Tbas adapter

CHANGELOG.md

@@ -9,20 +9,21 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 ## Unreleased
 ### Added
 -
+
 -
 - Add Sign metric ([#730](https://github.com/tinkoff-ai/etna/pull/730))
 -
 -
 -
 - Add AutoARIMA model ([#679](https://github.com/tinkoff-ai/etna/pull/679))
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--
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+- 
+- 
+- 
+- 
+- 
 - Add parameters `start`, `end` to some eda methods ([#665](https://github.com/tinkoff-ai/etna/pull/665))
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+- Add BATS and TBATS model adapters ([#678](https://github.com/tinkoff-ai/etna/pull/734))
+- 
 ### Changed
 -
 - 

etna/models/__init__.py

@@ -19,6 +19,8 @@
 from etna.models.seasonal_ma import SeasonalMovingAverageModel
 from etna.models.sklearn import SklearnMultiSegmentModel
 from etna.models.sklearn import SklearnPerSegmentModel
+from etna.models.tbats import BATSModel
+from etna.models.tbats import TBATSModel
 
 if SETTINGS.prophet_required:
     from etna.models.prophet import ProphetModel

etna/models/tbats.py

@@ -0,0 +1,178 @@
+from typing import Iterable
+from typing import Optional
+from typing import Tuple
+
+import pandas as pd
+from tbats.abstract import ContextInterface
+from tbats.abstract import Estimator
+from tbats.bats import BATS
+from tbats.tbats import TBATS
+from tbats.tbats.Model import Model
+
+from etna.models.base import BaseAdapter
+from etna.models.base import PerSegmentPredictionIntervalModel
+
+
+class _TBATSAdapter(BaseAdapter):
+    def __init__(self, model: Estimator):
+        self.model = model
+        self._fitted_model: Optional[Model] = None
+
+    def fit(self, df: pd.DataFrame, regressors: Iterable[str]):
+        target = df["target"]
+        self._fitted_model = self.model.fit(target)
+        return self
+
+    def predict(self, df: pd.DataFrame, prediction_interval: bool, quantiles: Iterable[float]) -> pd.DataFrame:
+        if self._fitted_model is None:
+            raise ValueError("Model is not fitted! Fit the model before calling predict method!")
+        y_pred = pd.DataFrame()
+        if prediction_interval:
+            for quantile in quantiles:
+                pred, confidence_intervals = self._fitted_model.forecast(steps=df.shape[0], confidence_level=quantile)
+                y_pred["target"] = pred
+                if quantile < 1 / 2:
+                    y_pred[f"target_{quantile:.4g}"] = confidence_intervals["lower_bound"]
+                else:
+                    y_pred[f"target_{quantile:.4g}"] = confidence_intervals["upper_bound"]
+        else:
+            pred = self._fitted_model.forecast(steps=df.shape[0])
+            y_pred["target"] = pred
+        return y_pred
+
+    def get_model(self) -> Estimator:
+        return self.model
+
+
+class BATSModel(PerSegmentPredictionIntervalModel):
+    """Class for holding segment interval BATS model."""
+
+    def __init__(
+        self,
+        use_box_cox: Optional[bool] = None,
+        box_cox_bounds: Tuple[int, int] = (0, 1),
+        use_trend: Optional[bool] = None,
+        use_damped_trend: Optional[bool] = None,
+        seasonal_periods: Optional[Iterable[int]] = None,
+        use_arma_errors: bool = True,
+        show_warnings: bool = True,
+        n_jobs: Optional[int] = None,
+        multiprocessing_start_method: str = "spawn",
+        context: Optional[ContextInterface] = None,
+    ):
+        """Create BATSPerSegmentModel with given parameters.
+
+        Parameters
+        ----------
+        use_box_cox: bool or None, optional (default=None)
+            If Box-Cox transformation of original series should be applied.
+            When None both cases shall be considered and better is selected by AIC.
+        box_cox_bounds: tuple, shape=(2,), optional (default=(0, 1))
+            Minimal and maximal Box-Cox parameter values.
+        use_trend: bool or None, optional (default=None)
+            Indicates whether to include a trend or not.
+            When None both cases shall be considered and better is selected by AIC.
+        use_damped_trend: bool or None, optional (default=None)
+            Indicates whether to include a damping parameter in the trend or not.
+            Applies only when trend is used.
+            When None both cases shall be considered and better is selected by AIC.
+        seasonal_periods: iterable or array-like of int values, optional (default=None)
+            Length of each of the periods (amount of observations in each period).
+            BATS accepts only int values here.
+            When None or empty array, non-seasonal model shall be fitted.
+        use_arma_errors: bool, optional (default=True)
+            When True BATS will try to improve the model by modelling residuals with ARMA.
