode, we can't say this is necessarily over. + // If there's more *pattern* left, then + if (partial) { + // ran out of file + this.debug('\n>>> no match, partial?', file, fr, pattern, pr) + if (fr === fl) return true + } + return false + } + + // something other than ** + // non-magic patterns just have to match exactly + // patterns with magic have been turned into regexps. + var hit + if (typeof p === 'string') { + if (options.nocase) { + hit = f.toLowerCase() === p.toLowerCase() + } else { + hit = f === p + } + this.debug('string match', p, f, hit) + } else { + hit = f.match(p) + this.debug('pattern match', p, f, hit) + } + + if (!hit) return false + } + + // Note: ending in / means that we'll get a final "" + // at the end of the pattern. This can only match a + // corresponding "" at the end of the file. + // If the file ends in /, then it can only match a + // a pattern that ends in /, unless the pattern just + // doesn't have any more for it. But, a/b/ should *not* + // match "a/b/*", even though "" matches against the + // [^/]*? pattern, except in partial mode, where it might + // simply not be reached yet. + // However, a/b/ should still satisfy a/* + + // now either we fell off the end of the pattern, or we're done. + if (fi === fl && pi === pl) { + // ran out of pattern and filename at the same time. + // an exact hit! + return true + } else if (fi === fl) { + // ran out of file, but still had pattern left. + // this is ok if we're doing the match as part of + // a glob fs traversal. + return partial + } else if (pi === pl) { + // ran out of pattern, still have file left. + // this is only acceptable if we're on the very last + // empty segment of a file with a trailing slash. + // a/* should match a/b/ + var emptyFileEnd = (fi === fl - 1) && (file[fi] === '') + return emptyFileEnd + } + + // should be unreachable. + throw new Error('wtf?') +} + +// replace stuff like \* with * +function globUnescape (s) { + return s.replace(/\\(.)/g, '$1') +} + +function regExpEscape (s) { + return s.replace(/[-[\]{}()*+?.,\\^$|#\s]/g, '\\$&') +} + +},{"brace-expansion":11,"path":22}],21:[function(require,module,exports){ +var wrappy = require('wrappy') +module.exports = wrappy(once) +module.exports.strict = wrappy(onceStrict) + +once.proto = once(function () { + Object.defineProperty(Function.prototype, 'once', { + value: function () { + return once(this) + }, + configurable: true + }) + + Object.defineProperty(Function.prototype, 'onceStrict', { + value: function () { + return onceStrict(this) + }, + configurable: true + }) +}) + +function once (fn) { + var f = function () { + if (f.called) return f.value + f.called = true + return f.value = fn.apply(this, arguments) + } + f.called = false + return f +} + +function onceStrict (fn) { + var f = function () { + if (f.called) + throw new Error(f.onceError) + f.called = true + return f.value = fn.apply(this, arguments) + } + var name = fn.name || 'Function wrapped with `once`' + f.onceError = name + " shouldn't be called more than once" + f.called = false + return f +} + +},{"wrappy":29}],22:[function(require,module,exports){ +(function (process){ +// Copyright Joyent, Inc. and other Node contributors. +// +// Permission is hereby granted, free of charge, to any person obtaining a +// copy of this software and associated documentation files (the +// "Software"), to deal in the Software without restriction, including +// without limitation the rights to use, copy, modify, merge, publish, +// distribute, sublicense, and/or sell copies of the Software, and to permit +// persons to whom the Software is furnished to do so, subject to the +// following conditions: +// +// The above copyright notice and this permission notice shall be included +// in all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS +// OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF +// MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN +// NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, +// DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR +// OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE +// USE OR OTHER DEALINGS IN THE SOFTWARE. + +// resolves . and .. elements in a path array with directory names there +// must be no slashes, empty elements, or device names (c:\) in the array +// (so also no leading and trailing slashes - it does not distinguish +// relative and absolute paths) +function normalizeArray(parts, allowAboveRoot) { + // if the path tries to go above the root, `up` ends up > 0 + var up = 0; + for (var i = parts.length - 1; i >= 0; i--) { + var last = parts[i]; + if (last === '.') { + parts.splice(i, 1); + } else if (last === '..') { + parts.splice(i, 1); + up++; + } else if (up) { + parts.splice(i, 1); + up--; + } + } + + // if the path is allowed to go above the root, restore leading ..s + if (allowAboveRoot) { + for (; up--; up) { + parts.unshift('..'); + } + } + + return parts; +} + +// Split a filename into [root, dir, basename, ext], unix version +// 'root' is just a slash, or nothing. +var splitPathRe = + /^(\/?|)([\s\S]*?)((?:\.{1,2}|[^\/]+?|)(\.[^.\/]*|))(?:[\/]*)$/; +var splitPath = function(filename) { + return splitPathRe.exec(filename).slice(1); +}; + +// path.resolve([from ...], to) +// posix version +exports.resolve = function() { + var resolvedPath = '', + resolvedAbsolute = false; + + for (var i = arguments.length - 1; i >= -1 && !resolvedAbsolute; i--) { + var path = (i >= 0) ? arguments[i] : process.cwd(); + + // Skip empty and invalid entries + if (typeof path !== 'string') { + throw new TypeError('Arguments to path.resolve must be strings'); + } else if (!path) { + continue; + } + + resolvedPath = path + '/' + resolvedPath; + resolvedAbsolute = path.charAt(0) === '/'; + } + + // At this point the path should be resolved to a full absolute path, but + // handle relative paths to be safe (might happen when process.cwd() fails) + + // Normalize the path + resolvedPath = normalizeArray(filter(resolvedPath.split('/'), function(p) { + return !!p; + }), !resolvedAbsolute).join('/'); + + return ((resolvedAbsolute ? '/' : '') + resolvedPath) || '.'; +}; + +// path.normalize(path) +// posix version +exports.normalize = function(path) { + var isAbsolute = exports.isAbsolute(path), + trailingSlash = substr(path, -1) === '/'; + + // Normalize the path + path = normalizeArray(filter(path.split('/'), function(p) { + return !!p; + }), !isAbsolute).join('/'); + + if (!path && !isAbsolute) { + path = '.'; + } + if (path && trailingSlash) { + path += '/'; + } + + return (isAbsolute ? '/' : '') + path; +}; + +// posix version +exports.isAbsolute = function(path) { + return path.charAt(0) === '/'; +}; + +// posix version +exports.join = function() { + var paths = Array.prototype.slice.call(arguments, 0); + return exports.normalize(filter(paths, function(p, index) { + if (typeof p !== 'string') { + throw new TypeError('Arguments to path.join must be strings'); + } + return p; + }).join('/')); +}; + + +// path.relative(from, to) +// posix version +exports.relative = function(from, to) { + from = exports.resolve(from).substr(1); + to = exports.resolve(to).substr(1); + + function trim(arr) { + var start = 0; + for (; start < arr.length; start++) { + if (arr[start] !== '') break; + } + + var end = arr.length - 1; + for (; end >= 0; end--) { + if (arr[end] !== '') break; + } + + if (start > end) return []; + return arr.slice(start, end - start + 1); + } + + var fromParts = trim(from.split('/')); + var toParts = trim(to.split('/')); + + var length = Math.min(fromParts.length, toParts.length); + var samePartsLength = length; + for (var i = 0; i < length; i++) { + if (fromParts[i] !== toParts[i]) { + samePartsLength = i; + break; + } + } + + var outputParts = []; + for (var i = samePartsLength; i < fromParts.length; i++) { + outputParts.push('..'); + } + + outputParts = outputParts.concat(toParts.slice(samePartsLength)); + + return outputParts.join('/'); +}; + +exports.sep = '/'; +exports.delimiter = ':'; + +exports.dirname = function(path) { + var result = splitPath(path), + root = result[0], + dir = result[1]; + + if (!root && !dir) { + // No dirname whatsoever + return '.'; + } + + if (dir) { + // It has a dirname, strip trailing slash + dir = dir.substr(0, dir.length - 1); + } + + return root + dir; +}; + + +exports.basename = function(path, ext) { + var f = splitPath(path)[2]; + // TODO: make this comparison case-insensitive on windows? + if (ext && f.substr(-1 * ext.length) === ext) { + f = f.substr(0, f.length - ext.length); + } + return f; +}; + + +exports.extname = function(path) { + return splitPath(path)[3]; +}; + +function filter (xs, f) { + if (xs.filter) return xs.filter(f); + var res = []; + for (var i = 0; i < xs.length; i++) { + if (f(xs[i], i, xs)) res.push(xs[i]); + } + return res; +} + +// String.prototype.substr - negative index don't work in IE8 +var substr = 'ab'.substr(-1) === 'b' + ? function (str, start, len) { return str.substr(start, len) } + : function (str, start, len) { + if (start < 0) start = str.length + start; + return str.substr(start, len); + } +; + +}).call(this,require('_process')) +},{"_process":24}],23:[function(require,module,exports){ +(function (process){ +'use strict'; + +function posix(path) { + return path.charAt(0) === '/'; +} + +function win32(path) { + // https://github.com/nodejs/node/blob/b3fcc245fb25539909ef1d5eaa01dbf92e168633/lib/path.js#L56 + var splitDeviceRe = /^([a-zA-Z]:|[\\\/]{2}[^\\\/]+[\\\/]+[^\\\/]+)?([\\\/])?([\s\S]*?)$/; + var result = splitDeviceRe.exec(path); + var device = result[1] || ''; + var isUnc = Boolean(device && device.charAt(1) !== ':'); + + // UNC paths are always absolute + return Boolean(result[2] || isUnc); +} + +module.exports = process.platform === 'win32' ? win32 : posix; +module.exports.posix = posix; +module.exports.win32 = win32; + +}).call(this,require('_process')) +},{"_process":24}],24:[function(require,module,exports){ +// shim for using process in browser +var process = module.exports = {}; + +// cached from whatever global is present so that test runners that stub it +// don't break things. But we need to wrap it in a try catch in case it is +// wrapped in strict mode code which doesn't define any globals. It's inside a +// function because try/catches deoptimize in certain engines. + +var cachedSetTimeout; +var cachedClearTimeout; + +function defaultSetTimout() { + throw new Error('setTimeout has not been defined'); +} +function defaultClearTimeout () { + throw new Error('clearTimeout has not been defined'); +} +(function () { + try { + if (typeof setTimeout === 'function') { + cachedSetTimeout = setTimeout; + } else { + cachedSetTimeout = defaultSetTimout; + } + } catch (e) { + cachedSetTimeout = defaultSetTimout; + } + try { + if (typeof clearTimeout === 'function') { + cachedClearTimeout = clearTimeout; + } else { + cachedClearTimeout = defaultClearTimeout; + } + } catch (e) { + cachedClearTimeout = defaultClearTimeout; + } +} ()) +function runTimeout(fun) { + if (cachedSetTimeout === setTimeout) { + //normal enviroments in sane situations + return setTimeout(fun, 0); + } + // if setTimeout wasn't available but was latter defined + if ((cachedSetTimeout === defaultSetTimout || !cachedSetTimeout) && setTimeout) { + cachedSetTimeout = setTimeout; + return setTimeout(fun, 0); + } + try { + // when when somebody has screwed with setTimeout but no I.E. maddness + return cachedSetTimeout(fun, 0); + } catch(e){ + try { + // When we are in I.E. but the script has been evaled so I.E. doesn't trust the global object when called normally + return cachedSetTimeout.call(null, fun, 0); + } catch(e){ + // same as above but when it's a version of I.E. that must have the global object for 'this', hopfully our context correct otherwise it will throw a global error + return cachedSetTimeout.call(this, fun, 0); + } + } + + +} +function runClearTimeout(marker) { + if (cachedClearTimeout === clearTimeout) { + //normal enviroments in sane situations + return clearTimeout(marker); + } + // if clearTimeout wasn't available but was latter defined + if ((cachedClearTimeout === defaultClearTimeout || !cachedClearTimeout) && clearTimeout) { + cachedClearTimeout = clearTimeout; + return clearTimeout(marker); + } + try { + // when when somebody has screwed with setTimeout but no I.E. maddness + return cachedClearTimeout(marker); + } catch (e){ + try { + // When we are in I.E. but the script has been evaled so I.E. doesn't trust the global object when called normally + return cachedClearTimeout.call(null, marker); + } catch (e){ + // same as above but when it's a version of I.E. that must have the global object for 'this', hopfully our context correct otherwise it will throw a global error. + // Some versions of I.E. have different rules for clearTimeout vs setTimeout + return cachedClearTimeout.call(this, marker); + } + } + + + +} +var queue = []; +var draining = false; +var currentQueue; +var queueIndex = -1; + +function cleanUpNextTick() { + if (!draining || !currentQueue) { + return; + } + draining = false; + if (currentQueue.length) { + queue = currentQueue.concat(queue); + } else { + queueIndex = -1; + } + if (queue.length) { + drainQueue(); + } +} + +function drainQueue() { + if (draining) { + return; + } + var timeout = runTimeout(cleanUpNextTick); + draining = true; + + var len = queue.length; + while(len) { + currentQueue = queue; + queue = []; + while (++queueIndex < len) { + if (currentQueue) { + currentQueue[queueIndex].run(); + } + } + queueIndex = -1; + len = queue.length; + } + currentQueue = null; + draining = false; + runClearTimeout(timeout); +} + +process.nextTick = function (fun) { + var args = new Array(arguments.length - 1); + if (arguments.length > 1) { + for (var i = 1; i < arguments.length; i++) { + args[i - 1] = arguments[i]; + } + } + queue.push(new Item(fun, args)); + if (queue.length === 1 && !draining) { + runTimeout(drainQueue); + } +}; + +// v8 likes predictible objects +function Item(fun, array) { + this.fun = fun; + this.array = array; +} +Item.prototype.run = function () { + this.fun.apply(null, this.array); +}; +process.title = 'browser'; +process.browser = true; +process.env = {}; +process.argv = []; +process.version = ''; // empty string to avoid regexp issues +process.versions = {}; + +function noop() {} + +process.on = noop; +process.addListener = noop; +process.once = noop; +process.off = noop; +process.removeListener = noop; +process.removeAllListeners = noop; +process.emit = noop; +process.prependListener = noop; +process.prependOnceListener = noop; + +process.listeners = function (name) { return [] } + +process.binding = function (name) { + throw new Error('process.binding is not supported'); +}; + +process.cwd = function () { return '/' }; +process.chdir = function (dir) { + throw new Error('process.chdir is not supported'); +}; +process.umask = function() { return 0; }; + +},{}],25:[function(require,module,exports){ +// Underscore.js 1.8.3 +// http://underscorejs.org +// (c) 2009-2015 Jeremy Ashkenas, DocumentCloud and Investigative Reporters & Editors +// Underscore may be freely distributed under the MIT license. + +(function() { + + // Baseline setup + // -------------- + + // Establish the root object, `window` in the browser, or `exports` on the server. + var root = this; + + // Save the previous value of the `_` variable. + var previousUnderscore = root._; + + // Save bytes in the minified (but not gzipped) version: + var ArrayProto = Array.prototype, ObjProto = Object.prototype, FuncProto = Function.prototype; + + // Create quick reference variables for speed access to core prototypes. + var + push = ArrayProto.push, + slice = ArrayProto.slice, + toString = ObjProto.toString, + hasOwnProperty = ObjProto.hasOwnProperty; + + // All **ECMAScript 5** native function implementations that we hope to use + // are declared here. + var + nativeIsArray = Array.isArray, + nativeKeys = Object.keys, + nativeBind = FuncProto.bind, + nativeCreate = Object.create; + + // Naked function reference for surrogate-prototype-swapping. + var Ctor = function(){}; + + // Create a safe reference to the Underscore object for use below. + var _ = function(obj) { + if (obj instanceof _) return obj; + if (!(this instanceof _)) return new _(obj); + this._wrapped = obj; + }; + + // Export the Underscore object for **Node.js**, with + // backwards-compatibility for the old `require()` API. If we're in + // the browser, add `_` as a global object. + if (typeof exports !== 'undefined') { + if (typeof module !== 'undefined' && module.exports) { + exports = module.exports = _; + } + exports._ = _; + } else { + root._ = _; + } + + // Current version. + _.VERSION = '1.8.3'; + + // Internal function that returns an efficient (for current engines) version + // of the passed-in callback, to be repeatedly applied in other Underscore + // functions. + var optimizeCb = function(func, context, argCount) { + if (context === void 0) return func; + switch (argCount == null ? 3 : argCount) { + case 1: return function(value) { + return func.call(context, value); + }; + case 2: return function(value, other) { + return func.call(context, value, other); + }; + case 3: return function(value, index, collection) { + return func.call(context, value, index, collection); + }; + case 4: return function(accumulator, value, index, collection) { + return func.call(context, accumulator, value, index, collection); + }; + } + return function() { + return func.apply(context, arguments); + }; + }; + + // A mostly-internal function to generate callbacks that can be applied + // to each element in a collection, returning the desired result — either + // identity, an arbitrary callback, a property matcher, or a property accessor. + var cb = function(value, context, argCount) { + if (value == null) return _.identity; + if (_.isFunction(value)) return optimizeCb(value, context, argCount); + if (_.isObject(value)) return _.matcher(value); + return _.property(value); + }; + _.iteratee = function(value, context) { + return cb(value, context, Infinity); + }; + + // An internal function for creating assigner functions. + var createAssigner = function(keysFunc, undefinedOnly) { + return function(obj) { + var length = arguments.length; + if (length < 2 || obj == null) return obj; + for (var index = 1; index < length; index++) { + var source = arguments[index], + keys = keysFunc(source), + l = keys.length; + for (var i = 0; i < l; i++) { + var key = keys[i]; + if (!undefinedOnly || obj[key] === void 0) obj[key] = source[key]; + } + } + return obj; + }; + }; + + // An internal function for creating a new object that inherits from another. + var baseCreate = function(prototype) { + if (!_.isObject(prototype)) return {}; + if (nativeCreate) return nativeCreate(prototype); + Ctor.prototype = prototype; + var result = new Ctor; + Ctor.prototype = null; + return result; + }; + + var property = function(key) { + return function(obj) { + return obj == null ? void 0 : obj[key]; + }; + }; + + // Helper for collection methods to determine whether a collection + // should be iterated as an array or as an object + // Related: http://people.mozilla.org/~jorendorff/es6-draft.html#sec-tolength + // Avoids a very nasty iOS 8 JIT bug on ARM-64. #2094 + var MAX_ARRAY_INDEX = Math.pow(2, 53) - 1; + var getLength = property('length'); + var isArrayLike = function(collection) { + var length = getLength(collection); + return typeof length == 'number' && length >= 0 && length <= MAX_ARRAY_INDEX; + }; + + // Collection Functions + // -------------------- + + // The cornerstone, an `each` implementation, aka `forEach`. + // Handles raw objects in addition to array-likes. Treats all + // sparse array-likes as if they were dense. + _.each = _.forEach = function(obj, iteratee, context) { + iteratee = optimizeCb(iteratee, context); + var i, length; + if (isArrayLike(obj)) { + for (i = 0, length = obj.length; i < length; i++) { + iteratee(obj[i], i, obj); + } + } else { + var keys = _.keys(obj); + for (i = 0, length = keys.length; i < length; i++) { + iteratee(obj[keys[i]], keys[i], obj); + } + } + return obj; + }; + + // Return the results of applying the iteratee to each element. + _.map = _.collect = function(obj, iteratee, context) { + iteratee = cb(iteratee, context); + var keys = !isArrayLike(obj) && _.keys(obj), + length = (keys || obj).length, + results = Array(length); + for (var index = 0; index < length; index++) { + var currentKey = keys ? keys[index] : index; + results[index] = iteratee(obj[currentKey], currentKey, obj); + } + return results; + }; + + // Create a reducing function iterating left or right. + function createReduce(dir) { + // Optimized iterator function as using arguments.length + // in the main function will deoptimize the, see #1991. + function iterator(obj, iteratee, memo, keys, index, length) { + for (; index >= 0 && index < length; index += dir) { + var currentKey = keys ? keys[index] : index; + memo = iteratee(memo, obj[currentKey], currentKey, obj); + } + return memo; + } + + return function(obj, iteratee, memo, context) { + iteratee = optimizeCb(iteratee, context, 4); + var keys = !isArrayLike(obj) && _.keys(obj), + length = (keys || obj).length, + index = dir > 0 ? 0 : length - 1; + // Determine the initial value if none is provided. + if (arguments.length < 3) { + memo = obj[keys ? keys[index] : index]; + index += dir; + } + return iterator(obj, iteratee, memo, keys, index, length); + }; + } + + // **Reduce** builds up a single result from a list of values, aka `inject`, + // or `foldl`. + _.reduce = _.foldl = _.inject = createReduce(1); + + // The right-associative version of reduce, also known as `foldr`. + _.reduceRight = _.foldr = createReduce(-1); + + // Return the first value which passes a truth test. Aliased as `detect`. + _.find = _.detect = function(obj, predicate, context) { + var key; + if (isArrayLike(obj)) { + key = _.findIndex(obj, predicate, context); + } else { + key = _.findKey(obj, predicate, context); + } + if (key !== void 0 && key !== -1) return obj[key]; + }; + + // Return all the elements that pass a truth test. + // Aliased as `select`. + _.filter = _.select = function(obj, predicate, context) { + var results = []; + predicate = cb(predicate, context); + _.each(obj, function(value, index, list) { + if (predicate(value, index, list)) results.push(value); + }); + return results; + }; + + // Return all the elements for which a truth test fails. + _.reject = function(obj, predicate, context) { + return _.filter(obj, _.negate(cb(predicate)), context); + }; + + // Determine whether all of the elements match a truth test. + // Aliased as `all`. + _.every = _.all = function(obj, predicate, context) { + predicate = cb(predicate, context); + var keys = !isArrayLike(obj) && _.keys(obj), + length = (keys || obj).length; + for (var index = 0; index < length; index++) { + var currentKey = keys ? keys[index] : index; + if (!predicate(obj[currentKey], currentKey, obj)) return false; + } + return true; + }; + + // Determine if at least one element in the object matches a truth test. + // Aliased as `any`. + _.some = _.any = function(obj, predicate, context) { + predicate = cb(predicate, context); + var keys = !isArrayLike(obj) && _.keys(obj), + length = (keys || obj).length; + for (var index = 0; index < length; index++) { + var currentKey = keys ? keys[index] : index; + if (predicate(obj[currentKey], currentKey, obj)) return true; + } + return false; + }; + + // Determine if the array or object contains a given item (using `===`). + // Aliased as `includes` and `include`. + _.contains = _.includes = _.include = function(obj, item, fromIndex, guard) { + if (!isArrayLike(obj)) obj = _.values(obj); + if (typeof fromIndex != 'number' || guard) fromIndex = 0; + return _.indexOf(obj, item, fromIndex) >= 0; + }; + + // Invoke a method (with arguments) on every item in a collection. + _.invoke = function(obj, method) { + var args = slice.call(arguments, 2); + var isFunc = _.isFunction(method); + return _.map(obj, function(value) { + var func = isFunc ? method : value[method]; + return func == null ? func : func.apply(value, args); + }); + }; + + // Convenience version of a common use case of `map`: fetching a property. + _.pluck = function(obj, key) { + return _.map(obj, _.property(key)); + }; + + // Convenience version of a common use case of `filter`: selecting only objects + // containing specific `key:value` pairs. + _.where = function(obj, attrs) { + return _.filter(obj, _.matcher(attrs)); + }; + + // Convenience version of a common use case of `find`: getting the first object + // containing specific `key:value` pairs. + _.findWhere = function(obj, attrs) { + return _.find(obj, _.matcher(attrs)); + }; + + // Return the maximum element (or element-based computation). + _.max = function(obj, iteratee, context) { + var result = -Infinity, lastComputed = -Infinity, + value, computed; + if (iteratee == null && obj != null) { + obj = isArrayLike(obj) ? obj : _.values(obj); + for (var i = 0, length = obj.length; i < length; i++) { + value = obj[i]; + if (value > result) { + result = value; + } + } + } else { + iteratee = cb(iteratee, context); + _.each(obj, function(value, index, list) { + computed = iteratee(value, index, list); + if (computed > lastComputed || computed === -Infinity && result === -Infinity) { + result = value; + lastComputed = computed; + } + }); + } + return result; + }; + + // Return the minimum element (or element-based computation). + _.min = function(obj, iteratee, context) { + var result = Infinity, lastComputed = Infinity, + value, computed; + if (iteratee == null && obj != null) { + obj = isArrayLike(obj) ? obj : _.values(obj); + for (var i = 0, length = obj.length; i < length; i++) { + value = obj[i]; + if (value < result) { + result = value; + } + } + } else { + iteratee = cb(iteratee, context); + _.each(obj, function(value, index, list) { + computed = iteratee(value, index, list); + if (computed < lastComputed || computed === Infinity && result === Infinity) { + result = value; + lastComputed = computed; + } + }); + } + return result; + }; + + // Shuffle a collection, using the modern version of the + // [Fisher-Yates shuffle](http://en.wikipedia.org/wiki/Fisher–Yates_shuffle). + _.shuffle = function(obj) { + var set = isArrayLike(obj) ? obj : _.values(obj); + var length = set.length; + var shuffled = Array(length); + for (var index = 0, rand; index < length; index++) { + rand = _.random(0, index); + if (rand !== index) shuffled[index] = shuffled[rand]; + shuffled[rand] = set[index]; + } + return shuffled; + }; + + // Sample **n** random values from a collection. + // If **n** is not specified, returns a single random element. + // The internal `guard` argument allows it to work with `map`. + _.sample = function(obj, n, guard) { + if (n == null || guard) { + if (!isArrayLike(obj)) obj = _.values(obj); + return obj[_.random(obj.length - 1)]; + } + return _.shuffle(obj).slice(0, Math.max(0, n)); + }; + + // Sort the object's values by a criterion produced by an iteratee. + _.sortBy = function(obj, iteratee, context) { + iteratee = cb(iteratee, context); + return _.pluck(_.map(obj, function(value, index, list) { + return { + value: value, + index: index, + criteria: iteratee(value, index, list) + }; + }).sort(function(left, right) { + var a = left.criteria; + var b = right.criteria; + if (a !== b) { + if (a > b || a === void 0) return 1; + if (a < b || b === void 0) return -1; + } + return left.index - right.index; + }), 'value'); + }; + + // An internal function used for aggregate "group by" operations. + var group = function(behavior) { + return function(obj, iteratee, context) { + var result = {}; + iteratee = cb(iteratee, context); + _.each(obj, function(value, index) { + var key = iteratee(value, index, obj); + behavior(result, value, key); + }); + return result; + }; + }; + + // Groups the object's values by a criterion. Pass either a string attribute + // to group by, or a function that returns the criterion. + _.groupBy = group(function(result, value, key) { + if (_.has(result, key)) result[key].push(value); else result[key] = [value]; + }); + + // Indexes the object's values by a criterion, similar to `groupBy`, but for + // when you know that your index values will be unique. + _.indexBy = group(function(result, value, key) { + result[key] = value; + }); + + // Counts instances of an object that group by a certain criterion. Pass + // either a string attribute to count by, or a function that returns the + // criterion. + _.countBy = group(function(result, value, key) { + if (_.has(result, key)) result[key]++; else result[key] = 1; + }); + + // Safely create a real, live array from anything iterable. + _.toArray = function(obj) { + if (!obj) return []; + if (_.isArray(obj)) return slice.call(obj); + if (isArrayLike(obj)) return _.map(obj, _.identity); + return _.values(obj); + }; + + // Return the number of elements in an object. + _.size = function(obj) { + if (obj == null) return 0; + return isArrayLike(obj) ? obj.length : _.keys(obj).length; + }; + + // Split a collection into two arrays: one whose elements all satisfy the given + // predicate, and one whose elements all do not satisfy the predicate. + _.partition = function(obj, predicate, context) { + predicate = cb(predicate, context); + var pass = [], fail = []; + _.each(obj, function(value, key, obj) { + (predicate(value, key, obj) ? pass : fail).push(value); + }); + return [pass, fail]; + }; + + // Array Functions + // --------------- + + // Get the first element of an array. Passing **n** will return the first N + // values in the array. Aliased as `head` and `take`. The **guard** check + // allows it to work with `_.map`. + _.first = _.head = _.take = function(array, n, guard) { + if (array == null) return void 0; + if (n == null || guard) return array[0]; + return _.initial(array, array.length - n); + }; + + // Returns everything but the last entry of the array. Especially useful on + // the arguments object. Passing **n** will return all the values in + // the array, excluding the last N. + _.initial = function(array, n, guard) { + return slice.call(array, 0, Math.max(0, array.length - (n == null || guard ? 1 : n))); + }; + + // Get the last element of an array. Passing **n** will return the last N + // values in the array. + _.last = function(array, n, guard) { + if (array == null) return void 0; + if (n == null || guard) return array[array.length - 1]; + return _.rest(array, Math.max(0, array.length - n)); + }; + + // Returns everything but the first entry of the array. Aliased as `tail` and `drop`. + // Especially useful on the arguments object. Passing an **n** will return + // the rest N values in the array. + _.rest = _.tail = _.drop = function(array, n, guard) { + return slice.call(array, n == null || guard ? 1 : n); + }; + + // Trim out all falsy values from an array. + _.compact = function(array) { + return _.filter(array, _.identity); + }; + + // Internal implementation of a recursive `flatten` function. + var flatten = function(input, shallow, strict, startIndex) { + var output = [], idx = 0; + for (var i = startIndex || 0, length = getLength(input); i < length; i++) { + var value = input[i]; + if (isArrayLike(value) && (_.isArray(value) || _.isArguments(value))) { + //flatten current level of array or arguments object + if (!shallow) value = flatten(value, shallow, strict); + var j = 0, len = value.length; + output.length += len; + while (j < len) { + output[idx++] = value[j++]; + } + } else if (!strict) { + output[idx++] = value; + } + } + return output; + }; + + // Flatten out an array, either recursively (by default), or just one level. + _.flatten = function(array, shallow) { + return flatten(array, shallow, false); + }; + + // Return a version of the array that does not contain the specified value(s). + _.without = function(array) { + return _.difference(array, slice.call(arguments, 1)); + }; + + // Produce a duplicate-free version of the array. If the array has already + // been sorted, you have the option of using a faster algorithm. + // Aliased as `unique`. + _.uniq = _.unique = function(array, isSorted, iteratee, context) { + if (!_.isBoolean(isSorted)) { + context = iteratee; + iteratee = isSorted; + isSorted = false; + } + if (iteratee != null) iteratee = cb(iteratee, context); + var result = []; + var seen = []; + for (var i = 0, length = getLength(array); i < length; i++) { + var value = array[i], + computed = iteratee ? iteratee(value, i, array) : value; + if (isSorted) { + if (!i || seen !== computed) result.push(value); + seen = computed; + } else if (iteratee) { + if (!_.contains(seen, computed)) { + seen.push(computed); + result.push(value); + } + } else if (!_.contains(result, value)) { + result.push(value); + } + } + return result; + }; + + // Produce an array that contains the union: each distinct element from all of + // the passed-in arrays. + _.union = function() { + return _.uniq(flatten(arguments, true, true)); + }; + + // Produce an array that contains every item shared between all the + // passed-in arrays. + _.intersection = function(array) { + var result = []; + var argsLength = arguments.length; + for (var i = 0, length = getLength(array); i < length; i++) { + var item = array[i]; + if (_.contains(result, item)) continue; + for (var j = 1; j < argsLength; j++) { + if (!_.contains(arguments[j], item)) break; + } + if (j === argsLength) result.push(item); + } + return result; + }; + + // Take the difference between one array and a number of other arrays. + // Only the elements present in just the first array will remain. + _.difference = function(array) { + var rest = flatten(arguments, true, true, 1); + return _.filter(array, function(value){ + return !_.contains(rest, value); + }); + }; + + // Zip together multiple lists into a single array -- elements that share + // an index go together. + _.zip = function() { + return _.unzip(arguments); + }; + + // Complement of _.zip. Unzip accepts an array of arrays and groups + // each array's elements on shared indices + _.unzip = function(array) { + var length = array && _.max(array, getLength).length || 0; + var result = Array(length); + + for (var index = 0; index < length; index++) { + result[index] = _.pluck(array, index); + } + return result; + }; + + // Converts lists into objects. Pass either a single array of `[key, value]` + // pairs, or two parallel arrays of the same length -- one of keys, and one of + // the corresponding values. + _.object = function(list, values) { + var result = {}; + for (var i = 0, length = getLength(list); i < length; i++) { + if (values) { + result[list[i]] = values[i]; + } else { + result[list[i][0]] = list[i][1]; + } + } + return result; + }; + + // Generator function to create the findIndex and findLastIndex functions + function createPredicateIndexFinder(dir) { + return function(array, predicate, context) { + predicate = cb(predicate, context); + var length = getLength(array); + var index = dir > 0 ? 0 : length - 1; + for (; index >= 0 && index < length; index += dir) { + if (predicate(array[index], index, array)) return index; + } + return -1; + }; + } + + // Returns the first index on an array-like that passes a predicate test + _.findIndex = createPredicateIndexFinder(1); + _.findLastIndex = createPredicateIndexFinder(-1); + + // Use a comparator function to figure out the smallest index at which + // an object should be inserted so as to maintain order. Uses binary search. + _.sortedIndex = function(array, obj, iteratee, context) { + iteratee = cb(iteratee, context, 1); + var value = iteratee(obj); + var low = 0, high = getLength(array); + while (low < high) { + var mid = Math.floor((low + high) / 2); + if (iteratee(array[mid]) < value) low = mid + 1; else high = mid; + } + return low; + }; + + // Generator function to create the indexOf and lastIndexOf functions + function createIndexFinder(dir, predicateFind, sortedIndex) { + return function(array, item, idx) { + var i = 0, length = getLength(array); + if (typeof idx == 'number') { + if (dir > 0) { + i = idx >= 0 ? idx : Math.max(idx + length, i); + } else { + length = idx >= 0 ? Math.min(idx + 1, length) : idx + length + 1; + } + } else if (sortedIndex && idx && length) { + idx = sortedIndex(array, item); + return array[idx] === item ? idx : -1; + } + if (item !== item) { + idx = predicateFind(slice.call(array, i, length), _.isNaN); + return idx >= 0 ? idx + i : -1; + } + for (idx = dir > 0 ? i : length - 1; idx >= 0 && idx < length; idx += dir) { + if (array[idx] === item) return idx; + } + return -1; + }; + } + + // Return the position of the first occurrence of an item in an array, + // or -1 if the item is not included in the array. + // If the array is large and already in sort order, pass `true` + // for **isSorted** to use binary search. + _.indexOf = createIndexFinder(1, _.findIndex, _.sortedIndex); + _.lastIndexOf = createIndexFinder(-1, _.findLastIndex); + + // Generate an integer Array containing an arithmetic progression. A port of + // the native Python `range()` function. See + // [the Python documentation](http://docs.python.org/library/functions.html#range). + _.range = function(start, stop, step) { + if (stop == null) { + stop = start || 0; + start = 0; + } + step = step || 1; + + var length = Math.max(Math.ceil((stop - start) / step), 0); + var range = Array(length); + + for (var idx = 0; idx < length; idx++, start += step) { + range[idx] = start; + } + + return range; + }; + + // Function (ahem) Functions + // ------------------ + + // Determines whether to execute a function as a constructor + // or a normal function with the provided arguments + var executeBound = function(sourceFunc, boundFunc, context, callingContext, args) { + if (!(callingContext instanceof boundFunc)) return sourceFunc.apply(context, args); + var self = baseCreate(sourceFunc.prototype); + var result = sourceFunc.apply(self, args); + if (_.isObject(result)) return result; + return self; + }; + + // Create a function bound to a given object (assigning `this`, and arguments, + // optionally). Delegates to **ECMAScript 5**'s native `Function.bind` if + // available. + _.bind = function(func, context) { + if (nativeBind && func.bind === nativeBind) return nativeBind.apply(func, slice.call(arguments, 1)); + if (!_.isFunction(func)) throw new TypeError('Bind must be called on a function'); + var args = slice.call(arguments, 2); + var bound = function() { + return executeBound(func, bound, context, this, args.concat(slice.call(arguments))); + }; + return bound; + }; + + // Partially apply a function by creating a version that has had some of its + // arguments pre-filled, without changing its dynamic `this` context. _ acts + // as a placeholder, allowing any combination of arguments to be pre-filled. + _.partial = function(func) { + var boundArgs = slice.call(arguments, 1); + var bound = function() { + var position = 0, length = boundArgs.length; + var args = Array(length); + for (var i = 0; i < length; i++) { + args[i] = boundArgs[i] === _ ? arguments[position++] : boundArgs[i]; + } + while (position < arguments.length) args.push(arguments[position++]); + return executeBound(func, bound, this, this, args); + }; + return bound; + }; + + // Bind a number of an object's methods to that object. Remaining arguments + // are the method names to be bound. Useful for ensuring that all callbacks + // defined on an object belong to it. + _.bindAll = function(obj) { + var i, length = arguments.length, key; + if (length <= 1) throw new Error('bindAll must be passed function names'); + for (i = 1; i < length; i++) { + key = arguments[i]; + obj[key] = _.bind(obj[key], obj); + } + return obj; + }; + + // Memoize an expensive function by storing its results. + _.memoize = function(func, hasher) { + var memoize = function(key) { + var cache = memoize.cache; + var address = '' + (hasher ? hasher.apply(this, arguments) : key); + if (!_.has(cache, address)) cache[address] = func.apply(this, arguments); + return cache[address]; + }; + memoize.cache = {}; + return memoize; + }; + + // Delays a function for the given number of milliseconds, and then calls + // it with the arguments supplied. + _.delay = function(func, wait) { + var args = slice.call(arguments, 2); + return setTimeout(function(){ + return func.apply(null, args); + }, wait); + }; + + // Defers a function, scheduling it to run after the current call stack has + // cleared. + _.defer = _.partial(_.delay, _, 1); + + // Returns a function, that, when invoked, will only be triggered at most once + // during a given window of time. Normally, the throttled function will run + // as much as it can, without ever going more than once per `wait` duration; + // but if you'd like to disable the execution on the leading edge, pass + // `{leading: false}`. To disable execution on the trailing edge, ditto. + _.throttle = function(func, wait, options) { + var context, args, result; + var timeout = null; + var previous = 0; + if (!options) options = {}; + var later = function() { + previous = options.leading === false ? 0 : _.now(); + timeout = null; + result = func.apply(context, args); + if (!timeout) context = args = null; + }; + return function() { + var now = _.now(); + if (!previous && options.leading === false) previous = now; + var remaining = wait - (now - previous); + context = this; + args = arguments; + if (remaining <= 0 || remaining > wait) { + if (timeout) { + clearTimeout(timeout); + timeout = null; + } + previous = now; + result = func.apply(context, args); + if (!timeout) context = args = null; + } else if (!timeout && options.trailing !== false) { + timeout = setTimeout(later, remaining); + } + return result; + }; + }; + + // Returns a function, that, as long as it continues to be invoked, will not + // be triggered. The function will be called after it stops being called for + // N milliseconds. If `immediate` is passed, trigger the function on the + // leading edge, instead of the trailing. + _.debounce = function(func, wait, immediate) { + var timeout, args, context, timestamp, result; + + var later = function() { + var last = _.now() - timestamp; + + if (last < wait && last >= 0) { + timeout = setTimeout(later, wait - last); + } else { + timeout = null; + if (!immediate) { + result = func.apply(context, args); + if (!timeout) context = args = null; + } + } + }; + + return function() { + context = this; + args = arguments; + timestamp = _.now(); + var callNow = immediate && !timeout; + if (!timeout) timeout = setTimeout(later, wait); + if (callNow) { + result = func.apply(context, args); + context = args = null; + } + + return result; + }; + }; + + // Returns the first function passed as an argument to the second, + // allowing you to adjust arguments, run code before and after, and + // conditionally execute the original function. + _.wrap = function(func, wrapper) { + return _.partial(wrapper, func); + }; + + // Returns a negated version of the passed-in predicate. + _.negate = function(predicate) { + return function() { + return !predicate.apply(this, arguments); + }; + }; + + // Returns a function that is the composition of a list of functions, each + // consuming the return value of the function that follows. + _.compose = function() { + var args = arguments; + var start = args.length - 1; + return function() { + var i = start; + var result = args[start].apply(this, arguments); + while (i--) result = args[i].call(this, result); + return result; + }; + }; + + // Returns a function that will only be executed on and after the Nth call. + _.after = function(times, func) { + return function() { + if (--times < 1) { + return func.apply(this, arguments); + } + }; + }; + + // Returns a function that will only be executed up to (but not including) the Nth call. + _.before = function(times, func) { + var memo; + return function() { + if (--times > 0) { + memo = func.apply(this, arguments); + } + if (times <= 1) func = null; + return memo; + }; + }; + + // Returns a function that will be executed at most one time, no matter how + // often you call it. Useful for lazy initialization. + _.once = _.partial(_.before, 2); + + // Object Functions + // ---------------- + + // Keys in IE < 9 that won't be iterated by `for key in ...` and thus missed. + var hasEnumBug = !{toString: null}.propertyIsEnumerable('toString'); + var nonEnumerableProps = ['valueOf', 'isPrototypeOf', 'toString', + 'propertyIsEnumerable', 'hasOwnProperty', 'toLocaleString']; + + function collectNonEnumProps(obj, keys) { + var nonEnumIdx = nonEnumerableProps.length; + var constructor = obj.constructor; + var proto = (_.isFunction(constructor) && constructor.prototype) || ObjProto; + + // Constructor is a special case. + var prop = 'constructor'; + if (_.has(obj, prop) && !_.contains(keys, prop)) keys.push(prop); + + while (nonEnumIdx--) { + prop = nonEnumerableProps[nonEnumIdx]; + if (prop in obj && obj[prop] !== proto[prop] && !_.contains(keys, prop)) { + keys.push(prop); + } + } + } + + // Retrieve the names of an object's own properties. + // Delegates to **ECMAScript 5**'s native `Object.keys` + _.keys = function(obj) { + if (!_.isObject(obj)) return []; + if (nativeKeys) return nativeKeys(obj); + var keys = []; + for (var key in obj) if (_.has(obj, key)) keys.push(key); + // Ahem, IE < 9. + if (hasEnumBug) collectNonEnumProps(obj, keys); + return keys; + }; + + // Retrieve all the property names of an object. + _.allKeys = function(obj) { + if (!_.isObject(obj)) return []; + var keys = []; + for (var key in obj) keys.push(key); + // Ahem, IE < 9. + if (hasEnumBug) collectNonEnumProps(obj, keys); + return keys; + }; + + // Retrieve the values of an object's properties. + _.values = function(obj) { + var keys = _.keys(obj); + var length = keys.length; + var values = Array(length); + for (var i = 0; i < length; i++) { + values[i] = obj[keys[i]]; + } + return values; + }; + + // Returns the results of applying the iteratee to each element of the object + // In contrast to _.map it returns an object + _.mapObject = function(obj, iteratee, context) { + iteratee = cb(iteratee, context); + var keys = _.keys(obj), + length = keys.length, + results = {}, + currentKey; + for (var index = 0; index < length; index++) { + currentKey = keys[index]; + results[currentKey] = iteratee(obj[currentKey], currentKey, obj); + } + return results; + }; + + // Convert an object into a list of `[key, value]` pairs. + _.pairs = function(obj) { + var keys = _.keys(obj); + var length = keys.length; + var pairs = Array(length); + for (var i = 0; i < length; i++) { + pairs[i] = [keys[i], obj[keys[i]]]; + } + return pairs; + }; + + // Invert the keys and values of an object. The values must be serializable. + _.invert = function(obj) { + var result = {}; + var keys = _.keys(obj); + for (var i = 0, length = keys.length; i < length; i++) { + result[obj[keys[i]]] = keys[i]; + } + return result; + }; + + // Return a sorted list of the function names available on the object. + // Aliased as `methods` + _.functions = _.methods = function(obj) { + var names = []; + for (var key in obj) { + if (_.isFunction(obj[key])) names.push(key); + } + return names.sort(); + }; + + // Extend a given object with all the properties in passed-in object(s). + _.extend = createAssigner(_.allKeys); + + // Assigns a given object with all the own properties in the passed-in object(s) + // (https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Object/assign) + _.extendOwn = _.assign = createAssigner(_.keys); + + // Returns the first key on an object that passes a predicate test + _.findKey = function(obj, predicate, context) { + predicate = cb(predicate, context); + var keys = _.keys(obj), key; + for (var i = 0, length = keys.length; i < length; i++) { + key = keys[i]; + if (predicate(obj[key], key, obj)) return key; + } + }; + + // Return a copy of the object only containing the whitelisted properties. + _.pick = function(object, oiteratee, context) { + var result = {}, obj = object, iteratee, keys; + if (obj == null) return result; + if (_.isFunction(oiteratee)) { + keys = _.allKeys(obj); + iteratee = optimizeCb(oiteratee, context); + } else { + keys = flatten(arguments, false, false, 1); + iteratee = function(value, key, obj) { return key in obj; }; + obj = Object(obj); + } + for (var i = 0, length = keys.length; i < length; i++) { + var key = keys[i]; + var value = obj[key]; + if (iteratee(value, key, obj)) result[key] = value; + } + return result; + }; + + // Return a copy of the object without the blacklisted properties. + _.omit = function(obj, iteratee, context) { + if (_.isFunction(iteratee)) { + iteratee = _.negate(iteratee); + } else { + var keys = _.map(flatten(arguments, false, false, 1), String); + iteratee = function(value, key) { + return !_.contains(keys, key); + }; + } + return _.pick(obj, iteratee, context); + }; + + // Fill in a given object with default properties. + _.defaults = createAssigner(_.allKeys, true); + + // Creates an object that inherits from the given prototype object. + // If additional properties are provided then they will be added to the + // created object. + _.create = function(prototype, props) { + var result = baseCreate(prototype); + if (props) _.extendOwn(result, props); + return result; + }; + + // Create a (shallow-cloned) duplicate of an object. + _.clone = function(obj) { + if (!_.isObject(obj)) return obj; + return _.isArray(obj) ? obj.slice() : _.extend({}, obj); + }; + + // Invokes interceptor with the obj, and then returns obj. + // The primary purpose of this method is to "tap into" a method chain, in + // order to perform operations on intermediate results within the chain. + _.tap = function(obj, interceptor) { + interceptor(obj); + return obj; + }; + + // Returns whether an object has a given set of `key:value` pairs. + _.isMatch = function(object, attrs) { + var keys = _.keys(attrs), length = keys.length; + if (object == null) return !length; + var obj = Object(object); + for (var i = 0; i < length; i++) { + var key = keys[i]; + if (attrs[key] !== obj[key] || !(key in obj)) return false; + } + return true; + }; + + + // Internal recursive comparison function for `isEqual`. + var eq = function(a, b, aStack, bStack) { + // Identical objects are equal. `0 === -0`, but they aren't identical. + // See the [Harmony `egal` proposal](http://wiki.ecmascript.org/doku.php?id=harmony:egal). + if (a === b) return a !== 0 || 1 / a === 1 / b; + // A strict comparison is necessary because `null == undefined`. + if (a == null || b == null) return a === b; + // Unwrap any wrapped objects. + if (a instanceof _) a = a._wrapped; + if (b instanceof _) b = b._wrapped; + // Compare `[[Class]]` names. + var className = toString.call(a); + if (className !== toString.call(b)) return false; + switch (className) { + // Strings, numbers, regular expressions, dates, and booleans are compared by value. + case '[object RegExp]': + // RegExps are coerced to strings for comparison (Note: '' + /a/i === '/a/i') + case '[object String]': + // Primitives and their corresponding object wrappers are equivalent; thus, `"5"` is + // equivalent to `new String("5")`. + return '' + a === '' + b; + case '[object Number]': + // `NaN`s are equivalent, but non-reflexive. + // Object(NaN) is equivalent to NaN + if (+a !== +a) return +b !== +b; + // An `egal` comparison is performed for other numeric values. + return +a === 0 ? 1 / +a === 1 / b : +a === +b; + case '[object Date]': + case '[object Boolean]': + // Coerce dates and booleans to numeric primitive values. Dates are compared by their + // millisecond representations. Note that invalid dates with millisecond representations + // of `NaN` are not equivalent. + return +a === +b; + } + + var areArrays = className === '[object Array]'; + if (!areArrays) { + if (typeof a != 'object' || typeof b != 'object') return false; + + // Objects with different constructors are not equivalent, but `Object`s or `Array`s + // from different frames are. + var aCtor = a.constructor, bCtor = b.constructor; + if (aCtor !== bCtor && !(_.isFunction(aCtor) && aCtor instanceof aCtor && + _.isFunction(bCtor) && bCtor instanceof bCtor) + && ('constructor' in a && 'constructor' in b)) { + return false; + } + } + // Assume equality for cyclic structures. The algorithm for detecting cyclic + // structures is adapted from ES 5.1 section 15.12.3, abstract operation `JO`. + + // Initializing stack of traversed objects. + // It's done here since we only need them for objects and arrays comparison. + aStack = aStack || []; + bStack = bStack || []; + var length = aStack.length; + while (length--) { + // Linear search. Performance is inversely proportional to the number of + // unique nested structures. + if (aStack[length] === a) return bStack[length] === b; + } + + // Add the first object to the stack of traversed objects. + aStack.push(a); + bStack.push(b); + + // Recursively compare objects and arrays. + if (areArrays) { + // Compare array lengths to determine if a deep comparison is necessary. + length = a.length; + if (length !== b.length) return false; + // Deep compare the contents, ignoring non-numeric properties. + while (length--) { + if (!eq(a[length], b[length], aStack, bStack)) return false; + } + } else { + // Deep compare objects. + var keys = _.keys(a), key; + length = keys.length; + // Ensure that both objects contain the same number of properties before comparing deep equality. + if (_.keys(b).length !== length) return false; + while (length--) { + // Deep compare each member + key = keys[length]; + if (!(_.has(b, key) && eq(a[key], b[key], aStack, bStack))) return false; + } + } + // Remove the first object from the stack of traversed objects. + aStack.pop(); + bStack.pop(); + return true; + }; + + // Perform a deep comparison to check if two objects are equal. + _.isEqual = function(a, b) { + return eq(a, b); + }; + + // Is a given array, string, or object empty? + // An "empty" object has no enumerable own-properties. + _.isEmpty = function(obj) { + if (obj == null) return true; + if (isArrayLike(obj) && (_.isArray(obj) || _.isString(obj) || _.isArguments(obj))) return obj.length === 0; + return _.keys(obj).length === 0; + }; + + // Is a given value a DOM element? + _.isElement = function(obj) { + return !!(obj && obj.nodeType === 1); + }; + + // Is a given value an array? + // Delegates to ECMA5's native Array.isArray + _.isArray = nativeIsArray || function(obj) { + return toString.call(obj) === '[object Array]'; + }; + + // Is a given variable an object? + _.isObject = function(obj) { + var type = typeof obj; + return type === 'function' || type === 'object' && !!obj; + }; + + // Add some isType methods: isArguments, isFunction, isString, isNumber, isDate, isRegExp, isError. + _.each(['Arguments', 'Function', 'String', 'Number', 'Date', 'RegExp', 'Error'], function(name) { + _['is' + name] = function(obj) { + return toString.call(obj) === '[object ' + name + ']'; + }; + }); + + // Define a fallback version of the method in browsers (ahem, IE < 9), where + // there isn't any inspectable "Arguments" type. + if (!_.isArguments(arguments)) { + _.isArguments = function(obj) { + return _.has(obj, 'callee'); + }; + } + + // Optimize `isFunction` if appropriate. Work around some typeof bugs in old v8, + // IE 11 (#1621), and in Safari 8 (#1929). + if (typeof /./ != 'function' && typeof Int8Array != 'object') { + _.isFunction = function(obj) { + return typeof obj == 'function' || false; + }; + } + + // Is a given object a finite number? + _.isFinite = function(obj) { + return isFinite(obj) && !isNaN(parseFloat(obj)); + }; + + // Is the given value `NaN`? (NaN is the only number which does not equal itself). + _.isNaN = function(obj) { + return _.isNumber(obj) && obj !== +obj; + }; + + // Is a given value a boolean? + _.isBoolean = function(obj) { + return obj === true || obj === false || toString.call(obj) === '[object Boolean]'; + }; + + // Is a given value equal to null? + _.isNull = function(obj) { + return obj === null; + }; + + // Is a given variable undefined? + _.isUndefined = function(obj) { + return obj === void 0; + }; + + // Shortcut function for checking if an object has a given property directly + // on itself (in other words, not on a prototype). + _.has = function(obj, key) { + return obj != null && hasOwnProperty.call(obj, key); + }; + + // Utility Functions + // ----------------- + + // Run Underscore.js in *noConflict* mode, returning the `_` variable to its + // previous owner. Returns a reference to the Underscore object. + _.noConflict = function() { + root._ = previousUnderscore; + return this; + }; + + // Keep the identity function around for default iteratees. + _.identity = function(value) { + return value; + }; + + // Predicate-generating functions. Often useful outside of Underscore. + _.constant = function(value) { + return function() { + return value; + }; + }; + + _.noop = function(){}; + + _.property = property; + + // Generates a function for a given object that returns a given property. + _.propertyOf = function(obj) { + return obj == null ? function(){} : function(key) { + return obj[key]; + }; + }; + + // Returns a predicate for checking whether an object has a given set of + // `key:value` pairs. + _.matcher = _.matches = function(attrs) { + attrs = _.extendOwn({}, attrs); + return function(obj) { + return _.isMatch(obj, attrs); + }; + }; + + // Run a function **n** times. + _.times = function(n, iteratee, context) { + var accum = Array(Math.max(0, n)); + iteratee = optimizeCb(iteratee, context, 1); + for (var i = 0; i < n; i++) accum[i] = iteratee(i); + return accum; + }; + + // Return a random integer between min and max (inclusive). + _.random = function(min, max) { + if (max == null) { + max = min; + min = 0; + } + return min + Math.floor(Math.random() * (max - min + 1)); + }; + + // A (possibly faster) way to get the current timestamp as an integer. + _.now = Date.now || function() { + return new Date().getTime(); + }; + + // List of HTML entities for escaping. + var escapeMap = { + '&': '&', + '<': '<', + '>': '>', + '"': '"', + "'": ''', + '`': '`' + }; + var unescapeMap = _.invert(escapeMap); + + // Functions for escaping and unescaping strings to/from HTML interpolation. + var createEscaper = function(map) { + var escaper = function(match) { + return map[match]; + }; + // Regexes for identifying a key that needs to be escaped + var source = '(?:' + _.keys(map).join('|') + ')'; + var testRegexp = RegExp(source); + var replaceRegexp = RegExp(source, 'g'); + return function(string) { + string = string == null ? '' : '' + string; + return testRegexp.test(string) ? string.replace(replaceRegexp, escaper) : string; + }; + }; + _.escape = createEscaper(escapeMap); + _.unescape = createEscaper(unescapeMap); + + // If the value of the named `property` is a function then invoke it with the + // `object` as context; otherwise, return it. + _.result = function(object, property, fallback) { + var value = object == null ? void 0 : object[property]; + if (value === void 0) { + value = fallback; + } + return _.isFunction(value) ? value.call(object) : value; + }; + + // Generate a unique integer id (unique within the entire client session). + // Useful for temporary DOM ids. + var idCounter = 0; + _.uniqueId = function(prefix) { + var id = ++idCounter + ''; + return prefix ? prefix + id : id; + }; + + // By default, Underscore uses ERB-style template delimiters, change the + // following template settings to use alternative delimiters. + _.templateSettings = { + evaluate : /<%([\s\S]+?)%>/g, + interpolate : /<%=([\s\S]+?)%>/g, + escape : /<%-([\s\S]+?)%>/g + }; + + // When customizing `templateSettings`, if you don't want to define an + // interpolation, evaluation or escaping regex, we need one that is + // guaranteed not to match. + var noMatch = /(.)^/; + + // Certain characters need to be escaped so that they can be put into a + // string literal. + var escapes = { + "'": "'", + '\\': '\\', + '\r': 'r', + '\n': 'n', + '\u2028': 'u2028', + '\u2029': 'u2029' + }; + + var escaper = /\\|'|\r|\n|\u2028|\u2029/g; + + var escapeChar = function(match) { + return '\\' + escapes[match]; + }; + + // JavaScript micro-templating, similar to John Resig's implementation. + // Underscore templating handles arbitrary delimiters, preserves whitespace, + // and correctly escapes quotes within interpolated code. + // NB: `oldSettings` only exists for backwards compatibility. + _.template = function(text, settings, oldSettings) { + if (!settings && oldSettings) settings = oldSettings; + settings = _.defaults({}, settings, _.templateSettings); + + // Combine delimiters into one regular expression via alternation. + var matcher = RegExp([ + (settings.escape || noMatch).source, + (settings.interpolate || noMatch).source, + (settings.evaluate || noMatch).source + ].join('|') + '|$', 'g'); + + // Compile the template source, escaping string literals appropriately. + var index = 0; + var source = "__p+='"; + text.replace(matcher, function(match, escape, interpolate, evaluate, offset) { + source += text.slice(index, offset).replace(escaper, escapeChar); + index = offset + match.length; + + if (escape) { + source += "'+\n((__t=(" + escape + "))==null?'':_.escape(__t))+\n'"; + } else if (interpolate) { + source += "'+\n((__t=(" + interpolate + "))==null?'':__t)+\n'"; + } else if (evaluate) { + source += "';\n" + evaluate + "\n__p+='"; + } + + // Adobe VMs need the match returned to produce the correct offest. + return match; + }); + source += "';\n"; + + // If a variable is not specified, place data values in local scope. + if (!settings.variable) source = 'with(obj||{}){\n' + source + '}\n'; + + source = "var __t,__p='',__j=Array.prototype.join," + + "print=function(){__p+=__j.call(arguments,'');};\n" + + source + 'return __p;\n'; + + try { + var render = new Function(settings.variable || 'obj', '_', source); + } catch (e) { + e.source = source; + throw e; + } + + var template = function(data) { + return render.call(this, data, _); + }; + + // Provide the compiled source as a convenience for precompilation. + var argument = settings.variable || 'obj'; + template.source = 'function(' + argument + '){\n' + source + '}'; + + return template; + }; + + // Add a "chain" function. Start chaining a wrapped Underscore object. + _.chain = function(obj) { + var instance = _(obj); + instance._chain = true; + return instance; + }; + + // OOP + // --------------- + // If Underscore is called as a function, it returns a wrapped object that + // can be used OO-style. This wrapper holds altered versions of all the + // underscore functions. Wrapped objects may be chained. + + // Helper function to continue chaining intermediate results. + var result = function(instance, obj) { + return instance._chain ? _(obj).chain() : obj; + }; + + // Add your own custom functions to the Underscore object. + _.mixin = function(obj) { + _.each(_.functions(obj), function(name) { + var func = _[name] = obj[name]; + _.prototype[name] = function() { + var args = [this._wrapped]; + push.apply(args, arguments); + return result(this, func.apply(_, args)); + }; + }); + }; + + // Add all of the Underscore functions to the wrapper object. + _.mixin(_); + + // Add all mutator Array functions to the wrapper. + _.each(['pop', 'push', 'reverse', 'shift', 'sort', 'splice', 'unshift'], function(name) { + var method = ArrayProto[name]; + _.prototype[name] = function() { + var obj = this._wrapped; + method.apply(obj, arguments); + if ((name === 'shift' || name === 'splice') && obj.length === 0) delete obj[0]; + return result(this, obj); + }; + }); + + // Add all accessor Array functions to the wrapper. + _.each(['concat', 'join', 'slice'], function(name) { + var method = ArrayProto[name]; + _.prototype[name] = function() { + return result(this, method.apply(this._wrapped, arguments)); + }; + }); + + // Extracts the result from a wrapped and chained object. + _.prototype.value = function() { + return this._wrapped; + }; + + // Provide unwrapping proxy for some methods used in engine operations + // such as arithmetic and JSON stringification. + _.prototype.valueOf = _.prototype.toJSON = _.prototype.value; + + _.prototype.toString = function() { + return '' + this._wrapped; + }; + + // AMD registration happens at the end for compatibility with AMD loaders + // that may not enforce next-turn semantics on modules. Even though general + // practice for AMD registration is to be anonymous, underscore registers + // as a named module because, like jQuery, it is a base library that is + // popular enough to be bundled in a third party lib, but not be part of + // an AMD load request. Those cases could generate an error when an + // anonymous define() is called outside of a loader request. + if (typeof define === 'function' && define.amd) { + define('underscore', [], function() { + return _; + }); + } +}.call(this)); + +},{}],26:[function(require,module,exports){ +arguments[4][19][0].apply(exports,arguments) +},{"dup":19}],27:[function(require,module,exports){ +module.exports = function isBuffer(arg) { + return arg && typeof arg === 'object' + && typeof arg.copy === 'function' + && typeof arg.fill === 'function' + && typeof arg.readUInt8 === 'function'; +} +},{}],28:[function(require,module,exports){ +(function (process,global){ +// Copyright Joyent, Inc. and other Node contributors. +// +// Permission is hereby granted, free of charge, to any person obtaining a +// copy of this software and associated documentation files (the +// "Software"), to deal in the Software without restriction, including +// without limitation the rights to use, copy, modify, merge, publish, +// distribute, sublicense, and/or sell copies of the Software, and to permit +// persons to whom the Software is furnished to do so, subject to the +// following conditions: +// +// The above copyright notice and this permission notice shall be included +// in all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS +// OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF +// MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN +// NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, +// DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR +// OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE +// USE OR OTHER DEALINGS IN THE SOFTWARE. + +var formatRegExp = /%[sdj%]/g; +exports.format = function(f) { + if (!isString(f)) { + var objects = []; + for (var i = 0; i < arguments.length; i++) { + objects.push(inspect(arguments[i])); + } + return objects.join(' '); + } + + var i = 1; + var args = arguments; + var len = args.length; + var str = String(f).replace(formatRegExp, function(x) { + if (x === '%%') return '%'; + if (i >= len) return x; + switch (x) { + case '%s': return String(args[i++]); + case '%d': return Number(args[i++]); + case '%j': + try { + return JSON.stringify(args[i++]); + } catch (_) { + return '[Circular]'; + } + default: + return x; + } + }); + for (var x = args[i]; i < len; x = args[++i]) { + if (isNull(x) || !isObject(x)) { + str += ' ' + x; + } else { + str += ' ' + inspect(x); + } + } + return str; +}; + + +// Mark that a method should not be used. +// Returns a modified function which warns once by default. +// If --no-deprecation is set, then it is a no-op. +exports.deprecate = function(fn, msg) { + // Allow for deprecating things in the process of starting up. + if (isUndefined(global.process)) { + return function() { + return exports.deprecate(fn, msg).apply(this, arguments); + }; + } + + if (process.noDeprecation === true) { + return fn; + } + + var warned = false; + function deprecated() { + if (!warned) { + if (process.throwDeprecation) { + throw new Error(msg); + } else if (process.traceDeprecation) { + console.trace(msg); + } else { + console.error(msg); + } + warned = true; + } + return fn.apply(this, arguments); + } + + return deprecated; +}; + + +var debugs = {}; +var debugEnviron; +exports.debuglog = function(set) { + if (isUndefined(debugEnviron)) + debugEnviron = process.env.NODE_DEBUG || ''; + set = set.toUpperCase(); + if (!debugs[set]) { + if (new RegExp('\\b' + set + '\\b', 'i').test(debugEnviron)) { + var pid = process.pid; + debugs[set] = function() { + var msg = exports.format.apply(exports, arguments); + console.error('%s %d: %s', set, pid, msg); + }; + } else { + debugs[set] = function() {}; + } + } + return debugs[set]; +}; + + +/** + * Echos the value of a value. Trys to print the value out + * in the best way possible given the different types. + * + * @param {Object} obj The object to print out. + * @param {Object} opts Optional options object that alters the output. + */ +/* legacy: obj, showHidden, depth, colors*/ +function inspect(obj, opts) { + // default options + var ctx = { + seen: [], + stylize: stylizeNoColor + }; + // legacy... + if (arguments.length >= 3) ctx.depth = arguments[2]; + if (arguments.length >= 4) ctx.colors = arguments[3]; + if (isBoolean(opts)) { + // legacy... + ctx.showHidden = opts; + } else if (opts) { + // got an "options" object + exports._extend(ctx, opts); + } + // set default options + if (isUndefined(ctx.showHidden)) ctx.showHidden = false; + if (isUndefined(ctx.depth)) ctx.depth = 2; + if (isUndefined(ctx.colors)) ctx.colors = false; + if (isUndefined(ctx.customInspect)) ctx.customInspect = true; + if (ctx.colors) ctx.stylize = stylizeWithColor; + return formatValue(ctx, obj, ctx.depth); +} +exports.inspect = inspect; + + +// http://en.wikipedia.org/wiki/ANSI_escape_code#graphics +inspect.colors = { + 'bold' : [1, 22], + 'italic' : [3, 23], + 'underline' : [4, 24], + 'inverse' : [7, 27], + 'white' : [37, 39], + 'grey' : [90, 39], + 'black' : [30, 39], + 'blue' : [34, 39], + 'cyan' : [36, 39], + 'green' : [32, 39], + 'magenta' : [35, 39], + 'red' : [31, 39], + 'yellow' : [33, 39] +}; + +// Don't use 'blue' not visible on cmd.exe +inspect.styles = { + 'special': 'cyan', + 'number': 'yellow', + 'boolean': 'yellow', + 'undefined': 'grey', + 'null': 'bold', + 'string': 'green', + 'date': 'magenta', + // "name": intentionally not styling + 'regexp': 'red' +}; + + +function stylizeWithColor(str, styleType) { + var style = inspect.styles[styleType]; + + if (style) { + return '\u001b[' + inspect.colors[style][0] + 'm' + str + + '\u001b[' + inspect.colors[style][1] + 'm'; + } else { + return str; + } +} + + +function stylizeNoColor(str, styleType) { + return str; +} + + +function arrayToHash(array) { + var hash = {}; + + array.forEach(function(val, idx) { + hash[val] = true; + }); + + return hash; +} + + +function formatValue(ctx, value, recurseTimes) { + // Provide a hook for user-specified inspect functions. + // Check that value is an object with an inspect function on it + if (ctx.customInspect && + value && + isFunction(value.inspect) && + // Filter out the util module, it's inspect function is special + value.inspect !== exports.inspect && + // Also filter out any prototype objects using the circular check. + !(value.constructor && value.constructor.prototype === value)) { + var ret = value.inspect(recurseTimes, ctx); + if (!isString(ret)) { + ret = formatValue(ctx, ret, recurseTimes); + } + return ret; + } + + // Primitive types cannot have properties + var primitive = formatPrimitive(ctx, value); + if (primitive) { + return primitive; + } + + // Look up the keys of the object. + var keys = Object.keys(value); + var visibleKeys = arrayToHash(keys); + + if (ctx.showHidden) { + keys = Object.getOwnPropertyNames(value); + } + + // IE doesn't make error fields non-enumerable + // http://msdn.microsoft.com/en-us/library/ie/dww52sbt(v=vs.94).aspx + if (isError(value) + && (keys.indexOf('message') >= 0 || keys.indexOf('description') >= 0)) { + return formatError(value); + } + + // Some type of object without properties can be shortcutted. + if (keys.length === 0) { + if (isFunction(value)) { + var name = value.name ? ': ' + value.name : ''; + return ctx.stylize('[Function' + name + ']', 'special'); + } + if (isRegExp(value)) { + return ctx.stylize(RegExp.prototype.toString.call(value), 'regexp'); + } + if (isDate(value)) { + return ctx.stylize(Date.prototype.toString.call(value), 'date'); + } + if (isError(value)) { + return formatError(value); + } + } + + var base = '', array = false, braces = ['{', '}']; + + // Make Array say that they are Array + if (isArray(value)) { + array = true; + braces = ['[', ']']; + } + + // Make functions say that they are functions + if (isFunction(value)) { + var n = value.name ? ': ' + value.name : ''; + base = ' [Function' + n + ']'; + } + + // Make RegExps say that they are RegExps + if (isRegExp(value)) { + base = ' ' + RegExp.prototype.toString.call(value); + } + + // Make dates with properties first say the date + if (isDate(value)) { + base = ' ' + Date.prototype.toUTCString.call(value); + } + + // Make error with message first say the error + if (isError(value)) { + base = ' ' + formatError(value); + } + + if (keys.length === 0 && (!array || value.length == 0)) { + return braces[0] + base + braces[1]; + } + + if (recurseTimes < 0) { + if (isRegExp(value)) { + return ctx.stylize(RegExp.prototype.toString.call(value), 'regexp'); + } else { + return ctx.stylize('[Object]', 'special'); + } + } + + ctx.seen.push(value); + + var output; + if (array) { + output = formatArray(ctx, value, recurseTimes, visibleKeys, keys); + } else { + output = keys.map(function(key) { + return formatProperty(ctx, value, recurseTimes, visibleKeys, key, array); + }); + } + + ctx.seen.pop(); + + return reduceToSingleString(output, base, braces); +} + + +function formatPrimitive(ctx, value) { + if (isUndefined(value)) + return ctx.stylize('undefined', 'undefined'); + if (isString(value)) { + var simple = '\'' + JSON.stringify(value).replace(/^"|"$/g, '') + .replace(/'/g, "\\'") + .replace(/\\"/g, '"') + '\''; + return ctx.stylize(simple, 'string'); + } + if (isNumber(value)) + return ctx.stylize('' + value, 'number'); + if (isBoolean(value)) + return ctx.stylize('' + value, 'boolean'); + // For some reason typeof null is "object", so special case here. + if (isNull(value)) + return ctx.stylize('null', 'null'); +} + + +function formatError(value) { + return '[' + Error.prototype.toString.call(value) + ']'; +} + + +function formatArray(ctx, value, recurseTimes, visibleKeys, keys) { + var output = []; + for (var i = 0, l = value.length; i < l; ++i) { + if (hasOwnProperty(value, String(i))) { + output.push(formatProperty(ctx, value, recurseTimes, visibleKeys, + String(i), true)); + } else { + output.push(''); + } + } + keys.forEach(function(key) { + if (!key.match(/^\d+$/)) { + output.push(formatProperty(ctx, value, recurseTimes, visibleKeys, + key, true)); + } + }); + return output; +} + + +function formatProperty(ctx, value, recurseTimes, visibleKeys, key, array) { + var name, str, desc; + desc = Object.getOwnPropertyDescriptor(value, key) || { value: value[key] }; + if (desc.get) { + if (desc.set) { + str = ctx.stylize('[Getter/Setter]', 'special'); + } else { + str = ctx.stylize('[Getter]', 'special'); + } + } else { + if (desc.set) { + str = ctx.stylize('[Setter]', 'special'); + } + } + if (!hasOwnProperty(visibleKeys, key)) { + name = '[' + key + ']'; + } + if (!str) { + if (ctx.seen.indexOf(desc.value) < 0) { + if (isNull(recurseTimes)) { + str = formatValue(ctx, desc.value, null); + } else { + str = formatValue(ctx, desc.value, recurseTimes - 1); + } + if (str.indexOf('\n') > -1) { + if (array) { + str = str.split('\n').map(function(line) { + return ' ' + line; + }).join('\n').substr(2); + } else { + str = '\n' + str.split('\n').map(function(line) { + return ' ' + line; + }).join('\n'); + } + } + } else { + str = ctx.stylize('[Circular]', 'special'); + } + } + if (isUndefined(name)) { + if (array && key.match(/^\d+$/)) { + return str; + } + name = JSON.stringify('' + key); + if (name.match(/^"([a-zA-Z_][a-zA-Z_0-9]*)"$/)) { + name = name.substr(1, name.length - 2); + name = ctx.stylize(name, 'name'); + } else { + name = name.replace(/'/g, "\\'") + .replace(/\\"/g, '"') + .replace(/(^"|"$)/g, "'"); + name = ctx.stylize(name, 'string'); + } + } + + return name + ': ' + str; +} + + +function reduceToSingleString(output, base, braces) { + var numLinesEst = 0; + var length = output.reduce(function(prev, cur) { + numLinesEst++; + if (cur.indexOf('\n') >= 0) numLinesEst++; + return prev + cur.replace(/\u001b\[\d\d?m/g, '').length + 1; + }, 0); + + if (length > 60) { + return braces[0] + + (base === '' ? '' : base + '\n ') + + ' ' + + output.join(',\n ') + + ' ' + + braces[1]; + } + + return braces[0] + base + ' ' + output.join(', ') + ' ' + braces[1]; +} + + +// NOTE: These type checking functions intentionally don't use `instanceof` +// because it is fragile and can be easily faked with `Object.create()`. +function isArray(ar) { + return Array.isArray(ar); +} +exports.isArray = isArray; + +function isBoolean(arg) { + return typeof arg === 'boolean'; +} +exports.isBoolean = isBoolean; + +function isNull(arg) { + return arg === null; +} +exports.isNull = isNull; + +function isNullOrUndefined(arg) { + return arg == null; +} +exports.isNullOrUndefined = isNullOrUndefined; + +function isNumber(arg) { + return typeof arg === 'number'; +} +exports.isNumber = isNumber; + +function isString(arg) { + return typeof arg === 'string'; +} +exports.isString = isString; + +function isSymbol(arg) { + return typeof arg === 'symbol'; +} +exports.isSymbol = isSymbol; + +function isUndefined(arg) { + return arg === void 0; +} +exports.isUndefined = isUndefined; + +function isRegExp(re) { + return isObject(re) && objectToString(re) === '[object RegExp]'; +} +exports.isRegExp = isRegExp; + +function isObject(arg) { + return typeof arg === 'object' && arg !== null; +} +exports.isObject = isObject; + +function isDate(d) { + return isObject(d) && objectToString(d) === '[object Date]'; +} +exports.isDate = isDate; + +function isError(e) { + return isObject(e) && + (objectToString(e) === '[object Error]' || e instanceof Error); +} +exports.isError = isError; + +function isFunction(arg) { + return typeof arg === 'function'; +} +exports.isFunction = isFunction; + +function isPrimitive(arg) { + return arg === null || + typeof arg === 'boolean' || + typeof arg === 'number' || + typeof arg === 'string' || + typeof arg === 'symbol' || // ES6 symbol + typeof arg === 'undefined'; +} +exports.isPrimitive = isPrimitive; + +exports.isBuffer = require('./support/isBuffer'); + +function objectToString(o) { + return Object.prototype.toString.call(o); +} + + +function pad(n) { + return n < 10 ? '0' + n.toString(10) : n.toString(10); +} + + +var months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', + 'Oct', 'Nov', 'Dec']; + +// 26 Feb 16:19:34 +function timestamp() { + var d = new Date(); + var time = [pad(d.getHours()), + pad(d.getMinutes()), + pad(d.getSeconds())].join(':'); + return [d.getDate(), months[d.getMonth()], time].join(' '); +} + + +// log is just a thin wrapper to console.log that prepends a timestamp +exports.log = function() { + console.log('%s - %s', timestamp(), exports.format.apply(exports, arguments)); +}; + + +/** + * Inherit the prototype methods from one constructor into another. + * + * The Function.prototype.inherits from lang.js rewritten as a standalone + * function (not on Function.prototype). NOTE: If this file is to be loaded + * during bootstrapping this function needs to be rewritten using some native + * functions as prototype setup using normal JavaScript does not work as + * expected during bootstrapping (see mirror.js in r114903). + * + * @param {function} ctor Constructor function which needs to inherit the + * prototype. + * @param {function} superCtor Constructor function to inherit prototype from. + */ +exports.inherits = require('inherits'); + +exports._extend = function(origin, add) { + // Don't do anything if add isn't an object + if (!add || !isObject(add)) return origin; + + var keys = Object.keys(add); + var i = keys.length; + while (i--) { + origin[keys[i]] = add[keys[i]]; + } + return origin; +}; + +function hasOwnProperty(obj, prop) { + return Object.prototype.hasOwnProperty.call(obj, prop); +} + +}).call(this,require('_process'),typeof global !== "undefined" ? global : typeof self !== "undefined" ? self : typeof window !== "undefined" ? window : {}) +},{"./support/isBuffer":27,"_process":24,"inherits":26}],29:[function(require,module,exports){ +// Returns a wrapper function that returns a wrapped callback +// The wrapper function should do some stuff, and return a +// presumably different callback function. +// This makes sure that own properties are retained, so that +// decorations and such are not lost along the way. +module.exports = wrappy +function wrappy (fn, cb) { + if (fn && cb) return wrappy(fn)(cb) + + if (typeof fn !== 'function') + throw new TypeError('need wrapper function') + + Object.keys(fn).forEach(function (k) { + wrapper[k] = fn[k] + }) + + return wrapper + + function wrapper() { + var args = new Array(arguments.length) + for (var i = 0; i < args.length; i++) { + args[i] = arguments[i] + } + var ret = fn.apply(this, args) + var cb = args[args.length-1] + if (typeof ret === 'function' && ret !== cb) { + Object.keys(cb).forEach(function (k) { + ret[k] = cb[k] + }) + } + return ret + } +} + +},{}]},{},[7])(7) +}); \ No newline at end of file diff --git a/assets/javascripts/workers/search.ecf98df9.min.js b/assets/javascripts/workers/search.ecf98df9.min.js new file mode 100644 index 00000000..f8c73991 --- /dev/null +++ b/assets/javascripts/workers/search.ecf98df9.min.js @@ -0,0 +1,48 @@ +"use strict";(()=>{var ge=Object.create;var W=Object.defineProperty,ye=Object.defineProperties,me=Object.getOwnPropertyDescriptor,ve=Object.getOwnPropertyDescriptors,xe=Object.getOwnPropertyNames,G=Object.getOwnPropertySymbols,Se=Object.getPrototypeOf,X=Object.prototype.hasOwnProperty,Qe=Object.prototype.propertyIsEnumerable;var J=(t,e,r)=>e in t?W(t,e,{enumerable:!0,configurable:!0,writable:!0,value:r}):t[e]=r,M=(t,e)=>{for(var r in e||(e={}))X.call(e,r)&&J(t,r,e[r]);if(G)for(var r of G(e))Qe.call(e,r)&&J(t,r,e[r]);return t},Z=(t,e)=>ye(t,ve(e));var K=(t,e)=>()=>(e||t((e={exports:{}}).exports,e),e.exports);var be=(t,e,r,n)=>{if(e&&typeof e=="object"||typeof e=="function")for(let i of xe(e))!X.call(t,i)&&i!==r&&W(t,i,{get:()=>e[i],enumerable:!(n=me(e,i))||n.enumerable});return t};var H=(t,e,r)=>(r=t!=null?ge(Se(t)):{},be(e||!t||!t.__esModule?W(r,"default",{value:t,enumerable:!0}):r,t));var z=(t,e,r)=>new Promise((n,i)=>{var s=u=>{try{a(r.next(u))}catch(c){i(c)}},o=u=>{try{a(r.throw(u))}catch(c){i(c)}},a=u=>u.done?n(u.value):Promise.resolve(u.value).then(s,o);a((r=r.apply(t,e)).next())});var re=K((ee,te)=>{/** + * lunr - http://lunrjs.com - A bit like Solr, but much smaller and not as bright - 2.3.9 + * Copyright (C) 2020 Oliver Nightingale + * @license MIT + */(function(){var t=function(e){var r=new t.Builder;return r.pipeline.add(t.trimmer,t.stopWordFilter,t.stemmer),r.searchPipeline.add(t.stemmer),e.call(r,r),r.build()};t.version="2.3.9";/*! + * lunr.utils + * Copyright (C) 2020 Oliver Nightingale + */t.utils={},t.utils.warn=function(e){return function(r){e.console&&console.warn&&console.warn(r)}}(this),t.utils.asString=function(e){return e==null?"":e.toString()},t.utils.clone=function(e){if(e==null)return e;for(var r=Object.create(null),n=Object.keys(e),i=0;i0){var h=t.utils.clone(r)||{};h.position=[a,c],h.index=s.length,s.push(new t.Token(n.slice(a,o),h))}a=o+1}}return s},t.tokenizer.separator=/[\s\-]+/;/*! + * lunr.Pipeline + * Copyright (C) 2020 Oliver Nightingale + */t.Pipeline=function(){this._stack=[]},t.Pipeline.registeredFunctions=Object.create(null),t.Pipeline.registerFunction=function(e,r){r in this.registeredFunctions&&t.utils.warn("Overwriting existing registered function: "+r),e.label=r,t.Pipeline.registeredFunctions[e.label]=e},t.Pipeline.warnIfFunctionNotRegistered=function(e){var r=e.label&&e.label in this.registeredFunctions;r||t.utils.warn(`Function is not registered with pipeline. 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r=this.highlight(e),n=ue(e).filter(o=>o.presence!==lunr.Query.presence.PROHIBITED),i=this.index.search(`${e}*`).reduce((o,{ref:a,score:u,matchData:c})=>{let h=this.documents.get(a);if(typeof h!="undefined"){let{location:y,title:g,text:b,tags:m,parent:Q}=h,p=ce(n,Object.keys(c.metadata)),d=+!Q+ +Object.values(p).every(w=>w);o.push(Z(M({location:y,title:r(g),text:r(b)},m&&{tags:m.map(r)}),{score:u*(1+d),terms:p}))}return o},[]).sort((o,a)=>a.score-o.score).reduce((o,a)=>{let u=this.documents.get(a.location);if(typeof u!="undefined"){let c="parent"in u?u.parent.location:u.location;o.set(c,[...o.get(c)||[],a])}return o},new Map),s;if(this.options.suggestions){let o=this.index.query(a=>{for(let u of n)a.term(u.term,{fields:["title"],presence:lunr.Query.presence.REQUIRED,wildcard:lunr.Query.wildcard.TRAILING})});s=o.length?Object.keys(o[0].matchData.metadata):[]}return M({items:[...i.values()]},typeof s!="undefined"&&{suggestions:s})}catch(r){console.warn(`Invalid query: ${e} \u2013 see https://bit.ly/2s3ChXG`)}return{items:[]}}};var Y;function ke(t){return z(this,null,function*(){let e="../lunr";if(typeof parent!="undefined"&&"IFrameWorker"in parent){let n=document.querySelector("script[src]"),[i]=n.src.split("/worker");e=e.replace("..",i)}let r=[];for(let n of t.lang){switch(n){case"ja":r.push(`${e}/tinyseg.js`);break;case"hi":case"th":r.push(`${e}/wordcut.js`);break}n!=="en"&&r.push(`${e}/min/lunr.${n}.min.js`)}t.lang.length>1&&r.push(`${e}/min/lunr.multi.min.js`),r.length&&(yield importScripts(`${e}/min/lunr.stemmer.support.min.js`,...r))})}function Te(t){return z(this,null,function*(){switch(t.type){case 0:return yield ke(t.data.config),Y=new U(t.data),{type:1};case 2:return{type:3,data:Y?Y.search(t.data):{items:[]}};default:throw new TypeError("Invalid message type")}})}self.lunr=le.default;addEventListener("message",t=>z(void 0,null,function*(){postMessage(yield Te(t.data))}));})(); +//# sourceMappingURL=search.ecf98df9.min.js.map + diff --git a/assets/javascripts/workers/search.ecf98df9.min.js.map b/assets/javascripts/workers/search.ecf98df9.min.js.map new file mode 100644 index 00000000..eada3e64 --- /dev/null +++ b/assets/javascripts/workers/search.ecf98df9.min.js.map @@ -0,0 +1,8 @@ +{ + "version": 3, + "sources": ["node_modules/lunr/lunr.js", "node_modules/escape-html/index.js", "src/assets/javascripts/integrations/search/worker/main/index.ts", "src/assets/javascripts/polyfills/index.ts", "src/assets/javascripts/integrations/search/document/index.ts", "src/assets/javascripts/integrations/search/highlighter/index.ts", "src/assets/javascripts/integrations/search/query/_/index.ts", "src/assets/javascripts/integrations/search/_/index.ts"], + "sourceRoot": "../../../..", + "sourcesContent": ["/**\n * lunr - http://lunrjs.com - A bit like Solr, but much smaller and not as bright - 2.3.9\n * Copyright (C) 2020 Oliver Nightingale\n * @license MIT\n */\n\n;(function(){\n\n/**\n * A convenience function for configuring and constructing\n * a new lunr Index.\n *\n * A lunr.Builder instance is created and the pipeline setup\n * with a trimmer, stop word filter and stemmer.\n *\n * This builder object is yielded to the configuration function\n * that is passed as a parameter, allowing the list of fields\n * and other builder parameters to be customised.\n *\n * All documents _must_ be added within the passed config function.\n *\n * @example\n * var idx = lunr(function () {\n * this.field('title')\n * this.field('body')\n * this.ref('id')\n *\n * documents.forEach(function (doc) {\n * this.add(doc)\n * }, this)\n * })\n *\n * @see {@link lunr.Builder}\n * @see {@link lunr.Pipeline}\n * @see {@link lunr.trimmer}\n * @see {@link lunr.stopWordFilter}\n * @see {@link lunr.stemmer}\n * @namespace {function} lunr\n */\nvar lunr = function (config) {\n var builder = new lunr.Builder\n\n builder.pipeline.add(\n lunr.trimmer,\n lunr.stopWordFilter,\n lunr.stemmer\n )\n\n builder.searchPipeline.add(\n lunr.stemmer\n )\n\n config.call(builder, builder)\n return builder.build()\n}\n\nlunr.version = \"2.3.9\"\n/*!\n * lunr.utils\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * A namespace containing utils for the rest of the lunr library\n * @namespace lunr.utils\n */\nlunr.utils = {}\n\n/**\n * Print a warning message to the console.\n *\n * @param {String} message The message to be printed.\n * @memberOf lunr.utils\n * @function\n */\nlunr.utils.warn = (function (global) {\n /* eslint-disable no-console */\n return function (message) {\n if (global.console && console.warn) {\n console.warn(message)\n }\n }\n /* eslint-enable no-console */\n})(this)\n\n/**\n * Convert an object to a string.\n *\n * In the case of `null` and `undefined` the function returns\n * the empty string, in all other cases the result of calling\n * `toString` on the passed object is returned.\n *\n * @param {Any} obj The object to convert to a string.\n * @return {String} string representation of the passed object.\n * @memberOf lunr.utils\n */\nlunr.utils.asString = function (obj) {\n if (obj === void 0 || obj === null) {\n return \"\"\n } else {\n return obj.toString()\n }\n}\n\n/**\n * Clones an object.\n *\n * Will create a copy of an existing object such that any mutations\n * on the copy cannot affect the original.\n *\n * Only shallow objects are supported, passing a nested object to this\n * function will cause a TypeError.\n *\n * Objects with primitives, and arrays of primitives are supported.\n *\n * @param {Object} obj The object to clone.\n * @return {Object} a clone of the passed object.\n * @throws {TypeError} when a nested object is passed.\n * @memberOf Utils\n */\nlunr.utils.clone = function (obj) {\n if (obj === null || obj === undefined) {\n return obj\n }\n\n var clone = Object.create(null),\n keys = Object.keys(obj)\n\n for (var i = 0; i < keys.length; i++) {\n var key = keys[i],\n val = obj[key]\n\n if (Array.isArray(val)) {\n clone[key] = val.slice()\n continue\n }\n\n if (typeof val === 'string' ||\n typeof val === 'number' ||\n typeof val === 'boolean') {\n clone[key] = val\n continue\n }\n\n throw new TypeError(\"clone is not deep and does not support nested objects\")\n }\n\n return clone\n}\nlunr.FieldRef = function (docRef, fieldName, stringValue) {\n this.docRef = docRef\n this.fieldName = fieldName\n this._stringValue = stringValue\n}\n\nlunr.FieldRef.joiner = \"/\"\n\nlunr.FieldRef.fromString = function (s) {\n var n = s.indexOf(lunr.FieldRef.joiner)\n\n if (n === -1) {\n throw \"malformed field ref string\"\n }\n\n var fieldRef = s.slice(0, n),\n docRef = s.slice(n + 1)\n\n return new lunr.FieldRef (docRef, fieldRef, s)\n}\n\nlunr.FieldRef.prototype.toString = function () {\n if (this._stringValue == undefined) {\n this._stringValue = this.fieldName + lunr.FieldRef.joiner + this.docRef\n }\n\n return this._stringValue\n}\n/*!\n * lunr.Set\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * A lunr set.\n *\n * @constructor\n */\nlunr.Set = function (elements) {\n this.elements = Object.create(null)\n\n if (elements) {\n this.length = elements.length\n\n for (var i = 0; i < this.length; i++) {\n this.elements[elements[i]] = true\n }\n } else {\n this.length = 0\n }\n}\n\n/**\n * A complete set that contains all elements.\n *\n * @static\n * @readonly\n * @type {lunr.Set}\n */\nlunr.Set.complete = {\n intersect: function (other) {\n return other\n },\n\n union: function () {\n return this\n },\n\n contains: function () {\n return true\n }\n}\n\n/**\n * An empty set that contains no elements.\n *\n * @static\n * @readonly\n * @type {lunr.Set}\n */\nlunr.Set.empty = {\n intersect: function () {\n return this\n },\n\n union: function (other) {\n return other\n },\n\n contains: function () {\n return false\n }\n}\n\n/**\n * Returns true if this set contains the specified object.\n *\n * @param {object} object - Object whose presence in this set is to be tested.\n * @returns {boolean} - True if this set contains the specified object.\n */\nlunr.Set.prototype.contains = function (object) {\n return !!this.elements[object]\n}\n\n/**\n * Returns a new set containing only the elements that are present in both\n * this set and the specified set.\n *\n * @param {lunr.Set} other - set to intersect with this set.\n * @returns {lunr.Set} a new set that is the intersection of this and the specified set.\n */\n\nlunr.Set.prototype.intersect = function (other) {\n var a, b, elements, intersection = []\n\n if (other === lunr.Set.complete) {\n return this\n }\n\n if (other === lunr.Set.empty) {\n return other\n }\n\n if (this.length < other.length) {\n a = this\n b = other\n } else {\n a = other\n b = this\n }\n\n elements = Object.keys(a.elements)\n\n for (var i = 0; i < elements.length; i++) {\n var element = elements[i]\n if (element in b.elements) {\n intersection.push(element)\n }\n }\n\n return new lunr.Set (intersection)\n}\n\n/**\n * Returns a new set combining the elements of this and the specified set.\n *\n * @param {lunr.Set} other - set to union with this set.\n * @return {lunr.Set} a new set that is the union of this and the specified set.\n */\n\nlunr.Set.prototype.union = function (other) {\n if (other === lunr.Set.complete) {\n return lunr.Set.complete\n }\n\n if (other === lunr.Set.empty) {\n return this\n }\n\n return new lunr.Set(Object.keys(this.elements).concat(Object.keys(other.elements)))\n}\n/**\n * A function to calculate the inverse document frequency for\n * a posting. This is shared between the builder and the index\n *\n * @private\n * @param {object} posting - The posting for a given term\n * @param {number} documentCount - The total number of documents.\n */\nlunr.idf = function (posting, documentCount) {\n var documentsWithTerm = 0\n\n for (var fieldName in posting) {\n if (fieldName == '_index') continue // Ignore the term index, its not a field\n documentsWithTerm += Object.keys(posting[fieldName]).length\n }\n\n var x = (documentCount - documentsWithTerm + 0.5) / (documentsWithTerm + 0.5)\n\n return Math.log(1 + Math.abs(x))\n}\n\n/**\n * A token wraps a string representation of a token\n * as it is passed through the text processing pipeline.\n *\n * @constructor\n * @param {string} [str=''] - The string token being wrapped.\n * @param {object} [metadata={}] - Metadata associated with this token.\n */\nlunr.Token = function (str, metadata) {\n this.str = str || \"\"\n this.metadata = metadata || {}\n}\n\n/**\n * Returns the token string that is being wrapped by this object.\n *\n * @returns {string}\n */\nlunr.Token.prototype.toString = function () {\n return this.str\n}\n\n/**\n * A token update function is used when updating or optionally\n * when cloning a token.\n *\n * @callback lunr.Token~updateFunction\n * @param {string} str - The string representation of the token.\n * @param {Object} metadata - All metadata associated with this token.\n */\n\n/**\n * Applies the given function to the wrapped string token.\n *\n * @example\n * token.update(function (str, metadata) {\n * return str.toUpperCase()\n * })\n *\n * @param {lunr.Token~updateFunction} fn - A function to apply to the token string.\n * @returns {lunr.Token}\n */\nlunr.Token.prototype.update = function (fn) {\n this.str = fn(this.str, this.metadata)\n return this\n}\n\n/**\n * Creates a clone of this token. Optionally a function can be\n * applied to the cloned token.\n *\n * @param {lunr.Token~updateFunction} [fn] - An optional function to apply to the cloned token.\n * @returns {lunr.Token}\n */\nlunr.Token.prototype.clone = function (fn) {\n fn = fn || function (s) { return s }\n return new lunr.Token (fn(this.str, this.metadata), this.metadata)\n}\n/*!\n * lunr.tokenizer\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * A function for splitting a string into tokens ready to be inserted into\n * the search index. Uses `lunr.tokenizer.separator` to split strings, change\n * the value of this property to change how strings are split into tokens.\n *\n * This tokenizer will convert its parameter to a string by calling `toString` and\n * then will split this string on the character in `lunr.tokenizer.separator`.\n * Arrays will have their elements converted to strings and wrapped in a lunr.Token.\n *\n * Optional metadata can be passed to the tokenizer, this metadata will be cloned and\n * added as metadata to every token that is created from the object to be tokenized.\n *\n * @static\n * @param {?(string|object|object[])} obj - The object to convert into tokens\n * @param {?object} metadata - Optional metadata to associate with every token\n * @returns {lunr.Token[]}\n * @see {@link lunr.Pipeline}\n */\nlunr.tokenizer = function (obj, metadata) {\n if (obj == null || obj == undefined) {\n return []\n }\n\n if (Array.isArray(obj)) {\n return obj.map(function (t) {\n return new lunr.Token(\n lunr.utils.asString(t).toLowerCase(),\n lunr.utils.clone(metadata)\n )\n })\n }\n\n var str = obj.toString().toLowerCase(),\n len = str.length,\n tokens = []\n\n for (var sliceEnd = 0, sliceStart = 0; sliceEnd <= len; sliceEnd++) {\n var char = str.charAt(sliceEnd),\n sliceLength = sliceEnd - sliceStart\n\n if ((char.match(lunr.tokenizer.separator) || sliceEnd == len)) {\n\n if (sliceLength > 0) {\n var tokenMetadata = lunr.utils.clone(metadata) || {}\n tokenMetadata[\"position\"] = [sliceStart, sliceLength]\n tokenMetadata[\"index\"] = tokens.length\n\n tokens.push(\n new lunr.Token (\n str.slice(sliceStart, sliceEnd),\n tokenMetadata\n )\n )\n }\n\n sliceStart = sliceEnd + 1\n }\n\n }\n\n return tokens\n}\n\n/**\n * The separator used to split a string into tokens. Override this property to change the behaviour of\n * `lunr.tokenizer` behaviour when tokenizing strings. By default this splits on whitespace and hyphens.\n *\n * @static\n * @see lunr.tokenizer\n */\nlunr.tokenizer.separator = /[\\s\\-]+/\n/*!\n * lunr.Pipeline\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * lunr.Pipelines maintain an ordered list of functions to be applied to all\n * tokens in documents entering the search index and queries being ran against\n * the index.\n *\n * An instance of lunr.Index created with the lunr shortcut will contain a\n * pipeline with a stop word filter and an English language stemmer. Extra\n * functions can be added before or after either of these functions or these\n * default functions can be removed.\n *\n * When run the pipeline will call each function in turn, passing a token, the\n * index of that token in the original list of all tokens and finally a list of\n * all the original tokens.\n *\n * The output of functions in the pipeline will be passed to the next function\n * in the pipeline. To exclude a token from entering the index the function\n * should return undefined, the rest of the pipeline will not be called with\n * this token.\n *\n * For serialisation of pipelines to work, all functions used in an instance of\n * a pipeline should be registered with lunr.Pipeline. Registered functions can\n * then be loaded. If trying to load a serialised pipeline that uses functions\n * that are not registered an error will be thrown.\n *\n * If not planning on serialising the pipeline then registering pipeline functions\n * is not necessary.\n *\n * @constructor\n */\nlunr.Pipeline = function () {\n this._stack = []\n}\n\nlunr.Pipeline.registeredFunctions = Object.create(null)\n\n/**\n * A pipeline function maps lunr.Token to lunr.Token. A lunr.Token contains the token\n * string as well as all known metadata. A pipeline function can mutate the token string\n * or mutate (or add) metadata for a given token.\n *\n * A pipeline function can indicate that the passed token should be discarded by returning\n * null, undefined or an empty string. This token will not be passed to any downstream pipeline\n * functions and will not be added to the index.\n *\n * Multiple tokens can be returned by returning an array of tokens. Each token will be passed\n * to any downstream pipeline functions and all will returned tokens will be added to the index.\n *\n * Any number of pipeline functions may be chained together using a lunr.Pipeline.\n *\n * @interface lunr.PipelineFunction\n * @param {lunr.Token} token - A token from the document being processed.\n * @param {number} i - The index of this token in the complete list of tokens for this document/field.\n * @param {lunr.Token[]} tokens - All tokens for this document/field.\n * @returns {(?lunr.Token|lunr.Token[])}\n */\n\n/**\n * Register a function with the pipeline.\n *\n * Functions that are used in the pipeline should be registered if the pipeline\n * needs to be serialised, or a serialised pipeline needs to be loaded.\n *\n * Registering a function does not add it to a pipeline, functions must still be\n * added to instances of the pipeline for them to be used when running a pipeline.\n *\n * @param {lunr.PipelineFunction} fn - The function to check for.\n * @param {String} label - The label to register this function with\n */\nlunr.Pipeline.registerFunction = function (fn, label) {\n if (label in this.registeredFunctions) {\n lunr.utils.warn('Overwriting existing registered function: ' + label)\n }\n\n fn.label = label\n lunr.Pipeline.registeredFunctions[fn.label] = fn\n}\n\n/**\n * Warns if the function is not registered as a Pipeline function.\n *\n * @param {lunr.PipelineFunction} fn - The function to check for.\n * @private\n */\nlunr.Pipeline.warnIfFunctionNotRegistered = function (fn) {\n var isRegistered = fn.label && (fn.label in this.registeredFunctions)\n\n if (!isRegistered) {\n lunr.utils.warn('Function is not registered with pipeline. This may cause problems when serialising the index.\\n', fn)\n }\n}\n\n/**\n * Loads a previously serialised pipeline.\n *\n * All functions to be loaded must already be registered with lunr.Pipeline.\n * If any function from the serialised data has not been registered then an\n * error will be thrown.\n *\n * @param {Object} serialised - The serialised pipeline to load.\n * @returns {lunr.Pipeline}\n */\nlunr.Pipeline.load = function (serialised) {\n var pipeline = new lunr.Pipeline\n\n serialised.forEach(function (fnName) {\n var fn = lunr.Pipeline.registeredFunctions[fnName]\n\n if (fn) {\n pipeline.add(fn)\n } else {\n throw new Error('Cannot load unregistered function: ' + fnName)\n }\n })\n\n return pipeline\n}\n\n/**\n * Adds new functions to the end of the pipeline.\n *\n * Logs a warning if the function has not been registered.\n *\n * @param {lunr.PipelineFunction[]} functions - Any number of functions to add to the pipeline.\n */\nlunr.Pipeline.prototype.add = function () {\n var fns = Array.prototype.slice.call(arguments)\n\n fns.forEach(function (fn) {\n lunr.Pipeline.warnIfFunctionNotRegistered(fn)\n this._stack.push(fn)\n }, this)\n}\n\n/**\n * Adds a single function after a function that already exists in the\n * pipeline.\n *\n * Logs a warning if the function has not been registered.\n *\n * @param {lunr.PipelineFunction} existingFn - A function that already exists in the pipeline.\n * @param {lunr.PipelineFunction} newFn - The new function to add to the pipeline.\n */\nlunr.Pipeline.prototype.after = function (existingFn, newFn) {\n lunr.Pipeline.warnIfFunctionNotRegistered(newFn)\n\n var pos = this._stack.indexOf(existingFn)\n if (pos == -1) {\n throw new Error('Cannot find existingFn')\n }\n\n pos = pos + 1\n this._stack.splice(pos, 0, newFn)\n}\n\n/**\n * Adds a single function before a function that already exists in the\n * pipeline.\n *\n * Logs a warning if the function has not been registered.\n *\n * @param {lunr.PipelineFunction} existingFn - A function that already exists in the pipeline.\n * @param {lunr.PipelineFunction} newFn - The new function to add to the pipeline.\n */\nlunr.Pipeline.prototype.before = function (existingFn, newFn) {\n lunr.Pipeline.warnIfFunctionNotRegistered(newFn)\n\n var pos = this._stack.indexOf(existingFn)\n if (pos == -1) {\n throw new Error('Cannot find existingFn')\n }\n\n this._stack.splice(pos, 0, newFn)\n}\n\n/**\n * Removes a function from the pipeline.\n *\n * @param {lunr.PipelineFunction} fn The function to remove from the pipeline.\n */\nlunr.Pipeline.prototype.remove = function (fn) {\n var pos = this._stack.indexOf(fn)\n if (pos == -1) {\n return\n }\n\n this._stack.splice(pos, 1)\n}\n\n/**\n * Runs the current list of functions that make up the pipeline against the\n * passed tokens.\n *\n * @param {Array} tokens The tokens to run through the pipeline.\n * @returns {Array}\n */\nlunr.Pipeline.prototype.run = function (tokens) {\n var stackLength = this._stack.length\n\n for (var i = 0; i < stackLength; i++) {\n var fn = this._stack[i]\n var memo = []\n\n for (var j = 0; j < tokens.length; j++) {\n var result = fn(tokens[j], j, tokens)\n\n if (result === null || result === void 0 || result === '') continue\n\n if (Array.isArray(result)) {\n for (var k = 0; k < result.length; k++) {\n memo.push(result[k])\n }\n } else {\n memo.push(result)\n }\n }\n\n tokens = memo\n }\n\n return tokens\n}\n\n/**\n * Convenience method for passing a string through a pipeline and getting\n * strings out. This method takes care of wrapping the passed string in a\n * token and mapping the resulting tokens back to strings.\n *\n * @param {string} str - The string to pass through the pipeline.\n * @param {?object} metadata - Optional metadata to associate with the token\n * passed to the pipeline.\n * @returns {string[]}\n */\nlunr.Pipeline.prototype.runString = function (str, metadata) {\n var token = new lunr.Token (str, metadata)\n\n return this.run([token]).map(function (t) {\n return t.toString()\n })\n}\n\n/**\n * Resets the pipeline by removing any existing processors.\n *\n */\nlunr.Pipeline.prototype.reset = function () {\n this._stack = []\n}\n\n/**\n * Returns a representation of the pipeline ready for serialisation.\n *\n * Logs a warning if the function has not been registered.\n *\n * @returns {Array}\n */\nlunr.Pipeline.prototype.toJSON = function () {\n return this._stack.map(function (fn) {\n lunr.Pipeline.warnIfFunctionNotRegistered(fn)\n\n return fn.label\n })\n}\n/*!\n * lunr.Vector\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * A vector is used to construct the vector space of documents and queries. These\n * vectors support operations to determine the similarity between two documents or\n * a document and a query.\n *\n * Normally no parameters are required for initializing a vector, but in the case of\n * loading a previously dumped vector the raw elements can be provided to the constructor.\n *\n * For performance reasons vectors are implemented with a flat array, where an elements\n * index is immediately followed by its value. E.g. [index, value, index, value]. This\n * allows the underlying array to be as sparse as possible and still offer decent\n * performance when being used for vector calculations.\n *\n * @constructor\n * @param {Number[]} [elements] - The flat list of element index and element value pairs.\n */\nlunr.Vector = function (elements) {\n this._magnitude = 0\n this.elements = elements || []\n}\n\n\n/**\n * Calculates the position within the vector to insert a given index.\n *\n * This is used internally by insert and upsert. If there are duplicate indexes then\n * the position is returned as if the value for that index were to be updated, but it\n * is the callers responsibility to check whether there is a duplicate at that index\n *\n * @param {Number} insertIdx - The index at which the element should be inserted.\n * @returns {Number}\n */\nlunr.Vector.prototype.positionForIndex = function (index) {\n // For an empty vector the tuple can be inserted at the beginning\n if (this.elements.length == 0) {\n return 0\n }\n\n var start = 0,\n end = this.elements.length / 2,\n sliceLength = end - start,\n pivotPoint = Math.floor(sliceLength / 2),\n pivotIndex = this.elements[pivotPoint * 2]\n\n while (sliceLength > 1) {\n if (pivotIndex < index) {\n start = pivotPoint\n }\n\n if (pivotIndex > index) {\n end = pivotPoint\n }\n\n if (pivotIndex == index) {\n break\n }\n\n sliceLength = end - start\n pivotPoint = start + Math.floor(sliceLength / 2)\n pivotIndex = this.elements[pivotPoint * 2]\n }\n\n if (pivotIndex == index) {\n return pivotPoint * 2\n }\n\n if (pivotIndex > index) {\n return pivotPoint * 2\n }\n\n if (pivotIndex < index) {\n return (pivotPoint + 1) * 2\n }\n}\n\n/**\n * Inserts an element at an index within the vector.\n *\n * Does not allow duplicates, will throw an error if there is already an entry\n * for this index.\n *\n * @param {Number} insertIdx - The index at which the element should be inserted.\n * @param {Number} val - The value to be inserted into the vector.\n */\nlunr.Vector.prototype.insert = function (insertIdx, val) {\n this.upsert(insertIdx, val, function () {\n throw \"duplicate index\"\n })\n}\n\n/**\n * Inserts or updates an existing index within the vector.\n *\n * @param {Number} insertIdx - The index at which the element should be inserted.\n * @param {Number} val - The value to be inserted into the vector.\n * @param {function} fn - A function that is called for updates, the existing value and the\n * requested value are passed as arguments\n */\nlunr.Vector.prototype.upsert = function (insertIdx, val, fn) {\n this._magnitude = 0\n var position = this.positionForIndex(insertIdx)\n\n if (this.elements[position] == insertIdx) {\n this.elements[position + 1] = fn(this.elements[position + 1], val)\n } else {\n this.elements.splice(position, 0, insertIdx, val)\n }\n}\n\n/**\n * Calculates the magnitude of this vector.\n *\n * @returns {Number}\n */\nlunr.Vector.prototype.magnitude = function () {\n if (this._magnitude) return this._magnitude\n\n var sumOfSquares = 0,\n elementsLength = this.elements.length\n\n for (var i = 1; i < elementsLength; i += 2) {\n var val = this.elements[i]\n sumOfSquares += val * val\n }\n\n return this._magnitude = Math.sqrt(sumOfSquares)\n}\n\n/**\n * Calculates the dot product of this vector and another vector.\n *\n * @param {lunr.Vector} otherVector - The vector to compute the dot product with.\n * @returns {Number}\n */\nlunr.Vector.prototype.dot = function (otherVector) {\n var dotProduct = 0,\n a = this.elements, b = otherVector.elements,\n aLen = a.length, bLen = b.length,\n aVal = 0, bVal = 0,\n i = 0, j = 0\n\n while (i < aLen && j < bLen) {\n aVal = a[i], bVal = b[j]\n if (aVal < bVal) {\n i += 2\n } else if (aVal > bVal) {\n j += 2\n } else if (aVal == bVal) {\n dotProduct += a[i + 1] * b[j + 1]\n i += 2\n j += 2\n }\n }\n\n return dotProduct\n}\n\n/**\n * Calculates the similarity between this vector and another vector.\n *\n * @param {lunr.Vector} otherVector - The other vector to calculate the\n * similarity with.\n * @returns {Number}\n */\nlunr.Vector.prototype.similarity = function (otherVector) {\n return this.dot(otherVector) / this.magnitude() || 0\n}\n\n/**\n * Converts the vector to an array of the elements within the vector.\n *\n * @returns {Number[]}\n */\nlunr.Vector.prototype.toArray = function () {\n var output = new Array (this.elements.length / 2)\n\n for (var i = 1, j = 0; i < this.elements.length; i += 2, j++) {\n output[j] = this.elements[i]\n }\n\n return output\n}\n\n/**\n * A JSON serializable representation of the vector.\n *\n * @returns {Number[]}\n */\nlunr.Vector.prototype.toJSON = function () {\n return this.elements\n}\n/* eslint-disable */\n/*!\n * lunr.stemmer\n * Copyright (C) 2020 Oliver Nightingale\n * Includes code from - http://tartarus.org/~martin/PorterStemmer/js.txt\n */\n\n/**\n * lunr.stemmer is an english language stemmer, this is a JavaScript\n * implementation of the PorterStemmer taken from http://tartarus.org/~martin\n *\n * @static\n * @implements {lunr.PipelineFunction}\n * @param {lunr.Token} token - The string to stem\n * @returns {lunr.Token}\n * @see {@link lunr.Pipeline}\n * @function\n */\nlunr.stemmer = (function(){\n var step2list = {\n \"ational\" : \"ate\",\n \"tional\" : \"tion\",\n \"enci\" : \"ence\",\n \"anci\" : \"ance\",\n \"izer\" : \"ize\",\n \"bli\" : \"ble\",\n \"alli\" : \"al\",\n \"entli\" : \"ent\",\n \"eli\" : \"e\",\n \"ousli\" : \"ous\",\n \"ization\" : \"ize\",\n \"ation\" : \"ate\",\n \"ator\" : \"ate\",\n \"alism\" : \"al\",\n \"iveness\" : \"ive\",\n \"fulness\" : \"ful\",\n \"ousness\" : \"ous\",\n \"aliti\" : \"al\",\n \"iviti\" : \"ive\",\n \"biliti\" : \"ble\",\n \"logi\" : \"log\"\n },\n\n step3list = {\n \"icate\" : \"ic\",\n \"ative\" : \"\",\n \"alize\" : \"al\",\n \"iciti\" : \"ic\",\n \"ical\" : \"ic\",\n \"ful\" : \"\",\n \"ness\" : \"\"\n },\n\n c = \"[^aeiou]\", // consonant\n v = \"[aeiouy]\", // vowel\n C = c + \"[^aeiouy]*\", // consonant sequence\n V = v + \"[aeiou]*\", // vowel sequence\n\n mgr0 = \"^(\" + C + \")?\" + V + C, // [C]VC... is m>0\n meq1 = \"^(\" + C + \")?\" + V + C + \"(\" + V + \")?$\", // [C]VC[V] is m=1\n mgr1 = \"^(\" + C + \")?\" + V + C + V + C, // [C]VCVC... is m>1\n s_v = \"^(\" + C + \")?\" + v; // vowel in stem\n\n var re_mgr0 = new RegExp(mgr0);\n var re_mgr1 = new RegExp(mgr1);\n var re_meq1 = new RegExp(meq1);\n var re_s_v = new RegExp(s_v);\n\n var re_1a = /^(.+?)(ss|i)es$/;\n var re2_1a = /^(.+?)([^s])s$/;\n var re_1b = /^(.+?)eed$/;\n var re2_1b = /^(.+?)(ed|ing)$/;\n var re_1b_2 = /.$/;\n var re2_1b_2 = /(at|bl|iz)$/;\n var re3_1b_2 = new RegExp(\"([^aeiouylsz])\\\\1$\");\n var re4_1b_2 = new RegExp(\"^\" + C + v + \"[^aeiouwxy]$\");\n\n var re_1c = /^(.+?[^aeiou])y$/;\n var re_2 = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;\n\n var re_3 = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;\n\n var re_4 = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;\n var re2_4 = /^(.+?)(s|t)(ion)$/;\n\n var re_5 = /^(.+?)e$/;\n var re_5_1 = /ll$/;\n var re3_5 = new RegExp(\"^\" + C + v + \"[^aeiouwxy]$\");\n\n var porterStemmer = function porterStemmer(w) {\n var stem,\n suffix,\n firstch,\n re,\n re2,\n re3,\n re4;\n\n if (w.length < 3) { return w; }\n\n firstch = w.substr(0,1);\n if (firstch == \"y\") {\n w = firstch.toUpperCase() + w.substr(1);\n }\n\n // Step 1a\n re = re_1a\n re2 = re2_1a;\n\n if (re.test(w)) { w = w.replace(re,\"$1$2\"); }\n else if (re2.test(w)) { w = w.replace(re2,\"$1$2\"); }\n\n // Step 1b\n re = re_1b;\n re2 = re2_1b;\n if (re.test(w)) {\n var fp = re.exec(w);\n re = re_mgr0;\n if (re.test(fp[1])) {\n re = re_1b_2;\n w = w.replace(re,\"\");\n }\n } else if (re2.test(w)) {\n var fp = re2.exec(w);\n stem = fp[1];\n re2 = re_s_v;\n if (re2.test(stem)) {\n w = stem;\n re2 = re2_1b_2;\n re3 = re3_1b_2;\n re4 = re4_1b_2;\n if (re2.test(w)) { w = w + \"e\"; }\n else if (re3.test(w)) { re = re_1b_2; w = w.replace(re,\"\"); }\n else if (re4.test(w)) { w = w + \"e\"; }\n }\n }\n\n // Step 1c - replace suffix y or Y by i if preceded by a non-vowel which is not the first letter of the word (so cry -> cri, by -> by, say -> say)\n re = re_1c;\n if (re.test(w)) {\n var fp = re.exec(w);\n stem = fp[1];\n w = stem + \"i\";\n }\n\n // Step 2\n re = re_2;\n if (re.test(w)) {\n var fp = re.exec(w);\n stem = fp[1];\n suffix = fp[2];\n re = re_mgr0;\n if (re.test(stem)) {\n w = stem + step2list[suffix];\n }\n }\n\n // Step 3\n re = re_3;\n if (re.test(w)) {\n var fp = re.exec(w);\n stem = fp[1];\n suffix = fp[2];\n re = re_mgr0;\n if (re.test(stem)) {\n w = stem + step3list[suffix];\n }\n }\n\n // Step 4\n re = re_4;\n re2 = re2_4;\n if (re.test(w)) {\n var fp = re.exec(w);\n stem = fp[1];\n re = re_mgr1;\n if (re.test(stem)) {\n w = stem;\n }\n } else if (re2.test(w)) {\n var fp = re2.exec(w);\n stem = fp[1] + fp[2];\n re2 = re_mgr1;\n if (re2.test(stem)) {\n w = stem;\n }\n }\n\n // Step 5\n re = re_5;\n if (re.test(w)) {\n var fp = re.exec(w);\n stem = fp[1];\n re = re_mgr1;\n re2 = re_meq1;\n re3 = re3_5;\n if (re.test(stem) || (re2.test(stem) && !(re3.test(stem)))) {\n w = stem;\n }\n }\n\n re = re_5_1;\n re2 = re_mgr1;\n if (re.test(w) && re2.test(w)) {\n re = re_1b_2;\n w = w.replace(re,\"\");\n }\n\n // and turn initial Y back to y\n\n if (firstch == \"y\") {\n w = firstch.toLowerCase() + w.substr(1);\n }\n\n return w;\n };\n\n return function (token) {\n return token.update(porterStemmer);\n }\n})();\n\nlunr.Pipeline.registerFunction(lunr.stemmer, 'stemmer')\n/*!\n * lunr.stopWordFilter\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * lunr.generateStopWordFilter builds a stopWordFilter function from the provided\n * list of stop words.\n *\n * The built in lunr.stopWordFilter is built using this generator and can be used\n * to generate custom stopWordFilters for applications or non English languages.\n *\n * @function\n * @param {Array} token The token to pass through the filter\n * @returns {lunr.PipelineFunction}\n * @see lunr.Pipeline\n * @see lunr.stopWordFilter\n */\nlunr.generateStopWordFilter = function (stopWords) {\n var words = stopWords.reduce(function (memo, stopWord) {\n memo[stopWord] = stopWord\n return memo\n }, {})\n\n return function (token) {\n if (token && words[token.toString()] !== token.toString()) return token\n }\n}\n\n/**\n * lunr.stopWordFilter is an English language stop word list filter, any words\n * contained in the list will not be passed through the filter.\n *\n * This is intended to be used in the Pipeline. If the token does not pass the\n * filter then undefined will be returned.\n *\n * @function\n * @implements {lunr.PipelineFunction}\n * @params {lunr.Token} token - A token to check for being a stop word.\n * @returns {lunr.Token}\n * @see {@link lunr.Pipeline}\n */\nlunr.stopWordFilter = lunr.generateStopWordFilter([\n 'a',\n 'able',\n 'about',\n 'across',\n 'after',\n 'all',\n 'almost',\n 'also',\n 'am',\n 'among',\n 'an',\n 'and',\n 'any',\n 'are',\n 'as',\n 'at',\n 'be',\n 'because',\n 'been',\n 'but',\n 'by',\n 'can',\n 'cannot',\n 'could',\n 'dear',\n 'did',\n 'do',\n 'does',\n 'either',\n 'else',\n 'ever',\n 'every',\n 'for',\n 'from',\n 'get',\n 'got',\n 'had',\n 'has',\n 'have',\n 'he',\n 'her',\n 'hers',\n 'him',\n 'his',\n 'how',\n 'however',\n 'i',\n 'if',\n 'in',\n 'into',\n 'is',\n 'it',\n 'its',\n 'just',\n 'least',\n 'let',\n 'like',\n 'likely',\n 'may',\n 'me',\n 'might',\n 'most',\n 'must',\n 'my',\n 'neither',\n 'no',\n 'nor',\n 'not',\n 'of',\n 'off',\n 'often',\n 'on',\n 'only',\n 'or',\n 'other',\n 'our',\n 'own',\n 'rather',\n 'said',\n 'say',\n 'says',\n 'she',\n 'should',\n 'since',\n 'so',\n 'some',\n 'than',\n 'that',\n 'the',\n 'their',\n 'them',\n 'then',\n 'there',\n 'these',\n 'they',\n 'this',\n 'tis',\n 'to',\n 'too',\n 'twas',\n 'us',\n 'wants',\n 'was',\n 'we',\n 'were',\n 'what',\n 'when',\n 'where',\n 'which',\n 'while',\n 'who',\n 'whom',\n 'why',\n 'will',\n 'with',\n 'would',\n 'yet',\n 'you',\n 'your'\n])\n\nlunr.Pipeline.registerFunction(lunr.stopWordFilter, 'stopWordFilter')\n/*!\n * lunr.trimmer\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * lunr.trimmer is a pipeline function for trimming non word\n * characters from the beginning and end of tokens before they\n * enter the index.\n *\n * This implementation may not work correctly for non latin\n * characters and should either be removed or adapted for use\n * with languages with non-latin characters.\n *\n * @static\n * @implements {lunr.PipelineFunction}\n * @param {lunr.Token} token The token to pass through the filter\n * @returns {lunr.Token}\n * @see lunr.Pipeline\n */\nlunr.trimmer = function (token) {\n return token.update(function (s) {\n return s.replace(/^\\W+/, '').replace(/\\W+$/, '')\n })\n}\n\nlunr.Pipeline.registerFunction(lunr.trimmer, 'trimmer')\n/*!\n * lunr.TokenSet\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * A token set is used to store the unique list of all tokens\n * within an index. Token sets are also used to represent an\n * incoming query to the index, this query token set and index\n * token set are then intersected to find which tokens to look\n * up in the inverted index.\n *\n * A token set can hold multiple tokens, as in the case of the\n * index token set, or it can hold a single token as in the\n * case of a simple query token set.\n *\n * Additionally token sets are used to perform wildcard matching.\n * Leading, contained and trailing wildcards are supported, and\n * from this edit distance matching can also be provided.\n *\n * Token sets are implemented as a minimal finite state automata,\n * where both common prefixes and suffixes are shared between tokens.\n * This helps to reduce the space used for storing the token set.\n *\n * @constructor\n */\nlunr.TokenSet = function () {\n this.final = false\n this.edges = {}\n this.id = lunr.TokenSet._nextId\n lunr.TokenSet._nextId += 1\n}\n\n/**\n * Keeps track of the next, auto increment, identifier to assign\n * to a new tokenSet.\n *\n * TokenSets require a unique identifier to be correctly minimised.\n *\n * @private\n */\nlunr.TokenSet._nextId = 1\n\n/**\n * Creates a TokenSet instance from the given sorted array of words.\n *\n * @param {String[]} arr - A sorted array of strings to create the set from.\n * @returns {lunr.TokenSet}\n * @throws Will throw an error if the input array is not sorted.\n */\nlunr.TokenSet.fromArray = function (arr) {\n var builder = new lunr.TokenSet.Builder\n\n for (var i = 0, len = arr.length; i < len; i++) {\n builder.insert(arr[i])\n }\n\n builder.finish()\n return builder.root\n}\n\n/**\n * Creates a token set from a query clause.\n *\n * @private\n * @param {Object} clause - A single clause from lunr.Query.\n * @param {string} clause.term - The query clause term.\n * @param {number} [clause.editDistance] - The optional edit distance for the term.\n * @returns {lunr.TokenSet}\n */\nlunr.TokenSet.fromClause = function (clause) {\n if ('editDistance' in clause) {\n return lunr.TokenSet.fromFuzzyString(clause.term, clause.editDistance)\n } else {\n return lunr.TokenSet.fromString(clause.term)\n }\n}\n\n/**\n * Creates a token set representing a single string with a specified\n * edit distance.\n *\n * Insertions, deletions, substitutions and transpositions are each\n * treated as an edit distance of 1.\n *\n * Increasing the allowed edit distance will have a dramatic impact\n * on the performance of both creating and intersecting these TokenSets.\n * It is advised to keep the edit distance less than 3.\n *\n * @param {string} str - The string to create the token set from.\n * @param {number} editDistance - The allowed edit distance to match.\n * @returns {lunr.Vector}\n */\nlunr.TokenSet.fromFuzzyString = function (str, editDistance) {\n var root = new lunr.TokenSet\n\n var stack = [{\n node: root,\n editsRemaining: editDistance,\n str: str\n }]\n\n while (stack.length) {\n var frame = stack.pop()\n\n // no edit\n if (frame.str.length > 0) {\n var char = frame.str.charAt(0),\n noEditNode\n\n if (char in frame.node.edges) {\n noEditNode = frame.node.edges[char]\n } else {\n noEditNode = new lunr.TokenSet\n frame.node.edges[char] = noEditNode\n }\n\n if (frame.str.length == 1) {\n noEditNode.final = true\n }\n\n stack.push({\n node: noEditNode,\n editsRemaining: frame.editsRemaining,\n str: frame.str.slice(1)\n })\n }\n\n if (frame.editsRemaining == 0) {\n continue\n }\n\n // insertion\n if (\"*\" in frame.node.edges) {\n var insertionNode = frame.node.edges[\"*\"]\n } else {\n var insertionNode = new lunr.TokenSet\n frame.node.edges[\"*\"] = insertionNode\n }\n\n if (frame.str.length == 0) {\n insertionNode.final = true\n }\n\n stack.push({\n node: insertionNode,\n editsRemaining: frame.editsRemaining - 1,\n str: frame.str\n })\n\n // deletion\n // can only do a deletion if we have enough edits remaining\n // and if there are characters left to delete in the string\n if (frame.str.length > 1) {\n stack.push({\n node: frame.node,\n editsRemaining: frame.editsRemaining - 1,\n str: frame.str.slice(1)\n })\n }\n\n // deletion\n // just removing the last character from the str\n if (frame.str.length == 1) {\n frame.node.final = true\n }\n\n // substitution\n // can only do a substitution if we have enough edits remaining\n // and if there are characters left to substitute\n if (frame.str.length >= 1) {\n if (\"*\" in frame.node.edges) {\n var substitutionNode = frame.node.edges[\"*\"]\n } else {\n var substitutionNode = new lunr.TokenSet\n frame.node.edges[\"*\"] = substitutionNode\n }\n\n if (frame.str.length == 1) {\n substitutionNode.final = true\n }\n\n stack.push({\n node: substitutionNode,\n editsRemaining: frame.editsRemaining - 1,\n str: frame.str.slice(1)\n })\n }\n\n // transposition\n // can only do a transposition if there are edits remaining\n // and there are enough characters to transpose\n if (frame.str.length > 1) {\n var charA = frame.str.charAt(0),\n charB = frame.str.charAt(1),\n transposeNode\n\n if (charB in frame.node.edges) {\n transposeNode = frame.node.edges[charB]\n } else {\n transposeNode = new lunr.TokenSet\n frame.node.edges[charB] = transposeNode\n }\n\n if (frame.str.length == 1) {\n transposeNode.final = true\n }\n\n stack.push({\n node: transposeNode,\n editsRemaining: frame.editsRemaining - 1,\n str: charA + frame.str.slice(2)\n })\n }\n }\n\n return root\n}\n\n/**\n * Creates a TokenSet from a string.\n *\n * The string may contain one or more wildcard characters (*)\n * that will allow wildcard matching when intersecting with\n * another TokenSet.\n *\n * @param {string} str - The string to create a TokenSet from.\n * @returns {lunr.TokenSet}\n */\nlunr.TokenSet.fromString = function (str) {\n var node = new lunr.TokenSet,\n root = node\n\n /*\n * Iterates through all characters within the passed string\n * appending a node for each character.\n *\n * When a wildcard character is found then a self\n * referencing edge is introduced to continually match\n * any number of any characters.\n */\n for (var i = 0, len = str.length; i < len; i++) {\n var char = str[i],\n final = (i == len - 1)\n\n if (char == \"*\") {\n node.edges[char] = node\n node.final = final\n\n } else {\n var next = new lunr.TokenSet\n next.final = final\n\n node.edges[char] = next\n node = next\n }\n }\n\n return root\n}\n\n/**\n * Converts this TokenSet into an array of strings\n * contained within the TokenSet.\n *\n * This is not intended to be used on a TokenSet that\n * contains wildcards, in these cases the results are\n * undefined and are likely to cause an infinite loop.\n *\n * @returns {string[]}\n */\nlunr.TokenSet.prototype.toArray = function () {\n var words = []\n\n var stack = [{\n prefix: \"\",\n node: this\n }]\n\n while (stack.length) {\n var frame = stack.pop(),\n edges = Object.keys(frame.node.edges),\n len = edges.length\n\n if (frame.node.final) {\n /* In Safari, at this point the prefix is sometimes corrupted, see:\n * https://github.com/olivernn/lunr.js/issues/279 Calling any\n * String.prototype method forces Safari to \"cast\" this string to what\n * it's supposed to be, fixing the bug. */\n frame.prefix.charAt(0)\n words.push(frame.prefix)\n }\n\n for (var i = 0; i < len; i++) {\n var edge = edges[i]\n\n stack.push({\n prefix: frame.prefix.concat(edge),\n node: frame.node.edges[edge]\n })\n }\n }\n\n return words\n}\n\n/**\n * Generates a string representation of a TokenSet.\n *\n * This is intended to allow TokenSets to be used as keys\n * in objects, largely to aid the construction and minimisation\n * of a TokenSet. As such it is not designed to be a human\n * friendly representation of the TokenSet.\n *\n * @returns {string}\n */\nlunr.TokenSet.prototype.toString = function () {\n // NOTE: Using Object.keys here as this.edges is very likely\n // to enter 'hash-mode' with many keys being added\n //\n // avoiding a for-in loop here as it leads to the function\n // being de-optimised (at least in V8). From some simple\n // benchmarks the performance is comparable, but allowing\n // V8 to optimize may mean easy performance wins in the future.\n\n if (this._str) {\n return this._str\n }\n\n var str = this.final ? '1' : '0',\n labels = Object.keys(this.edges).sort(),\n len = labels.length\n\n for (var i = 0; i < len; i++) {\n var label = labels[i],\n node = this.edges[label]\n\n str = str + label + node.id\n }\n\n return str\n}\n\n/**\n * Returns a new TokenSet that is the intersection of\n * this TokenSet and the passed TokenSet.\n *\n * This intersection will take into account any wildcards\n * contained within the TokenSet.\n *\n * @param {lunr.TokenSet} b - An other TokenSet to intersect with.\n * @returns {lunr.TokenSet}\n */\nlunr.TokenSet.prototype.intersect = function (b) {\n var output = new lunr.TokenSet,\n frame = undefined\n\n var stack = [{\n qNode: b,\n output: output,\n node: this\n }]\n\n while (stack.length) {\n frame = stack.pop()\n\n // NOTE: As with the #toString method, we are using\n // Object.keys and a for loop instead of a for-in loop\n // as both of these objects enter 'hash' mode, causing\n // the function to be de-optimised in V8\n var qEdges = Object.keys(frame.qNode.edges),\n qLen = qEdges.length,\n nEdges = Object.keys(frame.node.edges),\n nLen = nEdges.length\n\n for (var q = 0; q < qLen; q++) {\n var qEdge = qEdges[q]\n\n for (var n = 0; n < nLen; n++) {\n var nEdge = nEdges[n]\n\n if (nEdge == qEdge || qEdge == '*') {\n var node = frame.node.edges[nEdge],\n qNode = frame.qNode.edges[qEdge],\n final = node.final && qNode.final,\n next = undefined\n\n if (nEdge in frame.output.edges) {\n // an edge already exists for this character\n // no need to create a new node, just set the finality\n // bit unless this node is already final\n next = frame.output.edges[nEdge]\n next.final = next.final || final\n\n } else {\n // no edge exists yet, must create one\n // set the finality bit and insert it\n // into the output\n next = new lunr.TokenSet\n next.final = final\n frame.output.edges[nEdge] = next\n }\n\n stack.push({\n qNode: qNode,\n output: next,\n node: node\n })\n }\n }\n }\n }\n\n return output\n}\nlunr.TokenSet.Builder = function () {\n this.previousWord = \"\"\n this.root = new lunr.TokenSet\n this.uncheckedNodes = []\n this.minimizedNodes = {}\n}\n\nlunr.TokenSet.Builder.prototype.insert = function (word) {\n var node,\n commonPrefix = 0\n\n if (word < this.previousWord) {\n throw new Error (\"Out of order word insertion\")\n }\n\n for (var i = 0; i < word.length && i < this.previousWord.length; i++) {\n if (word[i] != this.previousWord[i]) break\n commonPrefix++\n }\n\n this.minimize(commonPrefix)\n\n if (this.uncheckedNodes.length == 0) {\n node = this.root\n } else {\n node = this.uncheckedNodes[this.uncheckedNodes.length - 1].child\n }\n\n for (var i = commonPrefix; i < word.length; i++) {\n var nextNode = new lunr.TokenSet,\n char = word[i]\n\n node.edges[char] = nextNode\n\n this.uncheckedNodes.push({\n parent: node,\n char: char,\n child: nextNode\n })\n\n node = nextNode\n }\n\n node.final = true\n this.previousWord = word\n}\n\nlunr.TokenSet.Builder.prototype.finish = function () {\n this.minimize(0)\n}\n\nlunr.TokenSet.Builder.prototype.minimize = function (downTo) {\n for (var i = this.uncheckedNodes.length - 1; i >= downTo; i--) {\n var node = this.uncheckedNodes[i],\n childKey = node.child.toString()\n\n if (childKey in this.minimizedNodes) {\n node.parent.edges[node.char] = this.minimizedNodes[childKey]\n } else {\n // Cache the key for this node since\n // we know it can't change anymore\n node.child._str = childKey\n\n this.minimizedNodes[childKey] = node.child\n }\n\n this.uncheckedNodes.pop()\n }\n}\n/*!\n * lunr.Index\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * An index contains the built index of all documents and provides a query interface\n * to the index.\n *\n * Usually instances of lunr.Index will not be created using this constructor, instead\n * lunr.Builder should be used to construct new indexes, or lunr.Index.load should be\n * used to load previously built and serialized indexes.\n *\n * @constructor\n * @param {Object} attrs - The attributes of the built search index.\n * @param {Object} attrs.invertedIndex - An index of term/field to document reference.\n * @param {Object} attrs.fieldVectors - Field vectors\n * @param {lunr.TokenSet} attrs.tokenSet - An set of all corpus tokens.\n * @param {string[]} attrs.fields - The names of indexed document fields.\n * @param {lunr.Pipeline} attrs.pipeline - The pipeline to use for search terms.\n */\nlunr.Index = function (attrs) {\n this.invertedIndex = attrs.invertedIndex\n this.fieldVectors = attrs.fieldVectors\n this.tokenSet = attrs.tokenSet\n this.fields = attrs.fields\n this.pipeline = attrs.pipeline\n}\n\n/**\n * A result contains details of a document matching a search query.\n * @typedef {Object} lunr.Index~Result\n * @property {string} ref - The reference of the document this result represents.\n * @property {number} score - A number between 0 and 1 representing how similar this document is to the query.\n * @property {lunr.MatchData} matchData - Contains metadata about this match including which term(s) caused the match.\n */\n\n/**\n * Although lunr provides the ability to create queries using lunr.Query, it also provides a simple\n * query language which itself is parsed into an instance of lunr.Query.\n *\n * For programmatically building queries it is advised to directly use lunr.Query, the query language\n * is best used for human entered text rather than program generated text.\n *\n * At its simplest queries can just be a single term, e.g. `hello`, multiple terms are also supported\n * and will be combined with OR, e.g `hello world` will match documents that contain either 'hello'\n * or 'world', though those that contain both will rank higher in the results.\n *\n * Wildcards can be included in terms to match one or more unspecified characters, these wildcards can\n * be inserted anywhere within the term, and more than one wildcard can exist in a single term. Adding\n * wildcards will increase the number of documents that will be found but can also have a negative\n * impact on query performance, especially with wildcards at the beginning of a term.\n *\n * Terms can be restricted to specific fields, e.g. `title:hello`, only documents with the term\n * hello in the title field will match this query. Using a field not present in the index will lead\n * to an error being thrown.\n *\n * Modifiers can also be added to terms, lunr supports edit distance and boost modifiers on terms. A term\n * boost will make documents matching that term score higher, e.g. `foo^5`. Edit distance is also supported\n * to provide fuzzy matching, e.g. 'hello~2' will match documents with hello with an edit distance of 2.\n * Avoid large values for edit distance to improve query performance.\n *\n * Each term also supports a presence modifier. By default a term's presence in document is optional, however\n * this can be changed to either required or prohibited. For a term's presence to be required in a document the\n * term should be prefixed with a '+', e.g. `+foo bar` is a search for documents that must contain 'foo' and\n * optionally contain 'bar'. Conversely a leading '-' sets the terms presence to prohibited, i.e. it must not\n * appear in a document, e.g. `-foo bar` is a search for documents that do not contain 'foo' but may contain 'bar'.\n *\n * To escape special characters the backslash character '\\' can be used, this allows searches to include\n * characters that would normally be considered modifiers, e.g. `foo\\~2` will search for a term \"foo~2\" instead\n * of attempting to apply a boost of 2 to the search term \"foo\".\n *\n * @typedef {string} lunr.Index~QueryString\n * @example Simple single term query\n * hello\n * @example Multiple term query\n * hello world\n * @example term scoped to a field\n * title:hello\n * @example term with a boost of 10\n * hello^10\n * @example term with an edit distance of 2\n * hello~2\n * @example terms with presence modifiers\n * -foo +bar baz\n */\n\n/**\n * Performs a search against the index using lunr query syntax.\n *\n * Results will be returned sorted by their score, the most relevant results\n * will be returned first. For details on how the score is calculated, please see\n * the {@link https://lunrjs.com/guides/searching.html#scoring|guide}.\n *\n * For more programmatic querying use lunr.Index#query.\n *\n * @param {lunr.Index~QueryString} queryString - A string containing a lunr query.\n * @throws {lunr.QueryParseError} If the passed query string cannot be parsed.\n * @returns {lunr.Index~Result[]}\n */\nlunr.Index.prototype.search = function (queryString) {\n return this.query(function (query) {\n var parser = new lunr.QueryParser(queryString, query)\n parser.parse()\n })\n}\n\n/**\n * A query builder callback provides a query object to be used to express\n * the query to perform on the index.\n *\n * @callback lunr.Index~queryBuilder\n * @param {lunr.Query} query - The query object to build up.\n * @this lunr.Query\n */\n\n/**\n * Performs a query against the index using the yielded lunr.Query object.\n *\n * If performing programmatic queries against the index, this method is preferred\n * over lunr.Index#search so as to avoid the additional query parsing overhead.\n *\n * A query object is yielded to the supplied function which should be used to\n * express the query to be run against the index.\n *\n * Note that although this function takes a callback parameter it is _not_ an\n * asynchronous operation, the callback is just yielded a query object to be\n * customized.\n *\n * @param {lunr.Index~queryBuilder} fn - A function that is used to build the query.\n * @returns {lunr.Index~Result[]}\n */\nlunr.Index.prototype.query = function (fn) {\n // for each query clause\n // * process terms\n // * expand terms from token set\n // * find matching documents and metadata\n // * get document vectors\n // * score documents\n\n var query = new lunr.Query(this.fields),\n matchingFields = Object.create(null),\n queryVectors = Object.create(null),\n termFieldCache = Object.create(null),\n requiredMatches = Object.create(null),\n prohibitedMatches = Object.create(null)\n\n /*\n * To support field level boosts a query vector is created per\n * field. An empty vector is eagerly created to support negated\n * queries.\n */\n for (var i = 0; i < this.fields.length; i++) {\n queryVectors[this.fields[i]] = new lunr.Vector\n }\n\n fn.call(query, query)\n\n for (var i = 0; i < query.clauses.length; i++) {\n /*\n * Unless the pipeline has been disabled for this term, which is\n * the case for terms with wildcards, we need to pass the clause\n * term through the search pipeline. A pipeline returns an array\n * of processed terms. Pipeline functions may expand the passed\n * term, which means we may end up performing multiple index lookups\n * for a single query term.\n */\n var clause = query.clauses[i],\n terms = null,\n clauseMatches = lunr.Set.empty\n\n if (clause.usePipeline) {\n terms = this.pipeline.runString(clause.term, {\n fields: clause.fields\n })\n } else {\n terms = [clause.term]\n }\n\n for (var m = 0; m < terms.length; m++) {\n var term = terms[m]\n\n /*\n * Each term returned from the pipeline needs to use the same query\n * clause object, e.g. the same boost and or edit distance. The\n * simplest way to do this is to re-use the clause object but mutate\n * its term property.\n */\n clause.term = term\n\n /*\n * From the term in the clause we create a token set which will then\n * be used to intersect the indexes token set to get a list of terms\n * to lookup in the inverted index\n */\n var termTokenSet = lunr.TokenSet.fromClause(clause),\n expandedTerms = this.tokenSet.intersect(termTokenSet).toArray()\n\n /*\n * If a term marked as required does not exist in the tokenSet it is\n * impossible for the search to return any matches. We set all the field\n * scoped required matches set to empty and stop examining any further\n * clauses.\n */\n if (expandedTerms.length === 0 && clause.presence === lunr.Query.presence.REQUIRED) {\n for (var k = 0; k < clause.fields.length; k++) {\n var field = clause.fields[k]\n requiredMatches[field] = lunr.Set.empty\n }\n\n break\n }\n\n for (var j = 0; j < expandedTerms.length; j++) {\n /*\n * For each term get the posting and termIndex, this is required for\n * building the query vector.\n */\n var expandedTerm = expandedTerms[j],\n posting = this.invertedIndex[expandedTerm],\n termIndex = posting._index\n\n for (var k = 0; k < clause.fields.length; k++) {\n /*\n * For each field that this query term is scoped by (by default\n * all fields are in scope) we need to get all the document refs\n * that have this term in that field.\n *\n * The posting is the entry in the invertedIndex for the matching\n * term from above.\n */\n var field = clause.fields[k],\n fieldPosting = posting[field],\n matchingDocumentRefs = Object.keys(fieldPosting),\n termField = expandedTerm + \"/\" + field,\n matchingDocumentsSet = new lunr.Set(matchingDocumentRefs)\n\n /*\n * if the presence of this term is required ensure that the matching\n * documents are added to the set of required matches for this clause.\n *\n */\n if (clause.presence == lunr.Query.presence.REQUIRED) {\n clauseMatches = clauseMatches.union(matchingDocumentsSet)\n\n if (requiredMatches[field] === undefined) {\n requiredMatches[field] = lunr.Set.complete\n }\n }\n\n /*\n * if the presence of this term is prohibited ensure that the matching\n * documents are added to the set of prohibited matches for this field,\n * creating that set if it does not yet exist.\n */\n if (clause.presence == lunr.Query.presence.PROHIBITED) {\n if (prohibitedMatches[field] === undefined) {\n prohibitedMatches[field] = lunr.Set.empty\n }\n\n prohibitedMatches[field] = prohibitedMatches[field].union(matchingDocumentsSet)\n\n /*\n * Prohibited matches should not be part of the query vector used for\n * similarity scoring and no metadata should be extracted so we continue\n * to the next field\n */\n continue\n }\n\n /*\n * The query field vector is populated using the termIndex found for\n * the term and a unit value with the appropriate boost applied.\n * Using upsert because there could already be an entry in the vector\n * for the term we are working with. In that case we just add the scores\n * together.\n */\n queryVectors[field].upsert(termIndex, clause.boost, function (a, b) { return a + b })\n\n /**\n * If we've already seen this term, field combo then we've already collected\n * the matching documents and metadata, no need to go through all that again\n */\n if (termFieldCache[termField]) {\n continue\n }\n\n for (var l = 0; l < matchingDocumentRefs.length; l++) {\n /*\n * All metadata for this term/field/document triple\n * are then extracted and collected into an instance\n * of lunr.MatchData ready to be returned in the query\n * results\n */\n var matchingDocumentRef = matchingDocumentRefs[l],\n matchingFieldRef = new lunr.FieldRef (matchingDocumentRef, field),\n metadata = fieldPosting[matchingDocumentRef],\n fieldMatch\n\n if ((fieldMatch = matchingFields[matchingFieldRef]) === undefined) {\n matchingFields[matchingFieldRef] = new lunr.MatchData (expandedTerm, field, metadata)\n } else {\n fieldMatch.add(expandedTerm, field, metadata)\n }\n\n }\n\n termFieldCache[termField] = true\n }\n }\n }\n\n /**\n * If the presence was required we need to update the requiredMatches field sets.\n * We do this after all fields for the term have collected their matches because\n * the clause terms presence is required in _any_ of the fields not _all_ of the\n * fields.\n */\n if (clause.presence === lunr.Query.presence.REQUIRED) {\n for (var k = 0; k < clause.fields.length; k++) {\n var field = clause.fields[k]\n requiredMatches[field] = requiredMatches[field].intersect(clauseMatches)\n }\n }\n }\n\n /**\n * Need to combine the field scoped required and prohibited\n * matching documents into a global set of required and prohibited\n * matches\n */\n var allRequiredMatches = lunr.Set.complete,\n allProhibitedMatches = lunr.Set.empty\n\n for (var i = 0; i < this.fields.length; i++) {\n var field = this.fields[i]\n\n if (requiredMatches[field]) {\n allRequiredMatches = allRequiredMatches.intersect(requiredMatches[field])\n }\n\n if (prohibitedMatches[field]) {\n allProhibitedMatches = allProhibitedMatches.union(prohibitedMatches[field])\n }\n }\n\n var matchingFieldRefs = Object.keys(matchingFields),\n results = [],\n matches = Object.create(null)\n\n /*\n * If the query is negated (contains only prohibited terms)\n * we need to get _all_ fieldRefs currently existing in the\n * index. This is only done when we know that the query is\n * entirely prohibited terms to avoid any cost of getting all\n * fieldRefs unnecessarily.\n *\n * Additionally, blank MatchData must be created to correctly\n * populate the results.\n */\n if (query.isNegated()) {\n matchingFieldRefs = Object.keys(this.fieldVectors)\n\n for (var i = 0; i < matchingFieldRefs.length; i++) {\n var matchingFieldRef = matchingFieldRefs[i]\n var fieldRef = lunr.FieldRef.fromString(matchingFieldRef)\n matchingFields[matchingFieldRef] = new lunr.MatchData\n }\n }\n\n for (var i = 0; i < matchingFieldRefs.length; i++) {\n /*\n * Currently we have document fields that match the query, but we\n * need to return documents. The matchData and scores are combined\n * from multiple fields belonging to the same document.\n *\n * Scores are calculated by field, using the query vectors created\n * above, and combined into a final document score using addition.\n */\n var fieldRef = lunr.FieldRef.fromString(matchingFieldRefs[i]),\n docRef = fieldRef.docRef\n\n if (!allRequiredMatches.contains(docRef)) {\n continue\n }\n\n if (allProhibitedMatches.contains(docRef)) {\n continue\n }\n\n var fieldVector = this.fieldVectors[fieldRef],\n score = queryVectors[fieldRef.fieldName].similarity(fieldVector),\n docMatch\n\n if ((docMatch = matches[docRef]) !== undefined) {\n docMatch.score += score\n docMatch.matchData.combine(matchingFields[fieldRef])\n } else {\n var match = {\n ref: docRef,\n score: score,\n matchData: matchingFields[fieldRef]\n }\n matches[docRef] = match\n results.push(match)\n }\n }\n\n /*\n * Sort the results objects by score, highest first.\n */\n return results.sort(function (a, b) {\n return b.score - a.score\n })\n}\n\n/**\n * Prepares the index for JSON serialization.\n *\n * The schema for this JSON blob will be described in a\n * separate JSON schema file.\n *\n * @returns {Object}\n */\nlunr.Index.prototype.toJSON = function () {\n var invertedIndex = Object.keys(this.invertedIndex)\n .sort()\n .map(function (term) {\n return [term, this.invertedIndex[term]]\n }, this)\n\n var fieldVectors = Object.keys(this.fieldVectors)\n .map(function (ref) {\n return [ref, this.fieldVectors[ref].toJSON()]\n }, this)\n\n return {\n version: lunr.version,\n fields: this.fields,\n fieldVectors: fieldVectors,\n invertedIndex: invertedIndex,\n pipeline: this.pipeline.toJSON()\n }\n}\n\n/**\n * Loads a previously serialized lunr.Index\n *\n * @param {Object} serializedIndex - A previously serialized lunr.Index\n * @returns {lunr.Index}\n */\nlunr.Index.load = function (serializedIndex) {\n var attrs = {},\n fieldVectors = {},\n serializedVectors = serializedIndex.fieldVectors,\n invertedIndex = Object.create(null),\n serializedInvertedIndex = serializedIndex.invertedIndex,\n tokenSetBuilder = new lunr.TokenSet.Builder,\n pipeline = lunr.Pipeline.load(serializedIndex.pipeline)\n\n if (serializedIndex.version != lunr.version) {\n lunr.utils.warn(\"Version mismatch when loading serialised index. Current version of lunr '\" + lunr.version + \"' does not match serialized index '\" + serializedIndex.version + \"'\")\n }\n\n for (var i = 0; i < serializedVectors.length; i++) {\n var tuple = serializedVectors[i],\n ref = tuple[0],\n elements = tuple[1]\n\n fieldVectors[ref] = new lunr.Vector(elements)\n }\n\n for (var i = 0; i < serializedInvertedIndex.length; i++) {\n var tuple = serializedInvertedIndex[i],\n term = tuple[0],\n posting = tuple[1]\n\n tokenSetBuilder.insert(term)\n invertedIndex[term] = posting\n }\n\n tokenSetBuilder.finish()\n\n attrs.fields = serializedIndex.fields\n\n attrs.fieldVectors = fieldVectors\n attrs.invertedIndex = invertedIndex\n attrs.tokenSet = tokenSetBuilder.root\n attrs.pipeline = pipeline\n\n return new lunr.Index(attrs)\n}\n/*!\n * lunr.Builder\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * lunr.Builder performs indexing on a set of documents and\n * returns instances of lunr.Index ready for querying.\n *\n * All configuration of the index is done via the builder, the\n * fields to index, the document reference, the text processing\n * pipeline and document scoring parameters are all set on the\n * builder before indexing.\n *\n * @constructor\n * @property {string} _ref - Internal reference to the document reference field.\n * @property {string[]} _fields - Internal reference to the document fields to index.\n * @property {object} invertedIndex - The inverted index maps terms to document fields.\n * @property {object} documentTermFrequencies - Keeps track of document term frequencies.\n * @property {object} documentLengths - Keeps track of the length of documents added to the index.\n * @property {lunr.tokenizer} tokenizer - Function for splitting strings into tokens for indexing.\n * @property {lunr.Pipeline} pipeline - The pipeline performs text processing on tokens before indexing.\n * @property {lunr.Pipeline} searchPipeline - A pipeline for processing search terms before querying the index.\n * @property {number} documentCount - Keeps track of the total number of documents indexed.\n * @property {number} _b - A parameter to control field length normalization, setting this to 0 disabled normalization, 1 fully normalizes field lengths, the default value is 0.75.\n * @property {number} _k1 - A parameter to control how quickly an increase in term frequency results in term frequency saturation, the default value is 1.2.\n * @property {number} termIndex - A counter incremented for each unique term, used to identify a terms position in the vector space.\n * @property {array} metadataWhitelist - A list of metadata keys that have been whitelisted for entry in the index.\n */\nlunr.Builder = function () {\n this._ref = \"id\"\n this._fields = Object.create(null)\n this._documents = Object.create(null)\n this.invertedIndex = Object.create(null)\n this.fieldTermFrequencies = {}\n this.fieldLengths = {}\n this.tokenizer = lunr.tokenizer\n this.pipeline = new lunr.Pipeline\n this.searchPipeline = new lunr.Pipeline\n this.documentCount = 0\n this._b = 0.75\n this._k1 = 1.2\n this.termIndex = 0\n this.metadataWhitelist = []\n}\n\n/**\n * Sets the document field used as the document reference. Every document must have this field.\n * The type of this field in the document should be a string, if it is not a string it will be\n * coerced into a string by calling toString.\n *\n * The default ref is 'id'.\n *\n * The ref should _not_ be changed during indexing, it should be set before any documents are\n * added to the index. Changing it during indexing can lead to inconsistent results.\n *\n * @param {string} ref - The name of the reference field in the document.\n */\nlunr.Builder.prototype.ref = function (ref) {\n this._ref = ref\n}\n\n/**\n * A function that is used to extract a field from a document.\n *\n * Lunr expects a field to be at the top level of a document, if however the field\n * is deeply nested within a document an extractor function can be used to extract\n * the right field for indexing.\n *\n * @callback fieldExtractor\n * @param {object} doc - The document being added to the index.\n * @returns {?(string|object|object[])} obj - The object that will be indexed for this field.\n * @example Extracting a nested field\n * function (doc) { return doc.nested.field }\n */\n\n/**\n * Adds a field to the list of document fields that will be indexed. Every document being\n * indexed should have this field. Null values for this field in indexed documents will\n * not cause errors but will limit the chance of that document being retrieved by searches.\n *\n * All fields should be added before adding documents to the index. Adding fields after\n * a document has been indexed will have no effect on already indexed documents.\n *\n * Fields can be boosted at build time. This allows terms within that field to have more\n * importance when ranking search results. Use a field boost to specify that matches within\n * one field are more important than other fields.\n *\n * @param {string} fieldName - The name of a field to index in all documents.\n * @param {object} attributes - Optional attributes associated with this field.\n * @param {number} [attributes.boost=1] - Boost applied to all terms within this field.\n * @param {fieldExtractor} [attributes.extractor] - Function to extract a field from a document.\n * @throws {RangeError} fieldName cannot contain unsupported characters '/'\n */\nlunr.Builder.prototype.field = function (fieldName, attributes) {\n if (/\\//.test(fieldName)) {\n throw new RangeError (\"Field '\" + fieldName + \"' contains illegal character '/'\")\n }\n\n this._fields[fieldName] = attributes || {}\n}\n\n/**\n * A parameter to tune the amount of field length normalisation that is applied when\n * calculating relevance scores. A value of 0 will completely disable any normalisation\n * and a value of 1 will fully normalise field lengths. The default is 0.75. Values of b\n * will be clamped to the range 0 - 1.\n *\n * @param {number} number - The value to set for this tuning parameter.\n */\nlunr.Builder.prototype.b = function (number) {\n if (number < 0) {\n this._b = 0\n } else if (number > 1) {\n this._b = 1\n } else {\n this._b = number\n }\n}\n\n/**\n * A parameter that controls the speed at which a rise in term frequency results in term\n * frequency saturation. The default value is 1.2. Setting this to a higher value will give\n * slower saturation levels, a lower value will result in quicker saturation.\n *\n * @param {number} number - The value to set for this tuning parameter.\n */\nlunr.Builder.prototype.k1 = function (number) {\n this._k1 = number\n}\n\n/**\n * Adds a document to the index.\n *\n * Before adding fields to the index the index should have been fully setup, with the document\n * ref and all fields to index already having been specified.\n *\n * The document must have a field name as specified by the ref (by default this is 'id') and\n * it should have all fields defined for indexing, though null or undefined values will not\n * cause errors.\n *\n * Entire documents can be boosted at build time. Applying a boost to a document indicates that\n * this document should rank higher in search results than other documents.\n *\n * @param {object} doc - The document to add to the index.\n * @param {object} attributes - Optional attributes associated with this document.\n * @param {number} [attributes.boost=1] - Boost applied to all terms within this document.\n */\nlunr.Builder.prototype.add = function (doc, attributes) {\n var docRef = doc[this._ref],\n fields = Object.keys(this._fields)\n\n this._documents[docRef] = attributes || {}\n this.documentCount += 1\n\n for (var i = 0; i < fields.length; i++) {\n var fieldName = fields[i],\n extractor = this._fields[fieldName].extractor,\n field = extractor ? extractor(doc) : doc[fieldName],\n tokens = this.tokenizer(field, {\n fields: [fieldName]\n }),\n terms = this.pipeline.run(tokens),\n fieldRef = new lunr.FieldRef (docRef, fieldName),\n fieldTerms = Object.create(null)\n\n this.fieldTermFrequencies[fieldRef] = fieldTerms\n this.fieldLengths[fieldRef] = 0\n\n // store the length of this field for this document\n this.fieldLengths[fieldRef] += terms.length\n\n // calculate term frequencies for this field\n for (var j = 0; j < terms.length; j++) {\n var term = terms[j]\n\n if (fieldTerms[term] == undefined) {\n fieldTerms[term] = 0\n }\n\n fieldTerms[term] += 1\n\n // add to inverted index\n // create an initial posting if one doesn't exist\n if (this.invertedIndex[term] == undefined) {\n var posting = Object.create(null)\n posting[\"_index\"] = this.termIndex\n this.termIndex += 1\n\n for (var k = 0; k < fields.length; k++) {\n posting[fields[k]] = Object.create(null)\n }\n\n this.invertedIndex[term] = posting\n }\n\n // add an entry for this term/fieldName/docRef to the invertedIndex\n if (this.invertedIndex[term][fieldName][docRef] == undefined) {\n this.invertedIndex[term][fieldName][docRef] = Object.create(null)\n }\n\n // store all whitelisted metadata about this token in the\n // inverted index\n for (var l = 0; l < this.metadataWhitelist.length; l++) {\n var metadataKey = this.metadataWhitelist[l],\n metadata = term.metadata[metadataKey]\n\n if (this.invertedIndex[term][fieldName][docRef][metadataKey] == undefined) {\n this.invertedIndex[term][fieldName][docRef][metadataKey] = []\n }\n\n this.invertedIndex[term][fieldName][docRef][metadataKey].push(metadata)\n }\n }\n\n }\n}\n\n/**\n * Calculates the average document length for this index\n *\n * @private\n */\nlunr.Builder.prototype.calculateAverageFieldLengths = function () {\n\n var fieldRefs = Object.keys(this.fieldLengths),\n numberOfFields = fieldRefs.length,\n accumulator = {},\n documentsWithField = {}\n\n for (var i = 0; i < numberOfFields; i++) {\n var fieldRef = lunr.FieldRef.fromString(fieldRefs[i]),\n field = fieldRef.fieldName\n\n documentsWithField[field] || (documentsWithField[field] = 0)\n documentsWithField[field] += 1\n\n accumulator[field] || (accumulator[field] = 0)\n accumulator[field] += this.fieldLengths[fieldRef]\n }\n\n var fields = Object.keys(this._fields)\n\n for (var i = 0; i < fields.length; i++) {\n var fieldName = fields[i]\n accumulator[fieldName] = accumulator[fieldName] / documentsWithField[fieldName]\n }\n\n this.averageFieldLength = accumulator\n}\n\n/**\n * Builds a vector space model of every document using lunr.Vector\n *\n * @private\n */\nlunr.Builder.prototype.createFieldVectors = function () {\n var fieldVectors = {},\n fieldRefs = Object.keys(this.fieldTermFrequencies),\n fieldRefsLength = fieldRefs.length,\n termIdfCache = Object.create(null)\n\n for (var i = 0; i < fieldRefsLength; i++) {\n var fieldRef = lunr.FieldRef.fromString(fieldRefs[i]),\n fieldName = fieldRef.fieldName,\n fieldLength = this.fieldLengths[fieldRef],\n fieldVector = new lunr.Vector,\n termFrequencies = this.fieldTermFrequencies[fieldRef],\n terms = Object.keys(termFrequencies),\n termsLength = terms.length\n\n\n var fieldBoost = this._fields[fieldName].boost || 1,\n docBoost = this._documents[fieldRef.docRef].boost || 1\n\n for (var j = 0; j < termsLength; j++) {\n var term = terms[j],\n tf = termFrequencies[term],\n termIndex = this.invertedIndex[term]._index,\n idf, score, scoreWithPrecision\n\n if (termIdfCache[term] === undefined) {\n idf = lunr.idf(this.invertedIndex[term], this.documentCount)\n termIdfCache[term] = idf\n } else {\n idf = termIdfCache[term]\n }\n\n score = idf * ((this._k1 + 1) * tf) / (this._k1 * (1 - this._b + this._b * (fieldLength / this.averageFieldLength[fieldName])) + tf)\n score *= fieldBoost\n score *= docBoost\n scoreWithPrecision = Math.round(score * 1000) / 1000\n // Converts 1.23456789 to 1.234.\n // Reducing the precision so that the vectors take up less\n // space when serialised. Doing it now so that they behave\n // the same before and after serialisation. Also, this is\n // the fastest approach to reducing a number's precision in\n // JavaScript.\n\n fieldVector.insert(termIndex, scoreWithPrecision)\n }\n\n fieldVectors[fieldRef] = fieldVector\n }\n\n this.fieldVectors = fieldVectors\n}\n\n/**\n * Creates a token set of all tokens in the index using lunr.TokenSet\n *\n * @private\n */\nlunr.Builder.prototype.createTokenSet = function () {\n this.tokenSet = lunr.TokenSet.fromArray(\n Object.keys(this.invertedIndex).sort()\n )\n}\n\n/**\n * Builds the index, creating an instance of lunr.Index.\n *\n * This completes the indexing process and should only be called\n * once all documents have been added to the index.\n *\n * @returns {lunr.Index}\n */\nlunr.Builder.prototype.build = function () {\n this.calculateAverageFieldLengths()\n this.createFieldVectors()\n this.createTokenSet()\n\n return new lunr.Index({\n invertedIndex: this.invertedIndex,\n fieldVectors: this.fieldVectors,\n tokenSet: this.tokenSet,\n fields: Object.keys(this._fields),\n pipeline: this.searchPipeline\n })\n}\n\n/**\n * Applies a plugin to the index builder.\n *\n * A plugin is a function that is called with the index builder as its context.\n * Plugins can be used to customise or extend the behaviour of the index\n * in some way. A plugin is just a function, that encapsulated the custom\n * behaviour that should be applied when building the index.\n *\n * The plugin function will be called with the index builder as its argument, additional\n * arguments can also be passed when calling use. The function will be called\n * with the index builder as its context.\n *\n * @param {Function} plugin The plugin to apply.\n */\nlunr.Builder.prototype.use = function (fn) {\n var args = Array.prototype.slice.call(arguments, 1)\n args.unshift(this)\n fn.apply(this, args)\n}\n/**\n * Contains and collects metadata about a matching document.\n * A single instance of lunr.MatchData is returned as part of every\n * lunr.Index~Result.\n *\n * @constructor\n * @param {string} term - The term this match data is associated with\n * @param {string} field - The field in which the term was found\n * @param {object} metadata - The metadata recorded about this term in this field\n * @property {object} metadata - A cloned collection of metadata associated with this document.\n * @see {@link lunr.Index~Result}\n */\nlunr.MatchData = function (term, field, metadata) {\n var clonedMetadata = Object.create(null),\n metadataKeys = Object.keys(metadata || {})\n\n // Cloning the metadata to prevent the original\n // being mutated during match data combination.\n // Metadata is kept in an array within the inverted\n // index so cloning the data can be done with\n // Array#slice\n for (var i = 0; i < metadataKeys.length; i++) {\n var key = metadataKeys[i]\n clonedMetadata[key] = metadata[key].slice()\n }\n\n this.metadata = Object.create(null)\n\n if (term !== undefined) {\n this.metadata[term] = Object.create(null)\n this.metadata[term][field] = clonedMetadata\n }\n}\n\n/**\n * An instance of lunr.MatchData will be created for every term that matches a\n * document. However only one instance is required in a lunr.Index~Result. This\n * method combines metadata from another instance of lunr.MatchData with this\n * objects metadata.\n *\n * @param {lunr.MatchData} otherMatchData - Another instance of match data to merge with this one.\n * @see {@link lunr.Index~Result}\n */\nlunr.MatchData.prototype.combine = function (otherMatchData) {\n var terms = Object.keys(otherMatchData.metadata)\n\n for (var i = 0; i < terms.length; i++) {\n var term = terms[i],\n fields = Object.keys(otherMatchData.metadata[term])\n\n if (this.metadata[term] == undefined) {\n this.metadata[term] = Object.create(null)\n }\n\n for (var j = 0; j < fields.length; j++) {\n var field = fields[j],\n keys = Object.keys(otherMatchData.metadata[term][field])\n\n if (this.metadata[term][field] == undefined) {\n this.metadata[term][field] = Object.create(null)\n }\n\n for (var k = 0; k < keys.length; k++) {\n var key = keys[k]\n\n if (this.metadata[term][field][key] == undefined) {\n this.metadata[term][field][key] = otherMatchData.metadata[term][field][key]\n } else {\n this.metadata[term][field][key] = this.metadata[term][field][key].concat(otherMatchData.metadata[term][field][key])\n }\n\n }\n }\n }\n}\n\n/**\n * Add metadata for a term/field pair to this instance of match data.\n *\n * @param {string} term - The term this match data is associated with\n * @param {string} field - The field in which the term was found\n * @param {object} metadata - The metadata recorded about this term in this field\n */\nlunr.MatchData.prototype.add = function (term, field, metadata) {\n if (!(term in this.metadata)) {\n this.metadata[term] = Object.create(null)\n this.metadata[term][field] = metadata\n return\n }\n\n if (!(field in this.metadata[term])) {\n this.metadata[term][field] = metadata\n return\n }\n\n var metadataKeys = Object.keys(metadata)\n\n for (var i = 0; i < metadataKeys.length; i++) {\n var key = metadataKeys[i]\n\n if (key in this.metadata[term][field]) {\n this.metadata[term][field][key] = this.metadata[term][field][key].concat(metadata[key])\n } else {\n this.metadata[term][field][key] = metadata[key]\n }\n }\n}\n/**\n * A lunr.Query provides a programmatic way of defining queries to be performed\n * against a {@link lunr.Index}.\n *\n * Prefer constructing a lunr.Query using the {@link lunr.Index#query} method\n * so the query object is pre-initialized with the right index fields.\n *\n * @constructor\n * @property {lunr.Query~Clause[]} clauses - An array of query clauses.\n * @property {string[]} allFields - An array of all available fields in a lunr.Index.\n */\nlunr.Query = function (allFields) {\n this.clauses = []\n this.allFields = allFields\n}\n\n/**\n * Constants for indicating what kind of automatic wildcard insertion will be used when constructing a query clause.\n *\n * This allows wildcards to be added to the beginning and end of a term without having to manually do any string\n * concatenation.\n *\n * The wildcard constants can be bitwise combined to select both leading and trailing wildcards.\n *\n * @constant\n * @default\n * @property {number} wildcard.NONE - The term will have no wildcards inserted, this is the default behaviour\n * @property {number} wildcard.LEADING - Prepend the term with a wildcard, unless a leading wildcard already exists\n * @property {number} wildcard.TRAILING - Append a wildcard to the term, unless a trailing wildcard already exists\n * @see lunr.Query~Clause\n * @see lunr.Query#clause\n * @see lunr.Query#term\n * @example query term with trailing wildcard\n * query.term('foo', { wildcard: lunr.Query.wildcard.TRAILING })\n * @example query term with leading and trailing wildcard\n * query.term('foo', {\n * wildcard: lunr.Query.wildcard.LEADING | lunr.Query.wildcard.TRAILING\n * })\n */\n\nlunr.Query.wildcard = new String (\"*\")\nlunr.Query.wildcard.NONE = 0\nlunr.Query.wildcard.LEADING = 1\nlunr.Query.wildcard.TRAILING = 2\n\n/**\n * Constants for indicating what kind of presence a term must have in matching documents.\n *\n * @constant\n * @enum {number}\n * @see lunr.Query~Clause\n * @see lunr.Query#clause\n * @see lunr.Query#term\n * @example query term with required presence\n * query.term('foo', { presence: lunr.Query.presence.REQUIRED })\n */\nlunr.Query.presence = {\n /**\n * Term's presence in a document is optional, this is the default value.\n */\n OPTIONAL: 1,\n\n /**\n * Term's presence in a document is required, documents that do not contain\n * this term will not be returned.\n */\n REQUIRED: 2,\n\n /**\n * Term's presence in a document is prohibited, documents that do contain\n * this term will not be returned.\n */\n PROHIBITED: 3\n}\n\n/**\n * A single clause in a {@link lunr.Query} contains a term and details on how to\n * match that term against a {@link lunr.Index}.\n *\n * @typedef {Object} lunr.Query~Clause\n * @property {string[]} fields - The fields in an index this clause should be matched against.\n * @property {number} [boost=1] - Any boost that should be applied when matching this clause.\n * @property {number} [editDistance] - Whether the term should have fuzzy matching applied, and how fuzzy the match should be.\n * @property {boolean} [usePipeline] - Whether the term should be passed through the search pipeline.\n * @property {number} [wildcard=lunr.Query.wildcard.NONE] - Whether the term should have wildcards appended or prepended.\n * @property {number} [presence=lunr.Query.presence.OPTIONAL] - The terms presence in any matching documents.\n */\n\n/**\n * Adds a {@link lunr.Query~Clause} to this query.\n *\n * Unless the clause contains the fields to be matched all fields will be matched. In addition\n * a default boost of 1 is applied to the clause.\n *\n * @param {lunr.Query~Clause} clause - The clause to add to this query.\n * @see lunr.Query~Clause\n * @returns {lunr.Query}\n */\nlunr.Query.prototype.clause = function (clause) {\n if (!('fields' in clause)) {\n clause.fields = this.allFields\n }\n\n if (!('boost' in clause)) {\n clause.boost = 1\n }\n\n if (!('usePipeline' in clause)) {\n clause.usePipeline = true\n }\n\n if (!('wildcard' in clause)) {\n clause.wildcard = lunr.Query.wildcard.NONE\n }\n\n if ((clause.wildcard & lunr.Query.wildcard.LEADING) && (clause.term.charAt(0) != lunr.Query.wildcard)) {\n clause.term = \"*\" + clause.term\n }\n\n if ((clause.wildcard & lunr.Query.wildcard.TRAILING) && (clause.term.slice(-1) != lunr.Query.wildcard)) {\n clause.term = \"\" + clause.term + \"*\"\n }\n\n if (!('presence' in clause)) {\n clause.presence = lunr.Query.presence.OPTIONAL\n }\n\n this.clauses.push(clause)\n\n return this\n}\n\n/**\n * A negated query is one in which every clause has a presence of\n * prohibited. These queries require some special processing to return\n * the expected results.\n *\n * @returns boolean\n */\nlunr.Query.prototype.isNegated = function () {\n for (var i = 0; i < this.clauses.length; i++) {\n if (this.clauses[i].presence != lunr.Query.presence.PROHIBITED) {\n return false\n }\n }\n\n return true\n}\n\n/**\n * Adds a term to the current query, under the covers this will create a {@link lunr.Query~Clause}\n * to the list of clauses that make up this query.\n *\n * The term is used as is, i.e. no tokenization will be performed by this method. Instead conversion\n * to a token or token-like string should be done before calling this method.\n *\n * The term will be converted to a string by calling `toString`. Multiple terms can be passed as an\n * array, each term in the array will share the same options.\n *\n * @param {object|object[]} term - The term(s) to add to the query.\n * @param {object} [options] - Any additional properties to add to the query clause.\n * @returns {lunr.Query}\n * @see lunr.Query#clause\n * @see lunr.Query~Clause\n * @example adding a single term to a query\n * query.term(\"foo\")\n * @example adding a single term to a query and specifying search fields, term boost and automatic trailing wildcard\n * query.term(\"foo\", {\n * fields: [\"title\"],\n * boost: 10,\n * wildcard: lunr.Query.wildcard.TRAILING\n * })\n * @example using lunr.tokenizer to convert a string to tokens before using them as terms\n * query.term(lunr.tokenizer(\"foo bar\"))\n */\nlunr.Query.prototype.term = function (term, options) {\n if (Array.isArray(term)) {\n term.forEach(function (t) { this.term(t, lunr.utils.clone(options)) }, this)\n return this\n }\n\n var clause = options || {}\n clause.term = term.toString()\n\n this.clause(clause)\n\n return this\n}\nlunr.QueryParseError = function (message, start, end) {\n this.name = \"QueryParseError\"\n this.message = message\n this.start = start\n this.end = end\n}\n\nlunr.QueryParseError.prototype = new Error\nlunr.QueryLexer = function (str) {\n this.lexemes = []\n this.str = str\n this.length = str.length\n this.pos = 0\n this.start = 0\n this.escapeCharPositions = []\n}\n\nlunr.QueryLexer.prototype.run = function () {\n var state = lunr.QueryLexer.lexText\n\n while (state) {\n state = state(this)\n }\n}\n\nlunr.QueryLexer.prototype.sliceString = function () {\n var subSlices = [],\n sliceStart = this.start,\n sliceEnd = this.pos\n\n for (var i = 0; i < this.escapeCharPositions.length; i++) {\n sliceEnd = this.escapeCharPositions[i]\n subSlices.push(this.str.slice(sliceStart, sliceEnd))\n sliceStart = sliceEnd + 1\n }\n\n subSlices.push(this.str.slice(sliceStart, this.pos))\n this.escapeCharPositions.length = 0\n\n return subSlices.join('')\n}\n\nlunr.QueryLexer.prototype.emit = function (type) {\n this.lexemes.push({\n type: type,\n str: this.sliceString(),\n start: this.start,\n end: this.pos\n })\n\n this.start = this.pos\n}\n\nlunr.QueryLexer.prototype.escapeCharacter = function () {\n this.escapeCharPositions.push(this.pos - 1)\n this.pos += 1\n}\n\nlunr.QueryLexer.prototype.next = function () {\n if (this.pos >= this.length) {\n return lunr.QueryLexer.EOS\n }\n\n var char = this.str.charAt(this.pos)\n this.pos += 1\n return char\n}\n\nlunr.QueryLexer.prototype.width = function () {\n return this.pos - this.start\n}\n\nlunr.QueryLexer.prototype.ignore = function () {\n if (this.start == this.pos) {\n this.pos += 1\n }\n\n this.start = this.pos\n}\n\nlunr.QueryLexer.prototype.backup = function () {\n this.pos -= 1\n}\n\nlunr.QueryLexer.prototype.acceptDigitRun = function () {\n var char, charCode\n\n do {\n char = this.next()\n charCode = char.charCodeAt(0)\n } while (charCode > 47 && charCode < 58)\n\n if (char != lunr.QueryLexer.EOS) {\n this.backup()\n }\n}\n\nlunr.QueryLexer.prototype.more = function () {\n return this.pos < this.length\n}\n\nlunr.QueryLexer.EOS = 'EOS'\nlunr.QueryLexer.FIELD = 'FIELD'\nlunr.QueryLexer.TERM = 'TERM'\nlunr.QueryLexer.EDIT_DISTANCE = 'EDIT_DISTANCE'\nlunr.QueryLexer.BOOST = 'BOOST'\nlunr.QueryLexer.PRESENCE = 'PRESENCE'\n\nlunr.QueryLexer.lexField = function (lexer) {\n lexer.backup()\n lexer.emit(lunr.QueryLexer.FIELD)\n lexer.ignore()\n return lunr.QueryLexer.lexText\n}\n\nlunr.QueryLexer.lexTerm = function (lexer) {\n if (lexer.width() > 1) {\n lexer.backup()\n lexer.emit(lunr.QueryLexer.TERM)\n }\n\n lexer.ignore()\n\n if (lexer.more()) {\n return lunr.QueryLexer.lexText\n }\n}\n\nlunr.QueryLexer.lexEditDistance = function (lexer) {\n lexer.ignore()\n lexer.acceptDigitRun()\n lexer.emit(lunr.QueryLexer.EDIT_DISTANCE)\n return lunr.QueryLexer.lexText\n}\n\nlunr.QueryLexer.lexBoost = function (lexer) {\n lexer.ignore()\n lexer.acceptDigitRun()\n lexer.emit(lunr.QueryLexer.BOOST)\n return lunr.QueryLexer.lexText\n}\n\nlunr.QueryLexer.lexEOS = function (lexer) {\n if (lexer.width() > 0) {\n lexer.emit(lunr.QueryLexer.TERM)\n }\n}\n\n// This matches the separator used when tokenising fields\n// within a document. These should match otherwise it is\n// not possible to search for some tokens within a document.\n//\n// It is possible for the user to change the separator on the\n// tokenizer so it _might_ clash with any other of the special\n// characters already used within the search string, e.g. :.\n//\n// This means that it is possible to change the separator in\n// such a way that makes some words unsearchable using a search\n// string.\nlunr.QueryLexer.termSeparator = lunr.tokenizer.separator\n\nlunr.QueryLexer.lexText = function (lexer) {\n while (true) {\n var char = lexer.next()\n\n if (char == lunr.QueryLexer.EOS) {\n return lunr.QueryLexer.lexEOS\n }\n\n // Escape character is '\\'\n if (char.charCodeAt(0) == 92) {\n lexer.escapeCharacter()\n continue\n }\n\n if (char == \":\") {\n return lunr.QueryLexer.lexField\n }\n\n if (char == \"~\") {\n lexer.backup()\n if (lexer.width() > 0) {\n lexer.emit(lunr.QueryLexer.TERM)\n }\n return lunr.QueryLexer.lexEditDistance\n }\n\n if (char == \"^\") {\n lexer.backup()\n if (lexer.width() > 0) {\n lexer.emit(lunr.QueryLexer.TERM)\n }\n return lunr.QueryLexer.lexBoost\n }\n\n // \"+\" indicates term presence is required\n // checking for length to ensure that only\n // leading \"+\" are considered\n if (char == \"+\" && lexer.width() === 1) {\n lexer.emit(lunr.QueryLexer.PRESENCE)\n return lunr.QueryLexer.lexText\n }\n\n // \"-\" indicates term presence is prohibited\n // checking for length to ensure that only\n // leading \"-\" are considered\n if (char == \"-\" && lexer.width() === 1) {\n lexer.emit(lunr.QueryLexer.PRESENCE)\n return lunr.QueryLexer.lexText\n }\n\n if (char.match(lunr.QueryLexer.termSeparator)) {\n return lunr.QueryLexer.lexTerm\n }\n }\n}\n\nlunr.QueryParser = function (str, query) {\n this.lexer = new lunr.QueryLexer (str)\n this.query = query\n this.currentClause = {}\n this.lexemeIdx = 0\n}\n\nlunr.QueryParser.prototype.parse = function () {\n this.lexer.run()\n this.lexemes = this.lexer.lexemes\n\n var state = lunr.QueryParser.parseClause\n\n while (state) {\n state = state(this)\n }\n\n return this.query\n}\n\nlunr.QueryParser.prototype.peekLexeme = function () {\n return this.lexemes[this.lexemeIdx]\n}\n\nlunr.QueryParser.prototype.consumeLexeme = function () {\n var lexeme = this.peekLexeme()\n this.lexemeIdx += 1\n return lexeme\n}\n\nlunr.QueryParser.prototype.nextClause = function () {\n var completedClause = this.currentClause\n this.query.clause(completedClause)\n this.currentClause = {}\n}\n\nlunr.QueryParser.parseClause = function (parser) {\n var lexeme = parser.peekLexeme()\n\n if (lexeme == undefined) {\n return\n }\n\n switch (lexeme.type) {\n case lunr.QueryLexer.PRESENCE:\n return lunr.QueryParser.parsePresence\n case lunr.QueryLexer.FIELD:\n return lunr.QueryParser.parseField\n case lunr.QueryLexer.TERM:\n return lunr.QueryParser.parseTerm\n default:\n var errorMessage = \"expected either a field or a term, found \" + lexeme.type\n\n if (lexeme.str.length >= 1) {\n errorMessage += \" with value '\" + lexeme.str + \"'\"\n }\n\n throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n }\n}\n\nlunr.QueryParser.parsePresence = function (parser) {\n var lexeme = parser.consumeLexeme()\n\n if (lexeme == undefined) {\n return\n }\n\n switch (lexeme.str) {\n case \"-\":\n parser.currentClause.presence = lunr.Query.presence.PROHIBITED\n break\n case \"+\":\n parser.currentClause.presence = lunr.Query.presence.REQUIRED\n break\n default:\n var errorMessage = \"unrecognised presence operator'\" + lexeme.str + \"'\"\n throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n }\n\n var nextLexeme = parser.peekLexeme()\n\n if (nextLexeme == undefined) {\n var errorMessage = \"expecting term or field, found nothing\"\n throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n }\n\n switch (nextLexeme.type) {\n case lunr.QueryLexer.FIELD:\n return lunr.QueryParser.parseField\n case lunr.QueryLexer.TERM:\n return lunr.QueryParser.parseTerm\n default:\n var errorMessage = \"expecting term or field, found '\" + nextLexeme.type + \"'\"\n throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)\n }\n}\n\nlunr.QueryParser.parseField = function (parser) {\n var lexeme = parser.consumeLexeme()\n\n if (lexeme == undefined) {\n return\n }\n\n if (parser.query.allFields.indexOf(lexeme.str) == -1) {\n var possibleFields = parser.query.allFields.map(function (f) { return \"'\" + f + \"'\" }).join(', '),\n errorMessage = \"unrecognised field '\" + lexeme.str + \"', possible fields: \" + possibleFields\n\n throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n }\n\n parser.currentClause.fields = [lexeme.str]\n\n var nextLexeme = parser.peekLexeme()\n\n if (nextLexeme == undefined) {\n var errorMessage = \"expecting term, found nothing\"\n throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n }\n\n switch (nextLexeme.type) {\n case lunr.QueryLexer.TERM:\n return lunr.QueryParser.parseTerm\n default:\n var errorMessage = \"expecting term, found '\" + nextLexeme.type + \"'\"\n throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)\n }\n}\n\nlunr.QueryParser.parseTerm = function (parser) {\n var lexeme = parser.consumeLexeme()\n\n if (lexeme == undefined) {\n return\n }\n\n parser.currentClause.term = lexeme.str.toLowerCase()\n\n if (lexeme.str.indexOf(\"*\") != -1) {\n parser.currentClause.usePipeline = false\n }\n\n var nextLexeme = parser.peekLexeme()\n\n if (nextLexeme == undefined) {\n parser.nextClause()\n return\n }\n\n switch (nextLexeme.type) {\n case lunr.QueryLexer.TERM:\n parser.nextClause()\n return lunr.QueryParser.parseTerm\n case lunr.QueryLexer.FIELD:\n parser.nextClause()\n return lunr.QueryParser.parseField\n case lunr.QueryLexer.EDIT_DISTANCE:\n return lunr.QueryParser.parseEditDistance\n case lunr.QueryLexer.BOOST:\n return lunr.QueryParser.parseBoost\n case lunr.QueryLexer.PRESENCE:\n parser.nextClause()\n return lunr.QueryParser.parsePresence\n default:\n var errorMessage = \"Unexpected lexeme type '\" + nextLexeme.type + \"'\"\n throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)\n }\n}\n\nlunr.QueryParser.parseEditDistance = function (parser) {\n var lexeme = parser.consumeLexeme()\n\n if (lexeme == undefined) {\n return\n }\n\n var editDistance = parseInt(lexeme.str, 10)\n\n if (isNaN(editDistance)) {\n var errorMessage = \"edit distance must be numeric\"\n throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n }\n\n parser.currentClause.editDistance = editDistance\n\n var nextLexeme = parser.peekLexeme()\n\n if (nextLexeme == undefined) {\n parser.nextClause()\n return\n }\n\n switch (nextLexeme.type) {\n case lunr.QueryLexer.TERM:\n parser.nextClause()\n return lunr.QueryParser.parseTerm\n case lunr.QueryLexer.FIELD:\n parser.nextClause()\n return lunr.QueryParser.parseField\n case lunr.QueryLexer.EDIT_DISTANCE:\n return lunr.QueryParser.parseEditDistance\n case lunr.QueryLexer.BOOST:\n return lunr.QueryParser.parseBoost\n case lunr.QueryLexer.PRESENCE:\n parser.nextClause()\n return lunr.QueryParser.parsePresence\n default:\n var errorMessage = \"Unexpected lexeme type '\" + nextLexeme.type + \"'\"\n throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)\n }\n}\n\nlunr.QueryParser.parseBoost = function (parser) {\n var lexeme = parser.consumeLexeme()\n\n if (lexeme == undefined) {\n return\n }\n\n var boost = parseInt(lexeme.str, 10)\n\n if (isNaN(boost)) {\n var errorMessage = \"boost must be numeric\"\n throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n }\n\n parser.currentClause.boost = boost\n\n var nextLexeme = parser.peekLexeme()\n\n if (nextLexeme == undefined) {\n parser.nextClause()\n return\n }\n\n switch (nextLexeme.type) {\n case lunr.QueryLexer.TERM:\n parser.nextClause()\n return lunr.QueryParser.parseTerm\n case lunr.QueryLexer.FIELD:\n parser.nextClause()\n return lunr.QueryParser.parseField\n case lunr.QueryLexer.EDIT_DISTANCE:\n return lunr.QueryParser.parseEditDistance\n case lunr.QueryLexer.BOOST:\n return lunr.QueryParser.parseBoost\n case lunr.QueryLexer.PRESENCE:\n parser.nextClause()\n return lunr.QueryParser.parsePresence\n default:\n var errorMessage = \"Unexpected lexeme type '\" + nextLexeme.type + \"'\"\n throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)\n }\n}\n\n /**\n * export the module via AMD, CommonJS or as a browser global\n * Export code from https://github.com/umdjs/umd/blob/master/returnExports.js\n */\n ;(function (root, factory) {\n if (typeof define === 'function' && define.amd) {\n // AMD. Register as an anonymous module.\n define(factory)\n } else if (typeof exports === 'object') {\n /**\n * Node. Does not work with strict CommonJS, but\n * only CommonJS-like enviroments that support module.exports,\n * like Node.\n */\n module.exports = factory()\n } else {\n // Browser globals (root is window)\n root.lunr = factory()\n }\n }(this, function () {\n /**\n * Just return a value to define the module export.\n * This example returns an object, but the module\n * can return a function as the exported value.\n */\n return lunr\n }))\n})();\n", "/*!\n * escape-html\n * Copyright(c) 2012-2013 TJ Holowaychuk\n * Copyright(c) 2015 Andreas Lubbe\n * Copyright(c) 2015 Tiancheng \"Timothy\" Gu\n * MIT Licensed\n */\n\n'use strict';\n\n/**\n * Module variables.\n * @private\n */\n\nvar matchHtmlRegExp = /[\"'&<>]/;\n\n/**\n * Module exports.\n * @public\n */\n\nmodule.exports = escapeHtml;\n\n/**\n * Escape special characters in the given string of html.\n *\n * @param {string} string The string to escape for inserting into HTML\n * @return {string}\n * @public\n */\n\nfunction escapeHtml(string) {\n var str = '' + string;\n var match = matchHtmlRegExp.exec(str);\n\n if (!match) {\n return str;\n }\n\n var escape;\n var html = '';\n var index = 0;\n var lastIndex = 0;\n\n for (index = match.index; index < str.length; index++) {\n switch (str.charCodeAt(index)) {\n case 34: // \"\n escape = '"';\n break;\n case 38: // &\n escape = '&';\n break;\n case 39: // '\n escape = ''';\n break;\n case 60: // <\n escape = '<';\n break;\n case 62: // >\n escape = '>';\n break;\n default:\n continue;\n }\n\n if (lastIndex !== index) {\n html += str.substring(lastIndex, index);\n }\n\n lastIndex = index + 1;\n html += escape;\n }\n\n return lastIndex !== index\n ? html + str.substring(lastIndex, index)\n : html;\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A RTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport lunr from \"lunr\"\n\nimport \"~/polyfills\"\n\nimport { Search, SearchIndexConfig } from \"../../_\"\nimport {\n SearchMessage,\n SearchMessageType\n} from \"../message\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Add support for usage with `iframe-worker` polyfill\n *\n * While `importScripts` is synchronous when executed inside of a web worker,\n * it's not possible to provide a synchronous polyfilled implementation. The\n * cool thing is that awaiting a non-Promise is a noop, so extending the type\n * definition to return a `Promise` shouldn't break anything.\n *\n * @see https://bit.ly/2PjDnXi - GitHub comment\n */\ndeclare global {\n function importScripts(...urls: string[]): Promise | void\n}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Search index\n */\nlet index: Search\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch (= import) multi-language support through `lunr-languages`\n *\n * This function automatically imports the stemmers necessary to process the\n * languages, which are defined through the search index configuration.\n *\n * If the worker runs inside of an `iframe` (when using `iframe-worker` as\n * a shim), the base URL for the stemmers to be loaded must be determined by\n * searching for the first `script` element with a `src` attribute, which will\n * contain the contents of this script.\n *\n * @param config - Search index configuration\n *\n * @returns Promise resolving with no result\n */\nasync function setupSearchLanguages(\n config: SearchIndexConfig\n): Promise {\n let base = \"../lunr\"\n\n /* Detect `iframe-worker` and fix base URL */\n if (typeof parent !== \"undefined\" && \"IFrameWorker\" in parent) {\n const worker = document.querySelector(\"script[src]\")!\n const [path] = worker.src.split(\"/worker\")\n\n /* Prefix base with path */\n base = base.replace(\"..\", path)\n }\n\n /* Add scripts for languages */\n const scripts = []\n for (const lang of config.lang) {\n switch (lang) {\n\n /* Add segmenter for Japanese */\n case \"ja\":\n scripts.push(`${base}/tinyseg.js`)\n break\n\n /* Add segmenter for Hindi and Thai */\n case \"hi\":\n case \"th\":\n scripts.push(`${base}/wordcut.js`)\n break\n }\n\n /* Add language support */\n if (lang !== \"en\")\n scripts.push(`${base}/min/lunr.${lang}.min.js`)\n }\n\n /* Add multi-language support */\n if (config.lang.length > 1)\n scripts.push(`${base}/min/lunr.multi.min.js`)\n\n /* Load scripts synchronously */\n if (scripts.length)\n await importScripts(\n `${base}/min/lunr.stemmer.support.min.js`,\n ...scripts\n )\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Message handler\n *\n * @param message - Source message\n *\n * @returns Target message\n */\nexport async function handler(\n message: SearchMessage\n): Promise {\n switch (message.type) {\n\n /* Search setup message */\n case SearchMessageType.SETUP:\n await setupSearchLanguages(message.data.config)\n index = new Search(message.data)\n return {\n type: SearchMessageType.READY\n }\n\n /* Search query message */\n case SearchMessageType.QUERY:\n return {\n type: SearchMessageType.RESULT,\n data: index ? index.search(message.data) : { items: [] }\n }\n\n /* All other messages */\n default:\n throw new TypeError(\"Invalid message type\")\n }\n}\n\n/* ----------------------------------------------------------------------------\n * Worker\n * ------------------------------------------------------------------------- */\n\n/* @ts-expect-error - expose Lunr.js in global scope, or stemmers won't work */\nself.lunr = lunr\n\n/* Handle messages */\naddEventListener(\"message\", async ev => {\n postMessage(await handler(ev.data))\n})\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Polyfills\n * ------------------------------------------------------------------------- */\n\n/* Polyfill `Object.entries` */\nif (!Object.entries)\n Object.entries = function (obj: object) {\n const data: [string, string][] = []\n for (const key of Object.keys(obj))\n // @ts-expect-error - ignore property access warning\n data.push([key, obj[key]])\n\n /* Return entries */\n return data\n }\n\n/* Polyfill `Object.values` */\nif (!Object.values)\n Object.values = function (obj: object) {\n const data: string[] = []\n for (const key of Object.keys(obj))\n // @ts-expect-error - ignore property access warning\n data.push(obj[key])\n\n /* Return values */\n return data\n }\n\n/* ------------------------------------------------------------------------- */\n\n/* Polyfills for `Element` */\nif (typeof Element !== \"undefined\") {\n\n /* Polyfill `Element.scrollTo` */\n if (!Element.prototype.scrollTo)\n Element.prototype.scrollTo = function (\n x?: ScrollToOptions | number, y?: number\n ): void {\n if (typeof x === \"object\") {\n this.scrollLeft = x.left!\n this.scrollTop = x.top!\n } else {\n this.scrollLeft = x!\n this.scrollTop = y!\n }\n }\n\n /* Polyfill `Element.replaceWith` */\n if (!Element.prototype.replaceWith)\n Element.prototype.replaceWith = function (\n ...nodes: Array\n ): void {\n const parent = this.parentNode\n if (parent) {\n if (nodes.length === 0)\n parent.removeChild(this)\n\n /* Replace children and create text nodes */\n for (let i = nodes.length - 1; i >= 0; i--) {\n let node = nodes[i]\n if (typeof node !== \"object\")\n node = document.createTextNode(node)\n else if (node.parentNode)\n node.parentNode.removeChild(node)\n\n /* Replace child or insert before previous sibling */\n if (!i)\n parent.replaceChild(node, this)\n else\n parent.insertBefore(this.previousSibling!, node)\n }\n }\n }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport escapeHTML from \"escape-html\"\n\nimport { SearchIndexDocument } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search document\n */\nexport interface SearchDocument extends SearchIndexDocument {\n parent?: SearchIndexDocument /* Parent article */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Search document mapping\n */\nexport type SearchDocumentMap = Map\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Create a search document mapping\n *\n * @param docs - Search index documents\n *\n * @returns Search document map\n */\nexport function setupSearchDocumentMap(\n docs: SearchIndexDocument[]\n): SearchDocumentMap {\n const documents = new Map()\n const parents = new Set()\n for (const doc of docs) {\n const [path, hash] = doc.location.split(\"#\")\n\n /* Extract location, title and tags */\n const location = doc.location\n const title = doc.title\n const tags = doc.tags\n\n /* Escape and cleanup text */\n const text = escapeHTML(doc.text)\n .replace(/\\s+(?=[,.:;!?])/g, \"\")\n .replace(/\\s+/g, \" \")\n\n /* Handle section */\n if (hash) {\n const parent = documents.get(path)!\n\n /* Ignore first section, override article */\n if (!parents.has(parent)) {\n parent.title = doc.title\n parent.text = text\n\n /* Remember that we processed the article */\n parents.add(parent)\n\n /* Add subsequent section */\n } else {\n documents.set(location, {\n location,\n title,\n text,\n parent\n })\n }\n\n /* Add article */\n } else {\n documents.set(location, {\n location,\n title,\n text,\n ...tags && { tags }\n })\n }\n }\n return documents\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport escapeHTML from \"escape-html\"\n\nimport { SearchIndexConfig } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search highlight function\n *\n * @param value - Value\n *\n * @returns Highlighted value\n */\nexport type SearchHighlightFn = (value: string) => string\n\n/**\n * Search highlight factory function\n *\n * @param query - Query value\n *\n * @returns Search highlight function\n */\nexport type SearchHighlightFactoryFn = (query: string) => SearchHighlightFn\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Create a search highlighter\n *\n * @param config - Search index configuration\n * @param escape - Whether to escape HTML\n *\n * @returns Search highlight factory function\n */\nexport function setupSearchHighlighter(\n config: SearchIndexConfig, escape: boolean\n): SearchHighlightFactoryFn {\n const separator = new RegExp(config.separator, \"img\")\n const highlight = (_: unknown, data: string, term: string) => {\n return `${data}${term}`\n }\n\n /* Return factory function */\n return (query: string) => {\n query = query\n .replace(/[\\s*+\\-:~^]+/g, \" \")\n .trim()\n\n /* Create search term match expression */\n const match = new RegExp(`(^|${config.separator})(${\n query\n .replace(/[|\\\\{}()[\\]^$+*?.-]/g, \"\\\\$&\")\n .replace(separator, \"|\")\n })`, \"img\")\n\n /* Highlight string value */\n return value => (\n escape\n ? escapeHTML(value)\n : value\n )\n .replace(match, highlight)\n .replace(/<\\/mark>(\\s+)]*>/img, \"$1\")\n }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search query clause\n */\nexport interface SearchQueryClause {\n presence: lunr.Query.presence /* Clause presence */\n term: string /* Clause term */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Search query terms\n */\nexport type SearchQueryTerms = Record\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Parse a search query for analysis\n *\n * @param value - Query value\n *\n * @returns Search query clauses\n */\nexport function parseSearchQuery(\n value: string\n): SearchQueryClause[] {\n const query = new (lunr as any).Query([\"title\", \"text\"])\n const parser = new (lunr as any).QueryParser(value, query)\n\n /* Parse and return query clauses */\n parser.parse()\n return query.clauses\n}\n\n/**\n * Analyze the search query clauses in regard to the search terms found\n *\n * @param query - Search query clauses\n * @param terms - Search terms\n *\n * @returns Search query terms\n */\nexport function getSearchQueryTerms(\n query: SearchQueryClause[], terms: string[]\n): SearchQueryTerms {\n const clauses = new Set(query)\n\n /* Match query clauses against terms */\n const result: SearchQueryTerms = {}\n for (let t = 0; t < terms.length; t++)\n for (const clause of clauses)\n if (terms[t].startsWith(clause.term)) {\n result[clause.term] = true\n clauses.delete(clause)\n }\n\n /* Annotate unmatched non-stopword query clauses */\n for (const clause of clauses)\n if (lunr.stopWordFilter?.(clause.term as any))\n result[clause.term] = false\n\n /* Return query terms */\n return result\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n SearchDocument,\n SearchDocumentMap,\n setupSearchDocumentMap\n} from \"../document\"\nimport {\n SearchHighlightFactoryFn,\n setupSearchHighlighter\n} from \"../highlighter\"\nimport { SearchOptions } from \"../options\"\nimport {\n SearchQueryTerms,\n getSearchQueryTerms,\n parseSearchQuery\n} from \"../query\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search index configuration\n */\nexport interface SearchIndexConfig {\n lang: string[] /* Search languages */\n separator: string /* Search separator */\n}\n\n/**\n * Search index document\n */\nexport interface SearchIndexDocument {\n location: string /* Document location */\n title: string /* Document title */\n text: string /* Document text */\n tags?: string[] /* Document tags */\n boost?: number /* Document boost */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Search index\n *\n * This interfaces describes the format of the `search_index.json` file which\n * is automatically built by the MkDocs search plugin.\n */\nexport interface SearchIndex {\n config: SearchIndexConfig /* Search index configuration */\n docs: SearchIndexDocument[] /* Search index documents */\n options: SearchOptions /* Search options */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Search metadata\n */\nexport interface SearchMetadata {\n score: number /* Score (relevance) */\n terms: SearchQueryTerms /* Search query terms */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Search result document\n */\nexport type SearchResultDocument = SearchDocument & SearchMetadata\n\n/**\n * Search result item\n */\nexport type SearchResultItem = SearchResultDocument[]\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Search result\n */\nexport interface SearchResult {\n items: SearchResultItem[] /* Search result items */\n suggestions?: string[] /* Search suggestions */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Compute the difference of two lists of strings\n *\n * @param a - 1st list of strings\n * @param b - 2nd list of strings\n *\n * @returns Difference\n */\nfunction difference(a: string[], b: string[]): string[] {\n const [x, y] = [new Set(a), new Set(b)]\n return [\n ...new Set([...x].filter(value => !y.has(value)))\n ]\n}\n\n/* ----------------------------------------------------------------------------\n * Class\n * ------------------------------------------------------------------------- */\n\n/**\n * Search index\n */\nexport class Search {\n\n /**\n * Search document mapping\n *\n * A mapping of URLs (including hash fragments) to the actual articles and\n * sections of the documentation. The search document mapping must be created\n * regardless of whether the index was prebuilt or not, as Lunr.js itself\n * only stores the actual index.\n */\n protected documents: SearchDocumentMap\n\n /**\n * Search highlight factory function\n */\n protected highlight: SearchHighlightFactoryFn\n\n /**\n * The underlying Lunr.js search index\n */\n protected index: lunr.Index\n\n /**\n * Search options\n */\n protected options: SearchOptions\n\n /**\n * Create the search integration\n *\n * @param data - Search index\n */\n public constructor({ config, docs, options }: SearchIndex) {\n this.options = options\n\n /* Set up document map and highlighter factory */\n this.documents = setupSearchDocumentMap(docs)\n this.highlight = setupSearchHighlighter(config, false)\n\n /* Set separator for tokenizer */\n lunr.tokenizer.separator = new RegExp(config.separator)\n\n /* Create search index */\n this.index = lunr(function () {\n\n /* Set up multi-language support */\n if (config.lang.length === 1 && config.lang[0] !== \"en\") {\n this.use((lunr as any)[config.lang[0]])\n } else if (config.lang.length > 1) {\n this.use((lunr as any).multiLanguage(...config.lang))\n }\n\n /* Compute functions to be removed from the pipeline */\n const fns = difference([\n \"trimmer\", \"stopWordFilter\", \"stemmer\"\n ], options.pipeline)\n\n /* Remove functions from the pipeline for registered languages */\n for (const lang of config.lang.map(language => (\n language === \"en\" ? lunr : (lunr as any)[language]\n ))) {\n for (const fn of fns) {\n this.pipeline.remove(lang[fn])\n this.searchPipeline.remove(lang[fn])\n }\n }\n\n /* Set up reference */\n this.ref(\"location\")\n\n /* Set up fields */\n this.field(\"title\", { boost: 1e3 })\n this.field(\"text\")\n this.field(\"tags\", { boost: 1e6, extractor: doc => {\n const { tags = [] } = doc as SearchDocument\n return tags.reduce((list, tag) => [\n ...list,\n ...lunr.tokenizer(tag)\n ], [] as lunr.Token[])\n } })\n\n /* Index documents */\n for (const doc of docs)\n this.add(doc, { boost: doc.boost })\n })\n }\n\n /**\n * Search for matching documents\n *\n * The search index which MkDocs provides is divided up into articles, which\n * contain the whole content of the individual pages, and sections, which only\n * contain the contents of the subsections obtained by breaking the individual\n * pages up at `h1` ... `h6`. As there may be many sections on different pages\n * with identical titles (for example within this very project, e.g. \"Usage\"\n * or \"Installation\"), they need to be put into the context of the containing\n * page. For this reason, section results are grouped within their respective\n * articles which are the top-level results that are returned.\n *\n * @param query - Query value\n *\n * @returns Search results\n */\n public search(query: string): SearchResult {\n if (query) {\n try {\n const highlight = this.highlight(query)\n\n /* Parse query to extract clauses for analysis */\n const clauses = parseSearchQuery(query)\n .filter(clause => (\n clause.presence !== lunr.Query.presence.PROHIBITED\n ))\n\n /* Perform search and post-process results */\n const groups = this.index.search(`${query}*`)\n\n /* Apply post-query boosts based on title and search query terms */\n .reduce((item, { ref, score, matchData }) => {\n const document = this.documents.get(ref)\n if (typeof document !== \"undefined\") {\n const { location, title, text, tags, parent } = document\n\n /* Compute and analyze search query terms */\n const terms = getSearchQueryTerms(\n clauses,\n Object.keys(matchData.metadata)\n )\n\n /* Highlight title and text and apply post-query boosts */\n const boost = +!parent + +Object.values(terms).every(t => t)\n item.push({\n location,\n title: highlight(title),\n text: highlight(text),\n ...tags && { tags: tags.map(highlight) },\n score: score * (1 + boost),\n terms\n })\n }\n return item\n }, [])\n\n /* Sort search results again after applying boosts */\n .sort((a, b) => b.score - a.score)\n\n /* Group search results by page */\n .reduce((items, result) => {\n const document = this.documents.get(result.location)\n if (typeof document !== \"undefined\") {\n const ref = \"parent\" in document\n ? document.parent!.location\n : document.location\n items.set(ref, [...items.get(ref) || [], result])\n }\n return items\n }, new Map())\n\n /* Generate search suggestions, if desired */\n let suggestions: string[] | undefined\n if (this.options.suggestions) {\n const titles = this.index.query(builder => {\n for (const clause of clauses)\n builder.term(clause.term, {\n fields: [\"title\"],\n presence: lunr.Query.presence.REQUIRED,\n wildcard: lunr.Query.wildcard.TRAILING\n })\n })\n\n /* Retrieve suggestions for best match */\n suggestions = titles.length\n ? 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    +

