feat: add ml LogisticRegression model params (#481)

* feat: add ml LogisticRegression model params

* fix tests

* fix tests

bigframes/ml/linear_model.py

@@ -38,7 +38,9 @@
     "learn_rate_strategy": "learnRateStrategy",
     "learn_rate": "learnRate",
     "early_stop": "earlyStop",
+    # To rename to tol.
     "min_rel_progress": "minRelativeProgress",
+    "tol": "minRelativeProgress",
     "ls_init_learn_rate": "initialLearnRate",
     "warm_start": "warmStart",
     "calculate_p_values": "calculatePValues",
@@ -59,7 +61,7 @@ def __init__(
         *,
         optimize_strategy: Literal[
             "auto_strategy", "batch_gradient_descent", "normal_equation"
-        ] = "normal_equation",
+        ] = "auto_strategy",
         fit_intercept: bool = True,
         l1_reg: Optional[float] = None,
         l2_reg: float = 0.0,
@@ -139,7 +141,7 @@ def _bqml_options(self) -> dict:
         if self.ls_init_learn_rate is not None:
             options["ls_init_learn_rate"] = self.ls_init_learn_rate
         # Even presenting warm_start returns error for NORMAL_EQUATION optimizer
-        if self.warm_start is True:
+        if self.warm_start:
             options["warm_start"] = self.warm_start
 
         return options
@@ -212,10 +214,34 @@ class LogisticRegression(
     def __init__(
         self,
         *,
+        optimize_strategy: Literal[
+            "auto_strategy", "batch_gradient_descent", "normal_equation"
+        ] = "auto_strategy",
         fit_intercept: bool = True,
+        l1_reg: Optional[float] = None,
+        l2_reg: float = 0.0,
+        max_iterations: int = 20,
+        warm_start: bool = False,
+        learn_rate: Optional[float] = None,
+        learn_rate_strategy: Literal["line_search", "constant"] = "line_search",
+        tol: float = 0.01,
+        ls_init_learn_rate: Optional[float] = None,
+        calculate_p_values: bool = False,
+        enable_global_explain: bool = False,
         class_weights: Optional[Union[Literal["balanced"], Dict[str, float]]] = None,
     ):
+        self.optimize_strategy = optimize_strategy
         self.fit_intercept = fit_intercept
+        self.l1_reg = l1_reg
+        self.l2_reg = l2_reg
+        self.max_iterations = max_iterations
+        self.warm_start = warm_start
+        self.learn_rate = learn_rate
+        self.learn_rate_strategy = learn_rate_strategy
+        self.tol = tol
+        self.ls_init_learn_rate = ls_init_learn_rate
+        self.calculate_p_values = calculate_p_values
+        self.enable_global_explain = enable_global_explain
         self.class_weights = class_weights
         self._auto_class_weight = class_weights == "balanced"
         self._bqml_model: Optional[core.BqmlModel] = None
@@ -231,8 +257,16 @@ def _from_bq(
 
         # See https://cloud.google.com/bigquery/docs/reference/rest/v2/models#trainingrun
         last_fitting = model.training_runs[-1]["trainingOptions"]
-        if "fitIntercept" in last_fitting:
-            kwargs["fit_intercept"] = last_fitting["fitIntercept"]
+        dummy_logistic = cls()
+        for bf_param, bf_value in dummy_logistic.__dict__.items():
+            bqml_param = _BQML_PARAMS_MAPPING.get(bf_param)
+            if bqml_param in last_fitting:
+                # Convert types
+                kwargs[bf_param] = (
+                    float(last_fitting[bqml_param])
+                    if bf_param in ["l1_reg", "learn_rate", "ls_init_learn_rate"]
+                    else type(bf_value)(last_fitting[bqml_param])
+                )
         if last_fitting["autoClassWeights"]:
             kwargs["class_weights"] = "balanced"
         # TODO(ashleyxu) support class_weights in the constructor.
@@ -244,16 +278,34 @@ def _from_bq(
         return new_logistic_regression
 
