feat: add params for LinearRegression model

bigframes/ml/linear_model.py

@@ -61,19 +61,25 @@ def __init__(
             "auto_strategy", "batch_gradient_descent", "normal_equation"
         ] = "normal_equation",
         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",
         early_stop: bool = True,
         min_rel_progress: float = 0.01,
-        ls_init_learn_rate: float = 0.1,
+        ls_init_learn_rate: Optional[float] = None,
         calculate_p_values: bool = False,
         enable_global_explain: bool = False,
     ):
         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.early_stop = early_stop
         self.min_rel_progress = min_rel_progress
@@ -99,17 +105,21 @@ def _from_bq(
         for bf_param, bf_value in dummy_linear.__dict__.items():
             bqml_param = _BQML_PARAMS_MAPPING.get(bf_param)
             if bqml_param in last_fitting:
-                kwargs[bf_param] = type(bf_value)(last_fitting[bqml_param])
+                # 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])
+                )
 
         new_linear_regression = cls(**kwargs)
         new_linear_regression._bqml_model = core.BqmlModel(session, model)
         return new_linear_regression
 
     @property
-    def _bqml_options(self) -> Dict[str, str | int | bool | float | List[str]]:
+    def _bqml_options(self) -> dict:
         """The model options as they will be set for BQML"""
-        # TODO: Support l1_reg, warm_start, and learn_rate with error catching.
-        return {
+        options = {
             "model_type": "LINEAR_REG",
             "data_split_method": "NO_SPLIT",
             "optimize_strategy": self.optimize_strategy,
@@ -119,10 +129,20 @@ def _bqml_options(self) -> Dict[str, str | int | bool | float | List[str]]:
             "learn_rate_strategy": self.learn_rate_strategy,
             "early_stop": self.early_stop,
             "min_rel_progress": self.min_rel_progress,
-            "ls_init_learn_rate": self.ls_init_learn_rate,
             "calculate_p_values": self.calculate_p_values,
             "enable_global_explain": self.enable_global_explain,
         }
+        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 is True:
+            options["warm_start"] = self.warm_start
+
+        return options
 
     def _fit(
         self,

bigframes/ml/sql.py

@@ -38,7 +38,9 @@ def encode_value(self, v: Union[str, int, float, Iterable[str]]) -> str:
             inner = ", ".join([self.encode_value(x) for x in v])
             return f"[{inner}]"
         else:
-            raise ValueError(f"Unexpected value type. {constants.FEEDBACK_LINK}")
+            raise ValueError(
+                f"Unexpected value type {type(v)}. {constants.FEEDBACK_LINK}"
+            )
 
     def build_parameters(self, **kwargs: Union[str, int, float, Iterable[str]]) -> str:
         """Encode a dict of values into a formatted Iterable of key-value pairs for SQL"""

tests/system/large/ml/test_linear_model.py

@@ -60,9 +60,11 @@ def test_linear_regression_configure_fit_score(penguins_df_default_index, datase
     assert reloaded_model.calculate_p_values is False
     assert reloaded_model.early_stop is True
     assert reloaded_model.enable_global_explain is False
+    assert reloaded_model.l1_reg is None
     assert reloaded_model.l2_reg == 0.0
+    assert reloaded_model.learn_rate is None
     assert reloaded_model.learn_rate_strategy == "line_search"
-    assert reloaded_model.ls_init_learn_rate == 0.1
+    assert reloaded_model.ls_init_learn_rate is None
     assert reloaded_model.max_iterations == 20
     assert reloaded_model.min_rel_progress == 0.01
 
@@ -71,7 +73,14 @@ def test_linear_regression_customized_params_fit_score(
     penguins_df_default_index, dataset_id
 ):
     model = bigframes.ml.linear_model.LinearRegression(
-        fit_intercept=False, l2_reg=0.1, min_rel_progress=0.01
+        fit_intercept=False,
+        l2_reg=0.2,
+        min_rel_progress=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()
@@ -92,12 +101,12 @@ def test_linear_regression_customized_params_fit_score(
     result = model.score(X_train, y_train).to_pandas()
     expected = pd.DataFrame(
         {
-            "mean_absolute_error": [226.108411],
-            "mean_squared_error": [80459.668456],
-            "mean_squared_log_error": [0.00497],
-            "median_absolute_error": [171.618872],
-            "r2_score": [0.875415],
-            "explained_variance": [0.875417],
+            "mean_absolute_error": [240],
+            "mean_squared_error": [91197],
+            "mean_squared_log_error": [0.00573],
+            "median_absolute_error": [197],
+            "r2_score": [0.858],
+            "explained_variance": [0.8588],
         },
         dtype="Float64",
     )
@@ -109,16 +118,21 @@ def test_linear_regression_customized_params_fit_score(
     assert (
         f"{dataset_id}.temp_configured_model" in reloaded_model._bqml_model.model_name
     )
-    assert reloaded_model.optimize_strategy == "NORMAL_EQUATION"
+    assert reloaded_model.optimize_strategy == "BATCH_GRADIENT_DESCENT"
     assert reloaded_model.fit_intercept is False
     assert reloaded_model.calculate_p_values is False
     assert reloaded_model.early_stop is True
     assert reloaded_model.enable_global_explain is False
-    assert reloaded_model.l2_reg == 0.1
-    assert reloaded_model.learn_rate_strategy == "line_search"
-    assert reloaded_model.ls_init_learn_rate == 0.1
-    assert reloaded_model.max_iterations == 20
-    assert reloaded_model.min_rel_progress == 0.01
+    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.min_rel_progress == 0.02
+    assert reloaded_model.learn_rate_strategy == "CONSTANT"
+    assert reloaded_model.learn_rate == 0.2
+
+
+# TODO(garrettwu): add tests for param warm_start. Requires a trained model.
 
 
 def test_logistic_regression_configure_fit_score(penguins_df_default_index, dataset_id):

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  ls_init_learn_rate=0.1,\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="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'
     )
 
 
@@ -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  ls_init_learn_rate=0.1,\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="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'
     )
 
 

third_party/bigframes_vendored/sklearn/linear_model/_base.py

@@ -71,18 +71,24 @@ class LinearRegression(RegressorMixin, LinearModel):
             Default ``True``. Whether to calculate the intercept for this
             model. If set to False, no intercept will be used in calculations
             (i.e. data is expected to be centered).
+        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".
         early_stop (bool, default True):
             Whether training should stop after the first iteration in which the relative loss improvement is less than the value specified for min_rel_progress. Default to True.
         min_rel_progress (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, default 0.1):
-            Sets the initial learning rate that learn_rate_strategy='line_search' uses. This option can only be used if line_search is specified. Default to 0.1.
+        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):