    cluster_experiments

    +

    Downloads +PyPI +Unit tests +CodeCov +License +Pypi version

    +

    A Python library for end-to-end A/B testing workflows, featuring: +- Experiment analysis and scorecards +- Power analysis (simulation-based and normal approximation) +- Variance reduction techniques (CUPED, CUPAC) +- Support for complex experimental designs (cluster randomization, switchback experiments)

    +

    Key Features

    +

    1. Power Analysis

    +
      +
    • Simulation-based: Run Monte Carlo simulations to estimate power
    • +
    • Normal approximation: Fast power estimation using CLT
    • +
    • Minimum Detectable Effect: Calculate required effect sizes
    • +
    • Multiple designs: Support for:
    • +
    • Simple randomization
    • +
    • Variance reduction techniques in power analysis
    • +
    • Cluster randomization
    • +
    • Switchback experiments
    • +
    • Dict config: Easy to configure power analysis with a dictionary
    • +
    +

    2. Experiment Analysis

    +
      +
    • Analysis Plans: Define structured analysis plans
    • +
    • Metrics:
    • +
    • Simple metrics
    • +
    • Ratio metrics
    • +
    • Dimensions: Slice results by dimensions
    • +
    • Statistical Methods:
    • +
    • GEE
    • +
    • Mixed Linear Models
    • +
    • Clustered / regular OLS
    • +
    • T-tests
    • +
    • Synthetic Control
    • +
    • Dict config: Easy to define analysis plans with a dictionary
    • +
    +

    3. Variance Reduction

    +
      +
    • CUPED (Controlled-experiment Using Pre-Experiment Data):
    • +
    • Use historical outcome data to reduce variance, choose any granularity
    • +
    • Support for several covariates
    • +
    • CUPAC (Control Using Predictors as Covariates):
    • +
    • Use any scikit-learn compatible estimator to predict the outcome with pre-experiment data
    • +
    +

    Quick Start

    +

    Power Analysis Example

    +
    import numpy as np
    +import pandas as pd
    +from cluster_experiments import PowerAnalysis, NormalPowerAnalysis
    +
    +# Create sample data
    +N = 1_000
    +df = pd.DataFrame({
    +    "target": np.random.normal(0, 1, size=N),
    +    "date": pd.to_datetime(
    +        np.random.randint(
    +            pd.Timestamp("2024-01-01").value,
    +            pd.Timestamp("2024-01-31").value,
    +            size=N,
    +        )
    +    ),
    +})
    +
    +# Simulation-based power analysis with CUPED
    +config = {
    +    "analysis": "ols",
    +    "perturbator": "constant",
    +    "splitter": "non_clustered",
    +    "n_simulations": 50,
    +}
    +pw = PowerAnalysis.from_dict(config)
    +power = pw.power_analysis(df, average_effect=0.1)
    +
    +# Normal approximation (faster)
    +npw = NormalPowerAnalysis.from_dict({
    +    "analysis": "ols",
    +    "splitter": "non_clustered",
    +    "n_simulations": 5,
    +    "time_col": "date",
    +})
    +power_normal = npw.power_analysis(df, average_effect=0.1)
    +power_line_normal = npw.power_line(df, average_effects=[0.1, 0.2, 0.3])
    +
    +
    +# MDE calculation
    +mde = npw.mde(df, power=0.8)
    +
    +# MDE line with length
    +mde_timeline = npw.mde_time_line(
    +    df,
    +    powers=[0.8],
    +    experiment_length=[7, 14, 21]
    +)
    +
    +print(power, power_line_normal, power_normal, mde, mde_timeline)
    +
    +

    Experiment Analysis Example

    +
    import numpy as np
    +import pandas as pd
    +from cluster_experiments import AnalysisPlan, SimpleMetric, Variant, Dimension
    +
    +N = 1_000
    +experiment_data = pd.DataFrame({
    +    "order_value": np.random.normal(100, 10, size=N),
    +    "delivery_time": np.random.normal(10, 1, size=N),
    +    "experiment_group": np.random.choice(["control", "treatment"], size=N),
    +    "city": np.random.choice(["NYC", "LA"], size=N),
    +    "customer_id": np.random.randint(1, 100, size=N),
    +    "customer_age": np.random.randint(20, 60, size=N),
    +})
    +
    +# Define metrics
    +aov = SimpleMetric(alias="AOV", name="order_value")
    +delivery_time = SimpleMetric(alias="Delivery Time", name="delivery_time")
    +
    +# Define variants and dimensions
    +variants = [
    +    Variant("control", is_control=True),
    +    Variant("treatment", is_control=False),
    +]
    +city_dimension = Dimension(name="city", values=["NYC", "LA"])
    +
    +# Create analysis plan
    +plan = AnalysisPlan.from_metrics(
    +    metrics=[aov, delivery_time],
    +    variants=variants,
    +    variant_col="experiment_group",
    +    dimensions=[city_dimension],
    +    analysis_type="clustered_ols",
    +    analysis_config={
    +        "cluster_cols": ["customer_id"],
    +    },
    +)
    +
    +# Run analysis
    +results = plan.analyze(experiment_data)
    +print(results.to_dataframe())
    +
    +

    Variance Reduction Example

    +
    import numpy as np
    +import pandas as pd
    +from cluster_experiments import (
    +    AnalysisPlan,
    +    SimpleMetric,
    +    Variant,
    +    Dimension,
    +    TargetAggregation,
    +    HypothesisTest
    +)
    +
    +N = 1000
    +
    +experiment_data = pd.DataFrame({
    +    "order_value": np.random.normal(100, 10, size=N),
    +    "delivery_time": np.random.normal(10, 1, size=N),
    +    "experiment_group": np.random.choice(["control", "treatment"], size=N),
    +    "city": np.random.choice(["NYC", "LA"], size=N),
    +    "customer_id": np.random.randint(1, 100, size=N),
    +    "customer_age": np.random.randint(20, 60, size=N),
    +})
    +
    +pre_experiment_data = pd.DataFrame({
    +    "order_value": np.random.normal(100, 10, size=N),
    +    "customer_id": np.random.randint(1, 100, size=N),
    +})
    +
    +# Define test
    +cupac_model = TargetAggregation(
    +    agg_col="customer_id",
    +    target_col="order_value"
    +)
    +
    +hypothesis_test = HypothesisTest(
    +    metric=SimpleMetric(alias="AOV", name="order_value"),
    +    analysis_type="clustered_ols",
    +    analysis_config={
    +        "cluster_cols": ["customer_id"],
    +        "covariates": ["customer_age", "estimate_order_value"],
    +    },
    +    cupac_config={
    +        "cupac_model": cupac_model,
    +        "target_col": "order_value",
    +    },
    +)
    +
    +# Create analysis plan
    +plan = AnalysisPlan(
    +    tests=[hypothesis_test],
    +    variants=[
    +        Variant("control", is_control=True),
    +        Variant("treatment", is_control=False),
    +    ],
    +    variant_col="experiment_group",
    +)
    +
    +# Run analysis
    +results = plan.analyze(experiment_data, pre_experiment_data)
    +print(results.to_dataframe())
    +
    +

    Installation

    +

    You can install this package via pip.

    +
    pip install cluster-experiments
    +
    +

    For detailed documentation and examples, visit our documentation site.

    +

    Features

    +

    The library offers the following classes:

    +
      +
    • Regarding power analysis:
        +
      • PowerAnalysis: to run power analysis on any experiment design, using simulation
      • +
      • PowerAnalysisWithPreExperimentData: to run power analysis on a clustered/switchback design, but adding pre-experiment df during split and perturbation (especially useful for Synthetic Control)
      • +
      • NormalPowerAnalysis: to run power analysis on any experiment design using the central limit theorem for the distribution of the estimator. It can be used to compute the minimum detectable effect (MDE) for a given power level.
      • +
      • ConstantPerturbator: to artificially perturb treated group with constant perturbations
      • +
      • BinaryPerturbator: to artificially perturb treated group for binary outcomes
      • +
      • RelativePositivePerturbator: to artificially perturb treated group with relative positive perturbations
      • +
      • NormalPerturbator: to artificially perturb treated group with normal distribution perturbations
      • +
      • BetaRelativePositivePerturbator: to artificially perturb treated group with relative positive beta distribution perturbations
      • +
      • BetaRelativePerturbator: to artificially perturb treated group with relative beta distribution perturbations in a specified support interval
      • +
      • SegmentedBetaRelativePerturbator: to artificially perturb treated group with relative beta distribution perturbations in a specified support interval, but using clusters
      • +
      +
    • +
    • Regarding splitting data:
        +
      • ClusteredSplitter: to split data based on clusters
      • +
      • FixedSizeClusteredSplitter: to split data based on clusters with a fixed size (example: only 1 treatment cluster and the rest in control)
      • +
      • BalancedClusteredSplitter: to split data based on clusters in a balanced way
      • +
      • NonClusteredSplitter: Regular data splitting, no clusters
      • +
      • StratifiedClusteredSplitter: to split based on clusters and strata, balancing the number of clusters in each stratus
      • +
      • RepeatedSampler: for backtests where we have access to counterfactuals, does not split the data, just duplicates the data for all groups
      • +
      • Switchback splitters (the same can be done with clustered splitters, but there is a convenient way to define switchback splitters using switch frequency):
          +
        • SwitchbackSplitter: to split data based on clusters and dates, for switchback experiments
        • +
        • BalancedSwitchbackSplitter: to split data based on clusters and dates, for switchback experiments, balancing treatment and control among all clusters
        • +
        • StratifiedSwitchbackSplitter: to split data based on clusters and dates, for switchback experiments, balancing the number of clusters in each stratus
        • +
        • Washover for switchback experiments:
            +
          • EmptyWashover: no washover done at all.
          • +
          • ConstantWashover: accepts a timedelta parameter and removes the data when we switch from A to B for the timedelta interval.
          • +
          +
        • +
        +
      • +
      +
    • +
    • Regarding analysis methods:
        +
      • GeeExperimentAnalysis: to run GEE analysis on the results of a clustered design
      • +
      • MLMExperimentAnalysis: to run Mixed Linear Model analysis on the results of a clustered design
      • +
      • TTestClusteredAnalysis: to run a t-test on aggregated data for clusters
      • +
      • PairedTTestClusteredAnalysis: to run a paired t-test on aggregated data for clusters
      • +
      • ClusteredOLSAnalysis: to run OLS analysis on the results of a clustered design
      • +
      • OLSAnalysis: to run OLS analysis for non-clustered data
      • +
      • TargetAggregation: to add pre-experimental data of the outcome to reduce variance
      • +
      • SyntheticControlAnalysis: to run synthetic control analysis
      • +
      +
    • +
    • Regarding experiment analysis workflow:
        +
      • Metric: abstract class to define a metric to be used in the analysis
      • +
      • SimpleMetric: to create a metric defined at the same level of the data used for the analysis
      • +
      • RatioMetric: to create a metric defined at a lower level than the data used for the analysis
      • +
      • Variant: to define a variant of the experiment
      • +
      • Dimension: to define a dimension to slice the results of the experiment
      • +
      • HypothesisTest: to define a Hypothesis Test with a metric, analysis method, optional analysis configuration, and optional dimensions
      • +
      • AnalysisPlan: to define a plan of analysis with a list of Hypothesis Tests for a dataset and the experiment variants. The analyze() method runs the analysis and returns the results
      • +
      • AnalysisResults: to store the results of the analysis
      • +
      +
    • +
    • Other:
        +
      • PowerConfig: to conveniently configure PowerAnalysis class
      • +
      • ConfidenceInterval: to store the data representation of a confidence interval
      • +
      • InferenceResults: to store the structure of complete statistical analysis results
      • +
      +
    • +
    +

    Contributing

    +

    In case you want to use venv as a virtual environment: +

    python -m venv venv
    +source venv/bin/activate
    +

    +

    After creating the virtual environment (or not), run: +

    git clone git@github.com:david26694/cluster-experiments.git
    +cd cluster-experiments
    +make install-dev
    +