+            Best model will be selected by AIC.
+            If False, ARMA residuals modeling will not be considered.
+        show_warnings: bool, optional (default=True)
+            If warnings should be shown or not.
+            Also see Model.warnings variable that contains all model related warnings.
+        n_jobs: int, optional (default=None)
+            How many jobs to run in parallel when fitting BATS model.
+            When not provided BATS shall try to utilize all available cpu cores.
+        multiprocessing_start_method: str, optional (default='spawn')
+            How threads should be started.
+            See https://docs.python.org/3/library/multiprocessing.html#contexts-and-start-methods
+        context: abstract.ContextInterface, optional (default=None)
+            For advanced users only. Provide this to override default behaviors
+        """
+        self.model = BATS(
+            use_box_cox=use_box_cox,
+            box_cox_bounds=box_cox_bounds,
+            use_trend=use_trend,
+            use_damped_trend=use_damped_trend,
+            seasonal_periods=seasonal_periods,
+            use_arma_errors=use_arma_errors,
+            show_warnings=show_warnings,
+            n_jobs=n_jobs,
+            multiprocessing_start_method=multiprocessing_start_method,
+            context=context,
+        )
+        super().__init__(base_model=_TBATSAdapter(self.model))
+
+
+class TBATSModel(PerSegmentPredictionIntervalModel):
+    """Class for holding segment interval TBATS model."""
+
+    def __init__(
+        self,
+        use_box_cox: Optional[bool] = None,
+        box_cox_bounds: Tuple[int, int] = (0, 1),
+        use_trend: Optional[bool] = None,
+        use_damped_trend: Optional[bool] = None,
+        seasonal_periods: Optional[Iterable[int]] = None,
+        use_arma_errors: bool = True,
+        show_warnings: bool = True,
+        n_jobs: Optional[int] = None,
+        multiprocessing_start_method: str = "spawn",
+        context: Optional[ContextInterface] = None,
+    ):
+        """Create TBATSPerSegmentModel with given parameters.
+
+        Parameters
+        ----------
+        use_box_cox: bool or None, optional (default=None)
+            If Box-Cox transformation of original series should be applied.
+            When None both cases shall be considered and better is selected by AIC.
+        box_cox_bounds: tuple, shape=(2,), optional (default=(0, 1))
+            Minimal and maximal Box-Cox parameter values.
+        use_trend: bool or None, optional (default=None)
+            Indicates whether to include a trend or not.
+            When None both cases shall be considered and better is selected by AIC.
+        use_damped_trend: bool or None, optional (default=None)
+            Indicates whether to include a damping parameter in the trend or not.
+            Applies only when trend is used.
+            When None both cases shall be considered and better is selected by AIC.
+        seasonal_periods: iterable or array-like of floats, optional (default=None)
+            Length of each of the periods (amount of observations in each period).
+            TBATS accepts int and float values here.
+            When None or empty array, non-seasonal model shall be fitted.
+        use_arma_errors: bool, optional (default=True)
+            When True BATS will try to improve the model by modelling residuals with ARMA.
+            Best model will be selected by AIC.
+            If False, ARMA residuals modeling will not be considered.
+        show_warnings: bool, optional (default=True)
+            If warnings should be shown or not.
+            Also see Model.warnings variable that contains all model related warnings.
+        n_jobs: int, optional (default=None)
+            How many jobs to run in parallel when fitting BATS model.
+            When not provided BATS shall try to utilize all available cpu cores.
+        multiprocessing_start_method: str, optional (default='spawn')
+            How threads should be started.
+            See https://docs.python.org/3/library/multiprocessing.html#contexts-and-start-methods
+        context: abstract.ContextInterface, optional (default=None)
+            For advanced users only. Provide this to override default behaviors
+        """
+        self.model = TBATS(
+            use_box_cox=use_box_cox,
+            box_cox_bounds=box_cox_bounds,
+            use_trend=use_trend,
+            use_damped_trend=use_damped_trend,
+            seasonal_periods=seasonal_periods,
+            use_arma_errors=use_arma_errors,
+            show_warnings=show_warnings,
+            n_jobs=n_jobs,
+            multiprocessing_start_method=multiprocessing_start_method,
+            context=context,
+        )
+        super().__init__(base_model=_TBATSAdapter(self.model))