     @property
-    def _bqml_options(self) -> Dict[str, str | int | float | List[str]]:
+    def _bqml_options(self) -> dict:
         """The model options as they will be set for BQML"""
-        return {
+        options = {
             "model_type": "LOGISTIC_REG",
             "data_split_method": "NO_SPLIT",
             "fit_intercept": self.fit_intercept,
             "auto_class_weights": self._auto_class_weight,
+            "optimize_strategy": self.optimize_strategy,
+            "l2_reg": self.l2_reg,
+            "max_iterations": self.max_iterations,
+            "learn_rate_strategy": self.learn_rate_strategy,
+            "min_rel_progress": self.tol,
+            "calculate_p_values": self.calculate_p_values,
+            "enable_global_explain": self.enable_global_explain,
             # TODO(ashleyxu): support class_weights (struct array as dict in our API)
             # "class_weights": self.class_weights,
         }
+        if self.l1_reg is not None:
+            options["l1_reg"] = self.l1_reg
+        if self.learn_rate is not None:
+            options["learn_rate"] = self.learn_rate
+        if self.ls_init_learn_rate is not None:
+            options["ls_init_learn_rate"] = self.ls_init_learn_rate
+        # Even presenting warm_start returns error for NORMAL_EQUATION optimizer
+        if self.warm_start:
+            options["warm_start"] = self.warm_start
+
+        return options
 
     def _fit(
         self,

tests/system/large/ml/test_linear_model.py

@@ -184,7 +184,15 @@ def test_logistic_regression_customized_params_fit_score(
     penguins_df_default_index, dataset_id
 ):
     model = bigframes.ml.linear_model.LogisticRegression(
-        fit_intercept=False, class_weights="balanced"
+        fit_intercept=False,
+        class_weights="balanced",
+        l2_reg=0.2,
+        tol=0.02,
+        l1_reg=0.2,
+        max_iterations=30,
+        optimize_strategy="batch_gradient_descent",
+        learn_rate_strategy="constant",
+        learn_rate=0.2,
     )
     df = penguins_df_default_index.dropna()
     X_train = df[
@@ -203,12 +211,12 @@ def test_logistic_regression_customized_params_fit_score(
     result = model.score(X_train, y_train).to_pandas()
     expected = pd.DataFrame(
         {
-            "precision": [0.58483],
-            "recall": [0.586616],
-            "accuracy": [0.877246],
-            "f1_score": [0.58571],
-            "log_loss": [1.032699],
-            "roc_auc": [0.924132],
+            "precision": [0.487],
+            "recall": [0.602],
+            "accuracy": [0.464],
+            "f1_score": [0.379],
+            "log_loss": [0.972],
+            "roc_auc": [0.700],
         },
         dtype="Float64",
     )
@@ -223,5 +231,15 @@ def test_logistic_regression_customized_params_fit_score(
         f"{dataset_id}.temp_configured_logistic_reg_model"
         in reloaded_model._bqml_model.model_name
     )
+    # TODO(garrettwu) optimize_strategy isn't logged in BQML
+    # assert reloaded_model.optimize_strategy == "BATCH_GRADIENT_DESCENT"
     assert reloaded_model.fit_intercept is False
-    assert reloaded_model.class_weights == "balanced"
+    assert reloaded_model.calculate_p_values is False
+    assert reloaded_model.enable_global_explain is False
+    assert reloaded_model.l1_reg == 0.2
+    assert reloaded_model.l2_reg == 0.2
+    assert reloaded_model.ls_init_learn_rate is None
+    assert reloaded_model.max_iterations == 30
+    assert reloaded_model.tol == 0.02
+    assert reloaded_model.learn_rate_strategy == "CONSTANT"
+    assert reloaded_model.learn_rate == 0.2

tests/unit/ml/test_golden_sql.py

@@ -105,7 +105,7 @@ def test_linear_regression_default_fit(
     model.fit(mock_X, mock_y)
 