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Power Analysis \u00b6 Simulation-based : Run Monte Carlo simulations to estimate power Normal approximation : Fast power estimation using CLT Minimum Detectable Effect : Calculate required effect sizes Multiple designs : Support for: Simple randomization Variance reduction techniques in power analysis Cluster randomization Switchback experiments Dict config : Easy to configure power analysis with a dictionary 2. Experiment Analysis \u00b6 Analysis Plans : Define structured analysis plans Metrics : Simple metrics Ratio metrics Dimensions : Slice results by dimensions Statistical Methods : GEE Mixed Linear Models Clustered / regular OLS T-tests Synthetic Control Dict config : Easy to define analysis plans with a dictionary 3. Variance Reduction \u00b6 CUPED (Controlled-experiment Using Pre-Experiment Data): Use historical outcome data to reduce variance, choose any granularity Support for several covariates CUPAC (Control Using Predictors as Covariates): Use any scikit-learn compatible estimator to predict the outcome with pre-experiment data Quick Start \u00b6 Power Analysis Example \u00b6 import numpy as np import pandas as pd from cluster_experiments import PowerAnalysis , NormalPowerAnalysis # Create sample data N = 1_000 df = pd . DataFrame ({ \"target\" : np . random . normal ( 0 , 1 , size = N ), \"date\" : pd . to_datetime ( np . random . randint ( pd . Timestamp ( \"2024-01-01\" ) . value , pd . Timestamp ( \"2024-01-31\" ) . value , size = N , ) ), }) # Simulation-based power analysis with CUPED config = { \"analysis\" : \"ols\" , \"perturbator\" : \"constant\" , \"splitter\" : \"non_clustered\" , \"n_simulations\" : 50 , } pw = PowerAnalysis . from_dict ( config ) power = pw . power_analysis ( df , average_effect = 0.1 ) # Normal approximation (faster) npw = NormalPowerAnalysis . from_dict ({ \"analysis\" : \"ols\" , \"splitter\" : \"non_clustered\" , \"n_simulations\" : 5 , \"time_col\" : \"date\" , }) power_normal = npw . power_analysis ( df , average_effect = 0.1 ) power_line_normal = npw . power_line ( df , average_effects = [ 0.1 , 0.2 , 0.3 ]) # MDE calculation mde = npw . mde ( df , power = 0.8 ) # MDE line with length mde_timeline = npw . mde_time_line ( df , powers = [ 0.8 ], experiment_length = [ 7 , 14 , 21 ] ) print ( power , power_line_normal , power_normal , mde , mde_timeline ) Experiment Analysis Example \u00b6 import numpy as np import pandas as pd from cluster_experiments import AnalysisPlan , SimpleMetric , Variant , Dimension N = 1_000 experiment_data = pd . DataFrame ({ \"order_value\" : np . random . normal ( 100 , 10 , size = N ), \"delivery_time\" : np . random . normal ( 10 , 1 , size = N ), \"experiment_group\" : np . random . choice ([ \"control\" , \"treatment\" ], size = N ), \"city\" : np . random . choice ([ \"NYC\" , \"LA\" ], size = N ), \"customer_id\" : np . random . randint ( 1 , 100 , size = N ), \"customer_age\" : np . random . randint ( 20 , 60 , size = N ), }) # Define metrics aov = SimpleMetric ( alias = \"AOV\" , name = \"order_value\" ) delivery_time = SimpleMetric ( alias = \"Delivery Time\" , name = \"delivery_time\" ) # Define variants and dimensions variants = [ Variant ( \"control\" , is_control = True ), Variant ( \"treatment\" , is_control = False ), ] city_dimension = Dimension ( name = \"city\" , values = [ \"NYC\" , \"LA\" ]) # Create analysis plan plan = AnalysisPlan . from_metrics ( metrics = [ aov , delivery_time ], variants = variants , variant_col = \"experiment_group\" , dimensions = [ city_dimension ], analysis_type = \"clustered_ols\" , analysis_config = { \"cluster_cols\" : [ \"customer_id\" ], }, ) # Run analysis results = plan . analyze ( experiment_data ) print ( results . to_dataframe ()) Variance Reduction Example \u00b6 import numpy as np import pandas as pd from cluster_experiments import ( AnalysisPlan , SimpleMetric , Variant , Dimension , TargetAggregation , HypothesisTest ) N = 1000 experiment_data = pd . DataFrame ({ \"order_value\" : np . random . normal ( 100 , 10 , size = N ), \"delivery_time\" : np . random . normal ( 10 , 1 , size = N ), \"experiment_group\" : np . random . choice ([ \"control\" , \"treatment\" ], size = N ), \"city\" : np . random . choice ([ \"NYC\" , \"LA\" ], size = N ), \"customer_id\" : np . random . randint ( 1 , 100 , size = N ), \"customer_age\" : np . random . randint ( 20 , 60 , size = N ), }) pre_experiment_data = pd . DataFrame ({ \"order_value\" : np . random . normal ( 100 , 10 , size = N ), \"customer_id\" : np . random . randint ( 1 , 100 , size = N ), }) # Define test cupac_model = TargetAggregation ( agg_col = \"customer_id\" , target_col = \"order_value\" ) hypothesis_test = HypothesisTest ( metric = SimpleMetric ( alias = \"AOV\" , name = \"order_value\" ), analysis_type = \"clustered_ols\" , analysis_config = { \"cluster_cols\" : [ \"customer_id\" ], \"covariates\" : [ \"customer_age\" , \"estimate_order_value\" ], }, cupac_config = { \"cupac_model\" : cupac_model , \"target_col\" : \"order_value\" , }, ) # Create analysis plan plan = AnalysisPlan ( tests = [ hypothesis_test ], variants = [ Variant ( \"control\" , is_control = True ), Variant ( \"treatment\" , is_control = False ), ], variant_col = \"experiment_group\" , ) # Run analysis results = plan . analyze ( experiment_data , pre_experiment_data ) print ( results . to_dataframe ()) Installation \u00b6 You can install this package via pip . pip install cluster-experiments For detailed documentation and examples, visit our documentation site . Features \u00b6 The library offers the following classes: Regarding power analysis: PowerAnalysis : to run power analysis on any experiment design, using simulation PowerAnalysisWithPreExperimentData : to run power analysis on a clustered/switchback design, but adding pre-experiment df during split and perturbation (especially useful for Synthetic Control) NormalPowerAnalysis : to run power analysis on any experiment design using the central limit theorem for the distribution of the estimator. It can be used to compute the minimum detectable effect (MDE) for a given power level. ConstantPerturbator : to artificially perturb treated group with constant perturbations BinaryPerturbator : to artificially perturb treated group for binary outcomes RelativePositivePerturbator : to artificially perturb treated group with relative positive perturbations NormalPerturbator : to artificially perturb treated group with normal distribution perturbations BetaRelativePositivePerturbator : to artificially perturb treated group with relative positive beta distribution perturbations BetaRelativePerturbator : to artificially perturb treated group with relative beta distribution perturbations in a specified support interval SegmentedBetaRelativePerturbator : to artificially perturb treated group with relative beta distribution perturbations in a specified support interval, but using clusters Regarding splitting data: ClusteredSplitter : to split data based on clusters FixedSizeClusteredSplitter : to split data based on clusters with a fixed size (example: only 1 treatment cluster and the rest in control) BalancedClusteredSplitter : to split data based on clusters in a balanced way NonClusteredSplitter : Regular data splitting, no clusters StratifiedClusteredSplitter : to split based on clusters and strata, balancing the number of clusters in each stratus RepeatedSampler : for backtests where we have access to counterfactuals, does not split the data, just duplicates the data for all groups Switchback splitters (the same can be done with clustered splitters, but there is a convenient way to define switchback splitters using switch frequency): SwitchbackSplitter : to split data based on clusters and dates, for switchback experiments BalancedSwitchbackSplitter : to split data based on clusters and dates, for switchback experiments, balancing treatment and control among all clusters StratifiedSwitchbackSplitter : to split data based on clusters and dates, for switchback experiments, balancing the number of clusters in each stratus Washover for switchback experiments: EmptyWashover : no washover done at all. ConstantWashover : accepts a timedelta parameter and removes the data when we switch from A to B for the timedelta interval. Regarding analysis methods: GeeExperimentAnalysis : to run GEE analysis on the results of a clustered design MLMExperimentAnalysis : to run Mixed Linear Model analysis on the results of a clustered design TTestClusteredAnalysis : to run a t-test on aggregated data for clusters PairedTTestClusteredAnalysis : to run a paired t-test on aggregated data for clusters ClusteredOLSAnalysis : to run OLS analysis on the results of a clustered design OLSAnalysis : to run OLS analysis for non-clustered data TargetAggregation : to add pre-experimental data of the outcome to reduce variance SyntheticControlAnalysis : to run synthetic control analysis Regarding experiment analysis workflow: Metric : abstract class to define a metric to be used in the analysis SimpleMetric : to create a metric defined at the same level of the data used for the analysis RatioMetric : to create a metric defined at a lower level than the data used for the analysis Variant : to define a variant of the experiment Dimension : to define a dimension to slice the results of the experiment HypothesisTest : to define a Hypothesis Test with a metric, analysis method, optional analysis configuration, and optional dimensions AnalysisPlan : to define a plan of analysis with a list of Hypothesis Tests for a dataset and the experiment variants. The analyze() method runs the analysis and returns the results AnalysisResults : to store the results of the analysis Other: PowerConfig : to conveniently configure PowerAnalysis class ConfidenceInterval : to store the data representation of a confidence interval InferenceResults : to store the structure of complete statistical analysis results Contributing \u00b6 In case you want to use venv as a virtual environment: python -m venv venv source venv/bin/activate After creating the virtual environment (or not), run: git clone git@github.com:david26694/cluster-experiments.git cd cluster-experiments make install-dev","title":"Index"},{"location":"index.html#cluster_experiments","text":"A Python library for end-to-end A/B testing workflows, featuring: - Experiment analysis and scorecards - Power analysis (simulation-based and normal approximation) - Variance reduction techniques (CUPED, CUPAC) - Support for complex experimental designs (cluster randomization, switchback experiments)","title":"cluster_experiments"},{"location":"index.html#key-features","text":"","title":"Key Features"},{"location":"index.html#1-power-analysis","text":"Simulation-based : Run Monte Carlo simulations to estimate power Normal approximation : Fast power estimation using CLT Minimum Detectable Effect : Calculate required effect sizes Multiple designs : Support for: Simple randomization Variance reduction techniques in power analysis Cluster randomization Switchback experiments Dict config : Easy to configure power analysis with a dictionary","title":"1. Power Analysis"},{"location":"index.html#2-experiment-analysis","text":"Analysis Plans : Define structured analysis plans Metrics : Simple metrics Ratio metrics Dimensions : Slice results by dimensions Statistical Methods : GEE Mixed Linear Models Clustered / regular OLS T-tests Synthetic Control Dict config : Easy to define analysis plans with a dictionary","title":"2. Experiment Analysis"},{"location":"index.html#3-variance-reduction","text":"CUPED (Controlled-experiment Using Pre-Experiment Data): Use historical outcome data to reduce variance, choose any granularity Support for several covariates CUPAC (Control Using Predictors as Covariates): Use any scikit-learn compatible estimator to predict the outcome with pre-experiment data","title":"3. Variance Reduction"},{"location":"index.html#quick-start","text":"","title":"Quick Start"},{"location":"index.html#power-analysis-example","text":"import numpy as np import pandas as pd from cluster_experiments import PowerAnalysis , NormalPowerAnalysis # Create sample data N = 1_000 df = pd . DataFrame ({ \"target\" : np . random . normal ( 0 , 1 , size = N ), \"date\" : pd . to_datetime ( np . random . randint ( pd . Timestamp ( \"2024-01-01\" ) . value , pd . Timestamp ( \"2024-01-31\" ) . value , size = N , ) ), }) # Simulation-based power analysis with CUPED config = { \"analysis\" : \"ols\" , \"perturbator\" : \"constant\" , \"splitter\" : \"non_clustered\" , \"n_simulations\" : 50 , } pw = PowerAnalysis . from_dict ( config ) power = pw . power_analysis ( df , average_effect = 0.1 ) # Normal approximation (faster) npw = NormalPowerAnalysis . from_dict ({ \"analysis\" : \"ols\" , \"splitter\" : \"non_clustered\" , \"n_simulations\" : 5 , \"time_col\" : \"date\" , }) power_normal = npw . power_analysis ( df , average_effect = 0.1 ) power_line_normal = npw . power_line ( df , average_effects = [ 0.1 , 0.2 , 0.3 ]) # MDE calculation mde = npw . mde ( df , power = 0.8 ) # MDE line with length mde_timeline = npw . mde_time_line ( df , powers = [ 0.8 ], experiment_length = [ 7 , 14 , 21 ] ) print ( power , power_line_normal , power_normal , mde , mde_timeline )","title":"Power Analysis Example"},{"location":"index.html#experiment-analysis-example","text":"import numpy as np import pandas as pd from cluster_experiments import AnalysisPlan , SimpleMetric , Variant , Dimension N = 1_000 experiment_data = pd . DataFrame ({ \"order_value\" : np . random . normal ( 100 , 10 , size = N ), \"delivery_time\" : np . random . normal ( 10 , 1 , size = N ), \"experiment_group\" : np . random . choice ([ \"control\" , \"treatment\" ], size = N ), \"city\" : np . random . choice ([ \"NYC\" , \"LA\" ], size = N ), \"customer_id\" : np . random . randint ( 1 , 100 , size = N ), \"customer_age\" : np . random . randint ( 20 , 60 , size = N ), }) # Define metrics aov = SimpleMetric ( alias = \"AOV\" , name = \"order_value\" ) delivery_time = SimpleMetric ( alias = \"Delivery Time\" , name = \"delivery_time\" ) # Define variants and dimensions variants = [ Variant ( \"control\" , is_control = True ), Variant ( \"treatment\" , is_control = False ), ] city_dimension = Dimension ( name = \"city\" , values = [ \"NYC\" , \"LA\" ]) # Create analysis plan plan = AnalysisPlan . from_metrics ( metrics = [ aov , delivery_time ], variants = variants , variant_col = \"experiment_group\" , dimensions = [ city_dimension ], analysis_type = \"clustered_ols\" , analysis_config = { \"cluster_cols\" : [ \"customer_id\" ], }, ) # Run analysis results = plan . analyze ( experiment_data ) print ( results . to_dataframe ())","title":"Experiment Analysis Example"},{"location":"index.html#variance-reduction-example","text":"import numpy as np import pandas as pd from cluster_experiments import ( AnalysisPlan , SimpleMetric , Variant , Dimension , TargetAggregation , HypothesisTest ) N = 1000 experiment_data = pd . DataFrame ({ \"order_value\" : np . random . normal ( 100 , 10 , size = N ), \"delivery_time\" : np . random . normal ( 10 , 1 , size = N ), \"experiment_group\" : np . random . choice ([ \"control\" , \"treatment\" ], size = N ), \"city\" : np . random . choice ([ \"NYC\" , \"LA\" ], size = N ), \"customer_id\" : np . random . randint ( 1 , 100 , size = N ), \"customer_age\" : np . random . randint ( 20 , 60 , size = N ), }) pre_experiment_data = pd . DataFrame ({ \"order_value\" : np . random . normal ( 100 , 10 , size = N ), \"customer_id\" : np . random . randint ( 1 , 100 , size = N ), }) # Define test cupac_model = TargetAggregation ( agg_col = \"customer_id\" , target_col = \"order_value\" ) hypothesis_test = HypothesisTest ( metric = SimpleMetric ( alias = \"AOV\" , name = \"order_value\" ), analysis_type = \"clustered_ols\" , analysis_config = { \"cluster_cols\" : [ \"customer_id\" ], \"covariates\" : [ \"customer_age\" , \"estimate_order_value\" ], }, cupac_config = { \"cupac_model\" : cupac_model , \"target_col\" : \"order_value\" , }, ) # Create analysis plan plan = AnalysisPlan ( tests = [ hypothesis_test ], variants = [ Variant ( \"control\" , is_control = True ), Variant ( \"treatment\" , is_control = False ), ], variant_col = \"experiment_group\" , ) # Run analysis results = plan . analyze ( experiment_data , pre_experiment_data ) print ( results . to_dataframe ())","title":"Variance Reduction Example"},{"location":"index.html#installation","text":"You can install this package via pip . pip install cluster-experiments For detailed documentation and examples, visit our documentation site .","title":"Installation"},{"location":"index.html#features","text":"The library offers the following classes: Regarding power analysis: PowerAnalysis : to run power analysis on any experiment design, using simulation PowerAnalysisWithPreExperimentData : to run power analysis on a clustered/switchback design, but adding pre-experiment df during split and perturbation (especially useful for Synthetic Control) NormalPowerAnalysis : to run power analysis on any experiment design using the central limit theorem for the distribution of the estimator. It can be used to compute the minimum detectable effect (MDE) for a given power level. ConstantPerturbator : to artificially perturb treated group with constant perturbations BinaryPerturbator : to artificially perturb treated group for binary outcomes RelativePositivePerturbator : to artificially perturb treated group with relative positive perturbations NormalPerturbator : to artificially perturb treated group with normal distribution perturbations BetaRelativePositivePerturbator : to artificially perturb treated group with relative positive beta distribution perturbations BetaRelativePerturbator : to artificially perturb treated group with relative beta distribution perturbations in a specified support interval SegmentedBetaRelativePerturbator : to artificially perturb treated group with relative beta distribution perturbations in a specified support interval, but using clusters Regarding splitting data: ClusteredSplitter : to split data based on clusters FixedSizeClusteredSplitter : to split data based on clusters with a fixed size (example: only 1 treatment cluster and the rest in control) BalancedClusteredSplitter : to split data based on clusters in a balanced way NonClusteredSplitter : Regular data splitting, no clusters StratifiedClusteredSplitter : to split based on clusters and strata, balancing the number of clusters in each stratus RepeatedSampler : for backtests where we have access to counterfactuals, does not split the data, just duplicates the data for all groups Switchback splitters (the same can be done with clustered splitters, but there is a convenient way to define switchback splitters using switch frequency): SwitchbackSplitter : to split data based on clusters and dates, for switchback experiments BalancedSwitchbackSplitter : to split data based on clusters and dates, for switchback experiments, balancing treatment and control among all clusters StratifiedSwitchbackSplitter : to split data based on clusters and dates, for switchback experiments, balancing the number of clusters in each stratus Washover for switchback experiments: EmptyWashover : no washover done at all. ConstantWashover : accepts a timedelta parameter and removes the data when we switch from A to B for the timedelta interval. Regarding analysis methods: GeeExperimentAnalysis : to run GEE analysis on the results of a clustered design MLMExperimentAnalysis : to run Mixed Linear Model analysis on the results of a clustered design TTestClusteredAnalysis : to run a t-test on aggregated data for clusters PairedTTestClusteredAnalysis : to run a paired t-test on aggregated data for clusters ClusteredOLSAnalysis : to run OLS analysis on the results of a clustered design OLSAnalysis : to run OLS analysis for non-clustered data TargetAggregation : to add pre-experimental data of the outcome to reduce variance SyntheticControlAnalysis : to run synthetic control analysis Regarding experiment analysis workflow: Metric : abstract class to define a metric to be used in the analysis SimpleMetric : to create a metric defined at the same level of the data used for the analysis RatioMetric : to create a metric defined at a lower level than the data used for the analysis Variant : to define a variant of the experiment Dimension : to define a dimension to slice the results of the experiment HypothesisTest : to define a Hypothesis Test with a metric, analysis method, optional analysis configuration, and optional dimensions AnalysisPlan : to define a plan of analysis with a list of Hypothesis Tests for a dataset and the experiment variants. The analyze() method runs the analysis and returns the results AnalysisResults : to store the results of the analysis Other: PowerConfig : to conveniently configure PowerAnalysis class ConfidenceInterval : to store the data representation of a confidence interval InferenceResults : to store the structure of complete statistical analysis results","title":"Features"},{"location":"index.html#contributing","text":"In case you want to use venv as a virtual environment: python -m venv venv source venv/bin/activate After creating the virtual environment (or not), run: git clone git@github.com:david26694/cluster-experiments.git cd cluster-experiments make install-dev","title":"Contributing"},{"location":"aa_test.html","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); }(this, function () { 'use strict'; 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These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); This notebook shows that, when using a clustered splitter, if the clusters explain a part of the variance, using a non-clustered analysis will lead to higher false positive rate than expected. In particular, we use a clustered splitter and: An OLS-clustered robust estimator, we see that it passes the AA test A simple OLS (without clustered standard errors), it fails the AA test as it returns a super high false positive rate In [1]: Copied! from datetime import date import numpy as np from cluster_experiments import PowerAnalysis , ConstantPerturbator , BalancedClusteredSplitter , ExperimentAnalysis , ClusteredOLSAnalysis import pandas as pd import statsmodels.api as sm # Create fake data N = 10_000 clusters = [ f \"Cluster { i } \" for i in range ( 10 )] dates = [ f \" { date ( 2022 , 1 , i ) : %Y-%m-%d } \" for i in range ( 1 , 15 )] df = pd . DataFrame ( { \"cluster\" : np . random . choice ( clusters , size = N ), \"date\" : np . random . choice ( dates , size = N ), } ) . assign ( # Target is a linear combination of cluster and day of week, plus some noise cluster_id = lambda df : df [ \"cluster\" ] . astype ( \"category\" ) . cat . codes , day_of_week = lambda df : pd . to_datetime ( df [ \"date\" ]) . dt . dayofweek , target = lambda df : df [ \"cluster_id\" ] + df [ \"day_of_week\" ] + np . random . normal ( size = N ), ) from datetime import date import numpy as np from cluster_experiments import PowerAnalysis, ConstantPerturbator, BalancedClusteredSplitter, ExperimentAnalysis, ClusteredOLSAnalysis import pandas as pd import statsmodels.api as sm # Create fake data N = 10_000 clusters = [f\"Cluster {i}\" for i in range(10)] dates = [f\"{date(2022, 1, i):%Y-%m-%d}\" for i in range(1, 15)] df = pd.DataFrame( { \"cluster\": np.random.choice(clusters, size=N), \"date\": np.random.choice(dates, size=N), } ).assign( # Target is a linear combination of cluster and day of week, plus some noise cluster_id=lambda df: df[\"cluster\"].astype(\"category\").cat.codes, day_of_week=lambda df: pd.to_datetime(df[\"date\"]).dt.dayofweek, target=lambda df: df[\"cluster_id\"] + df[\"day_of_week\"] + np.random.normal(size=N), ) In [2]: Copied! df . head () df.head() Out[2]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } cluster date cluster_id day_of_week target 0 Cluster 3 2022-01-08 3 5 7.534487 1 Cluster 2 2022-01-06 2 3 5.039041 2 Cluster 1 2022-01-14 1 4 5.341845 3 Cluster 7 2022-01-12 7 2 9.468617 4 Cluster 0 2022-01-10 0 0 -0.644678 Some clusters have a higher average outcome than others In [3]: Copied! df . groupby ( \"cluster\" ) . agg ({ \"target\" : [ \"mean\" , \"std\" ]}) df.groupby(\"cluster\").agg({\"target\": [\"mean\", \"std\"]}) Out[3]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead tr th { text-align: left; } .dataframe thead tr:last-of-type th { text-align: right; } target mean std cluster Cluster 0 3.027335 2.223308 Cluster 1 3.907833 2.211297 Cluster 2 4.895215 2.270596 Cluster 3 6.045043 2.269786 Cluster 4 6.902209 2.224554 Cluster 5 8.028794 2.313159 Cluster 6 9.046213 2.253462 Cluster 7 10.055748 2.226720 Cluster 8 11.048716 2.273583 Cluster 9 11.939075 2.216478 In [4]: Copied! # Simple ols to run the analysis class NonClusteredOLS ( ExperimentAnalysis ): def analysis_pvalue ( self , df : pd . DataFrame , verbose : bool = False ) -> float : \"\"\"Returns the p-value of the analysis Arguments: df: dataframe containing the data to analyze verbose (Optional): bool, prints the regression summary if True \"\"\" results_ols = sm . OLS . from_formula ( \"target ~ treatment\" , data = df ) . fit () return results_ols . pvalues [ self . treatment_col ] # Simple ols to run the analysis class NonClusteredOLS(ExperimentAnalysis): def analysis_pvalue(self, df: pd.DataFrame, verbose: bool = False) -> float: \"\"\"Returns the p-value of the analysis Arguments: df: dataframe containing the data to analyze verbose (Optional): bool, prints the regression summary if True \"\"\" results_ols = sm.OLS.from_formula(\"target ~ treatment\", data=df).fit() return results_ols.pvalues[self.treatment_col] In [5]: Copied! cluster_cols = [ \"cluster\" , \"date\" ] splitter = BalancedClusteredSplitter ( cluster_cols = cluster_cols , ) perturbator = ConstantPerturbator () alpha = 0.05 n_simulations = 100 # Right power analysis, we use clustered splitter and ols clustered analysis pw_right = PowerAnalysis ( splitter = splitter , perturbator = perturbator , alpha = alpha , n_simulations = n_simulations , analysis = ClusteredOLSAnalysis ( cluster_cols = cluster_cols , ), ) # Wrong power analysis, we use clustered splitter and regular ols pw_wrong = PowerAnalysis ( splitter = splitter , perturbator = perturbator , alpha = alpha , n_simulations = n_simulations , analysis = NonClusteredOLS ( # We pass cluster_cols here, but we don't use them!!! cluster_cols = cluster_cols , ), ) cluster_cols = [\"cluster\", \"date\"] splitter = BalancedClusteredSplitter( cluster_cols=cluster_cols, ) perturbator = ConstantPerturbator() alpha = 0.05 n_simulations = 100 # Right power analysis, we use clustered splitter and ols clustered analysis pw_right = PowerAnalysis( splitter=splitter, perturbator=perturbator, alpha=alpha, n_simulations=n_simulations, analysis=ClusteredOLSAnalysis( cluster_cols=cluster_cols, ), ) # Wrong power analysis, we use clustered splitter and regular ols pw_wrong = PowerAnalysis( splitter=splitter, perturbator=perturbator, alpha=alpha, n_simulations=n_simulations, analysis=NonClusteredOLS( # We pass cluster_cols here, but we don't use them!!! cluster_cols=cluster_cols, ), ) Right way of doing it: in the AA test we get a power similar to the type I error of the test In [6]: Copied! pw_right . power_analysis ( df , average_effect = 0.0 ) pw_right.power_analysis(df, average_effect=0.0) Out[6]: 0.06 Wrong way of doing it: the AA test fails, we have too much power In [7]: Copied! pw_wrong . power_analysis ( df , average_effect = 0.0 ) pw_wrong.power_analysis(df, average_effect=0.0) Out[7]: 0.79","title":"AA test clustered"},{"location":"analysis_with_different_hypotheses.html","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); 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We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Power comparison under different hypotheses \u00b6 The goal of this notebook is to understand how different hypotheses change the power of an experiment. We start from some theory, moving to a pratical perspective using simulations and the implementations from this package. In hypothesis testing, various hypotheses can be examined, but the most common are: Two-sided : the effect is different from zero (commonly noted as H0: beta = 0, H1: beta != 0) Less : the effect is less than zero (H0: beta >= 0, H1: beta < 0) Greater : the effect is greater than zero (H0: beta <= 0, H1: beta > 0) In most cases the one-sided (less or greater) p-value is half the two-sided p-value. So, if the two-sided p-value is 5%, the one-sided p-value is 2.5%. However, if the actual difference (effect) went opposite to the predicted direction, in this case the one-sided p-value equals one minus half the two-sided value. So if the two-sided p-value is 2.5%, the one-tailed p-value is 97.5%. Generate random clustered data \u00b6 In [1]: Copied! from datetime import date import numpy as np import pandas as pd from cluster_experiments import PowerAnalysis import matplotlib.pyplot as plt import warnings from plotnine import ggplot , theme_classic , facet_wrap , geom_density , labs , geom_vline , aes , geom_line , geom_histogram , geom_text , scale_y_continuous warnings . filterwarnings ( 'ignore' ) np . random . seed ( 42 ) N = 1000 clusters = [ f \"Cluster { i } \" for i in range ( 50 )] dates = [ f \" { date ( 2022 , 1 , i ) : %Y-%m-%d } \" for i in range ( 1 , 32 )] df = pd . DataFrame ( { \"cluster\" : np . random . choice ( clusters , size = N ), \"target\" : np . random . normal ( 0 , 1 , size = N ), \"date\" : np . random . choice ( dates , size = N ), } ) from datetime import date import numpy as np import pandas as pd from cluster_experiments import PowerAnalysis import matplotlib.pyplot as plt import warnings from plotnine import ggplot,theme_classic, facet_wrap, geom_density, labs, geom_vline, aes, geom_line, geom_histogram, geom_text,scale_y_continuous warnings.filterwarnings('ignore') np.random.seed(42) N = 1000 clusters = [f\"Cluster {i}\" for i in range(50)] dates = [f\"{date(2022, 1, i):%Y-%m-%d}\" for i in range(1, 32)] df = pd.DataFrame( { \"cluster\": np.random.choice(clusters, size=N), \"target\": np.random.normal(0, 1, size=N), \"date\": np.random.choice(dates, size=N), } ) OLS \u00b6 Let's start with a simple OLS model, not clustered, to understand the difference in power. We will try 2 different splitters: constant and normal In [2]: Copied! splitters = [ 'constant' , 'normal' ] results = [] for hypothesis in [ \"two-sided\" , \"less\" , \"greater\" ]: for splitter in splitters : config = { \"analysis\" : 'ols' , \"perturbator\" : splitter , \"splitter\" : \"non_clustered\" , \"n_simulations\" : 50 , \"hypothesis\" : hypothesis , \"seed\" : 41 } pw = PowerAnalysis . from_dict ( config ) power_dict = pw . power_line ( df , average_effects = list ( np . linspace ( 0.000001 , 0.5 , 15 ))) power_df = pd . DataFrame ( list ( power_dict . items ()), columns = [ 'average_effect' , 'power' ]) power_df [ 'hypothesis' ] = hypothesis power_df [ 'splitter' ] = splitter results . append ( power_df ) final_df = pd . concat ( results , ignore_index = True ) splitters = ['constant', 'normal'] results = [] for hypothesis in [\"two-sided\", \"less\", \"greater\"]: for splitter in splitters: config = { \"analysis\": 'ols', \"perturbator\": splitter, \"splitter\": \"non_clustered\", \"n_simulations\": 50, \"hypothesis\": hypothesis, \"seed\":41 } pw = PowerAnalysis.from_dict(config) power_dict = pw.power_line(df, average_effects=list(np.linspace(0.000001, 0.5, 15))) power_df = pd.DataFrame(list(power_dict.items()), columns=['average_effect', 'power']) power_df['hypothesis'] = hypothesis power_df['splitter'] = splitter results.append(power_df) final_df = pd.concat(results, ignore_index=True) In [3]: Copied! final_df . head () final_df.head() Out[3]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } average_effect power hypothesis splitter 0 0.000001 0.04 two-sided constant 1 0.035715 0.06 two-sided constant 2 0.071429 0.22 two-sided constant 3 0.107144 0.40 two-sided constant 4 0.142858 0.64 two-sided constant In [4]: Copied! def plot ( breakdown : str , ncol_facetting : int ): p = ( ggplot ( final_df , aes ( x = 'average_effect' , y = 'power' , color = 'hypothesis' )) + geom_line () + theme_classic () + facet_wrap ( breakdown , ncol = 1 )) print ( p ) def plot(breakdown:str, ncol_facetting:int): p = (ggplot(final_df, aes(x='average_effect', y = 'power', color='hypothesis')) + geom_line() + theme_classic() + facet_wrap(breakdown, ncol = 1)) print(p) We can clearly see that using the correct side (higher, as the effect is positive) increase the power of the experiment. It's also great to see such a low power in case of hypothesis 'less'. In [5]: Copied! plot ( breakdown = 'splitter' , ncol_facetting = 1 ) plot(breakdown = 'splitter', ncol_facetting = 1) Now we will quantify this difference in power between greater and two-sided In [6]: Copied! pivot_df = ( final_df . pivot ( index = [ 'splitter' , 'average_effect' ], columns = 'hypothesis' , values = 'power' ) . reset_index () . assign ( diff = lambda x : x [ 'greater' ] - x [ 'two-sided' ]) ) pivot_df = ( final_df .pivot(index = [ 'splitter', 'average_effect'], columns = 'hypothesis', values = 'power') .reset_index() .assign(diff = lambda x: x['greater'] - x['two-sided']) ) In [7]: Copied! mean_diff = pivot_df [ 'diff' ] . mean () max_count = np . max ( pivot_df [ 'diff' ] . value_counts ()) mean_diff = pivot_df['diff'].mean() max_count = np.max(pivot_df['diff'].value_counts()) Below we plot the distribution of the difference between 2-sided and 'greater' hypotheses. The mean equals to 4% is the estimated increase in power when using a one-sided experiment. From the 30 iterations, we see that 12 times it didn't change the power, in 2 cases it actually decrease it and in 2 cases the incease in power was 15pp. In [8]: Copied! ( ggplot ( pivot_df , aes ( x = 'diff' )) + geom_histogram ( alpha = 0.5 , bins = 10 , position = \"identity\" ) + geom_density () + geom_vline ( xintercept = mean_diff , color = \"red\" , linetype = \"dashed\" , size = 1 ) + geom_text ( x = mean_diff , y = np . max ( pivot_df [ 'diff' ] . value_counts ()), label = f 'Mean: { mean_diff : .2f } ' , va = 'bottom' , ha = 'left' , color = \"red\" ) + scale_y_continuous ( breaks = range ( 0 , int ( max_count ) + 1 )) + labs ( title = 'Histogram of power difference between greater and two sided hypothesis' , x = 'Difference in power' ) ) (ggplot(pivot_df, aes(x = 'diff')) + geom_histogram( alpha=0.5, bins = 10, position=\"identity\") + geom_density() + geom_vline(xintercept=mean_diff, color=\"red\", linetype=\"dashed\", size=1) + geom_text(x=mean_diff, y=np.max(pivot_df['diff'].value_counts()), label=f'Mean: {mean_diff:.2f}', va='bottom', ha='left', color=\"red\") + scale_y_continuous(breaks=range(0, int(max_count) + 1)) + labs(title = 'Histogram of power difference between greater and two sided hypothesis', x='Difference in power') ) Out[8]:
    Clustered methods \u00b6 Now let's move to clustered methods. To keep the notebook tidy, we will just run the constant perturbator. In [9]: Copied! results = [] for hypothesis in [ \"two-sided\" , \"less\" , \"greater\" ]: for analysis in [ \"ols_clustered\" , \"gee\" , 'ttest_clustered' ]: config = { \"analysis\" : analysis , \"perturbator\" : \"constant\" , \"splitter\" : \"clustered\" , \"n_simulations\" : 50 , \"hypothesis\" : hypothesis , \"cluster_cols\" : [ 'cluster' , 'date' ], \"seed\" : 41 } pw = PowerAnalysis . from_dict ( config ) power_dict = pw . power_line ( df , average_effects = list ( np . linspace ( 0.000001 , 0.5 , 15 ))) power_df = pd . DataFrame ( list ( power_dict . items ()), columns = [ 'average_effect' , 'power' ]) power_df [ 'hypothesis' ] = hypothesis power_df [ 'analysis' ] = analysis results . append ( power_df ) final_df = pd . concat ( results , ignore_index = True ) results = [] for hypothesis in [\"two-sided\", \"less\", \"greater\"]: for analysis in [\"ols_clustered\", \"gee\", 'ttest_clustered']: config = { \"analysis\": analysis, \"perturbator\": \"constant\", \"splitter\": \"clustered\", \"n_simulations\": 50, \"hypothesis\": hypothesis, \"cluster_cols\": ['cluster', 'date'], \"seed\":41 } pw = PowerAnalysis.from_dict(config) power_dict = pw.power_line(df, average_effects=list(np.linspace(0.000001, 0.5, 15))) power_df = pd.DataFrame(list(power_dict.items()), columns=['average_effect', 'power']) power_df['hypothesis'] = hypothesis power_df['analysis'] = analysis results.append(power_df) final_df = pd.concat(results, ignore_index=True) Here, again, we see an increase in power using the correct one-sided hypothesis compared to two-sided. In [10]: Copied! plot ( breakdown = 'analysis' , ncol_facetting = 1 ) plot(breakdown = 'analysis', ncol_facetting=1)","title":"Power comparison under different hypotheses"},{"location":"analysis_with_different_hypotheses.html#power-comparison-under-different-hypotheses","text":"The goal of this notebook is to understand how different hypotheses change the power of an experiment. We start from some theory, moving to a pratical perspective using simulations and the implementations from this package. In hypothesis testing, various hypotheses can be examined, but the most common are: Two-sided : the effect is different from zero (commonly noted as H0: beta = 0, H1: beta != 0) Less : the effect is less than zero (H0: beta >= 0, H1: beta < 0) Greater : the effect is greater than zero (H0: beta <= 0, H1: beta > 0) In most cases the one-sided (less or greater) p-value is half the two-sided p-value. So, if the two-sided p-value is 5%, the one-sided p-value is 2.5%. However, if the actual difference (effect) went opposite to the predicted direction, in this case the one-sided p-value equals one minus half the two-sided value. So if the two-sided p-value is 2.5%, the one-tailed p-value is 97.5%.","title":"Power comparison under different hypotheses"},{"location":"analysis_with_different_hypotheses.html#generate-random-clustered-data","text":"In [1]: Copied! from datetime import date import numpy as np import pandas as pd from cluster_experiments import PowerAnalysis import matplotlib.pyplot as plt import warnings from plotnine import ggplot , theme_classic , facet_wrap , geom_density , labs , geom_vline , aes , geom_line , geom_histogram , geom_text , scale_y_continuous warnings . filterwarnings ( 'ignore' ) np . random . seed ( 42 ) N = 1000 clusters = [ f \"Cluster { i } \" for i in range ( 50 )] dates = [ f \" { date ( 2022 , 1 , i ) : %Y-%m-%d } \" for i in range ( 1 , 32 )] df = pd . DataFrame ( { \"cluster\" : np . random . choice ( clusters , size = N ), \"target\" : np . random . normal ( 0 , 1 , size = N ), \"date\" : np . random . choice ( dates , size = N ), } ) from datetime import date import numpy as np import pandas as pd from cluster_experiments import PowerAnalysis import matplotlib.pyplot as plt import warnings from plotnine import ggplot,theme_classic, facet_wrap, geom_density, labs, geom_vline, aes, geom_line, geom_histogram, geom_text,scale_y_continuous warnings.filterwarnings('ignore') np.random.seed(42) N = 1000 clusters = [f\"Cluster {i}\" for i in range(50)] dates = [f\"{date(2022, 1, i):%Y-%m-%d}\" for i in range(1, 32)] df = pd.DataFrame( { \"cluster\": np.random.choice(clusters, size=N), \"target\": np.random.normal(0, 1, size=N), \"date\": np.random.choice(dates, size=N), } )","title":"Generate random clustered data"},{"location":"analysis_with_different_hypotheses.html#ols","text":"Let's start with a simple OLS model, not clustered, to understand the difference in power. We will try 2 different splitters: constant and normal In [2]: Copied! splitters = [ 'constant' , 'normal' ] results = [] for hypothesis in [ \"two-sided\" , \"less\" , \"greater\" ]: for splitter in splitters : config = { \"analysis\" : 'ols' , \"perturbator\" : splitter , \"splitter\" : \"non_clustered\" , \"n_simulations\" : 50 , \"hypothesis\" : hypothesis , \"seed\" : 41 } pw = PowerAnalysis . from_dict ( config ) power_dict = pw . power_line ( df , average_effects = list ( np . linspace ( 0.000001 , 0.5 , 15 ))) power_df = pd . DataFrame ( list ( power_dict . items ()), columns = [ 'average_effect' , 'power' ]) power_df [ 'hypothesis' ] = hypothesis power_df [ 'splitter' ] = splitter results . append ( power_df ) final_df = pd . concat ( results , ignore_index = True ) splitters = ['constant', 'normal'] results = [] for hypothesis in [\"two-sided\", \"less\", \"greater\"]: for splitter in splitters: config = { \"analysis\": 'ols', \"perturbator\": splitter, \"splitter\": \"non_clustered\", \"n_simulations\": 50, \"hypothesis\": hypothesis, \"seed\":41 } pw = PowerAnalysis.from_dict(config) power_dict = pw.power_line(df, average_effects=list(np.linspace(0.000001, 0.5, 15))) power_df = pd.DataFrame(list(power_dict.items()), columns=['average_effect', 'power']) power_df['hypothesis'] = hypothesis power_df['splitter'] = splitter results.append(power_df) final_df = pd.concat(results, ignore_index=True) In [3]: Copied! final_df . head () final_df.head() Out[3]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } average_effect power hypothesis splitter 0 0.000001 0.04 two-sided constant 1 0.035715 0.06 two-sided constant 2 0.071429 0.22 two-sided constant 3 0.107144 0.40 two-sided constant 4 0.142858 0.64 two-sided constant In [4]: Copied! def plot ( breakdown : str , ncol_facetting : int ): p = ( ggplot ( final_df , aes ( x = 'average_effect' , y = 'power' , color = 'hypothesis' )) + geom_line () + theme_classic () + facet_wrap ( breakdown , ncol = 1 )) print ( p ) def plot(breakdown:str, ncol_facetting:int): p = (ggplot(final_df, aes(x='average_effect', y = 'power', color='hypothesis')) + geom_line() + theme_classic() + facet_wrap(breakdown, ncol = 1)) print(p) We can clearly see that using the correct side (higher, as the effect is positive) increase the power of the experiment. It's also great to see such a low power in case of hypothesis 'less'. In [5]: Copied! plot ( breakdown = 'splitter' , ncol_facetting = 1 ) plot(breakdown = 'splitter', ncol_facetting = 1) Now we will quantify this difference in power between greater and two-sided In [6]: Copied! pivot_df = ( final_df . pivot ( index = [ 'splitter' , 'average_effect' ], columns = 'hypothesis' , values = 'power' ) . reset_index () . assign ( diff = lambda x : x [ 'greater' ] - x [ 'two-sided' ]) ) pivot_df = ( final_df .pivot(index = [ 'splitter', 'average_effect'], columns = 'hypothesis', values = 'power') .reset_index() .assign(diff = lambda x: x['greater'] - x['two-sided']) ) In [7]: Copied! mean_diff = pivot_df [ 'diff' ] . mean () max_count = np . max ( pivot_df [ 'diff' ] . value_counts ()) mean_diff = pivot_df['diff'].mean() max_count = np.max(pivot_df['diff'].value_counts()) Below we plot the distribution of the difference between 2-sided and 'greater' hypotheses. The mean equals to 4% is the estimated increase in power when using a one-sided experiment. From the 30 iterations, we see that 12 times it didn't change the power, in 2 cases it actually decrease it and in 2 cases the incease in power was 15pp. In [8]: Copied! ( ggplot ( pivot_df , aes ( x = 'diff' )) + geom_histogram ( alpha = 0.5 , bins = 10 , position = \"identity\" ) + geom_density () + geom_vline ( xintercept = mean_diff , color = \"red\" , linetype = \"dashed\" , size = 1 ) + geom_text ( x = mean_diff , y = np . max ( pivot_df [ 'diff' ] . value_counts ()), label = f 'Mean: { mean_diff : .2f } ' , va = 'bottom' , ha = 'left' , color = \"red\" ) + scale_y_continuous ( breaks = range ( 0 , int ( max_count ) + 1 )) + labs ( title = 'Histogram of power difference between greater and two sided hypothesis' , x = 'Difference in power' ) ) (ggplot(pivot_df, aes(x = 'diff')) + geom_histogram( alpha=0.5, bins = 10, position=\"identity\") + geom_density() + geom_vline(xintercept=mean_diff, color=\"red\", linetype=\"dashed\", size=1) + geom_text(x=mean_diff, y=np.max(pivot_df['diff'].value_counts()), label=f'Mean: {mean_diff:.2f}', va='bottom', ha='left', color=\"red\") + scale_y_continuous(breaks=range(0, int(max_count) + 1)) + labs(title = 'Histogram of power difference between greater and two sided hypothesis', x='Difference in power') ) Out[8]:
    ","title":"OLS"},{"location":"analysis_with_different_hypotheses.html#clustered-methods","text":"Now let's move to clustered methods. To keep the notebook tidy, we will just run the constant perturbator. In [9]: Copied! results = [] for hypothesis in [ \"two-sided\" , \"less\" , \"greater\" ]: for analysis in [ \"ols_clustered\" , \"gee\" , 'ttest_clustered' ]: config = { \"analysis\" : analysis , \"perturbator\" : \"constant\" , \"splitter\" : \"clustered\" , \"n_simulations\" : 50 , \"hypothesis\" : hypothesis , \"cluster_cols\" : [ 'cluster' , 'date' ], \"seed\" : 41 } pw = PowerAnalysis . from_dict ( config ) power_dict = pw . power_line ( df , average_effects = list ( np . linspace ( 0.000001 , 0.5 , 15 ))) power_df = pd . DataFrame ( list ( power_dict . items ()), columns = [ 'average_effect' , 'power' ]) power_df [ 'hypothesis' ] = hypothesis power_df [ 'analysis' ] = analysis results . append ( power_df ) final_df = pd . concat ( results , ignore_index = True ) results = [] for hypothesis in [\"two-sided\", \"less\", \"greater\"]: for analysis in [\"ols_clustered\", \"gee\", 'ttest_clustered']: config = { \"analysis\": analysis, \"perturbator\": \"constant\", \"splitter\": \"clustered\", \"n_simulations\": 50, \"hypothesis\": hypothesis, \"cluster_cols\": ['cluster', 'date'], \"seed\":41 } pw = PowerAnalysis.from_dict(config) power_dict = pw.power_line(df, average_effects=list(np.linspace(0.000001, 0.5, 15))) power_df = pd.DataFrame(list(power_dict.items()), columns=['average_effect', 'power']) power_df['hypothesis'] = hypothesis power_df['analysis'] = analysis results.append(power_df) final_df = pd.concat(results, ignore_index=True) Here, again, we see an increase in power using the correct one-sided hypothesis compared to two-sided. 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When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: 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#7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Examples on how to create: a custom perturbator a custom splitter a custom hypothesis test The names of you custom classes don't need to be CustomX, they are completely free. The only requirement is that they inherit from the base class. For example, if you want to create a custom perturbator, you need to inherit from the Perturbator base class. The same applies to the other classes. In [1]: Copied! from cluster_experiments import ExperimentAnalysis import pandas as pd from scipy.stats import ttest_ind class CustomExperimentAnalysis ( ExperimentAnalysis ): def analysis_pvalue ( self , df : pd . DataFrame , verbose : bool = True ) -> float : treatment_data = df . query ( f \" { self . treatment_col } == 1\" )[ self . target_col ] control_data = df . query ( f \" { self . treatment_col } == 0\" )[ self . target_col ] t_test_results = ttest_ind ( treatment_data , control_data , equal_var = False ) return t_test_results . pvalue from cluster_experiments import ExperimentAnalysis import pandas as pd from scipy.stats import ttest_ind class CustomExperimentAnalysis(ExperimentAnalysis): def analysis_pvalue(self, df: pd.DataFrame, verbose: bool = True) -> float: treatment_data = df.query(f\"{self.treatment_col} == 1\")[self.target_col] control_data = df.query(f\"{self.treatment_col} == 0\")[self.target_col] t_test_results = ttest_ind(treatment_data, control_data, equal_var=False) return t_test_results.pvalue In [2]: Copied! from cluster_experiments import RandomSplitter import numpy as np class CustomRandomSplitter ( RandomSplitter ): def assign_treatment_df ( self , df : pd . DataFrame ) -> pd . DataFrame : df = df . copy () # Power users get treatment with 90% probability df_power_users = df . query ( \"power_user\" ) df_power_users [ self . treatment_col ] = np . random . choice ( [ \"A\" , \"B\" ], size = len ( df_power_users ), p = [ 0.1 , 0.9 ] ) # Non-power users get treatment with 10% probability df_non_power_users = df . query ( \"not power_user\" ) df_non_power_users [ self . treatment_col ] = np . random . choice ( [ \"A\" , \"B\" ], size = len ( df_non_power_users ), p = [ 0.9 , 0.1 ] ) return pd . concat ([ df_power_users , df_non_power_users ]) from cluster_experiments import RandomSplitter import numpy as np class CustomRandomSplitter(RandomSplitter): def assign_treatment_df(self, df: pd.DataFrame) -> pd.DataFrame: df = df.copy() # Power users get treatment with 90% probability df_power_users = df.query(\"power_user\") df_power_users[self.treatment_col] = np.random.choice( [\"A\", \"B\"], size=len(df_power_users), p=[0.1, 0.9] ) # Non-power users get treatment with 10% probability df_non_power_users = df.query(\"not power_user\") df_non_power_users[self.treatment_col] = np.random.choice( [\"A\", \"B\"], size=len(df_non_power_users), p=[0.9, 0.1] ) return pd.concat([df_power_users, df_non_power_users]) In [3]: Copied! from cluster_experiments import Perturbator import pandas as pd class CustomPerturbator ( Perturbator ): def perturbate ( self , df : pd . DataFrame , average_effect : float ) -> pd . DataFrame : df = df . copy () . reset_index ( drop = True ) n = ( df [ self . treatment_col ] == self . treatment ) . sum () df . loc [ df [ self . treatment_col ] == self . treatment , self . target_col ] += np . random . normal ( average_effect , 1 , size = n ) return df from cluster_experiments import Perturbator import pandas as pd class CustomPerturbator(Perturbator): def perturbate(self, df: pd.DataFrame, average_effect: float) -> pd.DataFrame: df = df.copy().reset_index(drop=True) n = (df[self.treatment_col] == self.treatment).sum() df.loc[ df[self.treatment_col] == self.treatment, self.target_col ] += np.random.normal(average_effect, 1, size=n) return df","title":"Custom classes"},{"location":"cupac_example.html","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); }(this, function () { 'use strict'; 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if (!body) { return Promise.reject(new Error()); } const node = createNode(text); body.appendChild(node); copyNode(node); body.removeChild(node); return Promise.resolve(); } function copy(button) { const id = button.getAttribute('for'); const text = button.getAttribute('value'); function trigger() { button.dispatchEvent(new CustomEvent('clipboard-copy', { bubbles: true })); } if (text) { copyText(text).then(trigger); } else if (id) { const root = 'getRootNode' in Element.prototype ? button.getRootNode() : button.ownerDocument; if (!(root instanceof Document || 'ShadowRoot' in window && root instanceof ShadowRoot)) return; const node = root.getElementById(id); if (node) copyTarget(node).then(trigger); } } function copyTarget(content) { if (content instanceof HTMLInputElement || content instanceof HTMLTextAreaElement) { return copyText(content.value); } else if (content instanceof HTMLAnchorElement && content.hasAttribute('href')) { return copyText(content.href); } else { return copyNode(content); } } function clicked(event) { const button = event.currentTarget; if (button instanceof HTMLElement) { copy(button); } } function keydown(event) { if (event.key === ' ' || event.key === 'Enter') { const button = event.currentTarget; if (button instanceof HTMLElement) { event.preventDefault(); copy(button); } } } function focused(event) { event.currentTarget.addEventListener('keydown', keydown); } function blurred(event) { event.currentTarget.removeEventListener('keydown', keydown); } class ClipboardCopyElement extends HTMLElement { constructor() { super(); this.addEventListener('click', clicked); this.addEventListener('focus', focused); this.addEventListener('blur', blurred); } connectedCallback() { if (!this.hasAttribute('tabindex')) { this.setAttribute('tabindex', '0'); } if (!this.hasAttribute('role')) { this.setAttribute('role', 'button'); } } get value() { return this.getAttribute('value') || ''; } set value(text) { this.setAttribute('value', text); } } if (!window.customElements.get('clipboard-copy')) { window.ClipboardCopyElement = ClipboardCopyElement; window.customElements.define('clipboard-copy', ClipboardCopyElement); } return ClipboardCopyElement; })); document.addEventListener('clipboard-copy', function(event) { const notice = event.target.querySelector('.notice') notice.hidden = false setTimeout(function() { notice.hidden = true }, 1000) }) pre { line-height: 125%; } td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; } span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; } td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } .highlight-ipynb .hll { background-color: var(--jp-cell-editor-active-background) } .highlight-ipynb { background: var(--jp-cell-editor-background); color: var(--jp-mirror-editor-variable-color) } .highlight-ipynb .c { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment */ .highlight-ipynb .err { color: var(--jp-mirror-editor-error-color) } /* Error */ .highlight-ipynb .k { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword */ .highlight-ipynb .o { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator */ .highlight-ipynb .p { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation */ .highlight-ipynb .ch { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Hashbang */ .highlight-ipynb .cm { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Multiline */ .highlight-ipynb .cp { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Preproc */ .highlight-ipynb .cpf { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.PreprocFile */ .highlight-ipynb .c1 { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Single */ .highlight-ipynb .cs { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Special */ .highlight-ipynb .kc { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Constant */ .highlight-ipynb .kd { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Declaration */ .highlight-ipynb .kn { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Namespace */ .highlight-ipynb .kp { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Pseudo */ .highlight-ipynb .kr { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Reserved */ .highlight-ipynb .kt { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Type */ .highlight-ipynb .m { color: var(--jp-mirror-editor-number-color) } /* Literal.Number */ .highlight-ipynb .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */ .highlight-ipynb .ow { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator.Word */ .highlight-ipynb .pm { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation.Marker */ .highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */ .highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */ .highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */ .highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */ .highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */ .highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */ .highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */ .highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */ .highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */ .highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */ .highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */ .highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */ .highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */ .highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */ .highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */ .highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */ .highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */ .highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */ .highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */ .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */ /* This file is taken from the built JupyterLab theme.css Found on share/nbconvert/templates/lab/static Some changes have been made and marked with CHANGE */ .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Cupac tutorial \u00b6 In this example we demonstrate usage of CUPAC as a way to increase power. We compare vainilla (non-cupac) gee estimation with gee estimation with cupac. In [1]: Copied! from datetime import date import numpy as np import pandas as pd from cluster_experiments import GeeExperimentAnalysis from cluster_experiments import ConstantPerturbator from cluster_experiments import PowerAnalysis from cluster_experiments import ClusteredSplitter from sklearn.ensemble import HistGradientBoostingRegressor def generate_random_data ( clusters , dates , N ): \"\"\"Generates target as a non-linear function of the covariates, the cluster mean and some residual\"\"\" # Every cluster has a mean df_clusters = pd . DataFrame ( { \"cluster\" : clusters , \"cluster_mean\" : np . random . normal ( 0 , 0.1 , size = len ( clusters )), } ) # The target is the sum of: covariates, cluster mean and random residual df = ( pd . DataFrame ( { \"cluster\" : np . random . choice ( clusters , size = N ), \"residual\" : np . random . normal ( 0 , 1 , size = N ), \"date\" : np . random . choice ( dates , size = N ), \"x1\" : np . random . normal ( 0 , 1 , size = N ), \"x2\" : np . random . normal ( 0 , 1 , size = N ), \"x3\" : np . random . normal ( 0 , 1 , size = N ), \"x4\" : np . random . normal ( 0 , 1 , size = N ), } ) . merge ( df_clusters , on = \"cluster\" ) . assign ( target = lambda x : x [ \"x1\" ] * x [ \"x2\" ] + x [ \"x3\" ] ** 2 + x [ \"x4\" ] + x [ \"cluster_mean\" ] + x [ \"residual\" ] ) ) return df from datetime import date import numpy as np import pandas as pd from cluster_experiments import GeeExperimentAnalysis from cluster_experiments import ConstantPerturbator from cluster_experiments import PowerAnalysis from cluster_experiments import ClusteredSplitter from sklearn.ensemble import HistGradientBoostingRegressor def generate_random_data(clusters, dates, N): \"\"\"Generates target as a non-linear function of the covariates, the cluster mean and some residual\"\"\" # Every cluster has a mean df_clusters = pd.DataFrame( { \"cluster\": clusters, \"cluster_mean\": np.random.normal(0, 0.1, size=len(clusters)), } ) # The target is the sum of: covariates, cluster mean and random residual df = ( pd.DataFrame( { \"cluster\": np.random.choice(clusters, size=N), \"residual\": np.random.normal(0, 1, size=N), \"date\": np.random.choice(dates, size=N), \"x1\": np.random.normal(0, 1, size=N), \"x2\": np.random.normal(0, 1, size=N), \"x3\": np.random.normal(0, 1, size=N), \"x4\": np.random.normal(0, 1, size=N), } ) .merge(df_clusters, on=\"cluster\") .assign( target=lambda x: x[\"x1\"] * x[\"x2\"] + x[\"x3\"] ** 2 + x[\"x4\"] + x[\"cluster_mean\"] + x[\"residual\"] ) ) return df Data generation \u00b6 We have data that contains: Cluster Date Some covariates Outcome (target) We want to run a switchback-clustered experiment for around 15 days. We take data from day 15 to day 31 as the analysis data. We take data from day 1 to day 14 as the pre-analysis data, which is going to be used to fit the cupac model. In [2]: Copied! clusters = [ f \"Cluster { i } \" for i in range ( 100 )] dates = [ f \" { date ( 2022 , 1 , i ) : %Y-%m-%d } \" for i in range ( 1 , 32 )] experiment_dates = [ f \" { date ( 2022 , 1 , i ) : %Y-%m-%d } \" for i in range ( 15 , 32 )] N = 10_000 df = generate_random_data ( clusters , dates , N ) . drop ( columns = [ \"residual\" , \"cluster_mean\" ]) df_analysis = df . query ( f \"date.isin( { experiment_dates } )\" ) df_pre = df . query ( f \"~date.isin( { experiment_dates } )\" ) df clusters = [f\"Cluster {i}\" for i in range(100)] dates = [f\"{date(2022, 1, i):%Y-%m-%d}\" for i in range(1, 32)] experiment_dates = [f\"{date(2022, 1, i):%Y-%m-%d}\" for i in range(15, 32)] N = 10_000 df = generate_random_data(clusters, dates, N).drop(columns=[\"residual\", \"cluster_mean\"]) df_analysis = df.query(f\"date.isin({experiment_dates})\") df_pre = df.query(f\"~date.isin({experiment_dates})\") df Out[2]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } cluster date x1 x2 x3 x4 target 0 Cluster 64 2022-01-26 0.407857 -0.821972 0.380603 -0.435979 0.259322 1 Cluster 64 2022-01-01 -0.338506 1.259993 -1.172503 -0.638529 0.109807 2 Cluster 64 2022-01-25 0.144844 -0.647623 1.126136 0.339519 1.672764 3 Cluster 64 2022-01-04 -0.081028 2.257395 1.786203 0.608843 4.281907 4 Cluster 64 2022-01-13 -0.381064 -0.636419 0.854746 1.072939 2.303696 ... ... ... ... ... ... ... ... 9995 Cluster 74 2022-01-05 0.781370 -0.216738 -0.786449 -0.806634 -0.552988 9996 Cluster 74 2022-01-16 -0.858006 0.801571 -1.044879 0.105053 0.638271 9997 Cluster 74 2022-01-22 0.943984 -0.975810 -0.839801 0.839736 1.674833 9998 Cluster 74 2022-01-24 1.269351 -0.037544 -1.506677 1.209883 2.423682 9999 Cluster 74 2022-01-30 -1.141438 0.977891 0.143942 -0.585707 -0.835189 10000 rows \u00d7 7 columns Vainilla method \u00b6 Just run a regular gee estimation, no covariate adjustment. In [3]: Copied! # Splitter and perturbator sw = ClusteredSplitter ( cluster_cols = [ \"cluster\" , \"date\" ], ) perturbator = ConstantPerturbator ( average_effect = 0.1 , ) # Vainilla GEE analysis = GeeExperimentAnalysis ( cluster_cols = [ \"cluster\" , \"date\" ], ) pw_vainilla = PowerAnalysis ( perturbator = perturbator , splitter = sw , analysis = analysis , n_simulations = 50 , ) power = pw_vainilla . power_analysis ( df_analysis ) print ( f \"Not using cupac: { power = } \" ) # Splitter and perturbator sw = ClusteredSplitter( cluster_cols=[\"cluster\", \"date\"], ) perturbator = ConstantPerturbator( average_effect=0.1, ) # Vainilla GEE analysis = GeeExperimentAnalysis( cluster_cols=[\"cluster\", \"date\"], ) pw_vainilla = PowerAnalysis( perturbator=perturbator, splitter=sw, analysis=analysis, n_simulations=50, ) power = pw_vainilla.power_analysis(df_analysis) print(f\"Not using cupac: {power = }\") Not using cupac: power = 0.5 We can see that power is not great Cupac method \u00b6 Use GBM model with covariates x1, x2, x3, x4 fitted on pre-analysis data; use the predictions of this model on analysis data to reduce variance. In [4]: Copied! # Cupac GEE analysis = GeeExperimentAnalysis ( cluster_cols = [ \"cluster\" , \"date\" ], covariates = [ \"estimate_target\" ] ) gbm = HistGradientBoostingRegressor () pw_cupac = PowerAnalysis ( perturbator = perturbator , splitter = sw , analysis = analysis , n_simulations = 50 , cupac_model = gbm , features_cupac_model = [ \"x1\" , \"x2\" , \"x3\" , \"x4\" ], ) power = pw_cupac . power_analysis ( df_analysis , df_pre ) print ( f \"Using cupac: { power = } \" ) # Cupac GEE analysis = GeeExperimentAnalysis( cluster_cols=[\"cluster\", \"date\"], covariates=[\"estimate_target\"] ) gbm = HistGradientBoostingRegressor() pw_cupac = PowerAnalysis( perturbator=perturbator, splitter=sw, analysis=analysis, n_simulations=50, cupac_model=gbm, features_cupac_model=[\"x1\", \"x2\", \"x3\", \"x4\"], ) power = pw_cupac.power_analysis(df_analysis, df_pre) print(f\"Using cupac: {power = }\") Using cupac: power = 0.92 Power has increased!","title":"Cupac example"},{"location":"cupac_example.html#cupac-tutorial","text":"In this example we demonstrate usage of CUPAC as a way to increase power. We compare vainilla (non-cupac) gee estimation with gee estimation with cupac. In [1]: Copied! from datetime import date import numpy as np import pandas as pd from cluster_experiments import GeeExperimentAnalysis from cluster_experiments import ConstantPerturbator from cluster_experiments import PowerAnalysis from cluster_experiments import ClusteredSplitter from sklearn.ensemble import HistGradientBoostingRegressor def generate_random_data ( clusters , dates , N ): \"\"\"Generates target as a non-linear function of the covariates, the cluster mean and some residual\"\"\" # Every cluster has a mean df_clusters = pd . DataFrame ( { \"cluster\" : clusters , \"cluster_mean\" : np . random . normal ( 0 , 0.1 , size = len ( clusters )), } ) # The target is the sum of: covariates, cluster mean and random residual df = ( pd . DataFrame ( { \"cluster\" : np . random . choice ( clusters , size = N ), \"residual\" : np . random . normal ( 0 , 1 , size = N ), \"date\" : np . random . choice ( dates , size = N ), \"x1\" : np . random . normal ( 0 , 1 , size = N ), \"x2\" : np . random . normal ( 0 , 1 , size = N ), \"x3\" : np . random . normal ( 0 , 1 , size = N ), \"x4\" : np . random . normal ( 0 , 1 , size = N ), } ) . merge ( df_clusters , on = \"cluster\" ) . assign ( target = lambda x : x [ \"x1\" ] * x [ \"x2\" ] + x [ \"x3\" ] ** 2 + x [ \"x4\" ] + x [ \"cluster_mean\" ] + x [ \"residual\" ] ) ) return df from datetime import date import numpy as np import pandas as pd from cluster_experiments import GeeExperimentAnalysis from cluster_experiments import ConstantPerturbator from cluster_experiments import PowerAnalysis from cluster_experiments import ClusteredSplitter from sklearn.ensemble import HistGradientBoostingRegressor def generate_random_data(clusters, dates, N): \"\"\"Generates target as a non-linear function of the covariates, the cluster mean and some residual\"\"\" # Every cluster has a mean df_clusters = pd.DataFrame( { \"cluster\": clusters, \"cluster_mean\": np.random.normal(0, 0.1, size=len(clusters)), } ) # The target is the sum of: covariates, cluster mean and random residual df = ( pd.DataFrame( { \"cluster\": np.random.choice(clusters, size=N), \"residual\": np.random.normal(0, 1, size=N), \"date\": np.random.choice(dates, size=N), \"x1\": np.random.normal(0, 1, size=N), \"x2\": np.random.normal(0, 1, size=N), \"x3\": np.random.normal(0, 1, size=N), \"x4\": np.random.normal(0, 1, size=N), } ) .merge(df_clusters, on=\"cluster\") .assign( target=lambda x: x[\"x1\"] * x[\"x2\"] + x[\"x3\"] ** 2 + x[\"x4\"] + x[\"cluster_mean\"] + x[\"residual\"] ) ) return df","title":"Cupac tutorial"},{"location":"cupac_example.html#data-generation","text":"We have data that contains: Cluster Date Some covariates Outcome (target) We want to run a switchback-clustered experiment for around 15 days. We take data from day 15 to day 31 as the analysis data. We take data from day 1 to day 14 as the pre-analysis data, which is going to be used to fit the cupac model. In [2]: Copied! clusters = [ f \"Cluster { i } \" for i in range ( 100 )] dates = [ f \" { date ( 2022 , 1 , i ) : %Y-%m-%d } \" for i in range ( 1 , 32 )] experiment_dates = [ f \" { date ( 2022 , 1 , i ) : %Y-%m-%d } \" for i in range ( 15 , 32 )] N = 10_000 df = generate_random_data ( clusters , dates , N ) . drop ( columns = [ \"residual\" , \"cluster_mean\" ]) df_analysis = df . query ( f \"date.isin( { experiment_dates } )\" ) df_pre = df . query ( f \"~date.isin( { experiment_dates } )\" ) df clusters = [f\"Cluster {i}\" for i in range(100)] dates = [f\"{date(2022, 1, i):%Y-%m-%d}\" for i in range(1, 32)] experiment_dates = [f\"{date(2022, 1, i):%Y-%m-%d}\" for i in range(15, 32)] N = 10_000 df = generate_random_data(clusters, dates, N).drop(columns=[\"residual\", \"cluster_mean\"]) df_analysis = df.query(f\"date.isin({experiment_dates})\") df_pre = df.query(f\"~date.isin({experiment_dates})\") df Out[2]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } cluster date x1 x2 x3 x4 target 0 Cluster 64 2022-01-26 0.407857 -0.821972 0.380603 -0.435979 0.259322 1 Cluster 64 2022-01-01 -0.338506 1.259993 -1.172503 -0.638529 0.109807 2 Cluster 64 2022-01-25 0.144844 -0.647623 1.126136 0.339519 1.672764 3 Cluster 64 2022-01-04 -0.081028 2.257395 1.786203 0.608843 4.281907 4 Cluster 64 2022-01-13 -0.381064 -0.636419 0.854746 1.072939 2.303696 ... ... ... ... ... ... ... ... 9995 Cluster 74 2022-01-05 0.781370 -0.216738 -0.786449 -0.806634 -0.552988 9996 Cluster 74 2022-01-16 -0.858006 0.801571 -1.044879 0.105053 0.638271 9997 Cluster 74 2022-01-22 0.943984 -0.975810 -0.839801 0.839736 1.674833 9998 Cluster 74 2022-01-24 1.269351 -0.037544 -1.506677 1.209883 2.423682 9999 Cluster 74 2022-01-30 -1.141438 0.977891 0.143942 -0.585707 -0.835189 10000 rows \u00d7 7 columns","title":"Data generation"},{"location":"cupac_example.html#vainilla-method","text":"Just run a regular gee estimation, no covariate adjustment. In [3]: Copied! # Splitter and perturbator sw = ClusteredSplitter ( cluster_cols = [ \"cluster\" , \"date\" ], ) perturbator = ConstantPerturbator ( average_effect = 0.1 , ) # Vainilla GEE analysis = GeeExperimentAnalysis ( cluster_cols = [ \"cluster\" , \"date\" ], ) pw_vainilla = PowerAnalysis ( perturbator = perturbator , splitter = sw , analysis = analysis , n_simulations = 50 , ) power = pw_vainilla . power_analysis ( df_analysis ) print ( f \"Not using cupac: { power = } \" ) # Splitter and perturbator sw = ClusteredSplitter( cluster_cols=[\"cluster\", \"date\"], ) perturbator = ConstantPerturbator( average_effect=0.1, ) # Vainilla GEE analysis = GeeExperimentAnalysis( cluster_cols=[\"cluster\", \"date\"], ) pw_vainilla = PowerAnalysis( perturbator=perturbator, splitter=sw, analysis=analysis, n_simulations=50, ) power = pw_vainilla.power_analysis(df_analysis) print(f\"Not using cupac: {power = }\") Not using cupac: power = 0.5 We can see that power is not great","title":"Vainilla method"},{"location":"cupac_example.html#cupac-method","text":"Use GBM model with covariates x1, x2, x3, x4 fitted on pre-analysis data; use the predictions of this model on analysis data to reduce variance. In [4]: Copied! # Cupac GEE analysis = GeeExperimentAnalysis ( cluster_cols = [ \"cluster\" , \"date\" ], covariates = [ \"estimate_target\" ] ) gbm = HistGradientBoostingRegressor () pw_cupac = PowerAnalysis ( perturbator = perturbator , splitter = sw , analysis = analysis , n_simulations = 50 , cupac_model = gbm , features_cupac_model = [ \"x1\" , \"x2\" , \"x3\" , \"x4\" ], ) power = pw_cupac . power_analysis ( df_analysis , df_pre ) print ( f \"Using cupac: { power = } \" ) # Cupac GEE analysis = GeeExperimentAnalysis( cluster_cols=[\"cluster\", \"date\"], covariates=[\"estimate_target\"] ) gbm = HistGradientBoostingRegressor() pw_cupac = PowerAnalysis( perturbator=perturbator, splitter=sw, analysis=analysis, n_simulations=50, cupac_model=gbm, features_cupac_model=[\"x1\", \"x2\", \"x3\", \"x4\"], ) power = pw_cupac.power_analysis(df_analysis, df_pre) print(f\"Using cupac: {power = }\") Using cupac: power = 0.92 Power has increased!","title":"Cupac method"},{"location":"experiment_analysis.html","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); 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These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: 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#7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Experiment Analysis \u00b6 This notebook demonstrates how to analyze the results of an experiment and get a useful scorecard as a summary of the analysis. The experiment is an A/B/C test where we randomise users of a food delivery app to test a new ranking algorithm, and we compare the monetary value and the delivery time of the orders created. First of all we import the necessary libraries and generate some fake data about our experiment. In [1]: Copied! import pandas as pd import numpy as np from cluster_experiments import ( AnalysisPlan , SimpleMetric , Dimension , Variant , HypothesisTest , TargetAggregation , ) import pandas as pd import numpy as np from cluster_experiments import ( AnalysisPlan, SimpleMetric, Dimension, Variant, HypothesisTest, TargetAggregation, ) In [3]: Copied! def generate_fake_data (): # Constants NUM_ORDERS = 10_000 NUM_CUSTOMERS = 3_000 EXPERIMENT_GROUPS = [ 'control' , 'treatment_1' , 'treatment_2' ] GROUP_SIZE = NUM_CUSTOMERS // len ( EXPERIMENT_GROUPS ) # Generate customer_ids customer_ids = np . arange ( 1 , NUM_CUSTOMERS + 1 ) # Shuffle and split customer_ids into experiment groups np . random . shuffle ( customer_ids ) experiment_group = np . repeat ( EXPERIMENT_GROUPS , GROUP_SIZE ) experiment_group = np . concatenate (( experiment_group , np . random . choice ( EXPERIMENT_GROUPS , NUM_CUSTOMERS - len ( experiment_group )))) # Assign customers to groups customer_group_mapping = dict ( zip ( customer_ids , experiment_group )) # Generate orders order_ids = np . arange ( 1 , NUM_ORDERS + 1 ) customers = np . random . choice ( customer_ids , NUM_ORDERS ) order_values = np . abs ( np . random . normal ( loc = 10 , scale = 2 , size = NUM_ORDERS )) # Normally distributed around 10 and positive order_delivery_times = np . abs ( np . random . normal ( loc = 30 , scale = 5 , size = NUM_ORDERS )) # Normally distributed around 30 minutes and positive order_city_codes = np . random . randint ( 1 , 3 , NUM_ORDERS ) # Random city codes between 1 and 2 # Create DataFrame data = { 'order_id' : order_ids , 'customer_id' : customers , 'experiment_group' : [ customer_group_mapping [ customer_id ] for customer_id in customers ], 'order_value' : order_values , 'order_delivery_time_in_minutes' : order_delivery_times , 'order_city_code' : order_city_codes } df = pd . DataFrame ( data ) df . order_city_code = df . order_city_code . astype ( str ) pre_exp_df = df . assign ( order_value = lambda df : df [ 'order_value' ] + np . random . normal ( loc = 0 , scale = 1 , size = NUM_ORDERS ), order_delivery_time_in_minutes = lambda df : df [ 'order_delivery_time_in_minutes' ] + np . random . normal ( loc = 0 , scale = 2 , size = NUM_ORDERS ) ) . sample ( int ( NUM_ORDERS / 3 )) return df , pre_exp_df df , pre_exp_df = generate_fake_data () # Show the first few rows of the DataFrame display ( df . head ()) display ( pre_exp_df . head ()) def generate_fake_data(): # Constants NUM_ORDERS = 10_000 NUM_CUSTOMERS = 3_000 EXPERIMENT_GROUPS = ['control', 'treatment_1', 'treatment_2'] GROUP_SIZE = NUM_CUSTOMERS // len(EXPERIMENT_GROUPS) # Generate customer_ids customer_ids = np.arange(1, NUM_CUSTOMERS + 1) # Shuffle and split customer_ids into experiment groups np.random.shuffle(customer_ids) experiment_group = np.repeat(EXPERIMENT_GROUPS, GROUP_SIZE) experiment_group = np.concatenate((experiment_group, np.random.choice(EXPERIMENT_GROUPS, NUM_CUSTOMERS - len(experiment_group)))) # Assign customers to groups customer_group_mapping = dict(zip(customer_ids, experiment_group)) # Generate orders order_ids = np.arange(1, NUM_ORDERS + 1) customers = np.random.choice(customer_ids, NUM_ORDERS) order_values = np.abs(np.random.normal(loc=10, scale=2, size=NUM_ORDERS)) # Normally distributed around 10 and positive order_delivery_times = np.abs(np.random.normal(loc=30, scale=5, size=NUM_ORDERS)) # Normally distributed around 30 minutes and positive order_city_codes = np.random.randint(1, 3, NUM_ORDERS) # Random city codes between 1 and 2 # Create DataFrame data = { 'order_id': order_ids, 'customer_id': customers, 'experiment_group': [customer_group_mapping[customer_id] for customer_id in customers], 'order_value': order_values, 'order_delivery_time_in_minutes': order_delivery_times, 'order_city_code': order_city_codes } df = pd.DataFrame(data) df.order_city_code = df.order_city_code.astype(str) pre_exp_df = df.assign( order_value = lambda df: df['order_value'] + np.random.normal(loc=0, scale=1, size=NUM_ORDERS), order_delivery_time_in_minutes = lambda df: df['order_delivery_time_in_minutes'] + np.random.normal(loc=0, scale=2, size=NUM_ORDERS) ).sample(int(NUM_ORDERS/3)) return df, pre_exp_df df, pre_exp_df = generate_fake_data() # Show the first few rows of the DataFrame display(df.head()) display(pre_exp_df.head()) .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } order_id customer_id experiment_group order_value order_delivery_time_in_minutes order_city_code 0 1 2121 treatment_1 8.617823 28.018369 2 1 2 1358 control 14.091706 35.770020 2 2 3 1891 control 10.710713 31.483367 1 3 4 2173 control 12.860734 29.235751 1 4 5 995 treatment_1 11.782487 33.076537 1 .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } order_id customer_id experiment_group order_value order_delivery_time_in_minutes order_city_code 6211 6212 2470 control 9.740159 28.986726 1 5971 5972 1586 treatment_2 10.682427 38.246326 1 6460 6461 1706 control 7.093542 19.930616 2 249 250 1828 treatment_2 5.679958 39.600876 2 5258 5259 1990 treatment_2 9.483023 24.158893 2 Now that we have a sample experimental dataset and also a pre-experimental dataset that we can use to showcase how to include cupac-style variance reduction in the analysis flow, we can proceed to define the building blocks of the analysis plan: metrics, dimensions and variants first. Metrics: AOV (Average Order Value) AVG DT (Average Delivery Time) Dimensions: order_city_code Variants: control treatment_1 treatment_2 In [4]: Copied! dimension__city_code = Dimension ( name = 'order_city_code' , values = [ '1' , '2' ] ) metric__order_value = SimpleMetric ( alias = 'AOV' , name = 'order_value' ) metric__delivery_time = SimpleMetric ( alias = 'AVG DT' , name = 'order_delivery_time_in_minutes' ) variants = [ Variant ( 'control' , is_control = True ), Variant ( 'treatment_1' , is_control = False ), Variant ( 'treatment_2' , is_control = False ) ] dimension__city_code = Dimension( name='order_city_code', values=['1','2'] ) metric__order_value = SimpleMetric( alias='AOV', name='order_value' ) metric__delivery_time = SimpleMetric( alias='AVG DT', name='order_delivery_time_in_minutes' ) variants = [ Variant('control', is_control=True), Variant('treatment_1', is_control=False), Variant('treatment_2', is_control=False) ] Now we can define the hypothesis tests that we want to run on the data. We will run two tests: A clustered OLS test for the order value: no variance reduction slice results by the city code of the orders A GEE test for the delivery time: with variance reduction using cupac (target aggregation) no slicing As you can see, each hypothesis test can be flexible enough to have its own logic. In [5]: Copied! test__order_value = HypothesisTest ( metric = metric__order_value , analysis_type = \"clustered_ols\" , analysis_config = { \"cluster_cols\" :[ \"customer_id\" ]}, dimensions = [ dimension__city_code ] ) cupac__model = TargetAggregation ( agg_col = \"customer_id\" , target_col = \"order_delivery_time_in_minutes\" ) test__delivery_time = HypothesisTest ( metric = metric__delivery_time , analysis_type = \"gee\" , analysis_config = { \"cluster_cols\" :[ \"customer_id\" ], \"covariates\" :[ \"estimate_order_delivery_time_in_minutes\" ]}, cupac_config = { \"cupac_model\" : cupac__model , \"target_col\" : \"order_delivery_time_in_minutes\" } ) test__order_value = HypothesisTest( metric=metric__order_value, analysis_type=\"clustered_ols\", analysis_config={\"cluster_cols\":[\"customer_id\"]}, dimensions=[dimension__city_code] ) cupac__model = TargetAggregation(agg_col=\"customer_id\", target_col=\"order_delivery_time_in_minutes\") test__delivery_time = HypothesisTest( metric=metric__delivery_time, analysis_type=\"gee\", analysis_config={\"cluster_cols\":[\"customer_id\"], \"covariates\":[\"estimate_order_delivery_time_in_minutes\"]}, cupac_config={\"cupac_model\":cupac__model, \"target_col\":\"order_delivery_time_in_minutes\"} ) Finally, we can define the analysis plan where we pack and run all the tests on the data. The results will be displayed in a DataFrame. Note that all tests included in a single analysis plan must run on the same exact dataset (or datasets, in case the pre-experimental data is provided and used). Should there be the need to use different datasets, the user must create separate analysis plans for each dataset. In [6]: Copied! analysis_plan = AnalysisPlan ( tests = [ test__order_value , test__delivery_time ], variants = variants , variant_col = 'experiment_group' , alpha = 0.01 ) results = analysis_plan . analyze ( exp_data = df , pre_exp_data = pre_exp_df ) results_df = results . to_dataframe () display ( results_df ) analysis_plan = AnalysisPlan( tests=[test__order_value, test__delivery_time], variants=variants, variant_col='experiment_group', alpha=0.01 ) results = analysis_plan.analyze(exp_data=df, pre_exp_data=pre_exp_df) results_df = results.to_dataframe() display(results_df) .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } metric_alias control_variant_name treatment_variant_name control_variant_mean treatment_variant_mean analysis_type ate ate_ci_lower ate_ci_upper p_value std_error dimension_name dimension_value alpha 0 AOV control treatment_1 9.987651 10.027835 clustered_ols 0.040185 -0.081815 0.162184 0.396195 0.047363 __total_dimension total 0.01 1 AOV control treatment_1 10.037250 9.999080 clustered_ols -0.038170 -0.211476 0.135135 0.570493 0.067281 order_city_code 1 0.01 2 AOV control treatment_1 9.936038 10.056783 clustered_ols 0.120746 -0.059459 0.300951 0.084361 0.069960 order_city_code 2 0.01 3 AOV control treatment_2 9.987651 10.048016 clustered_ols 0.060365 -0.061972 0.182701 0.203729 0.047494 __total_dimension total 0.01 4 AOV control treatment_2 10.037250 10.026263 clustered_ols -0.010987 -0.187316 0.165342 0.872492 0.068455 order_city_code 1 0.01 5 AOV control treatment_2 9.936038 10.070004 clustered_ols 0.133967 -0.039486 0.307419 0.046652 0.067339 order_city_code 2 0.01 6 AVG DT control treatment_1 29.964957 29.985370 gee 0.031963 -0.250092 0.314017 0.770368 0.109500 __total_dimension total 0.01 7 AVG DT control treatment_2 29.964957 30.005273 gee 0.129250 -0.149748 0.408248 0.232756 0.108314 __total_dimension total 0.01 Shortcut: creating a simple analysis plan skipping hypothesis tests definition \u00b6 In case the user does not need to define custom hypothesis tests, they can use the AnalysisPlan.from_metrics method to create a simple analysis plan where the user only needs to define the metrics, dimensions and variants. The method will automatically create the necessary hypothesis tests and run them on the data. This works for the cases where all the desired tests should run with the same analysis type and configuration, and with the same dimensions to slice upon. In case the user needs to run tests with differences in such components, they should use the standard way of defining the analysis plan as illustrated in the previous section. Below is an example of how to create a simple analysis plan using the same metrics, dimensions and variants as before. The results will be displayed in a DataFrame. Additionally, we also show how setting verbose=True will log the setup of all the comparisons that are performed when running the analysis plan. In [7]: Copied! simple_analysis_plan = AnalysisPlan . from_metrics ( metrics = [ metric__delivery_time , metric__order_value ], variants = variants , variant_col = 'experiment_group' , alpha = 0.01 , dimensions = [ dimension__city_code ], analysis_type = \"clustered_ols\" , analysis_config = { \"cluster_cols\" :[ \"customer_id\" ]}, ) simple_results = simple_analysis_plan . analyze ( exp_data = df , verbose = True ) simple_results_df = simple_results . to_dataframe () display ( simple_results_df ) simple_analysis_plan = AnalysisPlan.from_metrics( metrics=[metric__delivery_time, metric__order_value], variants=variants, variant_col='experiment_group', alpha=0.01, dimensions=[dimension__city_code], analysis_type=\"clustered_ols\", analysis_config={\"cluster_cols\":[\"customer_id\"]}, ) simple_results = simple_analysis_plan.analyze(exp_data=df, verbose=True) simple_results_df = simple_results.to_dataframe() display(simple_results_df) 2024-11-06 20:03:19,308 - Metric: AVG DT, Treatment: treatment_1, Dimension: __total_dimension, Value: total 2024-11-06 20:03:19,385 - Metric: AVG DT, Treatment: treatment_1, Dimension: order_city_code, Value: 1 2024-11-06 20:03:19,413 - Metric: AVG DT, Treatment: treatment_1, Dimension: order_city_code, Value: 2 2024-11-06 20:03:19,440 - Metric: AVG DT, Treatment: treatment_2, Dimension: __total_dimension, Value: total 2024-11-06 20:03:19,482 - Metric: AVG DT, Treatment: treatment_2, Dimension: order_city_code, Value: 1 2024-11-06 20:03:19,513 - Metric: AVG DT, Treatment: treatment_2, Dimension: order_city_code, Value: 2 2024-11-06 20:03:19,556 - Metric: AOV, Treatment: treatment_1, Dimension: __total_dimension, Value: total 2024-11-06 20:03:19,609 - Metric: AOV, Treatment: treatment_1, Dimension: order_city_code, Value: 1 2024-11-06 20:03:19,639 - Metric: AOV, Treatment: treatment_1, Dimension: order_city_code, Value: 2 2024-11-06 20:03:19,669 - Metric: AOV, Treatment: treatment_2, Dimension: __total_dimension, Value: total 2024-11-06 20:03:19,705 - Metric: AOV, Treatment: treatment_2, Dimension: order_city_code, Value: 1 2024-11-06 20:03:19,732 - Metric: AOV, Treatment: treatment_2, Dimension: order_city_code, Value: 2 .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } metric_alias control_variant_name treatment_variant_name control_variant_mean treatment_variant_mean analysis_type ate ate_ci_lower ate_ci_upper p_value std_error dimension_name dimension_value alpha 0 AVG DT control treatment_1 29.964957 29.985370 clustered_ols 0.020413 -0.300872 0.341698 0.870000 0.124731 __total_dimension total 0.01 1 AVG DT control treatment_1 29.862094 29.800854 clustered_ols -0.061240 -0.508945 0.386466 0.724585 0.173810 order_city_code 1 0.01 2 AVG DT control treatment_1 30.071997 30.171119 clustered_ols 0.099122 -0.347323 0.545568 0.567389 0.173321 order_city_code 2 0.01 3 AVG DT control treatment_2 29.964957 30.005273 clustered_ols 0.040316 -0.279913 0.360546 0.745716 0.124321 __total_dimension total 0.01 4 AVG DT control treatment_2 29.862094 30.069727 clustered_ols 0.207634 -0.232033 0.647301 0.223817 0.170689 order_city_code 1 0.01 5 AVG DT control treatment_2 30.071997 29.940118 clustered_ols -0.131878 -0.581788 0.318032 0.450230 0.174666 order_city_code 2 0.01 6 AOV control treatment_1 9.987651 10.027835 clustered_ols 0.040185 -0.081815 0.162184 0.396195 0.047363 __total_dimension total 0.01 7 AOV control treatment_1 10.037250 9.999080 clustered_ols -0.038170 -0.211476 0.135135 0.570493 0.067281 order_city_code 1 0.01 8 AOV control treatment_1 9.936038 10.056783 clustered_ols 0.120746 -0.059459 0.300951 0.084361 0.069960 order_city_code 2 0.01 9 AOV control treatment_2 9.987651 10.048016 clustered_ols 0.060365 -0.061972 0.182701 0.203729 0.047494 __total_dimension total 0.01 10 AOV control treatment_2 10.037250 10.026263 clustered_ols -0.010987 -0.187316 0.165342 0.872492 0.068455 order_city_code 1 0.01 11 AOV control treatment_2 9.936038 10.070004 clustered_ols 0.133967 -0.039486 0.307419 0.046652 0.067339 order_city_code 2 0.01 Bonus: Plugging in a custom analysis method \u00b6 In case the user needs to run a custom analysis method that is not covered by the standard analysis types provided by the library, they can define a custom analysis class and plug it into the analysis plan. Below is an example of how to do this. In this example, we define a custom analysis class that extends the ClusteredOLSAnalysis class provided by the library. The custom class will be used to run a clustered OLS analysis with a custom logic. The analysis plan will be created with the custom analysis type mapper that will map the custom analysis type to the custom analysis class. In [9]: Copied! from cluster_experiments.experiment_analysis import ClusteredOLSAnalysis # assuming we define a meaningful custom ExperimentAnalysis class class CustomExperimentAnalysis ( ClusteredOLSAnalysis ): def __init__ ( self , ** kwargs ): super () . __init__ ( ** kwargs ) custom_simple_analysis_plan = AnalysisPlan . from_metrics ( metrics = [ metric__order_value ], variants = variants , variant_col = 'experiment_group' , alpha = 0.01 , dimensions = [ dimension__city_code ], analysis_type = \"custom_clustered_ols\" , analysis_config = { \"cluster_cols\" :[ \"customer_id\" ]}, custom_analysis_type_mapper = { \"custom_clustered_ols\" : CustomExperimentAnalysis } ) custom_simple_results = custom_simple_analysis_plan . analyze ( exp_data = df ) custom_simple_results_df = custom_simple_results . to_dataframe () display ( custom_simple_results_df ) from cluster_experiments.experiment_analysis import ClusteredOLSAnalysis # assuming we define a meaningful custom ExperimentAnalysis class class CustomExperimentAnalysis(ClusteredOLSAnalysis): def __init__(self, **kwargs): super().__init__(**kwargs) custom_simple_analysis_plan = AnalysisPlan.from_metrics( metrics=[metric__order_value], variants=variants, variant_col='experiment_group', alpha=0.01, dimensions=[dimension__city_code], analysis_type=\"custom_clustered_ols\", analysis_config={\"cluster_cols\":[\"customer_id\"]}, custom_analysis_type_mapper={\"custom_clustered_ols\": CustomExperimentAnalysis} ) custom_simple_results = custom_simple_analysis_plan.analyze(exp_data=df) custom_simple_results_df = custom_simple_results.to_dataframe() display(custom_simple_results_df) .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } metric_alias control_variant_name treatment_variant_name control_variant_mean treatment_variant_mean analysis_type ate ate_ci_lower ate_ci_upper p_value std_error dimension_name dimension_value alpha 0 AOV control treatment_1 9.987651 10.027835 custom_clustered_ols 0.040185 -0.081815 0.162184 0.396195 0.047363 __total_dimension total 0.01 1 AOV control treatment_1 10.037250 9.999080 custom_clustered_ols -0.038170 -0.211476 0.135135 0.570493 0.067281 order_city_code 1 0.01 2 AOV control treatment_1 9.936038 10.056783 custom_clustered_ols 0.120746 -0.059459 0.300951 0.084361 0.069960 order_city_code 2 0.01 3 AOV control treatment_2 9.987651 10.048016 custom_clustered_ols 0.060365 -0.061972 0.182701 0.203729 0.047494 __total_dimension total 0.01 4 AOV control treatment_2 10.037250 10.026263 custom_clustered_ols -0.010987 -0.187316 0.165342 0.872492 0.068455 order_city_code 1 0.01 5 AOV control treatment_2 9.936038 10.070004 custom_clustered_ols 0.133967 -0.039486 0.307419 0.046652 0.067339 order_city_code 2 0.01 Now it's your turn! Have fun experimenting with the library and analyzing your data!","title":"Experiment Analysis"},{"location":"experiment_analysis.html#experiment-analysis","text":"This notebook demonstrates how to analyze the results of an experiment and get a useful scorecard as a summary of the analysis. The experiment is an A/B/C test where we randomise users of a food delivery app to test a new ranking algorithm, and we compare the monetary value and the delivery time of the orders created. First of all we import the necessary libraries and generate some fake data about our experiment. In [1]: Copied! import pandas as pd import numpy as np from cluster_experiments import ( AnalysisPlan , SimpleMetric , Dimension , Variant , HypothesisTest , TargetAggregation , ) import pandas as pd import numpy as np from cluster_experiments import ( AnalysisPlan, SimpleMetric, Dimension, Variant, HypothesisTest, TargetAggregation, ) In [3]: Copied! def generate_fake_data (): # Constants NUM_ORDERS = 10_000 NUM_CUSTOMERS = 3_000 EXPERIMENT_GROUPS = [ 'control' , 'treatment_1' , 'treatment_2' ] GROUP_SIZE = NUM_CUSTOMERS // len ( EXPERIMENT_GROUPS ) # Generate customer_ids customer_ids = np . arange ( 1 , NUM_CUSTOMERS + 1 ) # Shuffle and split customer_ids into experiment groups np . random . shuffle ( customer_ids ) experiment_group = np . repeat ( EXPERIMENT_GROUPS , GROUP_SIZE ) experiment_group = np . concatenate (( experiment_group , np . random . choice ( EXPERIMENT_GROUPS , NUM_CUSTOMERS - len ( experiment_group )))) # Assign customers to groups customer_group_mapping = dict ( zip ( customer_ids , experiment_group )) # Generate orders order_ids = np . arange ( 1 , NUM_ORDERS + 1 ) customers = np . random . choice ( customer_ids , NUM_ORDERS ) order_values = np . abs ( np . random . normal ( loc = 10 , scale = 2 , size = NUM_ORDERS )) # Normally distributed around 10 and positive order_delivery_times = np . abs ( np . random . normal ( loc = 30 , scale = 5 , size = NUM_ORDERS )) # Normally distributed around 30 minutes and positive order_city_codes = np . random . randint ( 1 , 3 , NUM_ORDERS ) # Random city codes between 1 and 2 # Create DataFrame data = { 'order_id' : order_ids , 'customer_id' : customers , 'experiment_group' : [ customer_group_mapping [ customer_id ] for customer_id in customers ], 'order_value' : order_values , 'order_delivery_time_in_minutes' : order_delivery_times , 'order_city_code' : order_city_codes } df = pd . DataFrame ( data ) df . order_city_code = df . order_city_code . astype ( str ) pre_exp_df = df . assign ( order_value = lambda df : df [ 'order_value' ] + np . random . normal ( loc = 0 , scale = 1 , size = NUM_ORDERS ), order_delivery_time_in_minutes = lambda df : df [ 'order_delivery_time_in_minutes' ] + np . random . normal ( loc = 0 , scale = 2 , size = NUM_ORDERS ) ) . sample ( int ( NUM_ORDERS / 3 )) return df , pre_exp_df df , pre_exp_df = generate_fake_data () # Show the first few rows of the DataFrame display ( df . head ()) display ( pre_exp_df . head ()) def generate_fake_data(): # Constants NUM_ORDERS = 10_000 NUM_CUSTOMERS = 3_000 EXPERIMENT_GROUPS = ['control', 'treatment_1', 'treatment_2'] GROUP_SIZE = NUM_CUSTOMERS // len(EXPERIMENT_GROUPS) # Generate customer_ids customer_ids = np.arange(1, NUM_CUSTOMERS + 1) # Shuffle and split customer_ids into experiment groups np.random.shuffle(customer_ids) experiment_group = np.repeat(EXPERIMENT_GROUPS, GROUP_SIZE) experiment_group = np.concatenate((experiment_group, np.random.choice(EXPERIMENT_GROUPS, NUM_CUSTOMERS - len(experiment_group)))) # Assign customers to groups customer_group_mapping = dict(zip(customer_ids, experiment_group)) # Generate orders order_ids = np.arange(1, NUM_ORDERS + 1) customers = np.random.choice(customer_ids, NUM_ORDERS) order_values = np.abs(np.random.normal(loc=10, scale=2, size=NUM_ORDERS)) # Normally distributed around 10 and positive order_delivery_times = np.abs(np.random.normal(loc=30, scale=5, size=NUM_ORDERS)) # Normally distributed around 30 minutes and positive order_city_codes = np.random.randint(1, 3, NUM_ORDERS) # Random city codes between 1 and 2 # Create DataFrame data = { 'order_id': order_ids, 'customer_id': customers, 'experiment_group': [customer_group_mapping[customer_id] for customer_id in customers], 'order_value': order_values, 'order_delivery_time_in_minutes': order_delivery_times, 'order_city_code': order_city_codes } df = pd.DataFrame(data) df.order_city_code = df.order_city_code.astype(str) pre_exp_df = df.assign( order_value = lambda df: df['order_value'] + np.random.normal(loc=0, scale=1, size=NUM_ORDERS), order_delivery_time_in_minutes = lambda df: df['order_delivery_time_in_minutes'] + np.random.normal(loc=0, scale=2, size=NUM_ORDERS) ).sample(int(NUM_ORDERS/3)) return df, pre_exp_df df, pre_exp_df = generate_fake_data() # Show the first few rows of the DataFrame display(df.head()) display(pre_exp_df.head()) .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } order_id customer_id experiment_group order_value order_delivery_time_in_minutes order_city_code 0 1 2121 treatment_1 8.617823 28.018369 2 1 2 1358 control 14.091706 35.770020 2 2 3 1891 control 10.710713 31.483367 1 3 4 2173 control 12.860734 29.235751 1 4 5 995 treatment_1 11.782487 33.076537 1 .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } order_id customer_id experiment_group order_value order_delivery_time_in_minutes order_city_code 6211 6212 2470 control 9.740159 28.986726 1 5971 5972 1586 treatment_2 10.682427 38.246326 1 6460 6461 1706 control 7.093542 19.930616 2 249 250 1828 treatment_2 5.679958 39.600876 2 5258 5259 1990 treatment_2 9.483023 24.158893 2 Now that we have a sample experimental dataset and also a pre-experimental dataset that we can use to showcase how to include cupac-style variance reduction in the analysis flow, we can proceed to define the building blocks of the analysis plan: metrics, dimensions and variants first. Metrics: AOV (Average Order Value) AVG DT (Average Delivery Time) Dimensions: order_city_code Variants: control treatment_1 treatment_2 In [4]: Copied! dimension__city_code = Dimension ( name = 'order_city_code' , values = [ '1' , '2' ] ) metric__order_value = SimpleMetric ( alias = 'AOV' , name = 'order_value' ) metric__delivery_time = SimpleMetric ( alias = 'AVG DT' , name = 'order_delivery_time_in_minutes' ) variants = [ Variant ( 'control' , is_control = True ), Variant ( 'treatment_1' , is_control = False ), Variant ( 'treatment_2' , is_control = False ) ] dimension__city_code = Dimension( name='order_city_code', values=['1','2'] ) metric__order_value = SimpleMetric( alias='AOV', name='order_value' ) metric__delivery_time = SimpleMetric( alias='AVG DT', name='order_delivery_time_in_minutes' ) variants = [ Variant('control', is_control=True), Variant('treatment_1', is_control=False), Variant('treatment_2', is_control=False) ] Now we can define the hypothesis tests that we want to run on the data. We will run two tests: A clustered OLS test for the order value: no variance reduction slice results by the city code of the orders A GEE test for the delivery time: with variance reduction using cupac (target aggregation) no slicing As you can see, each hypothesis test can be flexible enough to have its own logic. In [5]: Copied! test__order_value = HypothesisTest ( metric = metric__order_value , analysis_type = \"clustered_ols\" , analysis_config = { \"cluster_cols\" :[ \"customer_id\" ]}, dimensions = [ dimension__city_code ] ) cupac__model = TargetAggregation ( agg_col = \"customer_id\" , target_col = \"order_delivery_time_in_minutes\" ) test__delivery_time = HypothesisTest ( metric = metric__delivery_time , analysis_type = \"gee\" , analysis_config = { \"cluster_cols\" :[ \"customer_id\" ], \"covariates\" :[ \"estimate_order_delivery_time_in_minutes\" ]}, cupac_config = { \"cupac_model\" : cupac__model , \"target_col\" : \"order_delivery_time_in_minutes\" } ) test__order_value = HypothesisTest( metric=metric__order_value, analysis_type=\"clustered_ols\", analysis_config={\"cluster_cols\":[\"customer_id\"]}, dimensions=[dimension__city_code] ) cupac__model = TargetAggregation(agg_col=\"customer_id\", target_col=\"order_delivery_time_in_minutes\") test__delivery_time = HypothesisTest( metric=metric__delivery_time, analysis_type=\"gee\", analysis_config={\"cluster_cols\":[\"customer_id\"], \"covariates\":[\"estimate_order_delivery_time_in_minutes\"]}, cupac_config={\"cupac_model\":cupac__model, \"target_col\":\"order_delivery_time_in_minutes\"} ) Finally, we can define the analysis plan where we pack and run all the tests on the data. The results will be displayed in a DataFrame. Note that all tests included in a single analysis plan must run on the same exact dataset (or datasets, in case the pre-experimental data is provided and used). Should there be the need to use different datasets, the user must create separate analysis plans for each dataset. In [6]: Copied! analysis_plan = AnalysisPlan ( tests = [ test__order_value , test__delivery_time ], variants = variants , variant_col = 'experiment_group' , alpha = 0.01 ) results = analysis_plan . analyze ( exp_data = df , pre_exp_data = pre_exp_df ) results_df = results . to_dataframe () display ( results_df ) analysis_plan = AnalysisPlan( tests=[test__order_value, test__delivery_time], variants=variants, variant_col='experiment_group', alpha=0.01 ) results = analysis_plan.analyze(exp_data=df, pre_exp_data=pre_exp_df) results_df = results.to_dataframe() display(results_df) .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } metric_alias control_variant_name treatment_variant_name control_variant_mean treatment_variant_mean analysis_type ate ate_ci_lower ate_ci_upper p_value std_error dimension_name dimension_value alpha 0 AOV control treatment_1 9.987651 10.027835 clustered_ols 0.040185 -0.081815 0.162184 0.396195 0.047363 __total_dimension total 0.01 1 AOV control treatment_1 10.037250 9.999080 clustered_ols -0.038170 -0.211476 0.135135 0.570493 0.067281 order_city_code 1 0.01 2 AOV control treatment_1 9.936038 10.056783 clustered_ols 0.120746 -0.059459 0.300951 0.084361 0.069960 order_city_code 2 0.01 3 AOV control treatment_2 9.987651 10.048016 clustered_ols 0.060365 -0.061972 0.182701 0.203729 0.047494 __total_dimension total 0.01 4 AOV control treatment_2 10.037250 10.026263 clustered_ols -0.010987 -0.187316 0.165342 0.872492 0.068455 order_city_code 1 0.01 5 AOV control treatment_2 9.936038 10.070004 clustered_ols 0.133967 -0.039486 0.307419 0.046652 0.067339 order_city_code 2 0.01 6 AVG DT control treatment_1 29.964957 29.985370 gee 0.031963 -0.250092 0.314017 0.770368 0.109500 __total_dimension total 0.01 7 AVG DT control treatment_2 29.964957 30.005273 gee 0.129250 -0.149748 0.408248 0.232756 0.108314 __total_dimension total 0.01","title":"Experiment Analysis"},{"location":"experiment_analysis.html#shortcut-creating-a-simple-analysis-plan-skipping-hypothesis-tests-definition","text":"In case the user does not need to define custom hypothesis tests, they can use the AnalysisPlan.from_metrics method to create a simple analysis plan where the user only needs to define the metrics, dimensions and variants. The method will automatically create the necessary hypothesis tests and run them on the data. This works for the cases where all the desired tests should run with the same analysis type and configuration, and with the same dimensions to slice upon. In case the user needs to run tests with differences in such components, they should use the standard way of defining the analysis plan as illustrated in the previous section. Below is an example of how to create a simple analysis plan using the same metrics, dimensions and variants as before. The results will be displayed in a DataFrame. Additionally, we also show how setting verbose=True will log the setup of all the comparisons that are performed when running the analysis plan. In [7]: Copied! simple_analysis_plan = AnalysisPlan . from_metrics ( metrics = [ metric__delivery_time , metric__order_value ], variants = variants , variant_col = 'experiment_group' , alpha = 0.01 , dimensions = [ dimension__city_code ], analysis_type = \"clustered_ols\" , analysis_config = { \"cluster_cols\" :[ \"customer_id\" ]}, ) simple_results = simple_analysis_plan . analyze ( exp_data = df , verbose = True ) simple_results_df = simple_results . to_dataframe () display ( simple_results_df ) simple_analysis_plan = AnalysisPlan.from_metrics( metrics=[metric__delivery_time, metric__order_value], variants=variants, variant_col='experiment_group', alpha=0.01, dimensions=[dimension__city_code], analysis_type=\"clustered_ols\", analysis_config={\"cluster_cols\":[\"customer_id\"]}, ) simple_results = simple_analysis_plan.analyze(exp_data=df, verbose=True) simple_results_df = simple_results.to_dataframe() display(simple_results_df) 2024-11-06 20:03:19,308 - Metric: AVG DT, Treatment: treatment_1, Dimension: __total_dimension, Value: total 2024-11-06 20:03:19,385 - Metric: AVG DT, Treatment: treatment_1, Dimension: order_city_code, Value: 1 2024-11-06 20:03:19,413 - Metric: AVG DT, Treatment: treatment_1, Dimension: order_city_code, Value: 2 2024-11-06 20:03:19,440 - Metric: AVG DT, Treatment: treatment_2, Dimension: __total_dimension, Value: total 2024-11-06 20:03:19,482 - Metric: AVG DT, Treatment: treatment_2, Dimension: order_city_code, Value: 1 2024-11-06 20:03:19,513 - Metric: AVG DT, Treatment: treatment_2, Dimension: order_city_code, Value: 2 2024-11-06 20:03:19,556 - Metric: AOV, Treatment: treatment_1, Dimension: __total_dimension, Value: total 2024-11-06 20:03:19,609 - Metric: AOV, Treatment: treatment_1, Dimension: order_city_code, Value: 1 2024-11-06 20:03:19,639 - Metric: AOV, Treatment: treatment_1, Dimension: order_city_code, Value: 2 2024-11-06 20:03:19,669 - Metric: AOV, Treatment: treatment_2, Dimension: __total_dimension, Value: total 2024-11-06 20:03:19,705 - Metric: AOV, Treatment: treatment_2, Dimension: order_city_code, Value: 1 2024-11-06 20:03:19,732 - Metric: AOV, Treatment: treatment_2, Dimension: order_city_code, Value: 2 .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } metric_alias control_variant_name treatment_variant_name control_variant_mean treatment_variant_mean analysis_type ate ate_ci_lower ate_ci_upper p_value std_error dimension_name dimension_value alpha 0 AVG DT control treatment_1 29.964957 29.985370 clustered_ols 0.020413 -0.300872 0.341698 0.870000 0.124731 __total_dimension total 0.01 1 AVG DT control treatment_1 29.862094 29.800854 clustered_ols -0.061240 -0.508945 0.386466 0.724585 0.173810 order_city_code 1 0.01 2 AVG DT control treatment_1 30.071997 30.171119 clustered_ols 0.099122 -0.347323 0.545568 0.567389 0.173321 order_city_code 2 0.01 3 AVG DT control treatment_2 29.964957 30.005273 clustered_ols 0.040316 -0.279913 0.360546 0.745716 0.124321 __total_dimension total 0.01 4 AVG DT control treatment_2 29.862094 30.069727 clustered_ols 0.207634 -0.232033 0.647301 0.223817 0.170689 order_city_code 1 0.01 5 AVG DT control treatment_2 30.071997 29.940118 clustered_ols -0.131878 -0.581788 0.318032 0.450230 0.174666 order_city_code 2 0.01 6 AOV control treatment_1 9.987651 10.027835 clustered_ols 0.040185 -0.081815 0.162184 0.396195 0.047363 __total_dimension total 0.01 7 AOV control treatment_1 10.037250 9.999080 clustered_ols -0.038170 -0.211476 0.135135 0.570493 0.067281 order_city_code 1 0.01 8 AOV control treatment_1 9.936038 10.056783 clustered_ols 0.120746 -0.059459 0.300951 0.084361 0.069960 order_city_code 2 0.01 9 AOV control treatment_2 9.987651 10.048016 clustered_ols 0.060365 -0.061972 0.182701 0.203729 0.047494 __total_dimension total 0.01 10 AOV control treatment_2 10.037250 10.026263 clustered_ols -0.010987 -0.187316 0.165342 0.872492 0.068455 order_city_code 1 0.01 11 AOV control treatment_2 9.936038 10.070004 clustered_ols 0.133967 -0.039486 0.307419 0.046652 0.067339 order_city_code 2 0.01","title":"Shortcut: creating a simple analysis plan skipping hypothesis tests definition"},{"location":"experiment_analysis.html#bonus-plugging-in-a-custom-analysis-method","text":"In case the user needs to run a custom analysis method that is not covered by the standard analysis types provided by the library, they can define a custom analysis class and plug it into the analysis plan. Below is an example of how to do this. In this example, we define a custom analysis class that extends the ClusteredOLSAnalysis class provided by the library. The custom class will be used to run a clustered OLS analysis with a custom logic. The analysis plan will be created with the custom analysis type mapper that will map the custom analysis type to the custom analysis class. In [9]: Copied! from cluster_experiments.experiment_analysis import ClusteredOLSAnalysis # assuming we define a meaningful custom ExperimentAnalysis class class CustomExperimentAnalysis ( ClusteredOLSAnalysis ): def __init__ ( self , ** kwargs ): super () . __init__ ( ** kwargs ) custom_simple_analysis_plan = AnalysisPlan . from_metrics ( metrics = [ metric__order_value ], variants = variants , variant_col = 'experiment_group' , alpha = 0.01 , dimensions = [ dimension__city_code ], analysis_type = \"custom_clustered_ols\" , analysis_config = { \"cluster_cols\" :[ \"customer_id\" ]}, custom_analysis_type_mapper = { \"custom_clustered_ols\" : CustomExperimentAnalysis } ) custom_simple_results = custom_simple_analysis_plan . analyze ( exp_data = df ) custom_simple_results_df = custom_simple_results . to_dataframe () display ( custom_simple_results_df ) from cluster_experiments.experiment_analysis import ClusteredOLSAnalysis # assuming we define a meaningful custom ExperimentAnalysis class class CustomExperimentAnalysis(ClusteredOLSAnalysis): def __init__(self, **kwargs): super().__init__(**kwargs) custom_simple_analysis_plan = AnalysisPlan.from_metrics( metrics=[metric__order_value], variants=variants, variant_col='experiment_group', alpha=0.01, dimensions=[dimension__city_code], analysis_type=\"custom_clustered_ols\", analysis_config={\"cluster_cols\":[\"customer_id\"]}, custom_analysis_type_mapper={\"custom_clustered_ols\": CustomExperimentAnalysis} ) custom_simple_results = custom_simple_analysis_plan.analyze(exp_data=df) custom_simple_results_df = custom_simple_results.to_dataframe() display(custom_simple_results_df) .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } metric_alias control_variant_name treatment_variant_name control_variant_mean treatment_variant_mean analysis_type ate ate_ci_lower ate_ci_upper p_value std_error dimension_name dimension_value alpha 0 AOV control treatment_1 9.987651 10.027835 custom_clustered_ols 0.040185 -0.081815 0.162184 0.396195 0.047363 __total_dimension total 0.01 1 AOV control treatment_1 10.037250 9.999080 custom_clustered_ols -0.038170 -0.211476 0.135135 0.570493 0.067281 order_city_code 1 0.01 2 AOV control treatment_1 9.936038 10.056783 custom_clustered_ols 0.120746 -0.059459 0.300951 0.084361 0.069960 order_city_code 2 0.01 3 AOV control treatment_2 9.987651 10.048016 custom_clustered_ols 0.060365 -0.061972 0.182701 0.203729 0.047494 __total_dimension total 0.01 4 AOV control treatment_2 10.037250 10.026263 custom_clustered_ols -0.010987 -0.187316 0.165342 0.872492 0.068455 order_city_code 1 0.01 5 AOV control treatment_2 9.936038 10.070004 custom_clustered_ols 0.133967 -0.039486 0.307419 0.046652 0.067339 order_city_code 2 0.01 Now it's your turn! Have fun experimenting with the library and analyzing your data!","title":"Bonus: Plugging in a custom analysis method"},{"location":"multivariate.html","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); }(this, function () { 'use strict'; function createNode(text) { const node = document.createElement('pre'); node.style.width = '1px'; node.style.height = '1px'; node.style.position = 'fixed'; node.style.top = '5px'; node.textContent = text; return node; } function copyNode(node) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(node.textContent); } const selection = getSelection(); if (selection == null) { return Promise.reject(new Error()); } selection.removeAllRanges(); 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These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: 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#7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); This notebook shows how to use the multivariate module. The idea is to use several treatments in the splitter and only one of them is used to run the hypothesis test. In [1]: Copied! import numpy as np import pandas as pd from cluster_experiments import PowerAnalysis import numpy as np import pandas as pd from cluster_experiments import PowerAnalysis In [2]: Copied! # Create fake data N = 1_000 df = pd . DataFrame ( { \"target\" : np . random . normal ( 0 , 1 , size = N ), } ) # Create fake data N = 1_000 df = pd.DataFrame( { \"target\": np.random.normal(0, 1, size=N), } ) In [3]: Copied! # Run power analysis using 3 variants config_abc = { \"analysis\" : \"ols_non_clustered\" , \"perturbator\" : \"constant\" , \"splitter\" : \"non_clustered\" , \"treatments\" : [ \"A\" , \"B\" , \"C\" ], \"control\" : \"A\" , \"treatment\" : \"B\" , \"n_simulations\" : 50 , } power_abc = PowerAnalysis . from_dict ( config_abc ) power_abc . power_analysis ( df , average_effect = 0.1 ) # Run power analysis using 3 variants config_abc = { \"analysis\": \"ols_non_clustered\", \"perturbator\": \"constant\", \"splitter\": \"non_clustered\", \"treatments\": [\"A\", \"B\", \"C\"], \"control\": \"A\", \"treatment\": \"B\", \"n_simulations\": 50, } power_abc = PowerAnalysis.from_dict(config_abc) power_abc.power_analysis(df, average_effect=0.1) Out[3]: 0.18 In [4]: Copied! # Run power analysis using 2 variants config_ab = { \"analysis\" : \"ols_non_clustered\" , \"perturbator\" : \"constant\" , \"splitter\" : \"non_clustered\" , \"treatments\" : [ \"A\" , \"B\" ], \"control\" : \"A\" , \"treatment\" : \"B\" , \"n_simulations\" : 50 , } power_ab = PowerAnalysis . from_dict ( config_ab ) power_ab . power_analysis ( df , average_effect = 0.1 ) # Run power analysis using 2 variants config_ab = { \"analysis\": \"ols_non_clustered\", \"perturbator\": \"constant\", \"splitter\": \"non_clustered\", \"treatments\": [\"A\", \"B\"], \"control\": \"A\", \"treatment\": \"B\", \"n_simulations\": 50, } power_ab = PowerAnalysis.from_dict(config_ab) power_ab.power_analysis(df, average_effect=0.1) Out[4]: 0.28 The power of the AB test is higher than the ABC test, which makes sense.","title":"Multiple treatments"},{"location":"normal_power.html","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); 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font-style: italic } /* Comment.Single */ .highlight-ipynb .cs { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Special */ .highlight-ipynb .kc { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Constant */ .highlight-ipynb .kd { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Declaration */ .highlight-ipynb .kn { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Namespace */ .highlight-ipynb .kp { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Pseudo */ .highlight-ipynb .kr { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Reserved */ .highlight-ipynb .kt { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Type */ .highlight-ipynb .m { color: var(--jp-mirror-editor-number-color) } /* Literal.Number */ .highlight-ipynb .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */ .highlight-ipynb .ow { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator.Word */ .highlight-ipynb .pm { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation.Marker */ .highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */ .highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */ .highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */ .highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */ .highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */ .highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */ .highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */ .highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */ .highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */ .highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */ .highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */ .highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */ .highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */ .highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */ .highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */ .highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */ .highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */ .highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */ .highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */ .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */ /* This file is taken from the built JupyterLab theme.css Found on share/nbconvert/templates/lab/static Some changes have been made and marked with CHANGE */ .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); This notebook shows how NormalPowerAnalysis and PowerAnalysis calculators give similar powers for a switchback experiment, and the normal one is way faster. In [1]: Copied! from datetime import date import numpy as np from cluster_experiments import PowerAnalysis , ConstantPerturbator , BalancedClusteredSplitter , ExperimentAnalysis , ClusteredOLSAnalysis , NormalPowerAnalysis import pandas as pd from time import time # Create fake data N = 10_000 clusters = [ f \"Cluster { i } \" for i in range ( 10 )] dates = [ f \" { date ( 2022 , 1 , i ) : %Y-%m-%d } \" for i in range ( 1 , 15 )] df = pd . DataFrame ( { \"cluster\" : np . random . choice ( clusters , size = N ), \"date\" : np . random . choice ( dates , size = N ), } ) . assign ( # Target is a linear combination of cluster and day of week, plus some noise cluster_id = lambda df : df [ \"cluster\" ] . astype ( \"category\" ) . cat . codes , day_of_week = lambda df : pd . to_datetime ( df [ \"date\" ]) . dt . dayofweek , target = lambda df : df [ \"cluster_id\" ] + df [ \"day_of_week\" ] + np . random . normal ( size = N ), ) from datetime import date import numpy as np from cluster_experiments import PowerAnalysis, ConstantPerturbator, BalancedClusteredSplitter, ExperimentAnalysis, ClusteredOLSAnalysis, NormalPowerAnalysis import pandas as pd from time import time # Create fake data N = 10_000 clusters = [f\"Cluster {i}\" for i in range(10)] dates = [f\"{date(2022, 1, i):%Y-%m-%d}\" for i in range(1, 15)] df = pd.DataFrame( { \"cluster\": np.random.choice(clusters, size=N), \"date\": np.random.choice(dates, size=N), } ).assign( # Target is a linear combination of cluster and day of week, plus some noise cluster_id=lambda df: df[\"cluster\"].astype(\"category\").cat.codes, day_of_week=lambda df: pd.to_datetime(df[\"date\"]).dt.dayofweek, target=lambda df: df[\"cluster_id\"] + df[\"day_of_week\"] + np.random.normal(size=N), ) In [2]: Copied! df . head () df.head() Out[2]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } cluster date cluster_id day_of_week target 0 Cluster 3 2022-01-12 3 2 5.206403 1 Cluster 3 2022-01-14 3 4 7.004311 2 Cluster 9 2022-01-06 9 3 11.554722 3 Cluster 1 2022-01-10 1 0 2.697168 4 Cluster 8 2022-01-14 8 4 10.311295 Some clusters have a higher average outcome than others In [3]: Copied! cluster_cols = [ \"cluster\" , \"date\" ] splitter = BalancedClusteredSplitter ( cluster_cols = cluster_cols , ) perturbator = ConstantPerturbator () analysis = ClusteredOLSAnalysis ( cluster_cols = cluster_cols , ) alpha = 0.05 n_simulations = 100 n_simulations_normal = 10 # Simulated power analysis, we use clustered splitter and ols clustered analysis pw_simulated = PowerAnalysis ( splitter = splitter , perturbator = perturbator , alpha = alpha , n_simulations = n_simulations , analysis = analysis , ) # Normal power analysis, uses Central limit theorem to estimate power, and needs less simulations pw_normal = NormalPowerAnalysis ( splitter = splitter , alpha = alpha , n_simulations = n_simulations_normal , analysis = analysis , ) cluster_cols = [\"cluster\", \"date\"] splitter = BalancedClusteredSplitter( cluster_cols=cluster_cols, ) perturbator = ConstantPerturbator() analysis = ClusteredOLSAnalysis( cluster_cols=cluster_cols, ) alpha = 0.05 n_simulations = 100 n_simulations_normal = 10 # Simulated power analysis, we use clustered splitter and ols clustered analysis pw_simulated = PowerAnalysis( splitter=splitter, perturbator=perturbator, alpha=alpha, n_simulations=n_simulations, analysis=analysis, ) # Normal power analysis, uses Central limit theorem to estimate power, and needs less simulations pw_normal = NormalPowerAnalysis( splitter=splitter, alpha=alpha, n_simulations=n_simulations_normal, analysis=analysis, ) In [4]: Copied! # power line for simulated effects = [ 0.25 , 0.5 , 0.75 , 1 , 1.25 , 1.5 , 1.75 ] time_pw = time () pw_simulated_line = pw_simulated . power_line ( df , average_effects = effects ) print ( f \"Simulated power line took { time () - time_pw } seconds\" ) # power line for simulated effects = [0.25, 0.5, 0.75, 1, 1.25, 1.5, 1.75] time_pw = time() pw_simulated_line = pw_simulated.power_line(df, average_effects=effects) print(f\"Simulated power line took {time() - time_pw} seconds\") Simulated power line took 35.480019092559814 seconds In [5]: Copied! # power line for normal time_pw = time () pw_normal_line = pw_normal . power_line ( df , average_effects = effects ) print ( f \"Time for normal power line: { time () - time_pw } seconds\" ) # power line for normal time_pw = time() pw_normal_line = pw_normal.power_line(df, average_effects=effects) print(f\"Time for normal power line: {time() - time_pw} seconds\") Time for normal power line: 0.5191936492919922 seconds In [6]: Copied! # power line for normal, single simulation time_pw = time () pw_normal . n_simulations = 1 pw_normal_line_single = pw_normal . power_line ( df , average_effects = effects ) print ( f \"Time for normal power line single simulation: { time () - time_pw } seconds\" ) # power line for normal, single simulation time_pw = time() pw_normal.n_simulations = 1 pw_normal_line_single = pw_normal.power_line(df, average_effects=effects) print(f\"Time for normal power line single simulation: {time() - time_pw} seconds\") Time for normal power line single simulation: 0.06762981414794922 seconds In [7]: Copied! pd . DataFrame ( { \"Average effect\" : effects , \"Simulated power\" : pw_simulated_line . values (), \"Normal power\" : pw_normal_line . values (), \"Normal power single simulation\" : pw_normal_line_single . values (), } ) . plot ( x = \"Average effect\" , y = [ \"Simulated power\" , \"Normal power\" , \"Normal power single simulation\" ], title = \"Power analysis\" , xlabel = \"Average effect\" , ylabel = \"Power\" , marker = \"o\" , ) pd.DataFrame( { \"Average effect\": effects, \"Simulated power\": pw_simulated_line.values(), \"Normal power\": pw_normal_line.values(), \"Normal power single simulation\": pw_normal_line_single.values(), } ).plot( x=\"Average effect\", y=[\"Simulated power\", \"Normal power\", \"Normal power single simulation\"], title=\"Power analysis\", xlabel=\"Average effect\", ylabel=\"Power\", marker=\"o\", ) Out[7]: In [8]: Copied! # pw_normal can also be used to find MDE mde = pw_normal . mde ( df , power = 0.7 ) print ( f \"Minimum detectable effect: { mde : .2f } \" ) # and we can retrieve the power for a given effect size power = pw_normal . power_analysis ( df , average_effect = mde ) print ( f \"Power for MDE: { power : .2f } , should be 0.7\" ) # pw_normal can also be used to find MDE mde = pw_normal.mde(df, power=0.7) print(f\"Minimum detectable effect: {mde:.2f}\") # and we can retrieve the power for a given effect size power = pw_normal.power_analysis(df, average_effect=mde) print(f\"Power for MDE: {power:.2f}, should be 0.7\") Minimum detectable effect: 1.44 Power for MDE: 0.70, should be 0.7","title":"Compare with simulation"},{"location":"normal_power_lines.html","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); 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In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Uses power and mde lines functionalities to showcase how to plot MDE and power as function of the sample size, given, respectively, the power and the MDE. In [1]: Copied! from datetime import date import numpy as np from cluster_experiments import NormalPowerAnalysis import pandas as pd import plotnine as p9 # Create fake data N = 10_000 clusters = [ f \"Cluster { i } \" for i in range ( 10 )] dates = [ f \" { date ( 2022 , 1 , i ) : %Y-%m-%d } \" for i in range ( 1 , 15 )] df = pd . DataFrame ( { \"cluster\" : np . random . choice ( clusters , size = N ), \"date\" : np . random . choice ( dates , size = N ), } ) . assign ( # Target is a linear combination of cluster and day of week, plus some noise cluster_id = lambda df : df [ \"cluster\" ] . astype ( \"category\" ) . cat . codes , day_of_week = lambda df : pd . to_datetime ( df [ \"date\" ]) . dt . dayofweek , target = lambda df : df [ \"cluster_id\" ] + df [ \"day_of_week\" ] + np . random . normal ( size = N ), date = lambda df : pd . to_datetime ( df [ \"date\" ]), ) from datetime import date import numpy as np from cluster_experiments import NormalPowerAnalysis import pandas as pd import plotnine as p9 # Create fake data N = 10_000 clusters = [f\"Cluster {i}\" for i in range(10)] dates = [f\"{date(2022, 1, i):%Y-%m-%d}\" for i in range(1, 15)] df = pd.DataFrame( { \"cluster\": np.random.choice(clusters, size=N), \"date\": np.random.choice(dates, size=N), } ).assign( # Target is a linear combination of cluster and day of week, plus some noise cluster_id=lambda df: df[\"cluster\"].astype(\"category\").cat.codes, day_of_week=lambda df: pd.to_datetime(df[\"date\"]).dt.dayofweek, target=lambda df: df[\"cluster_id\"] + df[\"day_of_week\"] + np.random.normal(size=N), date=lambda df: pd.to_datetime(df[\"date\"]), ) In [2]: Copied! # Set-up power analysis for switchback experiment pw_normal = NormalPowerAnalysis . from_dict ( { \"splitter\" : \"clustered\" , \"analysis\" : \"clustered_ols\" , \"cluster_cols\" : [ \"cluster\" , \"date\" ], \"n_simulations\" : 5 , \"hypothesis\" : \"two-sided\" , \"seed\" : 20240922 , \"time_col\" : \"date\" , } ) # Set-up power analysis for switchback experiment pw_normal = NormalPowerAnalysis.from_dict( { \"splitter\": \"clustered\", \"analysis\": \"clustered_ols\", \"cluster_cols\": [\"cluster\", \"date\"], \"n_simulations\": 5, \"hypothesis\": \"two-sided\", \"seed\": 20240922, \"time_col\": \"date\", } ) In [3]: Copied! %% time # compute power line for different lengths different average effects power_line = pw_normal . power_time_line ( df , experiment_length = range ( 1 , 14 ), average_effects = range ( 5 ) ) %%time # compute power line for different lengths different average effects power_line = pw_normal.power_time_line( df, experiment_length=range(1, 14), average_effects=range(5) ) CPU times: user 2.06 s, sys: 51.3 ms, total: 2.11 s Wall time: 2.13 s In [4]: Copied! # plot line p9 . ggplot ( pd . DataFrame ( power_line ), p9 . aes ( x = \"experiment_length\" , y = \"power\" , color = \"effect\" , group = \"effect\" ), ) + p9 . geom_line () + p9 . geom_point () + p9 . theme_minimal () + p9 . labs ( x = \"Experiment Length\" , y = \"Power\" ) + p9 . ggtitle ( \"Power lines\" ) # plot line p9.ggplot( pd.DataFrame(power_line), p9.aes(x=\"experiment_length\", y=\"power\", color=\"effect\", group=\"effect\"), ) + p9.geom_line() + p9.geom_point() + p9.theme_minimal() + p9.labs( x=\"Experiment Length\", y=\"Power\" ) + p9.ggtitle(\"Power lines\") Out[4]: In [5]: Copied! %% time # compute mde line for different lengths and different powers mde_line = pw_normal . mde_time_line ( df , experiment_length = range ( 1 , 14 ), powers = [ 0.7 , 0.8 , 0.9 ] ) %%time # compute mde line for different lengths and different powers mde_line = pw_normal.mde_time_line( df, experiment_length=range(1, 14), powers=[0.7, 0.8, 0.9] ) CPU times: user 1.97 s, sys: 37.7 ms, total: 2 s Wall time: 2.1 s In [6]: Copied! # plot line p9 . ggplot ( pd . DataFrame ( mde_line ), p9 . aes ( x = \"experiment_length\" , y = \"mde\" , color = \"power\" , group = \"power\" ), ) + p9 . geom_line () + p9 . geom_point () + p9 . theme_minimal () + p9 . labs ( x = \"Experiment Length\" , y = \"Minimum Detectable Effect\" ) + p9 . ggtitle ( \"MDE lines\" ) # plot line p9.ggplot( pd.DataFrame(mde_line), p9.aes(x=\"experiment_length\", y=\"mde\", color=\"power\", group=\"power\"), ) + p9.geom_line() + p9.geom_point() + p9.theme_minimal() + p9.labs( x=\"Experiment Length\", y=\"Minimum Detectable Effect\" ) + p9.ggtitle(\"MDE lines\") Out[6]: ","title":"Time-lines"},{"location":"paired_ttest.html","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); }(this, function () { 'use strict'; function createNode(text) { const node = document.createElement('pre'); node.style.width = '1px'; node.style.height = '1px'; node.style.position = 'fixed'; node.style.top = '5px'; node.textContent = text; return node; } function copyNode(node) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(node.textContent); } const selection = getSelection(); if (selection == null) { return Promise.reject(new Error()); } selection.removeAllRanges(); const range = document.createRange(); range.selectNodeContents(node); selection.addRange(range); document.execCommand('copy'); selection.removeAllRanges(); return Promise.resolve(); } function copyText(text) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(text); } const body = document.body; if (!body) { return Promise.reject(new Error()); } const node = createNode(text); 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} } function keydown(event) { if (event.key === ' ' || event.key === 'Enter') { const button = event.currentTarget; if (button instanceof HTMLElement) { event.preventDefault(); copy(button); } } } function focused(event) { event.currentTarget.addEventListener('keydown', keydown); } function blurred(event) { event.currentTarget.removeEventListener('keydown', keydown); } class ClipboardCopyElement extends HTMLElement { constructor() { super(); this.addEventListener('click', clicked); this.addEventListener('focus', focused); this.addEventListener('blur', blurred); } connectedCallback() { if (!this.hasAttribute('tabindex')) { this.setAttribute('tabindex', '0'); } if (!this.hasAttribute('role')) { this.setAttribute('role', 'button'); } } get value() { return this.getAttribute('value') || ''; } set value(text) { this.setAttribute('value', text); } } if (!window.customElements.get('clipboard-copy')) { window.ClipboardCopyElement = ClipboardCopyElement; window.customElements.define('clipboard-copy', ClipboardCopyElement); } return ClipboardCopyElement; })); document.addEventListener('clipboard-copy', function(event) { const notice = event.target.querySelector('.notice') notice.hidden = false setTimeout(function() { notice.hidden = true }, 1000) }) pre { line-height: 125%; } td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; } span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; } td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } .highlight-ipynb .hll { background-color: var(--jp-cell-editor-active-background) } .highlight-ipynb { background: var(--jp-cell-editor-background); color: var(--jp-mirror-editor-variable-color) } .highlight-ipynb .c { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment */ .highlight-ipynb .err { color: var(--jp-mirror-editor-error-color) } /* Error */ .highlight-ipynb .k { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword */ .highlight-ipynb .o { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator */ .highlight-ipynb .p { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation */ .highlight-ipynb .ch { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Hashbang */ .highlight-ipynb .cm { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Multiline */ .highlight-ipynb .cp { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Preproc */ .highlight-ipynb .cpf { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.PreprocFile */ .highlight-ipynb .c1 { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Single */ .highlight-ipynb .cs { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Special */ .highlight-ipynb .kc { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Constant */ .highlight-ipynb .kd { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Declaration */ .highlight-ipynb .kn { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Namespace */ .highlight-ipynb .kp { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Pseudo */ .highlight-ipynb .kr { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Reserved */ .highlight-ipynb .kt { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Type */ .highlight-ipynb .m { color: var(--jp-mirror-editor-number-color) } /* Literal.Number */ .highlight-ipynb .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */ .highlight-ipynb .ow { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator.Word */ .highlight-ipynb .pm { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation.Marker */ .highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */ .highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */ .highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */ .highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */ .highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */ .highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */ .highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */ .highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */ .highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */ .highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */ .highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */ .highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */ .highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */ .highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */ .highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */ .highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */ .highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */ .highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */ .highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */ .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */ /* This file is taken from the built JupyterLab theme.css Found on share/nbconvert/templates/lab/static Some changes have been made and marked with CHANGE */ .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); This notebook shows how the PairedTTestClusteredAnalysis class is performing the paired t test. It's important to get a grasp on the difference between cluster and strata columns. In [1]: Copied! from cluster_experiments.experiment_analysis import PairedTTestClusteredAnalysis import pandas as pd from cluster_experiments.experiment_analysis import PairedTTestClusteredAnalysis import pandas as pd In [2]: Copied! # Let's generate some fake switchback data (the clusters here would be city and date df = pd . DataFrame ( { \"country_code\" : [ \"ES\" ] * 4 + [ \"IT\" ] * 4 + [ \"PL\" ] * 4 + [ \"RO\" ] * 4 , \"date\" : [ \"2022-01-01\" , \"2022-01-02\" , \"2022-01-03\" , \"2022-01-04\" ] * 4 , \"treatment\" : [ \"A\" , 'B' ] * 8 , \"target\" : [ 0.01 ] * 15 + [ 0.1 ], } ) # Let's generate some fake switchback data (the clusters here would be city and date df = pd.DataFrame( { \"country_code\": [\"ES\"] * 4 + [\"IT\"] * 4 + [\"PL\"] * 4 + [\"RO\"] * 4, \"date\": [\"2022-01-01\", \"2022-01-02\", \"2022-01-03\", \"2022-01-04\"] * 4, \"treatment\": [\"A\", 'B'] * 8, \"target\": [0.01] * 15 + [0.1], } ) Let's see what the PairedTTestClusteredAnalysis class is doing under the hood. As I am passing already the treatment column, there's no need for splitter nor perturbator In [3]: Copied! analysis = PairedTTestClusteredAnalysis ( cluster_cols = [ \"country_code\" , \"date\" ], strata_cols = [ 'country_code' ] ) analysis . _preprocessing ( df , verbose = True ) analysis = PairedTTestClusteredAnalysis( cluster_cols=[\"country_code\", \"date\"], strata_cols = ['country_code'] ) analysis._preprocessing(df, verbose=True) performing paired t test in this data treatment A B country_code ES 0.01 0.010 IT 0.01 0.010 PL 0.01 0.010 RO 0.01 0.055 Out[3]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } treatment A B country_code ES 0.01 0.010 IT 0.01 0.010 PL 0.01 0.010 RO 0.01 0.055 Keep in mind that strata_cols needs to be a subset of cluster_cols and it will be used as the index for pivoting. In [4]: Copied! analysis . analysis_pvalue ( df , verbose = True ) analysis.analysis_pvalue(df, verbose=True) paired t test results: TtestResult(statistic=-1.0, pvalue=0.39100221895577053, df=3) Out[4]: 0.39100221895577053 In [4]: Copied!","title":"Paired T test"},{"location":"plot_calendars.html","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); }(this, function () { 'use strict'; function createNode(text) { const node = document.createElement('pre'); node.style.width = '1px'; node.style.height = '1px'; node.style.position = 'fixed'; node.style.top = '5px'; node.textContent = text; return node; } function copyNode(node) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(node.textContent); 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font-style: italic } /* Comment.Hashbang */ .highlight-ipynb .cm { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Multiline */ .highlight-ipynb .cp { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Preproc */ .highlight-ipynb .cpf { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.PreprocFile */ .highlight-ipynb .c1 { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Single */ .highlight-ipynb .cs { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Special */ .highlight-ipynb .kc { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Constant */ .highlight-ipynb .kd { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Declaration */ .highlight-ipynb .kn { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Namespace */ .highlight-ipynb .kp { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Pseudo */ .highlight-ipynb .kr { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Reserved */ .highlight-ipynb .kt { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Type */ .highlight-ipynb .m { color: var(--jp-mirror-editor-number-color) } /* Literal.Number */ .highlight-ipynb .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */ .highlight-ipynb .ow { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator.Word */ .highlight-ipynb .pm { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation.Marker */ .highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */ .highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */ .highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */ .highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */ .highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */ .highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */ .highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */ .highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */ .highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */ .highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */ .highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */ .highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */ .highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */ .highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */ .highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */ .highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */ .highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */ .highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */ .highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */ .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */ /* This file is taken from the built JupyterLab theme.css Found on share/nbconvert/templates/lab/static Some changes have been made and marked with CHANGE */ .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); This notebook shows the visualizations of different calendars that can be generated with switchback splitters In [1]: Copied! from cluster_experiments import PowerAnalysis import pandas as pd import numpy as np from plotnine import ggplot , geom_tile , element_text , element_blank , theme , labs , facet_wrap , aes , theme_set , theme_minimal # Remove warnings import warnings warnings . filterwarnings ( 'ignore' ) np . random . seed ( 42 ) theme_set ( theme_minimal ()) from cluster_experiments import PowerAnalysis import pandas as pd import numpy as np from plotnine import ggplot, geom_tile, element_text, element_blank, theme, labs, facet_wrap, aes, theme_set, theme_minimal # Remove warnings import warnings warnings.filterwarnings('ignore') np.random.seed(42) theme_set(theme_minimal()) In [2]: Copied! def plot_calendar ( splitter , df , calendar_name ): treatments = splitter . assign_treatment_df ( df ) treatment_df = ( treatments . loc [:, [ \"city\" , \"treatment\" , \"time\" ]] . drop_duplicates () ) # Convert time column to datetime format and extract week and day treatment_df [ 'time' ] = pd . to_datetime ( treatment_df [ 'time' ]) treatment_df [ 'week' ] = treatment_df [ 'time' ] . dt . isocalendar () . week . astype ( str ) treatment_df [ 'day' ] = treatment_df [ 'time' ] . dt . dayofweek . astype ( str ) # day of week with format number + name treatment_df [ 'day_of_week' ] = treatment_df [ 'day' ] + ' - ' + treatment_df [ 'time' ] . dt . day_name () return ( ggplot ( treatment_df , aes ( x = 'day_of_week' , y = 'week' , fill = 'treatment' )) + geom_tile () + facet_wrap ( 'city' , nrow = 2 ) + labs ( title = f 'Switchback calendar in different cities, using { calendar_name } ' , x = 'Day' , y = 'Week' ) + theme ( axis_text_x = element_text ( angle = 90 , vjust = 1.0 ), axis_text_y = element_text ( size = 8 ), strip_background = element_blank (), strip_text = element_text ( size = 10 ))) def plot_calendar(splitter, df, calendar_name): treatments = splitter.assign_treatment_df(df) treatment_df = ( treatments .loc[:, [\"city\", \"treatment\", \"time\"]] .drop_duplicates() ) # Convert time column to datetime format and extract week and day treatment_df['time'] = pd.to_datetime(treatment_df['time']) treatment_df['week'] = treatment_df['time'].dt.isocalendar().week.astype(str) treatment_df['day'] = treatment_df['time'].dt.dayofweek.astype(str) # day of week with format number + name treatment_df['day_of_week'] = treatment_df['day'] + ' - ' + treatment_df['time'].dt.day_name() return (ggplot(treatment_df, aes(x='day_of_week', y='week', fill='treatment')) + geom_tile() + facet_wrap('city', nrow=2) + labs(title=f'Switchback calendar in different cities, using {calendar_name}', x='Day', y='Week') + theme(axis_text_x=element_text(angle=90, vjust=1.0), axis_text_y=element_text(size=8), strip_background=element_blank(), strip_text=element_text(size=10))) In [3]: Copied! # Define data with random dates df_raw = pd . DataFrame ( { \"time\" : pd . date_range ( \"2021-03-01\" , \"2021-03-15\" , freq = \"1min\" )[ np . random . randint ( 14 * 24 * 60 , size = 14 * 24 * 60 ) ], \"y\" : np . random . randn ( 14 * 24 * 60 ), } ) . assign ( day_of_week = lambda df : df . time . dt . dayofweek , hour_of_day = lambda df : df . time . dt . hour , week = lambda df : df . time . dt . isocalendar () . week , ) df = pd . concat ([ df_raw . assign ( city = city ) for city in ( \"TGN\" , \"NYC\" , \"LON\" , \"REU\" )]) # Define data with random dates df_raw = pd.DataFrame( { \"time\": pd.date_range(\"2021-03-01\", \"2021-03-15\", freq=\"1min\")[ np.random.randint(14 * 24 * 60, size=14 * 24 * 60) ], \"y\": np.random.randn(14 * 24 * 60), } ).assign( day_of_week=lambda df: df.time.dt.dayofweek, hour_of_day=lambda df: df.time.dt.hour, week=lambda df: df.time.dt.isocalendar().week, ) df = pd.concat([df_raw.assign(city=city) for city in (\"TGN\", \"NYC\", \"LON\", \"REU\")]) In [4]: Copied! # We have data of 2-weeks in 4 cities df . head () # We have data of 2-weeks in 4 cities df.head() Out[4]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } time y day_of_week hour_of_day week city 0 2021-03-11 23:15:00 0.827658 3 23 10 TGN 1 2021-03-01 14:20:00 0.843550 0 14 9 TGN 2 2021-03-04 17:50:00 0.306790 3 17 9 TGN 3 2021-03-09 07:24:00 -2.133119 1 7 10 TGN 4 2021-03-08 20:04:00 -1.026710 0 20 10 TGN In [5]: Copied! # Completely random calendar config = { \"time_col\" : \"time\" , \"switch_frequency\" : \"1d\" , \"perturbator\" : \"constant\" , \"analysis\" : \"ols_clustered\" , \"splitter\" : \"switchback\" , \"cluster_cols\" : [ \"time\" , \"city\" ], \"target_col\" : \"y\" , } power = PowerAnalysis . from_dict ( config ) plot_calendar ( power . splitter , df , \"random calendar\" ) # Completely random calendar config = { \"time_col\": \"time\", \"switch_frequency\": \"1d\", \"perturbator\": \"constant\", \"analysis\": \"ols_clustered\", \"splitter\": \"switchback\", \"cluster_cols\": [\"time\", \"city\"], \"target_col\": \"y\", } power = PowerAnalysis.from_dict(config) plot_calendar(power.splitter, df, \"random calendar\") Out[5]: In [6]: Copied! # Balanced calendar config = { \"time_col\" : \"time\" , \"switch_frequency\" : \"1d\" , \"perturbator\" : \"constant\" , \"analysis\" : \"ols_clustered\" , \"splitter\" : \"switchback_balance\" , \"cluster_cols\" : [ \"time\" , \"city\" ], \"target_col\" : \"y\" , } power = PowerAnalysis . from_dict ( config ) plot_calendar ( power . splitter , df , \"balanced Calendar\" ) # Balanced calendar config = { \"time_col\": \"time\", \"switch_frequency\": \"1d\", \"perturbator\": \"constant\", \"analysis\": \"ols_clustered\", \"splitter\": \"switchback_balance\", \"cluster_cols\": [\"time\", \"city\"], \"target_col\": \"y\", } power = PowerAnalysis.from_dict(config) plot_calendar(power.splitter, df, \"balanced Calendar\") Out[6]: In [7]: Copied! # Stratified - city level config = { \"time_col\" : \"time\" , \"switch_frequency\" : \"1d\" , \"perturbator\" : \"constant\" , \"analysis\" : \"ols_clustered\" , \"splitter\" : \"switchback_stratified\" , \"cluster_cols\" : [ \"time\" , \"city\" ], \"strata_cols\" : [ \"city\" ], \"target_col\" : \"y\" , } power = PowerAnalysis . from_dict ( config ) plot_calendar ( power . splitter , df , \"stratified calendar (city level)\" ) # Stratified - city level config = { \"time_col\": \"time\", \"switch_frequency\": \"1d\", \"perturbator\": \"constant\", \"analysis\": \"ols_clustered\", \"splitter\": \"switchback_stratified\", \"cluster_cols\": [\"time\", \"city\"], \"strata_cols\": [\"city\"], \"target_col\": \"y\", } power = PowerAnalysis.from_dict(config) plot_calendar(power.splitter, df, \"stratified calendar (city level)\") Out[7]: In [8]: Copied! # Stratified - city + week level config = { \"time_col\" : \"time\" , \"switch_frequency\" : \"1d\" , \"perturbator\" : \"constant\" , \"analysis\" : \"ols_clustered\" , \"splitter\" : \"switchback_stratified\" , \"cluster_cols\" : [ \"time\" , \"city\" ], \"strata_cols\" : [ \"week\" , \"city\" ], \"target_col\" : \"y\" , } power = PowerAnalysis . from_dict ( config ) plot_calendar ( power . splitter , df , \"stratified calendar (week + city level)\" ) # Stratified - city + week level config = { \"time_col\": \"time\", \"switch_frequency\": \"1d\", \"perturbator\": \"constant\", \"analysis\": \"ols_clustered\", \"splitter\": \"switchback_stratified\", \"cluster_cols\": [\"time\", \"city\"], \"strata_cols\": [\"week\", \"city\"], \"target_col\": \"y\", } power = PowerAnalysis.from_dict(config) plot_calendar(power.splitter, df, \"stratified calendar (week + city level)\") Out[8]: In [9]: Copied! # Stratified - city + day of week level config = { \"time_col\" : \"time\" , \"switch_frequency\" : \"1d\" , \"perturbator\" : \"constant\" , \"analysis\" : \"ols_clustered\" , \"splitter\" : \"switchback_stratified\" , \"cluster_cols\" : [ \"time\" , \"city\" ], \"strata_cols\" : [ \"day_of_week\" , \"city\" ], \"target_col\" : \"y\" , } power = PowerAnalysis . from_dict ( config ) plot_calendar ( power . splitter , df , \"stratified calendar (day of week + city level)\" ) # Stratified - city + day of week level config = { \"time_col\": \"time\", \"switch_frequency\": \"1d\", \"perturbator\": \"constant\", \"analysis\": \"ols_clustered\", \"splitter\": \"switchback_stratified\", \"cluster_cols\": [\"time\", \"city\"], \"strata_cols\": [\"day_of_week\", \"city\"], \"target_col\": \"y\", } power = PowerAnalysis.from_dict(config) plot_calendar(power.splitter, df, \"stratified calendar (day of week + city level)\") Out[9]: In [ ]: Copied!","title":"Switchback calendar visualization"},{"location":"plot_calendars_hours.html","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); 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In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); This notebook shows the visualizations of different calendars that can be generated with switchback splitters In [1]: Copied! from cluster_experiments import PowerAnalysis import pandas as pd import numpy as np import plotnine as pn # Remove warnings import warnings warnings . filterwarnings ( 'ignore' ) np . random . seed ( 42 ) pn . theme_set ( pn . theme_minimal ()) from cluster_experiments import PowerAnalysis import pandas as pd import numpy as np import plotnine as pn # Remove warnings import warnings warnings.filterwarnings('ignore') np.random.seed(42) pn.theme_set(pn.theme_minimal()) In [2]: Copied! def plot_calendar ( splitter , df , calendar_name ): treatments = splitter . assign_treatment_df ( df ) treatment_df = ( treatments . loc [:, [ \"city\" , \"treatment\" , \"time\" ]] . drop_duplicates () ) # Convert time column to datetime format and extract week and day treatment_df [ 'week_number' ] = treatment_df [ 'time' ] . dt . isocalendar () . week . astype ( int ) . astype ( str ) treatment_df [ 'week_number_num' ] = treatment_df [ 'time' ] . dt . isocalendar () . week # add time with the week set to the minimum week number treatment_df [ 'time_in_week' ] = treatment_df [ 'time' ] - pd . to_timedelta ( treatment_df [ 'week_number_num' ], unit = 'W' ) + pd . to_timedelta ( treatment_df [ 'week_number_num' ] . min (), unit = 'W' ) # Custom date formatter function def custom_date_formatter ( dates ): return [ date . strftime ( ' %a %H:%M' ) for date in dates ] # Create the plot plot = ( pn . ggplot ( treatment_df , pn . aes ( x = 'time_in_week' , y = 'week_number' , fill = 'treatment' )) + pn . geom_tile () + pn . theme_minimal () + pn . labs ( title = f \"Treatment assignment using { calendar_name } \" , x = \"Time since start of week\" , y = \"Week number\" , fill = \"Treatment\" ) + pn . facet_wrap ( '~city' ) + pn . scale_x_datetime ( labels = custom_date_formatter ) # vertical labels + pn . theme ( axis_text_x = pn . element_text ( angle = 90 , vjust = 1.0 )) ) # Display the plot return plot def plot_calendar(splitter, df, calendar_name): treatments = splitter.assign_treatment_df(df) treatment_df = ( treatments .loc[:, [\"city\", \"treatment\", \"time\"]] .drop_duplicates() ) # Convert time column to datetime format and extract week and day treatment_df['week_number'] = treatment_df['time'].dt.isocalendar().week.astype(int).astype(str) treatment_df['week_number_num'] = treatment_df['time'].dt.isocalendar().week # add time with the week set to the minimum week number treatment_df['time_in_week'] = treatment_df['time'] - pd.to_timedelta(treatment_df['week_number_num'], unit='W') + pd.to_timedelta(treatment_df['week_number_num'].min(), unit='W') # Custom date formatter function def custom_date_formatter(dates): return [date.strftime('%a %H:%M') for date in dates] # Create the plot plot = ( pn.ggplot(treatment_df, pn.aes(x='time_in_week', y='week_number', fill='treatment')) + pn.geom_tile() + pn.theme_minimal() + pn.labs( title=f\"Treatment assignment using {calendar_name}\", x=\"Time since start of week\", y=\"Week number\", fill=\"Treatment\" ) + pn.facet_wrap('~city') + pn.scale_x_datetime(labels=custom_date_formatter) # vertical labels + pn.theme(axis_text_x=pn.element_text(angle=90, vjust=1.0)) ) # Display the plot return plot In [3]: Copied! # Define data with random dates df_raw = pd . DataFrame ( { \"time\" : pd . date_range ( \"2021-03-01\" , \"2021-03-15\" , freq = \"1s\" )[ np . random . randint ( 14 * 24 * 60 * 60 , size = 14 * 24 * 60 * 60 ) ], \"y\" : np . random . randn ( 14 * 24 * 60 * 60 ), } ) . assign ( day_of_week = lambda df : df . time . dt . dayofweek , hour_of_day = lambda df : df . time . dt . hour , hour_4h = lambda df : df . time . dt . hour // 4 , week = lambda df : df . time . dt . isocalendar () . week , ) df = pd . concat ([ df_raw . assign ( city = city ) for city in ( \"TGN\" , \"NYC\" , \"LON\" , \"REU\" )]) # Define data with random dates df_raw = pd.DataFrame( { \"time\": pd.date_range(\"2021-03-01\", \"2021-03-15\", freq=\"1s\")[ np.random.randint(14 * 24 * 60 * 60, size=14 * 24 * 60 * 60) ], \"y\": np.random.randn(14 * 24 * 60 * 60), } ).assign( day_of_week=lambda df: df.time.dt.dayofweek, hour_of_day=lambda df: df.time.dt.hour, hour_4h=lambda df: df.time.dt.hour // 4, week=lambda df: df.time.dt.isocalendar().week, ) df = pd.concat([df_raw.assign(city=city) for city in (\"TGN\", \"NYC\", \"LON\", \"REU\")]) In [4]: Copied! # We have data of 2-weeks in 4 cities df . head () # We have data of 2-weeks in 4 cities df.head() Out[4]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } time y day_of_week hour_of_day hour_4h week city 0 2021-03-02 09:52:38 0.328784 1 9 2 9 TGN 1 2021-03-08 18:25:55 0.489899 0 18 4 10 TGN 2 2021-03-02 12:38:52 -0.559446 1 12 3 9 TGN 3 2021-03-03 23:59:38 1.644260 2 23 5 9 TGN 4 2021-03-02 06:37:48 -0.418535 1 6 1 9 TGN In [5]: Copied! # Completely random calendar from datetime import timedelta config = { \"time_col\" : \"time\" , \"switch_frequency\" : \"4h\" , \"perturbator\" : \"constant\" , \"analysis\" : \"ols_clustered\" , \"splitter\" : \"switchback\" , \"cluster_cols\" : [ \"time\" , \"city\" ], \"target_col\" : \"y\" , \"washover\" : \"constant_washover\" , \"washover_time_delta\" : timedelta ( hours = 2 ), } power = PowerAnalysis . from_dict ( config ) plot_calendar ( power . splitter , df , \"random calendar\" ) # Completely random calendar from datetime import timedelta config = { \"time_col\": \"time\", \"switch_frequency\": \"4h\", \"perturbator\": \"constant\", \"analysis\": \"ols_clustered\", \"splitter\": \"switchback\", \"cluster_cols\": [\"time\", \"city\"], \"target_col\": \"y\", \"washover\": \"constant_washover\", \"washover_time_delta\": timedelta(hours=2), } power = PowerAnalysis.from_dict(config) plot_calendar(power.splitter, df, \"random calendar\") Out[5]: In [6]: Copied! # Balanced calendar config = { \"time_col\" : \"time\" , \"switch_frequency\" : \"4h\" , \"perturbator\" : \"constant\" , \"analysis\" : \"ols_clustered\" , \"splitter\" : \"switchback_balance\" , \"cluster_cols\" : [ \"time\" , \"city\" ], \"target_col\" : \"y\" , } power = PowerAnalysis . from_dict ( config ) plot_calendar ( power . splitter , df , \"balanced Calendar\" ) # Balanced calendar config = { \"time_col\": \"time\", \"switch_frequency\": \"4h\", \"perturbator\": \"constant\", \"analysis\": \"ols_clustered\", \"splitter\": \"switchback_balance\", \"cluster_cols\": [\"time\", \"city\"], \"target_col\": \"y\", } power = PowerAnalysis.from_dict(config) plot_calendar(power.splitter, df, \"balanced Calendar\") Out[6]: In [7]: Copied! # Stratified - city level config = { \"time_col\" : \"time\" , \"switch_frequency\" : \"4h\" , \"perturbator\" : \"constant\" , \"analysis\" : \"ols_clustered\" , \"splitter\" : \"switchback_stratified\" , \"cluster_cols\" : [ \"time\" , \"city\" ], \"strata_cols\" : [ \"city\" ], \"target_col\" : \"y\" , } power = PowerAnalysis . from_dict ( config ) plot_calendar ( power . splitter , df , \"stratified calendar (city level)\" ) # Stratified - city level config = { \"time_col\": \"time\", \"switch_frequency\": \"4h\", \"perturbator\": \"constant\", \"analysis\": \"ols_clustered\", \"splitter\": \"switchback_stratified\", \"cluster_cols\": [\"time\", \"city\"], \"strata_cols\": [\"city\"], \"target_col\": \"y\", } power = PowerAnalysis.from_dict(config) plot_calendar(power.splitter, df, \"stratified calendar (city level)\") Out[7]: In [8]: Copied! # Stratified - city + week level config = { \"time_col\" : \"time\" , \"switch_frequency\" : \"4h\" , \"perturbator\" : \"constant\" , \"analysis\" : \"ols_clustered\" , \"splitter\" : \"switchback_stratified\" , \"cluster_cols\" : [ \"time\" , \"city\" ], \"strata_cols\" : [ \"week\" , \"city\" ], \"target_col\" : \"y\" , } power = PowerAnalysis . from_dict ( config ) plot_calendar ( power . splitter , df , \"stratified calendar (week + city level)\" ) # Stratified - city + week level config = { \"time_col\": \"time\", \"switch_frequency\": \"4h\", \"perturbator\": \"constant\", \"analysis\": \"ols_clustered\", \"splitter\": \"switchback_stratified\", \"cluster_cols\": [\"time\", \"city\"], \"strata_cols\": [\"week\", \"city\"], \"target_col\": \"y\", } power = PowerAnalysis.from_dict(config) plot_calendar(power.splitter, df, \"stratified calendar (week + city level)\") Out[8]: In [9]: Copied! # Stratified - city + day of week level config = { \"time_col\" : \"time\" , \"switch_frequency\" : \"4h\" , \"perturbator\" : \"constant\" , \"analysis\" : \"ols_clustered\" , \"splitter\" : \"switchback_stratified\" , \"cluster_cols\" : [ \"time\" , \"city\" ], \"strata_cols\" : [ \"day_of_week\" , \"city\" ], \"target_col\" : \"y\" , } power = PowerAnalysis . from_dict ( config ) plot_calendar ( power . splitter , df , \"stratified calendar (day of week + city level)\" ) # Stratified - city + day of week level config = { \"time_col\": \"time\", \"switch_frequency\": \"4h\", \"perturbator\": \"constant\", \"analysis\": \"ols_clustered\", \"splitter\": \"switchback_stratified\", \"cluster_cols\": [\"time\", \"city\"], \"strata_cols\": [\"day_of_week\", \"city\"], \"target_col\": \"y\", } power = PowerAnalysis.from_dict(config) plot_calendar(power.splitter, df, \"stratified calendar (day of week + city level)\") Out[9]: In [10]: Copied! # Stratified - city + day of week + hour of day level config = { \"time_col\" : \"time\" , \"switch_frequency\" : \"4h\" , \"perturbator\" : \"constant\" , \"analysis\" : \"ols_clustered\" , \"splitter\" : \"switchback_stratified\" , \"cluster_cols\" : [ \"time\" , \"city\" ], \"strata_cols\" : [ \"day_of_week\" , \"hour_4h\" , \"city\" ], \"target_col\" : \"y\" , } power = PowerAnalysis . from_dict ( config ) plot_calendar ( power . splitter , df , \"stratified calendar (day of week + hour of day + city level)\" ) # Stratified - city + day of week + hour of day level config = { \"time_col\": \"time\", \"switch_frequency\": \"4h\", \"perturbator\": \"constant\", \"analysis\": \"ols_clustered\", \"splitter\": \"switchback_stratified\", \"cluster_cols\": [\"time\", \"city\"], \"strata_cols\": [\"day_of_week\", \"hour_4h\", \"city\"], \"target_col\": \"y\", } power = PowerAnalysis.from_dict(config) plot_calendar(power.splitter, df, \"stratified calendar (day of week + hour of day + city level)\") Out[10]: ","title":"Visualization - 4-hour switches"},{"location":"switchback.html","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); }(this, function () { 'use strict'; function createNode(text) { const node = document.createElement('pre'); node.style.width = '1px'; node.style.height = '1px'; node.style.position = 'fixed'; node.style.top = '5px'; node.textContent = text; return node; } function copyNode(node) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(node.textContent); } const 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In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: 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In particular, it shows how to create a PowerAnalysis object with a switchback splitter, using a time column and 30 min splits. It uses the splitter of the PowerAnalysis object to simulate the treatment assignment, and shows how the stratification of the clusters works. In the end, it also shows how to run the power analysis. In [1]: Copied! from cluster_experiments import PowerAnalysis import pandas as pd import numpy as np np . random . seed ( 42 ) from cluster_experiments import PowerAnalysis import pandas as pd import numpy as np np.random.seed(42) In [2]: Copied! # Define bihourly switchback splitter config = { \"time_col\" : \"time\" , \"switch_frequency\" : \"30min\" , \"perturbator\" : \"constant\" , \"analysis\" : \"ols_clustered\" , \"splitter\" : \"switchback_stratified\" , \"cluster_cols\" : [ \"time\" , \"city\" ], \"strata_cols\" : [ \"day_of_week\" , \"hour_of_day\" , \"city\" ], \"target_col\" : \"y\" , } power = PowerAnalysis . from_dict ( config ) # Define bihourly switchback splitter config = { \"time_col\": \"time\", \"switch_frequency\": \"30min\", \"perturbator\": \"constant\", \"analysis\": \"ols_clustered\", \"splitter\": \"switchback_stratified\", \"cluster_cols\": [\"time\", \"city\"], \"strata_cols\": [\"day_of_week\", \"hour_of_day\", \"city\"], \"target_col\": \"y\", } power = PowerAnalysis.from_dict(config) In [3]: Copied! # Define data with random dates df_raw = pd . DataFrame ( { # Generate 10k random timestamps from 2021-01-01 to 2021-01-10 \"time\" : pd . date_range ( \"2021-01-01\" , \"2021-01-08\" , freq = \"1min\" )[ np . random . randint ( 7 * 24 * 60 , size = 7 * 24 * 60 ) ], \"y\" : np . random . randn ( 7 * 24 * 60 ), } ) . assign ( day_of_week = lambda df : df . time . dt . dayofweek , hour_of_day = lambda df : df . time . dt . hour ) df = pd . concat ([ df_raw . assign ( city = city ) for city in ( \"TGN\" , \"NYC\" , \"LON\" , \"REU\" )]) # Define data with random dates df_raw = pd.DataFrame( { # Generate 10k random timestamps from 2021-01-01 to 2021-01-10 \"time\": pd.date_range(\"2021-01-01\", \"2021-01-08\", freq=\"1min\")[ np.random.randint(7 * 24 * 60, size=7 * 24 * 60) ], \"y\": np.random.randn(7 * 24 * 60), } ).assign( day_of_week=lambda df: df.time.dt.dayofweek, hour_of_day=lambda df: df.time.dt.hour ) df = pd.concat([df_raw.assign(city=city) for city in (\"TGN\", \"NYC\", \"LON\", \"REU\")]) In [4]: Copied! df . head ( 10 ) df.head(10) Out[4]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } time y day_of_week hour_of_day city 0 2021-01-06 01:10:00 -0.216104 2 1 TGN 1 2021-01-01 14:20:00 -1.016524 4 14 TGN 2 2021-01-04 17:50:00 -2.326362 0 17 TGN 3 2021-01-04 14:31:00 -0.358456 0 14 TGN 4 2021-01-04 23:34:00 -0.490571 0 23 TGN 5 2021-01-05 08:25:00 -0.149901 1 8 TGN 6 2021-01-01 07:46:00 -0.628898 4 7 TGN 7 2021-01-04 01:46:00 1.829330 0 1 TGN 8 2021-01-04 20:58:00 0.517337 0 20 TGN 9 2021-01-06 18:42:00 -0.499613 2 18 TGN In [5]: Copied! treatments = power . splitter . assign_treatment_df ( df ) treatments = power.splitter.assign_treatment_df(df) In [6]: Copied! # For every city, we have a balanced AB split ( treatments . loc [:, [ \"city\" , \"treatment\" , \"time\" ]] . drop_duplicates () . groupby ([ \"city\" , \"treatment\" ]) . size () . head ( 10 ) ) # For every city, we have a balanced AB split ( treatments .loc[:, [\"city\", \"treatment\", \"time\"]] .drop_duplicates() .groupby([\"city\", \"treatment\"]) .size() .head(10) ) Out[6]: city treatment LON A 168 B 168 NYC A 168 B 168 REU A 168 B 168 TGN A 168 B 168 dtype: int64 In [7]: Copied! # For every hour of day, we have a balanced AB split ( treatments . loc [:, [ \"city\" , \"treatment\" , \"time\" , \"hour_of_day\" ]] . drop_duplicates () . groupby ([ \"hour_of_day\" , \"treatment\" ]) . size () . head ( 10 ) ) # For every hour of day, we have a balanced AB split ( treatments .loc[:, [\"city\", \"treatment\", \"time\", \"hour_of_day\"]] .drop_duplicates() .groupby([\"hour_of_day\", \"treatment\"]) .size() .head(10) ) Out[7]: hour_of_day treatment 0 A 28 B 28 1 A 28 B 28 2 A 28 B 28 3 A 28 B 28 4 A 28 B 28 dtype: int64 In [8]: Copied! # For every day of week, we have a balanced AB split ( treatments . loc [:, [ \"city\" , \"treatment\" , \"time\" , \"day_of_week\" ]] . drop_duplicates () . groupby ([ \"day_of_week\" , \"treatment\" ]) . size () . head ( 10 ) ) # For every day of week, we have a balanced AB split ( treatments .loc[:, [\"city\", \"treatment\", \"time\", \"day_of_week\"]] .drop_duplicates() .groupby([\"day_of_week\", \"treatment\"]) .size() .head(10) ) Out[8]: day_of_week treatment 0 A 96 B 96 1 A 96 B 96 2 A 96 B 96 3 A 96 B 96 4 A 96 B 96 dtype: int64 In [9]: Copied! # In the first 30 minutes of the day, LON, NYC, REU, and TGN have a constant treatment treatments . query ( \"time < '2021-01-01 00:30:00'\" ) . groupby ([ \"city\" , \"treatment\" ]) . size () # In the first 30 minutes of the day, LON, NYC, REU, and TGN have a constant treatment treatments.query(\"time < '2021-01-01 00:30:00'\").groupby([\"city\", \"treatment\"]).size() Out[9]: city treatment LON B 36 NYC B 36 REU A 36 TGN B 36 dtype: int64 In [10]: Copied! # We can run power analysis power . power_analysis ( df , average_effect = 0.01 ) # We can run power analysis power.power_analysis(df, average_effect=0.01) Out[10]: 0.17","title":"Stratified switchback"},{"location":"synthetic_control.html","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); 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In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Synthetic Control \u00b6 This NB serves as a tutorial on how to run power analysis using synthetic control analysis. It considers that looking at some time frame and some clusters, one of the clusters will receive a treatment and the rest remain in control. This treatment cluster will receive some effect from the Perturbator. Then, we run a power analysis to understand how many times we can capture this perturbation. In the end we compare the results with Clustered OLS method. Generate data \u00b6 We will generate some random data with normal distribution of mean 10 and standard deviation of 10. In [9]: Copied! from itertools import product import numpy as np import pandas as pd from cluster_experiments.experiment_analysis import SyntheticControlAnalysis , ClusteredOLSAnalysis from cluster_experiments.perturbator import ConstantPerturbator from cluster_experiments.power_analysis import PowerAnalysisWithPreExperimentData , PowerAnalysis from cluster_experiments.random_splitter import FixedSizeClusteredSplitter import plotnine as p9 def generate_data ( N , start_date , end_date ): dates = pd . date_range ( start_date , end_date , freq = \"d\" ) users = [ f \"User { i } \" for i in range ( N )] combinations = list ( product ( users , dates )) target_values = np . random . normal ( 100 , 10 , size = len ( combinations )) df = pd . DataFrame ( combinations , columns = [ \"user\" , \"date\" ]) df [ \"target\" ] = target_values return df from itertools import product import numpy as np import pandas as pd from cluster_experiments.experiment_analysis import SyntheticControlAnalysis, ClusteredOLSAnalysis from cluster_experiments.perturbator import ConstantPerturbator from cluster_experiments.power_analysis import PowerAnalysisWithPreExperimentData, PowerAnalysis from cluster_experiments.random_splitter import FixedSizeClusteredSplitter import plotnine as p9 def generate_data(N, start_date, end_date): dates = pd.date_range(start_date, end_date, freq=\"d\") users = [f\"User {i}\" for i in range(N)] combinations = list(product(users, dates)) target_values = np.random.normal(100, 10, size=len(combinations)) df = pd.DataFrame(combinations, columns=[\"user\", \"date\"]) df[\"target\"] = target_values return df In [10]: Copied! df = generate_data ( 20 , \"2022-01-01\" , \"2022-01-30\" ) intervention_date = \"2022-01-15\" effects = [ 0 , 1 , 5 , 10 , 20 ] df = generate_data(20, \"2022-01-01\", \"2022-01-30\") intervention_date = \"2022-01-15\" effects = [0, 1, 5, 10, 20] The graph below shows the evolution of target for each user. When running the synthetic control analysis: Select randomly one user to be in treatment group. Use pre experiment data (in this case case from 1 to 15th of Jan) to find the best combination of weights Apply weights to donors and generate synthetic control user In [11]: Copied! ( p9 . ggplot ( df , p9 . aes ( x = \"date\" , y = \"target\" , color = 'user' )) + p9 . geom_line () + p9 . labs ( title = \"Data\" , x = \"Date\" , y = \"Target Value\" ) + p9 . theme ( axis_text_x = p9 . element_text ( angle = 60 ), figure_size = ( 12 , 6 ))) (p9.ggplot(df, p9.aes(x=\"date\", y=\"target\", color = 'user')) + p9.geom_line() + p9.labs(title=\"Data\", x=\"Date\", y=\"Target Value\") + p9.theme(axis_text_x=p9.element_text(angle=60), figure_size=(12, 6))) In [12]: Copied! df df Out[12]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } user date target 0 User 0 2022-01-01 99.884697 1 User 0 2022-01-02 107.471770 2 User 0 2022-01-03 86.269132 3 User 0 2022-01-04 116.092658 4 User 0 2022-01-05 100.131065 ... ... ... ... 595 User 19 2022-01-26 101.861088 596 User 19 2022-01-27 89.713442 597 User 19 2022-01-28 116.505479 598 User 19 2022-01-29 111.858909 599 User 19 2022-01-30 107.270857 600 rows \u00d7 3 columns Point Estimates \u00b6 In [13]: Copied! sw = FixedSizeClusteredSplitter ( n_treatment_clusters = 1 , cluster_cols = [ \"user\" ]) perturbator = ConstantPerturbator ( average_effect = 10 , ) analysis = SyntheticControlAnalysis ( cluster_cols = [ \"user\" ], time_col = \"date\" , intervention_date = intervention_date ) pw = PowerAnalysisWithPreExperimentData ( splitter = sw , analysis = analysis , n_simulations = 200 , perturbator = perturbator ) sw = FixedSizeClusteredSplitter(n_treatment_clusters=1, cluster_cols=[\"user\"]) perturbator = ConstantPerturbator( average_effect=10, ) analysis = SyntheticControlAnalysis( cluster_cols=[\"user\"], time_col=\"date\", intervention_date=intervention_date ) pw = PowerAnalysisWithPreExperimentData( splitter=sw, analysis=analysis, n_simulations=200, perturbator=perturbator ) In [14]: Copied! point_estimates = list ( pw . simulate_point_estimate ( df )) point_estimates = list(pw.simulate_point_estimate(df)) In [15]: Copied! mean_pe = pd . Series ( point_estimates ) . mean () ( p9 . ggplot ( pd . DataFrame ( point_estimates , columns = [ \"effect\" ]), p9 . aes ( x = \"effect\" )) + p9 . geom_histogram ( bins = 18 ) + p9 . scale_x_continuous ( limits = ( 0 , 15 )) + p9 . labs ( title = \"Point Estimate Distribution\" ) + p9 . geom_vline ( xintercept = 10 , color = \"red\" ) + p9 . geom_text ( x = 10 - 2 , y = 20 , label = \"True Effect\" , color = \"red\" , size = 8 ) + p9 . geom_vline ( xintercept = mean_pe , color = \"blue\" ) + p9 . geom_text ( x = mean_pe + 2 , y = 20 , label = \"Mean of point estimates\" , color = \"blue\" , size = 8 ) ) mean_pe = pd.Series(point_estimates).mean() (p9.ggplot(pd.DataFrame(point_estimates, columns=[\"effect\"]), p9.aes(x=\"effect\")) + p9.geom_histogram(bins=18) + p9.scale_x_continuous(limits=(0, 15)) + p9.labs(title = \"Point Estimate Distribution\") + p9.geom_vline(xintercept=10, color=\"red\") + p9.geom_text(x=10-2, y=20, label=\"True Effect\", color=\"red\", size = 8) + p9.geom_vline(xintercept=mean_pe, color=\"blue\") + p9.geom_text(x=mean_pe+2, y=20, label=\"Mean of point estimates\", color=\"blue\", size = 8) ) /home/asaas/PycharmProjects/cluster-experiments/.venv/lib/python3.10/site-packages/plotnine/layer.py:364: PlotnineWarning: geom_histogram : Removed 2 rows containing missing values. Power line and Comparison with OLS \u00b6 This power line takes a long time to run because we calculate p values using a permutation test. In [16]: Copied! p_synth = pw . power_line ( df , average_effects = effects ) p_synth = pw.power_line(df, average_effects=effects) In [17]: Copied! analysis = ClusteredOLSAnalysis ( cluster_cols = [ \"user\" ] ) pw = PowerAnalysis ( perturbator = perturbator , splitter = sw , analysis = analysis , n_simulations = 200 ) df = df . query ( f \"date > ' { intervention_date } '\" ) p_ols = pw . power_line ( df , average_effects = effects , n_jobs = 5 ) analysis = ClusteredOLSAnalysis( cluster_cols=[\"user\"] ) pw = PowerAnalysis( perturbator=perturbator, splitter=sw, analysis=analysis, n_simulations=200 ) df = df.query(f\"date > '{intervention_date}'\") p_ols = pw.power_line(df, average_effects=effects, n_jobs=5) In [18]: Copied! df_results = pd . DataFrame ({ 'effect' : list ( p_synth . keys ()), 'power' : list ( p_synth . values ())}) df_results = pd.DataFrame({'effect' :list(p_synth.keys()), 'power' : list(p_synth.values())}) In [19]: Copied! synth_df = pd . DataFrame ({ 'effect' : p_synth . keys (), 'method' : 'Synthetic' , 'power' : p_synth . values ()}) ols_df = pd . DataFrame ({ 'effect' : p_ols . keys (), 'method' : 'OLS' , 'power' : p_ols . values ()}) df_results = pd . concat ([ synth_df , ols_df ]) synth_df = pd.DataFrame({'effect' : p_synth.keys(), 'method': 'Synthetic', 'power': p_synth.values()}) ols_df = pd.DataFrame({'effect' : p_ols.keys(), 'method': 'OLS', 'power': p_ols.values()}) df_results = pd.concat([synth_df, ols_df]) In [20]: Copied! ( p9 . ggplot ( df_results , p9 . aes ( x = 'effect' , y = 'power' , color = 'method' )) + p9 . geom_line () + p9 . geom_point () + p9 . labs ( title = \"Comparison between Synthetic Control and OLS\" , x = \"Average effect size\" , y = \"Power\" )) ( p9.ggplot(df_results, p9.aes(x='effect', y='power', color='method')) + p9.geom_line() + p9.geom_point() + p9.labs( title=\"Comparison between Synthetic Control and OLS\", x=\"Average effect size\", y=\"Power\" )) At first sight, we might think that OLS has more power than synthetic control. But we can clearly see that the OLS doesn't behave as expected when using only 1 treatment cluster. This gets clear when we see the power at 0 effect, in which should be equal to alpha (5% in this case). Therefore, is more appropriate to use synthetic control.","title":"Synthetic Control"},{"location":"synthetic_control.html#synthetic-control","text":"This NB serves as a tutorial on how to run power analysis using synthetic control analysis. It considers that looking at some time frame and some clusters, one of the clusters will receive a treatment and the rest remain in control. This treatment cluster will receive some effect from the Perturbator. Then, we run a power analysis to understand how many times we can capture this perturbation. In the end we compare the results with Clustered OLS method.","title":"Synthetic Control"},{"location":"synthetic_control.html#generate-data","text":"We will generate some random data with normal distribution of mean 10 and standard deviation of 10. In [9]: Copied! from itertools import product import numpy as np import pandas as pd from cluster_experiments.experiment_analysis import SyntheticControlAnalysis , ClusteredOLSAnalysis from cluster_experiments.perturbator import ConstantPerturbator from cluster_experiments.power_analysis import PowerAnalysisWithPreExperimentData , PowerAnalysis from cluster_experiments.random_splitter import FixedSizeClusteredSplitter import plotnine as p9 def generate_data ( N , start_date , end_date ): dates = pd . date_range ( start_date , end_date , freq = \"d\" ) users = [ f \"User { i } \" for i in range ( N )] combinations = list ( product ( users , dates )) target_values = np . random . normal ( 100 , 10 , size = len ( combinations )) df = pd . DataFrame ( combinations , columns = [ \"user\" , \"date\" ]) df [ \"target\" ] = target_values return df from itertools import product import numpy as np import pandas as pd from cluster_experiments.experiment_analysis import SyntheticControlAnalysis, ClusteredOLSAnalysis from cluster_experiments.perturbator import ConstantPerturbator from cluster_experiments.power_analysis import PowerAnalysisWithPreExperimentData, PowerAnalysis from cluster_experiments.random_splitter import FixedSizeClusteredSplitter import plotnine as p9 def generate_data(N, start_date, end_date): dates = pd.date_range(start_date, end_date, freq=\"d\") users = [f\"User {i}\" for i in range(N)] combinations = list(product(users, dates)) target_values = np.random.normal(100, 10, size=len(combinations)) df = pd.DataFrame(combinations, columns=[\"user\", \"date\"]) df[\"target\"] = target_values return df In [10]: Copied! df = generate_data ( 20 , \"2022-01-01\" , \"2022-01-30\" ) intervention_date = \"2022-01-15\" effects = [ 0 , 1 , 5 , 10 , 20 ] df = generate_data(20, \"2022-01-01\", \"2022-01-30\") intervention_date = \"2022-01-15\" effects = [0, 1, 5, 10, 20] The graph below shows the evolution of target for each user. When running the synthetic control analysis: Select randomly one user to be in treatment group. Use pre experiment data (in this case case from 1 to 15th of Jan) to find the best combination of weights Apply weights to donors and generate synthetic control user In [11]: Copied! ( p9 . ggplot ( df , p9 . aes ( x = \"date\" , y = \"target\" , color = 'user' )) + p9 . geom_line () + p9 . labs ( title = \"Data\" , x = \"Date\" , y = \"Target Value\" ) + p9 . theme ( axis_text_x = p9 . element_text ( angle = 60 ), figure_size = ( 12 , 6 ))) (p9.ggplot(df, p9.aes(x=\"date\", y=\"target\", color = 'user')) + p9.geom_line() + p9.labs(title=\"Data\", x=\"Date\", y=\"Target Value\") + p9.theme(axis_text_x=p9.element_text(angle=60), figure_size=(12, 6))) In [12]: Copied! df df Out[12]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } user date target 0 User 0 2022-01-01 99.884697 1 User 0 2022-01-02 107.471770 2 User 0 2022-01-03 86.269132 3 User 0 2022-01-04 116.092658 4 User 0 2022-01-05 100.131065 ... ... ... ... 595 User 19 2022-01-26 101.861088 596 User 19 2022-01-27 89.713442 597 User 19 2022-01-28 116.505479 598 User 19 2022-01-29 111.858909 599 User 19 2022-01-30 107.270857 600 rows \u00d7 3 columns","title":"Generate data"},{"location":"synthetic_control.html#point-estimates","text":"In [13]: Copied! sw = FixedSizeClusteredSplitter ( n_treatment_clusters = 1 , cluster_cols = [ \"user\" ]) perturbator = ConstantPerturbator ( average_effect = 10 , ) analysis = SyntheticControlAnalysis ( cluster_cols = [ \"user\" ], time_col = \"date\" , intervention_date = intervention_date ) pw = PowerAnalysisWithPreExperimentData ( splitter = sw , analysis = analysis , n_simulations = 200 , perturbator = perturbator ) sw = FixedSizeClusteredSplitter(n_treatment_clusters=1, cluster_cols=[\"user\"]) perturbator = ConstantPerturbator( average_effect=10, ) analysis = SyntheticControlAnalysis( cluster_cols=[\"user\"], time_col=\"date\", intervention_date=intervention_date ) pw = PowerAnalysisWithPreExperimentData( splitter=sw, analysis=analysis, n_simulations=200, perturbator=perturbator ) In [14]: Copied! point_estimates = list ( pw . simulate_point_estimate ( df )) point_estimates = list(pw.simulate_point_estimate(df)) In [15]: Copied! mean_pe = pd . Series ( point_estimates ) . mean () ( p9 . ggplot ( pd . DataFrame ( point_estimates , columns = [ \"effect\" ]), p9 . aes ( x = \"effect\" )) + p9 . geom_histogram ( bins = 18 ) + p9 . scale_x_continuous ( limits = ( 0 , 15 )) + p9 . labs ( title = \"Point Estimate Distribution\" ) + p9 . geom_vline ( xintercept = 10 , color = \"red\" ) + p9 . geom_text ( x = 10 - 2 , y = 20 , label = \"True Effect\" , color = \"red\" , size = 8 ) + p9 . geom_vline ( xintercept = mean_pe , color = \"blue\" ) + p9 . geom_text ( x = mean_pe + 2 , y = 20 , label = \"Mean of point estimates\" , color = \"blue\" , size = 8 ) ) mean_pe = pd.Series(point_estimates).mean() (p9.ggplot(pd.DataFrame(point_estimates, columns=[\"effect\"]), p9.aes(x=\"effect\")) + p9.geom_histogram(bins=18) + p9.scale_x_continuous(limits=(0, 15)) + p9.labs(title = \"Point Estimate Distribution\") + p9.geom_vline(xintercept=10, color=\"red\") + p9.geom_text(x=10-2, y=20, label=\"True Effect\", color=\"red\", size = 8) + p9.geom_vline(xintercept=mean_pe, color=\"blue\") + p9.geom_text(x=mean_pe+2, y=20, label=\"Mean of point estimates\", color=\"blue\", size = 8) ) /home/asaas/PycharmProjects/cluster-experiments/.venv/lib/python3.10/site-packages/plotnine/layer.py:364: PlotnineWarning: geom_histogram : Removed 2 rows containing missing values.","title":"Point Estimates"},{"location":"synthetic_control.html#power-line-and-comparison-with-ols","text":"This power line takes a long time to run because we calculate p values using a permutation test. In [16]: Copied! p_synth = pw . power_line ( df , average_effects = effects ) p_synth = pw.power_line(df, average_effects=effects) In [17]: Copied! analysis = ClusteredOLSAnalysis ( cluster_cols = [ \"user\" ] ) pw = PowerAnalysis ( perturbator = perturbator , splitter = sw , analysis = analysis , n_simulations = 200 ) df = df . query ( f \"date > ' { intervention_date } '\" ) p_ols = pw . power_line ( df , average_effects = effects , n_jobs = 5 ) analysis = ClusteredOLSAnalysis( cluster_cols=[\"user\"] ) pw = PowerAnalysis( perturbator=perturbator, splitter=sw, analysis=analysis, n_simulations=200 ) df = df.query(f\"date > '{intervention_date}'\") p_ols = pw.power_line(df, average_effects=effects, n_jobs=5) In [18]: Copied! df_results = pd . DataFrame ({ 'effect' : list ( p_synth . keys ()), 'power' : list ( p_synth . values ())}) df_results = pd.DataFrame({'effect' :list(p_synth.keys()), 'power' : list(p_synth.values())}) In [19]: Copied! synth_df = pd . DataFrame ({ 'effect' : p_synth . keys (), 'method' : 'Synthetic' , 'power' : p_synth . values ()}) ols_df = pd . DataFrame ({ 'effect' : p_ols . keys (), 'method' : 'OLS' , 'power' : p_ols . values ()}) df_results = pd . concat ([ synth_df , ols_df ]) synth_df = pd.DataFrame({'effect' : p_synth.keys(), 'method': 'Synthetic', 'power': p_synth.values()}) ols_df = pd.DataFrame({'effect' : p_ols.keys(), 'method': 'OLS', 'power': p_ols.values()}) df_results = pd.concat([synth_df, ols_df]) In [20]: Copied! ( p9 . ggplot ( df_results , p9 . aes ( x = 'effect' , y = 'power' , color = 'method' )) + p9 . geom_line () + p9 . geom_point () + p9 . labs ( title = \"Comparison between Synthetic Control and OLS\" , x = \"Average effect size\" , y = \"Power\" )) ( p9.ggplot(df_results, p9.aes(x='effect', y='power', color='method')) + p9.geom_line() + p9.geom_point() + p9.labs( title=\"Comparison between Synthetic Control and OLS\", x=\"Average effect size\", y=\"Power\" )) At first sight, we might think that OLS has more power than synthetic control. But we can clearly see that the OLS doesn't behave as expected when using only 1 treatment cluster. This gets clear when we see the power at 0 effect, in which should be equal to alpha (5% in this case). 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When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: 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#7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); This notebook shows how to use the washover module. In particular, it shows how to apply a ConstantWashover object with a 30min time delta. In [1]: Copied! ## Import Libraries import numpy as np import pandas as pd from datetime import datetime , timedelta from cluster_experiments import ConstantWashover ## Import Libraries import numpy as np import pandas as pd from datetime import datetime, timedelta from cluster_experiments import ConstantWashover In [2]: Copied! ## Generate a dummy dataset of 4 time periods of 2 hours with 10 orders each np . random . seed ( 42 ) num_rows = 5 def random_timestamp ( start_time , end_time ): time_delta = end_time - start_time random_seconds = np . random . randint ( 0 , time_delta . total_seconds ()) return start_time + timedelta ( seconds = random_seconds ) def generate_data ( start_time , end_time , treatment ): data = { 'order_id' : np . random . randint ( 10 ** 9 , 10 ** 10 , size = num_rows ), 'city_code' : 'VAL' , 'activation_time_local' : [ random_timestamp ( start_time , end_time ) for _ in range ( num_rows )], 'bin_start_time_local' : start_time , 'treatment' : treatment } return pd . DataFrame ( data ) start_times = [ datetime ( 2024 , 1 , 22 , 9 , 0 ), datetime ( 2024 , 1 , 22 , 11 , 0 ), datetime ( 2024 , 1 , 22 , 13 , 0 ), datetime ( 2024 , 1 , 22 , 15 , 0 )] treatments = [ 'control' , 'variation' , 'variation' , 'control' ] dataframes = [ generate_data ( start , start + timedelta ( hours = 2 ), treatment ) for start , treatment in zip ( start_times , treatments )] df = pd . concat ( dataframes ) . sort_values ( by = 'activation_time_local' ) . reset_index ( drop = True ) df . head ( 20 ) ## Generate a dummy dataset of 4 time periods of 2 hours with 10 orders each np.random.seed(42) num_rows = 5 def random_timestamp(start_time, end_time): time_delta = end_time - start_time random_seconds = np.random.randint(0, time_delta.total_seconds()) return start_time + timedelta(seconds=random_seconds) def generate_data(start_time, end_time, treatment): data = { 'order_id': np.random.randint(10**9, 10**10, size=num_rows), 'city_code': 'VAL', 'activation_time_local': [random_timestamp(start_time, end_time) for _ in range(num_rows)], 'bin_start_time_local': start_time, 'treatment': treatment } return pd.DataFrame(data) start_times = [datetime(2024, 1, 22, 9, 0), datetime(2024, 1, 22, 11, 0), datetime(2024, 1, 22, 13, 0), datetime(2024, 1, 22, 15, 0)] treatments = ['control', 'variation', 'variation', 'control'] dataframes = [generate_data(start, start + timedelta(hours=2), treatment) for start, treatment in zip(start_times, treatments)] df = pd.concat(dataframes).sort_values(by='activation_time_local').reset_index(drop=True) df.head(20) Out[2]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } order_id city_code activation_time_local bin_start_time_local treatment 0 8395928407 VAL 2024-01-22 09:19:44 2024-01-22 09:00:00 control 1 5298312065 VAL 2024-01-22 10:15:55 2024-01-22 09:00:00 control 2 3563451924 VAL 2024-01-22 10:24:11 2024-01-22 09:00:00 control 3 1787846414 VAL 2024-01-22 10:28:31 2024-01-22 09:00:00 control 4 5537253172 VAL 2024-01-22 10:47:00 2024-01-22 09:00:00 control 5 2855189739 VAL 2024-01-22 11:21:07 2024-01-22 11:00:00 variation 6 8667272366 VAL 2024-01-22 11:25:28 2024-01-22 11:00:00 variation 7 7548779029 VAL 2024-01-22 11:31:39 2024-01-22 11:00:00 variation 8 6152559666 VAL 2024-01-22 11:53:22 2024-01-22 11:00:00 variation 9 2250819632 VAL 2024-01-22 12:56:15 2024-01-22 11:00:00 variation 10 8767007473 VAL 2024-01-22 13:22:43 2024-01-22 13:00:00 variation 11 3609385266 VAL 2024-01-22 13:34:01 2024-01-22 13:00:00 variation 12 9370399619 VAL 2024-01-22 13:43:32 2024-01-22 13:00:00 variation 13 1279394470 VAL 2024-01-22 13:47:04 2024-01-22 13:00:00 variation 14 5147358011 VAL 2024-01-22 14:57:21 2024-01-22 13:00:00 variation 15 7643070057 VAL 2024-01-22 15:02:41 2024-01-22 15:00:00 control 16 5164334270 VAL 2024-01-22 16:11:37 2024-01-22 15:00:00 control 17 7528642437 VAL 2024-01-22 16:34:35 2024-01-22 15:00:00 control 18 2111451555 VAL 2024-01-22 16:49:05 2024-01-22 15:00:00 control 19 8140478823 VAL 2024-01-22 16:54:33 2024-01-22 15:00:00 control In [3]: Copied! ## Define washover with 30 min duration washover = ConstantWashover ( washover_time_delta = timedelta ( minutes = 30 )) ## Apply washover to the dataframe, the orders with activation time within the first 30 minutes after every change in the treatment column, clustering by city and 2h time bin, will be dropped df_analysis_washover = washover . washover ( df = df , truncated_time_col = 'bin_start_time_local' , treatment_col = 'treatment' , cluster_cols = [ 'city_code' , 'bin_start_time_local' ], original_time_col = 'activation_time_local' , ) ## Define washover with 30 min duration washover = ConstantWashover(washover_time_delta=timedelta(minutes=30)) ## Apply washover to the dataframe, the orders with activation time within the first 30 minutes after every change in the treatment column, clustering by city and 2h time bin, will be dropped df_analysis_washover = washover.washover( df=df, truncated_time_col='bin_start_time_local', treatment_col='treatment', cluster_cols=['city_code','bin_start_time_local'], original_time_col='activation_time_local', ) In [4]: Copied! ## Show the rows that have been dropped anti_joined_df = df . merge ( df_analysis_washover [ 'order_id' ], how = 'left' , indicator = True , on = 'order_id' ) anti_joined_df = anti_joined_df [ anti_joined_df [ '_merge' ] == 'left_only' ] . drop ( columns = [ '_merge' ]) anti_joined_df . head ( 10 ) ## Show the rows that have been dropped anti_joined_df = df.merge(df_analysis_washover['order_id'], how='left', indicator=True, on='order_id') anti_joined_df = anti_joined_df[anti_joined_df['_merge'] == 'left_only'].drop(columns=['_merge']) anti_joined_df.head(10) Out[4]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } order_id city_code activation_time_local bin_start_time_local treatment 0 8395928407 VAL 2024-01-22 09:19:44 2024-01-22 09:00:00 control 5 2855189739 VAL 2024-01-22 11:21:07 2024-01-22 11:00:00 variation 6 8667272366 VAL 2024-01-22 11:25:28 2024-01-22 11:00:00 variation 15 7643070057 VAL 2024-01-22 15:02:41 2024-01-22 15:00:00 control In [5]: Copied! ## Check DF shapes print ( 'df:' , df . shape ) print ( 'df_analysis_washover:' , df_analysis_washover . shape ) ## Check DF shapes print('df:', df.shape) print('df_analysis_washover:', df_analysis_washover.shape) df: (20, 5) df_analysis_washover: (16, 5)","title":"Washover"},{"location":"api/analysis_plan.html","text":"from cluster_experiments.inference.analysis_plan import * \u00b6 AnalysisPlan \u00b6 A class used to represent an Analysis Plan with a list of hypothesis tests and a list of variants. All the hypothesis tests in the same analysis plan will be analysed with the same dataframe, which will need to be passed in the analyze() method. Attributes \u00b6 tests : List[HypothesisTest] A list of HypothesisTest instances variants : List[Variant] A list of Variant instances variant_col : str name of the column with the experiment groups alpha : float significance level used to construct confidence intervals Source code in cluster_experiments/inference/analysis_plan.py class AnalysisPlan : \"\"\" A class used to represent an Analysis Plan with a list of hypothesis tests and a list of variants. All the hypothesis tests in the same analysis plan will be analysed with the same dataframe, which will need to be passed in the analyze() method. Attributes ---------- tests : List[HypothesisTest] A list of HypothesisTest instances variants : List[Variant] A list of Variant instances variant_col : str name of the column with the experiment groups alpha : float significance level used to construct confidence intervals \"\"\" def __init__ ( self , tests : List [ HypothesisTest ], variants : List [ Variant ], variant_col : str = \"treatment\" , alpha : float = 0.05 , ): \"\"\" Parameters ---------- tests : List[HypothesisTest] A list of HypothesisTest instances variants : List[Variant] A list of Variant instances variant_col : str The name of the column containing the variant names. alpha : float significance level used to construct confidence intervals \"\"\" self . tests = tests self . variants = variants self . variant_col = variant_col self . alpha = alpha self . _validate_inputs () def _validate_inputs ( self ): \"\"\" Validates the inputs for the AnalysisPlan class. Raises ------ TypeError If tests is not a list of HypothesisTest instances or if variants is not a list of Variant instances. ValueError If tests or variants are empty lists. \"\"\" if not isinstance ( self . tests , list ) or not all ( isinstance ( test , HypothesisTest ) for test in self . tests ): raise TypeError ( \"Tests must be a list of HypothesisTest instances\" ) if not isinstance ( self . variants , list ) or not all ( isinstance ( variant , Variant ) for variant in self . variants ): raise TypeError ( \"Variants must be a list of Variant instances\" ) if not isinstance ( self . variant_col , str ): raise TypeError ( \"Variant_col must be a string\" ) if not self . tests : raise ValueError ( \"Tests list cannot be empty\" ) if not self . variants : raise ValueError ( \"Variants list cannot be empty\" ) def analyze ( self , exp_data : pd . DataFrame , pre_exp_data : Optional [ pd . DataFrame ] = None , verbose : bool = False , ) -> AnalysisPlanResults : \"\"\" Method to run the experiment analysis. \"\"\" # Validate input data at the beginning self . _validate_data ( exp_data , pre_exp_data ) analysis_results = AnalysisPlanResults () for test in self . tests : exp_data = test . add_covariates ( exp_data , pre_exp_data ) for treatment_variant in self . treatment_variants : for dimension in test . dimensions : for dimension_value in dimension . iterate_dimension_values (): if verbose : logger . info ( f \"Metric: { test . metric . alias } , \" f \"Treatment: { treatment_variant . name } , \" f \"Dimension: { dimension . name } , \" f \"Value: { dimension_value } \" ) test_results = test . get_test_results ( exp_data = exp_data , control_variant = self . control_variant , treatment_variant = treatment_variant , variant_col = self . variant_col , dimension = dimension , dimension_value = dimension_value , alpha = self . alpha , ) analysis_results = analysis_results + test_results return analysis_results def _validate_data ( self , exp_data : pd . DataFrame , pre_exp_data : Optional [ pd . DataFrame ] = None ): \"\"\" Validates the input dataframes for the analyze method. Parameters ---------- exp_data : pd.DataFrame The experimental data pre_exp_data : Optional[pd.DataFrame] The pre-experimental data (optional) Raises ------ ValueError If exp_data is not a DataFrame or is empty If pre_exp_data is provided and is not a DataFrame or is empty \"\"\" if not isinstance ( exp_data , pd . DataFrame ): raise ValueError ( \"exp_data must be a pandas DataFrame\" ) if exp_data . empty : raise ValueError ( \"exp_data cannot be empty\" ) if pre_exp_data is not None : if not isinstance ( pre_exp_data , pd . DataFrame ): raise ValueError ( \"pre_exp_data must be a pandas DataFrame if provided\" ) if pre_exp_data . empty : raise ValueError ( \"pre_exp_data cannot be empty if provided\" ) @property def control_variant ( self ) -> Variant : \"\"\" Returns the control variant from the list of variants. Raises an error if no control variant is found. Returns ------- Variant The control variant Raises ------ ValueError If no control variant is found \"\"\" for variant in self . variants : if variant . is_control : return variant raise ValueError ( \"No control variant found\" ) @property def treatment_variants ( self ) -> List [ Variant ]: \"\"\" Returns the treatment variants from the list of variants. Raises an error if no treatment variants are found. Returns ------- List[Variant] A list of treatment variants Raises ------ ValueError If no treatment variants are found \"\"\" treatments = [ variant for variant in self . variants if not variant . is_control ] if not treatments : raise ValueError ( \"No treatment variants found\" ) return treatments @classmethod def from_metrics ( cls , metrics : List [ Metric ], variants : List [ Variant ], variant_col : str = \"treatment\" , alpha : float = 0.05 , dimensions : Optional [ List [ Dimension ]] = None , analysis_type : str = \"default\" , analysis_config : Optional [ Dict [ str , Any ]] = None , custom_analysis_type_mapper : Optional [ Dict [ str , ExperimentAnalysis ]] = None , ) -> \"AnalysisPlan\" : \"\"\" Creates a simplified AnalysisPlan instance from a list of metrics. It will create HypothesisTest objects under the hood. This shortcut does not support cupac, and uses the same dimensions, analysis type and analysis config for all metrics. Parameters ---------- metrics : List[Metric] A list of Metric instances variants : List[Variant] A list of Variant instances variant_col : str The name of the column containing the variant names. alpha : float Significance level used to construct confidence intervals dimensions : Optional[List[Dimension]] A list of Dimension instances (optional) analysis_type : str The type of analysis to be conducted (default: \"default\") analysis_config : Optional[Dict[str, Any]] A dictionary containing analysis configuration options (optional) custom_analysis_type_mapper : Optional[Dict[str, ExperimentAnalysis]] An optional dictionary mapping the names of custom analysis types to the corresponding ExperimentAnalysis classes Returns ------- AnalysisPlan An instance of AnalysisPlan \"\"\" tests = [ HypothesisTest ( metric = metric , dimensions = dimensions or [], analysis_type = analysis_type , analysis_config = analysis_config or {}, custom_analysis_type_mapper = custom_analysis_type_mapper or {}, ) for metric in metrics ] return cls ( tests = tests , variants = variants , variant_col = variant_col , alpha = alpha , ) control_variant : Variant property readonly \u00b6 Returns the control variant from the list of variants. Raises an error if no control variant is found. Returns \u00b6 Variant The control variant Raises \u00b6 ValueError If no control variant is found treatment_variants : List [ cluster_experiments . inference . variant . Variant ] property readonly \u00b6 Returns the treatment variants from the list of variants. Raises an error if no treatment variants are found. Returns \u00b6 List[Variant] A list of treatment variants Raises \u00b6 ValueError If no treatment variants are found __init__ ( self , tests , variants , variant_col = 'treatment' , alpha = 0.05 ) special \u00b6 Parameters \u00b6 tests : List[HypothesisTest] A list of HypothesisTest instances variants : List[Variant] A list of Variant instances variant_col : str The name of the column containing the variant names. alpha : float significance level used to construct confidence intervals Source code in cluster_experiments/inference/analysis_plan.py def __init__ ( self , tests : List [ HypothesisTest ], variants : List [ Variant ], variant_col : str = \"treatment\" , alpha : float = 0.05 , ): \"\"\" Parameters ---------- tests : List[HypothesisTest] A list of HypothesisTest instances variants : List[Variant] A list of Variant instances variant_col : str The name of the column containing the variant names. alpha : float significance level used to construct confidence intervals \"\"\" self . tests = tests self . variants = variants self . variant_col = variant_col self . alpha = alpha self . _validate_inputs () analyze ( self , exp_data , pre_exp_data = None , verbose = False ) \u00b6 Method to run the experiment analysis. Source code in cluster_experiments/inference/analysis_plan.py def analyze ( self , exp_data : pd . DataFrame , pre_exp_data : Optional [ pd . DataFrame ] = None , verbose : bool = False , ) -> AnalysisPlanResults : \"\"\" Method to run the experiment analysis. \"\"\" # Validate input data at the beginning self . _validate_data ( exp_data , pre_exp_data ) analysis_results = AnalysisPlanResults () for test in self . tests : exp_data = test . add_covariates ( exp_data , pre_exp_data ) for treatment_variant in self . treatment_variants : for dimension in test . dimensions : for dimension_value in dimension . iterate_dimension_values (): if verbose : logger . info ( f \"Metric: { test . metric . alias } , \" f \"Treatment: { treatment_variant . name } , \" f \"Dimension: { dimension . name } , \" f \"Value: { dimension_value } \" ) test_results = test . get_test_results ( exp_data = exp_data , control_variant = self . control_variant , treatment_variant = treatment_variant , variant_col = self . variant_col , dimension = dimension , dimension_value = dimension_value , alpha = self . alpha , ) analysis_results = analysis_results + test_results return analysis_results from_metrics ( metrics , variants , variant_col = 'treatment' , alpha = 0.05 , dimensions = None , analysis_type = 'default' , analysis_config = None , custom_analysis_type_mapper = None ) classmethod \u00b6 Creates a simplified AnalysisPlan instance from a list of metrics. It will create HypothesisTest objects under the hood. This shortcut does not support cupac, and uses the same dimensions, analysis type and analysis config for all metrics. Parameters \u00b6 metrics : List[Metric] A list of Metric instances variants : List[Variant] A list of Variant instances variant_col : str The name of the column containing the variant names. alpha : float Significance level used to construct confidence intervals dimensions : Optional[List[Dimension]] A list of Dimension instances (optional) analysis_type : str The type of analysis to be conducted (default: \"default\") analysis_config : Optional[Dict[str, Any]] A dictionary containing analysis configuration options (optional) custom_analysis_type_mapper : Optional[Dict[str, ExperimentAnalysis]] An optional dictionary mapping the names of custom analysis types to the corresponding ExperimentAnalysis classes Returns \u00b6 AnalysisPlan An instance of AnalysisPlan Source code in cluster_experiments/inference/analysis_plan.py @classmethod def from_metrics ( cls , metrics : List [ Metric ], variants : List [ Variant ], variant_col : str = \"treatment\" , alpha : float = 0.05 , dimensions : Optional [ List [ Dimension ]] = None , analysis_type : str = \"default\" , analysis_config : Optional [ Dict [ str , Any ]] = None , custom_analysis_type_mapper : Optional [ Dict [ str , ExperimentAnalysis ]] = None , ) -> \"AnalysisPlan\" : \"\"\" Creates a simplified AnalysisPlan instance from a list of metrics. It will create HypothesisTest objects under the hood. This shortcut does not support cupac, and uses the same dimensions, analysis type and analysis config for all metrics. Parameters ---------- metrics : List[Metric] A list of Metric instances variants : List[Variant] A list of Variant instances variant_col : str The name of the column containing the variant names. alpha : float Significance level used to construct confidence intervals dimensions : Optional[List[Dimension]] A list of Dimension instances (optional) analysis_type : str The type of analysis to be conducted (default: \"default\") analysis_config : Optional[Dict[str, Any]] A dictionary containing analysis configuration options (optional) custom_analysis_type_mapper : Optional[Dict[str, ExperimentAnalysis]] An optional dictionary mapping the names of custom analysis types to the corresponding ExperimentAnalysis classes Returns ------- AnalysisPlan An instance of AnalysisPlan \"\"\" tests = [ HypothesisTest ( metric = metric , dimensions = dimensions or [], analysis_type = analysis_type , analysis_config = analysis_config or {}, custom_analysis_type_mapper = custom_analysis_type_mapper or {}, ) for metric in metrics ] return cls ( tests = tests , variants = variants , variant_col = variant_col , alpha = alpha , )","title":"Analysis Plan"},{"location":"api/analysis_plan.html#from-cluster_experimentsinferenceanalysis_plan-import","text":"","title":"from cluster_experiments.inference.analysis_plan import *"},{"location":"api/analysis_plan.html#cluster_experiments.inference.analysis_plan.AnalysisPlan","text":"A class used to represent an Analysis Plan with a list of hypothesis tests and a list of variants. All the hypothesis tests in the same analysis plan will be analysed with the same dataframe, which will need to be passed in the analyze() method.","title":"AnalysisPlan"},{"location":"api/analysis_plan.html#cluster_experiments.inference.analysis_plan.AnalysisPlan--attributes","text":"tests : List[HypothesisTest] A list of HypothesisTest instances variants : List[Variant] A list of Variant instances variant_col : str name of the column with the experiment groups alpha : float significance level used to construct confidence intervals Source code in cluster_experiments/inference/analysis_plan.py class AnalysisPlan : \"\"\" A class used to represent an Analysis Plan with a list of hypothesis tests and a list of variants. All the hypothesis tests in the same analysis plan will be analysed with the same dataframe, which will need to be passed in the analyze() method. Attributes ---------- tests : List[HypothesisTest] A list of HypothesisTest instances variants : List[Variant] A list of Variant instances variant_col : str name of the column with the experiment groups alpha : float significance level used to construct confidence intervals \"\"\" def __init__ ( self , tests : List [ HypothesisTest ], variants : List [ Variant ], variant_col : str = \"treatment\" , alpha : float = 0.05 , ): \"\"\" Parameters ---------- tests : List[HypothesisTest] A list of HypothesisTest instances variants : List[Variant] A list of Variant instances variant_col : str The name of the column containing the variant names. alpha : float significance level used to construct confidence intervals \"\"\" self . tests = tests self . variants = variants self . variant_col = variant_col self . alpha = alpha self . _validate_inputs () def _validate_inputs ( self ): \"\"\" Validates the inputs for the AnalysisPlan class. Raises ------ TypeError If tests is not a list of HypothesisTest instances or if variants is not a list of Variant instances. ValueError If tests or variants are empty lists. \"\"\" if not isinstance ( self . tests , list ) or not all ( isinstance ( test , HypothesisTest ) for test in self . tests ): raise TypeError ( \"Tests must be a list of HypothesisTest instances\" ) if not isinstance ( self . variants , list ) or not all ( isinstance ( variant , Variant ) for variant in self . variants ): raise TypeError ( \"Variants must be a list of Variant instances\" ) if not isinstance ( self . variant_col , str ): raise TypeError ( \"Variant_col must be a string\" ) if not self . tests : raise ValueError ( \"Tests list cannot be empty\" ) if not self . variants : raise ValueError ( \"Variants list cannot be empty\" ) def analyze ( self , exp_data : pd . DataFrame , pre_exp_data : Optional [ pd . DataFrame ] = None , verbose : bool = False , ) -> AnalysisPlanResults : \"\"\" Method to run the experiment analysis. \"\"\" # Validate input data at the beginning self . _validate_data ( exp_data , pre_exp_data ) analysis_results = AnalysisPlanResults () for test in self . tests : exp_data = test . add_covariates ( exp_data , pre_exp_data ) for treatment_variant in self . treatment_variants : for dimension in test . dimensions : for dimension_value in dimension . iterate_dimension_values (): if verbose : logger . info ( f \"Metric: { test . metric . alias } , \" f \"Treatment: { treatment_variant . name } , \" f \"Dimension: { dimension . name } , \" f \"Value: { dimension_value } \" ) test_results = test . get_test_results ( exp_data = exp_data , control_variant = self . control_variant , treatment_variant = treatment_variant , variant_col = self . variant_col , dimension = dimension , dimension_value = dimension_value , alpha = self . alpha , ) analysis_results = analysis_results + test_results return analysis_results def _validate_data ( self , exp_data : pd . DataFrame , pre_exp_data : Optional [ pd . DataFrame ] = None ): \"\"\" Validates the input dataframes for the analyze method. Parameters ---------- exp_data : pd.DataFrame The experimental data pre_exp_data : Optional[pd.DataFrame] The pre-experimental data (optional) Raises ------ ValueError If exp_data is not a DataFrame or is empty If pre_exp_data is provided and is not a DataFrame or is empty \"\"\" if not isinstance ( exp_data , pd . DataFrame ): raise ValueError ( \"exp_data must be a pandas DataFrame\" ) if exp_data . empty : raise ValueError ( \"exp_data cannot be empty\" ) if pre_exp_data is not None : if not isinstance ( pre_exp_data , pd . DataFrame ): raise ValueError ( \"pre_exp_data must be a pandas DataFrame if provided\" ) if pre_exp_data . empty : raise ValueError ( \"pre_exp_data cannot be empty if provided\" ) @property def control_variant ( self ) -> Variant : \"\"\" Returns the control variant from the list of variants. Raises an error if no control variant is found. Returns ------- Variant The control variant Raises ------ ValueError If no control variant is found \"\"\" for variant in self . variants : if variant . is_control : return variant raise ValueError ( \"No control variant found\" ) @property def treatment_variants ( self ) -> List [ Variant ]: \"\"\" Returns the treatment variants from the list of variants. Raises an error if no treatment variants are found. Returns ------- List[Variant] A list of treatment variants Raises ------ ValueError If no treatment variants are found \"\"\" treatments = [ variant for variant in self . variants if not variant . is_control ] if not treatments : raise ValueError ( \"No treatment variants found\" ) return treatments @classmethod def from_metrics ( cls , metrics : List [ Metric ], variants : List [ Variant ], variant_col : str = \"treatment\" , alpha : float = 0.05 , dimensions : Optional [ List [ Dimension ]] = None , analysis_type : str = \"default\" , analysis_config : Optional [ Dict [ str , Any ]] = None , custom_analysis_type_mapper : Optional [ Dict [ str , ExperimentAnalysis ]] = None , ) -> \"AnalysisPlan\" : \"\"\" Creates a simplified AnalysisPlan instance from a list of metrics. It will create HypothesisTest objects under the hood. This shortcut does not support cupac, and uses the same dimensions, analysis type and analysis config for all metrics. Parameters ---------- metrics : List[Metric] A list of Metric instances variants : List[Variant] A list of Variant instances variant_col : str The name of the column containing the variant names. alpha : float Significance level used to construct confidence intervals dimensions : Optional[List[Dimension]] A list of Dimension instances (optional) analysis_type : str The type of analysis to be conducted (default: \"default\") analysis_config : Optional[Dict[str, Any]] A dictionary containing analysis configuration options (optional) custom_analysis_type_mapper : Optional[Dict[str, ExperimentAnalysis]] An optional dictionary mapping the names of custom analysis types to the corresponding ExperimentAnalysis classes Returns ------- AnalysisPlan An instance of AnalysisPlan \"\"\" tests = [ HypothesisTest ( metric = metric , dimensions = dimensions or [], analysis_type = analysis_type , analysis_config = analysis_config or {}, custom_analysis_type_mapper = custom_analysis_type_mapper or {}, ) for metric in metrics ] return cls ( tests = tests , variants = variants , variant_col = variant_col , alpha = alpha , )","title":"Attributes"},{"location":"api/analysis_plan.html#cluster_experiments.inference.analysis_plan.AnalysisPlan.control_variant","text":"Returns the control variant from the list of variants. Raises an error if no control variant is found.","title":"control_variant"},{"location":"api/analysis_plan.html#cluster_experiments.inference.analysis_plan.AnalysisPlan.control_variant--returns","text":"Variant The control variant","title":"Returns"},{"location":"api/analysis_plan.html#cluster_experiments.inference.analysis_plan.AnalysisPlan.control_variant--raises","text":"ValueError If no control variant is found","title":"Raises"},{"location":"api/analysis_plan.html#cluster_experiments.inference.analysis_plan.AnalysisPlan.treatment_variants","text":"Returns the treatment variants from the list of variants. Raises an error if no treatment variants are found.","title":"treatment_variants"},{"location":"api/analysis_plan.html#cluster_experiments.inference.analysis_plan.AnalysisPlan.treatment_variants--returns","text":"List[Variant] A list of treatment variants","title":"Returns"},{"location":"api/analysis_plan.html#cluster_experiments.inference.analysis_plan.AnalysisPlan.treatment_variants--raises","text":"ValueError If no treatment variants are found","title":"Raises"},{"location":"api/analysis_plan.html#cluster_experiments.inference.analysis_plan.AnalysisPlan.__init__","text":"","title":"__init__()"},{"location":"api/analysis_plan.html#cluster_experiments.inference.analysis_plan.AnalysisPlan.__init__--parameters","text":"tests : List[HypothesisTest] A list of HypothesisTest instances variants : List[Variant] A list of Variant instances variant_col : str The name of the column containing the variant names. alpha : float significance level used to construct confidence intervals Source code in cluster_experiments/inference/analysis_plan.py def __init__ ( self , tests : List [ HypothesisTest ], variants : List [ Variant ], variant_col : str = \"treatment\" , alpha : float = 0.05 , ): \"\"\" Parameters ---------- tests : List[HypothesisTest] A list of HypothesisTest instances variants : List[Variant] A list of Variant instances variant_col : str The name of the column containing the variant names. alpha : float significance level used to construct confidence intervals \"\"\" self . tests = tests self . variants = variants self . variant_col = variant_col self . alpha = alpha self . _validate_inputs ()","title":"Parameters"},{"location":"api/analysis_plan.html#cluster_experiments.inference.analysis_plan.AnalysisPlan.analyze","text":"Method to run the experiment analysis. Source code in cluster_experiments/inference/analysis_plan.py def analyze ( self , exp_data : pd . DataFrame , pre_exp_data : Optional [ pd . DataFrame ] = None , verbose : bool = False , ) -> AnalysisPlanResults : \"\"\" Method to run the experiment analysis. \"\"\" # Validate input data at the beginning self . _validate_data ( exp_data , pre_exp_data ) analysis_results = AnalysisPlanResults () for test in self . tests : exp_data = test . add_covariates ( exp_data , pre_exp_data ) for treatment_variant in self . treatment_variants : for dimension in test . dimensions : for dimension_value in dimension . iterate_dimension_values (): if verbose : logger . info ( f \"Metric: { test . metric . alias } , \" f \"Treatment: { treatment_variant . name } , \" f \"Dimension: { dimension . name } , \" f \"Value: { dimension_value } \" ) test_results = test . get_test_results ( exp_data = exp_data , control_variant = self . control_variant , treatment_variant = treatment_variant , variant_col = self . variant_col , dimension = dimension , dimension_value = dimension_value , alpha = self . alpha , ) analysis_results = analysis_results + test_results return analysis_results","title":"analyze()"},{"location":"api/analysis_plan.html#cluster_experiments.inference.analysis_plan.AnalysisPlan.from_metrics","text":"Creates a simplified AnalysisPlan instance from a list of metrics. It will create HypothesisTest objects under the hood. This shortcut does not support cupac, and uses the same dimensions, analysis type and analysis config for all metrics.","title":"from_metrics()"},{"location":"api/analysis_plan.html#cluster_experiments.inference.analysis_plan.AnalysisPlan.from_metrics--parameters","text":"metrics : List[Metric] A list of Metric instances variants : List[Variant] A list of Variant instances variant_col : str The name of the column containing the variant names. alpha : float Significance level used to construct confidence intervals dimensions : Optional[List[Dimension]] A list of Dimension instances (optional) analysis_type : str The type of analysis to be conducted (default: \"default\") analysis_config : Optional[Dict[str, Any]] A dictionary containing analysis configuration options (optional) custom_analysis_type_mapper : Optional[Dict[str, ExperimentAnalysis]] An optional dictionary mapping the names of custom analysis types to the corresponding ExperimentAnalysis classes","title":"Parameters"},{"location":"api/analysis_plan.html#cluster_experiments.inference.analysis_plan.AnalysisPlan.from_metrics--returns","text":"AnalysisPlan An instance of AnalysisPlan Source code in cluster_experiments/inference/analysis_plan.py @classmethod def from_metrics ( cls , metrics : List [ Metric ], variants : List [ Variant ], variant_col : str = \"treatment\" , alpha : float = 0.05 , dimensions : Optional [ List [ Dimension ]] = None , analysis_type : str = \"default\" , analysis_config : Optional [ Dict [ str , Any ]] = None , custom_analysis_type_mapper : Optional [ Dict [ str , ExperimentAnalysis ]] = None , ) -> \"AnalysisPlan\" : \"\"\" Creates a simplified AnalysisPlan instance from a list of metrics. It will create HypothesisTest objects under the hood. This shortcut does not support cupac, and uses the same dimensions, analysis type and analysis config for all metrics. Parameters ---------- metrics : List[Metric] A list of Metric instances variants : List[Variant] A list of Variant instances variant_col : str The name of the column containing the variant names. alpha : float Significance level used to construct confidence intervals dimensions : Optional[List[Dimension]] A list of Dimension instances (optional) analysis_type : str The type of analysis to be conducted (default: \"default\") analysis_config : Optional[Dict[str, Any]] A dictionary containing analysis configuration options (optional) custom_analysis_type_mapper : Optional[Dict[str, ExperimentAnalysis]] An optional dictionary mapping the names of custom analysis types to the corresponding ExperimentAnalysis classes Returns ------- AnalysisPlan An instance of AnalysisPlan \"\"\" tests = [ HypothesisTest ( metric = metric , dimensions = dimensions or [], analysis_type = analysis_type , analysis_config = analysis_config or {}, custom_analysis_type_mapper = custom_analysis_type_mapper or {}, ) for metric in metrics ] return cls ( tests = tests , variants = variants , variant_col = variant_col , alpha = alpha , )","title":"Returns"},{"location":"api/analysis_results.html","text":"from cluster_experiments.inference.analysis_results import * \u00b6 AnalysisPlanResults dataclass \u00b6 A dataclass used to represent the results of the experiment analysis. Attributes \u00b6 metric_alias : List[str] The alias of the metric used in the test control_variant_name : List[str] The name of the control variant treatment_variant_name : List[str] The name of the treatment variant control_variant_mean : List[float] The mean value of the control variant treatment_variant_mean : List[float] The mean value of the treatment variant analysis_type : List[str] The type of analysis performed ate : List[float] The average treatment effect ate_ci_lower : List[float] The lower bound of the confidence interval for the ATE ate_ci_upper : List[float] The upper bound of the confidence interval for the ATE p_value : List[float] The p-value of the test std_error : List[float] The standard error of the test dimension_name : List[str] The name of the dimension dimension_value : List[str] The value of the dimension !!! alpha \"List[float]\" The significance level of the test Source code in cluster_experiments/inference/analysis_results.py class AnalysisPlanResults : \"\"\" A dataclass used to represent the results of the experiment analysis. Attributes ---------- metric_alias : List[str] The alias of the metric used in the test control_variant_name : List[str] The name of the control variant treatment_variant_name : List[str] The name of the treatment variant control_variant_mean : List[float] The mean value of the control variant treatment_variant_mean : List[float] The mean value of the treatment variant analysis_type : List[str] The type of analysis performed ate : List[float] The average treatment effect ate_ci_lower : List[float] The lower bound of the confidence interval for the ATE ate_ci_upper : List[float] The upper bound of the confidence interval for the ATE p_value : List[float] The p-value of the test std_error : List[float] The standard error of the test dimension_name : List[str] The name of the dimension dimension_value : List[str] The value of the dimension alpha: List[float] The significance level of the test \"\"\" metric_alias : List [ str ] = field ( default_factory = lambda : []) control_variant_name : List [ str ] = field ( default_factory = lambda : []) treatment_variant_name : List [ str ] = field ( default_factory = lambda : []) control_variant_mean : List [ float ] = field ( default_factory = lambda : []) treatment_variant_mean : List [ float ] = field ( default_factory = lambda : []) analysis_type : List [ str ] = field ( default_factory = lambda : []) ate : List [ float ] = field ( default_factory = lambda : []) ate_ci_lower : List [ float ] = field ( default_factory = lambda : []) ate_ci_upper : List [ float ] = field ( default_factory = lambda : []) p_value : List [ float ] = field ( default_factory = lambda : []) std_error : List [ float ] = field ( default_factory = lambda : []) dimension_name : List [ str ] = field ( default_factory = lambda : []) dimension_value : List [ str ] = field ( default_factory = lambda : []) alpha : List [ float ] = field ( default_factory = lambda : []) def __add__ ( self , other ): if not isinstance ( other , AnalysisPlanResults ): return NotImplemented return AnalysisPlanResults ( metric_alias = self . metric_alias + other . metric_alias , control_variant_name = self . control_variant_name + other . control_variant_name , treatment_variant_name = self . treatment_variant_name + other . treatment_variant_name , control_variant_mean = self . control_variant_mean + other . control_variant_mean , treatment_variant_mean = self . treatment_variant_mean + other . treatment_variant_mean , analysis_type = self . analysis_type + other . analysis_type , ate = self . ate + other . ate , ate_ci_lower = self . ate_ci_lower + other . ate_ci_lower , ate_ci_upper = self . ate_ci_upper + other . ate_ci_upper , p_value = self . p_value + other . p_value , std_error = self . std_error + other . std_error , dimension_name = self . dimension_name + other . dimension_name , dimension_value = self . dimension_value + other . dimension_value , alpha = self . alpha + other . alpha , ) def to_dataframe ( self ): return pd . DataFrame ( asdict ( self ))","title":"`from cluster_experiments.inference.analysis_results import *`"},{"location":"api/analysis_results.html#from-cluster_experimentsinferenceanalysis_results-import","text":"","title":"from cluster_experiments.inference.analysis_results import *"},{"location":"api/analysis_results.html#cluster_experiments.inference.analysis_results.AnalysisPlanResults","text":"A dataclass used to represent the results of the experiment analysis.","title":"AnalysisPlanResults"},{"location":"api/analysis_results.html#cluster_experiments.inference.analysis_results.AnalysisPlanResults--attributes","text":"metric_alias : List[str] The alias of the metric used in the test control_variant_name : List[str] The name of the control variant treatment_variant_name : List[str] The name of the treatment variant control_variant_mean : List[float] The mean value of the control variant treatment_variant_mean : List[float] The mean value of the treatment variant analysis_type : List[str] The type of analysis performed ate : List[float] The average treatment effect ate_ci_lower : List[float] The lower bound of the confidence interval for the ATE ate_ci_upper : List[float] The upper bound of the confidence interval for the ATE p_value : List[float] The p-value of the test std_error : List[float] The standard error of the test dimension_name : List[str] The name of the dimension dimension_value : List[str] The value of the dimension !!! alpha \"List[float]\" The significance level of the test Source code in cluster_experiments/inference/analysis_results.py class AnalysisPlanResults : \"\"\" A dataclass used to represent the results of the experiment analysis. Attributes ---------- metric_alias : List[str] The alias of the metric used in the test control_variant_name : List[str] The name of the control variant treatment_variant_name : List[str] The name of the treatment variant control_variant_mean : List[float] The mean value of the control variant treatment_variant_mean : List[float] The mean value of the treatment variant analysis_type : List[str] The type of analysis performed ate : List[float] The average treatment effect ate_ci_lower : List[float] The lower bound of the confidence interval for the ATE ate_ci_upper : List[float] The upper bound of the confidence interval for the ATE p_value : List[float] The p-value of the test std_error : List[float] The standard error of the test dimension_name : List[str] The name of the dimension dimension_value : List[str] The value of the dimension alpha: List[float] The significance level of the test \"\"\" metric_alias : List [ str ] = field ( default_factory = lambda : []) control_variant_name : List [ str ] = field ( default_factory = lambda : []) treatment_variant_name : List [ str ] = field ( default_factory = lambda : []) control_variant_mean : List [ float ] = field ( default_factory = lambda : []) treatment_variant_mean : List [ float ] = field ( default_factory = lambda : []) analysis_type : List [ str ] = field ( default_factory = lambda : []) ate : List [ float ] = field ( default_factory = lambda : []) ate_ci_lower : List [ float ] = field ( default_factory = lambda : []) ate_ci_upper : List [ float ] = field ( default_factory = lambda : []) p_value : List [ float ] = field ( default_factory = lambda : []) std_error : List [ float ] = field ( default_factory = lambda : []) dimension_name : List [ str ] = field ( default_factory = lambda : []) dimension_value : List [ str ] = field ( default_factory = lambda : []) alpha : List [ float ] = field ( default_factory = lambda : []) def __add__ ( self , other ): if not isinstance ( other , AnalysisPlanResults ): return NotImplemented return AnalysisPlanResults ( metric_alias = self . metric_alias + other . metric_alias , control_variant_name = self . control_variant_name + other . control_variant_name , treatment_variant_name = self . treatment_variant_name + other . treatment_variant_name , control_variant_mean = self . control_variant_mean + other . control_variant_mean , treatment_variant_mean = self . treatment_variant_mean + other . treatment_variant_mean , analysis_type = self . analysis_type + other . analysis_type , ate = self . ate + other . ate , ate_ci_lower = self . ate_ci_lower + other . ate_ci_lower , ate_ci_upper = self . ate_ci_upper + other . ate_ci_upper , p_value = self . p_value + other . p_value , std_error = self . std_error + other . std_error , dimension_name = self . dimension_name + other . dimension_name , dimension_value = self . dimension_value + other . dimension_value , alpha = self . alpha + other . alpha , ) def to_dataframe ( self ): return pd . DataFrame ( asdict ( self ))","title":"Attributes"},{"location":"api/cupac_model.html","text":"from cluster_experiments.cupac import * \u00b6 CupacHandler \u00b6 CupacHandler class. It handles operations related to the cupac model. Its main goal is to call the add_covariates method, where it will add the ouptut from the cupac model, and this should be used as covariates in the regression method for the hypothesis test. Source code in cluster_experiments/cupac.py class CupacHandler : \"\"\" CupacHandler class. It handles operations related to the cupac model. Its main goal is to call the add_covariates method, where it will add the ouptut from the cupac model, and this should be used as covariates in the regression method for the hypothesis test. \"\"\" def __init__ ( self , cupac_model : Optional [ BaseEstimator ] = None , target_col : str = \"target\" , features_cupac_model : Optional [ List [ str ]] = None , cache_fit : bool = True , ): self . cupac_model : BaseEstimator = cupac_model or EmptyRegressor () self . target_col = target_col self . cupac_outcome_name = f \"estimate_ { target_col } \" self . features_cupac_model : List [ str ] = features_cupac_model or [] self . is_cupac = not isinstance ( self . cupac_model , EmptyRegressor ) self . cache_fit = cache_fit def _prep_data_cupac ( self , df : pd . DataFrame , pre_experiment_df : pd . DataFrame ) -> Tuple [ pd . DataFrame , pd . DataFrame , pd . Series ]: \"\"\"Prepares data for training and prediction\"\"\" df = df . copy () pre_experiment_df = pre_experiment_df . copy () df_predict = df . drop ( columns = [ self . target_col ]) # Split data into X and y pre_experiment_x = pre_experiment_df . drop ( columns = [ self . target_col ]) pre_experiment_y = pre_experiment_df [ self . target_col ] # Keep only cupac features if self . features_cupac_model : pre_experiment_x = pre_experiment_x [ self . features_cupac_model ] df_predict = df_predict [ self . features_cupac_model ] return df_predict , pre_experiment_x , pre_experiment_y def add_covariates ( self , df : pd . DataFrame , pre_experiment_df : Optional [ pd . DataFrame ] = None ) -> pd . DataFrame : \"\"\" Train model to predict outcome variable (based on pre-experiment data) and add the prediction to the experiment dataframe. Only do this if we use cupac Args: pre_experiment_df: Dataframe with pre-experiment data. df: Dataframe with outcome and treatment variables. \"\"\" self . check_cupac_inputs ( pre_experiment_df ) # Early return if no need to add covariates if not self . need_covariates ( pre_experiment_df ): return df df = df . copy () pre_experiment_df = pre_experiment_df . copy () df_predict , pre_experiment_x , pre_experiment_y = self . _prep_data_cupac ( df = df , pre_experiment_df = pre_experiment_df ) # Fit model if it has not been fitted before self . _fit_cupac_model ( pre_experiment_x , pre_experiment_y ) # Predict estimated_target = self . _predict_cupac_model ( df_predict ) # Add cupac outcome name to df df [ self . cupac_outcome_name ] = estimated_target return df def _fit_cupac_model ( self , pre_experiment_x : pd . DataFrame , pre_experiment_y : pd . Series ): \"\"\"Fits the cupac model. Caches the fitted model in the object, so we only fit it once. We can disable this by setting cache_fit to False. \"\"\" if not self . cache_fit : self . cupac_model . fit ( pre_experiment_x , pre_experiment_y ) return try : check_is_fitted ( self . cupac_model ) except NotFittedError : self . cupac_model . fit ( pre_experiment_x , pre_experiment_y ) def _predict_cupac_model ( self , df_predict : pd . DataFrame ) -> ArrayLike : \"\"\"Predicts the cupac model\"\"\" if hasattr ( self . cupac_model , \"predict_proba\" ): return self . cupac_model . predict_proba ( df_predict )[:, 1 ] if hasattr ( self . cupac_model , \"predict\" ): return self . cupac_model . predict ( df_predict ) raise ValueError ( \"cupac_model should have predict or predict_proba method.\" ) def need_covariates ( self , pre_experiment_df : Optional [ pd . DataFrame ] = None ) -> bool : return pre_experiment_df is not None and self . is_cupac def check_cupac_inputs ( self , pre_experiment_df : Optional [ pd . DataFrame ] = None ): if self . is_cupac and pre_experiment_df is None : raise ValueError ( \"If cupac is used, pre_experiment_df should be provided.\" ) if not self . is_cupac and pre_experiment_df is not None : raise ValueError ( \"If cupac is not used, pre_experiment_df should not be provided - \" \"remove pre_experiment_df argument or set cupac_model to not None.\" ) add_covariates ( self , df , pre_experiment_df = None ) \u00b6 Train model to predict outcome variable (based on pre-experiment data) and add the prediction to the experiment dataframe. Only do this if we use cupac Parameters: Name Type Description Default pre_experiment_df Optional[pandas.core.frame.DataFrame] Dataframe with pre-experiment data. None df DataFrame Dataframe with outcome and treatment variables. required Source code in cluster_experiments/cupac.py def add_covariates ( self , df : pd . DataFrame , pre_experiment_df : Optional [ pd . DataFrame ] = None ) -> pd . DataFrame : \"\"\" Train model to predict outcome variable (based on pre-experiment data) and add the prediction to the experiment dataframe. Only do this if we use cupac Args: pre_experiment_df: Dataframe with pre-experiment data. df: Dataframe with outcome and treatment variables. \"\"\" self . check_cupac_inputs ( pre_experiment_df ) # Early return if no need to add covariates if not self . need_covariates ( pre_experiment_df ): return df df = df . copy () pre_experiment_df = pre_experiment_df . copy () df_predict , pre_experiment_x , pre_experiment_y = self . _prep_data_cupac ( df = df , pre_experiment_df = pre_experiment_df ) # Fit model if it has not been fitted before self . _fit_cupac_model ( pre_experiment_x , pre_experiment_y ) # Predict estimated_target = self . _predict_cupac_model ( df_predict ) # Add cupac outcome name to df df [ self . cupac_outcome_name ] = estimated_target return df EmptyRegressor ( BaseEstimator ) \u00b6 Empty regressor class. It does not do anything, used to glue the code of other estimators and PowerAnalysis Each Regressor should have: - fit method: Uses pre experiment data to fit some kind of model to be used as a covariate and reduce variance. - predict method: Uses the fitted model to add the covariate on the experiment data. It can add aggregates of the target in older data as a covariate, or a model (cupac) to predict the target. Source code in cluster_experiments/cupac.py class EmptyRegressor ( BaseEstimator ): \"\"\" Empty regressor class. It does not do anything, used to glue the code of other estimators and PowerAnalysis Each Regressor should have: - fit method: Uses pre experiment data to fit some kind of model to be used as a covariate and reduce variance. - predict method: Uses the fitted model to add the covariate on the experiment data. It can add aggregates of the target in older data as a covariate, or a model (cupac) to predict the target. \"\"\" @classmethod def from_config ( cls , config ): return cls () TargetAggregation ( BaseEstimator ) \u00b6 Adds average of target using pre-experiment data Parameters: Name Type Description Default agg_col str Column to group by to aggregate target required target_col str Column to aggregate 'target' smoothing_factor int Smoothing factor for the smoothed mean 20 Usage: import pandas as pd from cluster_experiments.cupac import TargetAggregation df = pd . DataFrame ({ \"agg_col\" : [ \"a\" , \"a\" , \"b\" , \"b\" , \"c\" , \"c\" ], \"target_col\" : [ 1 , 2 , 3 , 4 , 5 , 6 ]}) new_df = pd . DataFrame ({ \"agg_col\" : [ \"a\" , \"a\" , \"b\" , \"b\" , \"c\" , \"c\" ]}) target_agg = TargetAggregation ( \"agg_col\" , \"target_col\" ) target_agg . fit ( df . drop ( columns = \"target_col\" ), df [ \"target_col\" ]) df_with_target_agg = target_agg . predict ( new_df ) print ( df_with_target_agg ) Source code in cluster_experiments/cupac.py class TargetAggregation ( BaseEstimator ): \"\"\" Adds average of target using pre-experiment data Args: agg_col: Column to group by to aggregate target target_col: Column to aggregate smoothing_factor: Smoothing factor for the smoothed mean Usage: ```python import pandas as pd from cluster_experiments.cupac import TargetAggregation df = pd.DataFrame({\"agg_col\": [\"a\", \"a\", \"b\", \"b\", \"c\", \"c\"], \"target_col\": [1, 2, 3, 4, 5, 6]}) new_df = pd.DataFrame({\"agg_col\": [\"a\", \"a\", \"b\", \"b\", \"c\", \"c\"]}) target_agg = TargetAggregation(\"agg_col\", \"target_col\") target_agg.fit(df.drop(columns=\"target_col\"), df[\"target_col\"]) df_with_target_agg = target_agg.predict(new_df) print(df_with_target_agg) ``` \"\"\" def __init__ ( self , agg_col : str , target_col : str = \"target\" , smoothing_factor : int = 20 , ): self . agg_col = agg_col self . target_col = target_col self . smoothing_factor = smoothing_factor self . is_empty = False self . mean_target_col = f \" { self . target_col } _mean\" self . smooth_mean_target_col = f \" { self . target_col } _smooth_mean\" self . pre_experiment_agg_df = pd . DataFrame () def _get_pre_experiment_mean ( self , pre_experiment_df : pd . DataFrame ) -> float : return pre_experiment_df [ self . target_col ] . mean () def fit ( self , X : pd . DataFrame , y : pd . Series ) -> \"TargetAggregation\" : \"\"\"Fits \"target encoder\" model to pre-experiment data\"\"\" pre_experiment_df = X . copy () pre_experiment_df [ self . target_col ] = y self . pre_experiment_mean = self . _get_pre_experiment_mean ( pre_experiment_df ) self . pre_experiment_agg_df = ( pre_experiment_df . assign ( count = 1 ) . groupby ( self . agg_col , as_index = False ) . agg ({ self . target_col : \"sum\" , \"count\" : \"sum\" }) . assign ( ** { self . mean_target_col : lambda x : x [ self . target_col ] / x [ \"count\" ], self . smooth_mean_target_col : lambda x : ( x [ self . target_col ] + self . smoothing_factor * self . pre_experiment_mean ) / ( x [ \"count\" ] + self . smoothing_factor ), } ) . drop ( columns = [ \"count\" , self . target_col ]) ) return self def predict ( self , X : pd . DataFrame ) -> ArrayLike : \"\"\"Adds average target of pre-experiment data to experiment data\"\"\" return ( X . merge ( self . pre_experiment_agg_df , how = \"left\" , on = self . agg_col )[ self . smooth_mean_target_col ] . fillna ( self . pre_experiment_mean ) . values ) @classmethod def from_config ( cls , config ): \"\"\"Creates TargetAggregation from PowerConfig\"\"\" return cls ( agg_col = config . agg_col , target_col = config . target_col , smoothing_factor = config . smoothing_factor , ) fit ( self , X , y ) \u00b6 Fits \"target encoder\" model to pre-experiment data Source code in cluster_experiments/cupac.py def fit ( self , X : pd . DataFrame , y : pd . Series ) -> \"TargetAggregation\" : \"\"\"Fits \"target encoder\" model to pre-experiment data\"\"\" pre_experiment_df = X . copy () pre_experiment_df [ self . target_col ] = y self . pre_experiment_mean = self . _get_pre_experiment_mean ( pre_experiment_df ) self . pre_experiment_agg_df = ( pre_experiment_df . assign ( count = 1 ) . groupby ( self . agg_col , as_index = False ) . agg ({ self . target_col : \"sum\" , \"count\" : \"sum\" }) . assign ( ** { self . mean_target_col : lambda x : x [ self . target_col ] / x [ \"count\" ], self . smooth_mean_target_col : lambda x : ( x [ self . target_col ] + self . smoothing_factor * self . pre_experiment_mean ) / ( x [ \"count\" ] + self . smoothing_factor ), } ) . drop ( columns = [ \"count\" , self . target_col ]) ) return self from_config ( config ) classmethod \u00b6 Creates TargetAggregation from PowerConfig Source code in cluster_experiments/cupac.py @classmethod def from_config ( cls , config ): \"\"\"Creates TargetAggregation from PowerConfig\"\"\" return cls ( agg_col = config . agg_col , target_col = config . target_col , smoothing_factor = config . smoothing_factor , ) predict ( self , X ) \u00b6 Adds average target of pre-experiment data to experiment data Source code in cluster_experiments/cupac.py def predict ( self , X : pd . DataFrame ) -> ArrayLike : \"\"\"Adds average target of pre-experiment data to experiment data\"\"\" return ( X . merge ( self . pre_experiment_agg_df , how = \"left\" , on = self . agg_col )[ self . smooth_mean_target_col ] . fillna ( self . pre_experiment_mean ) . values )","title":"Pre experiment outcome model"},{"location":"api/cupac_model.html#from-cluster_experimentscupac-import","text":"","title":"from cluster_experiments.cupac import *"},{"location":"api/cupac_model.html#cluster_experiments.cupac.CupacHandler","text":"CupacHandler class. It handles operations related to the cupac model. Its main goal is to call the add_covariates method, where it will add the ouptut from the cupac model, and this should be used as covariates in the regression method for the hypothesis test. Source code in cluster_experiments/cupac.py class CupacHandler : \"\"\" CupacHandler class. It handles operations related to the cupac model. Its main goal is to call the add_covariates method, where it will add the ouptut from the cupac model, and this should be used as covariates in the regression method for the hypothesis test. \"\"\" def __init__ ( self , cupac_model : Optional [ BaseEstimator ] = None , target_col : str = \"target\" , features_cupac_model : Optional [ List [ str ]] = None , cache_fit : bool = True , ): self . cupac_model : BaseEstimator = cupac_model or EmptyRegressor () self . target_col = target_col self . cupac_outcome_name = f \"estimate_ { target_col } \" self . features_cupac_model : List [ str ] = features_cupac_model or [] self . is_cupac = not isinstance ( self . cupac_model , EmptyRegressor ) self . cache_fit = cache_fit def _prep_data_cupac ( self , df : pd . DataFrame , pre_experiment_df : pd . DataFrame ) -> Tuple [ pd . DataFrame , pd . DataFrame , pd . Series ]: \"\"\"Prepares data for training and prediction\"\"\" df = df . copy () pre_experiment_df = pre_experiment_df . copy () df_predict = df . drop ( columns = [ self . target_col ]) # Split data into X and y pre_experiment_x = pre_experiment_df . drop ( columns = [ self . target_col ]) pre_experiment_y = pre_experiment_df [ self . target_col ] # Keep only cupac features if self . features_cupac_model : pre_experiment_x = pre_experiment_x [ self . features_cupac_model ] df_predict = df_predict [ self . features_cupac_model ] return df_predict , pre_experiment_x , pre_experiment_y def add_covariates ( self , df : pd . DataFrame , pre_experiment_df : Optional [ pd . DataFrame ] = None ) -> pd . DataFrame : \"\"\" Train model to predict outcome variable (based on pre-experiment data) and add the prediction to the experiment dataframe. Only do this if we use cupac Args: pre_experiment_df: Dataframe with pre-experiment data. df: Dataframe with outcome and treatment variables. \"\"\" self . check_cupac_inputs ( pre_experiment_df ) # Early return if no need to add covariates if not self . need_covariates ( pre_experiment_df ): return df df = df . copy () pre_experiment_df = pre_experiment_df . copy () df_predict , pre_experiment_x , pre_experiment_y = self . _prep_data_cupac ( df = df , pre_experiment_df = pre_experiment_df ) # Fit model if it has not been fitted before self . _fit_cupac_model ( pre_experiment_x , pre_experiment_y ) # Predict estimated_target = self . _predict_cupac_model ( df_predict ) # Add cupac outcome name to df df [ self . cupac_outcome_name ] = estimated_target return df def _fit_cupac_model ( self , pre_experiment_x : pd . DataFrame , pre_experiment_y : pd . Series ): \"\"\"Fits the cupac model. Caches the fitted model in the object, so we only fit it once. We can disable this by setting cache_fit to False. \"\"\" if not self . cache_fit : self . cupac_model . fit ( pre_experiment_x , pre_experiment_y ) return try : check_is_fitted ( self . cupac_model ) except NotFittedError : self . cupac_model . fit ( pre_experiment_x , pre_experiment_y ) def _predict_cupac_model ( self , df_predict : pd . DataFrame ) -> ArrayLike : \"\"\"Predicts the cupac model\"\"\" if hasattr ( self . cupac_model , \"predict_proba\" ): return self . cupac_model . predict_proba ( df_predict )[:, 1 ] if hasattr ( self . cupac_model , \"predict\" ): return self . cupac_model . predict ( df_predict ) raise ValueError ( \"cupac_model should have predict or predict_proba method.\" ) def need_covariates ( self , pre_experiment_df : Optional [ pd . DataFrame ] = None ) -> bool : return pre_experiment_df is not None and self . is_cupac def check_cupac_inputs ( self , pre_experiment_df : Optional [ pd . DataFrame ] = None ): if self . is_cupac and pre_experiment_df is None : raise ValueError ( \"If cupac is used, pre_experiment_df should be provided.\" ) if not self . is_cupac and pre_experiment_df is not None : raise ValueError ( \"If cupac is not used, pre_experiment_df should not be provided - \" \"remove pre_experiment_df argument or set cupac_model to not None.\" )","title":"CupacHandler"},{"location":"api/cupac_model.html#cluster_experiments.cupac.CupacHandler.add_covariates","text":"Train model to predict outcome variable (based on pre-experiment data) and add the prediction to the experiment dataframe. Only do this if we use cupac Parameters: Name Type Description Default pre_experiment_df Optional[pandas.core.frame.DataFrame] Dataframe with pre-experiment data. None df DataFrame Dataframe with outcome and treatment variables. required Source code in cluster_experiments/cupac.py def add_covariates ( self , df : pd . DataFrame , pre_experiment_df : Optional [ pd . DataFrame ] = None ) -> pd . DataFrame : \"\"\" Train model to predict outcome variable (based on pre-experiment data) and add the prediction to the experiment dataframe. Only do this if we use cupac Args: pre_experiment_df: Dataframe with pre-experiment data. df: Dataframe with outcome and treatment variables. \"\"\" self . check_cupac_inputs ( pre_experiment_df ) # Early return if no need to add covariates if not self . need_covariates ( pre_experiment_df ): return df df = df . copy () pre_experiment_df = pre_experiment_df . copy () df_predict , pre_experiment_x , pre_experiment_y = self . _prep_data_cupac ( df = df , pre_experiment_df = pre_experiment_df ) # Fit model if it has not been fitted before self . _fit_cupac_model ( pre_experiment_x , pre_experiment_y ) # Predict estimated_target = self . _predict_cupac_model ( df_predict ) # Add cupac outcome name to df df [ self . cupac_outcome_name ] = estimated_target return df","title":"add_covariates()"},{"location":"api/cupac_model.html#cluster_experiments.cupac.EmptyRegressor","text":"Empty regressor class. It does not do anything, used to glue the code of other estimators and PowerAnalysis Each Regressor should have: - fit method: Uses pre experiment data to fit some kind of model to be used as a covariate and reduce variance. - predict method: Uses the fitted model to add the covariate on the experiment data. It can add aggregates of the target in older data as a covariate, or a model (cupac) to predict the target. Source code in cluster_experiments/cupac.py class EmptyRegressor ( BaseEstimator ): \"\"\" Empty regressor class. It does not do anything, used to glue the code of other estimators and PowerAnalysis Each Regressor should have: - fit method: Uses pre experiment data to fit some kind of model to be used as a covariate and reduce variance. - predict method: Uses the fitted model to add the covariate on the experiment data. It can add aggregates of the target in older data as a covariate, or a model (cupac) to predict the target. \"\"\" @classmethod def from_config ( cls , config ): return cls ()","title":"EmptyRegressor"},{"location":"api/cupac_model.html#cluster_experiments.cupac.TargetAggregation","text":"Adds average of target using pre-experiment data Parameters: Name Type Description Default agg_col str Column to group by to aggregate target required target_col str Column to aggregate 'target' smoothing_factor int Smoothing factor for the smoothed mean 20 Usage: import pandas as pd from cluster_experiments.cupac import TargetAggregation df = pd . DataFrame ({ \"agg_col\" : [ \"a\" , \"a\" , \"b\" , \"b\" , \"c\" , \"c\" ], \"target_col\" : [ 1 , 2 , 3 , 4 , 5 , 6 ]}) new_df = pd . DataFrame ({ \"agg_col\" : [ \"a\" , \"a\" , \"b\" , \"b\" , \"c\" , \"c\" ]}) target_agg = TargetAggregation ( \"agg_col\" , \"target_col\" ) target_agg . fit ( df . drop ( columns = \"target_col\" ), df [ \"target_col\" ]) df_with_target_agg = target_agg . predict ( new_df ) print ( df_with_target_agg ) Source code in cluster_experiments/cupac.py class TargetAggregation ( BaseEstimator ): \"\"\" Adds average of target using pre-experiment data Args: agg_col: Column to group by to aggregate target target_col: Column to aggregate smoothing_factor: Smoothing factor for the smoothed mean Usage: ```python import pandas as pd from cluster_experiments.cupac import TargetAggregation df = pd.DataFrame({\"agg_col\": [\"a\", \"a\", \"b\", \"b\", \"c\", \"c\"], \"target_col\": [1, 2, 3, 4, 5, 6]}) new_df = pd.DataFrame({\"agg_col\": [\"a\", \"a\", \"b\", \"b\", \"c\", \"c\"]}) target_agg = TargetAggregation(\"agg_col\", \"target_col\") target_agg.fit(df.drop(columns=\"target_col\"), df[\"target_col\"]) df_with_target_agg = target_agg.predict(new_df) print(df_with_target_agg) ``` \"\"\" def __init__ ( self , agg_col : str , target_col : str = \"target\" , smoothing_factor : int = 20 , ): self . agg_col = agg_col self . target_col = target_col self . smoothing_factor = smoothing_factor self . is_empty = False self . mean_target_col = f \" { self . target_col } _mean\" self . smooth_mean_target_col = f \" { self . target_col } _smooth_mean\" self . pre_experiment_agg_df = pd . DataFrame () def _get_pre_experiment_mean ( self , pre_experiment_df : pd . DataFrame ) -> float : return pre_experiment_df [ self . target_col ] . mean () def fit ( self , X : pd . DataFrame , y : pd . Series ) -> \"TargetAggregation\" : \"\"\"Fits \"target encoder\" model to pre-experiment data\"\"\" pre_experiment_df = X . copy () pre_experiment_df [ self . target_col ] = y self . pre_experiment_mean = self . _get_pre_experiment_mean ( pre_experiment_df ) self . pre_experiment_agg_df = ( pre_experiment_df . assign ( count = 1 ) . groupby ( self . agg_col , as_index = False ) . agg ({ self . target_col : \"sum\" , \"count\" : \"sum\" }) . assign ( ** { self . mean_target_col : lambda x : x [ self . target_col ] / x [ \"count\" ], self . smooth_mean_target_col : lambda x : ( x [ self . target_col ] + self . smoothing_factor * self . pre_experiment_mean ) / ( x [ \"count\" ] + self . smoothing_factor ), } ) . drop ( columns = [ \"count\" , self . target_col ]) ) return self def predict ( self , X : pd . DataFrame ) -> ArrayLike : \"\"\"Adds average target of pre-experiment data to experiment data\"\"\" return ( X . merge ( self . pre_experiment_agg_df , how = \"left\" , on = self . agg_col )[ self . smooth_mean_target_col ] . fillna ( self . pre_experiment_mean ) . values ) @classmethod def from_config ( cls , config ): \"\"\"Creates TargetAggregation from PowerConfig\"\"\" return cls ( agg_col = config . agg_col , target_col = config . target_col , smoothing_factor = config . smoothing_factor , )","title":"TargetAggregation"},{"location":"api/cupac_model.html#cluster_experiments.cupac.TargetAggregation.fit","text":"Fits \"target encoder\" model to pre-experiment data Source code in cluster_experiments/cupac.py def fit ( self , X : pd . DataFrame , y : pd . Series ) -> \"TargetAggregation\" : \"\"\"Fits \"target encoder\" model to pre-experiment data\"\"\" pre_experiment_df = X . copy () pre_experiment_df [ self . target_col ] = y self . pre_experiment_mean = self . _get_pre_experiment_mean ( pre_experiment_df ) self . pre_experiment_agg_df = ( pre_experiment_df . assign ( count = 1 ) . groupby ( self . agg_col , as_index = False ) . agg ({ self . target_col : \"sum\" , \"count\" : \"sum\" }) . assign ( ** { self . mean_target_col : lambda x : x [ self . target_col ] / x [ \"count\" ], self . smooth_mean_target_col : lambda x : ( x [ self . target_col ] + self . smoothing_factor * self . pre_experiment_mean ) / ( x [ \"count\" ] + self . smoothing_factor ), } ) . drop ( columns = [ \"count\" , self . target_col ]) ) return self","title":"fit()"},{"location":"api/cupac_model.html#cluster_experiments.cupac.TargetAggregation.from_config","text":"Creates TargetAggregation from PowerConfig Source code in cluster_experiments/cupac.py @classmethod def from_config ( cls , config ): \"\"\"Creates TargetAggregation from PowerConfig\"\"\" return cls ( agg_col = config . agg_col , target_col = config . target_col , smoothing_factor = config . smoothing_factor , )","title":"from_config()"},{"location":"api/cupac_model.html#cluster_experiments.cupac.TargetAggregation.predict","text":"Adds average target of pre-experiment data to experiment data Source code in cluster_experiments/cupac.py def predict ( self , X : pd . DataFrame ) -> ArrayLike : \"\"\"Adds average target of pre-experiment data to experiment data\"\"\" return ( X . merge ( self . pre_experiment_agg_df , how = \"left\" , on = self . agg_col )[ self . smooth_mean_target_col ] . fillna ( self . pre_experiment_mean ) . values )","title":"predict()"},{"location":"api/dimension.html","text":"from cluster_experiments.inference.dimension import * \u00b6 DefaultDimension ( Dimension ) dataclass \u00b6 A class used to represent a Dimension with a default value representing total, i.e. no slicing. Source code in cluster_experiments/inference/dimension.py class DefaultDimension ( Dimension ): \"\"\" A class used to represent a Dimension with a default value representing total, i.e. no slicing. \"\"\" def __init__ ( self ): super () . __init__ ( name = \"__total_dimension\" , values = [ \"total\" ]) __init__ ( self ) special \u00b6 Initialize self. See help(type(self)) for accurate signature. Source code in cluster_experiments/inference/dimension.py def __init__ ( self ): super () . __init__ ( name = \"__total_dimension\" , values = [ \"total\" ]) Dimension dataclass \u00b6 A class used to represent a Dimension with a name and values. Attributes \u00b6 name : str The name of the dimension values : List[str] A list of strings representing the possible values of the dimension Source code in cluster_experiments/inference/dimension.py class Dimension : \"\"\" A class used to represent a Dimension with a name and values. Attributes ---------- name : str The name of the dimension values : List[str] A list of strings representing the possible values of the dimension \"\"\" name : str values : List [ str ] def __post_init__ ( self ): \"\"\" Validates the inputs after initialization. \"\"\" self . _validate_inputs () def _validate_inputs ( self ): \"\"\" Validates the inputs for the Dimension class. Raises ------ TypeError If the name is not a string or if values is not a list of strings. \"\"\" if not isinstance ( self . name , str ): raise TypeError ( \"Dimension name must be a string\" ) if not isinstance ( self . values , list ) or not all ( isinstance ( val , str ) for val in self . values ): raise TypeError ( \"Dimension values must be a list of strings\" ) def iterate_dimension_values ( self ): \"\"\" A generator method to yield name and values from the dimension. Yields ------ Any A unique value from the dimension. \"\"\" seen = set () for value in self . values : if value not in seen : seen . add ( value ) yield value __post_init__ ( self ) special \u00b6 Validates the inputs after initialization. Source code in cluster_experiments/inference/dimension.py def __post_init__ ( self ): \"\"\" Validates the inputs after initialization. \"\"\" self . _validate_inputs () iterate_dimension_values ( self ) \u00b6 A generator method to yield name and values from the dimension. Yields \u00b6 Any A unique value from the dimension. Source code in cluster_experiments/inference/dimension.py def iterate_dimension_values ( self ): \"\"\" A generator method to yield name and values from the dimension. Yields ------ Any A unique value from the dimension. \"\"\" seen = set () for value in self . values : if value not in seen : seen . add ( value ) yield value","title":"Dimension"},{"location":"api/dimension.html#from-cluster_experimentsinferencedimension-import","text":"","title":"from cluster_experiments.inference.dimension import *"},{"location":"api/dimension.html#cluster_experiments.inference.dimension.DefaultDimension","text":"A class used to represent a Dimension with a default value representing total, i.e. no slicing. Source code in cluster_experiments/inference/dimension.py class DefaultDimension ( Dimension ): \"\"\" A class used to represent a Dimension with a default value representing total, i.e. no slicing. \"\"\" def __init__ ( self ): super () . __init__ ( name = \"__total_dimension\" , values = [ \"total\" ])","title":"DefaultDimension"},{"location":"api/dimension.html#cluster_experiments.inference.dimension.DefaultDimension.__init__","text":"Initialize self. See help(type(self)) for accurate signature. Source code in cluster_experiments/inference/dimension.py def __init__ ( self ): super () . __init__ ( name = \"__total_dimension\" , values = [ \"total\" ])","title":"__init__()"},{"location":"api/dimension.html#cluster_experiments.inference.dimension.Dimension","text":"A class used to represent a Dimension with a name and values.","title":"Dimension"},{"location":"api/dimension.html#cluster_experiments.inference.dimension.Dimension--attributes","text":"name : str The name of the dimension values : List[str] A list of strings representing the possible values of the dimension Source code in cluster_experiments/inference/dimension.py class Dimension : \"\"\" A class used to represent a Dimension with a name and values. Attributes ---------- name : str The name of the dimension values : List[str] A list of strings representing the possible values of the dimension \"\"\" name : str values : List [ str ] def __post_init__ ( self ): \"\"\" Validates the inputs after initialization. \"\"\" self . _validate_inputs () def _validate_inputs ( self ): \"\"\" Validates the inputs for the Dimension class. Raises ------ TypeError If the name is not a string or if values is not a list of strings. \"\"\" if not isinstance ( self . name , str ): raise TypeError ( \"Dimension name must be a string\" ) if not isinstance ( self . values , list ) or not all ( isinstance ( val , str ) for val in self . values ): raise TypeError ( \"Dimension values must be a list of strings\" ) def iterate_dimension_values ( self ): \"\"\" A generator method to yield name and values from the dimension. Yields ------ Any A unique value from the dimension. \"\"\" seen = set () for value in self . values : if value not in seen : seen . add ( value ) yield value","title":"Attributes"},{"location":"api/dimension.html#cluster_experiments.inference.dimension.Dimension.__post_init__","text":"Validates the inputs after initialization. Source code in cluster_experiments/inference/dimension.py def __post_init__ ( self ): \"\"\" Validates the inputs after initialization. \"\"\" self . _validate_inputs ()","title":"__post_init__()"},{"location":"api/dimension.html#cluster_experiments.inference.dimension.Dimension.iterate_dimension_values","text":"A generator method to yield name and values from the dimension.","title":"iterate_dimension_values()"},{"location":"api/dimension.html#cluster_experiments.inference.dimension.Dimension.iterate_dimension_values--yields","text":"Any A unique value from the dimension. Source code in cluster_experiments/inference/dimension.py def iterate_dimension_values ( self ): \"\"\" A generator method to yield name and values from the dimension. Yields ------ Any A unique value from the dimension. \"\"\" seen = set () for value in self . values : if value not in seen : seen . add ( value ) yield value","title":"Yields"},{"location":"api/experiment_analysis.html","text":"from cluster_experiments.experiment_analysis import * \u00b6 ClusteredOLSAnalysis ( ExperimentAnalysis ) \u00b6 Class to run OLS clustered analysis Parameters: Name Type Description Default cluster_cols List[str] list of columns to use as clusters required target_col str name of the column containing the variable to measure 'target' treatment_col str name of the column containing the treatment variable 'treatment' treatment str name of the treatment to use as the treated group 'B' covariates Optional[List[str]] list of columns to use as covariates None hypothesis str one of \"two-sided\", \"less\", \"greater\" indicating the alternative hypothesis 'two-sided' Usage: from cluster_experiments.experiment_analysis import ClusteredOLSAnalysis import pandas as pd df = pd . DataFrame ({ 'x' : [ 1 , 2 , 3 , 0 , 0 , 1 , 2 , 0 ], 'treatment' : [ \"A\" ] * 2 + [ \"B\" ] * 2 + [ \"A\" ] * 2 + [ \"B\" ] * 2 , 'cluster' : [ 1 , 1 , 2 , 2 , 3 , 3 , 4 , 4 ], }) ClusteredOLSAnalysis ( cluster_cols = [ 'cluster' ], target_col = 'x' , ) . get_pvalue ( df ) Source code in cluster_experiments/experiment_analysis.py class ClusteredOLSAnalysis ( ExperimentAnalysis ): \"\"\" Class to run OLS clustered analysis Arguments: cluster_cols: list of columns to use as clusters target_col: name of the column containing the variable to measure treatment_col: name of the column containing the treatment variable treatment: name of the treatment to use as the treated group covariates: list of columns to use as covariates hypothesis: one of \"two-sided\", \"less\", \"greater\" indicating the alternative hypothesis Usage: ```python from cluster_experiments.experiment_analysis import ClusteredOLSAnalysis import pandas as pd df = pd.DataFrame({ 'x': [1, 2, 3, 0, 0, 1, 2, 0], 'treatment': [\"A\"] * 2 + [\"B\"] * 2 + [\"A\"] * 2 + [\"B\"] * 2, 'cluster': [1, 1, 2, 2, 3, 3, 4, 4], }) ClusteredOLSAnalysis( cluster_cols=['cluster'], target_col='x', ).get_pvalue(df) ``` \"\"\" def __init__ ( self , cluster_cols : List [ str ], target_col : str = \"target\" , treatment_col : str = \"treatment\" , treatment : str = \"B\" , covariates : Optional [ List [ str ]] = None , hypothesis : str = \"two-sided\" , ): super () . __init__ ( target_col = target_col , treatment_col = treatment_col , cluster_cols = cluster_cols , treatment = treatment , covariates = covariates , hypothesis = hypothesis , ) self . regressors = [ self . treatment_col ] + self . covariates self . formula = f \" { self . target_col } ~ { ' + ' . join ( self . regressors ) } \" self . cov_type = \"cluster\" def fit_ols_clustered ( self , df : pd . DataFrame ): \"\"\"Returns the fitted OLS model\"\"\" return sm . OLS . from_formula ( self . formula , data = df ,) . fit ( cov_type = self . cov_type , cov_kwds = { \"groups\" : self . _get_cluster_column ( df )}, ) def analysis_pvalue ( self , df : pd . DataFrame , verbose : bool = False ) -> float : \"\"\"Returns the p-value of the analysis Arguments: df: dataframe containing the data to analyze verbose (Optional): bool, prints the regression summary if True \"\"\" results_ols = self . fit_ols_clustered ( df ) if verbose : print ( results_ols . summary ()) p_value = self . pvalue_based_on_hypothesis ( results_ols ) return p_value def analysis_point_estimate ( self , df : pd . DataFrame , verbose : bool = False ) -> float : \"\"\"Returns the point estimate of the analysis Arguments: df: dataframe containing the data to analyze verbose (Optional): bool, prints the regression summary if True \"\"\" results_ols = self . fit_ols_clustered ( df ) return results_ols . params [ self . treatment_col ] def analysis_standard_error ( self , df : pd . DataFrame , verbose : bool = False ) -> float : \"\"\"Returns the standard error of the analysis Arguments: df: dataframe containing the data to analyze verbose (Optional): bool, prints the regression summary if True \"\"\" results_ols = self . fit_ols_clustered ( df ) return results_ols . bse [ self . treatment_col ] def analysis_confidence_interval ( self , df : pd . DataFrame , alpha : float , verbose : bool = False ) -> ConfidenceInterval : \"\"\"Returns the confidence interval of the analysis Arguments: df: dataframe containing the data to analyze alpha: significance level verbose (Optional): bool, prints the regression summary if True \"\"\" results_ols = self . fit_ols_clustered ( df ) # Extract the confidence interval for the treatment column conf_int_df = results_ols . conf_int ( alpha = alpha ) lower_bound , upper_bound = conf_int_df . loc [ self . treatment_col ] if verbose : print ( results_ols . summary ()) # Return the confidence interval return ConfidenceInterval ( lower = lower_bound , upper = upper_bound , alpha = alpha ) def analysis_inference_results ( self , df : pd . DataFrame , alpha : float , verbose : bool = False ) -> InferenceResults : \"\"\"Returns the inference results of the analysis Arguments: df: dataframe containing the data to analyze alpha: significance level verbose (Optional): bool, prints the regression summary if True \"\"\" results_ols = self . fit_ols_clustered ( df ) std_error = results_ols . bse [ self . treatment_col ] ate = results_ols . params [ self . treatment_col ] p_value = self . pvalue_based_on_hypothesis ( results_ols ) # Extract the confidence interval for the treatment column conf_int_df = results_ols . conf_int ( alpha = alpha ) lower_bound , upper_bound = conf_int_df . loc [ self . treatment_col ] if verbose : print ( results_ols . summary ()) # Return the confidence interval return InferenceResults ( ate = ate , p_value = p_value , std_error = std_error , conf_int = ConfidenceInterval ( lower = lower_bound , upper = upper_bound , alpha = alpha ), ) analysis_confidence_interval ( self , df , alpha , verbose = False ) \u00b6 Returns the confidence interval of the analysis Parameters: Name Type Description Default df DataFrame dataframe containing the data to analyze required alpha float significance level required verbose Optional bool, prints the regression summary if True False Source code in cluster_experiments/experiment_analysis.py def analysis_confidence_interval ( self , df : pd . DataFrame , alpha : float , verbose : bool = False ) -> ConfidenceInterval : \"\"\"Returns the confidence interval of the analysis Arguments: df: dataframe containing the data to analyze alpha: significance level verbose (Optional): bool, prints the regression summary if True \"\"\" results_ols = self . fit_ols_clustered ( df ) # Extract the confidence interval for the treatment column conf_int_df = results_ols . conf_int ( alpha = alpha ) lower_bound , upper_bound = conf_int_df . loc [ self . treatment_col ] if verbose : print ( results_ols . summary ()) # Return the confidence interval return ConfidenceInterval ( lower = lower_bound , upper = upper_bound , alpha = alpha ) analysis_inference_results ( self , df , alpha , verbose = False ) \u00b6 Returns the inference results of the analysis Parameters: Name Type Description Default df DataFrame dataframe containing the data to analyze required alpha float significance level required verbose Optional bool, prints the regression summary if True False Source code in cluster_experiments/experiment_analysis.py def analysis_inference_results ( self , df : pd . DataFrame , alpha : float , verbose : bool = False ) -> InferenceResults : \"\"\"Returns the inference results of the analysis Arguments: df: dataframe containing the data to analyze alpha: significance level verbose (Optional): bool, prints the regression summary if True \"\"\" results_ols = self . fit_ols_clustered ( df ) std_error = results_ols . bse [ self . treatment_col ] ate = results_ols . params [ self . treatment_col ] p_value = self . pvalue_based_on_hypothesis ( results_ols ) # Extract the confidence interval for the treatment column conf_int_df = results_ols . conf_int ( alpha = alpha ) lower_bound , upper_bound = conf_int_df . loc [ self . treatment_col ] if verbose : print ( results_ols . summary ()) # Return the confidence interval return InferenceResults ( ate = ate , p_value = p_value , std_error = std_error , conf_int = ConfidenceInterval ( lower = lower_bound , upper = upper_bound , alpha = alpha ), ) analysis_point_estimate ( self , df , verbose = False ) \u00b6 Returns the point estimate of the analysis Parameters: Name Type Description Default df DataFrame dataframe containing the data to analyze required verbose Optional bool, prints the regression summary if True False Source code in cluster_experiments/experiment_analysis.py def analysis_point_estimate ( self , df : pd . DataFrame , verbose : bool = False ) -> float : \"\"\"Returns the point estimate of the analysis Arguments: df: dataframe containing the data to analyze verbose (Optional): bool, prints the regression summary if True \"\"\" results_ols = self . fit_ols_clustered ( df ) return results_ols . params [ self . treatment_col ] analysis_pvalue ( self , df , verbose = False ) \u00b6 Returns the p-value of the analysis Parameters: Name Type Description Default df DataFrame dataframe containing the data to analyze required verbose Optional bool, prints the regression summary if True False Source code in cluster_experiments/experiment_analysis.py def analysis_pvalue ( self , df : pd . DataFrame , verbose : bool = False ) -> float : \"\"\"Returns the p-value of the analysis Arguments: df: dataframe containing the data to analyze verbose (Optional): bool, prints the regression summary if True \"\"\" results_ols = self . fit_ols_clustered ( df ) if verbose : print ( results_ols . summary ()) p_value = self . pvalue_based_on_hypothesis ( results_ols ) return p_value analysis_standard_error ( self , df , verbose = False ) \u00b6 Returns the standard error of the analysis Parameters: Name Type Description Default df DataFrame dataframe containing the data to analyze required verbose Optional bool, prints the regression summary if True False Source code in cluster_experiments/experiment_analysis.py def analysis_standard_error ( self , df : pd . DataFrame , verbose : bool = False ) -> float : \"\"\"Returns the standard error of the analysis Arguments: df: dataframe containing the data to analyze verbose (Optional): bool, prints the regression summary if True \"\"\" results_ols = self . fit_ols_clustered ( df ) return results_ols . bse [ self . treatment_col ] fit_ols_clustered ( self , df ) \u00b6 Returns the fitted OLS model Source code in cluster_experiments/experiment_analysis.py def fit_ols_clustered ( self , df : pd . DataFrame ): \"\"\"Returns the fitted OLS model\"\"\" return sm . OLS . from_formula ( self . formula , data = df ,) . fit ( cov_type = self . cov_type , cov_kwds = { \"groups\" : self . _get_cluster_column ( df )}, ) ConfidenceInterval dataclass \u00b6 Class to define the structure of a confidence interval. Source code in cluster_experiments/experiment_analysis.py class ConfidenceInterval : \"\"\" Class to define the structure of a confidence interval. \"\"\" lower : float upper : float alpha : float ExperimentAnalysis ( ABC ) \u00b6 Abstract class to run the analysis of a given experiment In order to create your own ExperimentAnalysis, you should create a derived class that implements the analysis_pvalue method. It can also be used as a component of the PowerAnalysis class. Parameters: Name Type Description Default cluster_cols List[str] list of columns to use as clusters required target_col str name of the column containing the variable to measure 'target' treatment_col str name of the column containing the treatment variable 'treatment' treatment str name of the treatment to use as the treated group 'B' covariates Optional[List[str]] list of columns to use as covariates None hypothesis str one of \"two-sided\", \"less\", \"greater\" indicating the alternative hypothesis 'two-sided' Source code in cluster_experiments/experiment_analysis.py class ExperimentAnalysis ( ABC ): \"\"\" Abstract class to run the analysis of a given experiment In order to create your own ExperimentAnalysis, you should create a derived class that implements the analysis_pvalue method. It can also be used as a component of the PowerAnalysis class. Arguments: cluster_cols: list of columns to use as clusters target_col: name of the column containing the variable to measure treatment_col: name of the column containing the treatment variable treatment: name of the treatment to use as the treated group covariates: list of columns to use as covariates hypothesis: one of \"two-sided\", \"less\", \"greater\" indicating the alternative hypothesis \"\"\" def __init__ ( self , cluster_cols : List [ str ], target_col : str = \"target\" , treatment_col : str = \"treatment\" , treatment : str = \"B\" , covariates : Optional [ List [ str ]] = None , hypothesis : str = \"two-sided\" , ): self . target_col = target_col self . treatment = treatment self . treatment_col = treatment_col self . cluster_cols = cluster_cols self . covariates = covariates or [] self . hypothesis = hypothesis def _get_cluster_column ( self , df : pd . DataFrame ) -> pd . Series : \"\"\"Paste all strings of cluster_cols in one single column\"\"\" df = df . copy () return df [ self . cluster_cols ] . astype ( str ) . sum ( axis = 1 ) def _create_binary_treatment ( self , df : pd . DataFrame ) -> pd . DataFrame : \"\"\"Transforms treatment column into 0 - 1 column\"\"\" df = df . copy () df [ self . treatment_col ] = ( df [ self . treatment_col ] == self . treatment ) . astype ( int ) return df @abstractmethod def analysis_pvalue ( self , df : pd . DataFrame , verbose : bool = False , ) -> float : \"\"\" Returns the p-value of the analysis. Expects treatment to be 0-1 variable Arguments: df: dataframe containing the data to analyze verbose (Optional): bool, prints the regression summary if True \"\"\" def analysis_point_estimate ( self , df : pd . DataFrame , verbose : bool = False , ) -> float : \"\"\" Returns the point estimate of the analysis. Expects treatment to be 0-1 variable Arguments: df: dataframe containing the data to analyze verbose (Optional): bool, prints the regression summary if True \"\"\" raise NotImplementedError ( \"Point estimate not implemented for this analysis\" ) def analysis_standard_error ( self , df : pd . DataFrame , verbose : bool = False , ) -> float : \"\"\" Returns the standard error of the analysis. Expects treatment to be 0-1 variable Arguments: df: dataframe containing the data to analyze verbose (Optional): bool, prints the regression summary if True \"\"\" raise NotImplementedError ( \"Standard error not implemented for this analysis\" ) def analysis_confidence_interval ( self , df : pd . DataFrame , alpha : float , verbose : bool = False , ) -> ConfidenceInterval : \"\"\" Returns the confidence interval of the analysis. Expects treatment to be 0-1 variable Arguments: df: dataframe containing the data to analyze alpha: significance level verbose (Optional): bool, prints the regression summary if True \"\"\" raise NotImplementedError ( \"Confidence Interval not implemented for this analysis\" ) def analysis_inference_results ( self , df : pd . DataFrame , alpha : float , verbose : bool = False , ) -> InferenceResults : \"\"\" Returns the InferenceResults object of the analysis. Expects treatment to be 0-1 variable Arguments: df: dataframe containing the data to analyze alpha: significance level verbose (Optional): bool, prints the regression summary if True \"\"\" raise NotImplementedError ( \"Inference results are not implemented for this analysis\" ) def _data_checks ( self , df : pd . DataFrame ) -> None : \"\"\"Checks that the data is correct\"\"\" if df [ self . target_col ] . isnull () . any (): raise ValueError ( f \"There are null values in outcome column { self . treatment_col } \" ) if not is_numeric_dtype ( df [ self . target_col ]): raise ValueError ( f \"Outcome column { self . target_col } should be numeric and not { df [ self . target_col ] . dtype } \" ) def get_pvalue ( self , df : pd . DataFrame ) -> float : \"\"\"Returns the p-value of the analysis Arguments: df: dataframe containing the data to analyze \"\"\" df = df . copy () df = self . _create_binary_treatment ( df ) self . _data_checks ( df = df ) return self . analysis_pvalue ( df ) def get_point_estimate ( self , df : pd . DataFrame ) -> float : \"\"\"Returns the point estimate of the analysis Arguments: df: dataframe containing the data to analyze \"\"\" df = df . copy () df = self . _create_binary_treatment ( df ) self . _data_checks ( df = df ) return self . analysis_point_estimate ( df ) def get_standard_error ( self , df : pd . DataFrame ) -> float : \"\"\"Returns the standard error of the analysis Arguments: df: dataframe containing the data to analyze \"\"\" df = df . copy () df = self . _create_binary_treatment ( df ) self . _data_checks ( df = df ) return self . analysis_standard_error ( df ) def get_confidence_interval ( self , df : pd . DataFrame , alpha : float ) -> ConfidenceInterval : \"\"\"Returns the confidence interval of the analysis Arguments: df: dataframe containing the data to analyze alpha: significance level \"\"\" df = df . copy () df = self . _create_binary_treatment ( df ) self . _data_checks ( df = df ) return self . analysis_confidence_interval ( df , alpha ) def get_inference_results ( self , df : pd . DataFrame , alpha : float ) -> InferenceResults : \"\"\"Returns the inference results of the analysis Arguments: df: dataframe containing the data to analyze alpha: significance level \"\"\" df = df . copy () df = self . _create_binary_treatment ( df ) self . _data_checks ( df = df ) return self . analysis_inference_results ( df , alpha ) def pvalue_based_on_hypothesis ( self , model_result ) -> float : # todo add typehint statsmodels result \"\"\"Returns the p-value of the analysis Arguments: model_result: statsmodels result object verbose (Optional): bool, prints the regression summary if True \"\"\" treatment_effect = model_result . params [ self . treatment_col ] p_value = model_result . pvalues [ self . treatment_col ] if HypothesisEntries ( self . hypothesis ) == HypothesisEntries . LESS : return p_value / 2 if treatment_effect <= 0 else 1 - p_value / 2 if HypothesisEntries ( self . hypothesis ) == HypothesisEntries . GREATER : return p_value / 2 if treatment_effect >= 0 else 1 - p_value / 2 if HypothesisEntries ( self . hypothesis ) == HypothesisEntries . TWO_SIDED : return p_value raise ValueError ( f \" { self . hypothesis } is not a valid HypothesisEntries\" ) def _split_pre_experiment_df ( self , df : pd . DataFrame ): raise NotImplementedError ( \"This method should be implemented in the child class\" ) @classmethod def from_config ( cls , config ): \"\"\"Creates an ExperimentAnalysis object from a PowerConfig object\"\"\" return cls ( cluster_cols = config . cluster_cols , target_col = config . target_col , treatment_col = config . treatment_col , treatment = config . treatment , covariates = config . covariates , hypothesis = config . hypothesis , ) analysis_confidence_interval ( self , df , alpha , verbose = False ) \u00b6 Returns the confidence interval of the analysis. Expects treatment to be 0-1 variable Parameters: Name Type Description Default df DataFrame dataframe containing the data to analyze required alpha float significance level required verbose Optional bool, prints the regression summary if True False Source code in cluster_experiments/experiment_analysis.py def analysis_confidence_interval ( self , df : pd . DataFrame , alpha : float , verbose : bool = False , ) -> ConfidenceInterval : \"\"\" Returns the confidence interval of the analysis. Expects treatment to be 0-1 variable Arguments: df: dataframe containing the data to analyze alpha: significance level verbose (Optional): bool, prints the regression summary if True \"\"\" raise NotImplementedError ( \"Confidence Interval not implemented for this analysis\" ) analysis_inference_results ( self , df , alpha , verbose = False ) \u00b6 Returns the InferenceResults object of the analysis. Expects treatment to be 0-1 variable Parameters: Name Type Description Default df DataFrame dataframe containing the data to analyze required alpha float significance level required verbose Optional bool, prints the regression summary if True False Source code in cluster_experiments/experiment_analysis.py def analysis_inference_results ( self , df : pd . DataFrame , alpha : float , verbose : bool = False , ) -> InferenceResults : \"\"\" Returns the InferenceResults object of the analysis. Expects treatment to be 0-1 variable Arguments: df: dataframe containing the data to analyze alpha: significance level verbose (Optional): bool, prints the regression summary if True \"\"\" raise NotImplementedError ( \"Inference results are not implemented for this analysis\" ) analysis_point_estimate ( self , df , verbose = False ) \u00b6 Returns the point estimate of the analysis. Expects treatment to be 0-1 variable Parameters: Name Type Description Default df DataFrame dataframe containing the data to analyze required verbose Optional bool, prints the regression summary if True False Source code in cluster_experiments/experiment_analysis.py def analysis_point_estimate ( self , df : pd . DataFrame , verbose : bool = False , ) -> float : \"\"\" Returns the point estimate of the analysis. Expects treatment to be 0-1 variable Arguments: df: dataframe containing the data to analyze verbose (Optional): bool, prints the regression summary if True \"\"\" raise NotImplementedError ( \"Point estimate not implemented for this analysis\" ) analysis_pvalue ( self , df , verbose = False ) \u00b6 Returns the p-value of the analysis. Expects treatment to be 0-1 variable Parameters: Name Type Description Default df DataFrame dataframe containing the data to analyze required verbose Optional bool, prints the regression summary if True False Source code in cluster_experiments/experiment_analysis.py @abstractmethod def analysis_pvalue ( self , df : pd . DataFrame , verbose : bool = False , ) -> float : \"\"\" Returns the p-value of the analysis. Expects treatment to be 0-1 variable Arguments: df: dataframe containing the data to analyze verbose (Optional): bool, prints the regression summary if True \"\"\" analysis_standard_error ( self , df , verbose = False ) \u00b6 Returns the standard error of the analysis. Expects treatment to be 0-1 variable Parameters: Name Type Description Default df DataFrame dataframe containing the data to analyze required verbose Optional bool, prints the regression summary if True False Source code in cluster_experiments/experiment_analysis.py def analysis_standard_error ( self , df : pd . DataFrame , verbose : bool = False , ) -> float : \"\"\" Returns the standard error of the analysis. Expects treatment to be 0-1 variable Arguments: df: dataframe containing the data to analyze verbose (Optional): bool, prints the regression summary if True \"\"\" raise NotImplementedError ( \"Standard error not implemented for this analysis\" ) from_config ( config ) classmethod \u00b6 Creates an ExperimentAnalysis object from a PowerConfig object Source code in cluster_experiments/experiment_analysis.py @classmethod def from_config ( cls , config ): \"\"\"Creates an ExperimentAnalysis object from a PowerConfig object\"\"\" return cls ( cluster_cols = config . cluster_cols , target_col = config . target_col , treatment_col = config . treatment_col , treatment = config . treatment , covariates = config . covariates , hypothesis = config . hypothesis , ) get_confidence_interval ( self , df , alpha ) \u00b6 Returns the confidence interval of the analysis Parameters: Name Type Description Default df DataFrame dataframe containing the data to analyze required alpha float significance level required Source code in cluster_experiments/experiment_analysis.py def get_confidence_interval ( self , df : pd . DataFrame , alpha : float ) -> ConfidenceInterval : \"\"\"Returns the confidence interval of the analysis Arguments: df: dataframe containing the data to analyze alpha: significance level \"\"\" df = df . copy () df = self . _create_binary_treatment ( df ) self . _data_checks ( df = df ) return self . analysis_confidence_interval ( df , alpha ) get_inference_results ( self , df , alpha ) \u00b6 Returns the inference results of the analysis Parameters: Name Type Description Default df DataFrame dataframe containing the data to analyze required alpha float significance level required Source code in cluster_experiments/experiment_analysis.py def get_inference_results ( self , df : pd . DataFrame , alpha : float ) -> InferenceResults : \"\"\"Returns the inference results of the analysis Arguments: df: dataframe containing the data to analyze alpha: significance level \"\"\" df = df . copy () df = self . _create_binary_treatment ( df ) self . _data_checks ( df = df ) return self . analysis_inference_results ( df , alpha ) get_point_estimate ( self , df ) \u00b6 Returns the point estimate of the analysis Parameters: Name Type Description Default df DataFrame dataframe containing the data to analyze required Source code in cluster_experiments/experiment_analysis.py def get_point_estimate ( self , df : pd . DataFrame ) -> float : \"\"\"Returns the point estimate of the analysis Arguments: df: dataframe containing the data to analyze \"\"\" df = df . copy () df = self . _create_binary_treatment ( df ) self . _data_checks ( df = df ) return self . analysis_point_estimate ( df ) get_pvalue ( self , df ) \u00b6 Returns the p-value of the analysis Parameters: Name Type Description Default df DataFrame dataframe containing the data to analyze required Source code in cluster_experiments/experiment_analysis.py def get_pvalue ( self , df : pd . DataFrame ) -> float : \"\"\"Returns the p-value of the analysis Arguments: df: dataframe containing the data to analyze \"\"\" df = df . copy () df = self . _create_binary_treatment ( df ) self . _data_checks ( df = df ) return self . analysis_pvalue ( df ) get_standard_error ( self , df ) \u00b6 Returns the standard error of the analysis Parameters: Name Type Description Default df DataFrame dataframe containing the data to analyze required Source code in cluster_experiments/experiment_analysis.py def get_standard_error ( self , df : pd . DataFrame ) -> float : \"\"\"Returns the standard error of the analysis Arguments: df: dataframe containing the data to analyze \"\"\" df = df . copy () df = self . _create_binary_treatment ( df ) self . _data_checks ( df = df ) return self . analysis_standard_error ( df ) pvalue_based_on_hypothesis ( self , model_result ) \u00b6 Returns the p-value of the analysis Parameters: Name Type Description Default model_result statsmodels result object required verbose Optional bool, prints the regression summary if True required Source code in cluster_experiments/experiment_analysis.py def pvalue_based_on_hypothesis ( self , model_result ) -> float : # todo add typehint statsmodels result \"\"\"Returns the p-value of the analysis Arguments: model_result: statsmodels result object verbose (Optional): bool, prints the regression summary if True \"\"\" treatment_effect = model_result . params [ self . treatment_col ] p_value = model_result . pvalues [ self . treatment_col ] if HypothesisEntries ( self . hypothesis ) == HypothesisEntries . LESS : return p_value / 2 if treatment_effect <= 0 else 1 - p_value / 2 if HypothesisEntries ( self . hypothesis ) == HypothesisEntries . GREATER : return p_value / 2 if treatment_effect >= 0 else 1 - p_value / 2 if HypothesisEntries ( self . hypothesis ) == HypothesisEntries . TWO_SIDED : return p_value raise ValueError ( f \" { self . hypothesis } is not a valid HypothesisEntries\" ) GeeExperimentAnalysis ( ExperimentAnalysis ) \u00b6 Class to run GEE clustered analysis Parameters: Name Type Description Default cluster_cols List[str] list of columns to use as clusters required target_col str name of the column containing the variable to measure 'target' treatment_col str name of the column containing the treatment variable 'treatment' treatment str name of the treatment to use as the treated group 'B' covariates Optional[List[str]] list of columns to use as covariates None hypothesis str one of \"two-sided\", \"less\", \"greater\" indicating the alternative hypothesis 'two-sided' Usage: from cluster_experiments.experiment_analysis import GeeExperimentAnalysis import pandas as pd df = pd . DataFrame ({ 'x' : [ 1 , 2 , 3 , 0 , 0 , 1 ], 'treatment' : [ \"A\" ] * 3 + [ \"B\" ] * 3 , 'cluster' : [ 1 ] * 6 , }) GeeExperimentAnalysis ( cluster_cols = [ 'cluster' ], target_col = 'x' , ) . get_pvalue ( df ) Source code in cluster_experiments/experiment_analysis.py class GeeExperimentAnalysis ( ExperimentAnalysis ): \"\"\" Class to run GEE clustered analysis Arguments: cluster_cols: list of columns to use as clusters target_col: name of the column containing the variable to measure treatment_col: name of the column containing the treatment variable treatment: name of the treatment to use as the treated group covariates: list of columns to use as covariates hypothesis: one of \"two-sided\", \"less\", \"greater\" indicating the alternative hypothesis Usage: ```python from cluster_experiments.experiment_analysis import GeeExperimentAnalysis import pandas as pd df = pd.DataFrame({ 'x': [1, 2, 3, 0, 0, 1], 'treatment': [\"A\"] * 3 + [\"B\"] * 3, 'cluster': [1] * 6, }) GeeExperimentAnalysis( cluster_cols=['cluster'], target_col='x', ).get_pvalue(df) ``` \"\"\" def __init__ ( self , cluster_cols : List [ str ], target_col : str = \"target\" , treatment_col : str = \"treatment\" , treatment : str = \"B\" , covariates : Optional [ List [ str ]] = None , hypothesis : str = \"two-sided\" , ): super () . __init__ ( target_col = target_col , treatment_col = treatment_col , cluster_cols = cluster_cols , treatment = treatment , covariates = covariates , hypothesis = hypothesis , ) self . regressors = [ self . treatment_col ] + self . covariates self . formula = f \" { self . target_col } ~ { ' + ' . join ( self . regressors ) } \" self . fam = sm . families . Gaussian () self . va = sm . cov_struct . Exchangeable () def fit_gee ( self , df : pd . DataFrame ) -> sm . GEE : \"\"\"Returns the fitted GEE model\"\"\" return sm . GEE . from_formula ( self . formula , data = df , groups = self . _get_cluster_column ( df ), family = self . fam , cov_struct = self . va , ) . fit () def analysis_pvalue ( self , df : pd . DataFrame , verbose : bool = False ) -> float : \"\"\"Returns the p-value of the analysis Arguments: df: dataframe containing the data to analyze verbose (Optional): bool, prints the regression summary if True \"\"\" results_gee = self . fit_gee ( df ) if verbose : print ( results_gee . summary ()) p_value = self . pvalue_based_on_hypothesis ( results_gee ) return p_value def analysis_point_estimate ( self , df : pd . DataFrame , verbose : bool = False ) -> float : \"\"\"Returns the point estimate of the analysis Arguments: df: dataframe containing the data to analyze verbose (Optional): bool, prints the regression summary if True \"\"\" results_gee = self . fit_gee ( df ) return results_gee . params [ self . treatment_col ] def analysis_standard_error ( self , df : pd . DataFrame , verbose : bool = False ) -> float : \"\"\"Returns the standard error of the analysis Arguments: df: dataframe containing the data to analyze verbose (Optional): bool, prints the regression summary if True \"\"\" results_gee = self . fit_gee ( df ) return results_gee . bse [ self . treatment_col ] def analysis_confidence_interval ( self , df : pd . DataFrame , alpha : float , verbose : bool = False ) -> ConfidenceInterval : \"\"\"Returns the confidence interval of the