     mock_session._start_query_ml_ddl.assert_called_once_with(
-        'CREATE OR REPLACE MODEL `test-project`.`_anon123`.`temp_model_id`\nOPTIONS(\n  model_type="LINEAR_REG",\n  data_split_method="NO_SPLIT",\n  optimize_strategy="normal_equation",\n  fit_intercept=True,\n  l2_reg=0.0,\n  max_iterations=20,\n  learn_rate_strategy="line_search",\n  early_stop=True,\n  min_rel_progress=0.01,\n  calculate_p_values=False,\n  enable_global_explain=False,\n  INPUT_LABEL_COLS=["input_column_label"])\nAS input_X_y_sql'
+        'CREATE OR REPLACE MODEL `test-project`.`_anon123`.`temp_model_id`\nOPTIONS(\n  model_type="LINEAR_REG",\n  data_split_method="NO_SPLIT",\n  optimize_strategy="auto_strategy",\n  fit_intercept=True,\n  l2_reg=0.0,\n  max_iterations=20,\n  learn_rate_strategy="line_search",\n  early_stop=True,\n  min_rel_progress=0.01,\n  calculate_p_values=False,\n  enable_global_explain=False,\n  INPUT_LABEL_COLS=["input_column_label"])\nAS input_X_y_sql'
     )
 
 
@@ -115,7 +115,7 @@ def test_linear_regression_params_fit(bqml_model_factory, mock_session, mock_X,
     model.fit(mock_X, mock_y)
 
     mock_session._start_query_ml_ddl.assert_called_once_with(
-        'CREATE OR REPLACE MODEL `test-project`.`_anon123`.`temp_model_id`\nOPTIONS(\n  model_type="LINEAR_REG",\n  data_split_method="NO_SPLIT",\n  optimize_strategy="normal_equation",\n  fit_intercept=False,\n  l2_reg=0.0,\n  max_iterations=20,\n  learn_rate_strategy="line_search",\n  early_stop=True,\n  min_rel_progress=0.01,\n  calculate_p_values=False,\n  enable_global_explain=False,\n  INPUT_LABEL_COLS=["input_column_label"])\nAS input_X_y_sql'
+        'CREATE OR REPLACE MODEL `test-project`.`_anon123`.`temp_model_id`\nOPTIONS(\n  model_type="LINEAR_REG",\n  data_split_method="NO_SPLIT",\n  optimize_strategy="auto_strategy",\n  fit_intercept=False,\n  l2_reg=0.0,\n  max_iterations=20,\n  learn_rate_strategy="line_search",\n  early_stop=True,\n  min_rel_progress=0.01,\n  calculate_p_values=False,\n  enable_global_explain=False,\n  INPUT_LABEL_COLS=["input_column_label"])\nAS input_X_y_sql'
     )
 
 
@@ -148,21 +148,29 @@ def test_logistic_regression_default_fit(
     model.fit(mock_X, mock_y)
 
     mock_session._start_query_ml_ddl.assert_called_once_with(
-        'CREATE OR REPLACE MODEL `test-project`.`_anon123`.`temp_model_id`\nOPTIONS(\n  model_type="LOGISTIC_REG",\n  data_split_method="NO_SPLIT",\n  fit_intercept=True,\n  auto_class_weights=False,\n  INPUT_LABEL_COLS=["input_column_label"])\nAS input_X_y_sql'
+        'CREATE OR REPLACE MODEL `test-project`.`_anon123`.`temp_model_id`\nOPTIONS(\n  model_type="LOGISTIC_REG",\n  data_split_method="NO_SPLIT",\n  fit_intercept=True,\n  auto_class_weights=False,\n  optimize_strategy="auto_strategy",\n  l2_reg=0.0,\n  max_iterations=20,\n  learn_rate_strategy="line_search",\n  min_rel_progress=0.01,\n  calculate_p_values=False,\n  enable_global_explain=False,\n  INPUT_LABEL_COLS=["input_column_label"])\nAS input_X_y_sql'
     )
 
 
 def test_logistic_regression_params_fit(
     bqml_model_factory, mock_session, mock_X, mock_y
 ):
     model = linear_model.LogisticRegression(
-        fit_intercept=False, class_weights="balanced"
+        fit_intercept=False,
+        class_weights="balanced",
+        l2_reg=0.2,
+        tol=0.02,
+        l1_reg=0.2,
+        max_iterations=30,
+        optimize_strategy="batch_gradient_descent",
+        learn_rate_strategy="constant",
+        learn_rate=0.2,
     )
     model._bqml_model_factory = bqml_model_factory
     model.fit(mock_X, mock_y)
 