analysis Arguments: df: dataframe containing the data to analyze alpha: significance level verbose (Optional): bool, prints the regression summary if True \"\"\" results_gee = self . fit_gee ( df ) # Extract the confidence interval for the treatment column conf_int_df = results_gee . conf_int ( alpha = alpha ) lower_bound , upper_bound = conf_int_df . loc [ self . treatment_col ] if verbose : print ( results_gee . summary ()) # Return the confidence interval return ConfidenceInterval ( lower = lower_bound , upper = upper_bound , alpha = alpha ) def analysis_inference_results ( self , df : pd . DataFrame , alpha : float , verbose : bool = False ) -> InferenceResults : \"\"\"Returns the inference results of the analysis Arguments: df: dataframe containing the data to analyze alpha: significance level verbose (Optional): bool, prints the regression summary if True \"\"\" results_gee = self . fit_gee ( df ) std_error = results_gee . bse [ self . treatment_col ] ate = results_gee . params [ self . treatment_col ] p_value = self . pvalue_based_on_hypothesis ( results_gee ) # Extract the confidence interval for the treatment column conf_int_df = results_gee . conf_int ( alpha = alpha ) lower_bound , upper_bound = conf_int_df . loc [ self . treatment_col ] if verbose : print ( results_gee . summary ()) # Return the confidence interval return InferenceResults ( ate = ate , p_value = p_value , std_error = std_error , conf_int = ConfidenceInterval ( lower = lower_bound , upper = upper_bound , alpha = alpha ), ) analysis_confidence_interval ( self , df , alpha , verbose = False ) \u00b6 Returns the confidence interval of the analysis Parameters: Name Type Description Default df DataFrame dataframe containing the data to analyze required alpha float significance level required verbose Optional bool, prints the regression summary if True False Source code in cluster_experiments/experiment_analysis.py def analysis_confidence_interval ( self , df : pd . DataFrame , alpha : float , verbose : bool = False ) -> ConfidenceInterval : \"\"\"Returns the confidence interval of the analysis Arguments: df: dataframe containing the data to analyze alpha: significance level verbose (Optional): bool, prints the regression summary if True \"\"\" results_gee = self . fit_gee ( df ) # Extract the confidence interval for the treatment column conf_int_df = results_gee . conf_int ( alpha = alpha ) lower_bound , upper_bound = conf_int_df . loc [ self . treatment_col ] if verbose : print ( results_gee . summary ()) # Return the confidence interval return ConfidenceInterval ( lower = lower_bound , upper = upper_bound , alpha = alpha ) analysis_inference_results ( self , df , alpha , verbose = False ) \u00b6 Returns the inference results of the analysis Parameters: Name Type Description Default df DataFrame dataframe containing the data to analyze required alpha float significance level required verbose Optional bool, prints the regression summary if True False Source code in cluster_experiments/experiment_analysis.py def analysis_inference_results ( self , df : pd . DataFrame , alpha : float , verbose : bool = False ) -> InferenceResults : \"\"\"Returns the inference results of the analysis Arguments: df: dataframe containing the data to analyze alpha: significance level verbose (Optional): bool, prints the regression summary if True \"\"\" results_gee = self . fit_gee ( df ) std_error = results_gee . bse [ self . treatment_col ] ate = results_gee . params [ self . treatment_col ] p_value = self . pvalue_based_on_hypothesis ( results_gee ) # Extract the confidence interval for the treatment column conf_int_df = results_gee . conf_int ( alpha = alpha ) lower_bound , upper_bound = conf_int_df . loc [ self . treatment_col ] if verbose : print ( results_gee . summary ()) # Return the confidence interval return InferenceResults ( ate = ate , p_value = p_value , std_error = std_error , conf_int = ConfidenceInterval ( lower = lower_bound , upper = upper_bound , alpha = alpha ), ) analysis_point_estimate ( self , df , verbose = False ) \u00b6 Returns the point estimate of the analysis Parameters: Name Type Description Default df DataFrame dataframe containing the data to analyze required verbose Optional bool, prints the regression summary if True False Source code in cluster_experiments/experiment_analysis.py def analysis_point_estimate ( self , df : pd . DataFrame , verbose : bool = False ) -> float : \"\"\"Returns the point estimate of the analysis Arguments: df: dataframe containing the data to analyze verbose (Optional): bool, prints the regression summary if True \"\"\" results_gee = self . fit_gee ( df ) return results_gee . params [ self . treatment_col ] analysis_pvalue ( self , df , verbose = False ) \u00b6 Returns the p-value of the analysis Parameters: Name Type Description Default df DataFrame dataframe containing the data to analyze required verbose Optional bool, prints the regression summary if True False Source code in cluster_experiments/experiment_analysis.py def analysis_pvalue ( self , df : pd . DataFrame , verbose : bool = False ) -> float : \"\"\"Returns the p-value of the analysis Arguments: df: dataframe containing the data to analyze verbose (Optional): bool, prints the regression summary if True \"\"\" results_gee = self . fit_gee ( df ) if verbose : print ( results_gee . summary ()) p_value = self . pvalue_based_on_hypothesis ( results_gee ) return p_value analysis_standard_error ( self , df , verbose = False ) \u00b6 Returns the standard error of the analysis Parameters: Name Type Description Default df DataFrame dataframe containing the data to analyze required verbose Optional bool, prints the regression summary if True False Source code in cluster_experiments/experiment_analysis.py def analysis_standard_error ( self , df : pd . DataFrame , verbose : bool = False ) -> float : \"\"\"Returns the standard error of the analysis Arguments: df: dataframe containing the data to analyze verbose (Optional): bool, prints the regression summary if True \"\"\" results_gee = self . fit_gee ( df ) return results_gee . bse [ self . treatment_col ] fit_gee ( self , df ) \u00b6 Returns the fitted GEE model Source code in cluster_experiments/experiment_analysis.py def fit_gee ( self , df : pd . DataFrame ) -> sm . GEE : \"\"\"Returns the fitted GEE model\"\"\" return sm . GEE . from_formula ( self . formula , data = df , groups = self . _get_cluster_column ( df ), family = self . fam , cov_struct = self . va , ) . fit () InferenceResults dataclass \u00b6 Class to define the structure of complete statistical analysis results. Source code in cluster_experiments/experiment_analysis.py class InferenceResults : \"\"\" Class to define the structure of complete statistical analysis results. \"\"\" ate : float p_value : float std_error : float conf_int : ConfidenceInterval MLMExperimentAnalysis ( ExperimentAnalysis ) \u00b6 Class to run Mixed Linear Models clustered analysis Parameters: Name Type Description Default cluster_cols List[str] list of columns to use as clusters required target_col str name of the column containing the variable to measure 'target' treatment_col str name of the column containing the treatment variable 'treatment' treatment str name of the treatment to use as the treated group 'B' covariates Optional[List[str]] list of columns to use as covariates None hypothesis str one of \"two-sided\", \"less\", \"greater\" indicating the alternative hypothesis 'two-sided' Usage: from cluster_experiments.experiment_analysis import MLMExperimentAnalysis import pandas as pd df = pd . DataFrame ({ 'x' : [ 1 , 2 , 3 , 0 , 0 , 1 ], 'treatment' : [ \"A\" ] * 3 + [ \"B\" ] * 3 , 'cluster' : [ 1 ] * 6 , }) MLMExperimentAnalysis ( cluster_cols = [ 'cluster' ], target_col = 'x' , ) . get_pvalue ( df ) Source code in cluster_experiments/experiment_analysis.py class MLMExperimentAnalysis ( ExperimentAnalysis ): \"\"\" Class to run Mixed Linear Models clustered analysis Arguments: cluster_cols: list of columns to use as clusters target_col: name of the column containing the variable to measure treatment_col: name of the column containing the treatment variable treatment: name of the treatment to use as the treated group covariates: list of columns to use as covariates hypothesis: one of \"two-sided\", \"less\", \"greater\" indicating the alternative hypothesis Usage: ```python from cluster_experiments.experiment_analysis import MLMExperimentAnalysis import pandas as pd df = pd.DataFrame({ 'x': [1, 2, 3, 0, 0, 1], 'treatment': [\"A\"] * 3 + [\"B\"] * 3, 'cluster': [1] * 6, }) MLMExperimentAnalysis( cluster_cols=['cluster'], target_col='x', ).get_pvalue(df) ``` \"\"\" def __init__ ( self , cluster_cols : List [ str ], target_col : str = \"target\" , treatment_col : str = \"treatment\" , treatment : str = \"B\" , covariates : Optional [ List [ str ]] = None , hypothesis : str = \"two-sided\" , ): super () . __init__ ( target_col = target_col , treatment_col = treatment_col , cluster_cols = cluster_cols , treatment = treatment , covariates = covariates , hypothesis = hypothesis , ) self . regressors = [ self . treatment_col ] + self . covariates self . formula = f \" { self . target_col } ~ { ' + ' . join ( self . regressors ) } \" self . re_formula = None self . vc_formula = None def fit_mlm ( self , df : pd . DataFrame ) -> sm . MixedLM : \"\"\"Returns the fitted MLM model\"\"\" return sm . MixedLM . from_formula ( formula = self . formula , data = df , groups = self . _get_cluster_column ( df ), re_formula = self . re_formula , vc_formula = self . vc_formula , ) . fit () def analysis_pvalue ( self , df : pd . DataFrame , verbose : bool = False ) -> float : \"\"\"Returns the p-value of the analysis Arguments: df: dataframe containing the data to analyze verbose (Optional): bool, prints the regression summary if True \"\"\" results_mlm = self . fit_mlm ( df ) if verbose : print ( results_mlm . summary ()) p_value = self . pvalue_based_on_hypothesis ( results_mlm ) return p_value def analysis_point_estimate ( self , df : pd . DataFrame , verbose : bool = False ) -> float : \"\"\"Returns the point estimate of the analysis Arguments: df: dataframe containing the data to analyze verbose (Optional): bool, prints the regression summary if True \"\"\" results_mlm = self . fit_mlm ( df ) return results_mlm . params [ self . treatment_col ] def analysis_standard_error ( self , df : pd . DataFrame , verbose : bool = False ) -> float : \"\"\"Returns the standard error of the analysis Arguments: df: dataframe containing the data to analyze verbose (Optional): bool, prints the regression summary if True \"\"\" results_mlm = self . fit_mlm ( df ) return results_mlm . bse [ self . treatment_col ] analysis_point_estimate ( self , df , verbose = False ) \u00b6 Returns the point estimate of the analysis Parameters: Name Type Description Default df DataFrame dataframe containing the data to analyze required verbose Optional bool, prints the regression summary if True False Source code in cluster_experiments/experiment_analysis.py def analysis_point_estimate ( self , df : pd . DataFrame , verbose : bool = False ) -> float : \"\"\"Returns the point estimate of the analysis Arguments: df: dataframe containing the data to analyze verbose (Optional): bool, prints the regression summary if True \"\"\" results_mlm = self . fit_mlm ( df ) return results_mlm . params [ self . treatment_col ] analysis_pvalue ( self , df , verbose = False ) \u00b6 Returns the p-value of the analysis Parameters: Name Type Description Default df DataFrame dataframe containing the data to analyze required verbose Optional bool, prints the regression summary if True False Source code in cluster_experiments/experiment_analysis.py def analysis_pvalue ( self , df : pd . DataFrame , verbose : bool = False ) -> float : \"\"\"Returns the p-value of the analysis Arguments: df: dataframe containing the data to analyze verbose (Optional): bool, prints the regression summary if True \"\"\" results_mlm = self . fit_mlm ( df ) if verbose : print ( results_mlm . summary ()) p_value = self . pvalue_based_on_hypothesis ( results_mlm ) return p_value analysis_standard_error ( self , df , verbose = False ) \u00b6 Returns the standard error of the analysis Parameters: Name Type Description Default df DataFrame dataframe containing the data to analyze required verbose Optional bool, prints the regression summary if True False Source code in cluster_experiments/experiment_analysis.py def analysis_standard_error ( self , df : pd . DataFrame , verbose : bool = False ) -> float : \"\"\"Returns the standard error of the analysis Arguments: df: dataframe containing the data to analyze verbose (Optional): bool, prints the regression summary if True \"\"\" results_mlm = self . fit_mlm ( df ) return results_mlm . bse [ self . treatment_col ] fit_mlm ( self , df ) \u00b6 Returns the fitted MLM model Source code in cluster_experiments/experiment_analysis.py def fit_mlm ( self , df : pd . DataFrame ) -> sm . MixedLM : \"\"\"Returns the fitted MLM model\"\"\" return sm . MixedLM . from_formula ( formula = self . formula , data = df , groups = self . _get_cluster_column ( df ), re_formula = self . re_formula , vc_formula = self . vc_formula , ) . fit () OLSAnalysis ( ExperimentAnalysis ) \u00b6 Class to run OLS analysis Parameters: Name Type Description Default target_col str name of the column containing the variable to measure 'target' treatment_col str name of the column containing the treatment variable 'treatment' treatment str name of the treatment to use as the treated group 'B' covariates Optional[List[str]] list of columns to use as covariates None hypothesis str one of \"two-sided\", \"less\", \"greater\" indicating the alternative hypothesis 'two-sided' Usage: from cluster_experiments.experiment_analysis import OLSAnalysis import pandas as pd df = pd . DataFrame ({ 'x' : [ 1 , 2 , 3 , 0 , 0 , 1 ], 'treatment' : [ \"A\" ] * 3 + [ \"B\" ] * 3 , }) OLSAnalysis ( target_col = 'x' , ) . get_pvalue ( df ) Source code in cluster_experiments/experiment_analysis.py class OLSAnalysis ( ExperimentAnalysis ): \"\"\" Class to run OLS analysis Arguments: target_col: name of the column containing the variable to measure treatment_col: name of the column containing the treatment variable treatment: name of the treatment to use as the treated group covariates: list of columns to use as covariates hypothesis: one of \"two-sided\", \"less\", \"greater\" indicating the alternative hypothesis Usage: ```python from cluster_experiments.experiment_analysis import OLSAnalysis import pandas as pd df = pd.DataFrame({ 'x': [1, 2, 3, 0, 0, 1], 'treatment': [\"A\"] * 3 + [\"B\"] * 3, }) OLSAnalysis( target_col='x', ).get_pvalue(df) ``` \"\"\" def __init__ ( self , target_col : str = \"target\" , treatment_col : str = \"treatment\" , treatment : str = \"B\" , covariates : Optional [ List [ str ]] = None , hypothesis : str = \"two-sided\" , ): self . target_col = target_col self . treatment = treatment self . treatment_col = treatment_col self . covariates = covariates or [] self . regressors = [ self . treatment_col ] + self . covariates self . formula = f \" { self . target_col } ~ { ' + ' . join ( self . regressors ) } \" self . hypothesis = hypothesis def fit_ols ( self , df : pd . DataFrame ): \"\"\"Returns the fitted OLS model\"\"\" return sm . OLS . from_formula ( self . formula , data = df ) . fit () def analysis_pvalue ( self , df : pd . DataFrame , verbose : bool = False ) -> float : \"\"\"Returns the p-value of the analysis Arguments: df: dataframe containing the data to analyze verbose (Optional): bool, prints the regression summary if True \"\"\" results_ols = self . fit_ols ( df = df ) if verbose : print ( results_ols . summary ()) p_value = self . pvalue_based_on_hypothesis ( results_ols ) return p_value def analysis_point_estimate ( self , df : pd . DataFrame , verbose : bool = False ) -> float : \"\"\"Returns the point estimate of the analysis Arguments: df: dataframe containing the data to analyze verbose (Optional): bool, prints the regression summary if True \"\"\" results_ols = self . fit_ols ( df = df ) return results_ols . params [ self . treatment_col ] def analysis_standard_error ( self , df : pd . DataFrame , verbose : bool = False ) -> float : \"\"\"Returns the standard error of the analysis Arguments: df: dataframe containing the data to analyze verbose (Optional): bool, prints the regression summary if True \"\"\" results_ols = self . fit_ols ( df = df ) return results_ols . bse [ self . treatment_col ] def analysis_confidence_interval ( self , df : pd . DataFrame , alpha : float , verbose : bool = False ) -> ConfidenceInterval : \"\"\"Returns the confidence interval of the analysis Arguments: df: dataframe containing the data to analyze alpha: significance level verbose (Optional): bool, prints the regression summary if True \"\"\" results_ols = self . fit_ols ( df ) # Extract the confidence interval for the treatment column conf_int_df = results_ols . conf_int ( alpha = alpha ) lower_bound , upper_bound = conf_int_df . loc [ self . treatment_col ] if verbose : print ( results_ols . summary ()) # Return the confidence interval return ConfidenceInterval ( lower = lower_bound , upper = upper_bound , alpha = alpha ) def analysis_inference_results ( self , df : pd . DataFrame , alpha : float , verbose : bool = False ) -> InferenceResults : \"\"\"Returns the inference results of the analysis Arguments: df: dataframe containing the data to analyze alpha: significance level verbose (Optional): bool, prints the regression summary if True \"\"\" results_ols = self . fit_ols ( df ) std_error = results_ols . bse [ self . treatment_col ] ate = results_ols . params [ self . treatment_col ] p_value = self . pvalue_based_on_hypothesis ( results_ols ) # Extract the confidence interval for the treatment column conf_int_df = results_ols . conf_int ( alpha = alpha ) lower_bound , upper_bound = conf_int_df . loc [ self . treatment_col ] if verbose : print ( results_ols . summary ()) # Return the confidence interval return InferenceResults ( ate = ate , p_value = p_value , std_error = std_error , conf_int = ConfidenceInterval ( lower = lower_bound , upper = upper_bound , alpha = alpha ), ) @classmethod def from_config ( cls , config ): \"\"\"Creates an OLSAnalysis object from a PowerConfig object\"\"\" return cls ( target_col = config . target_col , treatment_col = config . treatment_col , treatment = config . treatment , covariates = config . covariates , hypothesis = config . hypothesis , ) analysis_confidence_interval ( self , df , alpha , verbose = False ) \u00b6 Returns the confidence interval of the analysis Parameters: Name Type Description Default df DataFrame dataframe containing the data to analyze required alpha float significance level required verbose Optional bool, prints the regression summary if True False Source code in cluster_experiments/experiment_analysis.py def analysis_confidence_interval ( self , df : pd . DataFrame , alpha : float , verbose : bool = False ) -> ConfidenceInterval : \"\"\"Returns the confidence interval of the analysis Arguments: df: dataframe containing the data to analyze alpha: significance level verbose (Optional): bool, prints the regression summary if True \"\"\" results_ols = self . fit_ols ( df ) # Extract the confidence interval for the treatment column conf_int_df = results_ols . conf_int ( alpha = alpha ) lower_bound , upper_bound = conf_int_df . loc [ self . treatment_col ] if verbose : print ( results_ols . summary ()) # Return the confidence interval return ConfidenceInterval ( lower = lower_bound , upper = upper_bound , alpha = alpha ) analysis_inference_results ( self , df , alpha , verbose = False ) \u00b6 Returns the inference results of the analysis Parameters: Name Type Description Default df DataFrame dataframe containing the data to analyze required alpha float significance level required verbose Optional bool, prints the regression summary if True False Source code in cluster_experiments/experiment_analysis.py def analysis_inference_results ( self , df : pd . DataFrame , alpha : float , verbose : bool = False ) -> InferenceResults : \"\"\"Returns the inference results of the analysis Arguments: df: dataframe containing the data to analyze alpha: significance level verbose (Optional): bool, prints the regression summary if True \"\"\" results_ols = self . fit_ols ( df ) std_error = results_ols . bse [ self . treatment_col ] ate = results_ols . params [ self . treatment_col ] p_value = self . pvalue_based_on_hypothesis ( results_ols ) # Extract the confidence interval for the treatment column conf_int_df = results_ols . conf_int ( alpha = alpha ) lower_bound , upper_bound = conf_int_df . loc [ self . treatment_col ] if verbose : print ( results_ols . summary ()) # Return the confidence interval return InferenceResults ( ate = ate , p_value = p_value , std_error = std_error , conf_int = ConfidenceInterval ( lower = lower_bound , upper = upper_bound , alpha = alpha ), ) analysis_point_estimate ( self , df , verbose = False ) \u00b6 Returns the point estimate of the analysis Parameters: Name Type Description Default df DataFrame dataframe containing the data to analyze required verbose Optional bool, prints the regression summary if True False Source code in cluster_experiments/experiment_analysis.py def analysis_point_estimate ( self , df : pd . DataFrame , verbose : bool = False ) -> float : \"\"\"Returns the point estimate of the analysis Arguments: df: dataframe containing the data to analyze verbose (Optional): bool, prints the regression summary if True \"\"\" results_ols = self . fit_ols ( df = df ) return results_ols . params [ self . treatment_col ] analysis_pvalue ( self , df , verbose = False ) \u00b6 Returns the p-value of the analysis Parameters: Name Type Description Default df DataFrame dataframe containing the data to analyze required verbose Optional bool, prints the regression summary if True False Source code in cluster_experiments/experiment_analysis.py def analysis_pvalue ( self , df : pd . DataFrame , verbose : bool = False ) -> float : \"\"\"Returns the p-value of the analysis Arguments: df: dataframe containing the data to analyze verbose (Optional): bool, prints the regression summary if True \"\"\" results_ols = self . fit_ols ( df = df ) if verbose : print ( results_ols . summary ()) p_value = self . pvalue_based_on_hypothesis ( results_ols ) return p_value analysis_standard_error ( self , df , verbose = False ) \u00b6 Returns the standard error of the analysis Parameters: Name Type Description Default df DataFrame dataframe containing the data to analyze required verbose Optional bool, prints the regression summary if True False Source code in cluster_experiments/experiment_analysis.py def analysis_standard_error ( self , df : pd . DataFrame , verbose : bool = False ) -> float : \"\"\"Returns the standard error of the analysis Arguments: df: dataframe containing the data to analyze verbose (Optional): bool, prints the regression summary if True \"\"\" results_ols = self . fit_ols ( df = df ) return results_ols . bse [ self . treatment_col ] fit_ols ( self , df ) \u00b6 Returns the fitted OLS model Source code in cluster_experiments/experiment_analysis.py def fit_ols ( self , df : pd . DataFrame ): \"\"\"Returns the fitted OLS model\"\"\" return sm . OLS . from_formula ( self . formula , data = df ) . fit () from_config ( config ) classmethod \u00b6 Creates an OLSAnalysis object from a PowerConfig object Source code in cluster_experiments/experiment_analysis.py @classmethod def from_config ( cls , config ): \"\"\"Creates an OLSAnalysis object from a PowerConfig object\"\"\" return cls ( target_col = config . target_col , treatment_col = config . treatment_col , treatment = config . treatment , covariates = config . covariates , hypothesis = config . hypothesis , ) PairedTTestClusteredAnalysis ( ExperimentAnalysis ) \u00b6 Class to run paired T-test analysis on aggregated data Parameters: Name Type Description Default cluster_cols List[str] list of columns to use as clusters required target_col str name of the column containing the variable to measure 'target' treatment_col str name of the column containing the treatment variable 'treatment' treatment str name of the treatment to use as the treated group 'B' strata_cols List[str] list of index columns for paired t test. Should be a subset or equal to cluster_cols required hypothesis str one of \"two-sided\", \"less\", \"greater\" indicating the alternative hypothesis 'two-sided' Usage: from cluster_experiments.experiment_analysis import PairedTTestClusteredAnalysis import pandas as pd df = pd . DataFrame ({ 'x' : [ 1 , 2 , 3 , 4 , 0 , 0 , 1 , 1 ], 'treatment' : [ \"A\" , \"B\" , \"A\" , \"B\" ] * 2 , 'cluster' : [ 1 , 2 , 3 , 4 , 1 , 2 , 3 , 4 ], }) PairedTTestClusteredAnalysis ( cluster_cols = [ 'cluster' ], strata_cols = [ 'cluster' ], target_col = 'x' , ) . get_pvalue ( df ) Source code in cluster_experiments/experiment_analysis.py class PairedTTestClusteredAnalysis ( ExperimentAnalysis ): \"\"\" Class to run paired T-test analysis on aggregated data Arguments: cluster_cols: list of columns to use as clusters target_col: name of the column containing the variable to measure treatment_col: name of the column containing the treatment variable treatment: name of the treatment to use as the treated group strata_cols: list of index columns for paired t test. Should be a subset or equal to cluster_cols hypothesis: one of \"two-sided\", \"less\", \"greater\" indicating the alternative hypothesis Usage: ```python from cluster_experiments.experiment_analysis import PairedTTestClusteredAnalysis import pandas as pd df = pd.DataFrame({ 'x': [1, 2, 3, 4, 0, 0, 1, 1], 'treatment': [\"A\", \"B\", \"A\", \"B\"] * 2, 'cluster': [1, 2, 3, 4, 1, 2, 3, 4], }) PairedTTestClusteredAnalysis( cluster_cols=['cluster'], strata_cols=['cluster'], target_col='x', ).get_pvalue(df) ``` \"\"\" def __init__ ( self , cluster_cols : List [ str ], strata_cols : List [ str ], target_col : str = \"target\" , treatment_col : str = \"treatment\" , treatment : str = \"B\" , hypothesis : str = \"two-sided\" , ): self . strata_cols = strata_cols self . target_col = target_col self . treatment = treatment self . treatment_col = treatment_col self . cluster_cols = cluster_cols self . hypothesis = hypothesis def _preprocessing ( self , df : pd . DataFrame , verbose : bool = False ) -> pd . DataFrame : df_grouped = df . groupby ( self . cluster_cols + [ self . treatment_col ], as_index = False )[ self . target_col ] . mean () n_control = df_grouped [ self . treatment_col ] . value_counts ()[ 0 ] n_treatment = df_grouped [ self . treatment_col ] . value_counts ()[ 1 ] if n_control != n_treatment : logging . warning ( f \"groups don't have same number of observations, { n_treatment =} and { n_control =} \" ) assert all ( [ x in self . cluster_cols for x in self . strata_cols ] ), f \"strata should be a subset or equal to cluster_cols ( { self . cluster_cols = } , { self . strata_cols = } )\" df_pivot = df_grouped . pivot_table ( columns = self . treatment_col , index = self . strata_cols , values = self . target_col , ) if df_pivot . isna () . sum () . sum () > 0 : logging . warning ( f \"There are missing pairs for some clusters, removing the lonely ones: { df_pivot [ df_pivot . isna () . any ( axis = 1 )] . to_dict () } \" ) if verbose : print ( f \"performing paired t test in this data \\n { df_pivot } \\n \" ) df_pivot = df_pivot . dropna () return df_pivot def analysis_pvalue ( self , df : pd . DataFrame , verbose : bool = False ) -> float : \"\"\"Returns the p-value of the analysis Arguments: df: dataframe containing the data to analyze verbose (Optional): bool, prints the extra info if True \"\"\" assert ( type ( self . cluster_cols ) is list ), \"cluster_cols needs to be a list of strings (even with one element)\" assert ( type ( self . strata_cols ) is list ), \"strata_cols needs to be a list of strings (even with one element)\" df_pivot = self . _preprocessing ( df = df ) t_test_results = ttest_rel ( df_pivot . iloc [:, 0 ], df_pivot . iloc [:, 1 ], alternative = self . hypothesis ) if verbose : print ( f \"paired t test results: \\n { t_test_results } \\n \" ) return t_test_results . pvalue @classmethod def from_config ( cls , config ): \"\"\"Creates a PairedTTestClusteredAnalysis object from a PowerConfig object\"\"\" return cls ( cluster_cols = config . cluster_cols , target_col = config . target_col , treatment_col = config . treatment_col , treatment = config . treatment , strata_cols = config . strata_cols , hypothesis = config . hypothesis , ) analysis_pvalue ( self , df , verbose = False ) \u00b6 Returns the p-value of the analysis Parameters: Name Type Description Default df DataFrame dataframe containing the data to analyze required verbose Optional bool, prints the extra info if True False Source code in cluster_experiments/experiment_analysis.py def analysis_pvalue ( self , df : pd . DataFrame , verbose : bool = False ) -> float : \"\"\"Returns the p-value of the analysis Arguments: df: dataframe containing the data to analyze verbose (Optional): bool, prints the extra info if True \"\"\" assert ( type ( self . cluster_cols ) is list ), \"cluster_cols needs to be a list of strings (even with one element)\" assert ( type ( self . strata_cols ) is list ), \"strata_cols needs to be a list of strings (even with one element)\" df_pivot = self . _preprocessing ( df = df ) t_test_results = ttest_rel ( df_pivot . iloc [:, 0 ], df_pivot . iloc [:, 1 ], alternative = self . hypothesis ) if verbose : print ( f \"paired t test results: \\n { t_test_results } \\n \" ) return t_test_results . pvalue from_config ( config ) classmethod \u00b6 Creates a PairedTTestClusteredAnalysis object from a PowerConfig object Source code in cluster_experiments/experiment_analysis.py @classmethod def from_config ( cls , config ): \"\"\"Creates a PairedTTestClusteredAnalysis object from a PowerConfig object\"\"\" return cls ( cluster_cols = config . cluster_cols , target_col = config . target_col , treatment_col = config . treatment_col , treatment = config . treatment , strata_cols = config . strata_cols , hypothesis = config . hypothesis , ) SyntheticControlAnalysis ( ExperimentAnalysis ) \u00b6 Class to run Synthetic control analysis. It expects only one treatment cluster. Parameters: Name Type Description Default target_col str The name of the column containing the variable to measure. 'target' treatment_col str The name of the column containing the treatment variable. 'treatment' treatment str The name of the treatment to use as the treated group. 'B' cluster_cols list A list of columns to use as clusters. required hypothesis str One of \"two-sided\", \"less\", \"greater\" indicating the hypothesis. 'two-sided' time_col str The name of the column containing the time data. 'date' intervention_date str The date when the intervention occurred. required Usage: from cluster_experiments.experiment_analysis import SyntheticControlAnalysis import pandas as pd import numpy as np from itertools import product dates = pd . date_range ( \"2022-01-01\" , \"2022-01-31\" , freq = \"d\" ) users = [ f \"User { i } \" for i in range ( 10 )] # Create a combination of each date with each user combinations = list ( product ( users , dates )) target_values = np . random . normal ( 0 , 1 , size = len ( combinations )) df = pd . DataFrame ( combinations , columns = [ \"user\" , \"date\" ]) df [ \"target\" ] = target_values df [ \"treatment\" ] = \"A\" df . loc [( df [ \"user\" ] == \"User 5\" ), \"treatment\" ] = \"B\" SyntheticControlAnalysis ( cluster_cols = [ \"user\" ], time_col = \"date\" , intervention_date = \"2022-01-15\" ) . get_pvalue ( df ) Source code in cluster_experiments/experiment_analysis.py class SyntheticControlAnalysis ( ExperimentAnalysis ): \"\"\" Class to run Synthetic control analysis. It expects only one treatment cluster. Arguments: target_col (str): The name of the column containing the variable to measure. treatment_col (str): The name of the column containing the treatment variable. treatment (str): The name of the treatment to use as the treated group. cluster_cols (list): A list of columns to use as clusters. hypothesis (str): One of \"two-sided\", \"less\", \"greater\" indicating the hypothesis. time_col (str): The name of the column containing the time data. intervention_date (str): The date when the intervention occurred. Usage: ```python from cluster_experiments.experiment_analysis import SyntheticControlAnalysis import pandas as pd import numpy as np from itertools import product dates = pd.date_range(\"2022-01-01\", \"2022-01-31\", freq=\"d\") users = [f\"User {i}\" for i in range(10)] # Create a combination of each date with each user combinations = list(product(users, dates)) target_values = np.random.normal(0, 1, size=len(combinations)) df = pd.DataFrame(combinations, columns=[\"user\", \"date\"]) df[\"target\"] = target_values df[\"treatment\"] = \"A\" df.loc[(df[\"user\"] == \"User 5\"), \"treatment\"] = \"B\" SyntheticControlAnalysis( cluster_cols=[\"user\"], time_col=\"date\", intervention_date=\"2022-01-15\" ).get_pvalue(df) ``` \"\"\" def __init__ ( self , intervention_date : str , cluster_cols : List [ str ], target_col : str = \"target\" , treatment_col : str = \"treatment\" , treatment : str = \"B\" , hypothesis : str = \"two-sided\" , time_col : str = \"date\" , ): super () . __init__ ( treatment = treatment , target_col = target_col , treatment_col = treatment_col , hypothesis = hypothesis , cluster_cols = cluster_cols , ) self . time_col = time_col self . intervention_date = intervention_date if time_col in cluster_cols : raise ValueError ( \"time columns should not be in cluster columns\" ) def _fit ( self , pre_experiment_df : pd . DataFrame , verbose : bool ) -> np . ndarray : \"\"\"Returns the weight of each donor\"\"\" if not any ( pre_experiment_df [ self . treatment_col ] == 1 ): raise ValueError ( \"No treatment unit found in the data.\" ) X = ( pre_experiment_df . query ( f \" { self . treatment_col } == 0\" ) . pivot ( index = self . cluster_cols , columns = self . time_col )[ self . target_col ] . T ) y = ( pre_experiment_df . query ( f \" { self . treatment_col } == 1\" ) . pivot ( index = self . cluster_cols , columns = self . time_col )[ self . target_col ] . T . iloc [:, 0 ] ) weights = get_w ( X , y , verbose ) return weights def _predict ( self , df : pd . DataFrame , weights : np . ndarray , treatment_cluster : str ) -> pd . DataFrame : \"\"\" This method adds a column with the synthetic results and filter only the treatment unit. First, it calculates the weights of each donor in the control group using the `fit_synthetic` method. It then uses these weights to create a synthetic control group that closely matches the treatment unit before the intervention. The synthetic control group is added to the treatment unit in the dataframe. \"\"\" synthetic = ( df [ self . _get_cluster_column ( df ) != treatment_cluster ] . pivot ( index = self . time_col , columns = self . cluster_cols )[ self . target_col ] . values . dot ( weights ) ) # add synthetic to treatment cluster return df [ self . _get_cluster_column ( df ) == treatment_cluster ] . assign ( synthetic = synthetic ) def fit_predict_synthetic ( self , df : pd . DataFrame , pre_experiment_df : pd . DataFrame , treatment_cluster : str , verbose : bool = False , ) -> pd . DataFrame : \"\"\" Fit the synthetic control model and predict the results for the treatment cluster. Args: df: The dataframe containing the data after the intervention. pre_experiment_df: The dataframe containing the data before the intervention. treatment_cluster: The name of the treatment cluster. verbose: If True, print the status of the optimization of weights. Returns: The dataframe with the synthetic results added to the treatment cluster. \"\"\" weights = self . _fit ( pre_experiment_df = pre_experiment_df , verbose = verbose ) prediction = self . _predict ( df = df , weights = weights , treatment_cluster = treatment_cluster ) return prediction def pvalue_based_on_hypothesis ( self , ate : np . float64 , avg_effects : Dict [ str , float ] ) -> float : \"\"\" Returns the p-value of the analysis. 1. Count how many times the average effect is greater than the real treatment unit 2. Average it with the number of units. The result is the p-value using Fisher permutation exact test. \"\"\" avg_effects = list ( avg_effects . values ()) if HypothesisEntries ( self . hypothesis ) == HypothesisEntries . LESS : return np . mean ( avg_effects < ate ) if HypothesisEntries ( self . hypothesis ) == HypothesisEntries . GREATER : return np . mean ( avg_effects > ate ) if HypothesisEntries ( self . hypothesis ) == HypothesisEntries . TWO_SIDED : avg_effects = np . abs ( avg_effects ) return np . mean ( avg_effects > ate ) raise ValueError ( f \" { self . hypothesis } is not a valid HypothesisEntries\" ) def _get_treatment_cluster ( self , df : pd . DataFrame ) -> str : \"\"\"Returns the first treatment cluster. The current implementation of Synthetic Control only accepts one treatment cluster. This will be left inside Synthetic class because it doesn't apply for other analyses\"\"\" treatment_df = df [ df [ self . treatment_col ] == 1 ] treatment_cluster = self . _get_cluster_column ( treatment_df ) . unique ()[ 0 ] return treatment_cluster def analysis_pvalue ( self , df : pd . DataFrame , verbose : bool = False ) -> float : \"\"\" Returns the p-value of the analysis. 1. Calculate the average effect after intervention for each unit. 2. Count how many times the average effect is greater than the real treatment unit 3. Average it with the number of units. The result is the p-value using Fisher permutation test \"\"\" clusters = self . _get_cluster_column ( df ) . unique () treatment_cluster = self . _get_treatment_cluster ( df ) synthetic_donors = { cluster : self . analysis_point_estimate ( treatment_cluster = cluster , df = df , verbose = verbose , ) for cluster in clusters } ate = synthetic_donors [ treatment_cluster ] synthetic_donors . pop ( treatment_cluster ) return self . pvalue_based_on_hypothesis ( ate = ate , avg_effects = synthetic_donors ) def analysis_point_estimate ( self , df : pd . DataFrame , treatment_cluster : Optional [ str ] = None , verbose : bool = False , ): \"\"\" Calculate the point estimate for the treatment effect for a specified cluster by averaging across the time windows. \"\"\" df , pre_experiment_df = self . _split_pre_experiment_df ( df ) if treatment_cluster is None : treatment_cluster = self . _get_treatment_cluster ( df ) df = self . fit_predict_synthetic ( df , pre_experiment_df , treatment_cluster , verbose = verbose ) df [ \"effect\" ] = df [ self . target_col ] - df [ \"synthetic\" ] avg_effect = df [ \"effect\" ] . mean () return avg_effect def _split_pre_experiment_df ( self , df : pd . DataFrame ): \"\"\"Split the dataframe into pre-experiment and experiment dataframes\"\"\" pre_experiment_df = df [( df [ self . time_col ] <= self . intervention_date )] df = df [( df [ self . time_col ] > self . intervention_date )] return df , pre_experiment_df analysis_point_estimate ( self , df , treatment_cluster = None , verbose = False ) \u00b6 Calculate the point estimate for the treatment effect for a specified cluster by averaging across the time windows. Source code in cluster_experiments/experiment_analysis.py def analysis_point_estimate ( self , df : pd . DataFrame , treatment_cluster : Optional [ str ] = None , verbose : bool = False , ): \"\"\" Calculate the point estimate for the treatment effect for a specified cluster by averaging across the time windows. \"\"\" df , pre_experiment_df = self . _split_pre_experiment_df ( df ) if treatment_cluster is None : treatment_cluster = self . _get_treatment_cluster ( df ) df = self . fit_predict_synthetic ( df , pre_experiment_df , treatment_cluster , verbose = verbose ) df [ \"effect\" ] = df [ self . target_col ] - df [ \"synthetic\" ] avg_effect = df [ \"effect\" ] . mean () return avg_effect analysis_pvalue ( self , df , verbose = False ) \u00b6 Returns the p-value of the analysis. 1. Calculate the average effect after intervention for each unit. 2. Count how many times the average effect is greater than the real treatment unit 3. Average it with the number of units. The result is the p-value using Fisher permutation test Source code in cluster_experiments/experiment_analysis.py def analysis_pvalue ( self , df : pd . DataFrame , verbose : bool = False ) -> float : \"\"\" Returns the p-value of the analysis. 1. Calculate the average effect after intervention for each unit. 2. Count how many times the average effect is greater than the real treatment unit 3. Average it with the number of units. The result is the p-value using Fisher permutation test \"\"\" clusters = self . _get_cluster_column ( df ) . unique () treatment_cluster = self . _get_treatment_cluster ( df ) synthetic_donors = { cluster : self . analysis_point_estimate ( treatment_cluster = cluster , df = df , verbose = verbose , ) for cluster in clusters } ate = synthetic_donors [ treatment_cluster ] synthetic_donors . pop ( treatment_cluster ) return self . pvalue_based_on_hypothesis ( ate = ate , avg_effects = synthetic_donors ) fit_predict_synthetic ( self , df , pre_experiment_df , treatment_cluster , verbose = False ) \u00b6 Fit the synthetic control model and predict the results for the treatment cluster. Parameters: Name Type Description Default df DataFrame The dataframe containing the data after the intervention. required pre_experiment_df DataFrame The dataframe containing the data before the intervention. required treatment_cluster str The name of the treatment cluster. required verbose bool If True, print the status of the optimization of weights. False Returns: Type Description DataFrame The dataframe with the synthetic results added to the treatment cluster. Source code in cluster_experiments/experiment_analysis.py def fit_predict_synthetic ( self , df : pd . DataFrame , pre_experiment_df : pd . DataFrame , treatment_cluster : str , verbose : bool = False , ) -> pd . DataFrame : \"\"\" Fit the synthetic control model and predict the results for the treatment cluster. Args: df: The dataframe containing the data after the intervention. pre_experiment_df: The dataframe containing the data before the intervention. treatment_cluster: The name of the treatment cluster. verbose: If True, print the status of the optimization of weights. Returns: The dataframe with the synthetic results added to the treatment cluster. \"\"\" weights = self . _fit ( pre_experiment_df = pre_experiment_df , verbose = verbose ) prediction = self . _predict ( df = df , weights = weights , treatment_cluster = treatment_cluster ) return prediction pvalue_based_on_hypothesis ( self , ate , avg_effects ) \u00b6 Returns the p-value of the analysis. 1. Count how many times the average effect is greater than the real treatment unit 2. Average it with the number of units. The result is the p-value using Fisher permutation exact test. Source code in cluster_experiments/experiment_analysis.py def pvalue_based_on_hypothesis ( self , ate : np . float64 , avg_effects : Dict [ str , float ] ) -> float : \"\"\" Returns the p-value of the analysis. 1. Count how many times the average effect is greater than the real treatment unit 2. Average it with the number of units. The result is the p-value using Fisher permutation exact test. \"\"\" avg_effects = list ( avg_effects . values ()) if HypothesisEntries ( self . hypothesis ) == HypothesisEntries . LESS : return np . mean ( avg_effects < ate ) if HypothesisEntries ( self . hypothesis ) == HypothesisEntries . GREATER : return np . mean ( avg_effects > ate ) if HypothesisEntries ( self . hypothesis ) == HypothesisEntries . TWO_SIDED : avg_effects = np . abs ( avg_effects ) return np . mean ( avg_effects > ate ) raise ValueError ( f \" { self . hypothesis } is not a valid HypothesisEntries\" ) TTestClusteredAnalysis ( ExperimentAnalysis ) \u00b6 Class to run T-test analysis on aggregated data Parameters: Name Type Description Default cluster_cols List[str] list of columns to use as clusters required target_col str name of the column containing the variable to measure 'target' treatment_col str name of the column containing the treatment variable 'treatment' treatment str name of the treatment to use as the treated group 'B' hypothesis str one of \"two-sided\", \"less\", \"greater\" indicating the alternative hypothesis 'two-sided' Usage: from cluster_experiments.experiment_analysis import TTestClusteredAnalysis import pandas as pd df = pd . DataFrame ({ 'x' : [ 1 , 2 , 3 , 4 , 0 , 0 , 1 , 1 ], 'treatment' : [ \"A\" , \"B\" , \"A\" , \"B\" ] * 2 , 'cluster' : [ 1 , 2 , 3 , 4 , 1 , 2 , 3 , 4 ], }) TTestClusteredAnalysis ( cluster_cols = [ 'cluster' ], target_col = 'x' , ) . get_pvalue ( df ) Source code in cluster_experiments/experiment_analysis.py class TTestClusteredAnalysis ( ExperimentAnalysis ): \"\"\" Class to run T-test analysis on aggregated data Arguments: cluster_cols: list of columns to use as clusters target_col: name of the column containing the variable to measure treatment_col: name of the column containing the treatment variable treatment: name of the treatment to use as the treated group hypothesis: one of \"two-sided\", \"less\", \"greater\" indicating the alternative hypothesis Usage: ```python from cluster_experiments.experiment_analysis import TTestClusteredAnalysis import pandas as pd df = pd.DataFrame({ 'x': [1, 2, 3, 4, 0, 0, 1, 1], 'treatment': [\"A\", \"B\", \"A\", \"B\"] * 2, 'cluster': [1, 2, 3, 4, 1, 2, 3, 4], }) TTestClusteredAnalysis( cluster_cols=['cluster'], target_col='x', ).get_pvalue(df) ``` \"\"\" def __init__ ( self , cluster_cols : List [ str ], target_col : str = \"target\" , treatment_col : str = \"treatment\" , treatment : str = \"B\" , hypothesis : str = \"two-sided\" , ): self . target_col = target_col self . treatment = treatment self . treatment_col = treatment_col self . cluster_cols = cluster_cols self . hypothesis = hypothesis def analysis_pvalue ( self , df : pd . DataFrame , verbose : bool = False ) -> float : \"\"\"Returns the p-value of the analysis Arguments: df: dataframe containing the data to analyze verbose (Optional): bool, prints the regression summary if True \"\"\" df_grouped = df . groupby ( self . cluster_cols + [ self . treatment_col ], as_index = False )[ self . target_col ] . mean () treatment_data = df_grouped . query ( f \" { self . treatment_col } == 1\" )[ self . target_col ] control_data = df_grouped . query ( f \" { self . treatment_col } == 0\" )[ self . target_col ] assert len ( treatment_data ), \"treatment data should have more than 1 cluster\" assert len ( control_data ), \"control data should have more than 1 cluster\" t_test_results = ttest_ind ( treatment_data , control_data , equal_var = False , alternative = self . hypothesis ) return t_test_results . pvalue @classmethod def from_config ( cls , config ): \"\"\"Creates a TTestClusteredAnalysis object from a PowerConfig object\"\"\" return cls ( cluster_cols = config . cluster_cols , target_col = config . target_col , treatment_col = config . treatment_col , treatment = config . treatment , hypothesis = config . hypothesis , ) analysis_pvalue ( self , df , verbose = False ) \u00b6 Returns the p-value of the analysis Parameters: Name Type Description Default df DataFrame dataframe containing the data to analyze required verbose Optional bool, prints the regression summary if True False Source code in cluster_experiments/experiment_analysis.py def analysis_pvalue ( self , df : pd . DataFrame , verbose : bool = False ) -> float : \"\"\"Returns the p-value of the analysis Arguments: df: dataframe containing the data to analyze verbose (Optional): bool, prints the regression summary if True \"\"\" df_grouped = df . groupby ( self . cluster_cols + [ self . treatment_col ], as_index = False )[ self . target_col ] . mean () treatment_data = df_grouped . query ( f \" { self . treatment_col } == 1\" )[ self . target_col ] control_data = df_grouped . query ( f \" { self . treatment_col } == 0\" )[ self . target_col ] assert len ( treatment_data ), \"treatment data should have more than 1 cluster\" assert len ( control_data ), \"control data should have more than 1 cluster\" t_test_results = ttest_ind ( treatment_data , control_data , equal_var = False , alternative = self . hypothesis ) return t_test_results . pvalue from_config ( config ) classmethod \u00b6 Creates a TTestClusteredAnalysis object from a PowerConfig object Source code in cluster_experiments/experiment_analysis.py @classmethod def from_config ( cls , config ): \"\"\"Creates a TTestClusteredAnalysis object from a PowerConfig object\"\"\" return cls ( cluster_cols = config . cluster_cols , target_col = config . target_col , treatment_col = config . treatment_col , treatment = config . treatment , hypothesis = config . hypothesis , )","title":"Experiment analysis methods"},{"location":"api/experiment_analysis.html#from-cluster_experimentsexperiment_analysis-import","text":"","title":"from cluster_experiments.experiment_analysis import *"},{"location":"api/experiment_analysis.html#cluster_experiments.experiment_analysis.ClusteredOLSAnalysis","text":"Class to run OLS clustered analysis Parameters: Name Type Description Default cluster_cols List[str] list of columns to use as clusters required target_col str name of the column containing the variable to measure 'target' treatment_col str name of the column containing the treatment variable 'treatment' treatment str name of the treatment to use as the treated group 'B' covariates Optional[List[str]] list of columns to use as covariates None hypothesis str one of \"two-sided\", \"less\", \"greater\" indicating the alternative hypothesis 'two-sided' Usage: from cluster_experiments.experiment_analysis import ClusteredOLSAnalysis import pandas as pd df = pd . DataFrame ({ 'x' : [ 1 , 2 , 3 , 0 , 0 , 1 , 2 , 0 ], 'treatment' : [ \"A\" ] * 2 + [ \"B\" ] * 2 + [ \"A\" ] * 2 + [ \"B\" ] * 2 , 'cluster' : [ 1 , 1 , 2 , 2 , 3 , 3 , 4 , 4 ], }) ClusteredOLSAnalysis ( cluster_cols = [ 'cluster' ], target_col = 'x' , ) . get_pvalue ( df ) Source code in cluster_experiments/experiment_analysis.py class ClusteredOLSAnalysis ( ExperimentAnalysis ): \"\"\" Class to run OLS clustered analysis Arguments: cluster_cols: list of columns to use as clusters target_col: name of the column containing the variable to measure treatment_col: name of the column containing the treatment variable treatment: name of the treatment to use as the treated group covariates: list of columns to use as covariates hypothesis: one of \"two-sided\", \"less\", \"greater\" indicating the alternative hypothesis Usage: ```python from cluster_experiments.experiment_analysis import ClusteredOLSAnalysis import pandas as pd df = pd.DataFrame({ 'x': [1, 2, 3, 0, 0, 1, 2, 0], 'treatment': [\"A\"] * 2 + [\"B\"] * 2 + [\"A\"] * 2 + [\"B\"] * 2, 'cluster': [1, 1, 2, 2, 3, 3, 4, 4], }) ClusteredOLSAnalysis( cluster_cols=['cluster'], target_col='x', ).get_pvalue(df) ``` \"\"\" def __init__ ( self , cluster_cols : List [ str ], target_col : str = \"target\" , treatment_col : str = \"treatment\" , treatment : str = \"B\" , covariates : Optional [ List [ str ]] = None , hypothesis : str = \"two-sided\" , ): super () . __init__ ( target_col = target_col , treatment_col = treatment_col , cluster_cols = cluster_cols , treatment = treatment , covariates = covariates , hypothesis = hypothesis , ) self . regressors = [ self . treatment_col ] + self . covariates self . formula = f \" { self . target_col } ~ { ' + ' . join ( self . regressors ) } \" self . cov_type = \"cluster\" def fit_ols_clustered ( self , df : pd . DataFrame ): \"\"\"Returns the fitted OLS model\"\"\" return sm . OLS . from_formula ( self . formula , data = df ,) . fit ( cov_type = self . cov_type , cov_kwds = { \"groups\" : self . _get_cluster_column ( df )}, ) def analysis_pvalue ( self , df : pd . DataFrame , verbose : bool = False ) -> float : \"\"\"Returns the p-value of the analysis Arguments: df: dataframe containing the data to analyze verbose (Optional): bool, prints the regression summary if True \"\"\" results_ols = self . fit_ols_clustered ( df ) if verbose : print ( results_ols . summary ()) p_value = self . pvalue_based_on_hypothesis ( results_ols ) return p_value def analysis_point_estimate ( self , df : pd . DataFrame , verbose : bool = False ) -> float : \"\"\"Returns the point estimate of the analysis Arguments: df: dataframe containing the data to analyze verbose (Optional): bool, prints the regression summary if True \"\"\" results_ols = self . fit_ols_clustered ( df ) return results_ols . params [ self . treatment_col ] def analysis_standard_error ( self , df : pd . DataFrame , verbose : bool = False ) -> float : \"\"\"Returns the standard error of the analysis Arguments: df: dataframe containing the data to analyze verbose (Optional): bool, prints the regression summary if True \"\"\" results_ols = self . fit_ols_clustered ( df ) return results_ols . bse [ self . treatment_col ] def analysis_confidence_interval ( self , df : pd . DataFrame , alpha : float , verbose : bool = False ) -> ConfidenceInterval : \"\"\"Returns the confidence interval of the analysis Arguments: df: dataframe containing the data to analyze alpha: significance level verbose (Optional): bool, prints the regression summary if True \"\"\" results_ols = self . fit_ols_clustered ( df ) # Extract the confidence interval for the treatment column conf_int_df = results_ols . conf_int ( alpha = alpha ) lower_bound , upper_bound = conf_int_df . loc [ self . treatment_col ] if verbose : print ( results_ols . summary ()) # Return the confidence interval return ConfidenceInterval ( lower = lower_bound , upper = upper_bound , alpha = alpha ) def analysis_inference_results ( self , df : pd . DataFrame , alpha : float , verbose : bool = False ) -> InferenceResults : \"\"\"Returns the inference results of the analysis Arguments: df: dataframe containing the data to analyze alpha: significance level verbose (Optional): bool, prints the regression summary if True \"\"\" results_ols = self . fit_ols_clustered ( df ) std_error = results_ols . bse [ self . treatment_col ] ate = results_ols . params [ self . treatment_col ] p_value = self . pvalue_based_on_hypothesis ( results_ols ) # Extract the confidence interval for the treatment column conf_int_df = results_ols . conf_int ( alpha = alpha ) lower_bound , upper_bound = conf_int_df . loc [ self . treatment_col ] if verbose : print ( results_ols . summary ()) # Return the confidence interval return InferenceResults ( ate = ate , p_value = p_value , std_error = std_error , conf_int = ConfidenceInterval ( lower = lower_bound , upper = upper_bound , alpha = alpha ), )","title":"ClusteredOLSAnalysis"},{"location":"api/experiment_analysis.html#cluster_experiments.experiment_analysis.ClusteredOLSAnalysis.analysis_confidence_interval","text":"Returns the confidence interval of the analysis Parameters: Name Type Description Default df DataFrame dataframe containing the data to analyze required alpha float significance level required verbose Optional bool, prints the regression summary if True False Source code in cluster_experiments/experiment_analysis.py def analysis_confidence_interval ( self , df : pd . DataFrame , alpha : float , verbose : bool = False ) -> ConfidenceInterval : \"\"\"Returns the confidence interval of the analysis Arguments: df: dataframe containing the data to analyze alpha: significance level verbose (Optional): bool, prints the regression summary if True \"\"\" results_ols = self . fit_ols_clustered ( df ) # Extract the confidence interval for the treatment column conf_int_df = results_ols . conf_int ( alpha = alpha ) lower_bound , upper_bound = conf_int_df . loc [ self . treatment_col ] if verbose : print ( results_ols . summary ()) # Return the confidence interval return ConfidenceInterval ( lower = lower_bound , upper = upper_bound , alpha = alpha )","title":"analysis_confidence_interval()"},{"location":"api/experiment_analysis.html#cluster_experiments.experiment_analysis.ClusteredOLSAnalysis.analysis_inference_results","text":"Returns the inference results of the analysis Parameters: Name Type Description Default df DataFrame dataframe containing the data to analyze required alpha float significance level required verbose Optional bool, prints the regression summary if True False Source code in cluster_experiments/experiment_analysis.py def analysis_inference_results ( self , df : pd . DataFrame , alpha : float , verbose : bool = False ) -> InferenceResults : \"\"\"Returns the inference results of the analysis Arguments: df: dataframe containing the data to analyze alpha: significance level verbose (Optional): bool, prints the regression summary if True \"\"\" results_ols = self . fit_ols_clustered ( df ) std_error = results_ols . bse [ self . treatment_col ] ate = results_ols . params [ self . treatment_col ] p_value = self . pvalue_based_on_hypothesis ( results_ols ) # Extract the confidence interval for the treatment column conf_int_df = results_ols . conf_int ( alpha = alpha ) lower_bound , upper_bound = conf_int_df . loc [ self . treatment_col ] if verbose : print ( results_ols . summary ()) # Return the confidence interval return InferenceResults ( ate = ate , p_value = p_value , std_error = std_error , conf_int = ConfidenceInterval ( lower = lower_bound , upper = upper_bound , alpha = alpha ), )","title":"analysis_inference_results()"},{"location":"api/experiment_analysis.html#cluster_experiments.experiment_analysis.ClusteredOLSAnalysis.analysis_point_estimate","text":"Returns the point estimate of the analysis Parameters: Name Type Description Default df DataFrame dataframe containing the data to analyze required verbose Optional bool, prints the regression summary if True False Source code in cluster_experiments/experiment_analysis.py def analysis_point_estimate ( self , df : pd . DataFrame , verbose : bool = False ) -> float : \"\"\"Returns the point estimate of the analysis Arguments: df: dataframe containing the data to analyze verbose (Optional): bool, prints the regression summary if True \"\"\" results_ols = self . fit_ols_clustered ( df ) return results_ols . params [ self . treatment_col ]","title":"analysis_point_estimate()"},{"location":"api/experiment_analysis.html#cluster_experiments.experiment_analysis.ClusteredOLSAnalysis.analysis_pvalue","text":"Returns the p-value of the analysis Parameters: Name Type Description Default df DataFrame dataframe containing the data to analyze required verbose Optional bool, prints the regression summary if True False Source code in cluster_experiments/experiment_analysis.py def analysis_pvalue ( self , df : pd . DataFrame , verbose : bool = False ) -> float : \"\"\"Returns the p-value of the analysis Arguments: df: dataframe containing the data to analyze verbose (Optional): bool, prints the regression summary if True \"\"\" results_ols = self . fit_ols_clustered ( df ) if verbose : print ( results_ols . summary ()) p_value = self . pvalue_based_on_hypothesis ( results_ols ) return p_value","title":"analysis_pvalue()"},{"location":"api/experiment_analysis.html#cluster_experiments.experiment_analysis.ClusteredOLSAnalysis.analysis_standard_error","text":"Returns the standard error of the analysis Parameters: Name Type Description Default df DataFrame dataframe containing the data to analyze required verbose Optional bool, prints the regression summary if True False Source code in cluster_experiments/experiment_analysis.py def analysis_standard_error ( self , df : pd . DataFrame , verbose : bool = False ) -> float : \"\"\"Returns the standard error of the analysis Arguments: df: dataframe containing the data to analyze verbose (Optional): bool, prints the regression summary if True \"\"\" results_ols = self . fit_ols_clustered ( df ) return results_ols . bse [ self . treatment_col ]","title":"analysis_standard_error()"},{"location":"api/experiment_analysis.html#cluster_experiments.experiment_analysis.ClusteredOLSAnalysis.fit_ols_clustered","text":"Returns the fitted OLS model Source code in cluster_experiments/experiment_analysis.py def fit_ols_clustered ( self , df : pd . DataFrame ): \"\"\"Returns the fitted OLS model\"\"\" return sm . OLS . from_formula ( self . formula , data = df ,) . fit ( cov_type = self . cov_type , cov_kwds = { \"groups\" : self . _get_cluster_column ( df )}, )","title":"fit_ols_clustered()"},{"location":"api/experiment_analysis.html#cluster_experiments.experiment_analysis.ConfidenceInterval","text":"Class to define the structure of a confidence interval. Source code in cluster_experiments/experiment_analysis.py class ConfidenceInterval : \"\"\" Class to define the structure of a confidence interval. \"\"\" lower : float upper : float alpha : float","title":"ConfidenceInterval"},{"location":"api/experiment_analysis.html#cluster_experiments.experiment_analysis.ExperimentAnalysis","text":"Abstract class to run the analysis of a given experiment In order to create your own ExperimentAnalysis, you should create a derived class that implements the analysis_pvalue method. It can also be used as a component of the PowerAnalysis class. Parameters: Name Type Description Default cluster_cols List[str] list of columns to use as clusters required target_col str name of the column containing the variable to measure 'target' treatment_col str name of the column containing the treatment variable 'treatment' treatment str name of the treatment to use as the treated group 'B' covariates Optional[List[str]] list of columns to use as covariates None hypothesis str one of \"two-sided\", \"less\", \"greater\" indicating the alternative hypothesis 'two-sided' Source code in cluster_experiments/experiment_analysis.py class ExperimentAnalysis ( ABC ): \"\"\" Abstract class to run the analysis of a given experiment In order to create your own ExperimentAnalysis, you should create a derived class that implements the analysis_pvalue method. It can also be used as a component of the PowerAnalysis class. Arguments: cluster_cols: list of columns to use as clusters target_col: name of the column containing the variable to measure treatment_col: name of the column containing the treatment variable treatment: name of the treatment to use as the treated group covariates: list of columns to use as covariates hypothesis: one of \"two-sided\", \"less\", \"greater\" indicating the alternative hypothesis \"\"\" def __init__ ( self , cluster_cols : List [ str ], target_col : str = \"target\" , treatment_col : str = \"treatment\" , treatment : str = \"B\" , covariates : Optional [ List [ str ]] = None , hypothesis : str = \"two-sided\" , ): self . target_col = target_col self . treatment = treatment self . treatment_col = treatment_col self . cluster_cols = cluster_cols self . covariates = covariates or [] self . hypothesis = hypothesis def _get_cluster_column ( self , df : pd . DataFrame ) -> pd . Series : \"\"\"Paste all strings of cluster_cols in one single column\"\"\" df = df . copy () return df [ self . cluster_cols ] . astype ( str ) . sum ( axis = 1 ) def _create_binary_treatment ( self , df : pd . DataFrame ) -> pd . DataFrame : \"\"\"Transforms treatment column into 0 - 1 column\"\"\" df = df . copy () df [ self . treatment_col ] = ( df [ self . treatment_col ] == self . treatment ) . astype ( int ) return df @abstractmethod def analysis_pvalue ( self , df : pd . DataFrame , verbose : bool = False , ) -> float : \"\"\" Returns the p-value of the analysis. Expects treatment to be 0-1 variable Arguments: df: dataframe containing the data to analyze verbose (Optional): bool, prints the regression summary if True \"\"\" def analysis_point_estimate ( self , df : pd . DataFrame , verbose : bool = False , ) -> float : \"\"\" Returns the point estimate of the analysis. Expects treatment to be 0-1 variable Arguments: df: dataframe containing the data to analyze verbose (Optional): bool, prints the regression summary if True \"\"\" raise NotImplementedError ( \"Point estimate not implemented for this analysis\" ) def analysis_standard_error ( self , df : pd . DataFrame , verbose : bool = False , ) -> float : \"\"\" Returns the standard error of the analysis. Expects treatment to be 0-1 variable Arguments: df: dataframe containing the data to analyze verbose (Optional): bool, prints the regression summary if True \"\"\" raise NotImplementedError ( \"Standard error not implemented for this analysis\" ) def analysis_confidence_interval ( self , df : pd . DataFrame , alpha : float , verbose : bool = False , ) -> ConfidenceInterval : \"\"\" Returns the confidence interval of the analysis. Expects treatment to be 0-1 variable Arguments: df: dataframe containing the data to analyze alpha: significance level verbose (Optional): bool, prints the regression summary if True \"\"\" raise NotImplementedError ( \"Confidence Interval not implemented for this analysis\" ) def analysis_inference_results ( self , df : pd . DataFrame , alpha : float , verbose : bool = False , ) -> InferenceResults : \"\"\" Returns the InferenceResults object of the analysis. Expects treatment to be 0-1 variable Arguments: df: dataframe containing the data to analyze alpha: significance level verbose (Optional): bool, prints the regression summary if True \"\"\" raise NotImplementedError ( \"Inference results are not implemented for this analysis\" ) def _data_checks ( self , df : pd . DataFrame ) -> None : \"\"\"Checks that the data is correct\"\"\" if df [ self . target_col ] . isnull () . any (): raise ValueError ( f \"There are null values in outcome column { self . treatment_col } \" ) if not is_numeric_dtype ( df [ self . target_col ]): raise ValueError ( f \"Outcome column { self . target_col } should be numeric and not { df [ self . target_col ] . dtype } \" ) def get_pvalue ( self , df : pd . DataFrame ) -> float : \"\"\"Returns the p-value of the analysis Arguments: df: dataframe containing the data to analyze \"\"\" df = df . copy () df = self . _create_binary_treatment ( df ) self . _data_checks ( df = df ) return self . analysis_pvalue ( df ) def get_point_estimate ( self , df : pd . DataFrame ) -> float : \"\"\"Returns the point estimate of the analysis Arguments: df: dataframe containing the data to analyze \"\"\" df = df . copy () df = self . _create_binary_treatment ( df ) self . _data_checks ( df = df ) return self . analysis_point_estimate ( df ) def get_standard_error ( self , df : pd . DataFrame ) -> float : \"\"\"Returns the standard error of the analysis Arguments: df: dataframe containing the data to analyze \"\"\" df = df . copy () df = self . _create_binary_treatment ( df ) self . _data_checks ( df = df ) return self . analysis_standard_error ( df ) def get_confidence_interval ( self , df : pd . DataFrame , alpha : float ) -> ConfidenceInterval : \"\"\"Returns the confidence interval of the analysis Arguments: df: dataframe containing the data to analyze alpha: significance level \"\"\" df = df . copy () df = self . _create_binary_treatment ( df ) self . _data_checks ( df = df ) return self . analysis_confidence_interval ( df , alpha ) def get_inference_results ( self , df : pd . DataFrame , alpha : float ) -> InferenceResults : \"\"\"Returns the inference results of the analysis Arguments: df: dataframe containing the data to analyze alpha: significance level \"\"\" df = df . copy () df = self . _create_binary_treatment ( df ) self . _data_checks ( df = df ) return self . analysis_inference_results ( df , alpha ) def pvalue_based_on_hypothesis ( self , model_result ) -> float : # todo add typehint statsmodels result \"\"\"Returns the p-value of the analysis Arguments: model_result: statsmodels result object verbose (Optional): bool, prints the regression summary if True \"\"\" treatment_effect = model_result . params [ self . treatment_col ] p_value = model_result . pvalues [ self . treatment_col ] if HypothesisEntries ( self . hypothesis ) == HypothesisEntries . LESS : return p_value / 2 if treatment_effect <= 0 else 1 - p_value / 2 if HypothesisEntries ( self . hypothesis ) == HypothesisEntries . GREATER : return p_value / 2 if treatment_effect >= 0 else 1 - p_value / 2 if HypothesisEntries ( self . hypothesis ) == HypothesisEntries . TWO_SIDED : return p_value raise ValueError ( f \" { self . hypothesis } is not a valid HypothesisEntries\" ) def _split_pre_experiment_df ( self , df : pd . DataFrame ): raise NotImplementedError ( \"This method should be implemented in the child class\" ) @classmethod def from_config ( cls , config ): \"\"\"Creates an ExperimentAnalysis object from a PowerConfig object\"\"\" return cls ( cluster_cols = config . cluster_cols , target_col = config . target_col , treatment_col = config . treatment_col , treatment = config . treatment , covariates = config . covariates , hypothesis = config . hypothesis , )","title":"ExperimentAnalysis"},{"location":"api/experiment_analysis.html#cluster_experiments.experiment_analysis.ExperimentAnalysis.analysis_confidence_interval","text":"Returns the confidence interval of the analysis. Expects treatment to be 0-1 variable Parameters: Name Type Description Default df DataFrame dataframe containing the data to analyze required alpha float significance level required verbose Optional bool, prints the regression summary if True False Source code in cluster_experiments/experiment_analysis.py def analysis_confidence_interval ( self , df : pd . DataFrame , alpha : float , verbose : bool = False , ) -> ConfidenceInterval : \"\"\" Returns the confidence interval of the analysis. Expects treatment to be 0-1 variable Arguments: df: dataframe containing the data to analyze alpha: significance level verbose (Optional): bool, prints the regression summary if True \"\"\" raise NotImplementedError ( \"Confidence Interval not implemented for this analysis\" )","title":"analysis_confidence_interval()"},{"location":"api/experiment_analysis.html#cluster_experiments.experiment_analysis.ExperimentAnalysis.analysis_inference_results","text":"Returns the InferenceResults object of the analysis. Expects treatment to be 0-1 variable Parameters: Name Type Description Default df DataFrame dataframe containing the data to analyze required alpha float significance level required verbose Optional bool, prints the regression summary if True False Source code in cluster_experiments/experiment_analysis.py def analysis_inference_results ( self , df : pd . DataFrame , alpha : float , verbose : bool = False , ) -> InferenceResults : \"\"\" Returns the InferenceResults object of the analysis. Expects treatment to be 0-1 variable Arguments: df: dataframe containing the data to analyze alpha: significance level verbose (Optional): bool, prints the regression summary if True \"\"\" raise NotImplementedError ( \"Inference results are not implemented for this analysis\" )","title":"analysis_inference_results()"},{"location":"api/experiment_analysis.html#cluster_experiments.experiment_analysis.ExperimentAnalysis.analysis_point_estimate","text":"Returns the point estimate of the analysis. Expects treatment to be 0-1 variable Parameters: Name Type Description Default df DataFrame dataframe containing the data to analyze required verbose Optional bool, prints the regression summary if True False Source code in cluster_experiments/experiment_analysis.py def analysis_point_estimate ( self , df : pd . DataFrame , verbose : bool = False , ) -> float : \"\"\" Returns the point estimate of the analysis. Expects treatment to be 0-1 variable Arguments: df: dataframe containing the data to analyze verbose (Optional): bool, prints the regression summary if True \"\"\" raise NotImplementedError ( \"Point estimate not implemented for this analysis\" )","title":"analysis_point_estimate()"},{"location":"api/experiment_analysis.html#cluster_experiments.experiment_analysis.ExperimentAnalysis.analysis_pvalue","text":"Returns the p-value of the analysis. Expects treatment to be 0-1 variable Parameters: Name Type Description Default df DataFrame dataframe containing the data to analyze required verbose Optional bool, prints the regression summary if True False Source code in cluster_experiments/experiment_analysis.py @abstractmethod def analysis_pvalue ( self , df : pd . DataFrame , verbose : bool = False , ) -> float : \"\"\" Returns the p-value of the analysis. Expects treatment to be 0-1 variable Arguments: df: dataframe containing the data to analyze verbose (Optional): bool, prints the regression summary if True \"\"\"","title":"analysis_pvalue()"},{"location":"api/experiment_analysis.html#cluster_experiments.experiment_analysis.ExperimentAnalysis.analysis_standard_error","text":"Returns the standard error of the analysis. Expects treatment to be 0-1 variable Parameters: Name Type Description Default df DataFrame dataframe containing the data to analyze required verbose Optional bool, prints the regression summary if True False Source code in cluster_experiments/experiment_analysis.py def analysis_standard_error ( self , df : pd . DataFrame , verbose : bool = False , ) -> float : \"\"\" Returns the standard error of the analysis. Expects treatment to be 0-1 variable Arguments: df: dataframe containing the data to analyze verbose (Optional): bool, prints the regression summary if True \"\"\" raise NotImplementedError ( \"Standard error not implemented for this analysis\" )","title":"analysis_standard_error()"},{"location":"api/experiment_analysis.html#cluster_experiments.experiment_analysis.ExperimentAnalysis.from_config","text":"Creates an ExperimentAnalysis object from a PowerConfig object Source code in cluster_experiments/experiment_analysis.py @classmethod def from_config ( cls , config ): \"\"\"Creates an ExperimentAnalysis object from a PowerConfig object\"\"\" return cls ( cluster_cols = config . cluster_cols , target_col = config . target_col , treatment_col = config . treatment_col , treatment = config . treatment , covariates = config . covariates , hypothesis = config . hypothesis , )","title":"from_config()"},{"location":"api/experiment_analysis.html#cluster_experiments.experiment_analysis.ExperimentAnalysis.get_confidence_interval","text":"Returns the confidence interval of the analysis Parameters: Name Type Description Default df DataFrame dataframe containing the data to analyze required alpha float significance level required Source code in cluster_experiments/experiment_analysis.py def get_confidence_interval ( self , df : pd . DataFrame , alpha : float ) -> ConfidenceInterval : \"\"\"Returns the confidence interval of the analysis Arguments: df: dataframe containing the data to analyze alpha: significance level \"\"\" df = df . copy () df = self . _create_binary_treatment ( df ) self . _data_checks ( df = df ) return self . analysis_confidence_interval ( df , alpha )","title":"get_confidence_interval()"},{"location":"api/experiment_analysis.html#cluster_experiments.experiment_analysis.ExperimentAnalysis.get_inference_results","text":"Returns the inference results of the analysis Parameters: Name Type Description Default df DataFrame dataframe containing the data to analyze required alpha float significance level required Source code in cluster_experiments/experiment_analysis.py def get_inference_results ( self , df : pd . DataFrame , alpha : float ) -> InferenceResults : \"\"\"Returns the inference results of the analysis Arguments: df: dataframe containing the data to analyze alpha: significance level \"\"\" df = df . copy () df = self . _create_binary_treatment ( df ) self . _data_checks ( df = df ) return self . analysis_inference_results ( df , alpha )","title":"get_inference_results()"},{"location":"api/experiment_analysis.html#cluster_experiments.experiment_analysis.ExperimentAnalysis.get_point_estimate","text":"Returns the point estimate of the analysis Parameters: Name Type Description Default df DataFrame dataframe containing the data to analyze required Source code in cluster_experiments/experiment_analysis.py def get_point_estimate ( self , df : pd . DataFrame ) -> float : \"\"\"Returns the point estimate of the analysis Arguments: df: dataframe containing the data to analyze \"\"\" df = df . copy () df = self . _create_binary_treatment ( df ) self . _data_checks ( df = df ) return self . analysis_point_estimate ( df )","title":"get_point_estimate()"},{"location":"api/experiment_analysis.html#cluster_experiments.experiment_analysis.ExperimentAnalysis.get_pvalue","text":"Returns the p-value of the analysis Parameters: Name Type Description Default df DataFrame dataframe containing the data to analyze required Source code in cluster_experiments/experiment_analysis.py def get_pvalue ( self , df : pd . DataFrame ) -> float : \"\"\"Returns the p-value of the analysis Arguments: df: dataframe containing the data to analyze \"\"\" df = df . copy () df = self . _create_binary_treatment ( df ) self . _data_checks ( df = df ) return self . analysis_pvalue ( df )","title":"get_pvalue()"},{"location":"api/experiment_analysis.html#cluster_experiments.experiment_analysis.ExperimentAnalysis.get_standard_error","text":"Returns the standard error of the analysis Parameters: Name Type Description Default df DataFrame dataframe containing the data to analyze required Source code in cluster_experiments/experiment_analysis.py def get_standard_error ( self , df : pd . DataFrame ) -> float : \"\"\"Returns the standard error of the analysis Arguments: df: dataframe containing the data to analyze \"\"\" df = df . copy () df = self . _create_binary_treatment ( df ) self . _data_checks ( df = df ) return self . analysis_standard_error ( df )","title":"get_standard_error()"},{"location":"api/experiment_analysis.html#cluster_experiments.experiment_analysis.ExperimentAnalysis.pvalue_based_on_hypothesis","text":"Returns the p-value of the analysis Parameters: Name Type Description Default model_result statsmodels result object required verbose Optional bool, prints the regression summary if True required Source code in cluster_experiments/experiment_analysis.py def pvalue_based_on_hypothesis ( self , model_result ) -> float : # todo add typehint statsmodels result \"\"\"Returns the p-value of the analysis Arguments: model_result: statsmodels result object verbose (Optional): bool, prints the regression summary if True \"\"\" treatment_effect = model_result . params [ self . treatment_col ] p_value = model_result . pvalues [ self . treatment_col ] if HypothesisEntries ( self . hypothesis ) == HypothesisEntries . LESS : return p_value / 2 if treatment_effect <= 0 else 1 - p_value / 2 if HypothesisEntries ( self . hypothesis ) == HypothesisEntries . GREATER : return p_value / 2 if treatment_effect >= 0 else 1 - p_value / 2 if HypothesisEntries ( self . hypothesis ) == HypothesisEntries . TWO_SIDED : return p_value raise ValueError ( f \" { self . hypothesis } is not a valid HypothesisEntries\" )","title":"pvalue_based_on_hypothesis()"},{"location":"api/experiment_analysis.html#cluster_experiments.experiment_analysis.GeeExperimentAnalysis","text":"Class to run GEE clustered analysis Parameters: Name Type Description Default cluster_cols List[str] list of columns to use as clusters required target_col str name of the column containing the variable to measure 'target' treatment_col str name of the column containing the treatment variable 'treatment' treatment str name of the treatment to use as the treated group 'B' covariates Optional[List[str]] list of columns to use as covariates None hypothesis str one of \"two-sided\", \"less\", \"greater\" indicating the alternative hypothesis 'two-sided' Usage: from cluster_experiments.experiment_analysis import GeeExperimentAnalysis import pandas as pd df = pd . DataFrame ({ 'x' : [ 1 , 2 , 3 , 0 , 0 , 1 ], 'treatment' : [ \"A\" ] * 3 + [ \"B\" ] * 3 , 'cluster' : [ 1 ] * 6 , }) GeeExperimentAnalysis ( cluster_cols = [ 'cluster' ], target_col = 'x' , ) . get_pvalue ( df ) Source code in cluster_experiments/experiment_analysis.py class GeeExperimentAnalysis ( ExperimentAnalysis ): \"\"\" Class to run GEE clustered analysis Arguments: cluster_cols: list of columns to use as clusters target_col: name of the column containing the variable to measure treatment_col: name of the column containing the treatment variable treatment: name of the treatment to use as the treated group covariates: list of columns to use as covariates hypothesis: one of \"two-sided\", \"less\", \"greater\" indicating the alternative hypothesis Usage: ```python from cluster_experiments.experiment_analysis import GeeExperimentAnalysis import pandas as pd df = pd.DataFrame({ 'x': [1, 2, 3, 0, 0, 1], 'treatment': [\"A\"] * 3 + [\"B\"] * 3, 'cluster': [1] * 6, }) GeeExperimentAnalysis( cluster_cols=['cluster'], target_col='x', ).get_pvalue(df) ``` \"\"\" def __init__ ( self , cluster_cols : List [ str ], target_col : str = \"target\" , treatment_col : str = \"treatment\" , treatment : str = \"B\" , covariates : Optional [ List [ str ]] = None , hypothesis : str = \"two-sided\" , ): super () . __init__ ( target_col = target_col , treatment_col = treatment_col , cluster_cols = cluster_cols , treatment = treatment , covariates = covariates , hypothesis = hypothesis , ) self . regressors = [ self . treatment_col ] + self . covariates self . formula = f \" { self . target_col } ~ { ' + ' . join ( self . regressors ) } \" self . fam = sm . families . Gaussian () self . va = sm . cov_struct . Exchangeable () def fit_gee ( self , df : pd . DataFrame ) -> sm . GEE : \"\"\"Returns the fitted GEE model\"\"\" return sm . GEE . from_formula ( self . formula , data = df , groups = self . _get_cluster_column ( df ), family = self . fam , cov_struct = self . va , ) . fit () def analysis_pvalue ( self , df : pd . DataFrame , verbose : bool = False ) -> float : \"\"\"Returns the p-value of the analysis Arguments: df: dataframe containing the data to analyze verbose (Optional): bool, prints the regression summary if True \"\"\" results_gee = self . fit_gee ( df ) if verbose : print ( results_gee . summary ()) p_value = self . pvalue_based_on_hypothesis ( results_gee ) return p_value def analysis_point_estimate ( self , df : pd . DataFrame , verbose : bool = False ) -> float : \"\"\"Returns the point estimate of the analysis Arguments: df: dataframe containing the data to analyze verbose (Optional): bool, prints the regression summary if True \"\"\" results_gee = self . fit_gee ( df ) return results_gee . params [ self . treatment_col ] def analysis_standard_error ( self , df : pd . DataFrame , verbose : bool = False ) -> float : \"\"\"Returns the standard error of the analysis Arguments: df: dataframe containing the data to analyze verbose (Optional): bool, prints the regression summary if True \"\"\" results_gee = self . fit_gee ( df ) return results_gee . bse [ self . treatment_col ] def analysis_confidence_interval ( self , df : pd . DataFrame , alpha : float , verbose : bool = False ) -> ConfidenceInterval : \"\"\"Returns the confidence interval of the analysis Arguments: df: dataframe containing the data to analyze alpha: significance level verbose (Optional): bool, prints the regression summary if True \"\"\" results_gee = self . fit_gee ( df ) # Extract the confidence interval for the treatment column conf_int_df = results_gee . conf_int ( alpha = alpha ) lower_bound , upper_bound = conf_int_df . loc [ self . treatment_col ] if verbose : print ( results_gee . summary ()) # Return the confidence interval return ConfidenceInterval ( lower = lower_bound , upper = upper_bound , alpha = alpha ) def analysis_inference_results ( self , df : pd . DataFrame , alpha : float , verbose : bool = False ) -> InferenceResults : \"\"\"Returns the inference results of the analysis Arguments: df: dataframe containing the data to analyze alpha: significance level verbose (Optional): bool, prints the regression summary if True \"\"\" results_gee = self . fit_gee ( df ) std_error = results_gee . bse [ self . treatment_col ] ate = results_gee . params [ self . treatment_col ] p_value = self . pvalue_based_on_hypothesis ( results_gee ) # Extract the confidence interval for the treatment column conf_int_df = results_gee . conf_int ( alpha = alpha ) lower_bound , upper_bound = conf_int_df . loc [ self . treatment_col ] if verbose : print ( results_gee . summary ()) # Return the confidence interval return InferenceResults ( ate = ate , p_value = p_value , std_error = std_error , conf_int = ConfidenceInterval ( lower = lower_bound , upper = upper_bound , alpha = alpha ), )","title":"GeeExperimentAnalysis"},{"location":"api/experiment_analysis.html#cluster_experiments.experiment_analysis.GeeExperimentAnalysis.analysis_confidence_interval","text":"Returns the confidence interval of the analysis Parameters: Name Type Description Default df DataFrame dataframe containing the data to analyze required alpha float significance level required verbose Optional bool, prints the regression summary if True False Source code in cluster_experiments/experiment_analysis.py def analysis_confidence_interval ( self , df : pd . DataFrame , alpha : float , verbose : bool = False ) -> ConfidenceInterval : \"\"\"Returns the confidence interval of the analysis Arguments: df: dataframe containing the data to analyze alpha: significance level verbose (Optional): bool, prints the regression summary if True \"\"\" results_gee = self . fit_gee ( df ) # Extract the confidence interval for the treatment column conf_int_df = results_gee . conf_int ( alpha = alpha ) lower_bound , upper_bound = conf_int_df . loc [ self . treatment_col ] if verbose : print ( results_gee . summary ()) # Return the confidence interval return ConfidenceInterval ( lower = lower_bound , upper = upper_bound , alpha = alpha )","title":"analysis_confidence_interval()"},{"location":"api/experiment_analysis.html#cluster_experiments.experiment_analysis.GeeExperimentAnalysis.analysis_inference_results","text":"Returns the inference results of the analysis Parameters: Name Type Description Default df DataFrame dataframe containing the data to analyze required alpha float significance level required verbose Optional bool, prints the regression summary if True False Source code in cluster_experiments/experiment_analysis.py def analysis_inference_results ( self , df : pd . DataFrame , alpha : float , verbose : bool = False ) -> InferenceResults : \"\"\"Returns the inference results of the analysis Arguments: df: dataframe containing the data to analyze alpha: significance level verbose (Optional): bool, prints the regression summary if True \"\"\" results_gee = self . fit_gee ( df ) std_error = results_gee . bse [ self . treatment_col ] ate = results_gee . params [ self . treatment_col ] p_value = self . pvalue_based_on_hypothesis ( results_gee ) # Extract the confidence interval for the treatment column conf_int_df = results_gee . conf_int ( alpha = alpha ) lower_bound , upper_bound = conf_int_df . loc [ self . treatment_col ] if verbose : print ( results_gee . summary ()) # Return the confidence interval return InferenceResults ( ate = ate , p_value = p_value , std_error = std_error , conf_int = ConfidenceInterval ( lower = lower_bound , upper = upper_bound , alpha = alpha ), )","title":"analysis_inference_results()"},{"location":"api/experiment_analysis.html#cluster_experiments.experiment_analysis.GeeExperimentAnalysis.analysis_point_estimate","text":"Returns the point estimate of the analysis Parameters: Name Type Description Default df DataFrame dataframe containing the data to analyze required verbose Optional bool, prints the regression summary if True False Source code in cluster_experiments/experiment_analysis.py def analysis_point_estimate ( self , df : pd . DataFrame , verbose : bool = False ) -> float : \"\"\"Returns the point estimate of the analysis Arguments: df: dataframe containing the data to analyze verbose (Optional): bool, prints the regression summary if True \"\"\" results_gee = self . fit_gee ( df ) return results_gee . params [ self . treatment_col ]","title":"analysis_point_estimate()"},{"location":"api/experiment_analysis.html#cluster_experiments.experiment_analysis.GeeExperimentAnalysis.analysis_pvalue","text":"Returns the p-value of the analysis Parameters: Name Type Description Default df DataFrame dataframe containing the data to analyze required verbose Optional bool, prints the regression summary if True False Source code in cluster_experiments/experiment_analysis.py def analysis_pvalue ( self , df : pd . DataFrame , verbose : bool = False ) -> float : \"\"\"Returns the p-value of the analysis Arguments: df: dataframe containing the data to analyze verbose (Optional): bool, prints the regression summary if True \"\"\" results_gee = self . fit_gee ( df ) if verbose : print ( results_gee . summary ()) p_value = self . pvalue_based_on_hypothesis ( results_gee ) return p_value","title":"analysis_pvalue()"},{"location":"api/experiment_analysis.html#cluster_experiments.experiment_analysis.GeeExperimentAnalysis.analysis_standard_error","text":"Returns the standard error of the analysis Parameters: Name Type Description Default df DataFrame dataframe containing the data to analyze required verbose Optional bool, prints the regression summary if True False Source code in cluster_experiments/experiment_analysis.py def analysis_standard_error ( self , df : pd . DataFrame , verbose : bool = False ) -> float : \"\"\"Returns the standard error of the analysis Arguments: df: dataframe containing the data to analyze verbose (Optional): bool, prints the regression summary if True \"\"\" results_gee = self . fit_gee ( df ) return results_gee . bse [ self . treatment_col ]","title":"analysis_standard_error()"},{"location":"api/experiment_analysis.html#cluster_experiments.experiment_analysis.GeeExperimentAnalysis.fit_gee","text":"Returns the fitted GEE model Source code in cluster_experiments/experiment_analysis.py def fit_gee ( self , df : pd . DataFrame ) -> sm . GEE : \"\"\"Returns the fitted GEE model\"\"\" return sm . GEE . from_formula ( self . formula , data = df , groups = self . _get_cluster_column ( df ), family = self . fam , cov_struct = self . va , ) . fit ()","title":"fit_gee()"},{"location":"api/experiment_analysis.html#cluster_experiments.experiment_analysis.InferenceResults","text":"Class to define the structure of complete statistical analysis results. Source code in cluster_experiments/experiment_analysis.py class InferenceResults : \"\"\" Class to define the structure of complete statistical analysis results. \"\"\" ate : float p_value : float std_error : float conf_int : ConfidenceInterval","title":"InferenceResults"},{"location":"api/experiment_analysis.html#cluster_experiments.experiment_analysis.MLMExperimentAnalysis","text":"Class to run Mixed Linear Models clustered analysis Parameters: Name Type Description Default cluster_cols List[str] list of columns to use as clusters required target_col str name of the column containing the variable to measure 'target' treatment_col str name of the column containing the treatment variable 'treatment' treatment str name of the treatment to use as the treated group 'B' covariates Optional[List[str]] list of columns to use as covariates None hypothesis str one of \"two-sided\", \"less\", \"greater\" indicating the alternative hypothesis 'two-sided' Usage: from cluster_experiments.experiment_analysis import MLMExperimentAnalysis import pandas as pd df = pd . DataFrame ({ 'x' : [ 1 , 2 , 3 , 0 , 0 , 1 ], 'treatment' : [ \"A\" ] * 3 + [ \"B\" ] * 3 , 'cluster' : [ 1 ] * 6 , }) MLMExperimentAnalysis ( cluster_cols = [ 'cluster' ], target_col = 'x' , ) . get_pvalue ( df ) Source code in cluster_experiments/experiment_analysis.py class MLMExperimentAnalysis ( ExperimentAnalysis ): \"\"\" Class to run Mixed Linear Models clustered analysis Arguments: cluster_cols: list of columns to use as clusters target_col: name of the column containing the variable to measure treatment_col: name of the column containing the treatment variable treatment: name of the treatment to use as the treated group covariates: list of columns to use as covariates hypothesis: one of \"two-sided\", \"less\", \"greater\" indicating the alternative hypothesis Usage: ```python from cluster_experiments.experiment_analysis import MLMExperimentAnalysis import pandas as pd df = pd.DataFrame({ 'x': [1, 2, 3, 0, 0, 1], 'treatment': [\"A\"] * 3 + [\"B\"] * 3, 'cluster': [1] * 6, }) MLMExperimentAnalysis( cluster_cols=['cluster'], target_col='x', ).get_pvalue(df) ``` \"\"\" def __init__ ( self , cluster_cols : List [ str ], target_col : str = \"target\" , treatment_col : str = \"treatment\" , treatment : str = \"B\" , covariates : Optional [ List [ str ]] = None , hypothesis : str = \"two-sided\" , ): super () . __init__ ( target_col = target_col , treatment_col = treatment_col , cluster_cols = cluster_cols , treatment = treatment , covariates = covariates , hypothesis = hypothesis , ) self . regressors = [ self . treatment_col ] + self . covariates self . formula = f \" { self . target_col } ~ { ' + ' . join ( self . regressors ) } \" self . re_formula = None self . vc_formula = None def fit_mlm ( self , df : pd . DataFrame ) -> sm . MixedLM : \"\"\"Returns the fitted MLM model\"\"\" return sm . MixedLM . from_formula ( formula = self . formula , data = df , groups = self . _get_cluster_column ( df ), re_formula = self . re_formula , vc_formula = self . vc_formula , ) . fit () def analysis_pvalue ( self , df : pd . DataFrame , verbose : bool = False ) -> float : \"\"\"Returns the p-value of the analysis Arguments: df: dataframe containing the data to analyze verbose (Optional): bool, prints the regression summary if True \"\"\" results_mlm = self . fit_mlm ( df ) if verbose : print ( results_mlm . summary ()) p_value = self . pvalue_based_on_hypothesis ( results_mlm ) return p_value def analysis_point_estimate ( self , df : pd . DataFrame , verbose : bool = False ) -> float : \"\"\"Returns the point estimate of the analysis Arguments: df: dataframe containing the data to analyze verbose (Optional): bool, prints the regression summary if True \"\"\" results_mlm = self . fit_mlm ( df ) return results_mlm . params [ self . treatment_col ] def analysis_standard_error ( self , df : pd . DataFrame , verbose : bool = False ) -> float : \"\"\"Returns the standard error of the analysis Arguments: df: dataframe containing the data to analyze verbose (Optional): bool, prints the regression summary if True \"\"\" results_mlm = self . fit_mlm ( df ) return results_mlm . bse [ self . treatment_col ]","title":"MLMExperimentAnalysis"},{"location":"api/experiment_analysis.html#cluster_experiments.experiment_analysis.MLMExperimentAnalysis.analysis_point_estimate","text":"Returns the point estimate of the analysis Parameters: Name Type Description Default df DataFrame dataframe containing the data to analyze required verbose Optional bool, prints the regression summary if True False Source code in cluster_experiments/experiment_analysis.py def analysis_point_estimate ( self , df : pd . DataFrame , verbose : bool = False ) -> float : \"\"\"Returns the point estimate of the analysis Arguments: df: dataframe containing the data to analyze verbose (Optional): bool, prints the regression summary if True \"\"\" results_mlm = self . fit_mlm ( df ) return results_mlm . params [ self . treatment_col ]","title":"analysis_point_estimate()"},{"location":"api/experiment_analysis.html#cluster_experiments.experiment_analysis.MLMExperimentAnalysis.analysis_pvalue","text":"Returns the p-value of the analysis Parameters: Name Type Description Default df DataFrame dataframe containing the data to analyze required verbose Optional bool, prints the regression summary if True False Source code in cluster_experiments/experiment_analysis.py def analysis_pvalue ( self , df : pd . DataFrame , verbose : bool = False ) -> float : \"\"\"Returns the p-value of the analysis Arguments: df: dataframe containing the data to analyze verbose (Optional): bool, prints the regression summary if True \"\"\" results_mlm = self . fit_mlm ( df ) if verbose : print ( results_mlm . summary ()) p_value = self . pvalue_based_on_hypothesis ( results_mlm ) return p_value","title":"analysis_pvalue()"},{"location":"api/experiment_analysis.html#cluster_experiments.experiment_analysis.MLMExperimentAnalysis.analysis_standard_error","text":"Returns the standard error of the analysis Parameters: Name Type Description Default df DataFrame dataframe containing the data to analyze required verbose Optional bool, prints the regression summary if True False Source code in cluster_experiments/experiment_analysis.py def analysis_standard_error ( self , df : pd . DataFrame , verbose : bool = False ) -> float : \"\"\"Returns the standard error of the analysis Arguments: df: dataframe containing the data to analyze verbose (Optional): bool, prints the regression summary if True \"\"\" results_mlm = self . fit_mlm ( df ) return results_mlm . bse [ self . treatment_col ]","title":"analysis_standard_error()"},{"location":"api/experiment_analysis.html#cluster_experiments.experiment_analysis.MLMExperimentAnalysis.fit_mlm","text":"Returns the fitted MLM model Source code in cluster_experiments/experiment_analysis.py def fit_mlm ( self , df : pd . DataFrame ) -> sm . MixedLM : \"\"\"Returns the fitted MLM model\"\"\" return sm . MixedLM . from_formula ( formula = self . formula , data = df , groups = self . _get_cluster_column ( df ), re_formula = self . re_formula , vc_formula = self . vc_formula , ) . fit ()","title":"fit_mlm()"},{"location":"api/experiment_analysis.html#cluster_experiments.experiment_analysis.OLSAnalysis","text":"Class to run OLS analysis Parameters: Name Type Description Default target_col str name of the column containing the variable to measure 'target' treatment_col str name of the column containing the treatment variable 'treatment' treatment str name of the treatment to use as the treated group 'B' covariates Optional[List[str]] list of columns to use as covariates None hypothesis str one of \"two-sided\", \"less\", \"greater\" indicating the alternative hypothesis 'two-sided' Usage: from cluster_experiments.experiment_analysis import OLSAnalysis import pandas as pd df = pd . DataFrame ({ 'x' : [ 1 , 2 , 3 , 0 , 0 , 1 ], 'treatment' : [ \"A\" ] * 3 + [ \"B\" ] * 3 , }) OLSAnalysis ( target_col = 'x' , ) . get_pvalue ( df ) Source code in cluster_experiments/experiment_analysis.py class OLSAnalysis ( ExperimentAnalysis ): \"\"\" Class to run OLS analysis Arguments: target_col: name of the column containing the variable to measure treatment_col: name of the column containing the treatment variable treatment: name of the treatment to use as the treated group covariates: list of columns to use as covariates hypothesis: one of \"two-sided\", \"less\", \"greater\" indicating the alternative hypothesis Usage: ```python from cluster_experiments.experiment_analysis import OLSAnalysis import pandas as pd df = pd.DataFrame({ 'x': [1, 2, 3, 0, 0, 1], 'treatment': [\"A\"] * 3 + [\"B\"] * 3, }) OLSAnalysis( target_col='x', ).get_pvalue(df) ``` \"\"\" def __init__ ( self , target_col : str = \"target\" , treatment_col : str = \"treatment\" , treatment : str = \"B\" , covariates : Optional [ List [ str ]] = None , hypothesis : str = \"two-sided\" , ): self . target_col = target_col self . treatment = treatment self . treatment_col = treatment_col self . covariates = covariates or [] self . regressors = [ self . treatment_col ] + self . covariates self . formula = f \" { self . target_col } ~ { ' + ' . join ( self . regressors ) } \" self . hypothesis = hypothesis def fit_ols ( self , df : pd . DataFrame ): \"\"\"Returns the fitted OLS model\"\"\" return sm . OLS . from_formula ( self . formula , data = df ) . fit () def analysis_pvalue ( self , df : pd . DataFrame , verbose : bool = False ) -> float : \"\"\"Returns the p-value of the analysis Arguments: df: dataframe containing the data to analyze verbose (Optional): bool, prints the regression summary if True \"\"\" results_ols = self . fit_ols ( df = df ) if verbose : print ( results_ols . summary ()) p_value = self . pvalue_based_on_hypothesis ( results_ols ) return p_value def analysis_point_estimate ( self , df : pd . DataFrame , verbose : bool = False ) -> float : \"\"\"Returns the point estimate of the analysis Arguments: df: dataframe containing the data to analyze verbose (Optional): bool, prints the regression summary if True \"\"\" results_ols = self . fit_ols ( df = df ) return results_ols . params [ self . treatment_col ] def analysis_standard_error ( self , df : pd . DataFrame , verbose : bool = False ) -> float : \"\"\"Returns the standard error of the analysis Arguments: df: dataframe containing the data to analyze verbose (Optional): bool, prints the regression summary if True \"\"\" results_ols = self . fit_ols ( df = df ) return results_ols . bse [ self . treatment_col ] def analysis_confidence_interval ( self , df : pd . DataFrame , alpha : float , verbose : bool = False ) -> ConfidenceInterval : \"\"\"Returns the confidence interval of the analysis Arguments: df: dataframe containing the data to analyze alpha: significance level verbose (Optional): bool, prints the regression summary if True \"\"\" results_ols = self . fit_ols ( df ) # Extract the confidence interval for the treatment column conf_int_df = results_ols . conf_int ( alpha = alpha ) lower_bound , upper_bound = conf_int_df . loc [ self . treatment_col ] if verbose : print ( results_ols . summary ()) # Return the confidence interval return ConfidenceInterval ( lower = lower_bound , upper = upper_bound , alpha = alpha ) def analysis_inference_results ( self , df : pd . DataFrame , alpha : float , verbose : bool = False ) -> InferenceResults : \"\"\"Returns the inference results of the analysis Arguments: df: dataframe containing the data to analyze alpha: significance level verbose (Optional): bool, prints the regression summary if True \"\"\" results_ols = self . fit_ols ( df ) std_error = results_ols . bse [ self . treatment_col ] ate = results_ols . params [ self . treatment_col ] p_value = self . pvalue_based_on_hypothesis ( results_ols ) # Extract the confidence interval for the treatment column conf_int_df = results_ols . conf_int ( alpha = alpha ) lower_bound , upper_bound = conf_int_df . loc [ self . treatment_col ] if verbose : print ( results_ols . summary ()) # Return the confidence interval return InferenceResults ( ate = ate , p_value = p_value , std_error = std_error , conf_int = ConfidenceInterval ( lower = lower_bound , upper = upper_bound , alpha = alpha ), ) @classmethod def from_config ( cls , config ): \"\"\"Creates an OLSAnalysis object from a PowerConfig object\"\"\" return cls ( target_col = config . target_col , treatment_col = config . treatment_col , treatment = config . treatment , covariates = config . covariates , hypothesis = config . hypothesis , )","title":"OLSAnalysis"},{"location":"api/experiment_analysis.html#cluster_experiments.experiment_analysis.OLSAnalysis.analysis_confidence_interval","text":"Returns the confidence interval of the analysis Parameters: Name Type Description Default df DataFrame dataframe containing the data to analyze required alpha float significance level required verbose Optional bool, prints the regression summary if True False Source code in cluster_experiments/experiment_analysis.py def analysis_confidence_interval ( self , df : pd . DataFrame , alpha : float , verbose : bool = False ) -> ConfidenceInterval : \"\"\"Returns the confidence interval of the analysis Arguments: df: dataframe containing the data to analyze alpha: significance level verbose (Optional): bool, prints the regression summary if True \"\"\" results_ols = self . fit_ols ( df ) # Extract the confidence interval for the treatment column conf_int_df = results_ols . conf_int ( alpha = alpha ) lower_bound , upper_bound = conf_int_df . loc [ self . treatment_col ] if verbose : print ( results_ols . summary ()) # Return the confidence interval return ConfidenceInterval ( lower = lower_bound , upper = upper_bound , alpha = alpha )","title":"analysis_confidence_interval()"},{"location":"api/experiment_analysis.html#cluster_experiments.experiment_analysis.OLSAnalysis.analysis_inference_results","text":"Returns the inference results of the analysis Parameters: Name Type Description Default df DataFrame dataframe containing the data to analyze required alpha float significance level required verbose Optional bool, prints the regression summary if True False Source code in cluster_experiments/experiment_analysis.py def analysis_inference_results ( self , df : pd . DataFrame , alpha : float , verbose : bool = False ) -> InferenceResults : \"\"\"Returns the inference results of the analysis Arguments: df: dataframe containing the data to analyze alpha: significance level verbose (Optional): bool, prints the regression summary if True \"\"\" results_ols = self . fit_ols ( df ) std_error = results_ols . bse [ self . treatment_col ] ate = results_ols . params [ self . treatment_col ] p_value = self . pvalue_based_on_hypothesis ( results_ols ) # Extract the confidence interval for the treatment column conf_int_df = results_ols . conf_int ( alpha = alpha ) lower_bound , upper_bound = conf_int_df . loc [ self . treatment_col ] if verbose : print ( results_ols . summary ()) # Return the confidence interval return InferenceResults ( ate = ate , p_value = p_value , std_error = std_error , conf_int = ConfidenceInterval ( lower = lower_bound , upper = upper_bound , alpha = alpha ), )","title":"analysis_inference_results()"},{"location":"api/experiment_analysis.html#cluster_experiments.experiment_analysis.OLSAnalysis.analysis_point_estimate","text":"Returns the point estimate of the analysis Parameters: Name Type Description Default df DataFrame dataframe containing the data to analyze required verbose Optional bool, prints the regression summary if True False Source code in cluster_experiments/experiment_analysis.py def analysis_point_estimate ( self , df : pd . DataFrame , verbose : bool = False ) -> float : \"\"\"Returns the point estimate of the analysis Arguments: df: dataframe containing the data to analyze verbose (Optional): bool, prints the regression summary if True \"\"\" results_ols = self . fit_ols ( df = df ) return results_ols . params [ self . treatment_col ]","title":"analysis_point_estimate()"},{"location":"api/experiment_analysis.html#cluster_experiments.experiment_analysis.OLSAnalysis.analysis_pvalue","text":"Returns the p-value of the analysis Parameters: Name Type Description Default df DataFrame dataframe containing the data to analyze required verbose Optional bool, prints the regression summary if True False Source code in cluster_experiments/experiment_analysis.py def analysis_pvalue ( self , df : pd . DataFrame , verbose : bool = False ) -> float : \"\"\"Returns the p-value of the analysis Arguments: df: dataframe containing the data to analyze verbose (Optional): bool, prints the regression summary if True \"\"\" results_ols = self . fit_ols ( df = df ) if verbose : print ( results_ols . summary ()) p_value = self . pvalue_based_on_hypothesis ( results_ols ) return p_value","title":"analysis_pvalue()"},{"location":"api/experiment_analysis.html#cluster_experiments.experiment_analysis.OLSAnalysis.analysis_standard_error","text":"Returns the standard error of the analysis Parameters: Name Type Description Default df DataFrame dataframe containing the data to analyze required verbose Optional bool, prints the regression summary if True False Source code in cluster_experiments/experiment_analysis.py def analysis_standard_error ( self , df : pd . DataFrame , verbose : bool = False ) -> float : \"\"\"Returns the standard error of the analysis Arguments: df: dataframe containing the data to analyze verbose (Optional): bool, prints the regression summary if True \"\"\" results_ols = self . fit_ols ( df = df ) return results_ols . bse [ self . treatment_col ]","title":"analysis_standard_error()"},{"location":"api/experiment_analysis.html#cluster_experiments.experiment_analysis.OLSAnalysis.fit_ols","text":"Returns the fitted OLS model Source code in cluster_experiments/experiment_analysis.py def fit_ols ( self , df : pd . DataFrame ): \"\"\"Returns the fitted OLS model\"\"\" return sm . OLS . from_formula ( self . formula , data = df ) . fit ()","title":"fit_ols()"},{"location":"api/experiment_analysis.html#cluster_experiments.experiment_analysis.OLSAnalysis.from_config","text":"Creates an OLSAnalysis object from a PowerConfig object Source code in cluster_experiments/experiment_analysis.py @classmethod def from_config ( cls , config ): \"\"\"Creates an OLSAnalysis object from a PowerConfig object\"\"\" return cls ( target_col = config . target_col , treatment_col = config . treatment_col , treatment = config . treatment , covariates = config . covariates , hypothesis = config . hypothesis , )","title":"from_config()"},{"location":"api/experiment_analysis.html#cluster_experiments.experiment_analysis.PairedTTestClusteredAnalysis","text":"Class to run paired T-test analysis on aggregated data Parameters: Name Type Description Default cluster_cols List[str] list of columns to use as clusters required target_col str name of the column containing the variable to measure 'target' treatment_col str name of the column containing the treatment variable 'treatment' treatment str name of the treatment to use as the treated group 'B' strata_cols List[str] list of index columns for paired t test. Should be a subset or equal to cluster_cols required hypothesis str one of \"two-sided\", \"less\", \"greater\" indicating the alternative hypothesis 'two-sided' Usage: from cluster_experiments.experiment_analysis import PairedTTestClusteredAnalysis import pandas as pd df = pd . DataFrame ({ 'x' : [ 1 , 2 , 3 , 4 , 0 , 0 , 1 , 1 ], 'treatment' : [ \"A\" , \"B\" , \"A\" , \"B\" ] * 2 , 'cluster' : [ 1 , 2 , 3 , 4 , 1 , 2 , 3 , 4 ], }) PairedTTestClusteredAnalysis ( cluster_cols = [ 'cluster' ], strata_cols = [ 'cluster' ], target_col = 'x' , ) . get_pvalue ( df ) Source code in cluster_experiments/experiment_analysis.py class PairedTTestClusteredAnalysis ( ExperimentAnalysis ): \"\"\" Class to run paired T-test analysis on aggregated data Arguments: cluster_cols: list of columns to use as clusters target_col: name of the column containing the variable to measure treatment_col: name of the column containing the treatment variable treatment: name of the treatment to use as the treated group strata_cols: list of index columns for paired t test. Should be a subset or equal to cluster_cols hypothesis: one of \"two-sided\", \"less\", \"greater\" indicating the alternative hypothesis Usage: ```python from cluster_experiments.experiment_analysis import PairedTTestClusteredAnalysis import pandas as pd df = pd.DataFrame({ 'x': [1, 2, 3, 4, 0, 0, 1, 1], 'treatment': [\"A\", \"B\", \"A\", \"B\"] * 2, 'cluster': [1, 2, 3, 4, 1, 2, 3, 4], }) PairedTTestClusteredAnalysis( cluster_cols=['cluster'], strata_cols=['cluster'], target_col='x', ).get_pvalue(df) ``` \"\"\" def __init__ ( self , cluster_cols : List [ str ], strata_cols : List [ str ], target_col : str = \"target\" , treatment_col : str = \"treatment\" , treatment : str = \"B\" , hypothesis : str = \"two-sided\" , ): self . strata_cols = strata_cols self . target_col = target_col self . treatment = treatment self . treatment_col = treatment_col self . cluster_cols = cluster_cols self . hypothesis = hypothesis def _preprocessing ( self , df : pd . DataFrame , verbose : bool = False ) -> pd . DataFrame : df_grouped = df . groupby ( self . cluster_cols + [ self . treatment_col ], as_index = False )[ self . target_col ] . mean () n_control = df_grouped [ self . treatment_col ] . value_counts ()[ 0 ] n_treatment = df_grouped [ self . treatment_col ] . value_counts ()[ 1 ] if n_control != n_treatment : logging . warning ( f \"groups don't have same number of observations, { n_treatment =} and { n_control =} \" ) assert all ( [ x in self . cluster_cols for x in self . strata_cols ] ), f \"strata should be a subset or equal to cluster_cols ( { self . cluster_cols = } , { self . strata_cols = } )\" df_pivot = df_grouped . pivot_table ( columns = self . treatment_col , index = self . strata_cols , values = self . target_col , ) if df_pivot . isna () . sum () . sum () > 0 : logging . warning ( f \"There are missing pairs for some clusters, removing the lonely ones: { df_pivot [ df_pivot . isna () . any ( axis = 1 )] . to_dict () } \" ) if verbose : print ( f \"performing paired t test in this data \\n { df_pivot } \\n \" ) df_pivot = df_pivot . dropna () return df_pivot def analysis_pvalue ( self , df : pd . DataFrame , verbose : bool = False ) -> float : \"\"\"Returns the p-value of the analysis Arguments: df: dataframe containing the data to analyze verbose (Optional): bool, prints the extra info if True \"\"\" assert ( type ( self . cluster_cols ) is list ), \"cluster_cols needs to be a list of strings (even with one element)\" assert ( type ( self . strata_cols ) is list ), \"strata_cols needs to be a list of strings (even with one element)\" df_pivot = self . _preprocessing ( df = df ) t_test_results = ttest_rel ( df_pivot . iloc [:, 0 ], df_pivot . iloc [:, 1 ], alternative = self . hypothesis ) if verbose : print ( f \"paired t test results: \\n { t_test_results } \\n \" ) return t_test_results . pvalue @classmethod def from_config ( cls , config ): \"\"\"Creates a PairedTTestClusteredAnalysis object from a PowerConfig object\"\"\" return cls ( cluster_cols = config . cluster_cols , target_col = config . target_col , treatment_col = config . treatment_col , treatment = config . treatment , strata_cols = config . strata_cols , hypothesis = config . hypothesis , )","title":"PairedTTestClusteredAnalysis"},{"location":"api/experiment_analysis.html#cluster_experiments.experiment_analysis.PairedTTestClusteredAnalysis.analysis_pvalue","text":"Returns the p-value of the analysis Parameters: Name Type Description Default df DataFrame dataframe containing the data to analyze required verbose Optional bool, prints the extra info if True False Source code in cluster_experiments/experiment_analysis.py def analysis_pvalue ( self , df : pd . DataFrame , verbose : bool = False ) -> float : \"\"\"Returns the p-value of the analysis Arguments: df: dataframe containing the data to analyze verbose (Optional): bool, prints the extra info if True \"\"\" assert ( type ( self . cluster_cols ) is list ), \"cluster_cols needs to be a list of strings (even with one element)\" assert ( type ( self . strata_cols ) is list ), \"strata_cols needs to be a list of strings (even with one element)\" df_pivot = self . _preprocessing ( df = df ) t_test_results = ttest_rel ( df_pivot . iloc [:, 0 ], df_pivot . iloc [:, 1 ], alternative = self . hypothesis ) if verbose : print ( f \"paired t test results: \\n { t_test_results } \\n \" ) return t_test_results . pvalue","title":"analysis_pvalue()"},{"location":"api/experiment_analysis.html#cluster_experiments.experiment_analysis.PairedTTestClusteredAnalysis.from_config","text":"Creates a PairedTTestClusteredAnalysis object from a PowerConfig object Source code in cluster_experiments/experiment_analysis.py @classmethod def from_config ( cls , config ): \"\"\"Creates a PairedTTestClusteredAnalysis object from a PowerConfig object\"\"\" return cls ( cluster_cols = config . cluster_cols , target_col = config . target_col , treatment_col = config . treatment_col , treatment = config . treatment , strata_cols = config . strata_cols , hypothesis = config . hypothesis , )","title":"from_config()"},{"location":"api/experiment_analysis.html#cluster_experiments.experiment_analysis.SyntheticControlAnalysis","text":"Class to run Synthetic control analysis. It expects only one treatment cluster. Parameters: Name Type Description Default target_col str The name of the column containing the variable to measure. 'target' treatment_col str The name of the column containing the treatment variable. 'treatment' treatment str The name of the treatment to use as the treated group. 'B' cluster_cols list A list of columns to use as clusters. required hypothesis str One of \"two-sided\", \"less\", \"greater\" indicating the hypothesis. 'two-sided' time_col str The name of the column containing the time data. 'date' intervention_date str The date when the intervention occurred. required Usage: from cluster_experiments.experiment_analysis import SyntheticControlAnalysis import pandas as pd import numpy as np from itertools import product dates = pd . date_range ( \"2022-01-01\" , \"2022-01-31\" , freq = \"d\" ) users = [ f \"User { i } \" for i in range ( 10 )] # Create a combination of each date with each user combinations = list ( product ( users , dates )) target_values = np . random . normal ( 0 , 1 , size = len ( combinations )) df = pd . DataFrame ( combinations , columns = [ \"user\" , \"date\" ]) df [ \"target\" ] = target_values df [ \"treatment\" ] = \"A\" df . loc [( df [ \"user\" ] == \"User 5\" ), \"treatment\" ] = \"B\" SyntheticControlAnalysis ( cluster_cols = [ \"user\" ], time_col = \"date\" , intervention_date = \"2022-01-15\" ) . get_pvalue ( df ) Source code in cluster_experiments/experiment_analysis.py class SyntheticControlAnalysis ( ExperimentAnalysis ): \"\"\" Class to run Synthetic control analysis. It expects only one treatment cluster. Arguments: target_col (str): The name of the column containing the variable to measure. treatment_col (str): The name of the column containing the treatment variable. treatment (str): The name of the treatment to use as the treated group. cluster_cols (list): A list of columns to use as clusters. hypothesis (str): One of \"two-sided\", \"less\", \"greater\" indicating the hypothesis. time_col (str): The name of the column containing the time data. intervention_date (str): The date when the intervention occurred. Usage: ```python from cluster_experiments.experiment_analysis import SyntheticControlAnalysis import pandas as pd import numpy as np from itertools import product dates = pd.date_range(\"2022-01-01\", \"2022-01-31\", freq=\"d\") users = [f\"User {i}\" for i in range(10)] # Create a combination of each date with each user combinations = list(product(users, dates)) target_values = np.random.normal(0, 1, size=len(combinations)) df = pd.DataFrame(combinations, columns=[\"user\", \"date\"]) df[\"target\"] = target_values df[\"treatment\"] = \"A\" df.loc[(df[\"user\"] == \"User 5\"), \"treatment\"] = \"B\" SyntheticControlAnalysis( cluster_cols=[\"user\"], time_col=\"date\", intervention_date=\"2022-01-15\" ).get_pvalue(df) ``` \"\"\" def __init__ ( self , intervention_date : str , cluster_cols : List [ str ], target_col : str = \"target\" , treatment_col : str = \"treatment\" , treatment : str = \"B\" , hypothesis : str = \"two-sided\" , time_col : str = \"date\" , ): super () . __init__ ( treatment = treatment , target_col = target_col , treatment_col = treatment_col , hypothesis = hypothesis , cluster_cols = cluster_cols , ) self . time_col = time_col self . intervention_date = intervention_date if time_col in cluster_cols : raise ValueError ( \"time columns should not be in cluster columns\" ) def _fit ( self , pre_experiment_df : pd . DataFrame , verbose : bool ) -> np . ndarray : \"\"\"Returns the weight of each donor\"\"\" if not any ( pre_experiment_df [ self . treatment_col ] == 1 ): raise ValueError ( \"No treatment unit found in the data.\" ) X = ( pre_experiment_df . query ( f \" { self . treatment_col } == 0\" ) . pivot ( index = self . cluster_cols , columns = self . time_col )[ self . target_col ] . T ) y = ( pre_experiment_df . query ( f \" { self . treatment_col } == 1\" ) . pivot ( index = self . cluster_cols , columns = self . time_col )[ self . target_col ] . T . iloc [:, 0 ] ) weights = get_w ( X , y , verbose ) return weights def _predict ( self , df : pd . DataFrame , weights : np . ndarray , treatment_cluster : str ) -> pd . DataFrame : \"\"\" This method adds a column with the synthetic results and filter only the treatment unit. First, it calculates the weights of each donor in the control group using the `fit_synthetic` method. It then uses these weights to create a synthetic control group that closely matches the treatment unit before the intervention. The synthetic control group is added to the treatment unit in the dataframe. \"\"\" synthetic = ( df [ self . _get_cluster_column ( df ) != treatment_cluster ] . pivot ( index = self . time_col , columns = self . cluster_cols )[ self . target_col ] . values . dot ( weights ) ) # add synthetic to treatment cluster return df [ self . _get_cluster_column ( df ) == treatment_cluster ] . assign ( synthetic = synthetic ) def fit_predict_synthetic ( self , df : pd . DataFrame , pre_experiment_df : pd . DataFrame , treatment_cluster : str , verbose : bool = False , ) -> pd . DataFrame : \"\"\" Fit the synthetic control model and predict the results for the treatment cluster. Args: df: The dataframe containing the data after the intervention. pre_experiment_df: The dataframe containing the data before the intervention. treatment_cluster: The name of the treatment cluster. verbose: If True, print the status of the optimization of weights. Returns: The dataframe with the synthetic results added to the treatment cluster. \"\"\" weights = self . _fit ( pre_experiment_df = pre_experiment_df , verbose = verbose ) prediction = self . _predict ( df = df , weights = weights , treatment_cluster = treatment_cluster ) return prediction def pvalue_based_on_hypothesis ( self , ate : np . float64 , avg_effects : Dict [ str , float ] ) -> float : \"\"\" Returns the p-value of the analysis. 1. Count how many times the average effect is greater than the real treatment unit 2. Average it with the number of units. The result is the p-value using Fisher permutation exact test. \"\"\" avg_effects = list ( avg_effects . values ()) if HypothesisEntries ( self . hypothesis ) == HypothesisEntries . LESS : return np . mean ( avg_effects < ate ) if HypothesisEntries ( self . hypothesis ) == HypothesisEntries . GREATER : return np . mean ( avg_effects > ate ) if HypothesisEntries ( self . hypothesis ) == HypothesisEntries . TWO_SIDED : avg_effects = np . abs ( avg_effects ) return np . mean ( avg_effects > ate ) raise ValueError ( f \" { self . hypothesis } is not a valid HypothesisEntries\" ) def _get_treatment_cluster ( self , df : pd . DataFrame ) -> str : \"\"\"Returns the first treatment cluster. The current implementation of Synthetic Control only accepts one treatment cluster. This will be left inside Synthetic class because it doesn't apply for other analyses\"\"\" treatment_df = df [ df [ self . treatment_col ] == 1 ] treatment_cluster = self . _get_cluster_column ( treatment_df ) . unique ()[ 0 ] return treatment_cluster def analysis_pvalue ( self , df : pd . DataFrame , verbose : bool = False ) -> float : \"\"\" Returns the p-value of the analysis. 1. Calculate the average effect after intervention for each unit. 2. Count how many times the average effect is greater than the real treatment unit 3. Average it with the number of units. The result is the p-value using Fisher permutation test \"\"\" clusters = self . _get_cluster_column ( df ) . unique () treatment_cluster = self . _get_treatment_cluster ( df ) synthetic_donors = { cluster : self . analysis_point_estimate ( treatment_cluster = cluster , df = df , verbose = verbose , ) for cluster in clusters } ate = synthetic_donors [ treatment_cluster ] synthetic_donors . pop ( treatment_cluster ) return self . pvalue_based_on_hypothesis ( ate = ate , avg_effects = synthetic_donors ) def analysis_point_estimate ( self , df : pd . DataFrame , treatment_cluster : Optional [ str ] = None , verbose : bool = False , ): \"\"\" Calculate the point estimate for the treatment effect for a specified cluster by averaging across the time windows. \"\"\" df , pre_experiment_df = self . _split_pre_experiment_df ( df ) if treatment_cluster is None : treatment_cluster = self . _get_treatment_cluster ( df ) df = self . fit_predict_synthetic ( df , pre_experiment_df , treatment_cluster , verbose = verbose ) df [ \"effect\" ] = df [ self . target_col ] - df [ \"synthetic\" ] avg_effect = df [ \"effect\" ] . mean () return avg_effect def _split_pre_experiment_df ( self , df : pd . DataFrame ): \"\"\"Split the dataframe into pre-experiment and experiment dataframes\"\"\" pre_experiment_df = df [( df [ self . time_col ] <= self . intervention_date )] df = df [( df [ self . time_col ] > self . intervention_date )] return df , pre_experiment_df","title":"SyntheticControlAnalysis"},{"location":"api/experiment_analysis.html#cluster_experiments.experiment_analysis.SyntheticControlAnalysis.analysis_point_estimate","text":"Calculate the point estimate for the treatment effect for a specified cluster by averaging across the time windows. Source code in cluster_experiments/experiment_analysis.py def analysis_point_estimate ( self , df : pd . DataFrame , treatment_cluster : Optional [ str ] = None , verbose : bool = False , ): \"\"\" Calculate the point estimate for the treatment effect for a specified cluster by averaging across the time windows. \"\"\" df , pre_experiment_df = self . _split_pre_experiment_df ( df ) if treatment_cluster is None : treatment_cluster = self . _get_treatment_cluster ( df ) df = self . fit_predict_synthetic ( df , pre_experiment_df , treatment_cluster , verbose = verbose ) df [ \"effect\" ] = df [ self . target_col ] - df [ \"synthetic\" ] avg_effect = df [ \"effect\" ] . mean () return avg_effect","title":"analysis_point_estimate()"},{"location":"api/experiment_analysis.html#cluster_experiments.experiment_analysis.SyntheticControlAnalysis.analysis_pvalue","text":"Returns the p-value of the analysis. 1. Calculate the average effect after intervention for each unit. 2. Count how many times the average effect is greater than the real treatment unit 3. Average it with the number of units. The result is the p-value using Fisher permutation test Source code in cluster_experiments/experiment_analysis.py def analysis_pvalue ( self , df : pd . DataFrame , verbose : bool = False ) -> float : \"\"\" Returns the p-value of the analysis. 1. Calculate the average effect after intervention for each unit. 2. Count how many times the average effect is greater than the real treatment unit 3. Average it with the number of units. The result is the p-value using Fisher permutation test \"\"\" clusters = self . _get_cluster_column ( df ) . unique () treatment_cluster = self . _get_treatment_cluster ( df ) synthetic_donors = { cluster : self . analysis_point_estimate ( treatment_cluster = cluster , df = df , verbose = verbose , ) for cluster in clusters } ate = synthetic_donors [ treatment_cluster ] synthetic_donors . pop ( treatment_cluster ) return self . pvalue_based_on_hypothesis ( ate = ate , avg_effects = synthetic_donors )","title":"analysis_pvalue()"},{"location":"api/experiment_analysis.html#cluster_experiments.experiment_analysis.SyntheticControlAnalysis.fit_predict_synthetic","text":"Fit the synthetic control model and predict the results for the treatment cluster. Parameters: Name Type Description Default df DataFrame The dataframe containing the data after the intervention. required pre_experiment_df DataFrame The dataframe containing the data before the intervention. required treatment_cluster str The name of the treatment cluster. required verbose bool If True, print the status of the optimization of weights. False Returns: Type Description DataFrame The dataframe with the synthetic results added to the treatment cluster. Source code in cluster_experiments/experiment_analysis.py def fit_predict_synthetic ( self , df : pd . DataFrame , pre_experiment_df : pd . DataFrame , treatment_cluster : str , verbose : bool = False , ) -> pd . DataFrame : \"\"\" Fit the synthetic control model and predict the results for the treatment cluster. Args: df: The dataframe containing the data after the intervention. pre_experiment_df: The dataframe containing the data before the intervention. treatment_cluster: The name of the treatment cluster. verbose: If True, print the status of the optimization of weights. Returns: The dataframe with the synthetic results added to the treatment cluster. \"\"\" weights = self . _fit ( pre_experiment_df = pre_experiment_df , verbose = verbose ) prediction = self . _predict ( df = df , weights = weights , treatment_cluster = treatment_cluster ) return prediction","title":"fit_predict_synthetic()"},{"location":"api/experiment_analysis.html#cluster_experiments.experiment_analysis.SyntheticControlAnalysis.pvalue_based_on_hypothesis","text":"Returns the p-value of the analysis. 1. Count how many times the average effect is greater than the real treatment unit 2. Average it with the number of units. The result is the p-value using Fisher permutation exact test. Source code in cluster_experiments/experiment_analysis.py def pvalue_based_on_hypothesis ( self , ate : np . float64 , avg_effects : Dict [ str , float ] ) -> float : \"\"\" Returns the p-value of the analysis. 1. Count how many times the average effect is greater than the real treatment unit 2. Average it with the number of units. The result is the p-value using Fisher permutation exact test. \"\"\" avg_effects = list ( avg_effects . values ()) if HypothesisEntries ( self . hypothesis ) == HypothesisEntries . LESS : return np . mean ( avg_effects < ate ) if HypothesisEntries ( self . hypothesis ) == HypothesisEntries . GREATER : return np . mean ( avg_effects > ate ) if HypothesisEntries ( self . hypothesis ) == HypothesisEntries . TWO_SIDED : avg_effects = np . abs ( avg_effects ) return np . mean ( avg_effects > ate ) raise ValueError ( f \" { self . hypothesis } is not a valid HypothesisEntries\" )","title":"pvalue_based_on_hypothesis()"},{"location":"api/experiment_analysis.html#cluster_experiments.experiment_analysis.TTestClusteredAnalysis","text":"Class to run T-test analysis on aggregated data Parameters: Name Type Description Default cluster_cols List[str] list of columns to use as clusters required target_col str name of the column containing the variable to measure 'target' treatment_col str name of the column containing the treatment variable 'treatment' treatment str name of the treatment to use as the treated group 'B' hypothesis str one of \"two-sided\", \"less\", \"greater\" indicating the alternative hypothesis 'two-sided' Usage: from cluster_experiments.experiment_analysis import TTestClusteredAnalysis import pandas as pd df = pd . DataFrame ({ 'x' : [ 1 , 2 , 3 , 4 , 0 , 0 , 1 , 1 ], 'treatment' : [ \"A\" , \"B\" , \"A\" , \"B\" ] * 2 , 'cluster' : [ 1 , 2 , 3 , 4 , 1 , 2 , 3 , 4 ], }) TTestClusteredAnalysis ( cluster_cols = [ 'cluster' ], target_col = 'x' , ) . get_pvalue ( df ) Source code in cluster_experiments/experiment_analysis.py class TTestClusteredAnalysis ( ExperimentAnalysis ): \"\"\" Class to run T-test analysis on aggregated data Arguments: cluster_cols: list of columns to use as clusters target_col: name of the column containing the variable to measure treatment_col: name of the column containing the treatment variable treatment: name of the treatment to use as the treated group hypothesis: one of \"two-sided\", \"less\", \"greater\" indicating the alternative hypothesis Usage: ```python from cluster_experiments.experiment_analysis import TTestClusteredAnalysis import pandas as pd df = pd.DataFrame({ 'x': [1, 2, 3, 4, 0, 0, 1, 1], 'treatment': [\"A\", \"B\", \"A\", \"B\"] * 2, 'cluster': [1, 2, 3, 4, 1, 2, 3, 4], }) TTestClusteredAnalysis( cluster_cols=['cluster'], target_col='x', ).get_pvalue(df) ``` \"\"\" def __init__ ( self , cluster_cols : List [ str ], target_col : str = \"target\" , treatment_col : str = \"treatment\" , treatment : str = \"B\" , hypothesis : str = \"two-sided\" , ): self . target_col = target_col self . treatment = treatment self . treatment_col = treatment_col self . cluster_cols = cluster_cols self . hypothesis = hypothesis def analysis_pvalue ( self , df : pd . DataFrame , verbose : bool = False ) -> float : \"\"\"Returns the p-value of the analysis Arguments: df: dataframe containing the data to analyze verbose (Optional): bool, prints the regression summary if True \"\"\" df_grouped = df . groupby ( self . cluster_cols + [ self . treatment_col ], as_index = False )[ self . target_col ] . mean () treatment_data = df_grouped . query ( f \" { self . treatment_col } == 1\" )[ self . target_col ] control_data = df_grouped . query ( f \" { self . treatment_col } == 0\" )[ self . target_col ] assert len ( treatment_data ), \"treatment data should have more than 1 cluster\" assert len ( control_data ), \"control data should have more than 1 cluster\" t_test_results = ttest_ind ( treatment_data , control_data , equal_var = False , alternative = self . hypothesis ) return t_test_results . pvalue @classmethod def from_config ( cls , config ): \"\"\"Creates a TTestClusteredAnalysis object from a PowerConfig object\"\"\" return cls ( cluster_cols = config . cluster_cols , target_col = config . target_col , treatment_col = config . treatment_col , treatment = config . treatment , hypothesis = config . hypothesis , )","title":"TTestClusteredAnalysis"},{"location":"api/experiment_analysis.html#cluster_experiments.experiment_analysis.TTestClusteredAnalysis.analysis_pvalue","text":"Returns the p-value of the analysis Parameters: Name Type Description Default df DataFrame dataframe containing the data to analyze required verbose Optional bool, prints the regression summary if True False Source code in cluster_experiments/experiment_analysis.py def analysis_pvalue ( self , df : pd . DataFrame , verbose : bool = False ) -> float : \"\"\"Returns the p-value of the analysis Arguments: df: dataframe containing the data to analyze verbose (Optional): bool, prints the regression summary if True \"\"\" df_grouped = df . groupby ( self . cluster_cols + [ self . treatment_col ], as_index = False )[ self . target_col ] . mean () treatment_data = df_grouped . query ( f \" { self . treatment_col } == 1\" )[ self . target_col ] control_data = df_grouped . query ( f \" { self . treatment_col } == 0\" )[ self . target_col ] assert len ( treatment_data ), \"treatment data should have more than 1 cluster\" assert len ( control_data ), \"control data should have more than 1 cluster\" t_test_results = ttest_ind ( treatment_data , control_data , equal_var = False , alternative = self . hypothesis ) return t_test_results . pvalue","title":"analysis_pvalue()"},{"location":"api/experiment_analysis.html#cluster_experiments.experiment_analysis.TTestClusteredAnalysis.from_config","text":"Creates a TTestClusteredAnalysis object from a PowerConfig object Source code in cluster_experiments/experiment_analysis.py @classmethod def from_config ( cls , config ): \"\"\"Creates a TTestClusteredAnalysis object from a PowerConfig object\"\"\" return cls ( cluster_cols = config . cluster_cols , target_col = config . target_col , treatment_col = config . treatment_col , treatment = config . treatment , hypothesis = config . hypothesis , )","title":"from_config()"},{"location":"api/hypothesis_test.html","text":"from cluster_experiments.inference.hypothesis_test import * \u00b6 HypothesisTest \u00b6 A class used to represent a Hypothesis Test with a metric, analysis, optional analysis configuration, and optional dimensions. Attributes \u00b6 metric : Metric An instance of the Metric class analysis_type : str string mapping to an ExperimentAnalysis class. Must be either in the built-in analysis_mapping or in the custom_analysis_type_mapper if provided. analysis_config : Optional[dict] An optional dictionary representing the configuration for the analysis dimensions : Optional[List[Dimension]] An optional list of Dimension instances cupac_config : Optional[dict] An optional dictionary representing the configuration for the cupac model custom_analysis_type_mapper : Optional[Dict[str, ExperimentAnalysis]] An optional dictionary mapping the names of custom analysis types to the corresponding ExperimentAnalysis classes Source code in cluster_experiments/inference/hypothesis_test.py class HypothesisTest : \"\"\" A class used to represent a Hypothesis Test with a metric, analysis, optional analysis configuration, and optional dimensions. Attributes ---------- metric : Metric An instance of the Metric class analysis_type : str string mapping to an ExperimentAnalysis class. Must be either in the built-in analysis_mapping or in the custom_analysis_type_mapper if provided. analysis_config : Optional[dict] An optional dictionary representing the configuration for the analysis dimensions : Optional[List[Dimension]] An optional list of Dimension instances cupac_config : Optional[dict] An optional dictionary representing the configuration for the cupac model custom_analysis_type_mapper : Optional[Dict[str, ExperimentAnalysis]] An optional dictionary mapping the names of custom analysis types to the corresponding ExperimentAnalysis classes \"\"\" def __init__ ( self , metric : Metric , analysis_type : str , analysis_config : Optional [ dict ] = None , dimensions : Optional [ List [ Dimension ]] = None , cupac_config : Optional [ dict ] = None , custom_analysis_type_mapper : Optional [ Dict [ str , ExperimentAnalysis ]] = None , ): \"\"\" Parameters ---------- metric : Metric An instance of the Metric class analysis_type : str string mapping to an ExperimentAnalysis class. Must be either in the built-in analysis_mapping or in the custom_analysis_type_mapper if provided. analysis_config : Optional[dict] An optional dictionary representing the configuration for the analysis dimensions : Optional[List[Dimension]] An optional list of Dimension instances cupac_config : Optional[dict] An optional dictionary representing the configuration for the cupac model custom_analysis_type_mapper : Optional[Dict[str, ExperimentAnalysis]] An optional dictionary mapping the names of custom analysis types to the corresponding ExperimentAnalysis classes \"\"\" self . _validate_inputs ( metric , analysis_type , analysis_config , dimensions , cupac_config , custom_analysis_type_mapper , ) self . metric = metric self . analysis_type = analysis_type self . analysis_config = analysis_config or {} self . dimensions = [ DefaultDimension ()] + ( dimensions or []) self . cupac_config = cupac_config or {} self . custom_analysis_type_mapper = custom_analysis_type_mapper or {} self . analysis_type_mapper = self . custom_analysis_type_mapper or analysis_mapping self . analysis_class = self . analysis_type_mapper [ self . analysis_type ] self . is_cupac = bool ( cupac_config ) self . cupac_handler = ( CupacHandler ( ** self . cupac_config ) if self . is_cupac else None ) self . cupac_covariate_col = ( self . cupac_handler . cupac_outcome_name if self . is_cupac else None ) self . new_analysis_config = None self . experiment_analysis = None @staticmethod def _validate_inputs ( metric : Metric , analysis_type : str , analysis_config : Optional [ dict ], dimensions : Optional [ List [ Dimension ]], cupac_config : Optional [ dict ] = None , custom_analysis_type_mapper : Optional [ Dict [ str , ExperimentAnalysis ]] = None , ): \"\"\" Validates the inputs for the HypothesisTest class. Parameters ---------- metric : Metric An instance of the Metric class analysis_type : str string mapper to an ExperimentAnalysis analysis_config : Optional[dict] An optional dictionary representing the configuration for the analysis dimensions : Optional[List[Dimension]] An optional list of Dimension instances cupac_config : Optional[dict] An optional dictionary representing the configuration for the cupac model custom_analysis_type_mapper : Optional[dict[str, ExperimentAnalysis]] An optional dictionary mapping the names of custom analysis types to the corresponding ExperimentAnalysis classes \"\"\" # Check if metric is a valid Metric instance if not isinstance ( metric , Metric ): raise TypeError ( \"Metric must be an instance of Metric\" ) # Check if analysis_type is a string if not isinstance ( analysis_type , str ): raise TypeError ( \"Analysis must be a string\" ) # Check if analysis_config is a dictionary when provided if analysis_config is not None and not isinstance ( analysis_config , dict ): raise TypeError ( \"analysis_config must be a dictionary if provided\" ) # Check if cupac_config is a dictionary when provided if cupac_config is not None and not isinstance ( cupac_config , dict ): raise TypeError ( \"cupac_config must be a dictionary if provided\" ) # Check if dimensions is a list of Dimension instances when provided if dimensions is not None and ( not isinstance ( dimensions , list ) or not all ( isinstance ( dim , Dimension ) for dim in dimensions ) ): raise TypeError ( \"Dimensions must be a list of Dimension instances if provided\" ) # Validate custom_analysis_type_mapper if provided if custom_analysis_type_mapper : # Ensure it's a dictionary if not isinstance ( custom_analysis_type_mapper , dict ): raise TypeError ( \"custom_analysis_type_mapper must be a dictionary if provided\" ) # Ensure all keys are strings and values are ExperimentAnalysis classes for key , value in custom_analysis_type_mapper . items (): if not isinstance ( key , str ): raise TypeError ( f \"Key ' { key } ' in custom_analysis_type_mapper must be a string\" ) if not issubclass ( value , ExperimentAnalysis ): raise TypeError ( f \"Value ' { value } ' for key ' { key } ' in custom_analysis_type_mapper must be a subclass of ExperimentAnalysis\" ) # Ensure the analysis_type is in the custom mapper if a custom mapper is provided if analysis_type not in custom_analysis_type_mapper : raise ValueError ( f \"Analysis type ' { analysis_type } ' not found in the provided custom_analysis_type_mapper\" ) # If no custom_analysis_type_mapper, check if analysis_type exists in the default mapping elif analysis_type not in analysis_mapping : raise ValueError ( f \"Analysis type ' { analysis_type } ' not found in analysis_mapping\" ) def get_inference_results ( self , df : pd . DataFrame , alpha : float ) -> InferenceResults : \"\"\" Performs inference analysis on the provided DataFrame using the analysis class. Parameters ---------- df : pd.DataFrame The dataframe containing the data for analysis. alpha : float The significance level to be used in the inference analysis. Returns ------- InferenceResults The results containing the statistics of the inference procedure. \"\"\" self . experiment_analysis = self . analysis_class ( ** self . new_analysis_config ) inference_results = self . experiment_analysis . get_inference_results ( df = df , alpha = alpha ) return inference_results def _prepare_analysis_config ( self , target_col : str , treatment_col : str , treatment : str ) -> None : \"\"\" Extends the analysis_config provided by the user, by adding or overriding the following keys: - target_col - treatment_col - treatment Also handles cupac covariate. Returns ------- dict The prepared analysis configuration, ready to be ingested by the experiment analysis class \"\"\" new_analysis_config = copy . deepcopy ( self . analysis_config ) new_analysis_config [ \"target_col\" ] = target_col new_analysis_config [ \"treatment_col\" ] = treatment_col new_analysis_config [ \"treatment\" ] = treatment covariates = new_analysis_config . get ( \"covariates\" , []) if self . cupac_covariate_col and self . cupac_covariate_col not in covariates : raise ValueError ( f \"You provided a cupac configuration but did not provide the cupac covariate called { self . cupac_covariate_col } in the analysis_config\" ) self . new_analysis_config = new_analysis_config @staticmethod def prepare_data ( data : pd . DataFrame , variant_col : str , treatment_variant : Variant , control_variant : Variant , dimension_name : str , dimension_value : str , ) -> pd . DataFrame : \"\"\" Prepares the data for the experiment analysis pipeline \"\"\" prepared_df = data . copy () prepared_df = prepared_df . assign ( __total_dimension = \"total\" ) prepared_df = prepared_df . query ( f \" { variant_col } .isin([' { treatment_variant . name } ',' { control_variant . name } '])\" ) . query ( f \" { dimension_name } == ' { dimension_value } '\" ) return prepared_df def add_covariates ( self , exp_data : pd . DataFrame , pre_exp_data : pd . DataFrame ) -> pd . DataFrame : \"\"\" If the test is a cupac test, adds the covariates to the experimental data. \"\"\" if self . is_cupac : exp_data = self . cupac_handler . add_covariates ( df = exp_data , pre_experiment_df = pre_exp_data ) return exp_data def get_test_results ( self , control_variant : Variant , treatment_variant : Variant , variant_col : str , exp_data : pd . DataFrame , dimension : Dimension , dimension_value : str , alpha : float , ) -> AnalysisPlanResults : \"\"\" Performs the hypothesis test on the provided data, for the given dimension value. Parameters ---------- control_variant : Variant The control variant treatment_variant : Variant The treatment variant variant_col : str The column name representing the variant exp_data : pd.DataFrame The dataframe containing the data for analysis. dimension : Dimension The dimension instance dimension_value : str The value of the dimension alpha : float The significance level to be used in the inference analysis. Returns ------- AnalysisPlanResults The results of the hypothesis test \"\"\" self . _prepare_analysis_config ( target_col = self . metric . target_column , treatment_col = variant_col , treatment = treatment_variant . name , ) prepared_df = self . prepare_data ( data = exp_data , variant_col = variant_col , treatment_variant = treatment_variant , control_variant = control_variant , dimension_name = dimension . name , dimension_value = dimension_value , ) inference_results = self . get_inference_results ( df = prepared_df , alpha = alpha ) control_variant_mean = self . metric . get_mean ( prepared_df . query ( f \" { variant_col } ==' { control_variant . name } '\" ) ) treatment_variant_mean = self . metric . get_mean ( prepared_df . query ( f \" { variant_col } ==' { treatment_variant . name } '\" ) ) test_results = AnalysisPlanResults ( metric_alias = [ self . metric . alias ], control_variant_name = [ control_variant . name ], treatment_variant_name = [ treatment_variant . name ], control_variant_mean = [ control_variant_mean ], treatment_variant_mean = [ treatment_variant_mean ], analysis_type = [ self . analysis_type ], ate = [ inference_results . ate ], ate_ci_lower = [ inference_results . conf_int . lower ], ate_ci_upper = [ inference_results . conf_int . upper ], p_value = [ inference_results . p_value ], std_error = [ inference_results . std_error ], dimension_name = [ dimension . name ], dimension_value = [ dimension_value ], alpha = [ alpha ], ) return test_results __init__ ( self , metric , analysis_type , analysis_config = None , dimensions = None , cupac_config = None , custom_analysis_type_mapper = None ) special \u00b6 Parameters \u00b6 metric : Metric An instance of the Metric class analysis_type : str string mapping to an ExperimentAnalysis class. Must be either in the built-in analysis_mapping or in the custom_analysis_type_mapper if provided. analysis_config : Optional[dict] An optional dictionary representing the configuration for the analysis dimensions : Optional[List[Dimension]] An optional list of Dimension instances cupac_config : Optional[dict] An optional dictionary representing the configuration for the cupac model custom_analysis_type_mapper : Optional[Dict[str, ExperimentAnalysis]] An optional dictionary mapping the names of custom analysis types to the corresponding ExperimentAnalysis classes Source code in cluster_experiments/inference/hypothesis_test.py def __init__ ( self , metric : Metric , analysis_type : str , analysis_config : Optional [ dict ] = None , dimensions : Optional [ List [ Dimension ]] = None , cupac_config : Optional [ dict ] = None , custom_analysis_type_mapper : Optional [ Dict [ str , ExperimentAnalysis ]] = None , ): \"\"\" Parameters ---------- metric : Metric An instance of the Metric class analysis_type : str string mapping to an ExperimentAnalysis class. Must be either in the built-in analysis_mapping or in the custom_analysis_type_mapper if provided. analysis_config : Optional[dict] An optional dictionary representing the configuration for the analysis dimensions : Optional[List[Dimension]] An optional list of Dimension instances cupac_config : Optional[dict] An optional dictionary representing the configuration for the cupac model custom_analysis_type_mapper : Optional[Dict[str, ExperimentAnalysis]] An optional dictionary mapping the names of custom analysis types to the corresponding ExperimentAnalysis classes \"\"\" self . _validate_inputs ( metric , analysis_type , analysis_config , dimensions , cupac_config , custom_analysis_type_mapper , ) self . metric = metric self . analysis_type = analysis_type self . analysis_config = analysis_config or {} self . dimensions = [ DefaultDimension ()] + ( dimensions or []) self . cupac_config = cupac_config or {} self . custom_analysis_type_mapper = custom_analysis_type_mapper or {} self . analysis_type_mapper = self . custom_analysis_type_mapper or analysis_mapping self . analysis_class = self . analysis_type_mapper [ self . analysis_type ] self . is_cupac = bool ( cupac_config ) self . cupac_handler = ( CupacHandler ( ** self . cupac_config ) if self . is_cupac else None ) self . cupac_covariate_col = ( self . cupac_handler . cupac_outcome_name if self . is_cupac else None ) self . new_analysis_config = None self . experiment_analysis = None add_covariates ( self , exp_data , pre_exp_data ) \u00b6 If the test is a cupac test, adds the covariates to the experimental data. Source code in cluster_experiments/inference/hypothesis_test.py def add_covariates ( self , exp_data : pd . DataFrame , pre_exp_data : pd . DataFrame ) -> pd . DataFrame : \"\"\" If the test is a cupac test, adds the covariates to the experimental data. \"\"\" if self . is_cupac : exp_data = self . cupac_handler . add_covariates ( df = exp_data , pre_experiment_df = pre_exp_data ) return exp_data get_inference_results ( self , df , alpha ) \u00b6 Performs inference analysis on the provided DataFrame using the analysis class. Parameters \u00b6 df : pd.DataFrame The dataframe containing the data for analysis. alpha : float The significance level to be used in the inference analysis. Returns \u00b6 InferenceResults The results containing the statistics of the inference procedure. Source code in cluster_experiments/inference/hypothesis_test.py def get_inference_results ( self , df : pd . DataFrame , alpha : float ) -> InferenceResults : \"\"\" Performs inference analysis on the provided DataFrame using the analysis class. Parameters ---------- df : pd.DataFrame The dataframe containing the data for analysis. alpha : float The significance level to be used in the inference analysis. Returns ------- InferenceResults The results containing the statistics of the inference procedure. \"\"\" self . experiment_analysis = self . analysis_class ( ** self . new_analysis_config ) inference_results = self . experiment_analysis . get_inference_results ( df = df , alpha = alpha ) return inference_results get_test_results ( self , control_variant , treatment_variant , variant_col , exp_data , dimension , dimension_value , alpha ) \u00b6 Performs the hypothesis test on the provided data, for the given dimension value. Parameters \u00b6 control_variant : Variant The control variant treatment_variant : Variant The treatment variant variant_col : str The column name representing the variant exp_data : pd.DataFrame The dataframe containing the data for analysis. dimension : Dimension The dimension instance dimension_value : str The value of the dimension alpha : float The significance level to be used in the inference analysis. Returns \u00b6 AnalysisPlanResults The results of the hypothesis test Source code in cluster_experiments/inference/hypothesis_test.py def get_test_results ( self , control_variant : Variant , treatment_variant : Variant , variant_col : str , exp_data : pd . DataFrame , dimension : Dimension , dimension_value : str , alpha : float , ) -> AnalysisPlanResults : \"\"\" Performs the hypothesis test on the provided data, for the given dimension value. Parameters ---------- control_variant : Variant The control variant treatment_variant : Variant The treatment variant variant_col : str The column name representing the variant exp_data : pd.DataFrame The dataframe containing the data for analysis. dimension : Dimension The dimension instance dimension_value : str The value of the dimension alpha : float The significance level to be used in the inference analysis. Returns ------- AnalysisPlanResults The results of the hypothesis test \"\"\" self . _prepare_analysis_config ( target_col = self . metric . target_column , treatment_col = variant_col , treatment = treatment_variant . name , ) prepared_df = self . prepare_data ( data = exp_data , variant_col = variant_col , treatment_variant = treatment_variant , control_variant = control_variant , dimension_name = dimension . name , dimension_value = dimension_value , ) inference_results = self . get_inference_results ( df = prepared_df , alpha = alpha ) control_variant_mean = self . metric . get_mean ( prepared_df . query ( f \" { variant_col } ==' { control_variant . name } '\" ) ) treatment_variant_mean = self . metric . get_mean ( prepared_df . query ( f \" { variant_col } ==' { treatment_variant . name } '\" ) ) test_results = AnalysisPlanResults ( metric_alias = [ self . metric . alias ], control_variant_name = [ control_variant . name ], treatment_variant_name = [ treatment_variant . name ], control_variant_mean = [ control_variant_mean ], treatment_variant_mean = [ treatment_variant_mean ], analysis_type = [ self . analysis_type ], ate = [ inference_results . ate ], ate_ci_lower = [ inference_results . conf_int . lower ], ate_ci_upper = [ inference_results . conf_int . upper ], p_value = [ inference_results . p_value ], std_error = [ inference_results . std_error ], dimension_name = [ dimension . name ], dimension_value = [ dimension_value ], alpha = [ alpha ], ) return test_results prepare_data ( data , variant_col , treatment_variant , control_variant , dimension_name , dimension_value ) staticmethod \u00b6 Prepares the data for the experiment analysis pipeline Source code in cluster_experiments/inference/hypothesis_test.py @staticmethod def prepare_data ( data : pd . DataFrame , variant_col : str , treatment_variant : Variant , control_variant : Variant , dimension_name : str , dimension_value : str , ) -> pd . DataFrame : \"\"\" Prepares the data for the experiment analysis pipeline \"\"\" prepared_df = data . copy () prepared_df = prepared_df . assign ( __total_dimension = \"total\" ) prepared_df = prepared_df . query ( f \" { variant_col } .isin([' { treatment_variant . name } ',' { control_variant . name } '])\" ) . query ( f \" { dimension_name } == ' { dimension_value } '\" ) return prepared_df","title":"Hypothesis Test"},{"location":"api/hypothesis_test.html#from-cluster_experimentsinferencehypothesis_test-import","text":"","title":"from cluster_experiments.inference.hypothesis_test import *"},{"location":"api/hypothesis_test.html#cluster_experiments.inference.hypothesis_test.HypothesisTest","text":"A class used to represent a Hypothesis Test with a metric, analysis, optional analysis configuration, and optional dimensions.","title":"HypothesisTest"},{"location":"api/hypothesis_test.html#cluster_experiments.inference.hypothesis_test.HypothesisTest--attributes","text":"metric : Metric An instance of the Metric class analysis_type : str string mapping to an ExperimentAnalysis class. Must be either in the built-in analysis_mapping or in the custom_analysis_type_mapper if provided. analysis_config : Optional[dict] An optional dictionary representing the configuration for the analysis dimensions : Optional[List[Dimension]] An optional list of Dimension instances cupac_config : Optional[dict] An optional dictionary representing the configuration for the cupac model custom_analysis_type_mapper : Optional[Dict[str, ExperimentAnalysis]] An optional dictionary mapping the names of custom analysis types to the corresponding ExperimentAnalysis classes Source code in cluster_experiments/inference/hypothesis_test.py class HypothesisTest : \"\"\" A class used to represent a Hypothesis Test with a metric, analysis, optional analysis configuration, and optional dimensions. Attributes ---------- metric : Metric An instance of the Metric class analysis_type : str string mapping to an ExperimentAnalysis class. Must be either in the built-in analysis_mapping or in the custom_analysis_type_mapper if provided. analysis_config : Optional[dict] An optional dictionary representing the configuration for the analysis dimensions : Optional[List[Dimension]] An optional list of Dimension instances cupac_config : Optional[dict] An optional dictionary representing the configuration for the cupac model custom_analysis_type_mapper : Optional[Dict[str, ExperimentAnalysis]] An optional dictionary mapping the names of custom analysis types to the corresponding ExperimentAnalysis classes \"\"\" def __init__ ( self , metric : Metric , analysis_type : str , analysis_config : Optional [ dict ] = None , dimensions : Optional [ List [ Dimension ]] = None , cupac_config : Optional [ dict ] = None , custom_analysis_type_mapper : Optional [ Dict [ str , ExperimentAnalysis ]] = None , ): \"\"\" Parameters ---------- metric : Metric An instance of the Metric class analysis_type : str string mapping to an ExperimentAnalysis class. Must be either in the built-in analysis_mapping or in the custom_analysis_type_mapper if provided. analysis_config : Optional[dict] An optional dictionary representing the configuration for the analysis dimensions : Optional[List[Dimension]] An optional list of Dimension instances cupac_config : Optional[dict] An optional dictionary representing the configuration for the cupac model custom_analysis_type_mapper : Optional[Dict[str, ExperimentAnalysis]] An optional dictionary mapping the names of custom analysis types to the corresponding ExperimentAnalysis classes \"\"\" self . _validate_inputs ( metric , analysis_type , analysis_config , dimensions , cupac_config , custom_analysis_type_mapper , ) self . metric = metric self . analysis_type = analysis_type self . analysis_config = analysis_config or {} self . dimensions = [ DefaultDimension ()] + ( dimensions or []) self . cupac_config = cupac_config or {} self . custom_analysis_type_mapper = custom_analysis_type_mapper or {} self . analysis_type_mapper = self . custom_analysis_type_mapper or analysis_mapping self . analysis_class = self . analysis_type_mapper [ self . analysis_type ] self . is_cupac = bool ( cupac_config ) self . cupac_handler = ( CupacHandler ( ** self . cupac_config ) if self . is_cupac else None ) self . cupac_covariate_col = ( self . cupac_handler . cupac_outcome_name if self . is_cupac else None ) self . new_analysis_config = None self . experiment_analysis = None @staticmethod def _validate_inputs ( metric : Metric , analysis_type : str , analysis_config : Optional [ dict ], dimensions : Optional [ List [ Dimension ]], cupac_config : Optional [ dict ] = None , custom_analysis_type_mapper : Optional [ Dict [ str , ExperimentAnalysis ]] = None , ): \"\"\" Validates the inputs for the HypothesisTest class. Parameters ---------- metric : Metric An instance of the Metric class analysis_type : str string mapper to an ExperimentAnalysis analysis_config : Optional[dict] An optional dictionary representing the configuration for the analysis dimensions : Optional[List[Dimension]] An optional list of Dimension instances cupac_config : Optional[dict] An optional dictionary representing the configuration for the cupac model custom_analysis_type_mapper : Optional[dict[str, ExperimentAnalysis]] An optional dictionary mapping the names of custom analysis types to the corresponding ExperimentAnalysis classes \"\"\" # Check if metric is a valid Metric instance if not isinstance ( metric , Metric ): raise TypeError ( \"Metric must be an instance of Metric\" ) # Check if analysis_type is a string if not isinstance ( analysis_type , str ): raise TypeError ( \"Analysis must be a string\" ) # Check if analysis_config is a dictionary when provided if analysis_config is not None and not isinstance ( analysis_config , dict ): raise TypeError ( \"analysis_config must be a dictionary if provided\" ) # Check if cupac_config is a dictionary when provided if cupac_config is not None and not isinstance ( cupac_config , dict ): raise TypeError ( \"cupac_config must be a dictionary if provided\" ) # Check if dimensions is a list of Dimension instances when provided if dimensions is not None and ( not isinstance ( dimensions , list ) or not all ( isinstance ( dim , Dimension ) for dim in dimensions ) ): raise TypeError ( \"Dimensions must be a list of Dimension instances if provided\" ) # Validate custom_analysis_type_mapper if provided if custom_analysis_type_mapper : # Ensure it's a dictionary if not isinstance ( custom_analysis_type_mapper , dict ): raise TypeError ( \"custom_analysis_type_mapper must be a dictionary if provided\" ) # Ensure all keys are strings and values are ExperimentAnalysis classes for key , value in custom_analysis_type_mapper . items (): if not isinstance ( key , str ): raise TypeError ( f \"Key ' { key } ' in custom_analysis_type_mapper must be a string\" ) if not issubclass ( value , ExperimentAnalysis ): raise TypeError ( f \"Value ' { value } ' for key ' { key } ' in custom_analysis_type_mapper must be a subclass of ExperimentAnalysis\" ) # Ensure the analysis_type is in the custom mapper if a custom mapper is provided if analysis_type not in custom_analysis_type_mapper : raise ValueError ( f \"Analysis type ' { analysis_type } ' not found in the provided custom_analysis_type_mapper\" ) # If no custom_analysis_type_mapper, check if analysis_type exists in the default mapping elif analysis_type not in analysis_mapping : raise ValueError ( f \"Analysis type ' { analysis_type } ' not found in analysis_mapping\" ) def get_inference_results ( self , df : pd . DataFrame , alpha : float ) -> InferenceResults : \"\"\" Performs inference analysis on the provided DataFrame using the analysis class. Parameters ---------- df : pd.DataFrame The dataframe containing the data for analysis. alpha : float The significance level to be used in the inference analysis. Returns ------- InferenceResults The results containing the statistics of the inference procedure. \"\"\" self . experiment_analysis = self . analysis_class ( ** self . new_analysis_config ) inference_results = self . experiment_analysis . get_inference_results ( df = df , alpha = alpha ) return inference_results def _prepare_analysis_config ( self , target_col : str , treatment_col : str , treatment : str ) -> None : \"\"\" Extends the analysis_config provided by the user, by adding or overriding the following keys: - target_col - treatment_col - treatment Also handles cupac covariate. Returns ------- dict The prepared analysis configuration, ready to be ingested by the experiment analysis class \"\"\" new_analysis_config = copy . deepcopy ( self . analysis_config ) new_analysis_config [ \"target_col\" ] = target_col new_analysis_config [ \"treatment_col\" ] = treatment_col new_analysis_config [ \"treatment\" ] = treatment covariates = new_analysis_config . get ( \"covariates\" , []) if self . cupac_covariate_col and self . cupac_covariate_col not in covariates : raise ValueError ( f \"You provided a cupac configuration but did not provide the cupac covariate called { self . cupac_covariate_col } in the analysis_config\" ) self . new_analysis_config = new_analysis_config @staticmethod def prepare_data ( data : pd . DataFrame , variant_col : str , treatment_variant : Variant , control_variant : Variant , dimension_name : str , dimension_value : str , ) -> pd . DataFrame : \"\"\" Prepares the data for the experiment analysis pipeline \"\"\" prepared_df = data . copy () prepared_df = prepared_df . assign ( __total_dimension = \"total\" ) prepared_df = prepared_df . query ( f \" { variant_col } .isin([' { treatment_variant . name } ',' { control_variant . name } '])\" ) . query ( f \" { dimension_name } == ' { dimension_value } '\" ) return prepared_df def add_covariates ( self , exp_data : pd . DataFrame , pre_exp_data : pd . DataFrame ) -> pd . DataFrame : \"\"\" If the test is a cupac test, adds the covariates to the experimental data. \"\"\" if self . is_cupac : exp_data = self . cupac_handler . add_covariates ( df = exp_data , pre_experiment_df = pre_exp_data ) return exp_data def get_test_results ( self , control_variant : Variant , treatment_variant : Variant , variant_col : str , exp_data : pd . DataFrame , dimension : Dimension , dimension_value : str , alpha : float , ) -> AnalysisPlanResults : \"\"\" Performs the hypothesis test on the provided data, for the given dimension value. Parameters ---------- control_variant : Variant The control variant treatment_variant : Variant The treatment variant variant_col : str The column name representing the variant exp_data : pd.DataFrame The dataframe containing the data for analysis. dimension : Dimension The dimension instance dimension_value : str The value of the dimension alpha : float The significance level to be used in the inference analysis. Returns ------- AnalysisPlanResults The results of the hypothesis test \"\"\" self . _prepare_analysis_config ( target_col = self . metric . target_column , treatment_col = variant_col , treatment = treatment_variant . name , ) prepared_df = self . prepare_data ( data = exp_data , variant_col = variant_col , treatment_variant = treatment_variant , control_variant = control_variant , dimension_name = dimension . name , dimension_value = dimension_value , ) inference_results = self . get_inference_results ( df = prepared_df , alpha = alpha ) control_variant_mean = self . metric . get_mean ( prepared_df . query ( f \" { variant_col } ==' { control_variant . name } '\" ) ) treatment_variant_mean = self . metric . get_mean ( prepared_df . query ( f \" { variant_col } ==' { treatment_variant . name } '\" ) ) test_results = AnalysisPlanResults ( metric_alias = [ self . metric . alias ], control_variant_name = [ control_variant . name ], treatment_variant_name = [ treatment_variant . name ], control_variant_mean = [ control_variant_mean ], treatment_variant_mean = [ treatment_variant_mean ], analysis_type = [ self . analysis_type ], ate = [ inference_results . ate ], ate_ci_lower = [ inference_results . conf_int . lower ], ate_ci_upper = [ inference_results . conf_int . upper ], p_value = [ inference_results . p_value ], std_error = [ inference_results . std_error ], dimension_name = [ dimension . name ], dimension_value = [ dimension_value ], alpha = [ alpha ], ) return test_results","title":"Attributes"},{"location":"api/hypothesis_test.html#cluster_experiments.inference.hypothesis_test.HypothesisTest.__init__","text":"","title":"__init__()"},{"location":"api/hypothesis_test.html#cluster_experiments.inference.hypothesis_test.HypothesisTest.__init__--parameters","text":"metric : Metric An instance of the Metric class analysis_type : str string mapping to an ExperimentAnalysis class. Must be either in the built-in analysis_mapping or in the custom_analysis_type_mapper if provided. analysis_config : Optional[dict] An optional dictionary representing the configuration for the analysis dimensions : Optional[List[Dimension]] An optional list of Dimension instances cupac_config : Optional[dict] An optional dictionary representing the configuration for the cupac model custom_analysis_type_mapper : Optional[Dict[str, ExperimentAnalysis]] An optional dictionary mapping the names of custom analysis types to the corresponding ExperimentAnalysis classes Source code in cluster_experiments/inference/hypothesis_test.py def __init__ ( self , metric : Metric , analysis_type : str , analysis_config : Optional [ dict ] = None , dimensions : Optional [ List [ Dimension ]] = None , cupac_config : Optional [ dict ] = None , custom_analysis_type_mapper : Optional [ Dict [ str , ExperimentAnalysis ]] = None , ): \"\"\" Parameters ---------- metric : Metric An instance of the Metric class analysis_type : str string mapping to an ExperimentAnalysis class. Must be either in the built-in analysis_mapping or in the custom_analysis_type_mapper if provided. analysis_config : Optional[dict] An optional dictionary representing the configuration for the analysis dimensions : Optional[List[Dimension]] An optional list of Dimension instances cupac_config : Optional[dict] An optional dictionary representing the configuration for the cupac model custom_analysis_type_mapper : Optional[Dict[str, ExperimentAnalysis]] An optional dictionary mapping the names of custom analysis types to the corresponding ExperimentAnalysis classes \"\"\" self . _validate_inputs ( metric , analysis_type , analysis_config , dimensions , cupac_config , custom_analysis_type_mapper , ) self . metric = metric self . analysis_type = analysis_type self . analysis_config = analysis_config or {} self . dimensions = [ DefaultDimension ()] + ( dimensions or []) self . cupac_config = cupac_config or {} self . custom_analysis_type_mapper = custom_analysis_type_mapper or {} self . analysis_type_mapper = self . custom_analysis_type_mapper or analysis_mapping self . analysis_class = self . analysis_type_mapper [ self . analysis_type ] self . is_cupac = bool ( cupac_config ) self . cupac_handler = ( CupacHandler ( ** self . cupac_config ) if self . is_cupac else None ) self . cupac_covariate_col = ( self . cupac_handler . cupac_outcome_name if self . is_cupac else None ) self . new_analysis_config = None self . experiment_analysis = None","title":"Parameters"},{"location":"api/hypothesis_test.html#cluster_experiments.inference.hypothesis_test.HypothesisTest.add_covariates","text":"If the test is a cupac test, adds the covariates to the experimental data. Source code in cluster_experiments/inference/hypothesis_test.py def add_covariates ( self , exp_data : pd . DataFrame , pre_exp_data : pd . DataFrame ) -> pd . DataFrame : \"\"\" If the test is a cupac test, adds the covariates to the experimental data. \"\"\" if self . is_cupac : exp_data = self . cupac_handler . add_covariates ( df = exp_data , pre_experiment_df = pre_exp_data ) return exp_data","title":"add_covariates()"},{"location":"api/hypothesis_test.html#cluster_experiments.inference.hypothesis_test.HypothesisTest.get_inference_results","text":"Performs inference analysis on the provided DataFrame using the analysis class.","title":"get_inference_results()"},{"location":"api/hypothesis_test.html#cluster_experiments.inference.hypothesis_test.HypothesisTest.get_inference_results--parameters","text":"df : pd.DataFrame The dataframe containing the data for analysis. alpha : float The significance level to be used in the inference analysis.","title":"Parameters"},{"location":"api/hypothesis_test.html#cluster_experiments.inference.hypothesis_test.HypothesisTest.get_inference_results--returns","text":"InferenceResults The results containing the statistics of the inference procedure. Source code in cluster_experiments/inference/hypothesis_test.py def get_inference_results ( self , df : pd . DataFrame , alpha : float ) -> InferenceResults : \"\"\" Performs inference analysis on the provided DataFrame using the analysis class. Parameters ---------- df : pd.DataFrame The dataframe containing the data for analysis. alpha : float The significance level to be used in the inference analysis. Returns ------- InferenceResults The results containing the statistics of the inference procedure. \"\"\" self . experiment_analysis = self . analysis_class ( ** self . new_analysis_config ) inference_results = self . experiment_analysis . get_inference_results ( df = df , alpha = alpha ) return inference_results","title":"Returns"},{"location":"api/hypothesis_test.html#cluster_experiments.inference.hypothesis_test.HypothesisTest.get_test_results","text":"Performs the hypothesis test on the provided data, for the given dimension value.","title":"get_test_results()"},{"location":"api/hypothesis_test.html#cluster_experiments.inference.hypothesis_test.HypothesisTest.get_test_results--parameters","text":"control_variant : Variant The control variant treatment_variant : Variant The treatment variant variant_col : str The column name representing the variant exp_data : pd.DataFrame The dataframe containing the data for analysis. dimension : Dimension The dimension instance dimension_value : str The value of the dimension alpha : float The significance level to be used in the inference analysis.","title":"Parameters"},{"location":"api/hypothesis_test.html#cluster_experiments.inference.hypothesis_test.HypothesisTest.get_test_results--returns","text":"AnalysisPlanResults The results of the hypothesis test Source code in cluster_experiments/inference/hypothesis_test.py def get_test_results ( self , control_variant : Variant , treatment_variant : Variant , variant_col : str , exp_data : pd . DataFrame , dimension : Dimension , dimension_value : str , alpha : float , ) -> AnalysisPlanResults : \"\"\" Performs the hypothesis test on the provided data, for the given dimension value. Parameters ---------- control_variant : Variant The control variant treatment_variant : Variant The treatment variant variant_col : str The column name representing the variant exp_data : pd.DataFrame The dataframe containing the data for analysis. dimension : Dimension The dimension instance dimension_value : str The value of the dimension alpha : float The significance level to be used in the inference analysis. Returns ------- AnalysisPlanResults The results of the hypothesis test \"\"\" self . _prepare_analysis_config ( target_col = self . metric . target_column , treatment_col = variant_col , treatment = treatment_variant . name , ) prepared_df = self . prepare_data ( data = exp_data , variant_col = variant_col , treatment_variant = treatment_variant , control_variant = control_variant , dimension_name = dimension . name , dimension_value = dimension_value , ) inference_results = self . get_inference_results ( df = prepared_df , alpha = alpha ) control_variant_mean = self . metric . get_mean ( prepared_df . query ( f \" { variant_col } ==' { control_variant . name } '\" ) ) treatment_variant_mean = self . metric . get_mean ( prepared_df . query ( f \" { variant_col } ==' { treatment_variant . name } '\" ) ) test_results = AnalysisPlanResults ( metric_alias = [ self . metric . alias ], control_variant_name = [ control_variant . name ], treatment_variant_name = [ treatment_variant . name ], control_variant_mean = [ control_variant_mean ], treatment_variant_mean = [ treatment_variant_mean ], analysis_type = [ self . analysis_type ], ate = [ inference_results . ate ], ate_ci_lower = [ inference_results . conf_int . lower ], ate_ci_upper = [ inference_results . conf_int . upper ], p_value = [ inference_results . p_value ], std_error = [ inference_results . std_error ], dimension_name = [ dimension . name ], dimension_value = [ dimension_value ], alpha = [ alpha ], ) return test_results","title":"Returns"},{"location":"api/hypothesis_test.html#cluster_experiments.inference.hypothesis_test.HypothesisTest.prepare_data","text":"Prepares the data for the experiment analysis pipeline Source code in cluster_experiments/inference/hypothesis_test.py @staticmethod def prepare_data ( data : pd . DataFrame , variant_col : str , treatment_variant : Variant , control_variant : Variant , dimension_name : str , dimension_value : str , ) -> pd . DataFrame : \"\"\" Prepares the data for the experiment analysis pipeline \"\"\" prepared_df = data . copy () prepared_df = prepared_df . assign ( __total_dimension = \"total\" ) prepared_df = prepared_df . query ( f \" { variant_col } .isin([' { treatment_variant . name } ',' { control_variant . name } '])\" ) . query ( f \" { dimension_name } == ' { dimension_value } '\" ) return prepared_df","title":"prepare_data()"},{"location":"api/metric.html","text":"from cluster_experiments.inference.metric import * \u00b6 Metric ( ABC ) \u00b6 An abstract base class used to represent a Metric with an alias. Attributes \u00b6 alias : str A string representing the alias of the metric Source code in cluster_experiments/inference/metric.py class Metric ( ABC ): \"\"\" An abstract base class used to represent a Metric with an alias. Attributes ---------- alias : str A string representing the alias of the metric \"\"\" def __init__ ( self , alias : str ): \"\"\" Parameters ---------- alias : str The alias of the metric \"\"\" self . alias = alias self . _validate_alias () def _validate_alias ( self ): \"\"\" Validates the alias input for the Metric class. Raises ------ TypeError If the alias is not a string \"\"\" if not isinstance ( self . alias , str ): raise TypeError ( \"Metric alias must be a string\" ) @property @abstractmethod def target_column ( self ) -> str : \"\"\" Abstract property to return the target column to feed the experiment analysis class, from the metric definition. Returns ------- str The target column name \"\"\" pass @abstractmethod def get_mean ( self , df : pd . DataFrame ) -> float : \"\"\" Abstract method to return the mean value of the metric, given a dataframe. Returns ------- float The mean value of the metric \"\"\" pass target_column : str property readonly \u00b6 Abstract property to return the target column to feed the experiment analysis class, from the metric definition. Returns \u00b6 str The target column name __init__ ( self , alias ) special \u00b6 Parameters \u00b6 alias : str The alias of the metric Source code in cluster_experiments/inference/metric.py def __init__ ( self , alias : str ): \"\"\" Parameters ---------- alias : str The alias of the metric \"\"\" self . alias = alias self . _validate_alias () get_mean ( self , df ) \u00b6 Abstract method to return the mean value of the metric, given a dataframe. Returns \u00b6 float The mean value of the metric Source code in cluster_experiments/inference/metric.py @abstractmethod def get_mean ( self , df : pd . DataFrame ) -> float : \"\"\" Abstract method to return the mean value of the metric, given a dataframe. Returns ------- float The mean value of the metric \"\"\" pass RatioMetric ( Metric ) \u00b6 A class used to represent a Ratio Metric with an alias, a numerator name, and a denominator name. To be used when the metric is defined at a lower level than the data used for the analysis. Example \u00b6 In a clustered experiment the participants were randomised based on their country of residence. The metric of interest is the salary of each participant. If the dataset fed into the analysis is at country-level, then a RatioMetric must be used: the numerator would be the sum of all salaries in the country, the denominator would be the number of participants in the country. Attributes \u00b6 alias : str A string representing the alias of the metric numerator_name : str A string representing the numerator name of the metric denominator_name : str A string representing the denominator name of the metric Source code in cluster_experiments/inference/metric.py class RatioMetric ( Metric ): \"\"\" A class used to represent a Ratio Metric with an alias, a numerator name, and a denominator name. To be used when the metric is defined at a lower level than the data used for the analysis. Example ---------- In a clustered experiment the participants were randomised based on their country of residence. The metric of interest is the salary of each participant. If the dataset fed into the analysis is at country-level, then a RatioMetric must be used: the numerator would be the sum of all salaries in the country, the denominator would be the number of participants in the country. Attributes ---------- alias : str A string representing the alias of the metric numerator_name : str A string representing the numerator name of the metric denominator_name : str A string representing the denominator name of the metric \"\"\" def __init__ ( self , alias : str , numerator_name : str , denominator_name : str ): \"\"\" Parameters ---------- alias : str The alias of the metric numerator_name : str The numerator name of the metric denominator_name : str The denominator name of the metric \"\"\" super () . __init__ ( alias ) self . numerator_name = numerator_name self . denominator_name = denominator_name self . _validate_names () def _validate_names ( self ): \"\"\" Validates the numerator and denominator names input for the RatioMetric class. Raises ------ TypeError If the numerator or denominator names are not strings \"\"\" if not isinstance ( self . numerator_name , str ) or not isinstance ( self . denominator_name , str ): raise TypeError ( \"RatioMetric names must be strings\" ) @property def target_column ( self ) -> str : \"\"\" Returns the target column for the RatioMetric. Returns ------- str The numerator name of the metric \"\"\" return self . numerator_name def get_mean ( self , df : pd . DataFrame ) -> float : \"\"\" Returns the mean value of the metric, given a dataframe. Returns ------- float The mean value of the metric \"\"\" return df [ self . numerator_name ] . mean () / df [ self . denominator_name ] . mean () target_column : str property readonly \u00b6 Returns the target column for the RatioMetric. Returns \u00b6 str The numerator name of the metric __init__ ( self , alias , numerator_name , denominator_name ) special \u00b6 Parameters \u00b6 alias : str The alias of the metric numerator_name : str The numerator name of the metric denominator_name : str The denominator name of the metric Source code in cluster_experiments/inference/metric.py def __init__ ( self , alias : str , numerator_name : str , denominator_name : str ): \"\"\" Parameters ---------- alias : str The alias of the metric numerator_name : str The numerator name of the metric denominator_name : str The denominator name of the metric \"\"\" super () . __init__ ( alias ) self . numerator_name = numerator_name self . denominator_name = denominator_name self . _validate_names () get_mean ( self , df ) \u00b6 Returns the mean value of the metric, given a dataframe. Returns \u00b6 float The mean value of the metric Source code in cluster_experiments/inference/metric.py def get_mean ( self , df : pd . DataFrame ) -> float : \"\"\" Returns the mean value of the metric, given a dataframe. Returns ------- float The mean value of the metric \"\"\" return df [ self . numerator_name ] . mean () / df [ self . denominator_name ] . mean () SimpleMetric ( Metric ) \u00b6 A class used to represent a Simple Metric with an alias and a name. To be used when the metric is defined at the same level of the data used for the analysis. Example \u00b6 In a clustered experiment the participants were randomised based on their country of residence. The metric of interest is the salary of each participant. If the dataset fed into the analysis is at participant-level, then a SimpleMetric must be used. However, if the dataset fed into the analysis is at country-level, then a RatioMetric must be used. Attributes \u00b6 alias : str A string representing the alias of the metric name : str A string representing the name of the metric Source code in cluster_experiments/inference/metric.py class SimpleMetric ( Metric ): \"\"\" A class used to represent a Simple Metric with an alias and a name. To be used when the metric is defined at the same level of the data used for the analysis. Example ---------- In a clustered experiment the participants were randomised based on their country of residence. The metric of interest is the salary of each participant. If the dataset fed into the analysis is at participant-level, then a SimpleMetric must be used. However, if the dataset fed into the analysis is at country-level, then a RatioMetric must be used. Attributes ---------- alias : str A string representing the alias of the metric name : str A string representing the name of the metric \"\"\" def __init__ ( self , alias : str , name : str ): \"\"\" Parameters ---------- alias : str The alias of the metric name : str The name of the metric \"\"\" super () . __init__ ( alias ) self . name = name self . _validate_name () def _validate_name ( self ): \"\"\" Validates the name input for the SimpleMetric class. Raises ------ TypeError If the name is not a string \"\"\" if not isinstance ( self . name , str ): raise TypeError ( \"SimpleMetric name must be a string\" ) @property def target_column ( self ) -> str : \"\"\" Returns the target column for the SimpleMetric. Returns ------- str The name of the metric \"\"\" return self . name def get_mean ( self , df : pd . DataFrame ) -> float : \"\"\" Returns the mean value of the metric, given a dataframe. Returns ------- float The mean value of the metric \"\"\" return df [ self . name ] . mean () target_column : str property readonly \u00b6 Returns the target column for the SimpleMetric. Returns \u00b6 str The name of the metric __init__ ( self , alias , name ) special \u00b6 Parameters \u00b6 alias : str The alias of the metric name : str The name of the metric Source code in cluster_experiments/inference/metric.py def __init__ ( self , alias : str , name : str ): \"\"\" Parameters ---------- alias : str The alias of the metric name : str The name of the metric \"\"\" super () . __init__ ( alias ) self . name = name self . _validate_name () get_mean ( self , df ) \u00b6 Returns the mean value of the metric, given a dataframe. Returns \u00b6 float The mean value of the metric Source code in cluster_experiments/inference/metric.py def get_mean ( self , df : pd . DataFrame ) -> float : \"\"\" Returns the mean value of the metric, given a dataframe. Returns ------- float The mean value of the metric \"\"\" return df [ self . name ] . mean ()","title":"Metric"},{"location":"api/metric.html#from-cluster_experimentsinferencemetric-import","text":"","title":"from cluster_experiments.inference.metric import *"},{"location":"api/metric.html#cluster_experiments.inference.metric.Metric","text":"An abstract base class used to represent a Metric with an alias.","title":"Metric"},{"location":"api/metric.html#cluster_experiments.inference.metric.Metric--attributes","text":"alias : str A string representing the alias of the metric Source code in cluster_experiments/inference/metric.py class Metric ( ABC ): \"\"\" An abstract base class used to represent a Metric with an alias. Attributes ---------- alias : str A string representing the alias of the metric \"\"\" def __init__ ( self , alias : str ): \"\"\" Parameters ---------- alias : str The alias of the metric \"\"\" self . alias = alias self . _validate_alias () def _validate_alias ( self ): \"\"\" Validates the alias input for the Metric class. Raises ------ TypeError If the alias is not a string \"\"\" if not isinstance ( self . alias , str ): raise TypeError ( \"Metric alias must be a string\" ) @property @abstractmethod def target_column ( self ) -> str : \"\"\" Abstract property to return the target column to feed the experiment analysis class, from the metric definition. Returns ------- str The target column name \"\"\" pass @abstractmethod def get_mean ( self , df : pd . DataFrame ) -> float : \"\"\" Abstract method to return the mean value of the metric, given a dataframe. Returns ------- float The mean value of the metric \"\"\" pass","title":"Attributes"},{"location":"api/metric.html#cluster_experiments.inference.metric.Metric.target_column","text":"Abstract property to return the target column to feed the experiment analysis class, from the metric definition.","title":"target_column"},{"location":"api/metric.html#cluster_experiments.inference.metric.Metric.target_column--returns","text":"str The target column name","title":"Returns"},{"location":"api/metric.html#cluster_experiments.inference.metric.Metric.__init__","text":"","title":"__init__()"},{"location":"api/metric.html#cluster_experiments.inference.metric.Metric.__init__--parameters","text":"alias : str The alias of the metric Source code in cluster_experiments/inference/metric.py def __init__ ( self , alias : str ): \"\"\" Parameters ---------- alias : str The alias of the metric \"\"\" self . alias = alias self . _validate_alias ()","title":"Parameters"},{"location":"api/metric.html#cluster_experiments.inference.metric.Metric.get_mean","text":"Abstract method to return the mean value of the metric, given a dataframe.","title":"get_mean()"},{"location":"api/metric.html#cluster_experiments.inference.metric.Metric.get_mean--returns","text":"float The mean value of the metric Source code in cluster_experiments/inference/metric.py @abstractmethod def get_mean ( self , df : pd . DataFrame ) -> float : \"\"\" Abstract method to return the mean value of the metric, given a dataframe. Returns ------- float The mean value of the metric \"\"\" pass","title":"Returns"},{"location":"api/metric.html#cluster_experiments.inference.metric.RatioMetric","text":"A class used to represent a Ratio Metric with an alias, a numerator name, and a denominator name. To be used when the metric is defined at a lower level than the data used for the analysis.","title":"RatioMetric"},{"location":"api/metric.html#cluster_experiments.inference.metric.RatioMetric--example","text":"In a clustered experiment the participants were randomised based on their country of residence. The metric of interest is the salary of each participant. If the dataset fed into the analysis is at country-level, then a RatioMetric must be used: the numerator would be the sum of all salaries in the country, the denominator would be the number of participants in the country.","title":"Example"},{"location":"api/metric.html#cluster_experiments.inference.metric.RatioMetric--attributes","text":"alias : str A string representing the alias of the metric numerator_name : str A string representing the numerator name of the metric denominator_name : str A string representing the denominator name of the metric Source code in cluster_experiments/inference/metric.py class RatioMetric ( Metric ): \"\"\" A class used to represent a Ratio Metric with an alias, a numerator name, and a denominator name. To be used when the metric is defined at a lower level than the data used for the analysis. Example ---------- In a clustered experiment the participants were randomised based on their country of residence. The metric of interest is the salary of each participant. If the dataset fed into the analysis is at country-level, then a RatioMetric must be used: the numerator would be the sum of all salaries in the country, the denominator would be the number of participants in the country. Attributes ---------- alias : str A string representing the alias of the metric numerator_name : str A string representing the numerator name of the metric denominator_name : str A string representing the denominator name of the metric \"\"\" def __init__ ( self , alias : str , numerator_name : str , denominator_name : str ): \"\"\" Parameters ---------- alias : str The alias of the metric numerator_name : str The numerator name of the metric denominator_name : str The denominator name of the metric \"\"\" super () . __init__ ( alias ) self . numerator_name = numerator_name self . denominator_name = denominator_name self . _validate_names () def _validate_names ( self ): \"\"\" Validates the numerator and denominator names input for the RatioMetric class. Raises ------ TypeError If the numerator or denominator names are not strings \"\"\" if not isinstance ( self . numerator_name , str ) or not isinstance ( self . denominator_name , str ): raise TypeError ( \"RatioMetric names must be strings\" ) @property def target_column ( self ) -> str : \"\"\" Returns the target column for the RatioMetric. Returns ------- str The numerator name of the metric \"\"\" return self . numerator_name def get_mean ( self , df : pd . DataFrame ) -> float : \"\"\" Returns the mean value of the metric, given a dataframe. Returns ------- float The mean value of the metric \"\"\" return df [ self . numerator_name ] . mean () / df [ self . denominator_name ] . mean ()","title":"Attributes"},{"location":"api/metric.html#cluster_experiments.inference.metric.RatioMetric.target_column","text":"Returns the target column for the RatioMetric.","title":"target_column"},{"location":"api/metric.html#cluster_experiments.inference.metric.RatioMetric.target_column--returns","text":"str The numerator name of the metric","title":"Returns"},{"location":"api/metric.html#cluster_experiments.inference.metric.RatioMetric.__init__","text":"","title":"__init__()"},{"location":"api/metric.html#cluster_experiments.inference.metric.RatioMetric.__init__--parameters","text":"alias : str The alias of the metric numerator_name : str The numerator name of the metric denominator_name : str The denominator name of the metric Source code in cluster_experiments/inference/metric.py def __init__ ( self , alias : str , numerator_name : str , denominator_name : str ): \"\"\" Parameters ---------- alias : str The alias of the metric numerator_name : str The numerator name of the metric denominator_name : str The denominator name of the metric \"\"\" super () . __init__ ( alias ) self . numerator_name = numerator_name self . denominator_name = denominator_name self . _validate_names ()","title":"Parameters"},{"location":"api/metric.html#cluster_experiments.inference.metric.RatioMetric.get_mean","text":"Returns the mean value of the metric, given a dataframe.","title":"get_mean()"},{"location":"api/metric.html#cluster_experiments.inference.metric.RatioMetric.get_mean--returns","text":"float The mean value of the metric Source code in cluster_experiments/inference/metric.py def get_mean ( self , df : pd . DataFrame ) -> float : \"\"\" Returns the mean value of the metric, given a dataframe. Returns ------- float The mean value of the metric \"\"\" return df [ self . numerator_name ] . mean () / df [ self . denominator_name ] . mean ()","title":"Returns"},{"location":"api/metric.html#cluster_experiments.inference.metric.SimpleMetric","text":"A class used to represent a Simple Metric with an alias and a name. To be used when the metric is defined at the same level of the data used for the analysis.","title":"SimpleMetric"},{"location":"api/metric.html#cluster_experiments.inference.metric.SimpleMetric--example","text":"In a clustered experiment the participants were randomised based on their country of residence. The metric of interest is the salary of each participant. If the dataset fed into the analysis is at participant-level, then a SimpleMetric must be used. However, if the dataset fed into the analysis is at country-level, then a RatioMetric must be used.","title":"Example"},{"location":"api/metric.html#cluster_experiments.inference.metric.SimpleMetric--attributes","text":"alias : str A string representing the alias of the metric name : str A string representing the name of the metric Source code in cluster_experiments/inference/metric.py class SimpleMetric ( Metric ): \"\"\" A class used to represent a Simple Metric with an alias and a name. To be used when the metric is defined at the same level of the data used for the analysis. Example ---------- In a clustered experiment the participants were randomised based on their country of residence. The metric of interest is the salary of each participant. If the dataset fed into the analysis is at participant-level, then a SimpleMetric must be used. However, if the dataset fed into the analysis is at country-level, then a RatioMetric must be used. Attributes ---------- alias : str A string representing the alias of the metric name : str A string representing the name of the metric \"\"\" def __init__ ( self , alias : str , name : str ): \"\"\" Parameters ---------- alias : str The alias of the metric name : str The name of the metric \"\"\" super () . __init__ ( alias ) self . name = name self . _validate_name () def _validate_name ( self ): \"\"\" Validates the name input for the SimpleMetric class. Raises ------ TypeError If the name is not a string \"\"\" if not isinstance ( self . name , str ): raise TypeError ( \"SimpleMetric name must be a string\" ) @property def target_column ( self ) -> str : \"\"\" Returns the target column for the SimpleMetric. Returns ------- str The name of the metric \"\"\" return self . name def get_mean ( self , df : pd . DataFrame ) -> float : \"\"\" Returns the mean value of the metric, given a dataframe. Returns ------- float The mean value of the metric \"\"\" return df [ self . name ] . mean ()","title":"Attributes"},{"location":"api/metric.html#cluster_experiments.inference.metric.SimpleMetric.target_column","text":"Returns the target column for the SimpleMetric.","title":"target_column"},{"location":"api/metric.html#cluster_experiments.inference.metric.SimpleMetric.target_column--returns","text":"str The name of the metric","title":"Returns"},{"location":"api/metric.html#cluster_experiments.inference.metric.SimpleMetric.__init__","text":"","title":"__init__()"},{"location":"api/metric.html#cluster_experiments.inference.metric.SimpleMetric.__init__--parameters","text":"alias : str The alias of the metric name : str The name of the metric Source code in cluster_experiments/inference/metric.py def __init__ ( self , alias : str , name : str ): \"\"\" Parameters ---------- alias : str The alias of the metric name : str The name of the metric \"\"\" super () . __init__ ( alias ) self . name = name self . _validate_name ()","title":"Parameters"},{"location":"api/metric.html#cluster_experiments.inference.metric.SimpleMetric.get_mean","text":"Returns the mean value of the metric, given a dataframe.","title":"get_mean()"},{"location":"api/metric.html#cluster_experiments.inference.metric.SimpleMetric.get_mean--returns","text":"float The mean value of the metric Source code in cluster_experiments/inference/metric.py def get_mean ( self , df : pd . DataFrame ) -> float : \"\"\" Returns the mean value of the metric, given a dataframe. Returns ------- float The mean value of the metric \"\"\" return df [ self . name ] . mean ()","title":"Returns"},{"location":"api/perturbator.html","text":"from cluster_experiments.perturbator import * \u00b6 BetaRelativePerturbator ( NormalPerturbator , RelativePositivePerturbator ) \u00b6 A stochastic Perturbator for continuous targets that applies a sampled effect from a scaled Beta distribution. It applies the effect multiplicatively. The sampled effect is defined for values in the specified range (range_min, range_max). It's recommended to set -10 in a \"symmetric way\" around 0, such that log(1 + range_min) = -log(1 + range_max). This ensures to have an \"symmetric range\" of perturbations that relatively decrease the target as perturbations that relatively increase the target. By \"symmetry\" of relative effects we mean that for an effect c > 0, an increase of the target t via t*(1 + c) is \"symmetric\" to a decrease of t via t/(1 + c). For example, an increase of 5x (i.e. by +400%, corresponding to c_inc=4) is \"symmetric\" to a decrease of 5x (i.e. a decrease of -80%, corresponding to c_dec = -0.8). In this case, 1 + c_dec = 1/(1 + c_inc), so the relative effects c_inc and c_dec are \"symmetric\" in the sense that they are inverse to each other. The number of samples with 0 as target remains unchanged. The stochastic effect is sampled from a beta distribution with parameters mean and variance, which is linearly scaled to the range (range_min, range_max). If variance is not provided, the variance is abs(mean). target -> target * (1 + effect), where effect ~ Beta(a, b) The common beta parameters are derived from the mean and scale parameters, combined with linear transformations to ensure the support in the given range. The resulting beta parameters are scaled by abs(mu) to narrow the beta distribution around the mean. mu_transformed <- (mu - range_min) / (range_max - range_min) scale_transformed <- (scale - range_min) / (range_max - range_min) a <- mu_transformed / (scale_transformed * scale_transformed) b <- (1-mu_transformed) / (scale_transformed * scale_transformed) effect_transformed ~ beta(a/abs(mu), b/abs(mu)) effect = effect_transformed * (range_max - range_min) + range_min Parameters: Name Type Description Default average_effect Optional[float] the average effect of the treatment. Defaults to None. None target_col str name of the target_col to use as the outcome. Defaults to \"target\". 'target' treatment_col str the name of the column that contains the treatment. Defaults to \"treatment\". 'treatment' treatment str name of the treatment to use as the treated group. Defaults to \"B\". 'B' scale Optional[float] the scale of the effect distribution. Defaults to None. If not provided, the variance of the beta distribution is abs(mean). None range_min float the minimum value of the target range, must be >-1. Defaults to -0.8, which allows for up to 5x decreases of the target. None range_max float the maximum value of the target range. Defaults to 4, which allows for up to 5x increases of the target. None reduce_variance Optional[bool] if True and if abs(average_effect)<1, we reduce the variance of the beta distribution by multiplying the beta parameters by 1/abs(average_effect). Defaults to None, which is equivalent to True. None Source code in cluster_experiments/perturbator.py class BetaRelativePerturbator ( NormalPerturbator , RelativePositivePerturbator ): \"\"\" A stochastic Perturbator for continuous targets that applies a sampled effect from a scaled Beta distribution. It applies the effect multiplicatively. The sampled effect is defined for values in the specified range (range_min, range_max). It's recommended to set -10 in a \"symmetric way\" around 0, such that log(1 + range_min) = -log(1 + range_max). This ensures to have an \"symmetric range\" of perturbations that relatively decrease the target as perturbations that relatively increase the target. By \"symmetry\" of relative effects we mean that for an effect c > 0, an increase of the target t via t*(1 + c) is \"symmetric\" to a decrease of t via t/(1 + c). For example, an increase of 5x (i.e. by +400%, corresponding to c_inc=4) is \"symmetric\" to a decrease of 5x (i.e. a decrease of -80%, corresponding to c_dec = -0.8). In this case, 1 + c_dec = 1/(1 + c_inc), so the relative effects c_inc and c_dec are \"symmetric\" in the sense that they are inverse to each other. The number of samples with 0 as target remains unchanged. The stochastic effect is sampled from a beta distribution with parameters mean and variance, which is linearly scaled to the range (range_min, range_max). If variance is not provided, the variance is abs(mean). ``` target -> target * (1 + effect), where effect ~ Beta(a, b) ``` The common beta parameters are derived from the mean and scale parameters, combined with linear transformations to ensure the support in the given range. The resulting beta parameters are scaled by abs(mu) to narrow the beta distribution around the mean. ``` mu_transformed <- (mu - range_min) / (range_max - range_min) scale_transformed <- (scale - range_min) / (range_max - range_min) a <- mu_transformed / (scale_transformed * scale_transformed) b <- (1-mu_transformed) / (scale_transformed * scale_transformed) effect_transformed ~ beta(a/abs(mu), b/abs(mu)) effect = effect_transformed * (range_max - range_min) + range_min ``` Arguments: average_effect (Optional[float], optional): the average effect of the treatment. Defaults to None. target_col (str, optional): name of the target_col to use as the outcome. Defaults to \"target\". treatment_col (str, optional): the name of the column that contains the treatment. Defaults to \"treatment\". treatment (str, optional): name of the treatment to use as the treated group. Defaults to \"B\". scale (Optional[float], optional): the scale of the effect distribution. Defaults to None. If not provided, the variance of the beta distribution is abs(mean). range_min (float, optional): the minimum value of the target range, must be >-1. Defaults to -0.8, which allows for up to 5x decreases of the target. range_max (float, optional): the maximum value of the target range. Defaults to 4, which allows for up to 5x increases of the target. reduce_variance (Optional[bool], optional): if True and if abs(average_effect)<1, we reduce the variance of the beta distribution by multiplying the beta parameters by 1/abs(average_effect). Defaults to None, which is equivalent to True. \"\"\" def __init__ ( self , average_effect : Optional [ float ] = None , target_col : str = \"target\" , treatment_col : str = \"treatment\" , treatment : str = \"B\" , scale : Optional [ float ] = None , range_min : Optional [ float ] = None , range_max : Optional [ float ] = None , reduce_variance : Optional [ bool ] = None , ): self . _check_range ( range_min , range_max ) super () . __init__ ( average_effect , target_col , treatment_col , treatment , scale ) self . _range_min = range_min or - 0.8 self . _range_max = range_max or 4 self . _reduce_variance = reduce_variance or True def perturbate ( self , df : pd . DataFrame , average_effect : Optional [ float ] = None ) -> pd . DataFrame : \"\"\" Usage: ```python from cluster_experiments.perturbator import BetaRelativePerturbator import pandas as pd df = pd.DataFrame({\"target\": [1, 2, 3], \"treatment\": [\"A\", \"B\", \"A\"]}) perturbator = BetaRelativePerturbator(range_min = -0.5, range_max = 2) # Increase target metric by 20% on average perturbator.perturbate(df, average_effect=0.2) ``` \"\"\" df = df . copy () . reset_index ( drop = True ) average_effect = self . get_average_effect ( average_effect ) self . check_relative_effect_bounds ( average_effect ) scale = self . get_scale ( average_effect ) self . check_relative_effect_bounds ( scale ) n = self . get_number_of_treated ( df ) sampled_effect = self . _sample_scaled_beta_effect ( average_effect , scale , n ) df = self . apply_multiplicative_effect ( df , sampled_effect ) return df @staticmethod def _check_range ( range_min : float , range_max : float ): if range_min < - 1 : raise ValueError ( f \"range_min needs to be greater than -1, got { range_min } \" ) if range_min >= range_max : raise ValueError ( f \"range_min needs to be smaller than range_max, got \" f \" { range_min = } and { range_max = } \" ) def check_relative_effect_bounds ( self , average_effect : float ) -> None : self . check_average_effect_greater_than ( average_effect , x = self . _range_min ) self . check_average_effect_smaller_than ( average_effect , x = self . _range_max ) def check_average_effect_greater_than ( self , average_effect : float , x : float ) -> Optional [ NoReturn ]: if average_effect <= x : raise ValueError ( f \"Simulated effect needs to be greater than range_min= { x } , got { average_effect } \" ) def check_average_effect_smaller_than ( self , average_effect : float , x : float ) -> Optional [ NoReturn ]: if average_effect >= x : raise ValueError ( f \"Simulated effect needs to be smaller than range_max= { x } , got { average_effect } \" ) def _reduce_variance_beta_params ( self , average_effect : float , a : float , b : float ) -> Tuple [ float , float ]: \"\"\" Multiplying the parameters of the beta distribution with a factor >1 reduces variance \"\"\" if abs ( average_effect ) < 1 : a *= 1 / abs ( average_effect ) b *= 1 / abs ( average_effect ) return a , b def _sample_scaled_beta_effect ( self , average_effect : float , scale : float , n : int ) -> np . ndarray : average_effect_inv_transf = self . _inv_transform_to_range ( average_effect ) scale_inv_transf = self . _inv_transform_to_range ( scale ) a = average_effect_inv_transf / ( scale_inv_transf * scale_inv_transf ) b = ( 1 - average_effect_inv_transf ) / ( scale_inv_transf * scale_inv_transf ) if self . _reduce_variance : a , b = self . _reduce_variance_beta_params ( average_effect , a , b ) beta = np . random . beta ( a , b , n ) return self . _transform_to_range ( beta ) def _transform_to_range ( self , x : Union [ float , np . ndarray ]): return x * ( self . _range_max - self . _range_min ) + self . _range_min def _inv_transform_to_range ( self , x : Union [ float , np . ndarray ]): return ( x - self . _range_min ) / ( self . _range_max - self . _range_min ) @classmethod def from_config ( cls , config ): \"\"\"Creates a Perturbator object from a PowerConfig object\"\"\" return cls ( average_effect = config . average_effect , target_col = config . target_col , treatment_col = config . treatment_col , treatment = config . treatment , scale = config . scale , range_min = config . range_min , range_max = config . range_max , ) from_config ( config ) classmethod \u00b6 Creates a Perturbator object from a PowerConfig object Source code in cluster_experiments/perturbator.py @classmethod def from_config ( cls , config ): \"\"\"Creates a Perturbator object from a PowerConfig object\"\"\" return cls ( average_effect = config . average_effect , target_col = config . target_col , treatment_col = config . treatment_col , treatment = config . treatment , scale = config . scale , range_min = config . range_min , range_max = config . range_max , ) perturbate ( self , df , average_effect = None ) \u00b6 Usage: from cluster_experiments.perturbator import BetaRelativePerturbator import pandas as pd df = pd . DataFrame ({ \"target\" : [ 1 , 2 , 3 ], \"treatment\" : [ \"A\" , \"B\" , \"A\" ]}) perturbator = BetaRelativePerturbator ( range_min = - 0.5 , range_max = 2 ) # Increase target metric by 20% on average perturbator . perturbate ( df , average_effect = 0.2 ) Source code in cluster_experiments/perturbator.py def perturbate ( self , df : pd . DataFrame , average_effect : Optional [ float ] = None ) -> pd . DataFrame : \"\"\" Usage: ```python from cluster_experiments.perturbator import BetaRelativePerturbator import pandas as pd df = pd.DataFrame({\"target\": [1, 2, 3], \"treatment\": [\"A\", \"B\", \"A\"]}) perturbator = BetaRelativePerturbator(range_min = -0.5, range_max = 2) # Increase target metric by 20% on average perturbator.perturbate(df, average_effect=0.2) ``` \"\"\" df = df . copy () . reset_index ( drop = True ) average_effect = self . get_average_effect ( average_effect ) self . check_relative_effect_bounds ( average_effect ) scale = self . get_scale ( average_effect ) self . check_relative_effect_bounds ( scale ) n = self . get_number_of_treated ( df ) sampled_effect = self . _sample_scaled_beta_effect ( average_effect , scale , n ) df = self . apply_multiplicative_effect ( df , sampled_effect ) return df BetaRelativePositivePerturbator ( NormalPerturbator , RelativePositivePerturbator ) \u00b6 A stochastic Perturbator for continuous, positively-defined targets that applies a sampled effect from the Beta distribution. It applies the effect multiplicatively. WARNING : the average effect is only defined for values between 0 and 1 (not included). Therefore, it only increments the target for the treated samples. The number of samples with 0 as target remains unchanged. The stochastic effect is sampled from a beta distribution with parameters mean and variance. If variance is not provided, the variance is abs(mean). Hence, the effect is bounded by 0 and 1. target -> target * (1 + effect), where effect ~ Beta(a, b); a, b > 0 and target > 0 for all samples The common beta parameters are derived from the mean and scale parameters (see how below). That's why the average effect is only defined for values between 0 and 1, otherwise one of the beta parameters would be negative or zero: a <- mu / (scale * scale) b <- (1-mu) / (scale * scale) effect ~ beta(a, b) source: https://stackoverflow.com/a/51143208 Example: a mean = 0.2 and variance = 0.1, give a = 20 and b = 80 Plot: https://www.wolframalpha.com/input?i=plot+distribution+of+beta%2820%2C+80%29 Parameters: Name Type Description Default average_effect Optional[float] the average effect of the treatment. Defaults to None. required target_col str name of the target_col to use as the outcome. Defaults to \"target\". required treatment_col str the name of the column that contains the treatment. Defaults to \"treatment\". required treatment str name of the treatment to use as the treated group. Defaults to \"B\". required scale Optional[float] the scale of the effect distribution. Defaults to None. required Source code in cluster_experiments/perturbator.py class BetaRelativePositivePerturbator ( NormalPerturbator , RelativePositivePerturbator ): \"\"\" A stochastic Perturbator for continuous, positively-defined targets that applies a sampled effect from the Beta distribution. It applies the effect multiplicatively. *WARNING*: the average effect is only defined for values between 0 and 1 (not included). Therefore, it only increments the target for the treated samples. The number of samples with 0 as target remains unchanged. The stochastic effect is sampled from a beta distribution with parameters mean and variance. If variance is not provided, the variance is abs(mean). Hence, the effect is bounded by 0 and 1. ``` target -> target * (1 + effect), where effect ~ Beta(a, b); a, b > 0 and target > 0 for all samples ``` The common beta parameters are derived from the mean and scale parameters (see how below). That's why the average effect is only defined for values between 0 and 1, otherwise one of the beta parameters would be negative or zero: ``` a <- mu / (scale * scale) b <- (1-mu) / (scale * scale) effect ~ beta(a, b) ``` source: https://stackoverflow.com/a/51143208 Example: a mean = 0.2 and variance = 0.1, give a = 20 and b = 80 Plot: https://www.wolframalpha.com/input?i=plot+distribution+of+beta%2820%2C+80%29 Arguments: average_effect (Optional[float], optional): the average effect of the treatment. Defaults to None. target_col (str, optional): name of the target_col to use as the outcome. Defaults to \"target\". treatment_col (str, optional): the name of the column that contains the treatment. Defaults to \"treatment\". treatment (str, optional): name of the treatment to use as the treated group. Defaults to \"B\". scale (Optional[float], optional): the scale of the effect distribution. Defaults to None. \"\"\" def perturbate ( self , df : pd . DataFrame , average_effect : Optional [ float ] = None ) -> pd . DataFrame : \"\"\" Usage: ```python from cluster_experiments.perturbator import BetaRelativePositivePerturbator import pandas as pd df = pd.DataFrame({\"target\": [1, 2, 3], \"treatment\": [\"A\", \"B\", \"A\"]}) perturbator = BetaRelativePositivePerturbator() # Increase target metric by 20% perturbator.perturbate(df, average_effect=0.2) # returns pd.DataFrame({\"target\": [1, 3, 3], \"treatment\": [\"A\", \"B\", \"A\"]}) ``` \"\"\" df = df . copy () . reset_index ( drop = True ) average_effect = self . get_average_effect ( average_effect ) self . check_beta_positive_effect ( df , average_effect ) scale = self . get_scale ( average_effect ) n = self . get_number_of_treated ( df ) sampled_effect = self . _sample_beta_effect ( average_effect , scale , n ) df = self . apply_multiplicative_effect ( df , sampled_effect ) return df def check_beta_positive_effect ( self , df , average_effect ): self . check_average_effect_greater_than ( average_effect , x = 0 ) self . check_average_effect_less_than ( average_effect , x = 1 ) self . check_target_is_not_negative ( df ) self . check_target_is_not_constant_zero ( df , average_effect ) def check_average_effect_less_than ( self , average_effect : float , x : float ) -> Optional [ NoReturn ]: if average_effect >= x : raise ValueError ( f \"Simulated effect needs to be less than { x * 100 : .0f } %, got \" f \" { average_effect * 100 : .1f } %\" ) def _sample_beta_effect ( self , average_effect : float , scale : float , n : int ) -> np . ndarray : a = average_effect / ( scale * scale ) b = ( 1 - average_effect ) / ( scale * scale ) return np . random . beta ( a , b , n ) perturbate ( self , df , average_effect = None ) \u00b6 Usage: from cluster_experiments.perturbator import BetaRelativePositivePerturbator import pandas as pd df = pd . DataFrame ({ \"target\" : [ 1 , 2 , 3 ], \"treatment\" : [ \"A\" , \"B\" , \"A\" ]}) perturbator = BetaRelativePositivePerturbator () # Increase target metric by 20% perturbator . perturbate ( df , average_effect = 0.2 ) # returns pd.DataFrame({\"target\": [1, 3, 3], \"treatment\": [\"A\", \"B\", \"A\"]}) Source code in cluster_experiments/perturbator.py def perturbate ( self , df : pd . DataFrame , average_effect : Optional [ float ] = None ) -> pd . DataFrame : \"\"\" Usage: ```python from cluster_experiments.perturbator import BetaRelativePositivePerturbator import pandas as pd df = pd.DataFrame({\"target\": [1, 2, 3], \"treatment\": [\"A\", \"B\", \"A\"]}) perturbator = BetaRelativePositivePerturbator() # Increase target metric by 20% perturbator.perturbate(df, average_effect=0.2) # returns pd.DataFrame({\"target\": [1, 3, 3], \"treatment\": [\"A\", \"B\", \"A\"]}) ``` \"\"\" df = df . copy () . reset_index ( drop = True ) average_effect = self . get_average_effect ( average_effect ) self . check_beta_positive_effect ( df , average_effect ) scale = self . get_scale ( average_effect ) n = self . get_number_of_treated ( df ) sampled_effect = self . _sample_beta_effect ( average_effect , scale , n ) df = self . apply_multiplicative_effect ( df , sampled_effect ) return df BinaryPerturbator ( Perturbator ) \u00b6 BinaryPerturbator is a Perturbator that adds is used to deal with binary outcome variables. It randomly selects some treated instances and flips their outcome from 0 to 1 or 1 to 0, depending on the effect being positive or negative Source code in cluster_experiments/perturbator.py class BinaryPerturbator ( Perturbator ): \"\"\" BinaryPerturbator is a Perturbator that adds is used to deal with binary outcome variables. It randomly selects some treated instances and flips their outcome from 0 to 1 or 1 to 0, depending on the effect being positive or negative \"\"\" def _sample_max ( self , df : pd . DataFrame , n : int ) -> pd . DataFrame : \"\"\"Like sample without replacement, but if you are to sample more than 100% of the data, it just returns the whole dataframe.\"\"\" if n >= len ( df ): return df return df . sample ( n = n ) def _data_checks ( self , df : pd . DataFrame , average_effect : float ) -> None : \"\"\"Check that outcome is indeed binary, and average effect is in (-1, 1)\"\"\" if set ( df [ self . target_col ] . unique ()) - { 0 , 1 }: raise ValueError ( f \"Target column must be binary, found { set ( df [ self . target_col ] . unique ()) } \" ) if average_effect > 1 or average_effect < - 1 : raise ValueError ( f \"Average effect must be in (-1, 1), found { average_effect } \" ) def perturbate ( self , df : pd . DataFrame , average_effect : Optional [ float ] = None ) -> pd . DataFrame : \"\"\" Usage: ```python from cluster_experiments.perturbator import BinaryPerturbator import pandas as pd df = pd.DataFrame({\"target\": [1, 0, 1], \"treatment\": [\"A\", \"B\", \"A\"]}) perturbator = BinaryPerturbator() perturbator.perturbate(df, average_effect=0.1) ``` \"\"\" df = df . copy () . reset_index ( drop = True ) average_effect = self . get_average_effect ( average_effect ) self . _data_checks ( df , average_effect ) from_target , to_target = 1 , 0 if average_effect > 0 : from_target , to_target = 0 , 1 n_transformed = abs ( int ( average_effect * len ( df . query ( self . treated_query )))) idx = list ( # Sample of negative cases in group B df . query ( f \" { self . target_col } == { from_target } & { self . treated_query } \" ) . pipe ( self . _sample_max , n = n_transformed ) . index . drop_duplicates () ) df . loc [ idx , self . target_col ] = to_target return df perturbate ( self , df , average_effect = None ) \u00b6 Usage: from cluster_experiments.perturbator import BinaryPerturbator import pandas as pd df = pd . DataFrame ({ \"target\" : [ 1 , 0 , 1 ], \"treatment\" : [ \"A\" , \"B\" , \"A\" ]}) perturbator = BinaryPerturbator () perturbator . perturbate ( df , average_effect = 0.1 ) Source code in cluster_experiments/perturbator.py def perturbate ( self , df : pd . DataFrame , average_effect : Optional [ float ] = None ) -> pd . DataFrame : \"\"\" Usage: ```python from cluster_experiments.perturbator import BinaryPerturbator import pandas as pd df = pd.DataFrame({\"target\": [1, 0, 1], \"treatment\": [\"A\", \"B\", \"A\"]}) perturbator = BinaryPerturbator() perturbator.perturbate(df, average_effect=0.1) ``` \"\"\" df = df . copy () . reset_index ( drop = True ) average_effect = self . get_average_effect ( average_effect ) self . _data_checks ( df , average_effect ) from_target , to_target = 1 , 0 if average_effect > 0 : from_target , to_target = 0 , 1 n_transformed = abs ( int ( average_effect * len ( df . query ( self . treated_query )))) idx = list ( # Sample of negative cases in group B df . query ( f \" { self . target_col } == { from_target } & { self . treated_query } \" ) . pipe ( self . _sample_max , n = n_transformed ) . index . drop_duplicates () ) df . loc [ idx , self . target_col ] = to_target return df ConstantPerturbator ( Perturbator ) \u00b6 ConstantPerturbator is a Perturbator that adds a constant effect to the target column of the treated instances. Source code in cluster_experiments/perturbator.py class ConstantPerturbator ( Perturbator ): \"\"\" ConstantPerturbator is a Perturbator that adds a constant effect to the target column of the treated instances. \"\"\" def perturbate ( self , df : pd . DataFrame , average_effect : Optional [ float ] = None ) -> pd . DataFrame : \"\"\" Usage: ```python from cluster_experiments.perturbator import ConstantPerturbator import pandas as pd df = pd.DataFrame({\"target\": [1, 2, 3], \"treatment\": [\"A\", \"B\", \"A\"]}) perturbator = ConstantPerturbator() perturbator.perturbate(df, average_effect=1) ``` \"\"\" df = df . copy () . reset_index ( drop = True ) average_effect = self . get_average_effect ( average_effect ) df = self . apply_additive_effect ( df , average_effect ) return df def apply_additive_effect ( self , df : pd . DataFrame , effect : Union [ float , np . ndarray ] ) -> pd . DataFrame : df . loc [ df [ self . treatment_col ] == self . treatment , self . target_col ] += effect return df perturbate ( self , df , average_effect = None ) \u00b6 Usage: from cluster_experiments.perturbator import ConstantPerturbator import pandas as pd df = pd . DataFrame ({ \"target\" : [ 1 , 2 , 3 ], \"treatment\" : [ \"A\" , \"B\" , \"A\" ]}) perturbator = ConstantPerturbator () perturbator . perturbate ( df , average_effect = 1 ) Source code in cluster_experiments/perturbator.py def perturbate ( self , df : pd . DataFrame , average_effect : Optional [ float ] = None ) -> pd . DataFrame : \"\"\" Usage: ```python from cluster_experiments.perturbator import ConstantPerturbator import pandas as pd df = pd.DataFrame({\"target\": [1, 2, 3], \"treatment\": [\"A\", \"B\", \"A\"]}) perturbator = ConstantPerturbator() perturbator.perturbate(df, average_effect=1) ``` \"\"\" df = df . copy () . reset_index ( drop = True ) average_effect = self . get_average_effect ( average_effect ) df = self . apply_additive_effect ( df , average_effect ) return df NormalPerturbator ( ConstantPerturbator ) \u00b6 The NormalPerturbator class implements a perturbator that adds a stochastic effect to the target column of the treated instances. The stochastic effect is sampled from a normal distribution with mean average_effect and variance scale. If scale is not provided, the variance is abs(average_effect). If scale is provided, a value not much bigger than the average_effect is suggested. target -> target + effect, where effect ~ Normal(average_effect, scale) Parameters: Name Type Description Default average_effect Optional[float] the average effect of the treatment. Defaults to None. None target_col str name of the target_col to use as the outcome. Defaults to \"target\". 'target' treatment_col str the name of the column that contains the treatment. Defaults to \"treatment\". 'treatment' treatment str name of the treatment to use as the treated group. Defaults to \"B\". 'B' scale Optional[float] the scale of the effect distribution. Defaults to None. None Source code in cluster_experiments/perturbator.py class NormalPerturbator ( ConstantPerturbator ): \"\"\"The NormalPerturbator class implements a perturbator that adds a stochastic effect to the target column of the treated instances. The stochastic effect is sampled from a normal distribution with mean average_effect and variance scale. If scale is not provided, the variance is abs(average_effect). If scale is provided, a value not much bigger than the average_effect is suggested. ``` target -> target + effect, where effect ~ Normal(average_effect, scale) ``` Arguments: average_effect (Optional[float], optional): the average effect of the treatment. Defaults to None. target_col (str, optional): name of the target_col to use as the outcome. Defaults to \"target\". treatment_col (str, optional): the name of the column that contains the treatment. Defaults to \"treatment\". treatment (str, optional): name of the treatment to use as the treated group. Defaults to \"B\". scale (Optional[float], optional): the scale of the effect distribution. Defaults to None. \"\"\" def __init__ ( self , average_effect : Optional [ float ] = None , target_col : str = \"target\" , treatment_col : str = \"treatment\" , treatment : str = \"B\" , scale : Optional [ float ] = None , ): super () . __init__ ( average_effect , target_col , treatment_col , treatment ) self . _scale = scale def perturbate ( self , df : pd . DataFrame , average_effect : Optional [ float ] = None ) -> pd . DataFrame : \"\"\"Perturbate with a normal effect with mean average_effect and std abs(average_effect). Arguments: df (pd.DataFrame): the dataframe to perturbate. average_effect (Optional[float], optional): the average effect. Defaults to None. Returns: pd.DataFrame: the perturbated dataframe. Usage: ```python from cluster_experiments.perturbator import NormalPerturbator import pandas as pd df = pd.DataFrame({\"target\": [1, 2, 3], \"treatment\": [\"A\", \"B\", \"A\"]}) perturbator = NormalPerturbator() perturbator.perturbate(df, average_effect=1) ``` \"\"\" df = df . copy () . reset_index ( drop = True ) average_effect = self . get_average_effect ( average_effect ) scale = self . get_scale ( average_effect ) n = self . get_number_of_treated ( df ) sampled_effect = self . _sample_normal_effect ( average_effect , scale , n ) df = self . apply_additive_effect ( df , sampled_effect ) return df def get_scale ( self , average_effect : float ) -> float : \"\"\"Get the scale of the normal distribution. If scale is not provided, the variance is abs(average_effect). Raises a ValueError if scale is not positive. \"\"\" scale = abs ( average_effect ) if self . _scale is None else self . _scale if scale <= 0 : raise ValueError ( f \"scale must be positive, got { scale } \" ) return scale def get_number_of_treated ( self , df : pd . DataFrame ) -> int : \"\"\"Get the number of treated instances in the dataframe\"\"\" return ( df [ self . treatment_col ] == self . treatment ) . sum () def _sample_normal_effect ( self , average_effect : float , scale : float , n : int ) -> np . ndarray : return np . random . normal ( average_effect , scale , n ) @classmethod def from_config ( cls , config ): \"\"\"Creates a Perturbator object from a PowerConfig object\"\"\" return cls ( average_effect = config . average_effect , target_col = config . target_col , treatment_col = config . treatment_col , treatment = config . treatment , scale = config . scale , ) from_config ( config ) classmethod \u00b6 Creates a Perturbator object from a PowerConfig object Source code in cluster_experiments/perturbator.py @classmethod def from_config ( cls , config ): \"\"\"Creates a Perturbator object from a PowerConfig object\"\"\" return cls ( average_effect = config . average_effect , target_col = config . target_col , treatment_col = config . treatment_col , treatment = config . treatment , scale = config . scale , ) get_number_of_treated ( self , df ) \u00b6 Get the number of treated instances in the dataframe Source code in cluster_experiments/perturbator.py def get_number_of_treated ( self , df : pd . DataFrame ) -> int : \"\"\"Get the number of treated instances in the dataframe\"\"\" return ( df [ self . treatment_col ] == self . treatment ) . sum () get_scale ( self , average_effect ) \u00b6 Get the scale of the normal distribution. If scale is not provided, the variance is abs(average_effect). Raises a ValueError if scale is not positive. Source code in cluster_experiments/perturbator.py def get_scale ( self , average_effect : float ) -> float : \"\"\"Get the scale of the normal distribution. If scale is not provided, the variance is abs(average_effect). Raises a ValueError if scale is not positive. \"\"\" scale = abs ( average_effect ) if self . _scale is None else self . _scale if scale <= 0 : raise ValueError ( f \"scale must be positive, got { scale } \" ) return scale perturbate ( self , df , average_effect = None ) \u00b6 Perturbate with a normal effect with mean average_effect and std abs(average_effect). Parameters: Name Type Description Default df pd.DataFrame the dataframe to perturbate. required average_effect Optional[float] the average effect. Defaults to None. None Returns: Type Description pd.DataFrame the perturbated dataframe. Usage: from cluster_experiments.perturbator import NormalPerturbator import pandas as pd df = pd . DataFrame ({ \"target\" : [ 1 , 2 , 3 ], \"treatment\" : [ \"A\" , \"B\" , \"A\" ]}) perturbator = NormalPerturbator () perturbator . perturbate ( df , average_effect = 1 ) Source code in cluster_experiments/perturbator.py def perturbate ( self , df : pd . DataFrame , average_effect : Optional [ float ] = None ) -> pd . DataFrame : \"\"\"Perturbate with a normal effect with mean average_effect and std abs(average_effect). Arguments: df (pd.DataFrame): the dataframe to perturbate. average_effect (Optional[float], optional): the average effect. Defaults to None. Returns: pd.DataFrame: the perturbated dataframe. Usage: ```python from cluster_experiments.perturbator import NormalPerturbator import pandas as pd df = pd.DataFrame({\"target\": [1, 2, 3], \"treatment\": [\"A\", \"B\", \"A\"]}) perturbator = NormalPerturbator() perturbator.perturbate(df, average_effect=1) ``` \"\"\" df = df . copy () . reset_index ( drop = True ) average_effect = self . get_average_effect ( average_effect ) scale = self . get_scale ( average_effect ) n = self . get_number_of_treated ( df ) sampled_effect = self . _sample_normal_effect ( average_effect , scale , n ) df = self . apply_additive_effect ( df , sampled_effect ) return df Perturbator ( ABC ) \u00b6 Abstract perturbator. Perturbators are used to simulate a fictitious effect when running a power analysis. The idea is that, when running a power analysis, we split our instances according to a RandomSplitter, and the instances that got the treatment, are perturbated with a fictional effect via the Perturbator. In order to create your own perturbator, you should create a derived class that implements the perturbate method. The perturbate method should add the average effect in the desired way and return the dataframe with the extra average effect, without affecting the initial dataframe. Keep in mind to use df = df.copy() in the first line of the perturbate method. Parameters: Name Type Description Default average_effect Optional[float] The average effect of the treatment None target_col str name of the target_col to use as the outcome 'target' treatment_col str The name of the column that contains the treatment 'treatment' treatment str name of the treatment to use as the treated group 'B' Source code in cluster_experiments/perturbator.py class Perturbator ( ABC ): \"\"\" Abstract perturbator. Perturbators are used to simulate a fictitious effect when running a power analysis. The idea is that, when running a power analysis, we split our instances according to a RandomSplitter, and the instances that got the treatment, are perturbated with a fictional effect via the Perturbator. In order to create your own perturbator, you should create a derived class that implements the perturbate method. The perturbate method should add the average effect in the desired way and return the dataframe with the extra average effect, without affecting the initial dataframe. Keep in mind to use `df = df.copy()` in the first line of the perturbate method. Arguments: average_effect: The average effect of the treatment target_col: name of the target_col to use as the outcome treatment_col: The name of the column that contains the treatment treatment: name of the treatment to use as the treated group \"\"\" def __init__ ( self , average_effect : Optional [ float ] = None , target_col : str = \"target\" , treatment_col : str = \"treatment\" , treatment : str = \"B\" , ): self . average_effect = average_effect self . target_col = target_col self . treatment_col = treatment_col self . treatment = treatment self . treated_query = f \" { self . treatment_col } == ' { self . treatment } '\" def get_average_effect ( self , average_effect : Optional [ float ] = None ) -> float : average_effect = ( average_effect if average_effect is not None else self . average_effect ) if average_effect is None : raise ValueError ( \"average_effect must be provided, either in the constructor or in the method call\" ) return average_effect @abstractmethod def perturbate ( self , df : pd . DataFrame , average_effect : Optional [ float ] = None ) -> pd . DataFrame : \"\"\"Method to perturbate a dataframe\"\"\" @classmethod def from_config ( cls , config ): \"\"\"Creates a Perturbator object from a PowerConfig object\"\"\" return cls ( average_effect = config . average_effect , target_col = config . target_col , treatment_col = config . treatment_col , treatment = config . treatment , ) from_config ( config ) classmethod \u00b6 Creates a Perturbator object from a PowerConfig object Source code in cluster_experiments/perturbator.py @classmethod def from_config ( cls , config ): \"\"\"Creates a Perturbator object from a PowerConfig object\"\"\" return cls ( average_effect = config . average_effect , target_col = config . target_col , treatment_col = config . treatment_col , treatment = config . treatment , ) perturbate ( self , df , average_effect = None ) \u00b6 Method to perturbate a dataframe Source code in cluster_experiments/perturbator.py @abstractmethod def perturbate ( self , df : pd . DataFrame , average_effect : Optional [ float ] = None ) -> pd . DataFrame : \"\"\"Method to perturbate a dataframe\"\"\" RelativePositivePerturbator ( Perturbator ) \u00b6 A Perturbator for continuous, positively-defined targets applies a simulated effect multiplicatively for the treated samples, ie. proportional to the target value for each sample. The number of samples with 0 as target remains unchanged. target -> target * (1 + average_effect), where -1 < average_effect < inf and target > 0 for all samples Source code in cluster_experiments/perturbator.py class RelativePositivePerturbator ( Perturbator ): \"\"\" A Perturbator for continuous, positively-defined targets applies a simulated effect multiplicatively for the treated samples, ie. proportional to the target value for each sample. The number of samples with 0 as target remains unchanged. ``` target -> target * (1 + average_effect), where -1 < average_effect < inf and target > 0 for all samples ``` \"\"\" def perturbate ( self , df : pd . DataFrame , average_effect : Optional [ float ] = None ) -> pd . DataFrame : \"\"\" Usage: ```python from cluster_experiments.perturbator import RelativePositivePerturbator import pandas as pd df = pd.DataFrame({\"target\": [1, 2, 3], \"treatment\": [\"A\", \"B\", \"A\"]}) perturbator = RelativePositivePerturbator() # Increase target metric by 50% perturbator.perturbate(df, average_effect=0.5) # returns pd.DataFrame({\"target\": [1, 3, 3], \"treatment\": [\"A\", \"B\", \"A\"]}) ``` \"\"\" df = df . copy () . reset_index ( drop = True ) average_effect = self . get_average_effect ( average_effect ) self . check_relative_positive_effect ( df , average_effect ) df = self . apply_multiplicative_effect ( df , average_effect ) return df def check_relative_positive_effect ( self , df : pd . DataFrame , average_effect : float ) -> None : self . check_average_effect_greater_than ( average_effect , x =- 1 ) self . check_target_is_not_negative ( df ) self . check_target_is_not_constant_zero ( df , average_effect ) def check_target_is_not_constant_zero ( self , df : pd . DataFrame , average_effect : float ) -> Optional [ NoReturn ]: treatment_zeros = ( ( df [ self . treatment_col ] != self . treatment ) | ( df [ self . target_col ] == 0 ) ) . mean () if 1.0 == treatment_zeros : raise ValueError ( f \"All treatment samples have { self . target_col } = 0, relative effect \" f \" { average_effect } will have no effect\" ) def check_target_is_not_negative ( self , df : pd . DataFrame ) -> Optional [ NoReturn ]: if any ( df [ self . target_col ] < 0 ): raise ValueError ( f \"All { self . target_col } values need to be positive or 0, \" f \"got { df [ self . target_col ] . min () } \" ) def check_average_effect_greater_than ( self , average_effect : float , x : float ) -> Optional [ NoReturn ]: if average_effect <= x : raise ValueError ( f \"Simulated effect needs to be greater than { x * 100 : .0f } %, got \" f \" { average_effect * 100 : .1f } %\" ) def apply_multiplicative_effect ( self , df : pd . DataFrame , effect : Union [ float , np . ndarray ] ) -> pd . DataFrame : df . loc [ df [ self . treatment_col ] == self . treatment , self . target_col ] *= 1 + effect return df perturbate ( self , df , average_effect = None ) \u00b6 Usage: from cluster_experiments.perturbator import RelativePositivePerturbator import pandas as pd df = pd . DataFrame ({ \"target\" : [ 1 , 2 , 3 ], \"treatment\" : [ \"A\" , \"B\" , \"A\" ]}) perturbator = RelativePositivePerturbator () # Increase target metric by 50% perturbator . perturbate ( df , average_effect = 0.5 ) # returns pd.DataFrame({\"target\": [1, 3, 3], \"treatment\": [\"A\", \"B\", \"A\"]}) Source code in cluster_experiments/perturbator.py def perturbate ( self , df : pd . DataFrame , average_effect : Optional [ float ] = None ) -> pd . DataFrame : \"\"\" Usage: ```python from cluster_experiments.perturbator import RelativePositivePerturbator import pandas as pd df = pd.DataFrame({\"target\": [1, 2, 3], \"treatment\": [\"A\", \"B\", \"A\"]}) perturbator = RelativePositivePerturbator() # Increase target metric by 50% perturbator.perturbate(df, average_effect=0.5) # returns pd.DataFrame({\"target\": [1, 3, 3], \"treatment\": [\"A\", \"B\", \"A\"]}) ``` \"\"\" df = df . copy () . reset_index ( drop = True ) average_effect = self . get_average_effect ( average_effect ) self . check_relative_positive_effect ( df , average_effect ) df = self . apply_multiplicative_effect ( df , average_effect ) return df SegmentedBetaRelativePerturbator ( BetaRelativePositivePerturbator ) \u00b6 A stochastic Perturbator for continuous targets that applies a sampled effect from the Beta distribution. It applies the effect multiplicatively and based on given segments. For each segment, the average segment effect is sampled from a beta distribution with support in (0, 1). Within each segment, the individual effects are sampled from a beta distribution with mean equal to the segment average effect and support in (range_min, range_max). The number of samples with 0 as target remains unchanged. For additional details and recommendations on the parameters, see the documentation for the BetaRelativePerturbator class. Parameters: Name Type Description Default average_effect Optional[float] the average effect of the treatment. Defaults to None. None target_col str name of the target_col to use as the outcome. Defaults to \"target\". 'target' treatment_col str the name of the column that contains the treatment. Defaults to \"treatment\". 'treatment' treatment str name of the treatment to use as the treated group. Defaults to \"B\". 'B' scale Optional[float] the scale of the effect distribution. Defaults to None. None range_min float the minimum value of the target range, must be >-1. Defaults to -0.8, which allows for up to 5x decreases of the target. None range_max float the maximum value of the target range. Defaults to 4, which allows for up to 5x increases of the target. None segment_cols Optional[List[str]] the columns to use for segmenting. Defaults to None. required Source code in cluster_experiments/perturbator.py class SegmentedBetaRelativePerturbator ( BetaRelativePositivePerturbator ): \"\"\" A stochastic Perturbator for continuous targets that applies a sampled effect from the Beta distribution. It applies the effect multiplicatively and based on given segments. For each segment, the average segment effect is sampled from a beta distribution with support in (0, 1). Within each segment, the individual effects are sampled from a beta distribution with mean equal to the segment average effect and support in (range_min, range_max). The number of samples with 0 as target remains unchanged. For additional details and recommendations on the parameters, see the documentation for the `BetaRelativePerturbator` class. Arguments: average_effect (Optional[float], optional): the average effect of the treatment. Defaults to None. target_col (str, optional): name of the target_col to use as the outcome. Defaults to \"target\". treatment_col (str, optional): the name of the column that contains the treatment. Defaults to \"treatment\". treatment (str, optional): name of the treatment to use as the treated group. Defaults to \"B\". scale (Optional[float], optional): the scale of the effect distribution. Defaults to None. range_min (float, optional): the minimum value of the target range, must be >-1. Defaults to -0.8, which allows for up to 5x decreases of the target. range_max (float, optional): the maximum value of the target range. Defaults to 4, which allows for up to 5x increases of the target. segment_cols (Optional[List[str]], optional): the columns to use for segmenting. Defaults to None. \"\"\" def __init__ ( self , segment_cols : List [ str ], average_effect : Optional [ float ] = None , target_col : str = \"target\" , treatment_col : str = \"treatment\" , treatment : str = \"B\" , scale : Optional [ float ] = None , range_min : Optional [ float ] = None , range_max : Optional [ float ] = None , ): super () . __init__ ( average_effect , target_col , treatment_col , treatment , scale ) self . _range_min = range_min or - 0.8 self . _range_max = range_max or 4 self . _segment_cols = segment_cols self . segment_col = self . _get_segment_col_name ( segment_cols ) @staticmethod def _get_segment_col_name ( segment_cols : List [ str ]): if not isinstance ( segment_cols , list ): raise ValueError ( f \"segment_cols must be of type List[str], got type { type ( segment_cols ) } \" ) return \"_cluster_\" + \"_\" . join ( segment_cols ) def _set_segment_col_values ( self , df : pd . DataFrame ): if self . segment_col in df . columns : raise ValueError ( f \"Cannot use { self . segment_col =} as perturbator clustering \" f \"column, as it already exists in the input dataframe!\" ) return df . copy () . assign ( ** { self . segment_col : df [ self . _segment_cols ] . astype ( str ) . sum ( axis = 1 )} ) def get_cluster_perturbator_fixed_params ( self , average_effect : Optional [ float ] = None ) -> Dict [ str , Any ]: average_effect = self . get_average_effect ( average_effect ) self . check_average_effect_greater_than ( average_effect , x = 0 ) self . check_average_effect_less_than ( average_effect , x = 1 ) scale = self . get_scale ( average_effect ) return { \"average_effect\" : average_effect , \"scale\" : scale , } def get_cluster_perturbator ( self , ** kwargs ) -> Perturbator : sampled_effect = self . _sample_beta_effect ( kwargs [ \"average_effect\" ], kwargs [ \"scale\" ], 1 ) cluster_perturbator = BetaRelativePerturbator ( average_effect = sampled_effect , target_col = self . target_col , treatment_col = self . treatment_col , treatment = self . treatment , range_min = self . _range_min , range_max = self . _range_max , ) return cluster_perturbator def perturbate ( self , df : pd . DataFrame , average_effect : Optional [ float ] = None , ) -> pd . DataFrame : df = df . copy () . reset_index ( drop = True ) df = self . _set_segment_col_values ( df ) cluster_perturbator_params = self . get_cluster_perturbator_fixed_params ( average_effect ) df_perturbed = pd . concat ( [ self . get_cluster_perturbator ( ** cluster_perturbator_params ) . perturbate ( df = df [ df [ self . segment_col ] == cluster ] . copy () ) for cluster in df [ self . segment_col ] . unique () ] ) return df_perturbed . drop ( columns = self . segment_col ) . reset_index ( drop = True ) @classmethod def from_config ( cls , config ): \"\"\"Creates a Perturbator object from a PowerConfig object\"\"\" return cls ( average_effect = config . average_effect , target_col = config . target_col , treatment_col = config . treatment_col , treatment = config . treatment , scale = config . scale , range_min = config . range_min , range_max = config . range_max , segment_cols = config . segment_cols , ) from_config ( config ) classmethod \u00b6 Creates a Perturbator object from a PowerConfig object Source code in cluster_experiments/perturbator.py @classmethod def from_config ( cls , config ): \"\"\"Creates a Perturbator object from a PowerConfig object\"\"\" return cls ( average_effect = config . average_effect , target_col = config . target_col , treatment_col = config . treatment_col , treatment = config . treatment , scale = config . scale , range_min = config . range_min , range_max = config . range_max , segment_cols = config . segment_cols , ) perturbate ( self , df , average_effect = None ) \u00b6 Usage: from cluster_experiments.perturbator import BetaRelativePositivePerturbator import pandas as pd df = pd . DataFrame ({ \"target\" : [ 1 , 2 , 3 ], \"treatment\" : [ \"A\" , \"B\" , \"A\" ]}) perturbator = BetaRelativePositivePerturbator () # Increase target metric by 20% perturbator . perturbate ( df , average_effect = 0.2 ) # returns pd.DataFrame({\"target\": [1, 3, 3], \"treatment\": [\"A\", \"B\", \"A\"]}) Source code in cluster_experiments/perturbator.py def perturbate ( self , df : pd . DataFrame , average_effect : Optional [ float ] = None , ) -> pd . DataFrame : df = df . copy () . reset_index ( drop = True ) df = self . _set_segment_col_values ( df ) cluster_perturbator_params = self . get_cluster_perturbator_fixed_params ( average_effect ) df_perturbed = pd . concat ( [ self . get_cluster_perturbator ( ** cluster_perturbator_params ) . perturbate ( df = df [ df [ self . segment_col ] == cluster ] . copy () ) for cluster in df [ self . segment_col ] . unique () ] ) return df_perturbed . drop ( columns = self . segment_col ) . reset_index ( drop = True ) UniformPerturbator ( Perturbator ) \u00b6 UniformPerturbator is a Perturbator that adds a constant effect to the target column of the treated instances. Source code in cluster_experiments/perturbator.py class UniformPerturbator ( Perturbator ): \"\"\" UniformPerturbator is a Perturbator that adds a constant effect to the target column of the treated instances. \"\"\" def __init__ ( self , average_effect : Optional [ float ] = None , target_col : str = \"target\" , treatment_col : str = \"treatment\" , treatment : str = \"B\" , ): super () . __init__ ( average_effect , target_col , treatment_col , treatment ) logging . warning ( \"UniformPerturbator is deprecated and will be removed in future versions. \" \"Use ConstantPerturbator instead.\" ) def perturbate ( self , df : pd . DataFrame , average_effect : Optional [ float ] = None ) -> pd . DataFrame : \"\"\" Usage: ```python from cluster_experiments.perturbator import UniformPerturbator import pandas as pd df = pd.DataFrame({\"target\": [1, 2, 3], \"treatment\": [\"A\", \"B\", \"A\"]}) perturbator = UniformPerturbator() perturbator.perturbate(df, average_effect=1) ``` \"\"\" df = df . copy () . reset_index ( drop = True ) average_effect = self . get_average_effect ( average_effect ) df = self . apply_additive_effect ( df , average_effect ) return df def apply_additive_effect ( self , df : pd . DataFrame , effect : Union [ float , np . ndarray ] ) -> pd . DataFrame : df . loc [ df [ self . treatment_col ] == self . treatment , self . target_col ] += effect return df perturbate ( self , df , average_effect = None ) \u00b6 Usage: from cluster_experiments.perturbator import UniformPerturbator import pandas as pd df = pd . DataFrame ({ \"target\" : [ 1 , 2 , 3 ], \"treatment\" : [ \"A\" , \"B\" , \"A\" ]}) perturbator = UniformPerturbator () perturbator . perturbate ( df , average_effect = 1 ) Source code in cluster_experiments/perturbator.py def perturbate ( self , df : pd . DataFrame , average_effect : Optional [ float ] = None ) -> pd . DataFrame : \"\"\" Usage: ```python from cluster_experiments.perturbator import UniformPerturbator import pandas as pd df = pd.DataFrame({\"target\": [1, 2, 3], \"treatment\": [\"A\", \"B\", \"A\"]}) perturbator = UniformPerturbator() perturbator.perturbate(df, average_effect=1) ``` \"\"\" df = df . copy () . reset_index ( drop = True ) average_effect = self . get_average_effect ( average_effect ) df = self . apply_additive_effect ( df , average_effect ) return df","title":"Perturbators"},{"location":"api/perturbator.html#from-cluster_experimentsperturbator-import","text":"","title":"from cluster_experiments.perturbator import *"},{"location":"api/perturbator.html#cluster_experiments.perturbator.BetaRelativePerturbator","text":"A stochastic Perturbator for continuous targets that applies a sampled effect from a scaled Beta distribution. It applies the effect multiplicatively. The sampled effect is defined for values in the specified range (range_min, range_max). It's recommended to set -10 in a \"symmetric way\" around 0, such that log(1 + range_min) = -log(1 + range_max). This ensures to have an \"symmetric range\" of perturbations that relatively decrease the target as perturbations that relatively increase the target. By \"symmetry\" of relative effects we mean that for an effect c > 0, an increase of the target t via t*(1 + c) is \"symmetric\" to a decrease of t via t/(1 + c). For example, an increase of 5x (i.e. by +400%, corresponding to c_inc=4) is \"symmetric\" to a decrease of 5x (i.e. a decrease of -80%, corresponding to c_dec = -0.8). In this case, 1 + c_dec = 1/(1 + c_inc), so the relative effects c_inc and c_dec are \"symmetric\" in the sense that they are inverse to each other. The number of samples with 0 as target remains unchanged. The stochastic effect is sampled from a beta distribution with parameters mean and variance, which is linearly scaled to the range (range_min, range_max). If variance is not provided, the variance is abs(mean). target -> target * (1 + effect), where effect ~ Beta(a, b) The common beta parameters are derived from the mean and scale parameters, combined with linear transformations to ensure the support in the given range. The resulting beta parameters are scaled by abs(mu) to narrow the beta distribution around the mean. mu_transformed <- (mu - range_min) / (range_max - range_min) scale_transformed <- (scale - range_min) / (range_max - range_min) a <- mu_transformed / (scale_transformed * scale_transformed) b <- (1-mu_transformed) / (scale_transformed * scale_transformed) effect_transformed ~ beta(a/abs(mu), b/abs(mu)) effect = effect_transformed * (range_max - range_min) + range_min Parameters: Name Type Description Default average_effect Optional[float] the average effect of the treatment. Defaults to None. None target_col str name of the target_col to use as the outcome. Defaults to \"target\". 'target' treatment_col str the name of the column that contains the treatment. Defaults to \"treatment\". 'treatment' treatment str name of the treatment to use as the treated group. Defaults to \"B\". 'B' scale Optional[float] the scale of the effect distribution. Defaults to None. If not provided, the variance of the beta distribution is abs(mean). None range_min float the minimum value of the target range, must be >-1. Defaults to -0.8, which allows for up to 5x decreases of the target. None range_max float the maximum value of the target range. Defaults to 4, which allows for up to 5x increases of the target. None reduce_variance Optional[bool] if True and if abs(average_effect)<1, we reduce the variance of the beta distribution by multiplying the beta parameters by 1/abs(average_effect). Defaults to None, which is equivalent to True. None Source code in cluster_experiments/perturbator.py class BetaRelativePerturbator ( NormalPerturbator , RelativePositivePerturbator ): \"\"\" A stochastic Perturbator for continuous targets that applies a sampled effect from a scaled Beta distribution. It applies the effect multiplicatively. The sampled effect is defined for values in the specified range (range_min, range_max). It's recommended to set -10 in a \"symmetric way\" around 0, such that log(1 + range_min) = -log(1 + range_max). This ensures to have an \"symmetric range\" of perturbations that relatively decrease the target as perturbations that relatively increase the target. By \"symmetry\" of relative effects we mean that for an effect c > 0, an increase of the target t via t*(1 + c) is \"symmetric\" to a decrease of t via t/(1 + c). For example, an increase of 5x (i.e. by +400%, corresponding to c_inc=4) is \"symmetric\" to a decrease of 5x (i.e. a decrease of -80%, corresponding to c_dec = -0.8). In this case, 1 + c_dec = 1/(1 + c_inc), so the relative effects c_inc and c_dec are \"symmetric\" in the sense that they are inverse to each other. The number of samples with 0 as target remains unchanged. The stochastic effect is sampled from a beta distribution with parameters mean and variance, which is linearly scaled to the range (range_min, range_max). If variance is not provided, the variance is abs(mean). ``` target -> target * (1 + effect), where effect ~ Beta(a, b) ``` The common beta parameters are derived from the mean and scale parameters, combined with linear transformations to ensure the support in the given range. The resulting beta parameters are scaled by abs(mu) to narrow the beta distribution around the mean. ``` mu_transformed <- (mu - range_min) / (range_max - range_min) scale_transformed <- (scale - range_min) / (range_max - range_min) a <- mu_transformed / (scale_transformed * scale_transformed) b <- (1-mu_transformed) / (scale_transformed * scale_transformed) effect_transformed ~ beta(a/abs(mu), b/abs(mu)) effect = effect_transformed * (range_max - range_min) + range_min ``` Arguments: average_effect (Optional[float], optional): the average effect of the treatment. Defaults to None. target_col (str, optional): name of the target_col to use as the outcome. Defaults to \"target\". treatment_col (str, optional): the name of the column that contains the treatment. Defaults to \"treatment\". treatment (str, optional): name of the treatment to use as the treated group. Defaults to \"B\". scale (Optional[float], optional): the scale of the effect distribution. Defaults to None. If not provided, the variance of the beta distribution is abs(mean). range_min (float, optional): the minimum value of the target range, must be >-1. Defaults to -0.8, which allows for up to 5x decreases of the target. range_max (float, optional): the maximum value of the target range. Defaults to 4, which allows for up to 5x increases of the target. reduce_variance (Optional[bool], optional): if True and if abs(average_effect)<1, we reduce the variance of the beta distribution by multiplying the beta parameters by 1/abs(average_effect). Defaults to None, which is equivalent to True. \"\"\" def __init__ ( self , average_effect : Optional [ float ] = None , target_col : str = \"target\" , treatment_col : str = \"treatment\" , treatment : str = \"B\" , scale : Optional [ float ] = None , range_min : Optional [ float ] = None , range_max : Optional [ float ] = None , reduce_variance : Optional [ bool ] = None , ): self . _check_range ( range_min , range_max ) super () . __init__ ( average_effect , target_col , treatment_col , treatment , scale ) self . _range_min = range_min or - 0.8 self . _range_max = range_max or 4 self . _reduce_variance = reduce_variance or True def perturbate ( self , df : pd . DataFrame , average_effect : Optional [ float ] = None ) -> pd . DataFrame : \"\"\" Usage: ```python from cluster_experiments.perturbator import BetaRelativePerturbator import pandas as pd df = pd.DataFrame({\"target\": [1, 2, 3], \"treatment\": [\"A\", \"B\", \"A\"]}) perturbator = BetaRelativePerturbator(range_min = -0.5, range_max = 2) # Increase target metric by 20% on average perturbator.perturbate(df, average_effect=0.2) ``` \"\"\" df = df . copy () . reset_index ( drop = True ) average_effect = self . get_average_effect ( average_effect ) self . check_relative_effect_bounds ( average_effect ) scale = self . get_scale ( average_effect ) self . check_relative_effect_bounds ( scale ) n = self . get_number_of_treated ( df ) sampled_effect = self . _sample_scaled_beta_effect ( average_effect , scale , n ) df = self . apply_multiplicative_effect ( df , sampled_effect ) return df @staticmethod def _check_range ( range_min : float , range_max : float ): if range_min < - 1 : raise ValueError ( f \"range_min needs to be greater than -1, got { range_min } \" ) if range_min >= range_max : raise ValueError ( f \"range_min needs to be smaller than range_max, got \" f \" { range_min = } and { range_max = } \" ) def check_relative_effect_bounds ( self , average_effect : float ) -> None : self . check_average_effect_greater_than ( average_effect , x = self . _range_min ) self . check_average_effect_smaller_than ( average_effect , x = self . _range_max ) def check_average_effect_greater_than ( self , average_effect : float , x : float ) -> Optional [ NoReturn ]: if average_effect <= x : raise ValueError ( f \"Simulated effect needs to be greater than range_min= { x } , got { average_effect } \" ) def check_average_effect_smaller_than ( self , average_effect : float , x : float ) -> Optional [ NoReturn ]: if average_effect >= x : raise ValueError ( f \"Simulated effect needs to be smaller than range_max= { x } , got { average_effect } \" ) def _reduce_variance_beta_params ( self , average_effect : float , a : float , b : float ) -> Tuple [ float , float ]: \"\"\" Multiplying the parameters of the beta distribution with a factor >1 reduces variance \"\"\" if abs ( average_effect ) < 1 : a *= 1 / abs ( average_effect ) b *= 1 / abs ( average_effect ) return a , b def _sample_scaled_beta_effect ( self , average_effect : float , scale : float , n : int ) -> np . ndarray : average_effect_inv_transf = self . _inv_transform_to_range ( average_effect ) scale_inv_transf = self . _inv_transform_to_range ( scale ) a = average_effect_inv_transf / ( scale_inv_transf * scale_inv_transf ) b = ( 1 - average_effect_inv_transf ) / ( scale_inv_transf * scale_inv_transf ) if self . _reduce_variance : a , b = self . _reduce_variance_beta_params ( average_effect , a , b ) beta = np . random . beta ( a , b , n ) return self . _transform_to_range ( beta ) def _transform_to_range ( self , x : Union [ float , np . ndarray ]): return x * ( self . _range_max - self . _range_min ) + self . _range_min def _inv_transform_to_range ( self , x : Union [ float , np . ndarray ]): return ( x - self . _range_min ) / ( self . _range_max - self . _range_min ) @classmethod def from_config ( cls , config ): \"\"\"Creates a Perturbator object from a PowerConfig object\"\"\" return cls ( average_effect = config . average_effect , target_col = config . target_col , treatment_col = config . treatment_col , treatment = config . treatment , scale = config . scale , range_min = config . range_min , range_max = config . range_max , )","title":"BetaRelativePerturbator"},{"location":"api/perturbator.html#cluster_experiments.perturbator.BetaRelativePerturbator.from_config","text":"Creates a Perturbator object from a PowerConfig object Source code in cluster_experiments/perturbator.py @classmethod def from_config ( cls , config ): \"\"\"Creates a Perturbator object from a PowerConfig object\"\"\" return cls ( average_effect = config . average_effect , target_col = config . target_col , treatment_col = config . treatment_col , treatment = config . treatment , scale = config . scale , range_min = config . range_min , range_max = config . range_max , )","title":"from_config()"},{"location":"api/perturbator.html#cluster_experiments.perturbator.BetaRelativePerturbator.perturbate","text":"Usage: from cluster_experiments.perturbator import BetaRelativePerturbator import pandas as pd df = pd . DataFrame ({ \"target\" : [ 1 , 2 , 3 ], \"treatment\" : [ \"A\" , \"B\" , \"A\" ]}) perturbator = BetaRelativePerturbator ( range_min = - 0.5 , range_max = 2 ) # Increase target metric by 20% on average perturbator . perturbate ( df , average_effect = 0.2 ) Source code in cluster_experiments/perturbator.py def perturbate ( self , df : pd . DataFrame , average_effect : Optional [ float ] = None ) -> pd . DataFrame : \"\"\" Usage: ```python from cluster_experiments.perturbator import BetaRelativePerturbator import pandas as pd df = pd.DataFrame({\"target\": [1, 2, 3], \"treatment\": [\"A\", \"B\", \"A\"]}) perturbator = BetaRelativePerturbator(range_min = -0.5, range_max = 2) # Increase target metric by 20% on average perturbator.perturbate(df, average_effect=0.2) ``` \"\"\" df = df . copy () . reset_index ( drop = True ) average_effect = self . get_average_effect ( average_effect ) self . check_relative_effect_bounds ( average_effect ) scale = self . get_scale ( average_effect ) self . check_relative_effect_bounds ( scale ) n = self . get_number_of_treated ( df ) sampled_effect = self . _sample_scaled_beta_effect ( average_effect , scale , n ) df = self . apply_multiplicative_effect ( df , sampled_effect ) return df","title":"perturbate()"},{"location":"api/perturbator.html#cluster_experiments.perturbator.BetaRelativePositivePerturbator","text":"A stochastic Perturbator for continuous, positively-defined targets that applies a sampled effect from the Beta distribution. It applies the effect multiplicatively. WARNING : the average effect is only defined for values between 0 and 1 (not included). Therefore, it only increments the target for the treated samples. The number of samples with 0 as target remains unchanged. The stochastic effect is sampled from a beta distribution with parameters mean and variance. If variance is not provided, the variance is abs(mean). Hence, the effect is bounded by 0 and 1. target -> target * (1 + effect), where effect ~ Beta(a, b); a, b > 0 and target > 0 for all samples The common beta parameters are derived from the mean and scale parameters (see how below). That's why the average effect is only defined for values between 0 and 1, otherwise one of the beta parameters would be negative or zero: a <- mu / (scale * scale) b <- (1-mu) / (scale * scale) effect ~ beta(a, b) source: https://stackoverflow.com/a/51143208 Example: a mean = 0.2 and variance = 0.1, give a = 20 and b = 80 Plot: https://www.wolframalpha.com/input?i=plot+distribution+of+beta%2820%2C+80%29 Parameters: Name Type Description Default average_effect Optional[float] the average effect of the treatment. Defaults to None. required target_col str name of the target_col to use as the outcome. Defaults to \"target\". required treatment_col str the name of the column that contains the treatment. Defaults to \"treatment\". required treatment str name of the treatment to use as the treated group. Defaults to \"B\". required scale Optional[float] the scale of the effect distribution. Defaults to None. required Source code in cluster_experiments/perturbator.py class BetaRelativePositivePerturbator ( NormalPerturbator , RelativePositivePerturbator ): \"\"\" A stochastic Perturbator for continuous, positively-defined targets that applies a sampled effect from the Beta distribution. It applies the effect multiplicatively. *WARNING*: the average effect is only defined for values between 0 and 1 (not included). Therefore, it only increments the target for the treated samples. The number of samples with 0 as target remains unchanged. The stochastic effect is sampled from a beta distribution with parameters mean and variance. If variance is not provided, the variance is abs(mean). Hence, the effect is bounded by 0 and 1. ``` target -> target * (1 + effect), where effect ~ Beta(a, b); a, b > 0 and target > 0 for all samples ``` The common beta parameters are derived from the mean and scale parameters (see how below). That's why the average effect is only defined for values between 0 and 1, otherwise one of the beta parameters would be negative or zero: ``` a <- mu / (scale * scale) b <- (1-mu) / (scale * scale) effect ~ beta(a, b) ``` source: https://stackoverflow.com/a/51143208 Example: a mean = 0.2 and variance = 0.1, give a = 20 and b = 80 Plot: https://www.wolframalpha.com/input?i=plot+distribution+of+beta%2820%2C+80%29 Arguments: average_effect (Optional[float], optional): the average effect of the treatment. Defaults to None. target_col (str, optional): name of the target_col to use as the outcome. Defaults to \"target\". treatment_col (str, optional): the name of the column that contains the treatment. Defaults to \"treatment\". treatment (str, optional): name of the treatment to use as the treated group. Defaults to \"B\". scale (Optional[float], optional): the scale of the effect distribution. Defaults to None. \"\"\" def perturbate ( self , df : pd . DataFrame , average_effect : Optional [ float ] = None ) -> pd . DataFrame : \"\"\" Usage: ```python from cluster_experiments.perturbator import BetaRelativePositivePerturbator import pandas as pd df = pd.DataFrame({\"target\": [1, 2, 3], \"treatment\": [\"A\", \"B\", \"A\"]}) perturbator = BetaRelativePositivePerturbator() # Increase target metric by 20% perturbator.perturbate(df, average_effect=0.2) # returns pd.DataFrame({\"target\": [1, 3, 3], \"treatment\": [\"A\", \"B\", \"A\"]}) ``` \"\"\" df = df . copy () . reset_index ( drop = True ) average_effect = self . get_average_effect ( average_effect ) self . check_beta_positive_effect ( df , average_effect ) scale = self . get_scale ( average_effect ) n = self . get_number_of_treated ( df ) sampled_effect = self . _sample_beta_effect ( average_effect , scale , n ) df = self . apply_multiplicative_effect ( df , sampled_effect ) return df def check_beta_positive_effect ( self , df , average_effect ): self . check_average_effect_greater_than ( average_effect , x = 0 ) self . check_average_effect_less_than ( average_effect , x = 1 ) self . check_target_is_not_negative ( df ) self . check_target_is_not_constant_zero ( df , average_effect ) def check_average_effect_less_than ( self , average_effect : float , x : float ) -> Optional [ NoReturn ]: if average_effect >= x : raise ValueError ( f \"Simulated effect needs to be less than { x * 100 : .0f } %, got \" f \" { average_effect * 100 : .1f } %\" ) def _sample_beta_effect ( self , average_effect : float , scale : float , n : int ) -> np . ndarray : a = average_effect / ( scale * scale ) b = ( 1 - average_effect ) / ( scale * scale ) return np . random . beta ( a , b , n )","title":"BetaRelativePositivePerturbator"},{"location":"api/perturbator.html#cluster_experiments.perturbator.BetaRelativePositivePerturbator.perturbate","text":"Usage: from cluster_experiments.perturbator import BetaRelativePositivePerturbator import pandas as pd df = pd . DataFrame ({ \"target\" : [ 1 , 2 , 3 ], \"treatment\" : [ \"A\" , \"B\" , \"A\" ]}) perturbator = BetaRelativePositivePerturbator () # Increase target metric by 20% perturbator . perturbate ( df , average_effect = 0.2 ) # returns pd.DataFrame({\"target\": [1, 3, 3], \"treatment\": [\"A\", \"B\", \"A\"]}) Source code in cluster_experiments/perturbator.py def perturbate ( self , df : pd . DataFrame , average_effect : Optional [ float ] = None ) -> pd . DataFrame : \"\"\" Usage: ```python from cluster_experiments.perturbator import BetaRelativePositivePerturbator import pandas as pd df = pd.DataFrame({\"target\": [1, 2, 3], \"treatment\": [\"A\", \"B\", \"A\"]}) perturbator = BetaRelativePositivePerturbator() # Increase target metric by 20% perturbator.perturbate(df, average_effect=0.2) # returns pd.DataFrame({\"target\": [1, 3, 3], \"treatment\": [\"A\", \"B\", \"A\"]}) ``` \"\"\" df = df . copy () . reset_index ( drop = True ) average_effect = self . get_average_effect ( average_effect ) self . check_beta_positive_effect ( df , average_effect ) scale = self . get_scale ( average_effect ) n = self . get_number_of_treated ( df ) sampled_effect = self . _sample_beta_effect ( average_effect , scale , n ) df = self . apply_multiplicative_effect ( df , sampled_effect ) return df","title":"perturbate()"},{"location":"api/perturbator.html#cluster_experiments.perturbator.BinaryPerturbator","text":"BinaryPerturbator is a Perturbator that adds is used to deal with binary outcome variables. It randomly selects some treated instances and flips their outcome from 0 to 1 or 1 to 0, depending on the effect being positive or negative Source code in cluster_experiments/perturbator.py class BinaryPerturbator ( Perturbator ): \"\"\" BinaryPerturbator is a Perturbator that adds is used to deal with binary outcome variables. It randomly selects some treated instances and flips their outcome from 0 to 1 or 1 to 0, depending on the effect being positive or negative \"\"\" def _sample_max ( self , df : pd . DataFrame , n : int ) -> pd . DataFrame : \"\"\"Like sample without replacement, but if you are to sample more than 100% of the data, it just returns the whole dataframe.\"\"\" if n >= len ( df ): return df return df . sample ( n = n ) def _data_checks ( self , df : pd . DataFrame , average_effect : float ) -> None : \"\"\"Check that outcome is indeed binary, and average effect is in (-1, 1)\"\"\" if set ( df [ self . target_col ] . unique ()) - { 0 , 1 }: raise ValueError ( f \"Target column must be binary, found { set ( df [ self . target_col ] . unique ()) } \" ) if average_effect > 1 or average_effect < - 1 : raise ValueError ( f \"Average effect must be in (-1, 1), found { average_effect } \" ) def perturbate ( self , df : pd . DataFrame , average_effect : Optional [ float ] = None ) -> pd . DataFrame : \"\"\" Usage: ```python from cluster_experiments.perturbator import BinaryPerturbator import pandas as pd df = pd.DataFrame({\"target\": [1, 0, 1], \"treatment\": [\"A\", \"B\", \"A\"]}) perturbator = BinaryPerturbator() perturbator.perturbate(df, average_effect=0.1) ``` \"\"\" df = df . copy () . reset_index ( drop = True ) average_effect = self . get_average_effect ( average_effect ) self . _data_checks ( df , average_effect ) from_target , to_target = 1 , 0 if average_effect > 0 : from_target , to_target = 0 , 1 n_transformed = abs ( int ( average_effect * len ( df . query ( self . treated_query )))) idx = list ( # Sample of negative cases in group B df . query ( f \" { self . target_col } == { from_target } & { self . treated_query } \" ) . pipe ( self . _sample_max , n = n_transformed ) . index . drop_duplicates () ) df . loc [ idx , self . target_col ] = to_target return df","title":"BinaryPerturbator"},{"location":"api/perturbator.html#cluster_experiments.perturbator.BinaryPerturbator.perturbate","text":"Usage: from cluster_experiments.perturbator import BinaryPerturbator import pandas as pd df = pd . DataFrame ({ \"target\" : [ 1 , 0 , 1 ], \"treatment\" : [ \"A\" , \"B\" , \"A\" ]}) perturbator = BinaryPerturbator () perturbator . perturbate ( df , average_effect = 0.1 ) Source code in cluster_experiments/perturbator.py def perturbate ( self , df : pd . DataFrame , average_effect : Optional [ float ] = None ) -> pd . DataFrame : \"\"\" Usage: ```python from cluster_experiments.perturbator import BinaryPerturbator import pandas as pd df = pd.DataFrame({\"target\": [1, 0, 1], \"treatment\": [\"A\", \"B\", \"A\"]}) perturbator = BinaryPerturbator() perturbator.perturbate(df, average_effect=0.1) ``` \"\"\" df = df . copy () . reset_index ( drop = True ) average_effect = self . get_average_effect ( average_effect ) self . _data_checks ( df , average_effect ) from_target , to_target = 1 , 0 if average_effect > 0 : from_target , to_target = 0 , 1 n_transformed = abs ( int ( average_effect * len ( df . query ( self . treated_query )))) idx = list ( # Sample of negative cases in group B df . query ( f \" { self . target_col } == { from_target } & { self . treated_query } \" ) . pipe ( self . _sample_max , n = n_transformed ) . index . drop_duplicates () ) df . loc [ idx , self . target_col ] = to_target return df","title":"perturbate()"},{"location":"api/perturbator.html#cluster_experiments.perturbator.ConstantPerturbator","text":"ConstantPerturbator is a Perturbator that adds a constant effect to the target column of the treated instances. Source code in cluster_experiments/perturbator.py class ConstantPerturbator ( Perturbator ): \"\"\" ConstantPerturbator is a Perturbator that adds a constant effect to the target column of the treated instances. \"\"\" def perturbate ( self , df : pd . DataFrame , average_effect : Optional [ float ] = None ) -> pd . DataFrame : \"\"\" Usage: ```python from cluster_experiments.perturbator import ConstantPerturbator import pandas as pd df = pd.DataFrame({\"target\": [1, 2, 3], \"treatment\": [\"A\", \"B\", \"A\"]}) perturbator = ConstantPerturbator() perturbator.perturbate(df, average_effect=1) ``` \"\"\" df = df . copy () . reset_index ( drop = True ) average_effect = self . get_average_effect ( average_effect ) df = self . apply_additive_effect ( df , average_effect ) return df def apply_additive_effect ( self , df : pd . DataFrame , effect : Union [ float , np . ndarray ] ) -> pd . DataFrame : df . loc [ df [ self . treatment_col ] == self . treatment , self . target_col ] += effect return df","title":"ConstantPerturbator"},{"location":"api/perturbator.html#cluster_experiments.perturbator.ConstantPerturbator.perturbate","text":"Usage: from cluster_experiments.perturbator import ConstantPerturbator import pandas as pd df = pd . DataFrame ({ \"target\" : [ 1 , 2 , 3 ], \"treatment\" : [ \"A\" , \"B\" , \"A\" ]}) perturbator = ConstantPerturbator () perturbator . perturbate ( df , average_effect = 1 ) Source code in cluster_experiments/perturbator.py def perturbate ( self , df : pd . DataFrame , average_effect : Optional [ float ] = None ) -> pd . DataFrame : \"\"\" Usage: ```python from cluster_experiments.perturbator import ConstantPerturbator import pandas as pd df = pd.DataFrame({\"target\": [1, 2, 3], \"treatment\": [\"A\", \"B\", \"A\"]}) perturbator = ConstantPerturbator() perturbator.perturbate(df, average_effect=1) ``` \"\"\" df = df . copy () . reset_index ( drop = True ) average_effect = self . get_average_effect ( average_effect ) df = self . apply_additive_effect ( df , average_effect ) return df","title":"perturbate()"},{"location":"api/perturbator.html#cluster_experiments.perturbator.NormalPerturbator","text":"The NormalPerturbator class implements a perturbator that adds a stochastic effect to the target column of the treated instances. The stochastic effect is sampled from a normal distribution with mean average_effect and variance scale. If scale is not provided, the variance is abs(average_effect). If scale is provided, a value not much bigger than the average_effect is suggested. target -> target + effect, where effect ~ Normal(average_effect, scale) Parameters: Name Type Description Default average_effect Optional[float] the average effect of the treatment. Defaults to None. None target_col str name of the target_col to use as the outcome. Defaults to \"target\". 'target' treatment_col str the name of the column that contains the treatment. Defaults to \"treatment\". 'treatment' treatment str name of the treatment to use as the treated group. Defaults to \"B\". 'B' scale Optional[float] the scale of the effect distribution. Defaults to None. None Source code in cluster_experiments/perturbator.py class NormalPerturbator ( ConstantPerturbator ): \"\"\"The NormalPerturbator class implements a perturbator that adds a stochastic effect to the target column of the treated instances. The stochastic effect is sampled from a normal distribution with mean average_effect and variance scale. If scale is not provided, the variance is abs(average_effect). If scale is provided, a value not much bigger than the average_effect is suggested. ``` target -> target + effect, where effect ~ Normal(average_effect, scale) ``` Arguments: average_effect (Optional[float], optional): the average effect of the treatment. Defaults to None. target_col (str, optional): name of the target_col to use as the outcome. Defaults to \"target\". treatment_col (str, optional): the name of the column that contains the treatment. Defaults to \"treatment\". treatment (str, optional): name of the treatment to use as the treated group. Defaults to \"B\". scale (Optional[float], optional): the scale of the effect distribution. Defaults to None. \"\"\" def __init__ ( self , average_effect : Optional [ float ] = None , target_col : str = \"target\" , treatment_col : str = \"treatment\" , treatment : str = \"B\" , scale : Optional [ float ] = None , ): super () . __init__ ( average_effect , target_col , treatment_col , treatment ) self . _scale = scale def perturbate ( self , df : pd . DataFrame , average_effect : Optional [ float ] = None ) -> pd . DataFrame : \"\"\"Perturbate with a normal effect with mean average_effect and std abs(average_effect). Arguments: df (pd.DataFrame): the dataframe to perturbate. average_effect (Optional[float], optional): the average effect. Defaults to None. Returns: pd.DataFrame: the perturbated dataframe. Usage: ```python from cluster_experiments.perturbator import NormalPerturbator import pandas as pd df = pd.DataFrame({\"target\": [1, 2, 3], \"treatment\": [\"A\", \"B\", \"A\"]}) perturbator = NormalPerturbator() perturbator.perturbate(df, average_effect=1) ``` \"\"\" df = df . copy () . reset_index ( drop = True ) average_effect = self . get_average_effect ( average_effect ) scale = self . get_scale ( average_effect ) n = self . get_number_of_treated ( df ) sampled_effect = self . _sample_normal_effect ( average_effect , scale , n ) df = self . apply_additive_effect ( df , sampled_effect ) return df def get_scale ( self , average_effect : float ) -> float : \"\"\"Get the scale of the normal distribution. If scale is not provided, the variance is abs(average_effect). Raises a ValueError if scale is not positive. \"\"\" scale = abs ( average_effect ) if self . _scale is None else self . _scale if scale <= 0 : raise ValueError ( f \"scale must be positive, got { scale } \" ) return scale def get_number_of_treated ( self , df : pd . DataFrame ) -> int : \"\"\"Get the number of treated instances in the dataframe\"\"\" return ( df [ self . treatment_col ] == self . treatment ) . sum () def _sample_normal_effect ( self , average_effect : float , scale : float , n : int ) -> np . ndarray : return np . random . normal ( average_effect , scale , n ) @classmethod def from_config ( cls , config ): \"\"\"Creates a Perturbator object from a PowerConfig object\"\"\" return cls ( average_effect = config . average_effect , target_col = config . target_col , treatment_col = config . treatment_col , treatment = config . treatment , scale = config . scale , )","title":"NormalPerturbator"},{"location":"api/perturbator.html#cluster_experiments.perturbator.NormalPerturbator.from_config","text":"Creates a Perturbator object from a PowerConfig object Source code in cluster_experiments/perturbator.py @classmethod def from_config ( cls , config ): \"\"\"Creates a Perturbator object from a PowerConfig object\"\"\" return cls ( average_effect = config . average_effect , target_col = config . target_col , treatment_col = config . treatment_col , treatment = config . treatment , scale = config . scale , )","title":"from_config()"},{"location":"api/perturbator.html#cluster_experiments.perturbator.NormalPerturbator.get_number_of_treated","text":"Get the number of treated instances in the dataframe Source code in cluster_experiments/perturbator.py def get_number_of_treated ( self , df : pd . DataFrame ) -> int : \"\"\"Get the number of treated instances in the dataframe\"\"\" return ( df [ self . treatment_col ] == self . treatment ) . sum ()","title":"get_number_of_treated()"},{"location":"api/perturbator.html#cluster_experiments.perturbator.NormalPerturbator.get_scale","text":"Get the scale of the normal distribution. If scale is not provided, the variance is abs(average_effect). Raises a ValueError if scale is not positive. Source code in cluster_experiments/perturbator.py def get_scale ( self , average_effect : float ) -> float : \"\"\"Get the scale of the normal distribution. If scale is not provided, the variance is abs(average_effect). Raises a ValueError if scale is not positive. \"\"\" scale = abs ( average_effect ) if self . _scale is None else self . _scale if scale <= 0 : raise ValueError ( f \"scale must be positive, got { scale } \" ) return scale","title":"get_scale()"},{"location":"api/perturbator.html#cluster_experiments.perturbator.NormalPerturbator.perturbate","text":"Perturbate with a normal effect with mean average_effect and std abs(average_effect). Parameters: Name Type Description Default df pd.DataFrame the dataframe to perturbate. required average_effect Optional[float] the average effect. Defaults to None. None Returns: Type Description pd.DataFrame the perturbated dataframe. Usage: from cluster_experiments.perturbator import NormalPerturbator import pandas as pd df = pd . DataFrame ({ \"target\" : [ 1 , 2 , 3 ], \"treatment\" : [ \"A\" , \"B\" , \"A\" ]}) perturbator = NormalPerturbator () perturbator . perturbate ( df , average_effect = 1 ) Source code in cluster_experiments/perturbator.py def perturbate ( self , df : pd . DataFrame , average_effect : Optional [ float ] = None ) -> pd . DataFrame : \"\"\"Perturbate with a normal effect with mean average_effect and std abs(average_effect). Arguments: df (pd.DataFrame): the dataframe to perturbate. average_effect (Optional[float], optional): the average effect. Defaults to None. Returns: pd.DataFrame: the perturbated dataframe. Usage: ```python from cluster_experiments.perturbator import NormalPerturbator import pandas as pd df = pd.DataFrame({\"target\": [1, 2, 3], \"treatment\": [\"A\", \"B\", \"A\"]}) perturbator = NormalPerturbator() perturbator.perturbate(df, average_effect=1) ``` \"\"\" df = df . copy () . reset_index ( drop = True ) average_effect = self . get_average_effect ( average_effect ) scale = self . get_scale ( average_effect ) n = self . get_number_of_treated ( df ) sampled_effect = self . _sample_normal_effect ( average_effect , scale , n ) df = self . apply_additive_effect ( df , sampled_effect ) return df","title":"perturbate()"},{"location":"api/perturbator.html#cluster_experiments.perturbator.Perturbator","text":"Abstract perturbator. Perturbators are used to simulate a fictitious effect when running a power analysis. The idea is that, when running a power analysis, we split our instances according to a RandomSplitter, and the instances that got the treatment, are perturbated with a fictional effect via the Perturbator. In order to create your own perturbator, you should create a derived class that implements the perturbate method. The perturbate method should add the average effect in the desired way and return the dataframe with the extra average effect, without affecting the initial dataframe. Keep in mind to use df = df.copy() in the first line of the perturbate method. Parameters: Name Type Description Default average_effect Optional[float] The average effect of the treatment None target_col str name of the target_col to use as the outcome 'target' treatment_col str The name of the column that contains the treatment 'treatment' treatment str name of the treatment to use as the treated group 'B' Source code in cluster_experiments/perturbator.py class Perturbator ( ABC ): \"\"\" Abstract perturbator. Perturbators are used to simulate a fictitious effect when running a power analysis. The idea is that, when running a power analysis, we split our instances according to a RandomSplitter, and the instances that got the treatment, are perturbated with a fictional effect via the Perturbator. In order to create your own perturbator, you should create a derived class that implements the perturbate method. The perturbate method should add the average effect in the desired way and return the dataframe with the extra average effect, without affecting the initial dataframe. Keep in mind to use `df = df.copy()` in the first line of the perturbate method. Arguments: average_effect: The average effect of the treatment target_col: name of the target_col to use as the outcome treatment_col: The name of the column that contains the treatment treatment: name of the treatment to use as the treated group \"\"\" def __init__ ( self , average_effect : Optional [ float ] = None , target_col : str = \"target\" , treatment_col : str = \"treatment\" , treatment : str = \"B\" , ): self . average_effect = average_effect self . target_col = target_col self . treatment_col = treatment_col self . treatment = treatment self . treated_query = f \" { self . treatment_col } == ' { self . treatment } '\" def get_average_effect ( self , average_effect : Optional [ float ] = None ) -> float : average_effect = ( average_effect if average_effect is not None else self . average_effect ) if average_effect is None : raise ValueError ( \"average_effect must be provided, either in the constructor or in the method call\" ) return average_effect @abstractmethod def perturbate ( self , df : pd . DataFrame , average_effect : Optional [ float ] = None ) -> pd . DataFrame : \"\"\"Method to perturbate a dataframe\"\"\" @classmethod def from_config ( cls , config ): \"\"\"Creates a Perturbator object from a PowerConfig object\"\"\" return cls ( average_effect = config . average_effect , target_col = config . target_col , treatment_col = config . treatment_col , treatment = config . treatment , )","title":"Perturbator"},{"location":"api/perturbator.html#cluster_experiments.perturbator.Perturbator.from_config","text":"Creates a Perturbator object from a PowerConfig object Source code in cluster_experiments/perturbator.py @classmethod def from_config ( cls , config ): \"\"\"Creates a Perturbator object from a PowerConfig object\"\"\" return cls ( average_effect = config . average_effect , target_col = config . target_col , treatment_col = config . treatment_col , treatment = config . treatment , )","title":"from_config()"},{"location":"api/perturbator.html#cluster_experiments.perturbator.Perturbator.perturbate","text":"Method to perturbate a dataframe Source code in cluster_experiments/perturbator.py @abstractmethod def perturbate ( self , df : pd . DataFrame , average_effect : Optional [ float ] = None ) -> pd . DataFrame : \"\"\"Method to perturbate a dataframe\"\"\"","title":"perturbate()"},{"location":"api/perturbator.html#cluster_experiments.perturbator.RelativePositivePerturbator","text":"A Perturbator for continuous, positively-defined targets applies a simulated effect multiplicatively for the treated samples, ie. proportional to the target value for each sample. The number of samples with 0 as target remains unchanged. target -> target * (1 + average_effect), where -1 < average_effect < inf and target > 0 for all samples Source code in cluster_experiments/perturbator.py class RelativePositivePerturbator ( Perturbator ): \"\"\" A Perturbator for continuous, positively-defined targets applies a simulated effect multiplicatively for the treated samples, ie. proportional to the target value for each sample. The number of samples with 0 as target remains unchanged. ``` target -> target * (1 + average_effect), where -1 < average_effect < inf and target > 0 for all samples ``` \"\"\" def perturbate ( self , df : pd . DataFrame , average_effect : Optional [ float ] = None ) -> pd . DataFrame : \"\"\" Usage: ```python from cluster_experiments.perturbator import RelativePositivePerturbator import pandas as pd df = pd.DataFrame({\"target\": [1, 2, 3], \"treatment\": [\"A\", \"B\", \"A\"]}) perturbator = RelativePositivePerturbator() # Increase target metric by 50% perturbator.perturbate(df, average_effect=0.5) # returns pd.DataFrame({\"target\": [1, 3, 3], \"treatment\": [\"A\", \"B\", \"A\"]}) ``` \"\"\" df = df . copy () . reset_index ( drop = True ) average_effect = self . get_average_effect ( average_effect ) self . check_relative_positive_effect ( df , average_effect ) df = self . apply_multiplicative_effect ( df , average_effect ) return df def check_relative_positive_effect ( self , df : pd . DataFrame , average_effect : float ) -> None : self . check_average_effect_greater_than ( average_effect , x =- 1 ) self . check_target_is_not_negative ( df ) self . check_target_is_not_constant_zero ( df , average_effect ) def check_target_is_not_constant_zero ( self , df : pd . DataFrame , average_effect : float ) -> Optional [ NoReturn ]: treatment_zeros = ( ( df [ self . treatment_col ] != self . treatment ) | ( df [ self . target_col ] == 0 ) ) . mean () if 1.0 == treatment_zeros : raise ValueError ( f \"All treatment samples have { self . target_col } = 0, relative effect \" f \" { average_effect } will have no effect\" ) def check_target_is_not_negative ( self , df : pd . DataFrame ) -> Optional [ NoReturn ]: if any ( df [ self . target_col ] < 0 ): raise ValueError ( f \"All { self . target_col } values need to be positive or 0, \" f \"got { df [ self . target_col ] . min () } \" ) def check_average_effect_greater_than ( self , average_effect : float , x : float ) -> Optional [ NoReturn ]: if average_effect <= x : raise ValueError ( f \"Simulated effect needs to be greater than { x * 100 : .0f } %, got \" f \" { average_effect * 100 : .1f } %\" ) def apply_multiplicative_effect ( self , df : pd . DataFrame , effect : Union [ float , np . ndarray ] ) -> pd . DataFrame : df . loc [ df [ self . treatment_col ] == self . treatment , self . target_col ] *= 1 + effect return df","title":"RelativePositivePerturbator"},{"location":"api/perturbator.html#cluster_experiments.perturbator.RelativePositivePerturbator.perturbate","text":"Usage: from cluster_experiments.perturbator import RelativePositivePerturbator import pandas as pd df = pd . DataFrame ({ \"target\" : [ 1 , 2 , 3 ], \"treatment\" : [ \"A\" , \"B\" , \"A\" ]}) perturbator = RelativePositivePerturbator () # Increase target metric by 50% perturbator . perturbate ( df , average_effect = 0.5 ) # returns pd.DataFrame({\"target\": [1, 3, 3], \"treatment\": [\"A\", \"B\", \"A\"]}) Source code in cluster_experiments/perturbator.py def perturbate ( self , df : pd . DataFrame , average_effect : Optional [ float ] = None ) -> pd . DataFrame : \"\"\" Usage: ```python from cluster_experiments.perturbator import RelativePositivePerturbator import pandas as pd df = pd.DataFrame({\"target\": [1, 2, 3], \"treatment\": [\"A\", \"B\", \"A\"]}) perturbator = RelativePositivePerturbator() # Increase target metric by 50% perturbator.perturbate(df, average_effect=0.5) # returns pd.DataFrame({\"target\": [1, 3, 3], \"treatment\": [\"A\", \"B\", \"A\"]}) ``` \"\"\" df = df . copy () . reset_index ( drop = True ) average_effect = self . get_average_effect ( average_effect ) self . check_relative_positive_effect ( df , average_effect ) df = self . apply_multiplicative_effect ( df , average_effect ) return df","title":"perturbate()"},{"location":"api/perturbator.html#cluster_experiments.perturbator.SegmentedBetaRelativePerturbator","text":"A stochastic Perturbator for continuous targets that applies a sampled effect from the Beta distribution. It applies the effect multiplicatively and based on given segments. For each segment, the average segment effect is sampled from a beta distribution with support in (0, 1). Within each segment, the individual effects are sampled from a beta distribution with mean equal to the segment average effect and support in (range_min, range_max). The number of samples with 0 as target remains unchanged. For additional details and recommendations on the parameters, see the documentation for the BetaRelativePerturbator class. Parameters: Name Type Description Default average_effect Optional[float] the average effect of the treatment. Defaults to None. None target_col str name of the target_col to use as the outcome. Defaults to \"target\". 