     mock_session._start_query_ml_ddl.assert_called_once_with(
-        'CREATE OR REPLACE MODEL `test-project`.`_anon123`.`temp_model_id`\nOPTIONS(\n  model_type="LOGISTIC_REG",\n  data_split_method="NO_SPLIT",\n  fit_intercept=False,\n  auto_class_weights=True,\n  INPUT_LABEL_COLS=["input_column_label"])\nAS input_X_y_sql'
+        'CREATE OR REPLACE MODEL `test-project`.`_anon123`.`temp_model_id`\nOPTIONS(\n  model_type="LOGISTIC_REG",\n  data_split_method="NO_SPLIT",\n  fit_intercept=False,\n  auto_class_weights=True,\n  optimize_strategy="batch_gradient_descent",\n  l2_reg=0.2,\n  max_iterations=30,\n  learn_rate_strategy="constant",\n  min_rel_progress=0.02,\n  calculate_p_values=False,\n  enable_global_explain=False,\n  l1_reg=0.2,\n  learn_rate=0.2,\n  INPUT_LABEL_COLS=["input_column_label"])\nAS input_X_y_sql'
     )
 
 

third_party/bigframes_vendored/sklearn/linear_model/_base.py

@@ -63,10 +63,10 @@ class LinearRegression(RegressorMixin, LinearModel):
     the dataset, and the targets predicted by the linear approximation.
 
     Args:
-        optimize_strategy (str, default "normal_equation"):
+        optimize_strategy (str, default "auto_strategy"):
             The strategy to train linear regression models. Possible values are
             "auto_strategy", "batch_gradient_descent", "normal_equation". Default
-            to "normal_equation".
+            to "auto_strategy".
         fit_intercept (bool, default True):
             Default ``True``. Whether to calculate the intercept for this
             model. If set to False, no intercept will be used in calculations

third_party/bigframes_vendored/sklearn/linear_model/_logistic.py

@@ -24,6 +24,10 @@ class LogisticRegression(LinearClassifierMixin, BaseEstimator):
     """Logistic Regression (aka logit, MaxEnt) classifier.
 
     Args:
+        optimize_strategy (str, default "auto_strategy"):
+            The strategy to train logistic regression models. Possible values are
+            "auto_strategy", "batch_gradient_descent", "normal_equation". Default
+            to "auto_strategy".
         fit_intercept (default True):
             Default True. Specifies if a constant (a.k.a. bias or intercept)
             should be added to the decision function.
@@ -35,6 +39,26 @@ class LogisticRegression(LinearClassifierMixin, BaseEstimator):
             frequencies in the input data as
             ``n_samples / (n_classes * np.bincount(y))``. Dict isn't
             supported now.
+        l1_reg (float or None, default None):
+            The amount of L1 regularization applied. Default to None. Can't be set in "normal_equation" mode. If unset, value 0 is used.
+        l2_reg (float, default 0.0):
+            The amount of L2 regularization applied. Default to 0.
+        max_iterations (int, default 20):
+            The maximum number of training iterations or steps. Default to 20.
+        warm_start (bool, default False):
+            Determines whether to train a model with new training data, new model options, or both. Unless you explicitly override them, the initial options used to train the model are used for the warm start run. Default to False.
+        learn_rate (float or None, default None):
+            The learn rate for gradient descent when learn_rate_strategy='constant'. If unset, value 0.1 is used. If learn_rate_strategy='line_search', an error is returned.
+        learn_rate_strategy (str, default "line_search"):
+            The strategy for specifying the learning rate during training. Default to "line_search".
+        tol (float, default 0.01):
+            The minimum relative loss improvement that is necessary to continue training when EARLY_STOP is set to true. For example, a value of 0.01 specifies that each iteration must reduce the loss by 1% for training to continue. Default to 0.01.
+        ls_init_learn_rate (float or None, default None):
+            Sets the initial learning rate that learn_rate_strategy='line_search' uses. This option can only be used if line_search is specified. If unset, value 0.1 is used.
+        calculate_p_values (bool, default False):
+            Specifies whether to compute p-values and standard errors during training. Default to False.
+        enable_global_explain (bool, default False):
+            Whether to compute global explanations using explainable AI to evaluate global feature importance to the model. Default to False.
     """
 
     def fit(