'target' treatment_col str the name of the column that contains the treatment. Defaults to \"treatment\". 'treatment' treatment str name of the treatment to use as the treated group. Defaults to \"B\". 'B' scale Optional[float] the scale of the effect distribution. Defaults to None. None range_min float the minimum value of the target range, must be >-1. Defaults to -0.8, which allows for up to 5x decreases of the target. None range_max float the maximum value of the target range. Defaults to 4, which allows for up to 5x increases of the target. None segment_cols Optional[List[str]] the columns to use for segmenting. Defaults to None. required Source code in cluster_experiments/perturbator.py class SegmentedBetaRelativePerturbator ( BetaRelativePositivePerturbator ): \"\"\" A stochastic Perturbator for continuous targets that applies a sampled effect from the Beta distribution. It applies the effect multiplicatively and based on given segments. For each segment, the average segment effect is sampled from a beta distribution with support in (0, 1). Within each segment, the individual effects are sampled from a beta distribution with mean equal to the segment average effect and support in (range_min, range_max). The number of samples with 0 as target remains unchanged. For additional details and recommendations on the parameters, see the documentation for the `BetaRelativePerturbator` class. Arguments: average_effect (Optional[float], optional): the average effect of the treatment. Defaults to None. target_col (str, optional): name of the target_col to use as the outcome. Defaults to \"target\". treatment_col (str, optional): the name of the column that contains the treatment. Defaults to \"treatment\". treatment (str, optional): name of the treatment to use as the treated group. Defaults to \"B\". scale (Optional[float], optional): the scale of the effect distribution. Defaults to None. range_min (float, optional): the minimum value of the target range, must be >-1. Defaults to -0.8, which allows for up to 5x decreases of the target. range_max (float, optional): the maximum value of the target range. Defaults to 4, which allows for up to 5x increases of the target. segment_cols (Optional[List[str]], optional): the columns to use for segmenting. Defaults to None. \"\"\" def __init__ ( self , segment_cols : List [ str ], average_effect : Optional [ float ] = None , target_col : str = \"target\" , treatment_col : str = \"treatment\" , treatment : str = \"B\" , scale : Optional [ float ] = None , range_min : Optional [ float ] = None , range_max : Optional [ float ] = None , ): super () . __init__ ( average_effect , target_col , treatment_col , treatment , scale ) self . _range_min = range_min or - 0.8 self . _range_max = range_max or 4 self . _segment_cols = segment_cols self . segment_col = self . _get_segment_col_name ( segment_cols ) @staticmethod def _get_segment_col_name ( segment_cols : List [ str ]): if not isinstance ( segment_cols , list ): raise ValueError ( f \"segment_cols must be of type List[str], got type { type ( segment_cols ) } \" ) return \"_cluster_\" + \"_\" . join ( segment_cols ) def _set_segment_col_values ( self , df : pd . DataFrame ): if self . segment_col in df . columns : raise ValueError ( f \"Cannot use { self . segment_col =} as perturbator clustering \" f \"column, as it already exists in the input dataframe!\" ) return df . copy () . assign ( ** { self . segment_col : df [ self . _segment_cols ] . astype ( str ) . sum ( axis = 1 )} ) def get_cluster_perturbator_fixed_params ( self , average_effect : Optional [ float ] = None ) -> Dict [ str , Any ]: average_effect = self . get_average_effect ( average_effect ) self . check_average_effect_greater_than ( average_effect , x = 0 ) self . check_average_effect_less_than ( average_effect , x = 1 ) scale = self . get_scale ( average_effect ) return { \"average_effect\" : average_effect , \"scale\" : scale , } def get_cluster_perturbator ( self , ** kwargs ) -> Perturbator : sampled_effect = self . _sample_beta_effect ( kwargs [ \"average_effect\" ], kwargs [ \"scale\" ], 1 ) cluster_perturbator = BetaRelativePerturbator ( average_effect = sampled_effect , target_col = self . target_col , treatment_col = self . treatment_col , treatment = self . treatment , range_min = self . _range_min , range_max = self . _range_max , ) return cluster_perturbator def perturbate ( self , df : pd . DataFrame , average_effect : Optional [ float ] = None , ) -> pd . DataFrame : df = df . copy () . reset_index ( drop = True ) df = self . _set_segment_col_values ( df ) cluster_perturbator_params = self . get_cluster_perturbator_fixed_params ( average_effect ) df_perturbed = pd . concat ( [ self . get_cluster_perturbator ( ** cluster_perturbator_params ) . perturbate ( df = df [ df [ self . segment_col ] == cluster ] . copy () ) for cluster in df [ self . segment_col ] . unique () ] ) return df_perturbed . drop ( columns = self . segment_col ) . reset_index ( drop = True ) @classmethod def from_config ( cls , config ): \"\"\"Creates a Perturbator object from a PowerConfig object\"\"\" return cls ( average_effect = config . average_effect , target_col = config . target_col , treatment_col = config . treatment_col , treatment = config . treatment , scale = config . scale , range_min = config . range_min , range_max = config . range_max , segment_cols = config . segment_cols , )","title":"SegmentedBetaRelativePerturbator"},{"location":"api/perturbator.html#cluster_experiments.perturbator.SegmentedBetaRelativePerturbator.from_config","text":"Creates a Perturbator object from a PowerConfig object Source code in cluster_experiments/perturbator.py @classmethod def from_config ( cls , config ): \"\"\"Creates a Perturbator object from a PowerConfig object\"\"\" return cls ( average_effect = config . average_effect , target_col = config . target_col , treatment_col = config . treatment_col , treatment = config . treatment , scale = config . scale , range_min = config . range_min , range_max = config . range_max , segment_cols = config . segment_cols , )","title":"from_config()"},{"location":"api/perturbator.html#cluster_experiments.perturbator.SegmentedBetaRelativePerturbator.perturbate","text":"Usage: from cluster_experiments.perturbator import BetaRelativePositivePerturbator import pandas as pd df = pd . DataFrame ({ \"target\" : [ 1 , 2 , 3 ], \"treatment\" : [ \"A\" , \"B\" , \"A\" ]}) perturbator = BetaRelativePositivePerturbator () # Increase target metric by 20% perturbator . perturbate ( df , average_effect = 0.2 ) # returns pd.DataFrame({\"target\": [1, 3, 3], \"treatment\": [\"A\", \"B\", \"A\"]}) Source code in cluster_experiments/perturbator.py def perturbate ( self , df : pd . DataFrame , average_effect : Optional [ float ] = None , ) -> pd . DataFrame : df = df . copy () . reset_index ( drop = True ) df = self . _set_segment_col_values ( df ) cluster_perturbator_params = self . get_cluster_perturbator_fixed_params ( average_effect ) df_perturbed = pd . concat ( [ self . get_cluster_perturbator ( ** cluster_perturbator_params ) . perturbate ( df = df [ df [ self . segment_col ] == cluster ] . copy () ) for cluster in df [ self . segment_col ] . unique () ] ) return df_perturbed . drop ( columns = self . segment_col ) . reset_index ( drop = True )","title":"perturbate()"},{"location":"api/perturbator.html#cluster_experiments.perturbator.UniformPerturbator","text":"UniformPerturbator is a Perturbator that adds a constant effect to the target column of the treated instances. Source code in cluster_experiments/perturbator.py class UniformPerturbator ( Perturbator ): \"\"\" UniformPerturbator is a Perturbator that adds a constant effect to the target column of the treated instances. \"\"\" def __init__ ( self , average_effect : Optional [ float ] = None , target_col : str = \"target\" , treatment_col : str = \"treatment\" , treatment : str = \"B\" , ): super () . __init__ ( average_effect , target_col , treatment_col , treatment ) logging . warning ( \"UniformPerturbator is deprecated and will be removed in future versions. \" \"Use ConstantPerturbator instead.\" ) def perturbate ( self , df : pd . DataFrame , average_effect : Optional [ float ] = None ) -> pd . DataFrame : \"\"\" Usage: ```python from cluster_experiments.perturbator import UniformPerturbator import pandas as pd df = pd.DataFrame({\"target\": [1, 2, 3], \"treatment\": [\"A\", \"B\", \"A\"]}) perturbator = UniformPerturbator() perturbator.perturbate(df, average_effect=1) ``` \"\"\" df = df . copy () . reset_index ( drop = True ) average_effect = self . get_average_effect ( average_effect ) df = self . apply_additive_effect ( df , average_effect ) return df def apply_additive_effect ( self , df : pd . DataFrame , effect : Union [ float , np . ndarray ] ) -> pd . DataFrame : df . loc [ df [ self . treatment_col ] == self . treatment , self . target_col ] += effect return df","title":"UniformPerturbator"},{"location":"api/perturbator.html#cluster_experiments.perturbator.UniformPerturbator.perturbate","text":"Usage: from cluster_experiments.perturbator import UniformPerturbator import pandas as pd df = pd . DataFrame ({ \"target\" : [ 1 , 2 , 3 ], \"treatment\" : [ \"A\" , \"B\" , \"A\" ]}) perturbator = UniformPerturbator () perturbator . perturbate ( df , average_effect = 1 ) Source code in cluster_experiments/perturbator.py def perturbate ( self , df : pd . DataFrame , average_effect : Optional [ float ] = None ) -> pd . DataFrame : \"\"\" Usage: ```python from cluster_experiments.perturbator import UniformPerturbator import pandas as pd df = pd.DataFrame({\"target\": [1, 2, 3], \"treatment\": [\"A\", \"B\", \"A\"]}) perturbator = UniformPerturbator() perturbator.perturbate(df, average_effect=1) ``` \"\"\" df = df . copy () . reset_index ( drop = True ) average_effect = self . get_average_effect ( average_effect ) df = self . apply_additive_effect ( df , average_effect ) return df","title":"perturbate()"},{"location":"api/power_analysis.html","text":"from cluster_experiments.power_analysis import * \u00b6 NormalPowerAnalysis \u00b6 Class used to run Power analysis, using the central limit theorem to estimate power based on standard errors of the estimator, and the fact that the coefficients of a regression are normally distributed. It does so by running simulations. In each simulation: 1. Assign treatment to dataframe randomly 2. Add pre-experiment data if needed 3. Get standard error from analysis Finally it returns the power of the analysis by counting how many times the effect was detected. Parameters: Name Type Description Default splitter RandomSplitter RandomSplitter class to randomly assign treatment to dataframe. required analysis ExperimentAnalysis ExperimentAnalysis class to use for analysis. required cupac_model Optional[sklearn.base.BaseEstimator] Sklearn estimator class to add pre-experiment data to dataframe. If None, no pre-experiment data will be added. None target_col str Name of the column with the outcome variable. 'target' treatment_col str Name of the column with the treatment variable. 'treatment' treatment str value of treatment_col considered to be treatment (not control) 'B' control str value of treatment_col considered to be control (not treatment) 'A' n_simulations int Number of simulations to run. 100 alpha float Significance level. 0.05 features_cupac_model Optional[List[str]] Covariates to be used in cupac model None seed Optional[int] Optional. Seed to use for the splitter. None Usage: from datetime import date import numpy as np import pandas as pd from cluster_experiments.experiment_analysis import GeeExperimentAnalysis from cluster_experiments.power_analysis import NormalPowerAnalysis from cluster_experiments.random_splitter import ClusteredSplitter N = 1_000 users = [ f \"User { i } \" for i in range ( 1000 )] clusters = [ f \"Cluster { i } \" for i in range ( 100 )] dates = [ f \" { date ( 2022 , 1 , i ) : %Y-%m-%d } \" for i in range ( 1 , 32 )] df = pd . DataFrame ( { \"cluster\" : np . random . choice ( clusters , size = N ), \"target\" : np . random . normal ( 0 , 1 , size = N ), \"user\" : np . random . choice ( users , size = N ), \"date\" : np . random . choice ( dates , size = N ), } ) experiment_dates = [ f \" { date ( 2022 , 1 , i ) : %Y-%m-%d } \" for i in range ( 15 , 32 )] sw = ClusteredSplitter ( cluster_cols = [ \"cluster\" , \"date\" ], ) analysis = GeeExperimentAnalysis ( cluster_cols = [ \"cluster\" , \"date\" ], ) pw = NormalPowerAnalysis ( splitter = sw , analysis = analysis , n_simulations = 50 ) power = pw . power_analysis ( df , average_effect = 0.1 ) print ( f \" { power = } \" ) Source code in cluster_experiments/power_analysis.py class NormalPowerAnalysis : \"\"\" Class used to run Power analysis, using the central limit theorem to estimate power based on standard errors of the estimator, and the fact that the coefficients of a regression are normally distributed. It does so by running simulations. In each simulation: 1. Assign treatment to dataframe randomly 2. Add pre-experiment data if needed 3. Get standard error from analysis Finally it returns the power of the analysis by counting how many times the effect was detected. Args: splitter: RandomSplitter class to randomly assign treatment to dataframe. analysis: ExperimentAnalysis class to use for analysis. cupac_model: Sklearn estimator class to add pre-experiment data to dataframe. If None, no pre-experiment data will be added. target_col: Name of the column with the outcome variable. treatment_col: Name of the column with the treatment variable. treatment: value of treatment_col considered to be treatment (not control) control: value of treatment_col considered to be control (not treatment) n_simulations: Number of simulations to run. alpha: Significance level. features_cupac_model: Covariates to be used in cupac model seed: Optional. Seed to use for the splitter. Usage: ```python from datetime import date import numpy as np import pandas as pd from cluster_experiments.experiment_analysis import GeeExperimentAnalysis from cluster_experiments.power_analysis import NormalPowerAnalysis from cluster_experiments.random_splitter import ClusteredSplitter N = 1_000 users = [f\"User {i}\" for i in range(1000)] clusters = [f\"Cluster {i}\" for i in range(100)] dates = [f\"{date(2022, 1, i):%Y-%m-%d}\" for i in range(1, 32)] df = pd.DataFrame( { \"cluster\": np.random.choice(clusters, size=N), \"target\": np.random.normal(0, 1, size=N), \"user\": np.random.choice(users, size=N), \"date\": np.random.choice(dates, size=N), } ) experiment_dates = [f\"{date(2022, 1, i):%Y-%m-%d}\" for i in range(15, 32)] sw = ClusteredSplitter( cluster_cols=[\"cluster\", \"date\"], ) analysis = GeeExperimentAnalysis( cluster_cols=[\"cluster\", \"date\"], ) pw = NormalPowerAnalysis( splitter=sw, analysis=analysis, n_simulations=50 ) power = pw.power_analysis(df, average_effect=0.1) print(f\"{power = }\") ``` \"\"\" def __init__ ( self , splitter : RandomSplitter , analysis : ExperimentAnalysis , cupac_model : Optional [ BaseEstimator ] = None , target_col : str = \"target\" , treatment_col : str = \"treatment\" , treatment : str = \"B\" , control : str = \"A\" , n_simulations : int = 100 , alpha : float = 0.05 , features_cupac_model : Optional [ List [ str ]] = None , seed : Optional [ int ] = None , hypothesis : str = \"two-sided\" , time_col : Optional [ str ] = None , ): self . splitter = splitter self . analysis = analysis self . n_simulations = n_simulations self . target_col = target_col self . treatment = treatment self . control = control self . treatment_col = treatment_col self . alpha = alpha self . hypothesis = hypothesis self . time_col = time_col self . cupac_handler = CupacHandler ( cupac_model = cupac_model , target_col = target_col , features_cupac_model = features_cupac_model , ) if seed is not None : random . seed ( seed ) # seed for splitter np . random . seed ( seed ) # numpy seed # may need to seed other stochasticity sources if added self . check_inputs () def _split ( self , df : pd . DataFrame ) -> pd . DataFrame : \"\"\" Split dataframe. Args: df: Dataframe with outcome variable \"\"\" treatment_df = self . splitter . assign_treatment_df ( df ) self . log_nulls ( treatment_df ) treatment_df = treatment_df . query ( f \" { self . treatment_col } .notnull()\" , engine = \"python\" ) . query ( f \" { self . treatment_col } .isin([' { self . treatment } ', ' { self . control } '])\" , engine = \"python\" , ) return treatment_df def _get_standard_error ( self , df : pd . DataFrame , n_simulations : int , verbose : bool , ) -> Generator [ float , None , None ]: for _ in tqdm ( range ( n_simulations ), disable = not verbose ): split_df = self . _split ( df ) yield self . analysis . get_standard_error ( split_df ) def _normal_power_calculation ( self , alpha : float , std_error : float , average_effect : float ) -> float : \"\"\"Returns the power of the analysis using the normal distribution. Arguments: alpha: significance level std_error: standard error of the analysis average_effect: effect size of the analysis \"\"\" if HypothesisEntries ( self . analysis . hypothesis ) == HypothesisEntries . LESS : z_alpha = norm . ppf ( alpha ) return float ( norm . cdf ( z_alpha - average_effect / std_error )) if HypothesisEntries ( self . analysis . hypothesis ) == HypothesisEntries . GREATER : z_alpha = norm . ppf ( 1 - alpha ) return 1 - float ( norm . cdf ( z_alpha - average_effect / std_error )) if HypothesisEntries ( self . analysis . hypothesis ) == HypothesisEntries . TWO_SIDED : z_alpha = norm . ppf ( 1 - alpha / 2 ) norm_cdf_right = norm . cdf ( z_alpha - average_effect / std_error ) norm_cdf_left = norm . cdf ( - z_alpha - average_effect / std_error ) return float ( norm_cdf_left + ( 1 - norm_cdf_right )) raise ValueError ( f \" { self . analysis . hypothesis } is not a valid HypothesisEntries\" ) def _normal_mde_calculation ( self , alpha : float , std_error : float , power : float ) -> float : \"\"\" Returns the minimum detectable effect of the analysis using the normal distribution. Args: alpha: Significance level. std_error: Standard error of the analysis. power: Power of the analysis. \"\"\" if HypothesisEntries ( self . analysis . hypothesis ) == HypothesisEntries . LESS : z_alpha = norm . ppf ( alpha ) z_beta = norm . ppf ( 1 - power ) elif HypothesisEntries ( self . analysis . hypothesis ) == HypothesisEntries . GREATER : z_alpha = norm . ppf ( 1 - alpha ) z_beta = norm . ppf ( power ) elif HypothesisEntries ( self . analysis . hypothesis ) == HypothesisEntries . TWO_SIDED : # we are neglecting norm_cdf_left z_alpha = norm . ppf ( 1 - alpha / 2 ) z_beta = norm . ppf ( power ) else : raise ValueError ( f \" { self . analysis . hypothesis } is not a valid HypothesisEntries\" ) return float ( z_alpha + z_beta ) * std_error def mde_power_line ( self , df : pd . DataFrame , pre_experiment_df : Optional [ pd . DataFrame ] = None , verbose : bool = False , powers : Iterable [ float ] = (), n_simulations : Optional [ int ] = None , alpha : Optional [ float ] = None , ) -> Dict [ float , float ]: \"\"\" Returns the minimum detectable effect of the analysis. Args: df: Dataframe with outcome and treatment variables. pre_experiment_df: Dataframe with pre-experiment data. verbose: Whether to show progress bar. power: Power of the analysis. n_simulations: Number of simulations to run. alpha: Significance level. \"\"\" alpha = self . alpha if alpha is None else alpha std_error = self . _get_average_standard_error ( df = df , pre_experiment_df = pre_experiment_df , verbose = verbose , n_simulations = n_simulations , ) return { power : self . _normal_mde_calculation ( alpha = alpha , std_error = std_error , power = power ) for power in powers } def mde ( self , df : pd . DataFrame , pre_experiment_df : Optional [ pd . DataFrame ] = None , verbose : bool = False , power : float = 0.8 , n_simulations : Optional [ int ] = None , alpha : Optional [ float ] = None , ) -> float : \"\"\" Returns the minimum detectable effect of the analysis. Args: df: Dataframe with outcome and treatment variables. pre_experiment_df: Dataframe with pre-experiment data. verbose: Whether to show progress bar. power: Power of the analysis. n_simulations: Number of simulations to run. alpha: Significance level. \"\"\" return self . mde_power_line ( df = df , pre_experiment_df = pre_experiment_df , verbose = verbose , powers = [ power ], n_simulations = n_simulations , alpha = alpha , )[ power ] def _get_average_standard_error ( self , df : pd . DataFrame , pre_experiment_df : Optional [ pd . DataFrame ] = None , verbose : bool = False , n_simulations : Optional [ int ] = None , ) -> float : \"\"\" Gets standard error to be used in normal power calculation. Args: df: Dataframe with outcome and treatment variables. pre_experiment_df: Dataframe with pre-experiment data. verbose: Whether to show progress bar. average_effects: Average effects to test. n_simulations: Number of simulations to run. alpha: Significance level. \"\"\" n_simulations = self . n_simulations if n_simulations is None else n_simulations df = df . copy () df = self . cupac_handler . add_covariates ( df , pre_experiment_df ) std_errors = list ( self . _get_standard_error ( df , n_simulations , verbose )) std_error_mean = float ( np . mean ( std_errors )) return std_error_mean def run_average_standard_error ( self , df : pd . DataFrame , pre_experiment_df : Optional [ pd . DataFrame ] = None , verbose : bool = False , n_simulations : Optional [ int ] = None , experiment_length : Iterable [ int ] = (), ) -> Generator [ Tuple [ float , int ], None , None ]: \"\"\" Run power analysis by simulation, using standard errors from the analysis. Args: df: Dataframe with outcome and treatment variables. pre_experiment_df: Dataframe with pre-experiment data. verbose: Whether to show progress bar. n_simulations: Number of simulations to run. experiment_length: Length of the experiment in days. \"\"\" n_simulations = self . n_simulations if n_simulations is None else n_simulations for n_days in experiment_length : df_time = df . copy () experiment_start = df_time [ self . time_col ] . min () df_time = df_time . loc [ df_time [ self . time_col ] < experiment_start + pd . Timedelta ( days = n_days ) ] std_error_mean = self . _get_average_standard_error ( df = df_time , pre_experiment_df = pre_experiment_df , verbose = verbose , n_simulations = n_simulations , ) yield std_error_mean , n_days def power_time_line ( self , df : pd . DataFrame , pre_experiment_df : Optional [ pd . DataFrame ] = None , verbose : bool = False , average_effects : Iterable [ float ] = (), experiment_length : Iterable [ int ] = (), n_simulations : Optional [ int ] = None , alpha : Optional [ float ] = None , ) -> List [ Dict ]: \"\"\" Run power analysis by simulation, using standard errors from the analysis. Args: df: Dataframe with outcome and treatment variables. pre_experiment_df: Dataframe with pre-experiment data. verbose: Whether to show progress bar. average_effects: Average effects to test. experiment_length: Length of the experiment in days. n_simulations: Number of simulations to run. alpha: Significance level. \"\"\" alpha = self . alpha if alpha is None else alpha results = [] for std_error_mean , n_days in self . run_average_standard_error ( df = df , pre_experiment_df = pre_experiment_df , verbose = verbose , n_simulations = n_simulations , experiment_length = experiment_length , ): for effect in average_effects : power = self . _normal_power_calculation ( alpha = alpha , std_error = std_error_mean , average_effect = effect ) results . append ( { \"effect\" : effect , \"power\" : power , \"experiment_length\" : n_days } ) return results def mde_time_line ( self , df : pd . DataFrame , pre_experiment_df : Optional [ pd . DataFrame ] = None , verbose : bool = False , powers : Iterable [ float ] = (), experiment_length : Iterable [ int ] = (), n_simulations : Optional [ int ] = None , alpha : Optional [ float ] = None , ) -> List [ Dict ]: alpha = self . alpha if alpha is None else alpha results = [] for std_error_mean , n_days in self . run_average_standard_error ( df = df , pre_experiment_df = pre_experiment_df , verbose = verbose , n_simulations = n_simulations , experiment_length = experiment_length , ): for power in powers : mde = self . _normal_mde_calculation ( alpha = alpha , std_error = std_error_mean , power = power ) results . append ( { \"power\" : power , \"mde\" : mde , \"experiment_length\" : n_days } ) return results def power_line ( self , df : pd . DataFrame , pre_experiment_df : Optional [ pd . DataFrame ] = None , verbose : bool = False , average_effects : Iterable [ float ] = (), n_simulations : Optional [ int ] = None , alpha : Optional [ float ] = None , ) -> Dict [ float , float ]: \"\"\" Run power analysis by simulation, using standard errors from the analysis. Args: df: Dataframe with outcome and treatment variables. pre_experiment_df: Dataframe with pre-experiment data. verbose: Whether to show progress bar. average_effects: Average effects to test. n_simulations: Number of simulations to run. alpha: Significance level. \"\"\" alpha = self . alpha if alpha is None else alpha std_error_mean = self . _get_average_standard_error ( df = df , pre_experiment_df = pre_experiment_df , verbose = verbose , n_simulations = n_simulations , ) return { effect : self . _normal_power_calculation ( alpha = alpha , std_error = std_error_mean , average_effect = effect ) for effect in average_effects } def power_analysis ( self , df : pd . DataFrame , pre_experiment_df : Optional [ pd . DataFrame ] = None , verbose : bool = False , average_effect : float = 0.0 , n_simulations : Optional [ int ] = None , alpha : Optional [ float ] = None , ) -> float : \"\"\" Run power analysis by simulation, using standard errors from the analysis. Args: df: Dataframe with outcome and treatment variables. pre_experiment_df: Dataframe with pre-experiment data. verbose: Whether to show progress bar. average_effect: Average effect of treatment. n_simulations: Number of simulations to run. alpha: Significance level. \"\"\" return self . power_line ( df = df , pre_experiment_df = pre_experiment_df , verbose = verbose , average_effects = [ average_effect ], n_simulations = n_simulations , alpha = alpha , )[ average_effect ] def log_nulls ( self , df : pd . DataFrame ) -> None : \"\"\"Warns about dropping nulls in treatment column\"\"\" n_nulls = len ( df . query ( f \" { self . treatment_col } .isnull()\" , engine = \"python\" )) if n_nulls > 0 : logging . warning ( f \"There are { n_nulls } null values in treatment, dropping them\" ) @classmethod def from_dict ( cls , config_dict : dict ) -> \"NormalPowerAnalysis\" : \"\"\"Constructs PowerAnalysis from dictionary\"\"\" config = PowerConfig ( ** config_dict ) return cls . from_config ( config ) @classmethod def from_config ( cls , config : PowerConfig ) -> \"NormalPowerAnalysis\" : \"\"\"Constructs PowerAnalysis from PowerConfig\"\"\" splitter_cls = _get_mapping_key ( splitter_mapping , config . splitter ) analysis_cls = _get_mapping_key ( analysis_mapping , config . analysis ) cupac_cls = _get_mapping_key ( cupac_model_mapping , config . cupac_model ) return cls ( splitter = splitter_cls . from_config ( config ), analysis = analysis_cls . from_config ( config ), cupac_model = cupac_cls . from_config ( config ), target_col = config . target_col , treatment_col = config . treatment_col , treatment = config . treatment , control = config . control , n_simulations = config . n_simulations , alpha = config . alpha , features_cupac_model = config . features_cupac_model , seed = config . seed , hypothesis = config . hypothesis , time_col = config . time_col , ) def check_treatment_col ( self ): \"\"\"Checks consistency of treatment column\"\"\" assert ( self . analysis . treatment_col == self . treatment_col ), f \"treatment_col in analysis ( { self . analysis . treatment_col } ) must be the same as treatment_col in PowerAnalysis ( { self . treatment_col } )\" assert ( self . analysis . treatment_col == self . splitter . treatment_col ), f \"treatment_col in analysis ( { self . analysis . treatment_col } ) must be the same as treatment_col in splitter ( { self . splitter . treatment_col } )\" def check_target_col ( self ): assert ( self . analysis . target_col == self . target_col ), f \"target_col in analysis ( { self . analysis . target_col } ) must be the same as target_col in PowerAnalysis ( { self . target_col } )\" def check_treatment ( self ): assert ( self . analysis . treatment == self . treatment ), f \"treatment in analysis ( { self . analysis . treatment } ) must be the same as treatment in PowerAnalysis ( { self . treatment } )\" assert ( self . analysis . treatment in self . splitter . treatments ), f \"treatment in analysis ( { self . analysis . treatment } ) must be in treatments in splitter ( { self . splitter . treatments } )\" assert ( self . control in self . splitter . treatments ), f \"control in power analysis ( { self . control } ) must be in treatments in splitter ( { self . splitter . treatments } )\" def check_covariates ( self ): if hasattr ( self . analysis , \"covariates\" ): cupac_in_covariates = ( self . cupac_handler . cupac_outcome_name in self . analysis . covariates ) assert cupac_in_covariates or not self . cupac_handler . is_cupac , ( f \"covariates in analysis must contain { self . cupac_handler . cupac_outcome_name } if cupac_model is not None. \" f \"If you want to use cupac_model, you must add the cupac outcome to the covariates of the analysis \" f \"You may want to do covariates=[' { self . cupac_handler . cupac_outcome_name } '] in your analysis method or your config\" ) if hasattr ( self . splitter , \"cluster_cols\" ): if set ( self . analysis . covariates ) . intersection ( set ( self . splitter . cluster_cols ) ): logging . warning ( f \"covariates in analysis ( { self . analysis . covariates } ) are also cluster_cols in splitter ( { self . splitter . cluster_cols } ). \" f \"Be specially careful when using switchback splitters, since the time splitter column is being overriden\" ) def check_clusters ( self ): has_analysis_clusters = hasattr ( self . analysis , \"cluster_cols\" ) has_splitter_clusters = hasattr ( self . splitter , \"cluster_cols\" ) not_cluster_cols_cond = not has_analysis_clusters or not has_splitter_clusters assert ( not_cluster_cols_cond or self . analysis . cluster_cols == self . splitter . cluster_cols ), f \"cluster_cols in analysis ( { self . analysis . cluster_cols } ) must be the same as cluster_cols in splitter ( { self . splitter . cluster_cols } )\" assert ( has_splitter_clusters or not has_analysis_clusters or not self . analysis . cluster_cols or isinstance ( self . splitter , RepeatedSampler ) ), \"analysis has cluster_cols but splitter does not.\" assert ( has_analysis_clusters or not has_splitter_clusters or not self . splitter . cluster_cols ), \"splitter has cluster_cols but analysis does not.\" has_time_col = hasattr ( self . splitter , \"time_col\" ) assert not ( has_time_col and has_splitter_clusters and self . splitter . time_col not in self . splitter . cluster_cols ), \"in switchback splitters, time_col must be in cluster_cols\" def check_inputs ( self ): self . check_covariates () self . check_treatment_col () self . check_target_col () self . check_treatment () self . check_clusters () check_treatment_col ( self ) \u00b6 Checks consistency of treatment column Source code in cluster_experiments/power_analysis.py def check_treatment_col ( self ): \"\"\"Checks consistency of treatment column\"\"\" assert ( self . analysis . treatment_col == self . treatment_col ), f \"treatment_col in analysis ( { self . analysis . treatment_col } ) must be the same as treatment_col in PowerAnalysis ( { self . treatment_col } )\" assert ( self . analysis . treatment_col == self . splitter . treatment_col ), f \"treatment_col in analysis ( { self . analysis . treatment_col } ) must be the same as treatment_col in splitter ( { self . splitter . treatment_col } )\" from_config ( config ) classmethod \u00b6 Constructs PowerAnalysis from PowerConfig Source code in cluster_experiments/power_analysis.py @classmethod def from_config ( cls , config : PowerConfig ) -> \"NormalPowerAnalysis\" : \"\"\"Constructs PowerAnalysis from PowerConfig\"\"\" splitter_cls = _get_mapping_key ( splitter_mapping , config . splitter ) analysis_cls = _get_mapping_key ( analysis_mapping , config . analysis ) cupac_cls = _get_mapping_key ( cupac_model_mapping , config . cupac_model ) return cls ( splitter = splitter_cls . from_config ( config ), analysis = analysis_cls . from_config ( config ), cupac_model = cupac_cls . from_config ( config ), target_col = config . target_col , treatment_col = config . treatment_col , treatment = config . treatment , control = config . control , n_simulations = config . n_simulations , alpha = config . alpha , features_cupac_model = config . features_cupac_model , seed = config . seed , hypothesis = config . hypothesis , time_col = config . time_col , ) from_dict ( config_dict ) classmethod \u00b6 Constructs PowerAnalysis from dictionary Source code in cluster_experiments/power_analysis.py @classmethod def from_dict ( cls , config_dict : dict ) -> \"NormalPowerAnalysis\" : \"\"\"Constructs PowerAnalysis from dictionary\"\"\" config = PowerConfig ( ** config_dict ) return cls . from_config ( config ) log_nulls ( self , df ) \u00b6 Warns about dropping nulls in treatment column Source code in cluster_experiments/power_analysis.py def log_nulls ( self , df : pd . DataFrame ) -> None : \"\"\"Warns about dropping nulls in treatment column\"\"\" n_nulls = len ( df . query ( f \" { self . treatment_col } .isnull()\" , engine = \"python\" )) if n_nulls > 0 : logging . warning ( f \"There are { n_nulls } null values in treatment, dropping them\" ) mde ( self , df , pre_experiment_df = None , verbose = False , power = 0.8 , n_simulations = None , alpha = None ) \u00b6 Returns the minimum detectable effect of the analysis. Parameters: Name Type Description Default df DataFrame Dataframe with outcome and treatment variables. required pre_experiment_df Optional[pandas.core.frame.DataFrame] Dataframe with pre-experiment data. None verbose bool Whether to show progress bar. False power float Power of the analysis. 0.8 n_simulations Optional[int] Number of simulations to run. None alpha Optional[float] Significance level. None Source code in cluster_experiments/power_analysis.py def mde ( self , df : pd . DataFrame , pre_experiment_df : Optional [ pd . DataFrame ] = None , verbose : bool = False , power : float = 0.8 , n_simulations : Optional [ int ] = None , alpha : Optional [ float ] = None , ) -> float : \"\"\" Returns the minimum detectable effect of the analysis. Args: df: Dataframe with outcome and treatment variables. pre_experiment_df: Dataframe with pre-experiment data. verbose: Whether to show progress bar. power: Power of the analysis. n_simulations: Number of simulations to run. alpha: Significance level. \"\"\" return self . mde_power_line ( df = df , pre_experiment_df = pre_experiment_df , verbose = verbose , powers = [ power ], n_simulations = n_simulations , alpha = alpha , )[ power ] mde_power_line ( self , df , pre_experiment_df = None , verbose = False , powers = (), n_simulations = None , alpha = None ) \u00b6 Returns the minimum detectable effect of the analysis. Parameters: Name Type Description Default df DataFrame Dataframe with outcome and treatment variables. required pre_experiment_df Optional[pandas.core.frame.DataFrame] Dataframe with pre-experiment data. None verbose bool Whether to show progress bar. False power Power of the analysis. required n_simulations Optional[int] Number of simulations to run. None alpha Optional[float] Significance level. None Source code in cluster_experiments/power_analysis.py def mde_power_line ( self , df : pd . DataFrame , pre_experiment_df : Optional [ pd . DataFrame ] = None , verbose : bool = False , powers : Iterable [ float ] = (), n_simulations : Optional [ int ] = None , alpha : Optional [ float ] = None , ) -> Dict [ float , float ]: \"\"\" Returns the minimum detectable effect of the analysis. Args: df: Dataframe with outcome and treatment variables. pre_experiment_df: Dataframe with pre-experiment data. verbose: Whether to show progress bar. power: Power of the analysis. n_simulations: Number of simulations to run. alpha: Significance level. \"\"\" alpha = self . alpha if alpha is None else alpha std_error = self . _get_average_standard_error ( df = df , pre_experiment_df = pre_experiment_df , verbose = verbose , n_simulations = n_simulations , ) return { power : self . _normal_mde_calculation ( alpha = alpha , std_error = std_error , power = power ) for power in powers } power_analysis ( self , df , pre_experiment_df = None , verbose = False , average_effect = 0.0 , n_simulations = None , alpha = None ) \u00b6 Run power analysis by simulation, using standard errors from the analysis. Parameters: Name Type Description Default df DataFrame Dataframe with outcome and treatment variables. required pre_experiment_df Optional[pandas.core.frame.DataFrame] Dataframe with pre-experiment data. None verbose bool Whether to show progress bar. False average_effect float Average effect of treatment. 0.0 n_simulations Optional[int] Number of simulations to run. None alpha Optional[float] Significance level. None Source code in cluster_experiments/power_analysis.py def power_analysis ( self , df : pd . DataFrame , pre_experiment_df : Optional [ pd . DataFrame ] = None , verbose : bool = False , average_effect : float = 0.0 , n_simulations : Optional [ int ] = None , alpha : Optional [ float ] = None , ) -> float : \"\"\" Run power analysis by simulation, using standard errors from the analysis. Args: df: Dataframe with outcome and treatment variables. pre_experiment_df: Dataframe with pre-experiment data. verbose: Whether to show progress bar. average_effect: Average effect of treatment. n_simulations: Number of simulations to run. alpha: Significance level. \"\"\" return self . power_line ( df = df , pre_experiment_df = pre_experiment_df , verbose = verbose , average_effects = [ average_effect ], n_simulations = n_simulations , alpha = alpha , )[ average_effect ] power_line ( self , df , pre_experiment_df = None , verbose = False , average_effects = (), n_simulations = None , alpha = None ) \u00b6 Run power analysis by simulation, using standard errors from the analysis. Parameters: Name Type Description Default df DataFrame Dataframe with outcome and treatment variables. required pre_experiment_df Optional[pandas.core.frame.DataFrame] Dataframe with pre-experiment data. None verbose bool Whether to show progress bar. False average_effects Iterable[float] Average effects to test. () n_simulations Optional[int] Number of simulations to run. None alpha Optional[float] Significance level. None Source code in cluster_experiments/power_analysis.py def power_line ( self , df : pd . DataFrame , pre_experiment_df : Optional [ pd . DataFrame ] = None , verbose : bool = False , average_effects : Iterable [ float ] = (), n_simulations : Optional [ int ] = None , alpha : Optional [ float ] = None , ) -> Dict [ float , float ]: \"\"\" Run power analysis by simulation, using standard errors from the analysis. Args: df: Dataframe with outcome and treatment variables. pre_experiment_df: Dataframe with pre-experiment data. verbose: Whether to show progress bar. average_effects: Average effects to test. n_simulations: Number of simulations to run. alpha: Significance level. \"\"\" alpha = self . alpha if alpha is None else alpha std_error_mean = self . _get_average_standard_error ( df = df , pre_experiment_df = pre_experiment_df , verbose = verbose , n_simulations = n_simulations , ) return { effect : self . _normal_power_calculation ( alpha = alpha , std_error = std_error_mean , average_effect = effect ) for effect in average_effects } power_time_line ( self , df , pre_experiment_df = None , verbose = False , average_effects = (), experiment_length = (), n_simulations = None , alpha = None ) \u00b6 Run power analysis by simulation, using standard errors from the analysis. Parameters: Name Type Description Default df DataFrame Dataframe with outcome and treatment variables. required pre_experiment_df Optional[pandas.core.frame.DataFrame] Dataframe with pre-experiment data. None verbose bool Whether to show progress bar. False average_effects Iterable[float] Average effects to test. () experiment_length Iterable[int] Length of the experiment in days. () n_simulations Optional[int] Number of simulations to run. None alpha Optional[float] Significance level. None Source code in cluster_experiments/power_analysis.py def power_time_line ( self , df : pd . DataFrame , pre_experiment_df : Optional [ pd . DataFrame ] = None , verbose : bool = False , average_effects : Iterable [ float ] = (), experiment_length : Iterable [ int ] = (), n_simulations : Optional [ int ] = None , alpha : Optional [ float ] = None , ) -> List [ Dict ]: \"\"\" Run power analysis by simulation, using standard errors from the analysis. Args: df: Dataframe with outcome and treatment variables. pre_experiment_df: Dataframe with pre-experiment data. verbose: Whether to show progress bar. average_effects: Average effects to test. experiment_length: Length of the experiment in days. n_simulations: Number of simulations to run. alpha: Significance level. \"\"\" alpha = self . alpha if alpha is None else alpha results = [] for std_error_mean , n_days in self . run_average_standard_error ( df = df , pre_experiment_df = pre_experiment_df , verbose = verbose , n_simulations = n_simulations , experiment_length = experiment_length , ): for effect in average_effects : power = self . _normal_power_calculation ( alpha = alpha , std_error = std_error_mean , average_effect = effect ) results . append ( { \"effect\" : effect , \"power\" : power , \"experiment_length\" : n_days } ) return results run_average_standard_error ( self , df , pre_experiment_df = None , verbose = False , n_simulations = None , experiment_length = ()) \u00b6 Run power analysis by simulation, using standard errors from the analysis. Parameters: Name Type Description Default df DataFrame Dataframe with outcome and treatment variables. required pre_experiment_df Optional[pandas.core.frame.DataFrame] Dataframe with pre-experiment data. None verbose bool Whether to show progress bar. False n_simulations Optional[int] Number of simulations to run. None experiment_length Iterable[int] Length of the experiment in days. () Source code in cluster_experiments/power_analysis.py def run_average_standard_error ( self , df : pd . DataFrame , pre_experiment_df : Optional [ pd . DataFrame ] = None , verbose : bool = False , n_simulations : Optional [ int ] = None , experiment_length : Iterable [ int ] = (), ) -> Generator [ Tuple [ float , int ], None , None ]: \"\"\" Run power analysis by simulation, using standard errors from the analysis. Args: df: Dataframe with outcome and treatment variables. pre_experiment_df: Dataframe with pre-experiment data. verbose: Whether to show progress bar. n_simulations: Number of simulations to run. experiment_length: Length of the experiment in days. \"\"\" n_simulations = self . n_simulations if n_simulations is None else n_simulations for n_days in experiment_length : df_time = df . copy () experiment_start = df_time [ self . time_col ] . min () df_time = df_time . loc [ df_time [ self . time_col ] < experiment_start + pd . Timedelta ( days = n_days ) ] std_error_mean = self . _get_average_standard_error ( df = df_time , pre_experiment_df = pre_experiment_df , verbose = verbose , n_simulations = n_simulations , ) yield std_error_mean , n_days PowerAnalysis \u00b6 Class used to run Power analysis. It does so by running simulations. In each simulation: 1. Assign treatment to dataframe randomly 2. Perturbate dataframe 3. Add pre-experiment data if needed 4. Run analysis Finally it returns the power of the analysis by counting how many times the effect was detected. Parameters: Name Type Description Default perturbator Perturbator Perturbator class to perturbate dataframe with treatment assigned. required splitter RandomSplitter RandomSplitter class to randomly assign treatment to dataframe. required analysis ExperimentAnalysis ExperimentAnalysis class to use for analysis. required cupac_model Optional[sklearn.base.BaseEstimator] Sklearn estimator class to add pre-experiment data to dataframe. If None, no pre-experiment data will be added. None target_col str Name of the column with the outcome variable. 'target' treatment_col str Name of the column with the treatment variable. 'treatment' treatment str value of treatment_col considered to be treatment (not control) 'B' control str value of treatment_col considered to be control (not treatment) 'A' n_simulations int Number of simulations to run. 100 alpha float Significance level. 0.05 features_cupac_model Optional[List[str]] Covariates to be used in cupac model None seed Optional[int] Optional. Seed to use for the splitter. None Usage: from datetime import date import numpy as np import pandas as pd from cluster_experiments.experiment_analysis import GeeExperimentAnalysis from cluster_experiments.perturbator import ConstantPerturbator from cluster_experiments.power_analysis import PowerAnalysis from cluster_experiments.random_splitter import ClusteredSplitter N = 1_000 users = [ f \"User { i } \" for i in range ( 1000 )] clusters = [ f \"Cluster { i } \" for i in range ( 100 )] dates = [ f \" { date ( 2022 , 1 , i ) : %Y-%m-%d } \" for i in range ( 1 , 32 )] df = pd . DataFrame ( { \"cluster\" : np . random . choice ( clusters , size = N ), \"target\" : np . random . normal ( 0 , 1 , size = N ), \"user\" : np . random . choice ( users , size = N ), \"date\" : np . random . choice ( dates , size = N ), } ) experiment_dates = [ f \" { date ( 2022 , 1 , i ) : %Y-%m-%d } \" for i in range ( 15 , 32 )] sw = ClusteredSplitter ( cluster_cols = [ \"cluster\" , \"date\" ], ) perturbator = ConstantPerturbator () analysis = GeeExperimentAnalysis ( cluster_cols = [ \"cluster\" , \"date\" ], ) pw = PowerAnalysis ( perturbator = perturbator , splitter = sw , analysis = analysis , n_simulations = 50 ) power = pw . power_analysis ( df , average_effect = 0.1 ) print ( f \" { power = } \" ) Source code in cluster_experiments/power_analysis.py class PowerAnalysis : \"\"\" Class used to run Power analysis. It does so by running simulations. In each simulation: 1. Assign treatment to dataframe randomly 2. Perturbate dataframe 3. Add pre-experiment data if needed 4. Run analysis Finally it returns the power of the analysis by counting how many times the effect was detected. Args: perturbator: Perturbator class to perturbate dataframe with treatment assigned. splitter: RandomSplitter class to randomly assign treatment to dataframe. analysis: ExperimentAnalysis class to use for analysis. cupac_model: Sklearn estimator class to add pre-experiment data to dataframe. If None, no pre-experiment data will be added. target_col: Name of the column with the outcome variable. treatment_col: Name of the column with the treatment variable. treatment: value of treatment_col considered to be treatment (not control) control: value of treatment_col considered to be control (not treatment) n_simulations: Number of simulations to run. alpha: Significance level. features_cupac_model: Covariates to be used in cupac model seed: Optional. Seed to use for the splitter. Usage: ```python from datetime import date import numpy as np import pandas as pd from cluster_experiments.experiment_analysis import GeeExperimentAnalysis from cluster_experiments.perturbator import ConstantPerturbator from cluster_experiments.power_analysis import PowerAnalysis from cluster_experiments.random_splitter import ClusteredSplitter N = 1_000 users = [f\"User {i}\" for i in range(1000)] clusters = [f\"Cluster {i}\" for i in range(100)] dates = [f\"{date(2022, 1, i):%Y-%m-%d}\" for i in range(1, 32)] df = pd.DataFrame( { \"cluster\": np.random.choice(clusters, size=N), \"target\": np.random.normal(0, 1, size=N), \"user\": np.random.choice(users, size=N), \"date\": np.random.choice(dates, size=N), } ) experiment_dates = [f\"{date(2022, 1, i):%Y-%m-%d}\" for i in range(15, 32)] sw = ClusteredSplitter( cluster_cols=[\"cluster\", \"date\"], ) perturbator = ConstantPerturbator() analysis = GeeExperimentAnalysis( cluster_cols=[\"cluster\", \"date\"], ) pw = PowerAnalysis( perturbator=perturbator, splitter=sw, analysis=analysis, n_simulations=50 ) power = pw.power_analysis(df, average_effect=0.1) print(f\"{power = }\") ``` \"\"\" def __init__ ( self , perturbator : Perturbator , splitter : RandomSplitter , analysis : ExperimentAnalysis , cupac_model : Optional [ BaseEstimator ] = None , target_col : str = \"target\" , treatment_col : str = \"treatment\" , treatment : str = \"B\" , control : str = \"A\" , n_simulations : int = 100 , alpha : float = 0.05 , features_cupac_model : Optional [ List [ str ]] = None , seed : Optional [ int ] = None , hypothesis : str = \"two-sided\" , ): self . perturbator = perturbator self . splitter = splitter self . analysis = analysis self . n_simulations = n_simulations self . target_col = target_col self . treatment = treatment self . control = control self . treatment_col = treatment_col self . alpha = alpha self . hypothesis = hypothesis self . cupac_handler = CupacHandler ( cupac_model = cupac_model , target_col = target_col , features_cupac_model = features_cupac_model , ) if seed is not None : random . seed ( seed ) # seed for splitter np . random . seed ( seed ) # seed for the binary perturbator # may need to seed other stochasticity sources if added self . check_inputs () def _simulate_perturbed_df ( self , df : pd . DataFrame , pre_experiment_df : Optional [ pd . DataFrame ] = None , verbose : bool = False , average_effect : Optional [ float ] = None , n_simulations : int = 100 , ) -> Generator [ pd . DataFrame , None , None ]: \"\"\"Yields splitted + perturbated dataframe for each iteration of the simulation.\"\"\" df = df . copy () df = self . cupac_handler . add_covariates ( df , pre_experiment_df ) for _ in tqdm ( range ( n_simulations ), disable = not verbose ): yield self . _split_and_perturbate ( df , average_effect ) def simulate_pvalue ( self , df : pd . DataFrame , pre_experiment_df : Optional [ pd . DataFrame ] = None , verbose : bool = False , average_effect : Optional [ float ] = None , n_simulations : int = 100 , ) -> Generator [ float , None , None ]: \"\"\" Yields p-values for each iteration of the simulation. In general, this is to be used in power_analysis method. However, if you're interested in the distribution of p-values, you can use this method to generate them. Args: df: Dataframe with outcome variable. pre_experiment_df: Dataframe with pre-experiment data. verbose: Whether to show progress bar. average_effect: Average effect of treatment. If None, it will use the perturbator average effect. n_simulations: Number of simulations to run. \"\"\" for perturbed_df in self . _simulate_perturbed_df ( df , pre_experiment_df = pre_experiment_df , verbose = verbose , average_effect = average_effect , n_simulations = n_simulations , ): yield self . analysis . get_pvalue ( perturbed_df ) def running_power_analysis ( self , df : pd . DataFrame , pre_experiment_df : Optional [ pd . DataFrame ] = None , verbose : bool = False , average_effect : Optional [ float ] = None , n_simulations : int = 100 , ) -> Generator [ float , None , None ]: \"\"\" Yields running power for each iteration of the simulation. if you're interested in getting the power at each iteration, you can use this method to generate them. Args: df: Dataframe with outcome variable. pre_experiment_df: Dataframe with pre-experiment data. verbose: Whether to show progress bar. average_effect: Average effect of treatment. If None, it will use the perturbator average effect. n_simulations: Number of simulations to run. \"\"\" n_rejected = 0 for i , perturbed_df in enumerate ( self . _simulate_perturbed_df ( df , pre_experiment_df = pre_experiment_df , verbose = verbose , average_effect = average_effect , n_simulations = n_simulations , ) ): p_value = self . analysis . get_pvalue ( perturbed_df ) n_rejected += int ( p_value < self . alpha ) yield n_rejected / ( i + 1 ) def simulate_point_estimate ( self , df : pd . DataFrame , pre_experiment_df : Optional [ pd . DataFrame ] = None , verbose : bool = False , average_effect : Optional [ float ] = None , n_simulations : int = 100 , ) -> Generator [ float , None , None ]: \"\"\" Yields point estimates for each iteration of the simulation. In general, this is to be used in power_analysis method. However, if you're interested in the distribution of point estimates, you can use this method to generate them. This is an experimental feature and it might change in the future. Args: df: Dataframe with outcome and treatment variables. pre_experiment_df: Dataframe with pre-experiment data. verbose: Whether to show progress bar. average_effect: Average effect of treatment. If None, it will use the perturbator average effect. n_simulations: Number of simulations to run. \"\"\" for perturbed_df in self . _simulate_perturbed_df ( df , pre_experiment_df = pre_experiment_df , verbose = verbose , average_effect = average_effect , n_simulations = n_simulations , ): yield self . analysis . get_point_estimate ( perturbed_df ) def power_analysis ( self , df : pd . DataFrame , pre_experiment_df : Optional [ pd . DataFrame ] = None , verbose : bool = False , average_effect : Optional [ float ] = None , n_simulations : Optional [ int ] = None , alpha : Optional [ float ] = None , n_jobs : int = 1 , ) -> float : \"\"\" Run power analysis by simulation Args: df: Dataframe with outcome and treatment variables. pre_experiment_df: Dataframe with pre-experiment data. verbose: Whether to show progress bar. average_effect: Average effect of treatment. If None, it will use the perturbator average effect. n_simulations: Number of simulations to run. alpha: Significance level. n_jobs: Number of jobs to run in parallel. If 1, it will run in serial. \"\"\" n_simulations = self . n_simulations if n_simulations is None else n_simulations alpha = self . alpha if alpha is None else alpha df = df . copy () df = self . cupac_handler . add_covariates ( df , pre_experiment_df ) if n_jobs == 1 : return self . _non_parallel_loop ( df , average_effect , n_simulations , alpha , verbose ) elif n_jobs > 1 or n_jobs == - 1 : return self . _parallel_loop ( df , average_effect , n_simulations , alpha , verbose , n_jobs ) else : raise ValueError ( \"n_jobs must be greater than 0, or -1.\" ) def _split ( self , df : pd . DataFrame ) -> pd . DataFrame : \"\"\" Split dataframe. Args: df: Dataframe with outcome variable \"\"\" treatment_df = self . splitter . assign_treatment_df ( df ) self . log_nulls ( treatment_df ) treatment_df = treatment_df . query ( f \" { self . treatment_col } .notnull()\" , engine = \"python\" ) . query ( f \" { self . treatment_col } .isin([' { self . treatment } ', ' { self . control } '])\" , engine = \"python\" , ) return treatment_df def _perturbate ( self , treatment_df : pd . DataFrame , average_effect : Optional [ float ] ) -> pd . DataFrame : \"\"\" Perturbate dataframe using perturbator. Args: df: Dataframe with outcome variable average_effect: Average effect of treatment. If None, it will use the perturbator average effect. \"\"\" perturbed_df = self . perturbator . perturbate ( treatment_df , average_effect = average_effect ) return perturbed_df def _split_and_perturbate ( self , df : pd . DataFrame , average_effect : Optional [ float ] ) -> pd . DataFrame : treatment_df = self . _split ( df ) perturbed_df = self . _perturbate ( treatment_df = treatment_df , average_effect = average_effect ) return perturbed_df def _run_simulation ( self , args : Tuple [ pd . DataFrame , Optional [ float ]]) -> float : df , average_effect = args perturbed_df = self . _split_and_perturbate ( df , average_effect ) return self . analysis . get_pvalue ( perturbed_df ) def _non_parallel_loop ( self , df : pd . DataFrame , average_effect : Optional [ float ], n_simulations : int , alpha : float , verbose : bool , ) -> float : \"\"\" Run power analysis by simulation in serial Args: df: Dataframe with outcome and treatment variables. average_effect: Average effect of treatment. If None, it will use the perturbator average effect. n_simulations: Number of simulations to run. alpha: Significance level. \"\"\" n_detected_mde = 0 for _ in tqdm ( range ( n_simulations ), disable = not verbose ): p_value = self . _run_simulation (( df , average_effect )) if verbose : print ( f \"p_value of simulation run: { p_value : .3f } \" ) n_detected_mde += p_value < alpha return n_detected_mde / n_simulations def _parallel_loop ( self , df : pd . DataFrame , average_effect : Optional [ float ], n_simulations : int , alpha : float , verbose : bool , n_jobs : int , ) -> float : \"\"\" Run power analysis by simulation in parallel Args: df: Dataframe with outcome and treatment variables. average_effect: Average effect of treatment. If None, it will use the perturbator average effect. n_simulations: Number of simulations to run. alpha: Significance level. n_jobs: Number of jobs to run in parallel. \"\"\" from multiprocessing import Pool , cpu_count n_jobs = n_jobs if n_jobs != - 1 else cpu_count () n_detected_mde = 0 with Pool ( processes = n_jobs ) as pool : args = [( df , average_effect ) for _ in range ( n_simulations )] results = pool . imap_unordered ( self . _run_simulation , args ) for p_value in tqdm ( results , total = n_simulations , disable = not verbose ): n_detected_mde += p_value < alpha return n_detected_mde / n_simulations def power_line ( self , df : pd . DataFrame , pre_experiment_df : Optional [ pd . DataFrame ] = None , verbose : bool = False , average_effects : Iterable [ float ] = (), n_simulations : Optional [ int ] = None , alpha : Optional [ float ] = None , n_jobs : int = 1 , ) -> Dict [ float , float ]: \"\"\"Runs power analysis with multiple average effects Args: df: Dataframe with outcome and treatment variables. pre_experiment_df: Dataframe with pre-experiment data. verbose: Whether to show progress bar. average_effects: Average effects to test. n_simulations: Number of simulations to run. alpha: Significance level. n_jobs: Number of jobs to run in parallel. Returns: Dictionary with average effects as keys and power as values. \"\"\" return { effect : self . power_analysis ( df = df , pre_experiment_df = pre_experiment_df , verbose = verbose , average_effect = effect , n_simulations = n_simulations , alpha = alpha , n_jobs = n_jobs , ) for effect in tqdm ( list ( average_effects ), disable = not verbose , desc = \"Effects loop\" ) } def log_nulls ( self , df : pd . DataFrame ) -> None : \"\"\"Warns about dropping nulls in treatment column\"\"\" n_nulls = len ( df . query ( f \" { self . treatment_col } .isnull()\" , engine = \"python\" )) if n_nulls > 0 : logging . warning ( f \"There are { n_nulls } null values in treatment, dropping them\" ) @classmethod def from_dict ( cls , config_dict : dict ) -> \"PowerAnalysis\" : \"\"\"Constructs PowerAnalysis from dictionary\"\"\" config = PowerConfig ( ** config_dict ) return cls . from_config ( config ) @classmethod def from_config ( cls , config : PowerConfig ) -> \"PowerAnalysis\" : \"\"\"Constructs PowerAnalysis from PowerConfig\"\"\" perturbator_cls = _get_mapping_key ( perturbator_mapping , config . perturbator ) splitter_cls = _get_mapping_key ( splitter_mapping , config . splitter ) analysis_cls = _get_mapping_key ( analysis_mapping , config . analysis ) cupac_cls = _get_mapping_key ( cupac_model_mapping , config . cupac_model ) return cls ( perturbator = perturbator_cls . from_config ( config ), splitter = splitter_cls . from_config ( config ), analysis = analysis_cls . from_config ( config ), cupac_model = cupac_cls . from_config ( config ), target_col = config . target_col , treatment_col = config . treatment_col , treatment = config . treatment , control = config . control , n_simulations = config . n_simulations , alpha = config . alpha , features_cupac_model = config . features_cupac_model , seed = config . seed , hypothesis = config . hypothesis , ) def check_treatment_col ( self ): \"\"\"Checks consistency of treatment column\"\"\" assert ( self . analysis . treatment_col == self . perturbator . treatment_col ), f \"treatment_col in analysis ( { self . analysis . treatment_col } ) must be the same as treatment_col in perturbator ( { self . perturbator . treatment_col } )\" assert ( self . analysis . treatment_col == self . treatment_col ), f \"treatment_col in analysis ( { self . analysis . treatment_col } ) must be the same as treatment_col in PowerAnalysis ( { self . treatment_col } )\" assert ( self . analysis . treatment_col == self . splitter . treatment_col ), f \"treatment_col in analysis ( { self . analysis . treatment_col } ) must be the same as treatment_col in splitter ( { self . splitter . treatment_col } )\" def check_target_col ( self ): assert ( self . analysis . target_col == self . perturbator . target_col ), f \"target_col in analysis ( { self . analysis . target_col } ) must be the same as target_col in perturbator ( { self . perturbator . target_col } )\" assert ( self . analysis . target_col == self . target_col ), f \"target_col in analysis ( { self . analysis . target_col } ) must be the same as target_col in PowerAnalysis ( { self . target_col } )\" def check_treatment ( self ): assert ( self . analysis . treatment == self . perturbator . treatment ), f \"treatment in analysis ( { self . analysis . treatment } ) must be the same as treatment in perturbator ( { self . perturbator . treatment } )\" assert ( self . analysis . treatment == self . treatment ), f \"treatment in analysis ( { self . analysis . treatment } ) must be the same as treatment in PowerAnalysis ( { self . treatment } )\" assert ( self . analysis . treatment in self . splitter . treatments ), f \"treatment in analysis ( { self . analysis . treatment } ) must be in treatments in splitter ( { self . splitter . treatments } )\" assert ( self . control in self . splitter . treatments ), f \"control in power analysis ( { self . control } ) must be in treatments in splitter ( { self . splitter . treatments } )\" def check_covariates ( self ): if hasattr ( self . analysis , \"covariates\" ): cupac_in_covariates = ( self . cupac_handler . cupac_outcome_name in self . analysis . covariates ) assert cupac_in_covariates or not self . cupac_handler . is_cupac , ( f \"covariates in analysis must contain { self . cupac_handler . cupac_outcome_name } if cupac_model is not None. \" f \"If you want to use cupac_model, you must add the cupac outcome to the covariates of the analysis \" f \"You may want to do covariates=[' { self . cupac_handler . cupac_outcome_name } '] in your analysis method or your config\" ) if hasattr ( self . splitter , \"cluster_cols\" ): if set ( self . analysis . covariates ) . intersection ( set ( self . splitter . cluster_cols ) ): logging . warning ( f \"covariates in analysis ( { self . analysis . covariates } ) are also cluster_cols in splitter ( { self . splitter . cluster_cols } ). \" f \"Be specially careful when using switchback splitters, since the time splitter column is being overriden\" ) def check_clusters ( self ): has_analysis_clusters = hasattr ( self . analysis , \"cluster_cols\" ) has_splitter_clusters = hasattr ( self . splitter , \"cluster_cols\" ) not_cluster_cols_cond = not has_analysis_clusters or not has_splitter_clusters assert ( not_cluster_cols_cond or self . analysis . cluster_cols == self . splitter . cluster_cols ), f \"cluster_cols in analysis ( { self . analysis . cluster_cols } ) must be the same as cluster_cols in splitter ( { self . splitter . cluster_cols } )\" assert ( has_splitter_clusters or not has_analysis_clusters or not self . analysis . cluster_cols or isinstance ( self . splitter , RepeatedSampler ) ), \"analysis has cluster_cols but splitter does not.\" assert ( has_analysis_clusters or not has_splitter_clusters or not self . splitter . cluster_cols ), \"splitter has cluster_cols but analysis does not.\" has_time_col = hasattr ( self . splitter , \"time_col\" ) assert not ( has_time_col and has_splitter_clusters and self . splitter . time_col not in self . splitter . cluster_cols ), \"in switchback splitters, time_col must be in cluster_cols\" def check_inputs ( self ): self . check_covariates () self . check_treatment_col () self . check_target_col () self . check_treatment () self . check_clusters () check_treatment_col ( self ) \u00b6 Checks consistency of treatment column Source code in cluster_experiments/power_analysis.py def check_treatment_col ( self ): \"\"\"Checks consistency of treatment column\"\"\" assert ( self . analysis . treatment_col == self . perturbator . treatment_col ), f \"treatment_col in analysis ( { self . analysis . treatment_col } ) must be the same as treatment_col in perturbator ( { self . perturbator . treatment_col } )\" assert ( self . analysis . treatment_col == self . treatment_col ), f \"treatment_col in analysis ( { self . analysis . treatment_col } ) must be the same as treatment_col in PowerAnalysis ( { self . treatment_col } )\" assert ( self . analysis . treatment_col == self . splitter . treatment_col ), f \"treatment_col in analysis ( { self . analysis . treatment_col } ) must be the same as treatment_col in splitter ( { self . splitter . treatment_col } )\" from_config ( config ) classmethod \u00b6 Constructs PowerAnalysis from PowerConfig Source code in cluster_experiments/power_analysis.py @classmethod def from_config ( cls , config : PowerConfig ) -> \"PowerAnalysis\" : \"\"\"Constructs PowerAnalysis from PowerConfig\"\"\" perturbator_cls = _get_mapping_key ( perturbator_mapping , config . perturbator ) splitter_cls = _get_mapping_key ( splitter_mapping , config . splitter ) analysis_cls = _get_mapping_key ( analysis_mapping , config . analysis ) cupac_cls = _get_mapping_key ( cupac_model_mapping , config . cupac_model ) return cls ( perturbator = perturbator_cls . from_config ( config ), splitter = splitter_cls . from_config ( config ), analysis = analysis_cls . from_config ( config ), cupac_model = cupac_cls . from_config ( config ), target_col = config . target_col , treatment_col = config . treatment_col , treatment = config . treatment , control = config . control , n_simulations = config . n_simulations , alpha = config . alpha , features_cupac_model = config . features_cupac_model , seed = config . seed , hypothesis = config . hypothesis , ) from_dict ( config_dict ) classmethod \u00b6 Constructs PowerAnalysis from dictionary Source code in cluster_experiments/power_analysis.py @classmethod def from_dict ( cls , config_dict : dict ) -> \"PowerAnalysis\" : \"\"\"Constructs PowerAnalysis from dictionary\"\"\" config = PowerConfig ( ** config_dict ) return cls . from_config ( config ) log_nulls ( self , df ) \u00b6 Warns about dropping nulls in treatment column Source code in cluster_experiments/power_analysis.py def log_nulls ( self , df : pd . DataFrame ) -> None : \"\"\"Warns about dropping nulls in treatment column\"\"\" n_nulls = len ( df . query ( f \" { self . treatment_col } .isnull()\" , engine = \"python\" )) if n_nulls > 0 : logging . warning ( f \"There are { n_nulls } null values in treatment, dropping them\" ) power_analysis ( self , df , pre_experiment_df = None , verbose = False , average_effect = None , n_simulations = None , alpha = None , n_jobs = 1 ) \u00b6 Run power analysis by simulation Parameters: Name Type Description Default df DataFrame Dataframe with outcome and treatment variables. required pre_experiment_df Optional[pandas.core.frame.DataFrame] Dataframe with pre-experiment data. None verbose bool Whether to show progress bar. False average_effect Optional[float] Average effect of treatment. If None, it will use the perturbator average effect. None n_simulations Optional[int] Number of simulations to run. None alpha Optional[float] Significance level. None n_jobs int Number of jobs to run in parallel. If 1, it will run in serial. 1 Source code in cluster_experiments/power_analysis.py def power_analysis ( self , df : pd . DataFrame , pre_experiment_df : Optional [ pd . DataFrame ] = None , verbose : bool = False , average_effect : Optional [ float ] = None , n_simulations : Optional [ int ] = None , alpha : Optional [ float ] = None , n_jobs : int = 1 , ) -> float : \"\"\" Run power analysis by simulation Args: df: Dataframe with outcome and treatment variables. pre_experiment_df: Dataframe with pre-experiment data. verbose: Whether to show progress bar. average_effect: Average effect of treatment. If None, it will use the perturbator average effect. n_simulations: Number of simulations to run. alpha: Significance level. n_jobs: Number of jobs to run in parallel. If 1, it will run in serial. \"\"\" n_simulations = self . n_simulations if n_simulations is None else n_simulations alpha = self . alpha if alpha is None else alpha df = df . copy () df = self . cupac_handler . add_covariates ( df , pre_experiment_df ) if n_jobs == 1 : return self . _non_parallel_loop ( df , average_effect , n_simulations , alpha , verbose ) elif n_jobs > 1 or n_jobs == - 1 : return self . _parallel_loop ( df , average_effect , n_simulations , alpha , verbose , n_jobs ) else : raise ValueError ( \"n_jobs must be greater than 0, or -1.\" ) power_line ( self , df , pre_experiment_df = None , verbose = False , average_effects = (), n_simulations = None , alpha = None , n_jobs = 1 ) \u00b6 Runs power analysis with multiple average effects Parameters: Name Type Description Default df DataFrame Dataframe with outcome and treatment variables. required pre_experiment_df Optional[pandas.core.frame.DataFrame] Dataframe with pre-experiment data. None verbose bool Whether to show progress bar. False average_effects Iterable[float] Average effects to test. () n_simulations Optional[int] Number of simulations to run. None alpha Optional[float] Significance level. None n_jobs int Number of jobs to run in parallel. 1 Returns: Type Description Dict[float, float] Dictionary with average effects as keys and power as values. Source code in cluster_experiments/power_analysis.py def power_line ( self , df : pd . DataFrame , pre_experiment_df : Optional [ pd . DataFrame ] = None , verbose : bool = False , average_effects : Iterable [ float ] = (), n_simulations : Optional [ int ] = None , alpha : Optional [ float ] = None , n_jobs : int = 1 , ) -> Dict [ float , float ]: \"\"\"Runs power analysis with multiple average effects Args: df: Dataframe with outcome and treatment variables. pre_experiment_df: Dataframe with pre-experiment data. verbose: Whether to show progress bar. average_effects: Average effects to test. n_simulations: Number of simulations to run. alpha: Significance level. n_jobs: Number of jobs to run in parallel. Returns: Dictionary with average effects as keys and power as values. \"\"\" return { effect : self . power_analysis ( df = df , pre_experiment_df = pre_experiment_df , verbose = verbose , average_effect = effect , n_simulations = n_simulations , alpha = alpha , n_jobs = n_jobs , ) for effect in tqdm ( list ( average_effects ), disable = not verbose , desc = \"Effects loop\" ) } running_power_analysis ( self , df , pre_experiment_df = None , verbose = False , average_effect = None , n_simulations = 100 ) \u00b6 Yields running power for each iteration of the simulation. if you're interested in getting the power at each iteration, you can use this method to generate them. Parameters: Name Type Description Default df DataFrame Dataframe with outcome variable. required pre_experiment_df Optional[pandas.core.frame.DataFrame] Dataframe with pre-experiment data. None verbose bool Whether to show progress bar. False average_effect Optional[float] Average effect of treatment. If None, it will use the perturbator average effect. None n_simulations int Number of simulations to run. 100 Source code in cluster_experiments/power_analysis.py def running_power_analysis ( self , df : pd . DataFrame , pre_experiment_df : Optional [ pd . DataFrame ] = None , verbose : bool = False , average_effect : Optional [ float ] = None , n_simulations : int = 100 , ) -> Generator [ float , None , None ]: \"\"\" Yields running power for each iteration of the simulation. if you're interested in getting the power at each iteration, you can use this method to generate them. Args: df: Dataframe with outcome variable. pre_experiment_df: Dataframe with pre-experiment data. verbose: Whether to show progress bar. average_effect: Average effect of treatment. If None, it will use the perturbator average effect. n_simulations: Number of simulations to run. \"\"\" n_rejected = 0 for i , perturbed_df in enumerate ( self . _simulate_perturbed_df ( df , pre_experiment_df = pre_experiment_df , verbose = verbose , average_effect = average_effect , n_simulations = n_simulations , ) ): p_value = self . analysis . get_pvalue ( perturbed_df ) n_rejected += int ( p_value < self . alpha ) yield n_rejected / ( i + 1 ) simulate_point_estimate ( self , df , pre_experiment_df = None , verbose = False , average_effect = None , n_simulations = 100 ) \u00b6 Yields point estimates for each iteration of the simulation. In general, this is to be used in power_analysis method. However, if you're interested in the distribution of point estimates, you can use this method to generate them. This is an experimental feature and it might change in the future. Parameters: Name Type Description Default df DataFrame Dataframe with outcome and treatment variables. required pre_experiment_df Optional[pandas.core.frame.DataFrame] Dataframe with pre-experiment data. None verbose bool Whether to show progress bar. False average_effect Optional[float] Average effect of treatment. If None, it will use the perturbator average effect. None n_simulations int Number of simulations to run. 100 Source code in cluster_experiments/power_analysis.py def simulate_point_estimate ( self , df : pd . DataFrame , pre_experiment_df : Optional [ pd . DataFrame ] = None , verbose : bool = False , average_effect : Optional [ float ] = None , n_simulations : int = 100 , ) -> Generator [ float , None , None ]: \"\"\" Yields point estimates for each iteration of the simulation. In general, this is to be used in power_analysis method. However, if you're interested in the distribution of point estimates, you can use this method to generate them. This is an experimental feature and it might change in the future. Args: df: Dataframe with outcome and treatment variables. pre_experiment_df: Dataframe with pre-experiment data. verbose: Whether to show progress bar. average_effect: Average effect of treatment. If None, it will use the perturbator average effect. n_simulations: Number of simulations to run. \"\"\" for perturbed_df in self . _simulate_perturbed_df ( df , pre_experiment_df = pre_experiment_df , verbose = verbose , average_effect = average_effect , n_simulations = n_simulations , ): yield self . analysis . get_point_estimate ( perturbed_df ) simulate_pvalue ( self , df , pre_experiment_df = None , verbose = False , average_effect = None , n_simulations = 100 ) \u00b6 Yields p-values for each iteration of the simulation. In general, this is to be used in power_analysis method. However, if you're interested in the distribution of p-values, you can use this method to generate them. Parameters: Name Type Description Default df DataFrame Dataframe with outcome variable. required pre_experiment_df Optional[pandas.core.frame.DataFrame] Dataframe with pre-experiment data. None verbose bool Whether to show progress bar. False average_effect Optional[float] Average effect of treatment. If None, it will use the perturbator average effect. None n_simulations int Number of simulations to run. 100 Source code in cluster_experiments/power_analysis.py def simulate_pvalue ( self , df : pd . DataFrame , pre_experiment_df : Optional [ pd . DataFrame ] = None , verbose : bool = False , average_effect : Optional [ float ] = None , n_simulations : int = 100 , ) -> Generator [ float , None , None ]: \"\"\" Yields p-values for each iteration of the simulation. In general, this is to be used in power_analysis method. However, if you're interested in the distribution of p-values, you can use this method to generate them. Args: df: Dataframe with outcome variable. pre_experiment_df: Dataframe with pre-experiment data. verbose: Whether to show progress bar. average_effect: Average effect of treatment. If None, it will use the perturbator average effect. n_simulations: Number of simulations to run. \"\"\" for perturbed_df in self . _simulate_perturbed_df ( df , pre_experiment_df = pre_experiment_df , verbose = verbose , average_effect = average_effect , n_simulations = n_simulations , ): yield self . analysis . get_pvalue ( perturbed_df ) PowerAnalysisWithPreExperimentData ( PowerAnalysis ) \u00b6 This is intended to work mainly for diff-in-diff or synthetic control-like estimators, and NOT for cases of CUPED/CUPAC. Same as PowerAnalysis, but allowing a perturbation only at experiment period and keeping pre-experiment df intact. Using this class, the pre experiment df is also available when the class is instantiated. Source code in cluster_experiments/power_analysis.py class PowerAnalysisWithPreExperimentData ( PowerAnalysis ): \"\"\" This is intended to work mainly for diff-in-diff or synthetic control-like estimators, and NOT for cases of CUPED/CUPAC. Same as PowerAnalysis, but allowing a perturbation only at experiment period and keeping pre-experiment df intact. Using this class, the pre experiment df is also available when the class is instantiated. \"\"\" def _perturbate ( self , treatment_df : pd . DataFrame , average_effect : Optional [ float ] ) -> pd . DataFrame : if not hasattr ( self . analysis , \"_split_pre_experiment_df\" ): raise AttributeError ( \"The PowerAnalysisWithPreExperimentData is intended to work mainly for diff-in-diff or synthetic control-like estimators.\" \"For other cases use the PowerAnalysis\" ) df , pre_experiment_df = self . analysis . _split_pre_experiment_df ( treatment_df ) perturbed_df = self . perturbator . perturbate ( df , average_effect = average_effect ) return pd . concat ([ perturbed_df , pre_experiment_df ])","title":"Power analysis"},{"location":"api/power_analysis.html#from-cluster_experimentspower_analysis-import","text":"","title":"from cluster_experiments.power_analysis import *"},{"location":"api/power_analysis.html#cluster_experiments.power_analysis.NormalPowerAnalysis","text":"Class used to run Power analysis, using the central limit theorem to estimate power based on standard errors of the estimator, and the fact that the coefficients of a regression are normally distributed. It does so by running simulations. In each simulation: 1. Assign treatment to dataframe randomly 2. Add pre-experiment data if needed 3. Get standard error from analysis Finally it returns the power of the analysis by counting how many times the effect was detected. Parameters: Name Type Description Default splitter RandomSplitter RandomSplitter class to randomly assign treatment to dataframe. required analysis ExperimentAnalysis ExperimentAnalysis class to use for analysis. required cupac_model Optional[sklearn.base.BaseEstimator] Sklearn estimator class to add pre-experiment data to dataframe. If None, no pre-experiment data will be added. None target_col str Name of the column with the outcome variable. 'target' treatment_col str Name of the column with the treatment variable. 'treatment' treatment str value of treatment_col considered to be treatment (not control) 'B' control str value of treatment_col considered to be control (not treatment) 'A' n_simulations int Number of simulations to run. 100 alpha float Significance level. 0.05 features_cupac_model Optional[List[str]] Covariates to be used in cupac model None seed Optional[int] Optional. Seed to use for the splitter. None Usage: from datetime import date import numpy as np import pandas as pd from cluster_experiments.experiment_analysis import GeeExperimentAnalysis from cluster_experiments.power_analysis import NormalPowerAnalysis from cluster_experiments.random_splitter import ClusteredSplitter N = 1_000 users = [ f \"User { i } \" for i in range ( 1000 )] clusters = [ f \"Cluster { i } \" for i in range ( 100 )] dates = [ f \" { date ( 2022 , 1 , i ) : %Y-%m-%d } \" for i in range ( 1 , 32 )] df = pd . DataFrame ( { \"cluster\" : np . random . choice ( clusters , size = N ), \"target\" : np . random . normal ( 0 , 1 , size = N ), \"user\" : np . random . choice ( users , size = N ), \"date\" : np . random . choice ( dates , size = N ), } ) experiment_dates = [ f \" { date ( 2022 , 1 , i ) : %Y-%m-%d } \" for i in range ( 15 , 32 )] sw = ClusteredSplitter ( cluster_cols = [ \"cluster\" , \"date\" ], ) analysis = GeeExperimentAnalysis ( cluster_cols = [ \"cluster\" , \"date\" ], ) pw = NormalPowerAnalysis ( splitter = sw , analysis = analysis , n_simulations = 50 ) power = pw . power_analysis ( df , average_effect = 0.1 ) print ( f \" { power = } \" ) Source code in cluster_experiments/power_analysis.py class NormalPowerAnalysis : \"\"\" Class used to run Power analysis, using the central limit theorem to estimate power based on standard errors of the estimator, and the fact that the coefficients of a regression are normally distributed. It does so by running simulations. In each simulation: 1. Assign treatment to dataframe randomly 2. Add pre-experiment data if needed 3. Get standard error from analysis Finally it returns the power of the analysis by counting how many times the effect was detected. Args: splitter: RandomSplitter class to randomly assign treatment to dataframe. analysis: ExperimentAnalysis class to use for analysis. cupac_model: Sklearn estimator class to add pre-experiment data to dataframe. If None, no pre-experiment data will be added. target_col: Name of the column with the outcome variable. treatment_col: Name of the column with the treatment variable. treatment: value of treatment_col considered to be treatment (not control) control: value of treatment_col considered to be control (not treatment) n_simulations: Number of simulations to run. alpha: Significance level. features_cupac_model: Covariates to be used in cupac model seed: Optional. Seed to use for the splitter. Usage: ```python from datetime import date import numpy as np import pandas as pd from cluster_experiments.experiment_analysis import GeeExperimentAnalysis from cluster_experiments.power_analysis import NormalPowerAnalysis from cluster_experiments.random_splitter import ClusteredSplitter N = 1_000 users = [f\"User {i}\" for i in range(1000)] clusters = [f\"Cluster {i}\" for i in range(100)] dates = [f\"{date(2022, 1, i):%Y-%m-%d}\" for i in range(1, 32)] df = pd.DataFrame( { \"cluster\": np.random.choice(clusters, size=N), \"target\": np.random.normal(0, 1, size=N), \"user\": np.random.choice(users, size=N), \"date\": np.random.choice(dates, size=N), } ) experiment_dates = [f\"{date(2022, 1, i):%Y-%m-%d}\" for i in range(15, 32)] sw = ClusteredSplitter( cluster_cols=[\"cluster\", \"date\"], ) analysis = GeeExperimentAnalysis( cluster_cols=[\"cluster\", \"date\"], ) pw = NormalPowerAnalysis( splitter=sw, analysis=analysis, n_simulations=50 ) power = pw.power_analysis(df, average_effect=0.1) print(f\"{power = }\") ``` \"\"\" def __init__ ( self , splitter : RandomSplitter , analysis : ExperimentAnalysis , cupac_model : Optional [ BaseEstimator ] = None , target_col : str = \"target\" , treatment_col : str = \"treatment\" , treatment : str = \"B\" , control : str = \"A\" , n_simulations : int = 100 , alpha : float = 0.05 , features_cupac_model : Optional [ List [ str ]] = None , seed : Optional [ int ] = None , hypothesis : str = \"two-sided\" , time_col : Optional [ str ] = None , ): self . splitter = splitter self . analysis = analysis self . n_simulations = n_simulations self . target_col = target_col self . treatment = treatment self . control = control self . treatment_col = treatment_col self . alpha = alpha self . hypothesis = hypothesis self . time_col = time_col self . cupac_handler = CupacHandler ( cupac_model = cupac_model , target_col = target_col , features_cupac_model = features_cupac_model , ) if seed is not None : random . seed ( seed ) # seed for splitter np . random . seed ( seed ) # numpy seed # may need to seed other stochasticity sources if added self . check_inputs () def _split ( self , df : pd . DataFrame ) -> pd . DataFrame : \"\"\" Split dataframe. Args: df: Dataframe with outcome variable \"\"\" treatment_df = self . splitter . assign_treatment_df ( df ) self . log_nulls ( treatment_df ) treatment_df = treatment_df . query ( f \" { self . treatment_col } .notnull()\" , engine = \"python\" ) . query ( f \" { self . treatment_col } .isin([' { self . treatment } ', ' { self . control } '])\" , engine = \"python\" , ) return treatment_df def _get_standard_error ( self , df : pd . DataFrame , n_simulations : int , verbose : bool , ) -> Generator [ float , None , None ]: for _ in tqdm ( range ( n_simulations ), disable = not verbose ): split_df = self . _split ( df ) yield self . analysis . get_standard_error ( split_df ) def _normal_power_calculation ( self , alpha : float , std_error : float , average_effect : float ) -> float : \"\"\"Returns the power of the analysis using the normal distribution. Arguments: alpha: significance level std_error: standard error of the analysis average_effect: effect size of the analysis \"\"\" if HypothesisEntries ( self . analysis . hypothesis ) == HypothesisEntries . LESS : z_alpha = norm . ppf ( alpha ) return float ( norm . cdf ( z_alpha - average_effect / std_error )) if HypothesisEntries ( self . analysis . hypothesis ) == HypothesisEntries . GREATER : z_alpha = norm . ppf ( 1 - alpha ) return 1 - float ( norm . cdf ( z_alpha - average_effect / std_error )) if HypothesisEntries ( self . analysis . hypothesis ) == HypothesisEntries . TWO_SIDED : z_alpha = norm . ppf ( 1 - alpha / 2 ) norm_cdf_right = norm . cdf ( z_alpha - average_effect / std_error ) norm_cdf_left = norm . cdf ( - z_alpha - average_effect / std_error ) return float ( norm_cdf_left + ( 1 - norm_cdf_right )) raise ValueError ( f \" { self . analysis . hypothesis } is not a valid HypothesisEntries\" ) def _normal_mde_calculation ( self , alpha : float , std_error : float , power : float ) -> float : \"\"\" Returns the minimum detectable effect of the analysis using the normal distribution. Args: alpha: Significance level. std_error: Standard error of the analysis. power: Power of the analysis. \"\"\" if HypothesisEntries ( self . analysis . hypothesis ) == HypothesisEntries . LESS : z_alpha = norm . ppf ( alpha ) z_beta = norm . ppf ( 1 - power ) elif HypothesisEntries ( self . analysis . hypothesis ) == HypothesisEntries . GREATER : z_alpha = norm . ppf ( 1 - alpha ) z_beta = norm . ppf ( power ) elif HypothesisEntries ( self . analysis . hypothesis ) == HypothesisEntries . TWO_SIDED : # we are neglecting norm_cdf_left z_alpha = norm . ppf ( 1 - alpha / 2 ) z_beta = norm . ppf ( power ) else : raise ValueError ( f \" { self . analysis . hypothesis } is not a valid HypothesisEntries\" ) return float ( z_alpha + z_beta ) * std_error def mde_power_line ( self , df : pd . DataFrame , pre_experiment_df : Optional [ pd . DataFrame ] = None , verbose : bool = False , powers : Iterable [ float ] = (), n_simulations : Optional [ int ] = None , alpha : Optional [ float ] = None , ) -> Dict [ float , float ]: \"\"\" Returns the minimum detectable effect of the analysis. Args: df: Dataframe with outcome and treatment variables. pre_experiment_df: Dataframe with pre-experiment data. verbose: Whether to show progress bar. power: Power of the analysis. n_simulations: Number of simulations to run. alpha: Significance level. \"\"\" alpha = self . alpha if alpha is None else alpha std_error = self . _get_average_standard_error ( df = df , pre_experiment_df = pre_experiment_df , verbose = verbose , n_simulations = n_simulations , ) return { power : self . _normal_mde_calculation ( alpha = alpha , std_error = std_error , power = power ) for power in powers } def mde ( self , df : pd . DataFrame , pre_experiment_df : Optional [ pd . DataFrame ] = None , verbose : bool = False , power : float = 0.8 , n_simulations : Optional [ int ] = None , alpha : Optional [ float ] = None , ) -> float : \"\"\" Returns the minimum detectable effect of the analysis. Args: df: Dataframe with outcome and treatment variables. pre_experiment_df: Dataframe with pre-experiment data. verbose: Whether to show progress bar. power: Power of the analysis. n_simulations: Number of simulations to run. alpha: Significance level. \"\"\" return self . mde_power_line ( df = df , pre_experiment_df = pre_experiment_df , verbose = verbose , powers = [ power ], n_simulations = n_simulations , alpha = alpha , )[ power ] def _get_average_standard_error ( self , df : pd . DataFrame , pre_experiment_df : Optional [ pd . DataFrame ] = None , verbose : bool = False , n_simulations : Optional [ int ] = None , ) -> float : \"\"\" Gets standard error to be used in normal power calculation. Args: df: Dataframe with outcome and treatment variables. pre_experiment_df: Dataframe with pre-experiment data. verbose: Whether to show progress bar. average_effects: Average effects to test. n_simulations: Number of simulations to run. alpha: Significance level. \"\"\" n_simulations = self . n_simulations if n_simulations is None else n_simulations df = df . copy () df = self . cupac_handler . add_covariates ( df , pre_experiment_df ) std_errors = list ( self . _get_standard_error ( df , n_simulations , verbose )) std_error_mean = float ( np . mean ( std_errors )) return std_error_mean def run_average_standard_error ( self , df : pd . DataFrame , pre_experiment_df : Optional [ pd . DataFrame ] = None , verbose : bool = False , n_simulations : Optional [ int ] = None , experiment_length : Iterable [ int ] = (), ) -> Generator [ Tuple [ float , int ], None , None ]: \"\"\" Run power analysis by simulation, using standard errors from the analysis. Args: df: Dataframe with outcome and treatment variables. pre_experiment_df: Dataframe with pre-experiment data. verbose: Whether to show progress bar. n_simulations: Number of simulations to run. experiment_length: Length of the experiment in days. \"\"\" n_simulations = self . n_simulations if n_simulations is None else n_simulations for n_days in experiment_length : df_time = df . copy () experiment_start = df_time [ self . time_col ] . min () df_time = df_time . loc [ df_time [ self . time_col ] < experiment_start + pd . Timedelta ( days = n_days ) ] std_error_mean = self . _get_average_standard_error ( df = df_time , pre_experiment_df = pre_experiment_df , verbose = verbose , n_simulations = n_simulations , ) yield std_error_mean , n_days def power_time_line ( self , df : pd . DataFrame , pre_experiment_df : Optional [ pd . DataFrame ] = None , verbose : bool = False , average_effects : Iterable [ float ] = (), experiment_length : Iterable [ int ] = (), n_simulations : Optional [ int ] = None , alpha : Optional [ float ] = None , ) -> List [ Dict ]: \"\"\" Run power analysis by simulation, using standard errors from the analysis. Args: df: Dataframe with outcome and treatment variables. pre_experiment_df: Dataframe with pre-experiment data. verbose: Whether to show progress bar. average_effects: Average effects to test. experiment_length: Length of the experiment in days. n_simulations: Number of simulations to run. alpha: Significance level. \"\"\" alpha = self . alpha if alpha is None else alpha results = [] for std_error_mean , n_days in self . run_average_standard_error ( df = df , pre_experiment_df = pre_experiment_df , verbose = verbose , n_simulations = n_simulations , experiment_length = experiment_length , ): for effect in average_effects : power = self . _normal_power_calculation ( alpha = alpha , std_error = std_error_mean , average_effect = effect ) results . append ( { \"effect\" : effect , \"power\" : power , \"experiment_length\" : n_days } ) return results def mde_time_line ( self , df : pd . DataFrame , pre_experiment_df : Optional [ pd . DataFrame ] = None , verbose : bool = False , powers : Iterable [ float ] = (), experiment_length : Iterable [ int ] = (), n_simulations : Optional [ int ] = None , alpha : Optional [ float ] = None , ) -> List [ Dict ]: alpha = self . alpha if alpha is None else alpha results = [] for std_error_mean , n_days in self . run_average_standard_error ( df = df , pre_experiment_df = pre_experiment_df , verbose = verbose , n_simulations = n_simulations , experiment_length = experiment_length , ): for power in powers : mde = self . _normal_mde_calculation ( alpha = alpha , std_error = std_error_mean , power = power ) results . append ( { \"power\" : power , \"mde\" : mde , \"experiment_length\" : n_days } ) return results def power_line ( self , df : pd . DataFrame , pre_experiment_df : Optional [ pd . DataFrame ] = None , verbose : bool = False , average_effects : Iterable [ float ] = (), n_simulations : Optional [ int ] = None , alpha : Optional [ float ] = None , ) -> Dict [ float , float ]: \"\"\" Run power analysis by simulation, using standard errors from the analysis. Args: df: Dataframe with outcome and treatment variables. pre_experiment_df: Dataframe with pre-experiment data. verbose: Whether to show progress bar. average_effects: Average effects to test. n_simulations: Number of simulations to run. alpha: Significance level. \"\"\" alpha = self . alpha if alpha is None else alpha std_error_mean = self . _get_average_standard_error ( df = df , pre_experiment_df = pre_experiment_df , verbose = verbose , n_simulations = n_simulations , ) return { effect : self . _normal_power_calculation ( alpha = alpha , std_error = std_error_mean , average_effect = effect ) for effect in average_effects } def power_analysis ( self , df : pd . DataFrame , pre_experiment_df : Optional [ pd . DataFrame ] = None , verbose : bool = False , average_effect : float = 0.0 , n_simulations : Optional [ int ] = None , alpha : Optional [ float ] = None , ) -> float : \"\"\" Run power analysis by simulation, using standard errors from the analysis. Args: df: Dataframe with outcome and treatment variables. pre_experiment_df: Dataframe with pre-experiment data. verbose: Whether to show progress bar. average_effect: Average effect of treatment. n_simulations: Number of simulations to run. alpha: Significance level. \"\"\" return self . power_line ( df = df , pre_experiment_df = pre_experiment_df , verbose = verbose , average_effects = [ average_effect ], n_simulations = n_simulations , alpha = alpha , )[ average_effect ] def log_nulls ( self , df : pd . DataFrame ) -> None : \"\"\"Warns about dropping nulls in treatment column\"\"\" n_nulls = len ( df . query ( f \" { self . treatment_col } .isnull()\" , engine = \"python\" )) if n_nulls > 0 : logging . warning ( f \"There are { n_nulls } null values in treatment, dropping them\" ) @classmethod def from_dict ( cls , config_dict : dict ) -> \"NormalPowerAnalysis\" : \"\"\"Constructs PowerAnalysis from dictionary\"\"\" config = PowerConfig ( ** config_dict ) return cls . from_config ( config ) @classmethod def from_config ( cls , config : PowerConfig ) -> \"NormalPowerAnalysis\" : \"\"\"Constructs PowerAnalysis from PowerConfig\"\"\" splitter_cls = _get_mapping_key ( splitter_mapping , config . splitter ) analysis_cls = _get_mapping_key ( analysis_mapping , config . analysis ) cupac_cls = _get_mapping_key ( cupac_model_mapping , config . cupac_model ) return cls ( splitter = splitter_cls . from_config ( config ), analysis = analysis_cls . from_config ( config ), cupac_model = cupac_cls . from_config ( config ), target_col = config . target_col , treatment_col = config . treatment_col , treatment = config . treatment , control = config . control , n_simulations = config . n_simulations , alpha = config . alpha , features_cupac_model = config . features_cupac_model , seed = config . seed , hypothesis = config . hypothesis , time_col = config . time_col , ) def check_treatment_col ( self ): \"\"\"Checks consistency of treatment column\"\"\" assert ( self . analysis . treatment_col == self . treatment_col ), f \"treatment_col in analysis ( { self . analysis . treatment_col } ) must be the same as treatment_col in PowerAnalysis ( { self . treatment_col } )\" assert ( self . analysis . treatment_col == self . splitter . treatment_col ), f \"treatment_col in analysis ( { self . analysis . treatment_col } ) must be the same as treatment_col in splitter ( { self . splitter . treatment_col } )\" def check_target_col ( self ): assert ( self . analysis . target_col == self . target_col ), f \"target_col in analysis ( { self . analysis . target_col } ) must be the same as target_col in PowerAnalysis ( { self . target_col } )\" def check_treatment ( self ): assert ( self . analysis . treatment == self . treatment ), f \"treatment in analysis ( { self . analysis . treatment } ) must be the same as treatment in PowerAnalysis ( { self . treatment } )\" assert ( self . analysis . treatment in self . splitter . treatments ), f \"treatment in analysis ( { self . analysis . treatment } ) must be in treatments in splitter ( { self . splitter . treatments } )\" assert ( self . control in self . splitter . treatments ), f \"control in power analysis ( { self . control } ) must be in treatments in splitter ( { self . splitter . treatments } )\" def check_covariates ( self ): if hasattr ( self . analysis , \"covariates\" ): cupac_in_covariates = ( self . cupac_handler . cupac_outcome_name in self . analysis . covariates ) assert cupac_in_covariates or not self . cupac_handler . is_cupac , ( f \"covariates in analysis must contain { self . cupac_handler . cupac_outcome_name } if cupac_model is not None. \" f \"If you want to use cupac_model, you must add the cupac outcome to the covariates of the analysis \" f \"You may want to do covariates=[' { self . cupac_handler . cupac_outcome_name } '] in your analysis method or your config\" ) if hasattr ( self . splitter , \"cluster_cols\" ): if set ( self . analysis . covariates ) . intersection ( set ( self . splitter . cluster_cols ) ): logging . warning ( f \"covariates in analysis ( { self . analysis . covariates } ) are also cluster_cols in splitter ( { self . splitter . cluster_cols } ). \" f \"Be specially careful when using switchback splitters, since the time splitter column is being overriden\" ) def check_clusters ( self ): has_analysis_clusters = hasattr ( self . analysis , \"cluster_cols\" ) has_splitter_clusters = hasattr ( self . splitter , \"cluster_cols\" ) not_cluster_cols_cond = not has_analysis_clusters or not has_splitter_clusters assert ( not_cluster_cols_cond or self . analysis . cluster_cols == self . splitter . cluster_cols ), f \"cluster_cols in analysis ( { self . analysis . cluster_cols } ) must be the same as cluster_cols in splitter ( { self . splitter . cluster_cols } )\" assert ( has_splitter_clusters or not has_analysis_clusters or not self . analysis . cluster_cols or isinstance ( self . splitter , RepeatedSampler ) ), \"analysis has cluster_cols but splitter does not.\" assert ( has_analysis_clusters or not has_splitter_clusters or not self . splitter . cluster_cols ), \"splitter has cluster_cols but analysis does not.\" has_time_col = hasattr ( self . splitter , \"time_col\" ) assert not ( has_time_col and has_splitter_clusters and self . splitter . time_col not in self . splitter . cluster_cols ), \"in switchback splitters, time_col must be in cluster_cols\" def check_inputs ( self ): self . check_covariates () self . check_treatment_col () self . check_target_col () self . check_treatment () self . check_clusters ()","title":"NormalPowerAnalysis"},{"location":"api/power_analysis.html#cluster_experiments.power_analysis.NormalPowerAnalysis.check_treatment_col","text":"Checks consistency of treatment column Source code in cluster_experiments/power_analysis.py def check_treatment_col ( self ): \"\"\"Checks consistency of treatment column\"\"\" assert ( self . analysis . treatment_col == self . treatment_col ), f \"treatment_col in analysis ( { self . analysis . treatment_col } ) must be the same as treatment_col in PowerAnalysis ( { self . treatment_col } )\" assert ( self . analysis . treatment_col == self . splitter . treatment_col ), f \"treatment_col in analysis ( { self . analysis . treatment_col } ) must be the same as treatment_col in splitter ( { self . splitter . treatment_col } )\"","title":"check_treatment_col()"},{"location":"api/power_analysis.html#cluster_experiments.power_analysis.NormalPowerAnalysis.from_config","text":"Constructs PowerAnalysis from PowerConfig Source code in cluster_experiments/power_analysis.py @classmethod def from_config ( cls , config : PowerConfig ) -> \"NormalPowerAnalysis\" : \"\"\"Constructs PowerAnalysis from PowerConfig\"\"\" splitter_cls = _get_mapping_key ( splitter_mapping , config . splitter ) analysis_cls = _get_mapping_key ( analysis_mapping , config . analysis ) cupac_cls = _get_mapping_key ( cupac_model_mapping , config . cupac_model ) return cls ( splitter = splitter_cls . from_config ( config ), analysis = analysis_cls . from_config ( config ), cupac_model = cupac_cls . from_config ( config ), target_col = config . target_col , treatment_col = config . treatment_col , treatment = config . treatment , control = config . control , n_simulations = config . n_simulations , alpha = config . alpha , features_cupac_model = config . features_cupac_model , seed = config . seed , hypothesis = config . hypothesis , time_col = config . time_col , )","title":"from_config()"},{"location":"api/power_analysis.html#cluster_experiments.power_analysis.NormalPowerAnalysis.from_dict","text":"Constructs PowerAnalysis from dictionary Source code in cluster_experiments/power_analysis.py @classmethod def from_dict ( cls , config_dict : dict ) -> \"NormalPowerAnalysis\" : \"\"\"Constructs PowerAnalysis from dictionary\"\"\" config = PowerConfig ( ** config_dict ) return cls . from_config ( config )","title":"from_dict()"},{"location":"api/power_analysis.html#cluster_experiments.power_analysis.NormalPowerAnalysis.log_nulls","text":"Warns about dropping nulls in treatment column Source code in cluster_experiments/power_analysis.py def log_nulls ( self , df : pd . DataFrame ) -> None : \"\"\"Warns about dropping nulls in treatment column\"\"\" n_nulls = len ( df . query ( f \" { self . treatment_col } .isnull()\" , engine = \"python\" )) if n_nulls > 0 : logging . warning ( f \"There are { n_nulls } null values in treatment, dropping them\" )","title":"log_nulls()"},{"location":"api/power_analysis.html#cluster_experiments.power_analysis.NormalPowerAnalysis.mde","text":"Returns the minimum detectable effect of the analysis. Parameters: Name Type Description Default df DataFrame Dataframe with outcome and treatment variables. required pre_experiment_df Optional[pandas.core.frame.DataFrame] Dataframe with pre-experiment data. None verbose bool Whether to show progress bar. False power float Power of the analysis. 0.8 n_simulations Optional[int] Number of simulations to run. None alpha Optional[float] Significance level. None Source code in cluster_experiments/power_analysis.py def mde ( self , df : pd . DataFrame , pre_experiment_df : Optional [ pd . DataFrame ] = None , verbose : bool = False , power : float = 0.8 , n_simulations : Optional [ int ] = None , alpha : Optional [ float ] = None , ) -> float : \"\"\" Returns the minimum detectable effect of the analysis. Args: df: Dataframe with outcome and treatment variables. pre_experiment_df: Dataframe with pre-experiment data. verbose: Whether to show progress bar. power: Power of the analysis. n_simulations: Number of simulations to run. alpha: Significance level. \"\"\" return self . mde_power_line ( df = df , pre_experiment_df = pre_experiment_df , verbose = verbose , powers = [ power ], n_simulations = n_simulations , alpha = alpha , )[ power ]","title":"mde()"},{"location":"api/power_analysis.html#cluster_experiments.power_analysis.NormalPowerAnalysis.mde_power_line","text":"Returns the minimum detectable effect of the analysis. Parameters: Name Type Description Default df DataFrame Dataframe with outcome and treatment variables. required pre_experiment_df Optional[pandas.core.frame.DataFrame] Dataframe with pre-experiment data. None verbose bool Whether to show progress bar. False power Power of the analysis. required n_simulations Optional[int] Number of simulations to run. None alpha Optional[float] Significance level. None Source code in cluster_experiments/power_analysis.py def mde_power_line ( self , df : pd . DataFrame , pre_experiment_df : Optional [ pd . DataFrame ] = None , verbose : bool = False , powers : Iterable [ float ] = (), n_simulations : Optional [ int ] = None , alpha : Optional [ float ] = None , ) -> Dict [ float , float ]: \"\"\" Returns the minimum detectable effect of the analysis. Args: df: Dataframe with outcome and treatment variables. pre_experiment_df: Dataframe with pre-experiment data. verbose: Whether to show progress bar. power: Power of the analysis. n_simulations: Number of simulations to run. alpha: Significance level. \"\"\" alpha = self . alpha if alpha is None else alpha std_error = self . _get_average_standard_error ( df = df , pre_experiment_df = pre_experiment_df , verbose = verbose , n_simulations = n_simulations , ) return { power : self . _normal_mde_calculation ( alpha = alpha , std_error = std_error , power = power ) for power in powers }","title":"mde_power_line()"},{"location":"api/power_analysis.html#cluster_experiments.power_analysis.NormalPowerAnalysis.power_analysis","text":"Run power analysis by simulation, using standard errors from the analysis. Parameters: Name Type Description Default df DataFrame Dataframe with outcome and treatment variables. required pre_experiment_df Optional[pandas.core.frame.DataFrame] Dataframe with pre-experiment data. None verbose bool Whether to show progress bar. False average_effect float Average effect of treatment. 0.0 n_simulations Optional[int] Number of simulations to run. None alpha Optional[float] Significance level. None Source code in cluster_experiments/power_analysis.py def power_analysis ( self , df : pd . DataFrame , pre_experiment_df : Optional [ pd . DataFrame ] = None , verbose : bool = False , average_effect : float = 0.0 , n_simulations : Optional [ int ] = None , alpha : Optional [ float ] = None , ) -> float : \"\"\" Run power analysis by simulation, using standard errors from the analysis. Args: df: Dataframe with outcome and treatment variables. pre_experiment_df: Dataframe with pre-experiment data. verbose: Whether to show progress bar. average_effect: Average effect of treatment. n_simulations: Number of simulations to run. alpha: Significance level. \"\"\" return self . power_line ( df = df , pre_experiment_df = pre_experiment_df , verbose = verbose , average_effects = [ average_effect ], n_simulations = n_simulations , alpha = alpha , )[ average_effect ]","title":"power_analysis()"},{"location":"api/power_analysis.html#cluster_experiments.power_analysis.NormalPowerAnalysis.power_line","text":"Run power analysis by simulation, using standard errors from the analysis. Parameters: Name Type Description Default df DataFrame Dataframe with outcome and treatment variables. required pre_experiment_df Optional[pandas.core.frame.DataFrame] Dataframe with pre-experiment data. None verbose bool Whether to show progress bar. False average_effects Iterable[float] Average effects to test. () n_simulations Optional[int] Number of simulations to run. None alpha Optional[float] Significance level. None Source code in cluster_experiments/power_analysis.py def power_line ( self , df : pd . DataFrame , pre_experiment_df : Optional [ pd . DataFrame ] = None , verbose : bool = False , average_effects : Iterable [ float ] = (), n_simulations : Optional [ int ] = None , alpha : Optional [ float ] = None , ) -> Dict [ float , float ]: \"\"\" Run power analysis by simulation, using standard errors from the analysis. Args: df: Dataframe with outcome and treatment variables. pre_experiment_df: Dataframe with pre-experiment data. verbose: Whether to show progress bar. average_effects: Average effects to test. n_simulations: Number of simulations to run. alpha: Significance level. \"\"\" alpha = self . alpha if alpha is None else alpha std_error_mean = self . _get_average_standard_error ( df = df , pre_experiment_df = pre_experiment_df , verbose = verbose , n_simulations = n_simulations , ) return { effect : self . _normal_power_calculation ( alpha = alpha , std_error = std_error_mean , average_effect = effect ) for effect in average_effects }","title":"power_line()"},{"location":"api/power_analysis.html#cluster_experiments.power_analysis.NormalPowerAnalysis.power_time_line","text":"Run power analysis by simulation, using standard errors from the analysis. Parameters: Name Type Description Default df DataFrame Dataframe with outcome and treatment variables. required pre_experiment_df Optional[pandas.core.frame.DataFrame] Dataframe with pre-experiment data. None verbose bool Whether to show progress bar. False average_effects Iterable[float] Average effects to test. () experiment_length Iterable[int] Length of the experiment in days. () n_simulations Optional[int] Number of simulations to run. None alpha Optional[float] Significance level. None Source code in cluster_experiments/power_analysis.py def power_time_line ( self , df : pd . DataFrame , pre_experiment_df : Optional [ pd . DataFrame ] = None , verbose : bool = False , average_effects : Iterable [ float ] = (), experiment_length : Iterable [ int ] = (), n_simulations : Optional [ int ] = None , alpha : Optional [ float ] = None , ) -> List [ Dict ]: \"\"\" Run power analysis by simulation, using standard errors from the analysis. Args: df: Dataframe with outcome and treatment variables. pre_experiment_df: Dataframe with pre-experiment data. verbose: Whether to show progress bar. average_effects: Average effects to test. experiment_length: Length of the experiment in days. n_simulations: Number of simulations to run. alpha: Significance level. \"\"\" alpha = self . alpha if alpha is None else alpha results = [] for std_error_mean , n_days in self . run_average_standard_error ( df = df , pre_experiment_df = pre_experiment_df , verbose = verbose , n_simulations = n_simulations , experiment_length = experiment_length , ): for effect in average_effects : power = self . _normal_power_calculation ( alpha = alpha , std_error = std_error_mean , average_effect = effect ) results . append ( { \"effect\" : effect , \"power\" : power , \"experiment_length\" : n_days } ) return results","title":"power_time_line()"},{"location":"api/power_analysis.html#cluster_experiments.power_analysis.NormalPowerAnalysis.run_average_standard_error","text":"Run power analysis by simulation, using standard errors from the analysis. Parameters: Name Type Description Default df DataFrame Dataframe with outcome and treatment variables. required pre_experiment_df Optional[pandas.core.frame.DataFrame] Dataframe with pre-experiment data. None verbose bool Whether to show progress bar. False n_simulations Optional[int] Number of simulations to run. None experiment_length Iterable[int] Length of the experiment in days. () Source code in cluster_experiments/power_analysis.py def run_average_standard_error ( self , df : pd . DataFrame , pre_experiment_df : Optional [ pd . DataFrame ] = None , verbose : bool = False , n_simulations : Optional [ int ] = None , experiment_length : Iterable [ int ] = (), ) -> Generator [ Tuple [ float , int ], None , None ]: \"\"\" Run power analysis by simulation, using standard errors from the analysis. Args: df: Dataframe with outcome and treatment variables. pre_experiment_df: Dataframe with pre-experiment data. verbose: Whether to show progress bar. n_simulations: Number of simulations to run. experiment_length: Length of the experiment in days. \"\"\" n_simulations = self . n_simulations if n_simulations is None else n_simulations for n_days in experiment_length : df_time = df . copy () experiment_start = df_time [ self . time_col ] . min () df_time = df_time . loc [ df_time [ self . time_col ] < experiment_start + pd . Timedelta ( days = n_days ) ] std_error_mean = self . _get_average_standard_error ( df = df_time , pre_experiment_df = pre_experiment_df , verbose = verbose , n_simulations = n_simulations , ) yield std_error_mean , n_days","title":"run_average_standard_error()"},{"location":"api/power_analysis.html#cluster_experiments.power_analysis.PowerAnalysis","text":"Class used to run Power analysis. It does so by running simulations. In each simulation: 1. Assign treatment to dataframe randomly 2. Perturbate dataframe 3. Add pre-experiment data if needed 4. Run analysis Finally it returns the power of the analysis by counting how many times the effect was detected. Parameters: Name Type Description Default perturbator Perturbator Perturbator class to perturbate dataframe with treatment assigned. required splitter RandomSplitter RandomSplitter class to randomly assign treatment to dataframe. required analysis ExperimentAnalysis ExperimentAnalysis class to use for analysis. required cupac_model Optional[sklearn.base.BaseEstimator] Sklearn estimator class to add pre-experiment data to dataframe. If None, no pre-experiment data will be added. None target_col str Name of the column with the outcome variable. 'target' treatment_col str Name of the column with the treatment variable. 'treatment' treatment str value of treatment_col considered to be treatment (not control) 'B' control str value of treatment_col considered to be control (not treatment) 'A' n_simulations int Number of simulations to run. 100 alpha float Significance level. 0.05 features_cupac_model Optional[List[str]] Covariates to be used in cupac model None seed Optional[int] Optional. Seed to use for the splitter. None Usage: from datetime import date import numpy as np import pandas as pd from cluster_experiments.experiment_analysis import GeeExperimentAnalysis from cluster_experiments.perturbator import ConstantPerturbator from cluster_experiments.power_analysis import PowerAnalysis from cluster_experiments.random_splitter import ClusteredSplitter N = 1_000 users = [ f \"User { i } \" for i in range ( 1000 )] clusters = [ f \"Cluster { i } \" for i in range ( 100 )] dates = [ f \" { date ( 2022 , 1 , i ) : %Y-%m-%d } \" for i in range ( 1 , 32 )] df = pd . DataFrame ( { \"cluster\" : np . random . choice ( clusters , size = N ), \"target\" : np . random . normal ( 0 , 1 , size = N ), \"user\" : np . random . choice ( users , size = N ), \"date\" : np . random . choice ( dates , size = N ), } ) experiment_dates = [ f \" { date ( 2022 , 1 , i ) : %Y-%m-%d } \" for i in range ( 15 , 32 )] sw = ClusteredSplitter ( cluster_cols = [ \"cluster\" , \"date\" ], ) perturbator = ConstantPerturbator () analysis = GeeExperimentAnalysis ( cluster_cols = [ \"cluster\" , \"date\" ], ) pw = PowerAnalysis ( perturbator = perturbator , splitter = sw , analysis = analysis , n_simulations = 50 ) power = pw . power_analysis ( df , average_effect = 0.1 ) print ( f \" { power = } \" ) Source code in cluster_experiments/power_analysis.py class PowerAnalysis : \"\"\" Class used to run Power analysis. It does so by running simulations. In each simulation: 1. Assign treatment to dataframe randomly 2. Perturbate dataframe 3. Add pre-experiment data if needed 4. Run analysis Finally it returns the power of the analysis by counting how many times the effect was detected. Args: perturbator: Perturbator class to perturbate dataframe with treatment assigned. splitter: RandomSplitter class to randomly assign treatment to dataframe. analysis: ExperimentAnalysis class to use for analysis. cupac_model: Sklearn estimator class to add pre-experiment data to dataframe. If None, no pre-experiment data will be added. target_col: Name of the column with the outcome variable. treatment_col: Name of the column with the treatment variable. treatment: value of treatment_col considered to be treatment (not control) control: value of treatment_col considered to be control (not treatment) n_simulations: Number of simulations to run. alpha: Significance level. features_cupac_model: Covariates to be used in cupac model seed: Optional. Seed to use for the splitter. Usage: ```python from datetime import date import numpy as np import pandas as pd from cluster_experiments.experiment_analysis import GeeExperimentAnalysis from cluster_experiments.perturbator import ConstantPerturbator from cluster_experiments.power_analysis import PowerAnalysis from cluster_experiments.random_splitter import ClusteredSplitter N = 1_000 users = [f\"User {i}\" for i in range(1000)] clusters = [f\"Cluster {i}\" for i in range(100)] dates = [f\"{date(2022, 1, i):%Y-%m-%d}\" for i in range(1, 32)] df = pd.DataFrame( { \"cluster\": np.random.choice(clusters, size=N), \"target\": np.random.normal(0, 1, size=N), \"user\": np.random.choice(users, size=N), \"date\": np.random.choice(dates, size=N), } ) experiment_dates = [f\"{date(2022, 1, i):%Y-%m-%d}\" for i in range(15, 32)] sw = ClusteredSplitter( cluster_cols=[\"cluster\", \"date\"], ) perturbator = ConstantPerturbator() analysis = GeeExperimentAnalysis( cluster_cols=[\"cluster\", \"date\"], ) pw = PowerAnalysis( perturbator=perturbator, splitter=sw, analysis=analysis, n_simulations=50 ) power = pw.power_analysis(df, average_effect=0.1) print(f\"{power = }\") ``` \"\"\" def __init__ ( self , perturbator : Perturbator , splitter : RandomSplitter , analysis : ExperimentAnalysis , cupac_model : Optional [ BaseEstimator ] = None , target_col : str = \"target\" , treatment_col : str = \"treatment\" , treatment : str = \"B\" , control : str = \"A\" , n_simulations : int = 100 , alpha : float = 0.05 , features_cupac_model : Optional [ List [ str ]] = None , seed : Optional [ int ] = None , hypothesis : str = \"two-sided\" , ): self . perturbator = perturbator self . splitter = splitter self . analysis = analysis self . n_simulations = n_simulations self . target_col = target_col self . treatment = treatment self . control = control self . treatment_col = treatment_col self . alpha = alpha self . hypothesis = hypothesis self . cupac_handler = CupacHandler ( cupac_model = cupac_model , target_col = target_col , features_cupac_model = features_cupac_model , ) if seed is not None : random . seed ( seed ) # seed for splitter np . random . seed ( seed ) # seed for the binary perturbator # may need to seed other stochasticity sources if added self . check_inputs () def _simulate_perturbed_df ( self , df : pd . DataFrame , pre_experiment_df : Optional [ pd . DataFrame ] = None , verbose : bool = False , average_effect : Optional [ float ] = None , n_simulations : int = 100 , ) -> Generator [ pd . DataFrame , None , None ]: \"\"\"Yields splitted + perturbated dataframe for each iteration of the simulation.\"\"\" df = df . copy () df = self . cupac_handler . add_covariates ( df , pre_experiment_df ) for _ in tqdm ( range ( n_simulations ), disable = not verbose ): yield self . _split_and_perturbate ( df , average_effect ) def simulate_pvalue ( self , df : pd . DataFrame , pre_experiment_df : Optional [ pd . DataFrame ] = None , verbose : bool = False , average_effect : Optional [ float ] = None , n_simulations : int = 100 , ) -> Generator [ float , None , None ]: \"\"\" Yields p-values for each iteration of the simulation. In general, this is to be used in power_analysis method. However, if you're interested in the distribution of p-values, you can use this method to generate them. Args: df: Dataframe with outcome variable. pre_experiment_df: Dataframe with pre-experiment data. verbose: Whether to show progress bar. average_effect: Average effect of treatment. If None, it will use the perturbator average effect. n_simulations: Number of simulations to run. \"\"\" for perturbed_df in self . _simulate_perturbed_df ( df , pre_experiment_df = pre_experiment_df , verbose = verbose , average_effect = average_effect , n_simulations = n_simulations , ): yield self . analysis . get_pvalue ( perturbed_df ) def running_power_analysis ( self , df : pd . DataFrame , pre_experiment_df : Optional [ pd . DataFrame ] = None , verbose : bool = False , average_effect : Optional [ float ] = None , n_simulations : int = 100 , ) -> Generator [ float , None , None ]: \"\"\" Yields running power for each iteration of the simulation. if you're interested in getting the power at each iteration, you can use this method to generate them. Args: df: Dataframe with outcome variable. pre_experiment_df: Dataframe with pre-experiment data. verbose: Whether to show progress bar. average_effect: Average effect of treatment. If None, it will use the perturbator average effect. n_simulations: Number of simulations to run. \"\"\" n_rejected = 0 for i , perturbed_df in enumerate ( self . _simulate_perturbed_df ( df , pre_experiment_df = pre_experiment_df , verbose = verbose , average_effect = average_effect , n_simulations = n_simulations , ) ): p_value = self . analysis . get_pvalue ( perturbed_df ) n_rejected += int ( p_value < self . alpha ) yield n_rejected / ( i + 1 ) def simulate_point_estimate ( self , df : pd . DataFrame , pre_experiment_df : Optional [ pd . DataFrame ] = None , verbose : bool = False , average_effect : Optional [ float ] = None , n_simulations : int = 100 , ) -> Generator [ float , None , None ]: \"\"\" Yields point estimates for each iteration of the simulation. In general, this is to be used in power_analysis method. However, if you're interested in the distribution of point estimates, you can use this method to generate them. This is an experimental feature and it might change in the future. Args: df: Dataframe with outcome and treatment variables. pre_experiment_df: Dataframe with pre-experiment data. verbose: Whether to show progress bar. average_effect: Average effect of treatment. If None, it will use the perturbator average effect. n_simulations: Number of simulations to run. \"\"\" for perturbed_df in self . _simulate_perturbed_df ( df , pre_experiment_df = pre_experiment_df , verbose = verbose , average_effect = average_effect , n_simulations = n_simulations , ): yield self . analysis . get_point_estimate ( perturbed_df ) def power_analysis ( self , df : pd . DataFrame , pre_experiment_df : Optional [ pd . DataFrame ] = None , verbose : bool = False , average_effect : Optional [ float ] = None , n_simulations : Optional [ int ] = None , alpha : Optional [ float ] = None , n_jobs : int = 1 , ) -> float : \"\"\" Run power analysis by simulation Args: df: Dataframe with outcome and treatment variables. pre_experiment_df: Dataframe with pre-experiment data. verbose: Whether to show progress bar. average_effect: Average effect of treatment. If None, it will use the perturbator average effect. n_simulations: Number of simulations to run. alpha: Significance level. n_jobs: Number of jobs to run in parallel. If 1, it will run in serial. \"\"\" n_simulations = self . n_simulations if n_simulations is None else n_simulations alpha = self . alpha if alpha is None else alpha df = df . copy () df = self . cupac_handler . add_covariates ( df , pre_experiment_df ) if n_jobs == 1 : return self . _non_parallel_loop ( df , average_effect , n_simulations , alpha , verbose ) elif n_jobs > 1 or n_jobs == - 1 : return self . _parallel_loop ( df , average_effect , n_simulations , alpha , verbose , n_jobs ) else : raise ValueError ( \"n_jobs must be greater than 0, or -1.\" ) def _split ( self , df : pd . DataFrame ) -> pd . DataFrame : \"\"\" Split dataframe. Args: df: Dataframe with outcome variable \"\"\" treatment_df = self . splitter . assign_treatment_df ( df ) self . log_nulls ( treatment_df ) treatment_df = treatment_df . query ( f \" { self . treatment_col } .notnull()\" , engine = \"python\" ) . query ( f \" { self . treatment_col } .isin([' { self . treatment } ', ' { self . control } '])\" , engine = \"python\" , ) return treatment_df def _perturbate ( self , treatment_df : pd . DataFrame , average_effect : Optional [ float ] ) -> pd . DataFrame : \"\"\" Perturbate dataframe using perturbator. Args: df: Dataframe with outcome variable average_effect: Average effect of treatment. If None, it will use the perturbator average effect. \"\"\" perturbed_df = self . perturbator . perturbate ( treatment_df , average_effect = average_effect ) return perturbed_df def _split_and_perturbate ( self , df : pd . DataFrame , average_effect : Optional [ float ] ) -> pd . DataFrame : treatment_df = self . _split ( df ) perturbed_df = self . _perturbate ( treatment_df = treatment_df , average_effect = average_effect ) return perturbed_df def _run_simulation ( self , args : Tuple [ pd . DataFrame , Optional [ float ]]) -> float : df , average_effect = args perturbed_df = self . _split_and_perturbate ( df , average_effect ) return self . analysis . get_pvalue ( perturbed_df ) def _non_parallel_loop ( self , df : pd . DataFrame , average_effect : Optional [ float ], n_simulations : int , alpha : float , verbose : bool , ) -> float : \"\"\" Run power analysis by simulation in serial Args: df: Dataframe with outcome and treatment variables. average_effect: Average effect of treatment. If None, it will use the perturbator average effect. n_simulations: Number of simulations to run. alpha: Significance level. \"\"\" n_detected_mde = 0 for _ in tqdm ( range ( n_simulations ), disable = not verbose ): p_value = self . _run_simulation (( df , average_effect )) if verbose : print ( f \"p_value of simulation run: { p_value : .3f } \" ) n_detected_mde += p_value < alpha return n_detected_mde / n_simulations def _parallel_loop ( self , df : pd . DataFrame , average_effect : Optional [ float ], n_simulations : int , alpha : float , verbose : bool , n_jobs : int , ) -> float : \"\"\" Run power analysis by simulation in parallel Args: df: Dataframe with outcome and treatment variables. average_effect: Average effect of treatment. If None, it will use the perturbator average effect. n_simulations: Number of simulations to run. alpha: Significance level. n_jobs: Number of jobs to run in parallel. \"\"\" from multiprocessing import Pool , cpu_count n_jobs = n_jobs if n_jobs != - 1 else cpu_count () n_detected_mde = 0 with Pool ( processes = n_jobs ) as pool : args = [( df , average_effect ) for _ in range ( n_simulations )] results = pool . imap_unordered ( self . _run_simulation , args ) for p_value in tqdm ( results , total = n_simulations , disable = not verbose ): n_detected_mde += p_value < alpha return n_detected_mde / n_simulations def power_line ( self , df : pd . DataFrame , pre_experiment_df : Optional [ pd . DataFrame ] = None , verbose : bool = False , average_effects : Iterable [ float ] = (), n_simulations : Optional [ int ] = None , alpha : Optional [ float ] = None , n_jobs : int = 1 , ) -> Dict [ float , float ]: \"\"\"Runs power analysis with multiple average effects Args: df: Dataframe with outcome and treatment variables. pre_experiment_df: Dataframe with pre-experiment data. verbose: Whether to show progress bar. average_effects: Average effects to test. n_simulations: Number of simulations to run. alpha: Significance level. n_jobs: Number of jobs to run in parallel. Returns: Dictionary with average effects as keys and power as values. \"\"\" return { effect : self . power_analysis ( df = df , pre_experiment_df = pre_experiment_df , verbose = verbose , average_effect = effect , n_simulations = n_simulations , alpha = alpha , n_jobs = n_jobs , ) for effect in tqdm ( list ( average_effects ), disable = not verbose , desc = \"Effects loop\" ) } def log_nulls ( self , df : pd . DataFrame ) -> None : \"\"\"Warns about dropping nulls in treatment column\"\"\" n_nulls = len ( df . query ( f \" { self . treatment_col } .isnull()\" , engine = \"python\" )) if n_nulls > 0 : logging . warning ( f \"There are { n_nulls } null values in treatment, dropping them\" ) @classmethod def from_dict ( cls , config_dict : dict ) -> \"PowerAnalysis\" : \"\"\"Constructs PowerAnalysis from dictionary\"\"\" config = PowerConfig ( ** config_dict ) return cls . from_config ( config ) @classmethod def from_config ( cls , config : PowerConfig ) -> \"PowerAnalysis\" : \"\"\"Constructs PowerAnalysis from PowerConfig\"\"\" perturbator_cls = _get_mapping_key ( perturbator_mapping , config . perturbator ) splitter_cls = _get_mapping_key ( splitter_mapping , config . splitter ) analysis_cls = _get_mapping_key ( analysis_mapping , config . analysis ) cupac_cls = _get_mapping_key ( cupac_model_mapping , config . cupac_model ) return cls ( perturbator = perturbator_cls . from_config ( config ), splitter = splitter_cls . from_config ( config ), analysis = analysis_cls . from_config ( config ), cupac_model = cupac_cls . from_config ( config ), target_col = config . target_col , treatment_col = config . treatment_col , treatment = config . treatment , control = config . control , n_simulations = config . n_simulations , alpha = config . alpha , features_cupac_model = config . features_cupac_model , seed = config . seed , hypothesis = config . hypothesis , ) def check_treatment_col ( self ): \"\"\"Checks consistency of treatment column\"\"\" assert ( self . analysis . treatment_col == self . perturbator . treatment_col ), f \"treatment_col in analysis ( { self . analysis . treatment_col } ) must be the same as treatment_col in perturbator ( { self . perturbator . treatment_col } )\" assert ( self . analysis . treatment_col == self . treatment_col ), f \"treatment_col in analysis ( { self . analysis . treatment_col } ) must be the same as treatment_col in PowerAnalysis ( { self . treatment_col } )\" assert ( self . analysis . treatment_col == self . splitter . treatment_col ), f \"treatment_col in analysis ( { self . analysis . treatment_col } ) must be the same as treatment_col in splitter ( { self . splitter . treatment_col } )\" def check_target_col ( self ): assert ( self . analysis . target_col == self . perturbator . target_col ), f \"target_col in analysis ( { self . analysis . target_col } ) must be the same as target_col in perturbator ( { self . perturbator . target_col } )\" assert ( self . analysis . target_col == self . target_col ), f \"target_col in analysis ( { self . analysis . target_col } ) must be the same as target_col in PowerAnalysis ( { self . target_col } )\" def check_treatment ( self ): assert ( self . analysis . treatment == self . perturbator . treatment ), f \"treatment in analysis ( { self . analysis . treatment } ) must be the same as treatment in perturbator ( { self . perturbator . treatment } )\" assert ( self . analysis . treatment == self . treatment ), f \"treatment in analysis ( { self . analysis . treatment } ) must be the same as treatment in PowerAnalysis ( { self . treatment } )\" assert ( self . analysis . treatment in self . splitter . treatments ), f \"treatment in analysis ( { self . analysis . treatment } ) must be in treatments in splitter ( { self . splitter . treatments } )\" assert ( self . control in self . splitter . treatments ), f \"control in power analysis ( { self . control } ) must be in treatments in splitter ( { self . splitter . treatments } )\" def check_covariates ( self ): if hasattr ( self . analysis , \"covariates\" ): cupac_in_covariates = ( self . cupac_handler . cupac_outcome_name in self . analysis . covariates ) assert cupac_in_covariates or not self . cupac_handler . is_cupac , ( f \"covariates in analysis must contain { self . cupac_handler . cupac_outcome_name } if cupac_model is not None. \" f \"If you want to use cupac_model, you must add the cupac outcome to the covariates of the analysis \" f \"You may want to do covariates=[' { self . cupac_handler . cupac_outcome_name } '] in your analysis method or your config\" ) if hasattr ( self . splitter , \"cluster_cols\" ): if set ( self . analysis . covariates ) . intersection ( set ( self . splitter . cluster_cols ) ): logging . warning ( f \"covariates in analysis ( { self . analysis . covariates } ) are also cluster_cols in splitter ( { self . splitter . cluster_cols } ). \" f \"Be specially careful when using switchback splitters, since the time splitter column is being overriden\" ) def check_clusters ( self ): has_analysis_clusters = hasattr ( self . analysis , \"cluster_cols\" ) has_splitter_clusters = hasattr ( self . splitter , \"cluster_cols\" ) not_cluster_cols_cond = not has_analysis_clusters or not has_splitter_clusters assert ( not_cluster_cols_cond or self . analysis . cluster_cols == self . splitter . cluster_cols ), f \"cluster_cols in analysis ( { self . analysis . cluster_cols } ) must be the same as cluster_cols in splitter ( { self . splitter . cluster_cols } )\" assert ( has_splitter_clusters or not has_analysis_clusters or not self . analysis . cluster_cols or isinstance ( self . splitter , RepeatedSampler ) ), \"analysis has cluster_cols but splitter does not.\" assert ( has_analysis_clusters or not has_splitter_clusters or not self . splitter . cluster_cols ), \"splitter has cluster_cols but analysis does not.\" has_time_col = hasattr ( self . splitter , \"time_col\" ) assert not ( has_time_col and has_splitter_clusters and self . splitter . time_col not in self . splitter . cluster_cols ), \"in switchback splitters, time_col must be in cluster_cols\" def check_inputs ( self ): self . check_covariates () self . check_treatment_col () self . check_target_col () self . check_treatment () self . check_clusters ()","title":"PowerAnalysis"},{"location":"api/power_analysis.html#cluster_experiments.power_analysis.PowerAnalysis.check_treatment_col","text":"Checks consistency of treatment column Source code in cluster_experiments/power_analysis.py def check_treatment_col ( self ): \"\"\"Checks consistency of treatment column\"\"\" assert ( self . analysis . treatment_col == self . perturbator . treatment_col ), f \"treatment_col in analysis ( { self . analysis . treatment_col } ) must be the same as treatment_col in perturbator ( { self . perturbator . treatment_col } )\" assert ( self . analysis . treatment_col == self . treatment_col ), f \"treatment_col in analysis ( { self . analysis . treatment_col } ) must be the same as treatment_col in PowerAnalysis ( { self . treatment_col } )\" assert ( self . analysis . treatment_col == self . splitter . treatment_col ), f \"treatment_col in analysis ( { self . analysis . treatment_col } ) must be the same as treatment_col in splitter ( { self . splitter . treatment_col } )\"","title":"check_treatment_col()"},{"location":"api/power_analysis.html#cluster_experiments.power_analysis.PowerAnalysis.from_config","text":"Constructs PowerAnalysis from PowerConfig Source code in cluster_experiments/power_analysis.py @classmethod def from_config ( cls , config : PowerConfig ) -> \"PowerAnalysis\" : \"\"\"Constructs PowerAnalysis from PowerConfig\"\"\" perturbator_cls = _get_mapping_key ( perturbator_mapping , config . perturbator ) splitter_cls = _get_mapping_key ( splitter_mapping , config . splitter ) analysis_cls = _get_mapping_key ( analysis_mapping , config . analysis ) cupac_cls = _get_mapping_key ( cupac_model_mapping , config . cupac_model ) return cls ( perturbator = perturbator_cls . from_config ( config ), splitter = splitter_cls . from_config ( config ), analysis = analysis_cls . from_config ( config ), cupac_model = cupac_cls . from_config ( config ), target_col = config . target_col , treatment_col = config . treatment_col , treatment = config . treatment , control = config . control , n_simulations = config . n_simulations , alpha = config . alpha , features_cupac_model = config . features_cupac_model , seed = config . seed , hypothesis = config . hypothesis , )","title":"from_config()"},{"location":"api/power_analysis.html#cluster_experiments.power_analysis.PowerAnalysis.from_dict","text":"Constructs PowerAnalysis from dictionary Source code in cluster_experiments/power_analysis.py @classmethod def from_dict ( cls , config_dict : dict ) -> \"PowerAnalysis\" : \"\"\"Constructs PowerAnalysis from dictionary\"\"\" config = PowerConfig ( ** config_dict ) return cls . from_config ( config )","title":"from_dict()"},{"location":"api/power_analysis.html#cluster_experiments.power_analysis.PowerAnalysis.log_nulls","text":"Warns about dropping nulls in treatment column Source code in cluster_experiments/power_analysis.py def log_nulls ( self , df : pd . DataFrame ) -> None : \"\"\"Warns about dropping nulls in treatment column\"\"\" n_nulls = len ( df . query ( f \" { self . treatment_col } .isnull()\" , engine = \"python\" )) if n_nulls > 0 : logging . warning ( f \"There are { n_nulls } null values in treatment, dropping them\" )","title":"log_nulls()"},{"location":"api/power_analysis.html#cluster_experiments.power_analysis.PowerAnalysis.power_analysis","text":"Run power analysis by simulation Parameters: Name Type Description Default df DataFrame Dataframe with outcome and treatment variables. required pre_experiment_df Optional[pandas.core.frame.DataFrame] Dataframe with pre-experiment data. None verbose bool Whether to show progress bar. False average_effect Optional[float] Average effect of treatment. If None, it will use the perturbator average effect. None n_simulations Optional[int] Number of simulations to run. None alpha Optional[float] Significance level. None n_jobs int Number of jobs to run in parallel. If 1, it will run in serial. 1 Source code in cluster_experiments/power_analysis.py def power_analysis ( self , df : pd . DataFrame , pre_experiment_df : Optional [ pd . DataFrame ] = None , verbose : bool = False , average_effect : Optional [ float ] = None , n_simulations : Optional [ int ] = None , alpha : Optional [ float ] = None , n_jobs : int = 1 , ) -> float : \"\"\" Run power analysis by simulation Args: df: Dataframe with outcome and treatment variables. pre_experiment_df: Dataframe with pre-experiment data. verbose: Whether to show progress bar. average_effect: Average effect of treatment. If None, it will use the perturbator average effect. n_simulations: Number of simulations to run. alpha: Significance level. n_jobs: Number of jobs to run in parallel. If 1, it will run in serial. \"\"\" n_simulations = self . n_simulations if n_simulations is None else n_simulations alpha = self . alpha if alpha is None else alpha df = df . copy () df = self . cupac_handler . add_covariates ( df , pre_experiment_df ) if n_jobs == 1 : return self . _non_parallel_loop ( df , average_effect , n_simulations , alpha , verbose ) elif n_jobs > 1 or n_jobs == - 1 : return self . _parallel_loop ( df , average_effect , n_simulations , alpha , verbose , n_jobs ) else : raise ValueError ( \"n_jobs must be greater than 0, or -1.\" )","title":"power_analysis()"},{"location":"api/power_analysis.html#cluster_experiments.power_analysis.PowerAnalysis.power_line","text":"Runs power analysis with multiple average effects Parameters: Name Type Description Default df DataFrame Dataframe with outcome and treatment variables. required pre_experiment_df Optional[pandas.core.frame.DataFrame] Dataframe with pre-experiment data. None verbose bool Whether to show progress bar. False average_effects Iterable[float] Average effects to test. () n_simulations Optional[int] Number of simulations to run. None alpha Optional[float] Significance level. None n_jobs int Number of jobs to run in parallel. 1 Returns: Type Description Dict[float, float] Dictionary with average effects as keys and power as values. Source code in cluster_experiments/power_analysis.py def power_line ( self , df : pd . DataFrame , pre_experiment_df : Optional [ pd . DataFrame ] = None , verbose : bool = False , average_effects : Iterable [ float ] = (), n_simulations : Optional [ int ] = None , alpha : Optional [ float ] = None , n_jobs : int = 1 , ) -> Dict [ float , float ]: \"\"\"Runs power analysis with multiple average effects Args: df: Dataframe with outcome and treatment variables. pre_experiment_df: Dataframe with pre-experiment data. verbose: Whether to show progress bar. average_effects: Average effects to test. n_simulations: Number of simulations to run. alpha: Significance level. n_jobs: Number of jobs to run in parallel. Returns: Dictionary with average effects as keys and power as values. \"\"\" return { effect : self . power_analysis ( df = df , pre_experiment_df = pre_experiment_df , verbose = verbose , average_effect = effect , n_simulations = n_simulations , alpha = alpha , n_jobs = n_jobs , ) for effect in tqdm ( list ( average_effects ), disable = not verbose , desc = \"Effects loop\" ) }","title":"power_line()"},{"location":"api/power_analysis.html#cluster_experiments.power_analysis.PowerAnalysis.running_power_analysis","text":"Yields running power for each iteration of the simulation. if you're interested in getting the power at each iteration, you can use this method to generate them. Parameters: Name Type Description Default df DataFrame Dataframe with outcome variable. required pre_experiment_df Optional[pandas.core.frame.DataFrame] Dataframe with pre-experiment data. None verbose bool Whether to show progress bar. False average_effect Optional[float] Average effect of treatment. If None, it will use the perturbator average effect. None n_simulations int Number of simulations to run. 100 Source code in cluster_experiments/power_analysis.py def running_power_analysis ( self , df : pd . DataFrame , pre_experiment_df : Optional [ pd . DataFrame ] = None , verbose : bool = False , average_effect : Optional [ float ] = None , n_simulations : int = 100 , ) -> Generator [ float , None , None ]: \"\"\" Yields running power for each iteration of the simulation. if you're interested in getting the power at each iteration, you can use this method to generate them. Args: df: Dataframe with outcome variable. pre_experiment_df: Dataframe with pre-experiment data. verbose: Whether to show progress bar. average_effect: Average effect of treatment. If None, it will use the perturbator average effect. n_simulations: Number of simulations to run. \"\"\" n_rejected = 0 for i , perturbed_df in enumerate ( self . _simulate_perturbed_df ( df , pre_experiment_df = pre_experiment_df , verbose = verbose , average_effect = average_effect , n_simulations = n_simulations , ) ): p_value = self . analysis . get_pvalue ( perturbed_df ) n_rejected += int ( p_value < self . alpha ) yield n_rejected / ( i + 1 )","title":"running_power_analysis()"},{"location":"api/power_analysis.html#cluster_experiments.power_analysis.PowerAnalysis.simulate_point_estimate","text":"Yields point estimates for each iteration of the simulation. In general, this is to be used in power_analysis method. However, if you're interested in the distribution of point estimates, you can use this method to generate them. This is an experimental feature and it might change in the future. Parameters: Name Type Description Default df DataFrame Dataframe with outcome and treatment variables. required pre_experiment_df Optional[pandas.core.frame.DataFrame] Dataframe with pre-experiment data. None verbose bool Whether to show progress bar. False average_effect Optional[float] Average effect of treatment. If None, it will use the perturbator average effect. None n_simulations int Number of simulations to run. 100 Source code in cluster_experiments/power_analysis.py def simulate_point_estimate ( self , df : pd . DataFrame , pre_experiment_df : Optional [ pd . DataFrame ] = None , verbose : bool = False , average_effect : Optional [ float ] = None , n_simulations : int = 100 , ) -> Generator [ float , None , None ]: \"\"\" Yields point estimates for each iteration of the simulation. In general, this is to be used in power_analysis method. However, if you're interested in the distribution of point estimates, you can use this method to generate them. This is an experimental feature and it might change in the future. Args: df: Dataframe with outcome and treatment variables. pre_experiment_df: Dataframe with pre-experiment data. verbose: Whether to show progress bar. average_effect: Average effect of treatment. If None, it will use the perturbator average effect. n_simulations: Number of simulations to run. \"\"\" for perturbed_df in self . _simulate_perturbed_df ( df , pre_experiment_df = pre_experiment_df , verbose = verbose , average_effect = average_effect , n_simulations = n_simulations , ): yield self . analysis . get_point_estimate ( perturbed_df )","title":"simulate_point_estimate()"},{"location":"api/power_analysis.html#cluster_experiments.power_analysis.PowerAnalysis.simulate_pvalue","text":"Yields p-values for each iteration of the simulation. In general, this is to be used in power_analysis method. However, if you're interested in the distribution of p-values, you can use this method to generate them. Parameters: Name Type Description Default df DataFrame Dataframe with outcome variable. required pre_experiment_df Optional[pandas.core.frame.DataFrame] Dataframe with pre-experiment data. None verbose bool Whether to show progress bar. False average_effect Optional[float] Average effect of treatment. If None, it will use the perturbator average effect. None n_simulations int Number of simulations to run. 100 Source code in cluster_experiments/power_analysis.py def simulate_pvalue ( self , df : pd . DataFrame , pre_experiment_df : Optional [ pd . DataFrame ] = None , verbose : bool = False , average_effect : Optional [ float ] = None , n_simulations : int = 100 , ) -> Generator [ float , None , None ]: \"\"\" Yields p-values for each iteration of the simulation. In general, this is to be used in power_analysis method. However, if you're interested in the distribution of p-values, you can use this method to generate them. Args: df: Dataframe with outcome variable. pre_experiment_df: Dataframe with pre-experiment data. verbose: Whether to show progress bar. average_effect: Average effect of treatment. If None, it will use the perturbator average effect. n_simulations: Number of simulations to run. \"\"\" for perturbed_df in self . _simulate_perturbed_df ( df , pre_experiment_df = pre_experiment_df , verbose = verbose , average_effect = average_effect , n_simulations = n_simulations , ): yield self . analysis . get_pvalue ( perturbed_df )","title":"simulate_pvalue()"},{"location":"api/power_analysis.html#cluster_experiments.power_analysis.PowerAnalysisWithPreExperimentData","text":"This is intended to work mainly for diff-in-diff or synthetic control-like estimators, and NOT for cases of CUPED/CUPAC. Same as PowerAnalysis, but allowing a perturbation only at experiment period and keeping pre-experiment df intact. Using this class, the pre experiment df is also available when the class is instantiated. Source code in cluster_experiments/power_analysis.py class PowerAnalysisWithPreExperimentData ( PowerAnalysis ): \"\"\" This is intended to work mainly for diff-in-diff or synthetic control-like estimators, and NOT for cases of CUPED/CUPAC. Same as PowerAnalysis, but allowing a perturbation only at experiment period and keeping pre-experiment df intact. Using this class, the pre experiment df is also available when the class is instantiated. \"\"\" def _perturbate ( self , treatment_df : pd . DataFrame , average_effect : Optional [ float ] ) -> pd . DataFrame : if not hasattr ( self . analysis , \"_split_pre_experiment_df\" ): raise AttributeError ( \"The PowerAnalysisWithPreExperimentData is intended to work mainly for diff-in-diff or synthetic control-like estimators.\" \"For other cases use the PowerAnalysis\" ) df , pre_experiment_df = self . analysis . _split_pre_experiment_df ( treatment_df ) perturbed_df = self . perturbator . perturbate ( df , average_effect = average_effect ) return pd . concat ([ perturbed_df , pre_experiment_df ])","title":"PowerAnalysisWithPreExperimentData"},{"location":"api/power_config.html","text":"from cluster_experiments.power_config import * \u00b6 PowerConfig dataclass \u00b6 Dataclass to create a power analysis from. Parameters: Name Type Description Default splitter str Splitter object to use required perturbator str Perturbator object to use, defaults to \"\" for normal power analysis '' analysis str ExperimentAnalysis object to use required washover str Washover object to use, defaults to \"\" '' cupac_model str CUPAC model to use '' n_simulations int number of simulations to run 100 cluster_cols Optional[List[str]] list of columns to use as clusters None target_col str column to use as target 'target' treatment_col str column to use as treatment 'treatment' treatment str what value of treatment_col should be considered as treatment 'B' control str what value of treatment_col should be considered as control 'A' strata_cols Optional[List[str]] columns to stratify with None splitter_weights Optional[List[float]] weights to use for the splitter, should have the same length as treatments, each weight should correspond to an element in treatments None switch_frequency Optional[str] how often to switch treatments None time_col Optional[str] column to use as time in switchback splitter None washover_time_delta Union[datetime.timedelta, int] optional, int indicating the washover time in minutes or datetime.timedelta object None covariates Optional[List[str]] list of columns to use as covariates None average_effect Optional[float] average effect to use in the perturbator None scale Optional[float] scale to use in stochastic perturbators None range_min Optional[float] minimum value of the target range for relative beta perturbator, must be >-1 None range_max Optional[float] maximum value of the target range for relative beta perturbator None reduce_variance Optional[bool] whether to reduce variance in the BetaRelative perturbator None segment_cols Optional[List[str]] list of segmentation columns for segmented perturbator None treatments Optional[List[str]] list of treatments to use None alpha float alpha value to use in the power analysis 0.05 agg_col str column to use for aggregation in the CUPAC model '' smoothing_factor float smoothing value to use in the CUPAC model 20 features_cupac_model Optional[List[str]] list of features to use in the CUPAC model None seed Optional[int] seed to make the power analysis reproducible None Usage: from cluster_experiments.power_config import PowerConfig from cluster_experiments.power_analysis import PowerAnalysis , NormalPowerAnalysis p = PowerConfig ( analysis = \"gee\" , splitter = \"clustered_balance\" , perturbator = \"constant\" , cluster_cols = [ \"city\" ], n_simulations = 100 , alpha = 0.05 , ) power_analysis = PowerAnalysis . from_config ( p ) normal_power_analysis = NormalPowerAnalysis . from_config ( p ) Source code in cluster_experiments/power_config.py class PowerConfig : \"\"\" Dataclass to create a power analysis from. Arguments: splitter: Splitter object to use perturbator: Perturbator object to use, defaults to \"\" for normal power analysis analysis: ExperimentAnalysis object to use washover: Washover object to use, defaults to \"\" cupac_model: CUPAC model to use n_simulations: number of simulations to run cluster_cols: list of columns to use as clusters target_col: column to use as target treatment_col: column to use as treatment treatment: what value of treatment_col should be considered as treatment control: what value of treatment_col should be considered as control strata_cols: columns to stratify with splitter_weights: weights to use for the splitter, should have the same length as treatments, each weight should correspond to an element in treatments switch_frequency: how often to switch treatments time_col: column to use as time in switchback splitter washover_time_delta: optional, int indicating the washover time in minutes or datetime.timedelta object covariates: list of columns to use as covariates average_effect: average effect to use in the perturbator scale: scale to use in stochastic perturbators range_min: minimum value of the target range for relative beta perturbator, must be >-1 range_max: maximum value of the target range for relative beta perturbator reduce_variance: whether to reduce variance in the BetaRelative perturbator segment_cols: list of segmentation columns for segmented perturbator treatments: list of treatments to use alpha: alpha value to use in the power analysis agg_col: column to use for aggregation in the CUPAC model smoothing_factor: smoothing value to use in the CUPAC model features_cupac_model: list of features to use in the CUPAC model seed: seed to make the power analysis reproducible Usage: ```python from cluster_experiments.power_config import PowerConfig from cluster_experiments.power_analysis import PowerAnalysis, NormalPowerAnalysis p = PowerConfig( analysis=\"gee\", splitter=\"clustered_balance\", perturbator=\"constant\", cluster_cols=[\"city\"], n_simulations=100, alpha=0.05, ) power_analysis = PowerAnalysis.from_config(p) normal_power_analysis = NormalPowerAnalysis.from_config(p) ``` \"\"\" # mappings splitter : str analysis : str perturbator : str = \"\" washover : str = \"\" # Needed cluster_cols : Optional [ List [ str ]] = None # optional mappings cupac_model : str = \"\" # Shared target_col : str = \"target\" treatment_col : str = \"treatment\" treatment : str = \"B\" # Perturbator average_effect : Optional [ float ] = None scale : Optional [ float ] = None range_min : Optional [ float ] = None range_max : Optional [ float ] = None reduce_variance : Optional [ bool ] = None segment_cols : Optional [ List [ str ]] = None # Splitter treatments : Optional [ List [ str ]] = None strata_cols : Optional [ List [ str ]] = None splitter_weights : Optional [ List [ float ]] = None switch_frequency : Optional [ str ] = None # Switchback time_col : Optional [ str ] = None washover_time_delta : Optional [ Union [ datetime . timedelta , int ]] = None # Analysis covariates : Optional [ List [ str ]] = None hypothesis : str = \"two-sided\" # Power analysis n_simulations : int = 100 alpha : float = 0.05 control : str = \"A\" # Cupac agg_col : str = \"\" smoothing_factor : float = 20 features_cupac_model : Optional [ List [ str ]] = None seed : Optional [ int ] = None def __post_init__ ( self ): if \"switchback\" not in self . splitter : if self . _are_different ( self . switch_frequency , None ): self . _set_and_log ( \"switch_frequency\" , None , \"splitter\" ) if self . _are_different ( self . washover_time_delta , None ): self . _set_and_log ( \"washover_time_delta\" , None , \"splitter\" ) if self . _are_different ( self . washover , \"\" ): self . _set_and_log ( \"washover\" , \"\" , \"splitter\" ) # an exception is made when we have no perturbator (normal power analysis) if self . _are_different ( self . time_col , None ) and self . perturbator != \"\" : self . _set_and_log ( \"time_col\" , None , \"splitter\" ) if self . perturbator not in { \"normal\" , \"beta_relative_positive\" }: if self . _are_different ( self . scale , None ): self . _set_and_log ( \"scale\" , None , \"perturbator\" ) if self . perturbator not in { \"beta_relative\" , \"segmented_beta_relative\" }: if self . _are_different ( self . range_min , None ): self . _set_and_log ( \"range_min\" , None , \"perturbator\" ) if self . _are_different ( self . range_max , None ): self . _set_and_log ( \"range_max\" , None , \"perturbator\" ) if self . _are_different ( self . reduce_variance , None ): self . _set_and_log ( \"reduce_variance\" , None , \"perturbator\" ) if self . perturbator not in { \"segmented_beta_relative\" }: if self . _are_different ( self . segment_cols , None ): self . _set_and_log ( \"segment_cols\" , None , \"perturbator\" ) if \"stratified\" not in self . splitter and \"paired_ttest\" not in self . analysis : if self . _are_different ( self . strata_cols , None ): self . _set_and_log ( \"strata_cols\" , None , \"splitter\" ) if \"stratified\" in self . splitter or \"balanced\" in self . splitter : if self . _are_different ( self . splitter_weights , None ): self . _set_and_log ( \"splitter_weights\" , None , \"splitter\" ) if self . cupac_model != \"mean_cupac_model\" : if self . _are_different ( self . agg_col , \"\" ): self . _set_and_log ( \"agg_col\" , \"\" , \"cupac_model\" ) if self . _are_different ( self . smoothing_factor , 20 ): self . _set_and_log ( \"smoothing_factor\" , 20 , \"cupac_model\" ) # for now, features_cupac_model are not used if self . _are_different ( self . features_cupac_model , None ): self . _set_and_log ( \"features_cupac_model\" , None , \"cupac_model\" ) if \"ttest\" in self . analysis : if self . _are_different ( self . covariates , None ): self . _set_and_log ( \"covariates\" , None , \"analysis\" ) if \"segmented\" in self . perturbator : self . _raise_error_if_missing ( \"segment_cols\" , \"perturbator\" ) def _are_different ( self , arg1 , arg2 ) -> bool : return arg1 != arg2 def _set_and_log ( self , attr , value , other_attr ): logging . warning ( f \" { attr } = { getattr ( self , attr ) } has no effect with \" f \" { other_attr } = { getattr ( self , other_attr ) } . \" f \"Overriding { attr } to { value } .\" ) setattr ( self , attr , value ) def _raise_error_if_missing ( self , attr , other_attr ): if getattr ( self , attr ) is None : raise MissingArgumentError ( f \" { attr } is required when using \" f \" { other_attr } = { getattr ( self , other_attr ) } .\" )","title":"Power config"},{"location":"api/power_config.html#from-cluster_experimentspower_config-import","text":"","title":"from cluster_experiments.power_config import *"},{"location":"api/power_config.html#cluster_experiments.power_config.PowerConfig","text":"Dataclass to create a power analysis from. Parameters: Name Type Description Default splitter str Splitter object to use required perturbator str Perturbator object to use, defaults to \"\" for normal power analysis '' analysis str ExperimentAnalysis object to use required washover str Washover object to use, defaults to \"\" '' cupac_model str CUPAC model to use '' n_simulations int number of simulations to run 100 cluster_cols Optional[List[str]] list of columns to use as clusters None target_col str column to use as target 'target' treatment_col str column to use as treatment 'treatment' treatment str what value of treatment_col should be considered as treatment 'B' control str what value of treatment_col should be considered as control 'A' strata_cols Optional[List[str]] columns to stratify with None splitter_weights Optional[List[float]] weights to use for the splitter, should have the same length as treatments, each weight should correspond to an element in treatments None switch_frequency Optional[str] how often to switch treatments None time_col Optional[str] column to use as time in switchback splitter None washover_time_delta Union[datetime.timedelta, int] optional, int indicating the washover time in minutes or datetime.timedelta object None covariates Optional[List[str]] list of columns to use as covariates None average_effect Optional[float] average effect to use in the perturbator None scale Optional[float] scale to use in stochastic perturbators None range_min Optional[float] minimum value of the target range for relative beta perturbator, must be >-1 None range_max Optional[float] maximum value of the target range for relative beta perturbator None reduce_variance Optional[bool] whether to reduce variance in the BetaRelative perturbator None segment_cols Optional[List[str]] list of segmentation columns for segmented perturbator None treatments Optional[List[str]] list of treatments to use None alpha float alpha value to use in the power analysis 0.05 agg_col str column to use for aggregation in the CUPAC model '' smoothing_factor float smoothing value to use in the CUPAC model 20 features_cupac_model Optional[List[str]] list of features to use in the CUPAC model None seed Optional[int] seed to make the power analysis reproducible None Usage: from cluster_experiments.power_config import PowerConfig from cluster_experiments.power_analysis import PowerAnalysis , NormalPowerAnalysis p = PowerConfig ( analysis = \"gee\" , splitter = \"clustered_balance\" , perturbator = \"constant\" , cluster_cols = [ \"city\" ], n_simulations = 100 , alpha = 0.05 , ) power_analysis = PowerAnalysis . from_config ( p ) normal_power_analysis = NormalPowerAnalysis . from_config ( p ) Source code in cluster_experiments/power_config.py class PowerConfig : \"\"\" Dataclass to create a power analysis from. Arguments: splitter: Splitter object to use perturbator: Perturbator object to use, defaults to \"\" for normal power analysis analysis: ExperimentAnalysis object to use washover: Washover object to use, defaults to \"\" cupac_model: CUPAC model to use n_simulations: number of simulations to run cluster_cols: list of columns to use as clusters target_col: column to use as target treatment_col: column to use as treatment treatment: what value of treatment_col should be considered as treatment control: what value of treatment_col should be considered as control strata_cols: columns to stratify with splitter_weights: weights to use for the splitter, should have the same length as treatments, each weight should correspond to an element in treatments switch_frequency: how often to switch treatments time_col: column to use as time in switchback splitter washover_time_delta: optional, int indicating the washover time in minutes or datetime.timedelta object covariates: list of columns to use as covariates average_effect: average effect to use in the perturbator scale: scale to use in stochastic perturbators range_min: minimum value of the target range for relative beta perturbator, must be >-1 range_max: maximum value of the target range for relative beta perturbator reduce_variance: whether to reduce variance in the BetaRelative perturbator segment_cols: list of segmentation columns for segmented perturbator treatments: list of treatments to use alpha: alpha value to use in the power analysis agg_col: column to use for aggregation in the CUPAC model smoothing_factor: smoothing value to use in the CUPAC model features_cupac_model: list of features to use in the CUPAC model seed: seed to make the power analysis reproducible Usage: ```python from cluster_experiments.power_config import PowerConfig from cluster_experiments.power_analysis import PowerAnalysis, NormalPowerAnalysis p = PowerConfig( analysis=\"gee\", splitter=\"clustered_balance\", perturbator=\"constant\", cluster_cols=[\"city\"], n_simulations=100, alpha=0.05, ) power_analysis = PowerAnalysis.from_config(p) normal_power_analysis = NormalPowerAnalysis.from_config(p) ``` \"\"\" # mappings splitter : str analysis : str perturbator : str = \"\" washover : str = \"\" # Needed cluster_cols : Optional [ List [ str ]] = None # optional mappings cupac_model : str = \"\" # Shared target_col : str = \"target\" treatment_col : str = \"treatment\" treatment : str = \"B\" # Perturbator average_effect : Optional [ float ] = None scale : Optional [ float ] = None range_min : Optional [ float ] = None range_max : Optional [ float ] = None reduce_variance : Optional [ bool ] = None segment_cols : Optional [ List [ str ]] = None # Splitter treatments : Optional [ List [ str ]] = None strata_cols : Optional [ List [ str ]] = None splitter_weights : Optional [ List [ float ]] = None switch_frequency : Optional [ str ] = None # Switchback time_col : Optional [ str ] = None washover_time_delta : Optional [ Union [ datetime . timedelta , int ]] = None # Analysis covariates : Optional [ List [ str ]] = None hypothesis : str = \"two-sided\" # Power analysis n_simulations : int = 100 alpha : float = 0.05 control : str = \"A\" # Cupac agg_col : str = \"\" smoothing_factor : float = 20 features_cupac_model : Optional [ List [ str ]] = None seed : Optional [ int ] = None def __post_init__ ( self ): if \"switchback\" not in self . splitter : if self . _are_different ( self . switch_frequency , None ): self . _set_and_log ( \"switch_frequency\" , None , \"splitter\" ) if self . _are_different ( self . washover_time_delta , None ): self . _set_and_log ( \"washover_time_delta\" , None , \"splitter\" ) if self . _are_different ( self . washover , \"\" ): self . _set_and_log ( \"washover\" , \"\" , \"splitter\" ) # an exception is made when we have no perturbator (normal power analysis) if self . _are_different ( self . time_col , None ) and self . perturbator != \"\" : self . _set_and_log ( \"time_col\" , None , \"splitter\" ) if self . perturbator not in { \"normal\" , \"beta_relative_positive\" }: if self . _are_different ( self . scale , None ): self . _set_and_log ( \"scale\" , None , \"perturbator\" ) if self . perturbator not in { \"beta_relative\" , \"segmented_beta_relative\" }: if self . _are_different ( self . range_min , None ): self . _set_and_log ( \"range_min\" , None , \"perturbator\" ) if self . _are_different ( self . range_max , None ): self . _set_and_log ( \"range_max\" , None , \"perturbator\" ) if self . _are_different ( self . reduce_variance , None ): self . _set_and_log ( \"reduce_variance\" , None , \"perturbator\" ) if self . perturbator not in { \"segmented_beta_relative\" }: if self . _are_different ( self . segment_cols , None ): self . _set_and_log ( \"segment_cols\" , None , \"perturbator\" ) if \"stratified\" not in self . splitter and \"paired_ttest\" not in self . analysis : if self . _are_different ( self . strata_cols , None ): self . _set_and_log ( \"strata_cols\" , None , \"splitter\" ) if \"stratified\" in self . splitter or \"balanced\" in self . splitter : if self . _are_different ( self . splitter_weights , None ): self . _set_and_log ( \"splitter_weights\" , None , \"splitter\" ) if self . cupac_model != \"mean_cupac_model\" : if self . _are_different ( self . agg_col , \"\" ): self . _set_and_log ( \"agg_col\" , \"\" , \"cupac_model\" ) if self . _are_different ( self . smoothing_factor , 20 ): self . _set_and_log ( \"smoothing_factor\" , 20 , \"cupac_model\" ) # for now, features_cupac_model are not used if self . _are_different ( self . features_cupac_model , None ): self . _set_and_log ( \"features_cupac_model\" , None , \"cupac_model\" ) if \"ttest\" in self . analysis : if self . _are_different ( self . covariates , None ): self . _set_and_log ( \"covariates\" , None , \"analysis\" ) if \"segmented\" in self . perturbator : self . _raise_error_if_missing ( \"segment_cols\" , \"perturbator\" ) def _are_different ( self , arg1 , arg2 ) -> bool : return arg1 != arg2 def _set_and_log ( self , attr , value , other_attr ): logging . warning ( f \" { attr } = { getattr ( self , attr ) } has no effect with \" f \" { other_attr } = { getattr ( self , other_attr ) } . \" f \"Overriding { attr } to { value } .\" ) setattr ( self , attr , value ) def _raise_error_if_missing ( self , attr , other_attr ): if getattr ( self , attr ) is None : raise MissingArgumentError ( f \" { attr } is required when using \" f \" { other_attr } = { getattr ( self , other_attr ) } .\" )","title":"PowerConfig"},{"location":"api/random_splitter.html","text":"from cluster_experiments.random_splitter import * \u00b6 BalancedClusteredSplitter ( ClusteredSplitter ) \u00b6 Like ClusteredSplitter, but ensures that treatments are balanced among clusters. That is, if we have 25 clusters and 2 treatments, 13 clusters should have treatment A and 12 clusters should have treatment B. Source code in cluster_experiments/random_splitter.py class BalancedClusteredSplitter ( ClusteredSplitter ): \"\"\"Like ClusteredSplitter, but ensures that treatments are balanced among clusters. That is, if we have 25 clusters and 2 treatments, 13 clusters should have treatment A and 12 clusters should have treatment B.\"\"\" def sample_treatment ( self , cluster_df : pd . DataFrame , ) -> List [ str ]: \"\"\" Samples treatments for each cluster Arguments: cluster_df: dataframe to assign treatments to \"\"\" n_clusters = len ( cluster_df ) n_treatments = len ( self . treatments ) n_per_treatment = n_clusters // n_treatments n_extra = n_clusters % n_treatments treatments = [] for i in range ( n_treatments ): treatments += [ self . treatments [ i ]] * ( n_per_treatment + ( i < n_extra )) random . shuffle ( treatments ) return treatments sample_treatment ( self , cluster_df ) \u00b6 Samples treatments for each cluster Parameters: Name Type Description Default cluster_df DataFrame dataframe to assign treatments to required Source code in cluster_experiments/random_splitter.py def sample_treatment ( self , cluster_df : pd . DataFrame , ) -> List [ str ]: \"\"\" Samples treatments for each cluster Arguments: cluster_df: dataframe to assign treatments to \"\"\" n_clusters = len ( cluster_df ) n_treatments = len ( self . treatments ) n_per_treatment = n_clusters // n_treatments n_extra = n_clusters % n_treatments treatments = [] for i in range ( n_treatments ): treatments += [ self . treatments [ i ]] * ( n_per_treatment + ( i < n_extra )) random . shuffle ( treatments ) return treatments BalancedSwitchbackSplitter ( BalancedClusteredSplitter , SwitchbackSplitter ) \u00b6 Like SwitchbackSplitter, but ensures that treatments are balanced among clusters. That is, if we have 25 clusters and 2 treatments, 13 clusters should have treatment A and 12 clusters should have treatment B. Source code in cluster_experiments/random_splitter.py class BalancedSwitchbackSplitter ( BalancedClusteredSplitter , SwitchbackSplitter ): \"\"\" Like SwitchbackSplitter, but ensures that treatments are balanced among clusters. That is, if we have 25 clusters and 2 treatments, 13 clusters should have treatment A and 12 clusters should have treatment B. \"\"\" pass ClusteredSplitter ( RandomSplitter ) \u00b6 Splits randomly using clusters Parameters: Name Type Description Default cluster_cols List[str] List of columns to use as clusters required treatments Optional[List[str]] list of treatments None treatment_col str Name of the column with the treatment variable. 'treatment' splitter_weights Optional[List[float]] weights to use for the splitter, should have the same length as treatments, each weight should correspond to an element in treatments None Usage: import pandas as pd from cluster_experiments.random_splitter import ClusteredSplitter splitter = ClusteredSplitter ( cluster_cols = [ \"city\" ]) df = pd . DataFrame ({ \"city\" : [ \"A\" , \"B\" , \"C\" ]}) df = splitter . assign_treatment_df ( df ) print ( df ) Source code in cluster_experiments/random_splitter.py class ClusteredSplitter ( RandomSplitter ): \"\"\" Splits randomly using clusters Arguments: cluster_cols: List of columns to use as clusters treatments: list of treatments treatment_col: Name of the column with the treatment variable. splitter_weights: weights to use for the splitter, should have the same length as treatments, each weight should correspond to an element in treatments Usage: ```python import pandas as pd from cluster_experiments.random_splitter import ClusteredSplitter splitter = ClusteredSplitter(cluster_cols=[\"city\"]) df = pd.DataFrame({\"city\": [\"A\", \"B\", \"C\"]}) df = splitter.assign_treatment_df(df) print(df) ``` \"\"\" def __init__ ( self , cluster_cols : List [ str ], treatments : Optional [ List [ str ]] = None , treatment_col : str = \"treatment\" , splitter_weights : Optional [ List [ float ]] = None , ) -> None : self . treatments = treatments or [ \"A\" , \"B\" ] self . cluster_cols = cluster_cols self . treatment_col = treatment_col self . splitter_weights = splitter_weights def assign_treatment_df ( self , df : pd . DataFrame , ) -> pd . DataFrame : \"\"\" Takes a df, randomizes treatments and adds the treatment column to the dataframe Arguments: df: dataframe to assign treatments to \"\"\" df = df . copy () # raise error if any nulls in cluster_cols if df [ self . cluster_cols ] . isnull () . values . any (): raise ValueError ( f \"Null values found in cluster_cols: { self . cluster_cols } . \" \"Please remove nulls before running the splitter.\" ) clusters_df = df . loc [:, self . cluster_cols ] . drop_duplicates () clusters_df [ self . treatment_col ] = self . sample_treatment ( clusters_df ) df = df . merge ( clusters_df , on = self . cluster_cols , how = \"left\" ) return df def sample_treatment ( self , cluster_df : pd . DataFrame , ) -> List [ str ]: \"\"\" Samples treatments for each cluster Arguments: cluster_df: dataframe to assign treatments to \"\"\" return random . choices ( self . treatments , k = len ( cluster_df ), weights = self . splitter_weights ) assign_treatment_df ( self , df ) \u00b6 Takes a df, randomizes treatments and adds the treatment column to the dataframe Parameters: Name Type Description Default df DataFrame dataframe to assign treatments to required Source code in cluster_experiments/random_splitter.py def assign_treatment_df ( self , df : pd . DataFrame , ) -> pd . DataFrame : \"\"\" Takes a df, randomizes treatments and adds the treatment column to the dataframe Arguments: df: dataframe to assign treatments to \"\"\" df = df . copy () # raise error if any nulls in cluster_cols if df [ self . cluster_cols ] . isnull () . values . any (): raise ValueError ( f \"Null values found in cluster_cols: { self . cluster_cols } . \" \"Please remove nulls before running the splitter.\" ) clusters_df = df . loc [:, self . cluster_cols ] . drop_duplicates () clusters_df [ self . treatment_col ] = self . sample_treatment ( clusters_df ) df = df . merge ( clusters_df , on = self . cluster_cols , how = \"left\" ) return df sample_treatment ( self , cluster_df ) \u00b6 Samples treatments for each cluster Parameters: Name Type Description Default cluster_df DataFrame dataframe to assign treatments to required Source code in cluster_experiments/random_splitter.py def sample_treatment ( self , cluster_df : pd . DataFrame , ) -> List [ str ]: \"\"\" Samples treatments for each cluster Arguments: cluster_df: dataframe to assign treatments to \"\"\" return random . choices ( self . treatments , k = len ( cluster_df ), weights = self . splitter_weights ) FixedSizeClusteredSplitter ( ClusteredSplitter ) \u00b6 This class represents a splitter that splits clusters into treatment groups with a predefined number of treatment clusters. This is particularly useful for synthetic control analysis, where we only want 1 cluster ( unit) to be in treatment group and the rest in control The cluster that receives treatment remains random. Attributes: Name Type Description cluster_cols List[str] List of columns to use as clusters. n_treatment_clusters int The predefined number of treatment clusters. Source code in cluster_experiments/random_splitter.py class FixedSizeClusteredSplitter ( ClusteredSplitter ): \"\"\" This class represents a splitter that splits clusters into treatment groups with a predefined number of treatment clusters. This is particularly useful for synthetic control analysis, where we only want 1 cluster ( unit) to be in treatment group and the rest in control The cluster that receives treatment remains random. Attributes: cluster_cols (List[str]): List of columns to use as clusters. n_treatment_clusters (int): The predefined number of treatment clusters. \"\"\" def __init__ ( self , cluster_cols : List [ str ], n_treatment_clusters : int ): super () . __init__ ( cluster_cols = cluster_cols ) self . n_treatment_clusters = n_treatment_clusters def sample_treatment ( self , cluster_df : pd . DataFrame , ) -> List [ str ]: \"\"\" Samples treatments for each cluster. Args: cluster_df (pd.DataFrame): Dataframe to assign treatments to. Returns: List[str]: A list of treatments for each cluster. \"\"\" n_control_treatment = [ len ( cluster_df ) - self . n_treatment_clusters , self . n_treatment_clusters , ] sample_treatment = [ treatment for treatment , count in zip ( self . treatments , n_control_treatment ) for _ in range ( count ) ] random . shuffle ( sample_treatment ) return sample_treatment sample_treatment ( self , cluster_df ) \u00b6 Samples treatments for each cluster. Parameters: Name Type Description Default cluster_df pd.DataFrame Dataframe to assign treatments to. required Returns: Type Description List[str] A list of treatments for each cluster. Source code in cluster_experiments/random_splitter.py def sample_treatment ( self , cluster_df : pd . DataFrame , ) -> List [ str ]: \"\"\" Samples treatments for each cluster. Args: cluster_df (pd.DataFrame): Dataframe to assign treatments to. Returns: List[str]: A list of treatments for each cluster. \"\"\" n_control_treatment = [ len ( cluster_df ) - self . n_treatment_clusters , self . n_treatment_clusters , ] sample_treatment = [ treatment for treatment , count in zip ( self . treatments , n_control_treatment ) for _ in range ( count ) ] random . shuffle ( sample_treatment ) return sample_treatment NonClusteredSplitter ( RandomSplitter ) \u00b6 Splits randomly without clusters Parameters: Name Type Description Default treatments Optional[List[str]] list of treatments None treatment_col str Name of the column with the treatment variable. 'treatment' Usage: import pandas as pd from cluster_experiments.random_splitter import NonClusteredSplitter splitter = NonClusteredSplitter ( treatments = [ \"A\" , \"B\" ], ) df = pd . DataFrame ({ \"city\" : [ \"A\" , \"B\" , \"C\" ]}) df = splitter . assign_treatment_df ( df ) print ( df ) Source code in cluster_experiments/random_splitter.py class NonClusteredSplitter ( RandomSplitter ): \"\"\" Splits randomly without clusters Arguments: treatments: list of treatments treatment_col: Name of the column with the treatment variable. Usage: ```python import pandas as pd from cluster_experiments.random_splitter import NonClusteredSplitter splitter = NonClusteredSplitter( treatments=[\"A\", \"B\"], ) df = pd.DataFrame({\"city\": [\"A\", \"B\", \"C\"]}) df = splitter.assign_treatment_df(df) print(df) ``` \"\"\" def __init__ ( self , treatments : Optional [ List [ str ]] = None , treatment_col : str = \"treatment\" , splitter_weights : Optional [ List [ float ]] = None , ) -> None : self . treatments = treatments or [ \"A\" , \"B\" ] self . treatment_col = treatment_col self . splitter_weights = splitter_weights def assign_treatment_df ( self , df : pd . DataFrame , ) -> pd . DataFrame : \"\"\" Takes a df, randomizes treatments and adds the treatment column to the dataframe Arguments: df: dataframe to assign treatments to \"\"\" df = df . copy () df [ self . treatment_col ] = random . choices ( self . treatments , k = len ( df ), weights = self . splitter_weights ) return df @classmethod def from_config ( cls , config ): \"\"\"Creates a NonClusteredSplitter from a PowerConfig\"\"\" return cls ( treatments = config . treatments , treatment_col = config . treatment_col , splitter_weights = config . splitter_weights , ) assign_treatment_df ( self , df ) \u00b6 Takes a df, randomizes treatments and adds the treatment column to the dataframe Parameters: Name Type Description Default df DataFrame dataframe to assign treatments to required Source code in cluster_experiments/random_splitter.py def assign_treatment_df ( self , df : pd . DataFrame , ) -> pd . DataFrame : \"\"\" Takes a df, randomizes treatments and adds the treatment column to the dataframe Arguments: df: dataframe to assign treatments to \"\"\" df = df . copy () df [ self . treatment_col ] = random . choices ( self . treatments , k = len ( df ), weights = self . splitter_weights ) return df from_config ( config ) classmethod \u00b6 Creates a NonClusteredSplitter from a PowerConfig Source code in cluster_experiments/random_splitter.py @classmethod def from_config ( cls , config ): \"\"\"Creates a NonClusteredSplitter from a PowerConfig\"\"\" return cls ( treatments = config . treatments , treatment_col = config . treatment_col , splitter_weights = config . splitter_weights , ) RandomSplitter ( ABC ) \u00b6 Abstract class to split instances in a switchback or clustered way. It can be used to create a calendar/split of clusters or to run a power analysis. In order to create your own RandomSplitter, you should write your own assign_treatment_df method, that takes a dataframe as an input and returns the same dataframe with the treatment_col column. Parameters: Name Type Description Default cluster_cols Optional[List[str]] List of columns to use as clusters None treatments Optional[List[str]] list of treatments None treatment_col str Name of the column with the treatment variable. 'treatment' splitter_weights Optional[List[float]] weights to use for the splitter, should have the same length as treatments, each weight should correspond to an element in treatments None Source code in cluster_experiments/random_splitter.py class RandomSplitter ( ABC ): \"\"\" Abstract class to split instances in a switchback or clustered way. It can be used to create a calendar/split of clusters or to run a power analysis. In order to create your own RandomSplitter, you should write your own assign_treatment_df method, that takes a dataframe as an input and returns the same dataframe with the treatment_col column. Arguments: cluster_cols: List of columns to use as clusters treatments: list of treatments treatment_col: Name of the column with the treatment variable. splitter_weights: weights to use for the splitter, should have the same length as treatments, each weight should correspond to an element in treatments \"\"\" def __init__ ( self , cluster_cols : Optional [ List [ str ]] = None , treatments : Optional [ List [ str ]] = None , treatment_col : str = \"treatment\" , splitter_weights : Optional [ List [ float ]] = None , ) -> None : self . treatments = treatments or [ \"A\" , \"B\" ] self . cluster_cols = cluster_cols or [] self . treatment_col = treatment_col self . splitter_weights = splitter_weights @abstractmethod def assign_treatment_df ( self , df : pd . DataFrame , ) -> pd . DataFrame : \"\"\" Takes a df, randomizes treatments and adds the treatment column to the dataframe Arguments: df: dataframe to assign treatments to \"\"\" @classmethod def from_config ( cls , config ): \"\"\"Creates a RandomSplitter from a PowerConfig\"\"\" return cls ( treatments = config . treatments , cluster_cols = config . cluster_cols , treatment_col = config . treatment_col , splitter_weights = config . splitter_weights , ) assign_treatment_df ( self , df ) \u00b6 Takes a df, randomizes treatments and adds the treatment column to the dataframe Parameters: Name Type Description Default df DataFrame dataframe to assign treatments to required Source code in cluster_experiments/random_splitter.py @abstractmethod def assign_treatment_df ( self , df : pd . DataFrame , ) -> pd . DataFrame : \"\"\" Takes a df, randomizes treatments and adds the treatment column to the dataframe Arguments: df: dataframe to assign treatments to \"\"\" from_config ( config ) classmethod \u00b6 Creates a RandomSplitter from a PowerConfig Source code in cluster_experiments/random_splitter.py @classmethod def from_config ( cls , config ): \"\"\"Creates a RandomSplitter from a PowerConfig\"\"\" return cls ( treatments = config . treatments , cluster_cols = config . cluster_cols , treatment_col = config . treatment_col , splitter_weights = config . splitter_weights , ) RepeatedSampler ( RandomSplitter ) \u00b6 Doesn't actually split the data, but repeatedly samples (i.e. duplicates) all rows for all treatments. This is useful for backtesting, where we assume to have access to all counterfactuals. Parameters: Name Type Description Default treatments Optional[List[str]] list of treatments None treatment_col str Name of the column with the treatment variable. 'treatment' Usage: import pandas as pd from cluster_experiments.random_splitter import RepeatedSampler splitter = RepeatedSampler ( treatments = [ \"A\" , \"B\" ], ) df = pd . DataFrame ({ \"city\" : [ \"A\" , \"B\" , \"C\" ]}) df = splitter . assign_treatment_df ( df ) print ( df ) Source code in cluster_experiments/random_splitter.py class RepeatedSampler ( RandomSplitter ): \"\"\" Doesn't actually split the data, but repeatedly samples (i.e. duplicates) all rows for all treatments. This is useful for backtesting, where we assume to have access to all counterfactuals. Arguments: treatments: list of treatments treatment_col: Name of the column with the treatment variable. Usage: ```python import pandas as pd from cluster_experiments.random_splitter import RepeatedSampler splitter = RepeatedSampler( treatments=[\"A\", \"B\"], ) df = pd.DataFrame({\"city\": [\"A\", \"B\", \"C\"]}) df = splitter.assign_treatment_df(df) print(df) ``` \"\"\" def __init__ ( self , treatments : Optional [ List [ str ]] = None , treatment_col : str = \"treatment\" , ) -> None : self . treatments = treatments or [ \"A\" , \"B\" ] self . treatment_col = treatment_col def assign_treatment_df ( self , df : pd . DataFrame , ) -> pd . DataFrame : df = df . copy () dfs = [] for treatment in self . treatments : df_treat = df . copy () . assign ( ** { self . treatment_col : treatment }) dfs . append ( df_treat ) return pd . concat ( dfs ) . reset_index ( drop = True ) @classmethod def from_config ( cls , config ): \"\"\"Creates a RepeatedSampler from a PowerConfig\"\"\" return cls ( treatments = config . treatments , treatment_col = config . treatment_col , ) assign_treatment_df ( self , df ) \u00b6 Takes a df, randomizes treatments and adds the treatment column to the dataframe Parameters: Name Type Description Default df DataFrame dataframe to assign treatments to required Source code in cluster_experiments/random_splitter.py def assign_treatment_df ( self , df : pd . DataFrame , ) -> pd . DataFrame : df = df . copy () dfs = [] for treatment in self . treatments : df_treat = df . copy () . assign ( ** { self . treatment_col : treatment }) dfs . append ( df_treat ) return pd . concat ( dfs ) . reset_index ( drop = True ) from_config ( config ) classmethod \u00b6 Creates a RepeatedSampler from a PowerConfig Source code in cluster_experiments/random_splitter.py @classmethod def from_config ( cls , config ): \"\"\"Creates a RepeatedSampler from a PowerConfig\"\"\" return cls ( treatments = config . treatments , treatment_col = config . treatment_col , ) StratifiedClusteredSplitter ( RandomSplitter ) \u00b6 Splits randomly with clusters, ensuring a balanced allocation of treatment groups across clusters and strata. To be used, for example, when having days as clusters and days of the week as stratus. This splitter will make sure that we won't have all Sundays in treatment and no Sundays in control. Parameters: Name Type Description Default cluster_cols Optional[List[str]] List of columns to use as clusters None treatments Optional[List[str]] list of treatments None treatment_col str Name of the column with the treatment variable. 'treatment' strata_cols Optional[List[str]] List of columns to use as strata None Usage: import pandas as pd from cluster_experiments.random_splitter import StratifiedClusteredSplitter splitter = StratifiedClusteredSplitter ( cluster_cols = [ \"city\" ], strata_cols = [ \"country\" ]) df = pd . DataFrame ({ \"city\" : [ \"A\" , \"B\" , \"C\" , \"D\" ], \"country\" :[ \"C1\" , \"C2\" , \"C2\" , \"C1\" ]}) df = splitter . assign_treatment_df ( df ) print ( df ) Source code in cluster_experiments/random_splitter.py class StratifiedClusteredSplitter ( RandomSplitter ): \"\"\" Splits randomly with clusters, ensuring a balanced allocation of treatment groups across clusters and strata. To be used, for example, when having days as clusters and days of the week as stratus. This splitter will make sure that we won't have all Sundays in treatment and no Sundays in control. Arguments: cluster_cols: List of columns to use as clusters treatments: list of treatments treatment_col: Name of the column with the treatment variable. strata_cols: List of columns to use as strata Usage: ```python import pandas as pd from cluster_experiments.random_splitter import StratifiedClusteredSplitter splitter = StratifiedClusteredSplitter(cluster_cols=[\"city\"],strata_cols=[\"country\"]) df = pd.DataFrame({\"city\": [\"A\", \"B\", \"C\",\"D\"], \"country\":[\"C1\",\"C2\",\"C2\",\"C1\"]}) df = splitter.assign_treatment_df(df) print(df) ``` \"\"\" def __init__ ( self , cluster_cols : Optional [ List [ str ]] = None , treatments : Optional [ List [ str ]] = None , treatment_col : str = \"treatment\" , strata_cols : Optional [ List [ str ]] = None , ) -> None : super () . __init__ ( cluster_cols = cluster_cols , treatments = treatments , treatment_col = treatment_col , ) if not strata_cols or strata_cols == [ \"\" ]: raise ValueError ( f \"Splitter { self . __class__ . __name__ } requires strata_cols,\" f \" got { strata_cols = } \" ) self . strata_cols = strata_cols def assign_treatment_df ( self , df : pd . DataFrame ) -> pd . DataFrame : df = df . copy () df_unique_shuffled = ( df . loc [:, list ( set ( self . cluster_cols + self . strata_cols ))] . drop_duplicates () . sample ( frac = 1 ) . reset_index ( drop = True ) ) # check that, for a given cluster, there is only 1 strata for strata_col in self . strata_cols : if ( df_unique_shuffled . groupby ( self . cluster_cols )[ strata_col ] . nunique () . max () > 1 ): raise ValueError ( f \"There are multiple values in { strata_col } for the same cluster item \\n \" \"You cannot stratify on this column\" , ) # random shuffling random_sorted_treatments = list ( np . random . permutation ( self . treatments )) df_unique_shuffled [ self . treatment_col ] = ( df_unique_shuffled . groupby ( self . strata_cols , as_index = False ) . cumcount () . mod ( len ( random_sorted_treatments )) . map ( dict ( enumerate ( random_sorted_treatments ))) ) df = df . merge ( df_unique_shuffled , on = self . cluster_cols + self . strata_cols , how = \"left\" ) return df @classmethod def from_config ( cls , config ): \"\"\"Creates a StratifiedClusteredSplitter from a PowerConfig\"\"\" return cls ( treatments = config . treatments , cluster_cols = config . cluster_cols , strata_cols = config . strata_cols , treatment_col = config . treatment_col , ) assign_treatment_df ( self , df ) \u00b6 Takes a df, randomizes treatments and adds the treatment column to the dataframe Parameters: Name Type Description Default df DataFrame dataframe to assign treatments to required Source code in cluster_experiments/random_splitter.py def assign_treatment_df ( self , df : pd . DataFrame ) -> pd . DataFrame : df = df . copy () df_unique_shuffled = ( df . loc [:, list ( set ( self . cluster_cols + self . strata_cols ))] . drop_duplicates () . sample ( frac = 1 ) . reset_index ( drop = True ) ) # check that, for a given cluster, there is only 1 strata for strata_col in self . strata_cols : if ( df_unique_shuffled . groupby ( self . cluster_cols )[ strata_col ] . nunique () . max () > 1 ): raise ValueError ( f \"There are multiple values in { strata_col } for the same cluster item \\n \" \"You cannot stratify on this column\" , ) # random shuffling random_sorted_treatments = list ( np . random . permutation ( self . treatments )) df_unique_shuffled [ self . treatment_col ] = ( df_unique_shuffled . groupby ( self . strata_cols , as_index = False ) . cumcount () . mod ( len ( random_sorted_treatments )) . map ( dict ( enumerate ( random_sorted_treatments ))) ) df = df . merge ( df_unique_shuffled , on = self . cluster_cols + self . strata_cols , how = \"left\" ) return df from_config ( config ) classmethod \u00b6 Creates a StratifiedClusteredSplitter from a PowerConfig Source code in cluster_experiments/random_splitter.py @classmethod def from_config ( cls , config ): \"\"\"Creates a StratifiedClusteredSplitter from a PowerConfig\"\"\" return cls ( treatments = config . treatments , cluster_cols = config . cluster_cols , strata_cols = config . strata_cols , treatment_col = config . treatment_col , ) StratifiedSwitchbackSplitter ( StratifiedClusteredSplitter , SwitchbackSplitter ) \u00b6 Splits randomly with clusters, ensuring a balanced allocation of treatment groups across clusters and strata. To be used, for example, when having days as clusters and days of the week as stratus. This splitter will make sure that we won't have all Sundays in treatment and no Sundays in control. It can be created using the time_col and switch_frequency arguments, just like the SwitchbackSplitter. Parameters: Name Type Description Default time_col str Name of the column with the time variable. 'date' switch_frequency str Frequency of the switchback. Must be a string (e.g. \"1D\") '1D' cluster_cols Optional[List[str]] List of columns to use as clusters None treatments Optional[List[str]] list of treatments None treatment_col str Name of the column with the treatment variable. 'treatment' splitter_weights Optional[List[float]] List of weights for the treatments. If None, all treatments will have the same weight. None strata_cols Optional[List[str]] List of columns to use as strata None Usage: import pandas as pd from cluster_experiments.random_splitter import StratifiedSwitchbackSplitter splitter = StratifiedSwitchbackSplitter ( time_col = \"date\" , switch_frequency = \"1D\" , strata_cols = [ \"country\" ], cluster_cols = [ \"country\" , \"date\" ]) df = pd . DataFrame ({ \"date\" : [ \"2020-01-01\" , \"2020-01-02\" , \"2020-01-03\" , \"2020-01-04\" ], \"country\" :[ \"C1\" , \"C2\" , \"C2\" , \"C1\" ]}) df = splitter . assign_treatment_df ( df ) print ( df ) Source code in cluster_experiments/random_splitter.py class StratifiedSwitchbackSplitter ( StratifiedClusteredSplitter , SwitchbackSplitter ): \"\"\" Splits randomly with clusters, ensuring a balanced allocation of treatment groups across clusters and strata. To be used, for example, when having days as clusters and days of the week as stratus. This splitter will make sure that we won't have all Sundays in treatment and no Sundays in control. It can be created using the time_col and switch_frequency arguments, just like the SwitchbackSplitter. Arguments: time_col: Name of the column with the time variable. switch_frequency: Frequency of the switchback. Must be a string (e.g. \"1D\") cluster_cols: List of columns to use as clusters treatments: list of treatments treatment_col: Name of the column with the treatment variable. splitter_weights: List of weights for the treatments. If None, all treatments will have the same weight. strata_cols: List of columns to use as strata Usage: ```python import pandas as pd from cluster_experiments.random_splitter import StratifiedSwitchbackSplitter splitter = StratifiedSwitchbackSplitter(time_col=\"date\",switch_frequency=\"1D\",strata_cols=[\"country\"], cluster_cols=[\"country\", \"date\"]) df = pd.DataFrame({\"date\": [\"2020-01-01\", \"2020-01-02\", \"2020-01-03\",\"2020-01-04\"], \"country\":[\"C1\",\"C2\",\"C2\",\"C1\"]}) df = splitter.assign_treatment_df(df) print(df) ``` \"\"\" def __init__ ( self , time_col : str = \"date\" , switch_frequency : str = \"1D\" , cluster_cols : Optional [ List [ str ]] = None , treatments : Optional [ List [ str ]] = None , treatment_col : str = \"treatment\" , splitter_weights : Optional [ List [ float ]] = None , washover : Optional [ Washover ] = None , strata_cols : Optional [ List [ str ]] = None , ) -> None : # Inherit init from SwitchbackSplitter SwitchbackSplitter . __init__ ( self , time_col = time_col , switch_frequency = switch_frequency , cluster_cols = cluster_cols , treatments = treatments , treatment_col = treatment_col , splitter_weights = splitter_weights , washover = washover , ) self . strata_cols = strata_cols or [ \"strata\" ] def assign_treatment_df ( self , df : pd . DataFrame ) -> pd . DataFrame : df = df . copy () df = self . _prepare_switchback_df ( df ) df = StratifiedClusteredSplitter . assign_treatment_df ( self , df ) return self . washover . washover ( df = df , treatment_col = self . treatment_col , truncated_time_col = self . time_col , cluster_cols = self . cluster_cols , ) @classmethod def from_config ( cls , config ) -> \"StratifiedSwitchbackSplitter\" : \"\"\"Creates a StratifiedSwitchbackSplitter from a PowerConfig\"\"\" washover_cls = _get_mapping_key ( washover_mapping , config . washover ) return cls ( treatments = config . treatments , cluster_cols = config . cluster_cols , strata_cols = config . strata_cols , treatment_col = config . treatment_col , time_col = config . time_col , switch_frequency = config . switch_frequency , splitter_weights = config . splitter_weights , washover = washover_cls . from_config ( config ), ) assign_treatment_df ( self , df ) \u00b6 Creates the switchback column, adds it to cluster_cols and then calls ClusteredSplitter assign_treatment_df Parameters: Name Type Description Default df DataFrame dataframe to assign treatments to required Source code in cluster_experiments/random_splitter.py def assign_treatment_df ( self , df : pd . DataFrame ) -> pd . DataFrame : df = df . copy () df = self . _prepare_switchback_df ( df ) df = StratifiedClusteredSplitter . assign_treatment_df ( self , df ) return self . washover . washover ( df = df , treatment_col = self . treatment_col , truncated_time_col = self . time_col , cluster_cols = self . cluster_cols , ) from_config ( config ) classmethod \u00b6 Creates a StratifiedSwitchbackSplitter from a PowerConfig Source code in cluster_experiments/random_splitter.py @classmethod def from_config ( cls , config ) -> \"StratifiedSwitchbackSplitter\" : \"\"\"Creates a StratifiedSwitchbackSplitter from a PowerConfig\"\"\" washover_cls = _get_mapping_key ( washover_mapping , config . washover ) return cls ( treatments = config . treatments , cluster_cols = config . cluster_cols , strata_cols = config . strata_cols , treatment_col = config . treatment_col , time_col = config . time_col , switch_frequency = config . switch_frequency , splitter_weights = config . splitter_weights , washover = washover_cls . from_config ( config ), ) SwitchbackSplitter ( ClusteredSplitter ) \u00b6 Splits randomly using clusters and time column It is a clustered splitter but one of the cluster columns is obtained by truncating the time column to the switch frequency. Parameters: Name Type Description Default time_col Optional[str] Name of the column with the time variable. None switch_frequency Optional[str] Frequency to switch treatments. Uses pandas frequency aliases (https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases) None cluster_cols Optional[List[str]] List of columns to use as clusters None treatments Optional[List[str]] list of treatments None treatment_col str Name of the column with the treatment variable. 'treatment' splitter_weights Optional[List[float]] weights to use for the splitter, should have the same length as treatments, each weight should correspond to an element in treatments None Usage: import pandas as pd from cluster_experiments.random_splitter import SwitchbackSplitter splitter = SwitchbackSplitter ( time_col = \"date\" , switch_frequency = \"1D\" , cluster_cols = [ \"date\" ]) df = pd . DataFrame ({ \"date\" : pd . date_range ( \"2020-01-01\" , \"2020-01-03\" )}) df = splitter . assign_treatment_df ( df ) print ( df ) Source code in cluster_experiments/random_splitter.py class SwitchbackSplitter ( ClusteredSplitter ): \"\"\" Splits randomly using clusters and time column It is a clustered splitter but one of the cluster columns is obtained by truncating the time column to the switch frequency. Arguments: time_col: Name of the column with the time variable. switch_frequency: Frequency to switch treatments. Uses pandas frequency aliases (https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases) cluster_cols: List of columns to use as clusters treatments: list of treatments treatment_col: Name of the column with the treatment variable. splitter_weights: weights to use for the splitter, should have the same length as treatments, each weight should correspond to an element in treatments Usage: ```python import pandas as pd from cluster_experiments.random_splitter import SwitchbackSplitter splitter = SwitchbackSplitter(time_col=\"date\", switch_frequency=\"1D\", cluster_cols=[\"date\"]) df = pd.DataFrame({\"date\": pd.date_range(\"2020-01-01\", \"2020-01-03\")}) df = splitter.assign_treatment_df(df) print(df) ``` \"\"\" def __init__ ( self , time_col : Optional [ str ] = None , switch_frequency : Optional [ str ] = None , cluster_cols : Optional [ List [ str ]] = None , treatments : Optional [ List [ str ]] = None , treatment_col : str = \"treatment\" , splitter_weights : Optional [ List [ float ]] = None , washover : Optional [ Washover ] = None , ) -> None : self . time_col = time_col or \"date\" self . switch_frequency = switch_frequency or \"1D\" self . cluster_cols = cluster_cols or [] self . treatments = treatments or [ \"A\" , \"B\" ] self . treatment_col = treatment_col self . splitter_weights = splitter_weights self . washover = washover or EmptyWashover () self . _check_clusters () def _check_clusters ( self ): \"\"\"Check if time_col is in cluster_cols\"\"\" assert ( self . time_col in self . cluster_cols ), \"in switchback splitters, time_col must be in cluster_cols\" def _get_time_col_cluster ( self , df : pd . DataFrame ) -> pd . Series : df = df . copy () df [ self . time_col ] = pd . to_datetime ( df [ self . time_col ]) # Given the switch frequency, truncate the time column to the switch frequency # Using pandas frequency aliases: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases if \"W\" in self . switch_frequency or \"M\" in self . switch_frequency : return df [ self . time_col ] . dt . to_period ( self . switch_frequency ) . dt . start_time return df [ self . time_col ] . dt . floor ( self . switch_frequency ) def _prepare_switchback_df ( self , df : pd . DataFrame ) -> pd . DataFrame : df = df . copy () # Build time_col switchback column # Overwriting column, this is the worst! If we use the column as a covariate, we're screwed. Needs improvement df [ _original_time_column ( self . time_col )] = df [ self . time_col ] df [ self . time_col ] = self . _get_time_col_cluster ( df ) return df def assign_treatment_df ( self , df : pd . DataFrame , ) -> pd . DataFrame : \"\"\" Creates the switchback column, adds it to cluster_cols and then calls ClusteredSplitter assign_treatment_df Arguments: df: dataframe to assign treatments to \"\"\" df = df . copy () df = self . _prepare_switchback_df ( df ) df = super () . assign_treatment_df ( df ) df = self . washover . washover ( df , truncated_time_col = self . time_col , treatment_col = self . treatment_col , cluster_cols = self . cluster_cols , ) return df @classmethod def from_config ( cls , config ) -> \"SwitchbackSplitter\" : washover_cls = _get_mapping_key ( washover_mapping , config . washover ) return cls ( time_col = config . time_col , switch_frequency = config . switch_frequency , cluster_cols = config . cluster_cols , treatments = config . treatments , treatment_col = config . treatment_col , splitter_weights = config . splitter_weights , washover = washover_cls . from_config ( config ), ) assign_treatment_df ( self , df ) \u00b6 Creates the switchback column, adds it to cluster_cols and then calls ClusteredSplitter assign_treatment_df Parameters: Name Type Description Default df DataFrame dataframe to assign treatments to required Source code in cluster_experiments/random_splitter.py def assign_treatment_df ( self , df : pd . DataFrame , ) -> pd . DataFrame : \"\"\" Creates the switchback column, adds it to cluster_cols and then calls ClusteredSplitter assign_treatment_df Arguments: df: dataframe to assign treatments to \"\"\" df = df . copy () df = self . _prepare_switchback_df ( df ) df = super () . assign_treatment_df ( df ) df = self . washover . washover ( df , truncated_time_col = self . time_col , treatment_col = self . treatment_col , cluster_cols = self . cluster_cols , ) return df from_config ( config ) classmethod \u00b6 Creates a RandomSplitter from a PowerConfig Source code in cluster_experiments/random_splitter.py @classmethod def from_config ( cls , config ) -> \"SwitchbackSplitter\" : washover_cls = _get_mapping_key ( washover_mapping , config . washover ) return cls ( time_col = config . time_col , switch_frequency = config . switch_frequency , cluster_cols = config . cluster_cols , treatments = config . treatments , treatment_col = config . treatment_col , splitter_weights = config . splitter_weights , washover = washover_cls . from_config ( config ), )","title":"Splitter"},{"location":"api/random_splitter.html#from-cluster_experimentsrandom_splitter-import","text":"","title":"from cluster_experiments.random_splitter import *"},{"location":"api/random_splitter.html#cluster_experiments.random_splitter.BalancedClusteredSplitter","text":"Like ClusteredSplitter, but ensures that treatments are balanced among clusters. That is, if we have 25 clusters and 2 treatments, 13 clusters should have treatment A and 12 clusters should have treatment B. Source code in cluster_experiments/random_splitter.py class BalancedClusteredSplitter ( ClusteredSplitter ): \"\"\"Like ClusteredSplitter, but ensures that treatments are balanced among clusters. That is, if we have 25 clusters and 2 treatments, 13 clusters should have treatment A and 12 clusters should have treatment B.\"\"\" def sample_treatment ( self , cluster_df : pd . DataFrame , ) -> List [ str ]: \"\"\" Samples treatments for each cluster Arguments: cluster_df: dataframe to assign treatments to \"\"\" n_clusters = len ( cluster_df ) n_treatments = len ( self . treatments ) n_per_treatment = n_clusters // n_treatments n_extra = n_clusters % n_treatments treatments = [] for i in range ( n_treatments ): treatments += [ self . treatments [ i ]] * ( n_per_treatment + ( i < n_extra )) random . shuffle ( treatments ) return treatments","title":"BalancedClusteredSplitter"},{"location":"api/random_splitter.html#cluster_experiments.random_splitter.BalancedClusteredSplitter.sample_treatment","text":"Samples treatments for each cluster Parameters: Name Type Description Default cluster_df DataFrame dataframe to assign treatments to required Source code in cluster_experiments/random_splitter.py def sample_treatment ( self , cluster_df : pd . DataFrame , ) -> List [ str ]: \"\"\" Samples treatments for each cluster Arguments: cluster_df: dataframe to assign treatments to \"\"\" n_clusters = len ( cluster_df ) n_treatments = len ( self . treatments ) n_per_treatment = n_clusters // n_treatments n_extra = n_clusters % n_treatments treatments = [] for i in range ( n_treatments ): treatments += [ self . treatments [ i ]] * ( n_per_treatment + ( i < n_extra )) random . shuffle ( treatments ) return treatments","title":"sample_treatment()"},{"location":"api/random_splitter.html#cluster_experiments.random_splitter.BalancedSwitchbackSplitter","text":"Like SwitchbackSplitter, but ensures that treatments are balanced among clusters. That is, if we have 25 clusters and 2 treatments, 13 clusters should have treatment A and 12 clusters should have treatment B. Source code in cluster_experiments/random_splitter.py class BalancedSwitchbackSplitter ( BalancedClusteredSplitter , SwitchbackSplitter ): \"\"\" Like SwitchbackSplitter, but ensures that treatments are balanced among clusters. That is, if we have 25 clusters and 2 treatments, 13 clusters should have treatment A and 12 clusters should have treatment B. \"\"\" pass","title":"BalancedSwitchbackSplitter"},{"location":"api/random_splitter.html#cluster_experiments.random_splitter.ClusteredSplitter","text":"Splits randomly using clusters Parameters: Name Type Description Default cluster_cols List[str] List of columns to use as clusters required treatments Optional[List[str]] list of treatments None treatment_col str Name of the column with the treatment variable. 'treatment' splitter_weights Optional[List[float]] weights to use for the splitter, should have the same length as treatments, each weight should correspond to an element in treatments None Usage: import pandas as pd from cluster_experiments.random_splitter import ClusteredSplitter splitter = ClusteredSplitter ( cluster_cols = [ \"city\" ]) df = pd . DataFrame ({ \"city\" : [ \"A\" , \"B\" , \"C\" ]}) df = splitter . assign_treatment_df ( df ) print ( df ) Source code in cluster_experiments/random_splitter.py class ClusteredSplitter ( RandomSplitter ): \"\"\" Splits randomly using clusters Arguments: cluster_cols: List of columns to use as clusters treatments: list of treatments treatment_col: Name of the column with the treatment variable. splitter_weights: weights to use for the splitter, should have the same length as treatments, each weight should correspond to an element in treatments Usage: ```python import pandas as pd from cluster_experiments.random_splitter import ClusteredSplitter splitter = ClusteredSplitter(cluster_cols=[\"city\"]) df = pd.DataFrame({\"city\": [\"A\", \"B\", \"C\"]}) df = splitter.assign_treatment_df(df) print(df) ``` \"\"\" def __init__ ( self , cluster_cols : List [ str ], treatments : Optional [ List [ str ]] = None , treatment_col : str = \"treatment\" , splitter_weights : Optional [ List [ float ]] = None , ) -> None : self . treatments = treatments or [ \"A\" , \"B\" ] self . cluster_cols = cluster_cols self . treatment_col = treatment_col self . splitter_weights = splitter_weights def assign_treatment_df ( self , df : pd . DataFrame , ) -> pd . DataFrame : \"\"\" Takes a df, randomizes treatments and adds the treatment column to the dataframe Arguments: df: dataframe to assign treatments to \"\"\" df = df . copy () # raise error if any nulls in cluster_cols if df [ self . cluster_cols ] . isnull () . values . any (): raise ValueError ( f \"Null values found in cluster_cols: { self . cluster_cols } . \" \"Please remove nulls before running the splitter.\" ) clusters_df = df . loc [:, self . cluster_cols ] . drop_duplicates () clusters_df [ self . treatment_col ] = self . sample_treatment ( clusters_df ) df = df . merge ( clusters_df , on = self . cluster_cols , how = \"left\" ) return df def sample_treatment ( self , cluster_df : pd . DataFrame , ) -> List [ str ]: \"\"\" Samples treatments for each cluster Arguments: cluster_df: dataframe to assign treatments to \"\"\" return random . choices ( self . treatments , k = len ( cluster_df ), weights = self . splitter_weights )","title":"ClusteredSplitter"},{"location":"api/random_splitter.html#cluster_experiments.random_splitter.ClusteredSplitter.assign_treatment_df","text":"Takes a df, randomizes treatments and adds the treatment column to the dataframe Parameters: Name Type Description Default df DataFrame dataframe to assign treatments to required Source code in cluster_experiments/random_splitter.py def assign_treatment_df ( self , df : pd . DataFrame , ) -> pd . DataFrame : \"\"\" Takes a df, randomizes treatments and adds the treatment column to the dataframe Arguments: df: dataframe to assign treatments to \"\"\" df = df . copy () # raise error if any nulls in cluster_cols if df [ self . cluster_cols ] . isnull () . values . any (): raise ValueError ( f \"Null values found in cluster_cols: { self . cluster_cols } . \" \"Please remove nulls before running the splitter.\" ) clusters_df = df . loc [:, self . cluster_cols ] . drop_duplicates () clusters_df [ self . treatment_col ] = self . sample_treatment ( clusters_df ) df = df . merge ( clusters_df , on = self . cluster_cols , how = \"left\" ) return df","title":"assign_treatment_df()"},{"location":"api/random_splitter.html#cluster_experiments.random_splitter.ClusteredSplitter.sample_treatment","text":"Samples treatments for each cluster Parameters: Name Type Description Default cluster_df DataFrame dataframe to assign treatments to required Source code in cluster_experiments/random_splitter.py def sample_treatment ( self , cluster_df : pd . DataFrame , ) -> List [ str ]: \"\"\" Samples treatments for each cluster Arguments: cluster_df: dataframe to assign treatments to \"\"\" return random . choices ( self . treatments , k = len ( cluster_df ), weights = self . splitter_weights )","title":"sample_treatment()"},{"location":"api/random_splitter.html#cluster_experiments.random_splitter.FixedSizeClusteredSplitter","text":"This class represents a splitter that splits clusters into treatment groups with a predefined number of treatment clusters. This is particularly useful for synthetic control analysis, where we only want 1 cluster ( unit) to be in treatment group and the rest in control The cluster that receives treatment remains random. Attributes: Name Type Description cluster_cols List[str] List of columns to use as clusters. n_treatment_clusters int The predefined number of treatment clusters. Source code in cluster_experiments/random_splitter.py class FixedSizeClusteredSplitter ( ClusteredSplitter ): \"\"\" This class represents a splitter that splits clusters into treatment groups with a predefined number of treatment clusters. This is particularly useful for synthetic control analysis, where we only want 1 cluster ( unit) to be in treatment group and the rest in control The cluster that receives treatment remains random. Attributes: cluster_cols (List[str]): List of columns to use as clusters. n_treatment_clusters (int): The predefined number of treatment clusters. \"\"\" def __init__ ( self , cluster_cols : List [ str ], n_treatment_clusters : int ): super () . __init__ ( cluster_cols = cluster_cols ) self . n_treatment_clusters = n_treatment_clusters def sample_treatment ( self , cluster_df : pd . DataFrame , ) -> List [ str ]: \"\"\" Samples treatments for each cluster. Args: cluster_df (pd.DataFrame): Dataframe to assign treatments to. Returns: List[str]: A list of treatments for each cluster. \"\"\" n_control_treatment = [ len ( cluster_df ) - self . n_treatment_clusters , self . n_treatment_clusters , ] sample_treatment = [ treatment for treatment , count in zip ( self . treatments , n_control_treatment ) for _ in range ( count ) ] random . shuffle ( sample_treatment ) return sample_treatment","title":"FixedSizeClusteredSplitter"},{"location":"api/random_splitter.html#cluster_experiments.random_splitter.FixedSizeClusteredSplitter.sample_treatment","text":"Samples treatments for each cluster. Parameters: Name Type Description Default cluster_df pd.DataFrame Dataframe to assign treatments to. required Returns: Type Description List[str] A list of treatments for each cluster. Source code in cluster_experiments/random_splitter.py def sample_treatment ( self , cluster_df : pd . DataFrame , ) -> List [ str ]: \"\"\" Samples treatments for each cluster. Args: cluster_df (pd.DataFrame): Dataframe to assign treatments to. Returns: List[str]: A list of treatments for each cluster. \"\"\" n_control_treatment = [ len ( cluster_df ) - self . n_treatment_clusters , self . n_treatment_clusters , ] sample_treatment = [ treatment for treatment , count in zip ( self . treatments , n_control_treatment ) for _ in range ( count ) ] random . shuffle ( sample_treatment ) return sample_treatment","title":"sample_treatment()"},{"location":"api/random_splitter.html#cluster_experiments.random_splitter.NonClusteredSplitter","text":"Splits randomly without clusters Parameters: Name Type Description Default treatments Optional[List[str]] list of treatments None treatment_col str Name of the column with the treatment variable. 'treatment' Usage: import pandas as pd from cluster_experiments.random_splitter import NonClusteredSplitter splitter = NonClusteredSplitter ( treatments = [ \"A\" , \"B\" ], ) df = pd . DataFrame ({ \"city\" : [ \"A\" , \"B\" , \"C\" ]}) df = splitter . assign_treatment_df ( df ) print ( df ) Source code in cluster_experiments/random_splitter.py class NonClusteredSplitter ( RandomSplitter ): \"\"\" Splits randomly without clusters Arguments: treatments: list of treatments treatment_col: Name of the column with the treatment variable. Usage: ```python import pandas as pd from cluster_experiments.random_splitter import NonClusteredSplitter splitter = NonClusteredSplitter( treatments=[\"A\", \"B\"], ) df = pd.DataFrame({\"city\": [\"A\", \"B\", \"C\"]}) df = splitter.assign_treatment_df(df) print(df) ``` \"\"\" def __init__ ( self , treatments : Optional [ List [ str ]] = None , treatment_col : str = \"treatment\" , splitter_weights : Optional [ List [ float ]] = None , ) -> None : self . treatments = treatments or [ \"A\" , \"B\" ] self . treatment_col = treatment_col self . splitter_weights = splitter_weights def assign_treatment_df ( self , df : pd . DataFrame , ) -> pd . DataFrame : \"\"\" Takes a df, randomizes treatments and adds the treatment column to the dataframe Arguments: df: dataframe to assign treatments to \"\"\" df = df . copy () df [ self . treatment_col ] = random . choices ( self . treatments , k = len ( df ), weights = self . splitter_weights ) return df @classmethod def from_config ( cls , config ): \"\"\"Creates a NonClusteredSplitter from a PowerConfig\"\"\" return cls ( treatments = config . treatments , treatment_col = config . treatment_col , splitter_weights = config . splitter_weights , )","title":"NonClusteredSplitter"},{"location":"api/random_splitter.html#cluster_experiments.random_splitter.NonClusteredSplitter.assign_treatment_df","text":"Takes a df, randomizes treatments and adds the treatment column to the dataframe Parameters: Name Type Description Default df DataFrame dataframe to assign treatments to required Source code in cluster_experiments/random_splitter.py def assign_treatment_df ( self , df : pd . DataFrame , ) -> pd . DataFrame : \"\"\" Takes a df, randomizes treatments and adds the treatment column to the dataframe Arguments: df: dataframe to assign treatments to \"\"\" df = df . copy () df [ self . treatment_col ] = random . choices ( self . treatments , k = len ( df ), weights = self . splitter_weights ) return df","title":"assign_treatment_df()"},{"location":"api/random_splitter.html#cluster_experiments.random_splitter.NonClusteredSplitter.from_config","text":"Creates a NonClusteredSplitter from a PowerConfig Source code in cluster_experiments/random_splitter.py @classmethod def from_config ( cls , config ): \"\"\"Creates a NonClusteredSplitter from a PowerConfig\"\"\" return cls ( treatments = config . treatments , treatment_col = config . treatment_col , splitter_weights = config . splitter_weights , )","title":"from_config()"},{"location":"api/random_splitter.html#cluster_experiments.random_splitter.RandomSplitter","text":"Abstract class to split instances in a switchback or clustered way. It can be used to create a calendar/split of clusters or to run a power analysis. In order to create your own RandomSplitter, you should write your own assign_treatment_df method, that takes a dataframe as an input and returns the same dataframe with the treatment_col column. Parameters: Name Type Description Default cluster_cols Optional[List[str]] List of columns to use as clusters None treatments Optional[List[str]] list of treatments None treatment_col str Name of the column with the treatment variable. 'treatment' splitter_weights Optional[List[float]] weights to use for the splitter, should have the same length as treatments, each weight should correspond to an element in treatments None Source code in cluster_experiments/random_splitter.py class RandomSplitter ( ABC ): \"\"\" Abstract class to split instances in a switchback or clustered way. It can be used to create a calendar/split of clusters or to run a power analysis. In order to create your own RandomSplitter, you should write your own assign_treatment_df method, that takes a dataframe as an input and returns the same dataframe with the treatment_col column. Arguments: cluster_cols: List of columns to use as clusters treatments: list of treatments treatment_col: Name of the column with the treatment variable. splitter_weights: weights to use for the splitter, should have the same length as treatments, each weight should correspond to an element in treatments \"\"\" def __init__ ( self , cluster_cols : Optional [ List [ str ]] = None , treatments : Optional [ List [ str ]] = None , treatment_col : str = \"treatment\" , splitter_weights : Optional [ List [ float ]] = None , ) -> None : self . treatments = treatments or [ \"A\" , \"B\" ] self . cluster_cols = cluster_cols or [] self . treatment_col = treatment_col self . splitter_weights = splitter_weights @abstractmethod def assign_treatment_df ( self , df : pd . DataFrame , ) -> pd . DataFrame : \"\"\" Takes a df, randomizes treatments and adds the treatment column to the dataframe Arguments: df: dataframe to assign treatments to \"\"\" @classmethod def from_config ( cls , config ): \"\"\"Creates a RandomSplitter from a PowerConfig\"\"\" return cls ( treatments = config . treatments , cluster_cols = config . cluster_cols , treatment_col = config . treatment_col , splitter_weights = config . splitter_weights , )","title":"RandomSplitter"},{"location":"api/random_splitter.html#cluster_experiments.random_splitter.RandomSplitter.assign_treatment_df","text":"Takes a df, randomizes treatments and adds the treatment column to the dataframe Parameters: Name Type Description Default df DataFrame dataframe to assign treatments to required Source code in cluster_experiments/random_splitter.py @abstractmethod def assign_treatment_df ( self , df : pd . DataFrame , ) -> pd . DataFrame : \"\"\" Takes a df, randomizes treatments and adds the treatment column to the dataframe Arguments: df: dataframe to assign treatments to \"\"\"","title":"assign_treatment_df()"},{"location":"api/random_splitter.html#cluster_experiments.random_splitter.RandomSplitter.from_config","text":"Creates a RandomSplitter from a PowerConfig Source code in cluster_experiments/random_splitter.py @classmethod def from_config ( cls , config ): \"\"\"Creates a RandomSplitter from a PowerConfig\"\"\" return cls ( treatments = config . treatments , cluster_cols = config . cluster_cols , treatment_col = config . treatment_col , splitter_weights = config . splitter_weights , )","title":"from_config()"},{"location":"api/random_splitter.html#cluster_experiments.random_splitter.RepeatedSampler","text":"Doesn't actually split the data, but repeatedly samples (i.e. duplicates) all rows for all treatments. This is useful for backtesting, where we assume to have access to all counterfactuals. Parameters: Name Type Description Default treatments Optional[List[str]] list of treatments None treatment_col str Name of the column with the treatment variable. 'treatment' Usage: import pandas as pd from cluster_experiments.random_splitter import RepeatedSampler splitter = RepeatedSampler ( treatments = [ \"A\" , \"B\" ], ) df = pd . DataFrame ({ \"city\" : [ \"A\" , \"B\" , \"C\" ]}) df = splitter . assign_treatment_df ( df ) print ( df ) Source code in cluster_experiments/random_splitter.py class RepeatedSampler ( RandomSplitter ): \"\"\" Doesn't actually split the data, but repeatedly samples (i.e. duplicates) all rows for all treatments. This is useful for backtesting, where we assume to have access to all counterfactuals. Arguments: treatments: list of treatments treatment_col: Name of the column with the treatment variable. Usage: ```python import pandas as pd from cluster_experiments.random_splitter import RepeatedSampler splitter = RepeatedSampler( treatments=[\"A\", \"B\"], ) df = pd.DataFrame({\"city\": [\"A\", \"B\", \"C\"]}) df = splitter.assign_treatment_df(df) print(df) ``` \"\"\" def __init__ ( self , treatments : Optional [ List [ str ]] = None , treatment_col : str = \"treatment\" , ) -> None : self . treatments = treatments or [ \"A\" , \"B\" ] self . treatment_col = treatment_col def assign_treatment_df ( self , df : pd . DataFrame , ) -> pd . DataFrame : df = df . copy () dfs = [] for treatment in self . treatments : df_treat = df . copy () . assign ( ** { self . treatment_col : treatment }) dfs . append ( df_treat ) return pd . concat ( dfs ) . reset_index ( drop = True ) @classmethod def from_config ( cls , config ): \"\"\"Creates a RepeatedSampler from a PowerConfig\"\"\" return cls ( treatments = config . treatments , treatment_col = config . treatment_col , )","title":"RepeatedSampler"},{"location":"api/random_splitter.html#cluster_experiments.random_splitter.RepeatedSampler.assign_treatment_df","text":"Takes a df, randomizes treatments and adds the treatment column to the dataframe Parameters: Name Type Description Default df DataFrame dataframe to assign treatments to required Source code in cluster_experiments/random_splitter.py def assign_treatment_df ( self , df : pd . DataFrame , ) -> pd . DataFrame : df = df . copy () dfs = [] for treatment in self . treatments : df_treat = df . copy () . assign ( ** { self . treatment_col : treatment }) dfs . append ( df_treat ) return pd . concat ( dfs ) . reset_index ( drop = True )","title":"assign_treatment_df()"},{"location":"api/random_splitter.html#cluster_experiments.random_splitter.RepeatedSampler.from_config","text":"Creates a RepeatedSampler from a PowerConfig Source code in cluster_experiments/random_splitter.py @classmethod def from_config ( cls , config ): \"\"\"Creates a RepeatedSampler from a PowerConfig\"\"\" return cls ( treatments = config . treatments , treatment_col = config . treatment_col , )","title":"from_config()"},{"location":"api/random_splitter.html#cluster_experiments.random_splitter.StratifiedClusteredSplitter","text":"Splits randomly with clusters, ensuring a balanced allocation of treatment groups across clusters and strata. To be used, for example, when having days as clusters and days of the week as stratus. This splitter will make sure that we won't have all Sundays in treatment and no Sundays in control. Parameters: Name Type Description Default cluster_cols Optional[List[str]] List of columns to use as clusters None treatments Optional[List[str]] list of treatments None treatment_col str Name of the column with the treatment variable. 'treatment' strata_cols Optional[List[str]] List of columns to use as strata None Usage: import pandas as pd from cluster_experiments.random_splitter import StratifiedClusteredSplitter splitter = StratifiedClusteredSplitter ( cluster_cols = [ \"city\" ], strata_cols = [ \"country\" ]) df = pd . DataFrame ({ \"city\" : [ \"A\" , \"B\" , \"C\" , \"D\" ], \"country\" :[ \"C1\" , \"C2\" , \"C2\" , \"C1\" ]}) df = splitter . assign_treatment_df ( df ) print ( df ) Source code in cluster_experiments/random_splitter.py class StratifiedClusteredSplitter ( RandomSplitter ): \"\"\" Splits randomly with clusters, ensuring a balanced allocation of treatment groups across clusters and strata. To be used, for example, when having days as clusters and days of the week as stratus. This splitter will make sure that we won't have all Sundays in treatment and no Sundays in control. Arguments: cluster_cols: List of columns to use as clusters treatments: list of treatments treatment_col: Name of the column with the treatment variable. strata_cols: List of columns to use as strata Usage: ```python import pandas as pd from cluster_experiments.random_splitter import StratifiedClusteredSplitter splitter = StratifiedClusteredSplitter(cluster_cols=[\"city\"],strata_cols=[\"country\"]) df = pd.DataFrame({\"city\": [\"A\", \"B\", \"C\",\"D\"], \"country\":[\"C1\",\"C2\",\"C2\",\"C1\"]}) df = splitter.assign_treatment_df(df) print(df) ``` \"\"\" def __init__ ( self , cluster_cols : Optional [ List [ str ]] = None , treatments : Optional [ List [ str ]] = None , treatment_col : str = \"treatment\" , strata_cols : Optional [ List [ str ]] = None , ) -> None : super () . __init__ ( cluster_cols = cluster_cols , treatments = treatments , treatment_col = treatment_col , ) if not strata_cols or strata_cols == [ \"\" ]: raise ValueError ( f \"Splitter { self . __class__ . __name__ } requires strata_cols,\" f \" got { strata_cols = } \" ) self . strata_cols = strata_cols def assign_treatment_df ( self , df : pd . DataFrame ) -> pd . DataFrame : df = df . copy () df_unique_shuffled = ( df . loc [:, list ( set ( self . cluster_cols + self . strata_cols ))] . drop_duplicates () . sample ( frac = 1 ) . reset_index ( drop = True ) ) # check that, for a given cluster, there is only 1 strata for strata_col in self . strata_cols : if ( df_unique_shuffled . groupby ( self . cluster_cols )[ strata_col ] . nunique () . max () > 1 ): raise ValueError ( f \"There are multiple values in { strata_col } for the same cluster item \\n \" \"You cannot stratify on this column\" , ) # random shuffling random_sorted_treatments = list ( np . random . permutation ( self . treatments )) df_unique_shuffled [ self . treatment_col ] = ( df_unique_shuffled . groupby ( self . strata_cols , as_index = False ) . cumcount () . mod ( len ( random_sorted_treatments )) . map ( dict ( enumerate ( random_sorted_treatments ))) ) df = df . merge ( df_unique_shuffled , on = self . cluster_cols + self . strata_cols , how = \"left\" ) return df @classmethod def from_config ( cls , config ): \"\"\"Creates a StratifiedClusteredSplitter from a PowerConfig\"\"\" return cls ( treatments = config . treatments , cluster_cols = config . cluster_cols , strata_cols = config . strata_cols , treatment_col = config . treatment_col , )","title":"StratifiedClusteredSplitter"},{"location":"api/random_splitter.html#cluster_experiments.random_splitter.StratifiedClusteredSplitter.assign_treatment_df","text":"Takes a df, randomizes treatments and adds the treatment column to the dataframe Parameters: Name Type Description Default df DataFrame dataframe to assign treatments to required Source code in cluster_experiments/random_splitter.py def assign_treatment_df ( self , df : pd . DataFrame ) -> pd . DataFrame : df = df . copy () df_unique_shuffled = ( df . loc [:, list ( set ( self . cluster_cols + self . strata_cols ))] . drop_duplicates () . sample ( frac = 1 ) . reset_index ( drop = True ) ) # check that, for a given cluster, there is only 1 strata for strata_col in self . strata_cols : if ( df_unique_shuffled . groupby ( self . cluster_cols )[ strata_col ] . nunique () . max () > 1 ): raise ValueError ( f \"There are multiple values in { strata_col } for the same cluster item \\n \" \"You cannot stratify on this column\" , ) # random shuffling random_sorted_treatments = list ( np . random . permutation ( self . treatments )) df_unique_shuffled [ self . treatment_col ] = ( df_unique_shuffled . groupby ( self . strata_cols , as_index = False ) . cumcount () . mod ( len ( random_sorted_treatments )) . map ( dict ( enumerate ( random_sorted_treatments ))) ) df = df . merge ( df_unique_shuffled , on = self . cluster_cols + self . strata_cols , how = \"left\" ) return df","title":"assign_treatment_df()"},{"location":"api/random_splitter.html#cluster_experiments.random_splitter.StratifiedClusteredSplitter.from_config","text":"Creates a StratifiedClusteredSplitter from a PowerConfig Source code in cluster_experiments/random_splitter.py @classmethod def from_config ( cls , config ): \"\"\"Creates a StratifiedClusteredSplitter from a PowerConfig\"\"\" return cls ( treatments = config . treatments , cluster_cols = config . cluster_cols , strata_cols = config . strata_cols , treatment_col = config . treatment_col , )","title":"from_config()"},{"location":"api/random_splitter.html#cluster_experiments.random_splitter.StratifiedSwitchbackSplitter","text":"Splits randomly with clusters, ensuring a balanced allocation of treatment groups across clusters and strata. To be used, for example, when having days as clusters and days of the week as stratus. This splitter will make sure that we won't have all Sundays in treatment and no Sundays in control. It can be created using the time_col and switch_frequency arguments, just like the SwitchbackSplitter. Parameters: Name Type Description Default time_col str Name of the column with the time variable. 'date' switch_frequency str Frequency of the switchback. Must be a string (e.g. \"1D\") '1D' cluster_cols Optional[List[str]] List of columns to use as clusters None treatments Optional[List[str]] list of treatments None treatment_col str Name of the column with the treatment variable. 'treatment' splitter_weights Optional[List[float]] List of weights for the treatments. If None, all treatments will have the same weight. None strata_cols Optional[List[str]] List of columns to use as strata None Usage: import pandas as pd from cluster_experiments.random_splitter import StratifiedSwitchbackSplitter splitter = StratifiedSwitchbackSplitter ( time_col = \"date\" , switch_frequency = \"1D\" , strata_cols = [ \"country\" ], cluster_cols = [ \"country\" , \"date\" ]) df = pd . DataFrame ({ \"date\" : [ \"2020-01-01\" , \"2020-01-02\" , \"2020-01-03\" , \"2020-01-04\" ], \"country\" :[ \"C1\" , \"C2\" , \"C2\" , \"C1\" ]}) df = splitter . assign_treatment_df ( df ) print ( df ) Source code in cluster_experiments/random_splitter.py class StratifiedSwitchbackSplitter ( StratifiedClusteredSplitter , SwitchbackSplitter ): \"\"\" Splits randomly with clusters, ensuring a balanced allocation of treatment groups across clusters and strata. To be used, for example, when having days as clusters and days of the week as stratus. This splitter will make sure that we won't have all Sundays in treatment and no Sundays in control. It can be created using the time_col and switch_frequency arguments, just like the SwitchbackSplitter. Arguments: time_col: Name of the column with the time variable. switch_frequency: Frequency of the switchback. Must be a string (e.g. \"1D\") cluster_cols: List of columns to use as clusters treatments: list of treatments treatment_col: Name of the column with the treatment variable. splitter_weights: List of weights for the treatments. If None, all treatments will have the same weight. strata_cols: List of columns to use as strata Usage: ```python import pandas as pd from cluster_experiments.random_splitter import StratifiedSwitchbackSplitter splitter = StratifiedSwitchbackSplitter(time_col=\"date\",switch_frequency=\"1D\",strata_cols=[\"country\"], cluster_cols=[\"country\", \"date\"]) df = pd.DataFrame({\"date\": [\"2020-01-01\", \"2020-01-02\", \"2020-01-03\",\"2020-01-04\"], \"country\":[\"C1\",\"C2\",\"C2\",\"C1\"]}) df = splitter.assign_treatment_df(df) print(df) ``` \"\"\" def __init__ ( self , time_col : str = \"date\" , switch_frequency : str = \"1D\" , cluster_cols : Optional [ List [ str ]] = None , treatments : Optional [ List [ str ]] = None , treatment_col : str = \"treatment\" , splitter_weights : Optional [ List [ float ]] = None , washover : Optional [ Washover ] = None , strata_cols : Optional [ List [ str ]] = None , ) -> None : # Inherit init from SwitchbackSplitter SwitchbackSplitter . __init__ ( self , time_col = time_col , switch_frequency = switch_frequency , cluster_cols = cluster_cols , treatments = treatments , treatment_col = treatment_col , splitter_weights = splitter_weights , washover = washover , ) self . strata_cols = strata_cols or [ \"strata\" ] def assign_treatment_df ( self , df : pd . DataFrame ) -> pd . DataFrame : df = df . copy () df = self . _prepare_switchback_df ( df ) df = StratifiedClusteredSplitter . assign_treatment_df ( self , df ) return self . washover . washover ( df = df , treatment_col = self . treatment_col , truncated_time_col = self . time_col , cluster_cols = self . cluster_cols , ) @classmethod def from_config ( cls , config ) -> \"StratifiedSwitchbackSplitter\" : \"\"\"Creates a StratifiedSwitchbackSplitter from a PowerConfig\"\"\" washover_cls = _get_mapping_key ( washover_mapping , config . washover ) return cls ( treatments = config . treatments , cluster_cols = config . cluster_cols , strata_cols = config . strata_cols , treatment_col = config . treatment_col , time_col = config . time_col , switch_frequency = config . switch_frequency , splitter_weights = config . splitter_weights , washover = washover_cls . from_config ( config ), )","title":"StratifiedSwitchbackSplitter"},{"location":"api/random_splitter.html#cluster_experiments.random_splitter.StratifiedSwitchbackSplitter.assign_treatment_df","text":"Creates the switchback column, adds it to cluster_cols and then calls ClusteredSplitter assign_treatment_df Parameters: Name Type Description Default df DataFrame dataframe to assign treatments to required Source code in cluster_experiments/random_splitter.py def assign_treatment_df ( self , df : pd . DataFrame ) -> pd . DataFrame : df = df . copy () df = self . _prepare_switchback_df ( df ) df = StratifiedClusteredSplitter . assign_treatment_df ( self , df ) return self . washover . washover ( df = df , treatment_col = self . treatment_col , truncated_time_col = self . time_col , cluster_cols = self . cluster_cols , )","title":"assign_treatment_df()"},{"location":"api/random_splitter.html#cluster_experiments.random_splitter.StratifiedSwitchbackSplitter.from_config","text":"Creates a StratifiedSwitchbackSplitter from a PowerConfig Source code in cluster_experiments/random_splitter.py @classmethod def from_config ( cls , config ) -> \"StratifiedSwitchbackSplitter\" : \"\"\"Creates a StratifiedSwitchbackSplitter from a PowerConfig\"\"\" washover_cls = _get_mapping_key ( washover_mapping , config . washover ) return cls ( treatments = config . treatments , cluster_cols = config . cluster_cols , strata_cols = config . strata_cols , treatment_col = config . treatment_col , time_col = config . time_col , switch_frequency = config . switch_frequency , splitter_weights = config . splitter_weights , washover = washover_cls . from_config ( config ), )","title":"from_config()"},{"location":"api/random_splitter.html#cluster_experiments.random_splitter.SwitchbackSplitter","text":"Splits randomly using clusters and time column It is a clustered splitter but one of the cluster columns is obtained by truncating the time column to the switch frequency. Parameters: Name Type Description Default time_col Optional[str] Name of the column with the time variable. None switch_frequency Optional[str] Frequency to switch treatments. Uses pandas frequency aliases (https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases) None cluster_cols Optional[List[str]] List of columns to use as clusters None treatments Optional[List[str]] list of treatments None treatment_col str Name of the column with the treatment variable. 'treatment' splitter_weights Optional[List[float]] weights to use for the splitter, should have the same length as treatments, each weight should correspond to an element in treatments None Usage: import pandas as pd from cluster_experiments.random_splitter import SwitchbackSplitter splitter = SwitchbackSplitter ( time_col = \"date\" , switch_frequency = \"1D\" , cluster_cols = [ \"date\" ]) df = pd . DataFrame ({ \"date\" : pd . date_range ( \"2020-01-01\" , \"2020-01-03\" )}) df = splitter . assign_treatment_df ( df ) print ( df ) Source code in cluster_experiments/random_splitter.py class SwitchbackSplitter ( ClusteredSplitter ): \"\"\" Splits randomly using clusters and time column It is a clustered splitter but one of the cluster columns is obtained by truncating the time column to the switch frequency. Arguments: time_col: Name of the column with the time variable. switch_frequency: Frequency to switch treatments. Uses pandas frequency aliases (https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases) cluster_cols: List of columns to use as clusters treatments: list of treatments treatment_col: Name of the column with the treatment variable. splitter_weights: weights to use for the splitter, should have the same length as treatments, each weight should correspond to an element in treatments Usage: ```python import pandas as pd from cluster_experiments.random_splitter import SwitchbackSplitter splitter = SwitchbackSplitter(time_col=\"date\", switch_frequency=\"1D\", cluster_cols=[\"date\"]) df = pd.DataFrame({\"date\": pd.date_range(\"2020-01-01\", \"2020-01-03\")}) df = splitter.assign_treatment_df(df) print(df) ``` \"\"\" def __init__ ( self , time_col : Optional [ str ] = None , switch_frequency : Optional [ str ] = None , cluster_cols : Optional [ List [ str ]] = None , treatments : Optional [ List [ str ]] = None , treatment_col : str = \"treatment\" , splitter_weights : Optional [ List [ float ]] = None , washover : Optional [ Washover ] = None , ) -> None : self . time_col = time_col or \"date\" self . switch_frequency = switch_frequency or \"1D\" self . cluster_cols = cluster_cols or [] self . treatments = treatments or [ \"A\" , \"B\" ] self . treatment_col = treatment_col self . splitter_weights = splitter_weights self . washover = washover or EmptyWashover () self . _check_clusters () def _check_clusters ( self ): \"\"\"Check if time_col is in cluster_cols\"\"\" assert ( self . time_col in self . cluster_cols ), \"in switchback splitters, time_col must be in cluster_cols\" def _get_time_col_cluster ( self , df : pd . DataFrame ) -> pd . Series : df = df . copy () df [ self . time_col ] = pd . to_datetime ( df [ self . time_col ]) # Given the switch frequency, truncate the time column to the switch frequency # Using pandas frequency aliases: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases if \"W\" in self . switch_frequency or \"M\" in self . switch_frequency : return df [ self . time_col ] . dt . to_period ( self . switch_frequency ) . dt . start_time return df [ self . time_col ] . dt . floor ( self . switch_frequency ) def _prepare_switchback_df ( self , df : pd . DataFrame ) -> pd . DataFrame : df = df . copy () # Build time_col switchback column # Overwriting column, this is the worst! If we use the column as a covariate, we're screwed. Needs improvement df [ _original_time_column ( self . time_col )] = df [ self . time_col ] df [ self . time_col ] = self . _get_time_col_cluster ( df ) return df def assign_treatment_df ( self , df : pd . DataFrame , ) -> pd . DataFrame : \"\"\" Creates the switchback column, adds it to cluster_cols and then calls ClusteredSplitter assign_treatment_df Arguments: df: dataframe to assign treatments to \"\"\" df = df . copy () df = self . _prepare_switchback_df ( df ) df = super () . assign_treatment_df ( df ) df = self . washover . washover ( df , truncated_time_col = self . time_col , treatment_col = self . treatment_col , cluster_cols = self . cluster_cols , ) return df @classmethod def from_config ( cls , config ) -> \"SwitchbackSplitter\" : washover_cls = _get_mapping_key ( washover_mapping , config . washover ) return cls ( time_col = config . time_col , switch_frequency = config . switch_frequency , cluster_cols = config . cluster_cols , treatments = config . treatments , treatment_col = config . treatment_col , splitter_weights = config . splitter_weights , washover = washover_cls . from_config ( config ), )","title":"SwitchbackSplitter"},{"location":"api/random_splitter.html#cluster_experiments.random_splitter.SwitchbackSplitter.assign_treatment_df","text":"Creates the switchback column, adds it to cluster_cols and then calls ClusteredSplitter assign_treatment_df Parameters: Name Type Description Default df DataFrame dataframe to assign treatments to required Source code in cluster_experiments/random_splitter.py def assign_treatment_df ( self , df : pd . DataFrame , ) -> pd . DataFrame : \"\"\" Creates the switchback column, adds it to cluster_cols and then calls ClusteredSplitter assign_treatment_df Arguments: df: dataframe to assign treatments to \"\"\" df = df . copy () df = self . _prepare_switchback_df ( df ) df = super () . assign_treatment_df ( df ) df = self . washover . washover ( df , truncated_time_col = self . time_col , treatment_col = self . treatment_col , cluster_cols = self . cluster_cols , ) return df","title":"assign_treatment_df()"},{"location":"api/random_splitter.html#cluster_experiments.random_splitter.SwitchbackSplitter.from_config","text":"Creates a RandomSplitter from a PowerConfig Source code in cluster_experiments/random_splitter.py @classmethod def from_config ( cls , config ) -> \"SwitchbackSplitter\" : washover_cls = _get_mapping_key ( washover_mapping , config . washover ) return cls ( time_col = config . time_col , switch_frequency = config . switch_frequency , cluster_cols = config . cluster_cols , treatments = config . treatments , treatment_col = config . treatment_col , splitter_weights = config . splitter_weights , washover = washover_cls . from_config ( config ), )","title":"from_config()"},{"location":"api/variant.html","text":"from cluster_experiments.inference.variant import * \u00b6 Variant dataclass \u00b6 A class used to represent a Variant with a name and a control flag. Attributes \u00b6 name : str The name of the variant is_control : bool A boolean indicating if the variant is a control variant Source code in cluster_experiments/inference/variant.py class Variant : \"\"\" A class used to represent a Variant with a name and a control flag. Attributes ---------- name : str The name of the variant is_control : bool A boolean indicating if the variant is a control variant \"\"\" name : str is_control : bool def __post_init__ ( self ): \"\"\" Validates the inputs after initialization. \"\"\" self . _validate_inputs () def _validate_inputs ( self ): \"\"\" Validates the inputs for the Variant class. Raises ------ TypeError If the name is not a string or if is_control is not a boolean. \"\"\" if not isinstance ( self . name , str ): raise TypeError ( \"Variant name must be a string\" ) if not isinstance ( self . is_control , bool ): raise TypeError ( \"Variant is_control must be a boolean\" ) __post_init__ ( self ) special \u00b6 Validates the inputs after initialization. Source code in cluster_experiments/inference/variant.py def __post_init__ ( self ): \"\"\" Validates the inputs after initialization. \"\"\" self . _validate_inputs ()","title":"Variant"},{"location":"api/variant.html#from-cluster_experimentsinferencevariant-import","text":"","title":"from cluster_experiments.inference.variant import *"},{"location":"api/variant.html#cluster_experiments.inference.variant.Variant","text":"A class used to represent a Variant with a name and a control flag.","title":"Variant"},{"location":"api/variant.html#cluster_experiments.inference.variant.Variant--attributes","text":"name : str The name of the variant is_control : bool A boolean indicating if the variant is a control variant Source code in cluster_experiments/inference/variant.py class Variant : \"\"\" A class used to represent a Variant with a name and a control flag. Attributes ---------- name : str The name of the variant is_control : bool A boolean indicating if the variant is a control variant \"\"\" name : str is_control : bool def __post_init__ ( self ): \"\"\" Validates the inputs after initialization. \"\"\" self . _validate_inputs () def _validate_inputs ( self ): \"\"\" Validates the inputs for the Variant class. Raises ------ TypeError If the name is not a string or if is_control is not a boolean. \"\"\" if not isinstance ( self . name , str ): raise TypeError ( \"Variant name must be a string\" ) if not isinstance ( self . is_control , bool ): raise TypeError ( \"Variant is_control must be a boolean\" )","title":"Attributes"},{"location":"api/variant.html#cluster_experiments.inference.variant.Variant.__post_init__","text":"Validates the inputs after initialization. Source code in cluster_experiments/inference/variant.py def __post_init__ ( self ): \"\"\" Validates the inputs after initialization. \"\"\" self . _validate_inputs ()","title":"__post_init__()"},{"location":"api/washover.html","text":"from cluster_experiments.washover import * \u00b6 ConstantWashover ( Washover ) \u00b6 Constant washover - we drop all rows in the washover period when there is a switch where the treatment is different. Source code in cluster_experiments/washover.py class ConstantWashover ( Washover ): \"\"\"Constant washover - we drop all rows in the washover period when there is a switch where the treatment is different.\"\"\" def __init__ ( self , washover_time_delta : datetime . timedelta ): self . washover_time_delta = washover_time_delta def washover ( self , df : pd . DataFrame , truncated_time_col : str , treatment_col : str , cluster_cols : List [ str ], original_time_col : Optional [ str ] = None , ) -> pd . DataFrame : \"\"\"Constant washover - we drop all rows in the washover period when there is a switch where the treatment is different. Args: df (pd.DataFrame): Input dataframe. truncated_time_col (str): Name of the truncated time column. treatment_col (str): Name of the treatment column. cluster_cols (List[str]): List of clusters of experiment. original_time_col (Optional[str], optional): Name of the original time column. Returns: pd.DataFrame: Same dataframe as input without the rows in the washover period. Usage: ```python import numpy as np import pandas as pd from datetime import datetime, timedelta from cluster_experiments import ConstantWashover np.random.seed(42) num_rows = 10 def random_timestamp(start_time, end_time): time_delta = end_time - start_time random_seconds = np.random.randint(0, time_delta.total_seconds()) return start_time + timedelta(seconds=random_seconds) def generate_data(start_time, end_time, treatment): data = { 'order_id': np.random.randint(10**9, 10**10, size=num_rows), 'city_code': 'VAL', 'activation_time_local': [random_timestamp(start_time, end_time) for _ in range(num_rows)], 'bin_start_time_local': start_time, 'treatment': treatment } return pd.DataFrame(data) start_times = [datetime(2024, 1, 22, 9, 0), datetime(2024, 1, 22, 11, 0), datetime(2024, 1, 22, 13, 0), datetime(2024, 1, 22, 15, 0)] treatments = ['control', 'variation', 'variation', 'control'] dataframes = [generate_data(start, start + timedelta(hours=2), treatment) for start, treatment in zip(start_times, treatments)] df = pd.concat(dataframes).sort_values(by='activation_time_local').reset_index(drop=True) ## Define washover with 30 min duration washover = ConstantWashover(washover_time_delta=timedelta(minutes=30)) ## Apply washover to the dataframe, the orders with activation time within the first 30 minutes after every change in the treatment column, clustering by city and 2h time bin, will be dropped df_analysis_washover = washover.washover( df=df, truncated_time_col='bin_start_time_local', treatment_col='treatment', cluster_cols=['city_code','bin_start_time_local'], original_time_col='activation_time_local', ) ``` \"\"\" # Set original time column original_time_col = ( original_time_col if original_time_col else _original_time_column ( truncated_time_col ) ) # Validate columns self . _validate_columns ( df , truncated_time_col , cluster_cols , original_time_col ) # Cluster columns that do not involve time non_time_cols = list ( set ( cluster_cols ) - set ([ truncated_time_col ])) # For each cluster, we need to check if treatment has changed wrt last time df_agg = df . sort_values ([ original_time_col ]) . copy () df_agg = df_agg . drop_duplicates ( subset = cluster_cols + [ treatment_col ]) if non_time_cols : df_agg [ \"__changed\" ] = ( df_agg . groupby ( non_time_cols )[ treatment_col ] . shift ( 1 ) != df_agg [ treatment_col ] ) else : df_agg [ \"__changed\" ] = ( df_agg [ treatment_col ] . shift ( 1 ) != df_agg [ treatment_col ] ) df_agg = df_agg . loc [:, cluster_cols + [ \"__changed\" ]] return ( df . merge ( df_agg , on = cluster_cols , how = \"inner\" ) . assign ( __time_since_switch = lambda x : x [ original_time_col ] . astype ( \"datetime64[ns]\" ) - x [ truncated_time_col ] . astype ( \"datetime64[ns]\" ), __after_washover = lambda x : x [ \"__time_since_switch\" ] > self . washover_time_delta , ) # add not changed in query . query ( \"__after_washover or not __changed\" ) . drop ( columns = [ \"__time_since_switch\" , \"__after_washover\" , \"__changed\" ]) ) @classmethod def from_config ( cls , config ) -> \"Washover\" : if not config . washover_time_delta : raise ValueError ( f \"Washover time delta must be specified for ConstantWashover, while it is { config . washover_time_delta = } \" ) washover_time_delta = config . washover_time_delta if isinstance ( washover_time_delta , int ): washover_time_delta = datetime . timedelta ( minutes = config . washover_time_delta ) return cls ( washover_time_delta = washover_time_delta ) washover ( self , df , truncated_time_col , treatment_col , cluster_cols , original_time_col = None ) \u00b6 Constant washover - we drop all rows in the washover period when there is a switch where the treatment is different. Parameters: Name Type Description Default df pd.DataFrame Input dataframe. required truncated_time_col str Name of the truncated time column. required treatment_col str Name of the treatment column. required cluster_cols List[str] List of clusters of experiment. required original_time_col Optional[str] Name of the original time column. None Returns: Type Description pd.DataFrame Same dataframe as input without the rows in the washover period. Usage: import numpy as np import pandas as pd from datetime import datetime , timedelta from cluster_experiments import ConstantWashover np . random . seed ( 42 ) num_rows = 10 def random_timestamp ( start_time , end_time ): time_delta = end_time - start_time random_seconds = np . random . randint ( 0 , time_delta . total_seconds ()) return start_time + timedelta ( seconds = random_seconds ) def generate_data ( start_time , end_time , treatment ): data = { 'order_id' : np . random . randint ( 10 ** 9 , 10 ** 10 , size = num_rows ), 'city_code' : 'VAL' , 'activation_time_local' : [ random_timestamp ( start_time , end_time ) for _ in range ( num_rows )], 'bin_start_time_local' : start_time , 'treatment' : treatment } return pd . DataFrame ( data ) start_times = [ datetime ( 2024 , 1 , 22 , 9 , 0 ), datetime ( 2024 , 1 , 22 , 11 , 0 ), datetime ( 2024 , 1 , 22 , 13 , 0 ), datetime ( 2024 , 1 , 22 , 15 , 0 )] treatments = [ 'control' , 'variation' , 'variation' , 'control' ] dataframes = [ generate_data ( start , start + timedelta ( hours = 2 ), treatment ) for start , treatment in zip ( start_times , treatments )] df = pd . concat ( dataframes ) . sort_values ( by = 'activation_time_local' ) . reset_index ( drop = True ) ## Define washover with 30 min duration washover = ConstantWashover ( washover_time_delta = timedelta ( minutes = 30 )) ## Apply washover to the dataframe, the orders with activation time within the first 30 minutes after every change in the treatment column, clustering by city and 2h time bin, will be dropped df_analysis_washover = washover . washover ( df = df , truncated_time_col = 'bin_start_time_local' , treatment_col = 'treatment' , cluster_cols = [ 'city_code' , 'bin_start_time_local' ], original_time_col = 'activation_time_local' , ) Source code in cluster_experiments/washover.py def washover ( self , df : pd . DataFrame , truncated_time_col : str , treatment_col : str , cluster_cols : List [ str ], original_time_col : Optional [ str ] = None , ) -> pd . DataFrame : \"\"\"Constant washover - we drop all rows in the washover period when there is a switch where the treatment is different. Args: df (pd.DataFrame): Input dataframe. truncated_time_col (str): Name of the truncated time column. treatment_col (str): Name of the treatment column. cluster_cols (List[str]): List of clusters of experiment. original_time_col (Optional[str], optional): Name of the original time column. Returns: pd.DataFrame: Same dataframe as input without the rows in the washover period. Usage: ```python import numpy as np import pandas as pd from datetime import datetime, timedelta from cluster_experiments import ConstantWashover np.random.seed(42) num_rows = 10 def random_timestamp(start_time, end_time): time_delta = end_time - start_time random_seconds = np.random.randint(0, time_delta.total_seconds()) return start_time + timedelta(seconds=random_seconds) def generate_data(start_time, end_time, treatment): data = { 'order_id': np.random.randint(10**9, 10**10, size=num_rows), 'city_code': 'VAL', 'activation_time_local': [random_timestamp(start_time, end_time) for _ in range(num_rows)], 'bin_start_time_local': start_time, 'treatment': treatment } return pd.DataFrame(data) start_times = [datetime(2024, 1, 22, 9, 0), datetime(2024, 1, 22, 11, 0), datetime(2024, 1, 22, 13, 0), datetime(2024, 1, 22, 15, 0)] treatments = ['control', 'variation', 'variation', 'control'] dataframes = [generate_data(start, start + timedelta(hours=2), treatment) for start, treatment in zip(start_times, treatments)] df = pd.concat(dataframes).sort_values(by='activation_time_local').reset_index(drop=True) ## Define washover with 30 min duration washover = ConstantWashover(washover_time_delta=timedelta(minutes=30)) ## Apply washover to the dataframe, the orders with activation time within the first 30 minutes after every change in the treatment column, clustering by city and 2h time bin, will be dropped df_analysis_washover = washover.washover( df=df, truncated_time_col='bin_start_time_local', treatment_col='treatment', cluster_cols=['city_code','bin_start_time_local'], original_time_col='activation_time_local', ) ``` \"\"\" # Set original time column original_time_col = ( original_time_col if original_time_col else _original_time_column ( truncated_time_col ) ) # Validate columns self . _validate_columns ( df , truncated_time_col , cluster_cols , original_time_col ) # Cluster columns that do not involve time non_time_cols = list ( set ( cluster_cols ) - set ([ truncated_time_col ])) # For each cluster, we need to check if treatment has changed wrt last time df_agg = df . sort_values ([ original_time_col ]) . copy () df_agg = df_agg . drop_duplicates ( subset = cluster_cols + [ treatment_col ]) if non_time_cols : df_agg [ \"__changed\" ] = ( df_agg . groupby ( non_time_cols )[ treatment_col ] . shift ( 1 ) != df_agg [ treatment_col ] ) else : df_agg [ \"__changed\" ] = ( df_agg [ treatment_col ] . shift ( 1 ) != df_agg [ treatment_col ] ) df_agg = df_agg . loc [:, cluster_cols + [ \"__changed\" ]] return ( df . merge ( df_agg , on = cluster_cols , how = \"inner\" ) . assign ( __time_since_switch = lambda x : x [ original_time_col ] . astype ( \"datetime64[ns]\" ) - x [ truncated_time_col ] . astype ( \"datetime64[ns]\" ), __after_washover = lambda x : x [ \"__time_since_switch\" ] > self . washover_time_delta , ) # add not changed in query . query ( \"__after_washover or not __changed\" ) . drop ( columns = [ \"__time_since_switch\" , \"__after_washover\" , \"__changed\" ]) ) EmptyWashover ( Washover ) \u00b6 No washover - assumes no spill-over effects from one treatment to another. Source code in cluster_experiments/washover.py class EmptyWashover ( Washover ): \"\"\"No washover - assumes no spill-over effects from one treatment to another.\"\"\" def washover ( self , df : pd . DataFrame , truncated_time_col : str , treatment_col : str , cluster_cols : List [ str ], original_time_col : Optional [ str ] = None , ) -> pd . DataFrame : \"\"\"No washover - returns the same dataframe as input. Args: df (pd.DataFrame): Input dataframe. truncated_time_col (str): Name of the truncated time column. treatment_col (str): Name of the treatment column. cluster_cols (List[str]): List of clusters of experiment. original_time_col (Optional[str], optional): Name of the original time column. Returns: pd.DataFrame: Same dataframe as input. Usage: ```python from cluster_experiments import SwitchbackSplitter from cluster_experiments import EmptyWashover washover = EmptyWashover() n = 10 df = pd.DataFrame( { # Random time each minute in 2022-01-01, length 10 \"time\": pd.date_range(\"2022-01-01\", \"2022-01-02\", freq=\"1min\")[ np.random.randint(24 * 60, size=n) ], \"city\": random.choices([\"TGN\", \"NYC\", \"LON\", \"REU\"], k=n), } ) splitter = SwitchbackSplitter( washover=washover, time_col=\"time\", cluster_cols=[\"city\", \"time\"], treatment_col=\"treatment\", switch_frequency=\"30T\", ) out_df = splitter.assign_treatment_df(df=washover_split_df) ``` \"\"\" return df washover ( self , df , truncated_time_col , treatment_col , cluster_cols , original_time_col = None ) \u00b6 No washover - returns the same dataframe as input. Parameters: Name Type Description Default df pd.DataFrame Input dataframe. required truncated_time_col str Name of the truncated time column. required treatment_col str Name of the treatment column. required cluster_cols List[str] List of clusters of experiment. required original_time_col Optional[str] Name of the original time column. None Returns: Type Description pd.DataFrame Same dataframe as input. Usage: from cluster_experiments import SwitchbackSplitter from cluster_experiments import EmptyWashover washover = EmptyWashover () n = 10 df = pd . DataFrame ( { # Random time each minute in 2022-01-01, length 10 \"time\" : pd . date_range ( \"2022-01-01\" , \"2022-01-02\" , freq = \"1min\" )[ np . random . randint ( 24 * 60 , size = n ) ], \"city\" : random . choices ([ \"TGN\" , \"NYC\" , \"LON\" , \"REU\" ], k = n ), } ) splitter = SwitchbackSplitter ( washover = washover , time_col = \"time\" , cluster_cols = [ \"city\" , \"time\" ], treatment_col = \"treatment\" , switch_frequency = \"30T\" , ) out_df = splitter . assign_treatment_df ( df = washover_split_df ) Source code in cluster_experiments/washover.py def washover ( self , df : pd . DataFrame , truncated_time_col : str , treatment_col : str , cluster_cols : List [ str ], original_time_col : Optional [ str ] = None , ) -> pd . DataFrame : \"\"\"No washover - returns the same dataframe as input. Args: df (pd.DataFrame): Input dataframe. truncated_time_col (str): Name of the truncated time column. treatment_col (str): Name of the treatment column. cluster_cols (List[str]): List of clusters of experiment. original_time_col (Optional[str], optional): Name of the original time column. Returns: pd.DataFrame: Same dataframe as input. Usage: ```python from cluster_experiments import SwitchbackSplitter from cluster_experiments import EmptyWashover washover = EmptyWashover() n = 10 df = pd.DataFrame( { # Random time each minute in 2022-01-01, length 10 \"time\": pd.date_range(\"2022-01-01\", \"2022-01-02\", freq=\"1min\")[ np.random.randint(24 * 60, size=n) ], \"city\": random.choices([\"TGN\", \"NYC\", \"LON\", \"REU\"], k=n), } ) splitter = SwitchbackSplitter( washover=washover, time_col=\"time\", cluster_cols=[\"city\", \"time\"], treatment_col=\"treatment\", switch_frequency=\"30T\", ) out_df = splitter.assign_treatment_df(df=washover_split_df) ``` \"\"\" return df Washover ( ABC ) \u00b6 Abstract class to model washovers in the switchback splitter. Source code in cluster_experiments/washover.py class Washover ( ABC ): \"\"\"Abstract class to model washovers in the switchback splitter.\"\"\" def _validate_columns ( self , df : pd . DataFrame , truncated_time_col : str , cluster_cols : List [ str ], original_time_col : str , ): \"\"\"Validate that all the columns required for the washover are present in the dataframe. Args: df (pd.DataFrame): Input dataframe. truncated_time_col (str): Name of the truncated time column. cluster_cols (List[str]): List of clusters of experiment. original_time_col (str): Name of the original time column. Returns: None: This method does not return any data; it only performs validation. \"\"\" if original_time_col not in df . columns : raise ValueError ( f \" { original_time_col = } is not in the dataframe columns and/or not specified as an input.\" ) if truncated_time_col not in cluster_cols : raise ValueError ( f \" { truncated_time_col = } is not in the cluster columns.\" ) for col in cluster_cols : if col not in df . columns : raise ValueError ( f \" { col = } cluster is not in the dataframe columns.\" ) @abstractmethod def washover ( self , df : pd . DataFrame , truncated_time_col : str , treatment_col : str , cluster_cols : List [ str ], original_time_col : Optional [ str ] = None , ) -> pd . DataFrame : \"\"\"Abstract method to add washvover to the dataframe.\"\"\" @classmethod def from_config ( cls , config ) -> \"Washover\" : return cls () washover ( self , df , truncated_time_col , treatment_col , cluster_cols , original_time_col = None ) \u00b6 Abstract method to add washvover to the dataframe. Source code in cluster_experiments/washover.py @abstractmethod def washover ( self , df : pd . DataFrame , truncated_time_col : str , treatment_col : str , cluster_cols : List [ str ], original_time_col : Optional [ str ] = None , ) -> pd . DataFrame : \"\"\"Abstract method to add washvover to the dataframe.\"\"\"","title":"Washover"},{"location":"api/washover.html#from-cluster_experimentswashover-import","text":"","title":"from cluster_experiments.washover import *"},{"location":"api/washover.html#cluster_experiments.washover.ConstantWashover","text":"Constant washover - we drop all rows in the washover period when there is a switch where the treatment is different. Source code in cluster_experiments/washover.py class ConstantWashover ( Washover ): \"\"\"Constant washover - we drop all rows in the washover period when there is a switch where the treatment is different.\"\"\" def __init__ ( self , washover_time_delta : datetime . timedelta ): self . washover_time_delta = washover_time_delta def washover ( self , df : pd . DataFrame , truncated_time_col : str , treatment_col : str , cluster_cols : List [ str ], original_time_col : Optional [ str ] = None , ) -> pd . DataFrame : \"\"\"Constant washover - we drop all rows in the washover period when there is a switch where the treatment is different. Args: df (pd.DataFrame): Input dataframe. truncated_time_col (str): Name of the truncated time column. treatment_col (str): Name of the treatment column. cluster_cols (List[str]): List of clusters of experiment. original_time_col (Optional[str], optional): Name of the original time column. Returns: pd.DataFrame: Same dataframe as input without the rows in the washover period. Usage: ```python import numpy as np import pandas as pd from datetime import datetime, timedelta from cluster_experiments import ConstantWashover np.random.seed(42) num_rows = 10 def random_timestamp(start_time, end_time): time_delta = end_time - start_time random_seconds = np.random.randint(0, time_delta.total_seconds()) return start_time + timedelta(seconds=random_seconds) def generate_data(start_time, end_time, treatment): data = { 'order_id': np.random.randint(10**9, 10**10, size=num_rows), 'city_code': 'VAL', 'activation_time_local': [random_timestamp(start_time, end_time) for _ in range(num_rows)], 'bin_start_time_local': start_time, 'treatment': treatment } return pd.DataFrame(data) start_times = [datetime(2024, 1, 22, 9, 0), datetime(2024, 1, 22, 11, 0), datetime(2024, 1, 22, 13, 0), datetime(2024, 1, 22, 15, 0)] treatments = ['control', 'variation', 'variation', 'control'] dataframes = [generate_data(start, start + timedelta(hours=2), treatment) for start, treatment in zip(start_times, treatments)] df = pd.concat(dataframes).sort_values(by='activation_time_local').reset_index(drop=True) ## Define washover with 30 min duration washover = ConstantWashover(washover_time_delta=timedelta(minutes=30)) ## Apply washover to the dataframe, the orders with activation time within the first 30 minutes after every change in the treatment column, clustering by city and 2h time bin, will be dropped df_analysis_washover = washover.washover( df=df, truncated_time_col='bin_start_time_local', treatment_col='treatment', cluster_cols=['city_code','bin_start_time_local'], original_time_col='activation_time_local', ) ``` \"\"\" # Set original time column original_time_col = ( original_time_col if original_time_col else _original_time_column ( truncated_time_col ) ) # Validate columns self . _validate_columns ( df , truncated_time_col , cluster_cols , original_time_col ) # Cluster columns that do not involve time non_time_cols = list ( set ( cluster_cols ) - set ([ truncated_time_col ])) # For each cluster, we need to check if treatment has changed wrt last time df_agg = df . sort_values ([ original_time_col ]) . copy () df_agg = df_agg . drop_duplicates ( subset = cluster_cols + [ treatment_col ]) if non_time_cols : df_agg [ \"__changed\" ] = ( df_agg . groupby ( non_time_cols )[ treatment_col ] . shift ( 1 ) != df_agg [ treatment_col ] ) else : df_agg [ \"__changed\" ] = ( df_agg [ treatment_col ] . shift ( 1 ) != df_agg [ treatment_col ] ) df_agg = df_agg . loc [:, cluster_cols + [ \"__changed\" ]] return ( df . merge ( df_agg , on = cluster_cols , how = \"inner\" ) . assign ( __time_since_switch = lambda x : x [ original_time_col ] . astype ( \"datetime64[ns]\" ) - x [ truncated_time_col ] . astype ( \"datetime64[ns]\" ), __after_washover = lambda x : x [ \"__time_since_switch\" ] > self . washover_time_delta , ) # add not changed in query . query ( \"__after_washover or not __changed\" ) . drop ( columns = [ \"__time_since_switch\" , \"__after_washover\" , \"__changed\" ]) ) @classmethod def from_config ( cls , config ) -> \"Washover\" : if not config . washover_time_delta : raise ValueError ( f \"Washover time delta must be specified for ConstantWashover, while it is { config . washover_time_delta = } \" ) washover_time_delta = config . washover_time_delta if isinstance ( washover_time_delta , int ): washover_time_delta = datetime . timedelta ( minutes = config . washover_time_delta ) return cls ( washover_time_delta = washover_time_delta )","title":"ConstantWashover"},{"location":"api/washover.html#cluster_experiments.washover.ConstantWashover.washover","text":"Constant washover - we drop all rows in the washover period when there is a switch where the treatment is different. Parameters: Name Type Description Default df pd.DataFrame Input dataframe. required truncated_time_col str Name of the truncated time column. required treatment_col str Name of the treatment column. required cluster_cols List[str] List of clusters of experiment. required original_time_col Optional[str] Name of the original time column. None Returns: Type Description pd.DataFrame Same dataframe as input without the rows in the washover period. Usage: import numpy as np import pandas as pd from datetime import datetime , timedelta from cluster_experiments import ConstantWashover np . random . seed ( 42 ) num_rows = 10 def random_timestamp ( start_time , end_time ): time_delta = end_time - start_time random_seconds = np . random . randint ( 0 , time_delta . total_seconds ()) return start_time + timedelta ( seconds = random_seconds ) def generate_data ( start_time , end_time , treatment ): data = { 'order_id' : np . random . randint ( 10 ** 9 , 10 ** 10 , size = num_rows ), 'city_code' : 'VAL' , 'activation_time_local' : [ random_timestamp ( start_time , end_time ) for _ in range ( num_rows )], 'bin_start_time_local' : start_time , 'treatment' : treatment } return pd . DataFrame ( data ) start_times = [ datetime ( 2024 , 1 , 22 , 9 , 0 ), datetime ( 2024 , 1 , 22 , 11 , 0 ), datetime ( 2024 , 1 , 22 , 13 , 0 ), datetime ( 2024 , 1 , 22 , 15 , 0 )] treatments = [ 'control' , 'variation' , 'variation' , 'control' ] dataframes = [ generate_data ( start , start + timedelta ( hours = 2 ), treatment ) for start , treatment in zip ( start_times , treatments )] df = pd . concat ( dataframes ) . sort_values ( by = 'activation_time_local' ) . reset_index ( drop = True ) ## Define washover with 30 min duration washover = ConstantWashover ( washover_time_delta = timedelta ( minutes = 30 )) ## Apply washover to the dataframe, the orders with activation time within the first 30 minutes after every change in the treatment column, clustering by city and 2h time bin, will be dropped df_analysis_washover = washover . washover ( df = df , truncated_time_col = 'bin_start_time_local' , treatment_col = 'treatment' , cluster_cols = [ 'city_code' , 'bin_start_time_local' ], original_time_col = 'activation_time_local' , ) Source code in cluster_experiments/washover.py def washover ( self , df : pd . DataFrame , truncated_time_col : str , treatment_col : str , cluster_cols : List [ str ], original_time_col : Optional [ str ] = None , ) -> pd . DataFrame : \"\"\"Constant washover - we drop all rows in the washover period when there is a switch where the treatment is different. Args: df (pd.DataFrame): Input dataframe. truncated_time_col (str): Name of the truncated time column. treatment_col (str): Name of the treatment column. cluster_cols (List[str]): List of clusters of experiment. original_time_col (Optional[str], optional): Name of the original time column. Returns: pd.DataFrame: Same dataframe as input without the rows in the washover period. Usage: ```python import numpy as np import pandas as pd from datetime import datetime, timedelta from cluster_experiments import ConstantWashover np.random.seed(42) num_rows = 10 def random_timestamp(start_time, end_time): time_delta = end_time - start_time random_seconds = np.random.randint(0, time_delta.total_seconds()) return start_time + timedelta(seconds=random_seconds) def generate_data(start_time, end_time, treatment): data = { 'order_id': np.random.randint(10**9, 10**10, size=num_rows), 'city_code': 'VAL', 'activation_time_local': [random_timestamp(start_time, end_time) for _ in range(num_rows)], 'bin_start_time_local': start_time, 'treatment': treatment } return pd.DataFrame(data) start_times = [datetime(2024, 1, 22, 9, 0), datetime(2024, 1, 22, 11, 0), datetime(2024, 1, 22, 13, 0), datetime(2024, 1, 22, 15, 0)] treatments = ['control', 'variation', 'variation', 'control'] dataframes = [generate_data(start, start + timedelta(hours=2), treatment) for start, treatment in zip(start_times, treatments)] df = pd.concat(dataframes).sort_values(by='activation_time_local').reset_index(drop=True) ## Define washover with 30 min duration washover = ConstantWashover(washover_time_delta=timedelta(minutes=30)) ## Apply washover to the dataframe, the orders with activation time within the first 30 minutes after every change in the treatment column, clustering by city and 2h time bin, will be dropped df_analysis_washover = washover.washover( df=df, truncated_time_col='bin_start_time_local', treatment_col='treatment', cluster_cols=['city_code','bin_start_time_local'], original_time_col='activation_time_local', ) ``` \"\"\" # Set original time column original_time_col = ( original_time_col if original_time_col else _original_time_column ( truncated_time_col ) ) # Validate columns self . _validate_columns ( df , truncated_time_col , cluster_cols , original_time_col ) # Cluster columns that do not involve time non_time_cols = list ( set ( cluster_cols ) - set ([ truncated_time_col ])) # For each cluster, we need to check if treatment has changed wrt last time df_agg = df . sort_values ([ original_time_col ]) . copy () df_agg = df_agg . drop_duplicates ( subset = cluster_cols + [ treatment_col ]) if non_time_cols : df_agg [ \"__changed\" ] = ( df_agg . groupby ( non_time_cols )[ treatment_col ] . shift ( 1 ) != df_agg [ treatment_col ] ) else : df_agg [ \"__changed\" ] = ( df_agg [ treatment_col ] . shift ( 1 ) != df_agg [ treatment_col ] ) df_agg = df_agg . loc [:, cluster_cols + [ \"__changed\" ]] return ( df . merge ( df_agg , on = cluster_cols , how = \"inner\" ) . assign ( __time_since_switch = lambda x : x [ original_time_col ] . astype ( \"datetime64[ns]\" ) - x [ truncated_time_col ] . astype ( \"datetime64[ns]\" ), __after_washover = lambda x : x [ \"__time_since_switch\" ] > self . washover_time_delta , ) # add not changed in query . query ( \"__after_washover or not __changed\" ) . drop ( columns = [ \"__time_since_switch\" , \"__after_washover\" , \"__changed\" ]) )","title":"washover()"},{"location":"api/washover.html#cluster_experiments.washover.EmptyWashover","text":"No washover - assumes no spill-over effects from one treatment to another. Source code in cluster_experiments/washover.py class EmptyWashover ( Washover ): \"\"\"No washover - assumes no spill-over effects from one treatment to another.\"\"\" def washover ( self , df : pd . DataFrame , truncated_time_col : str , treatment_col : str , cluster_cols : List [ str ], original_time_col : Optional [ str ] = None , ) -> pd . DataFrame : \"\"\"No washover - returns the same dataframe as input. Args: df (pd.DataFrame): Input dataframe. truncated_time_col (str): Name of the truncated time column. treatment_col (str): Name of the treatment column. cluster_cols (List[str]): List of clusters of experiment. original_time_col (Optional[str], optional): Name of the original time column. Returns: pd.DataFrame: Same dataframe as input. Usage: ```python from cluster_experiments import SwitchbackSplitter from cluster_experiments import EmptyWashover washover = EmptyWashover() n = 10 df = pd.DataFrame( { # Random time each minute in 2022-01-01, length 10 \"time\": pd.date_range(\"2022-01-01\", \"2022-01-02\", freq=\"1min\")[ np.random.randint(24 * 60, size=n) ], \"city\": random.choices([\"TGN\", \"NYC\", \"LON\", \"REU\"], k=n), } ) splitter = SwitchbackSplitter( washover=washover, time_col=\"time\", cluster_cols=[\"city\", \"time\"], treatment_col=\"treatment\", switch_frequency=\"30T\", ) out_df = splitter.assign_treatment_df(df=washover_split_df) ``` \"\"\" return df","title":"EmptyWashover"},{"location":"api/washover.html#cluster_experiments.washover.EmptyWashover.washover","text":"No washover - returns the same dataframe as input. Parameters: Name Type Description Default df pd.DataFrame Input dataframe. required truncated_time_col str Name of the truncated time column. required treatment_col str Name of the treatment column. required cluster_cols List[str] List of clusters of experiment. required original_time_col Optional[str] Name of the original time column. None Returns: Type Description pd.DataFrame Same dataframe as input. Usage: from cluster_experiments import SwitchbackSplitter from cluster_experiments import EmptyWashover washover = EmptyWashover () n = 10 df = pd . DataFrame ( { # Random time each minute in 2022-01-01, length 10 \"time\" : pd . date_range ( \"2022-01-01\" , \"2022-01-02\" , freq = \"1min\" )[ np . random . randint ( 24 * 60 , size = n ) ], \"city\" : random . choices ([ \"TGN\" , \"NYC\" , \"LON\" , \"REU\" ], k = n ), } ) splitter = SwitchbackSplitter ( washover = washover , time_col = \"time\" , cluster_cols = [ \"city\" , \"time\" ], treatment_col = \"treatment\" , switch_frequency = \"30T\" , ) out_df = splitter . assign_treatment_df ( df = washover_split_df ) Source code in cluster_experiments/washover.py def washover ( self , df : pd . DataFrame , truncated_time_col : str , treatment_col : str , cluster_cols : List [ str ], original_time_col : Optional [ str ] = None , ) -> pd . DataFrame : \"\"\"No washover - returns the same dataframe as input. Args: df (pd.DataFrame): Input dataframe. truncated_time_col (str): Name of the truncated time column. treatment_col (str): Name of the treatment column. cluster_cols (List[str]): List of clusters of experiment. original_time_col (Optional[str], optional): Name of the original time column. Returns: pd.DataFrame: Same dataframe as input. Usage: ```python from cluster_experiments import SwitchbackSplitter from cluster_experiments import EmptyWashover washover = EmptyWashover() n = 10 df = pd.DataFrame( { # Random time each minute in 2022-01-01, length 10 \"time\": pd.date_range(\"2022-01-01\", \"2022-01-02\", freq=\"1min\")[ np.random.randint(24 * 60, size=n) ], \"city\": random.choices([\"TGN\", \"NYC\", \"LON\", \"REU\"], k=n), } ) splitter = SwitchbackSplitter( washover=washover, time_col=\"time\", cluster_cols=[\"city\", \"time\"], treatment_col=\"treatment\", switch_frequency=\"30T\", ) out_df = splitter.assign_treatment_df(df=washover_split_df) ``` \"\"\" return df","title":"washover()"},{"location":"api/washover.html#cluster_experiments.washover.Washover","text":"Abstract class to model washovers in the switchback splitter. Source code in cluster_experiments/washover.py class Washover ( ABC ): \"\"\"Abstract class to model washovers in the switchback splitter.\"\"\" def _validate_columns ( self , df : pd . DataFrame , truncated_time_col : str , cluster_cols : List [ str ], original_time_col : str , ): \"\"\"Validate that all the columns required for the washover are present in the dataframe. Args: df (pd.DataFrame): Input dataframe. truncated_time_col (str): Name of the truncated time column. cluster_cols (List[str]): List of clusters of experiment. original_time_col (str): Name of the original time column. Returns: None: This method does not return any data; it only performs validation. \"\"\" if original_time_col not in df . columns : raise ValueError ( f \" { original_time_col = } is not in the dataframe columns and/or not specified as an input.\" ) if truncated_time_col not in cluster_cols : raise ValueError ( f \" { truncated_time_col = } is not in the cluster columns.\" ) for col in cluster_cols : if col not in df . columns : raise ValueError ( f \" { col = } cluster is not in the dataframe columns.\" ) @abstractmethod def washover ( self , df : pd . DataFrame , truncated_time_col : str , treatment_col : str , cluster_cols : List [ str ], original_time_col : Optional [ str ] = None , ) -> pd . DataFrame : \"\"\"Abstract method to add washvover to the dataframe.\"\"\" @classmethod def from_config ( cls , config ) -> \"Washover\" : return cls ()","title":"Washover"},{"location":"api/washover.html#cluster_experiments.washover.Washover.washover","text":"Abstract method to add washvover to the dataframe. Source code in cluster_experiments/washover.py @abstractmethod def washover ( self , df : pd . DataFrame , truncated_time_col : str , treatment_col : str , cluster_cols : List [ str ], original_time_col : Optional [ str ] = None , ) -> pd . 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