feat: set alpha parameter for regularization of `ElasticNetRegressi…

…on` (#238)

Closes #165.

### Summary of Changes

add alpha parameter for `elasticnetregression`.

---------

Co-authored-by: megalinter-bot <129584137+megalinter-bot@users.noreply.github.com>
Co-authored-by: Lars Reimann <mail@larsreimann.com>

src/safeds/ml/classical/regression/_elastic_net_regression.py

@@ -2,6 +2,7 @@
 
 import warnings
 from typing import TYPE_CHECKING
+from warnings import warn
 
 from sklearn.linear_model import ElasticNet as sk_ElasticNet
 
@@ -14,24 +15,59 @@
 
 
 class ElasticNetRegression(Regressor):
-    """Elastic net regression."""
+    """Elastic net regression.
+
+    Parameters
+    ----------
+    alpha : float
+        Controls the regularization of the model. The higher the value, the more regularized it becomes.
+
+    lasso_ratio: float
+        Number between 0 and 1 that controls the ratio between Lasso- and Ridge regularization.
+        lasso_ratio=0 is essentially RidgeRegression
+        lasso_ratio=1 is essentially LassoRegression
+
+    Raises
+    ------
+    ValueError
+        If alpha is negative.
+    """
+
+    def __init__(self, alpha: float = 1.0, lasso_ratio: float = 0.5) -> None:
+        if alpha < 0:
+            raise ValueError("alpha must be non-negative")
+        if alpha == 0:
+            warn(
+                (
+                    "Setting alpha to zero makes this model equivalent to LinearRegression. You should use "
+                    "LinearRegression instead for better numerical stability."
+                ),
+                UserWarning,
+                stacklevel=2,
+            )
+
+        self._alpha = alpha
 
-    def __init__(self, lasso_ratio: float = 0.5) -> None:
         if lasso_ratio < 0 or lasso_ratio > 1:
             raise ValueError("lasso_ratio must be between 0 and 1.")
         elif lasso_ratio == 0:
             warnings.warn(
-                "ElasticNetRegression with lasso_ratio = 0 is essentially RidgeRegression."
-                " Use RidgeRegression instead for better numerical stability.",
-                stacklevel=1,
+                (
+                    "ElasticNetRegression with lasso_ratio = 0 is essentially RidgeRegression."
+                    " Use RidgeRegression instead for better numerical stability."
+                ),
+                stacklevel=2,
             )
         elif lasso_ratio == 1:
             warnings.warn(
-                "ElasticNetRegression with lasso_ratio = 0 is essentially LassoRegression."
-                " Use LassoRegression instead for better numerical stability.",
-                stacklevel=1,
+                (
+                    "ElasticNetRegression with lasso_ratio = 0 is essentially LassoRegression."
+                    " Use LassoRegression instead for better numerical stability."
+                ),
+                stacklevel=2,
             )
         self.lasso_ratio = lasso_ratio
+
         self._wrapped_regressor: sk_ElasticNet | None = None
         self._feature_names: list[str] | None = None
         self._target_name: str | None = None
@@ -57,10 +93,10 @@ def fit(self, training_set: TaggedTable) -> ElasticNetRegression:
         LearningError
             If the training data contains invalid values or if the training failed.
         """
-        wrapped_regressor = sk_ElasticNet(l1_ratio=self.lasso_ratio)
+        wrapped_regressor = sk_ElasticNet(alpha=self._alpha, l1_ratio=self.lasso_ratio)
         fit(wrapped_regressor, training_set)
 
-        result = ElasticNetRegression(self.lasso_ratio)
+        result = ElasticNetRegression(alpha=self._alpha, lasso_ratio=self.lasso_ratio)
         result._wrapped_regressor = wrapped_regressor
         result._feature_names = training_set.features.column_names
         result._target_name = training_set.target.name

tests/safeds/ml/classical/regression/test_elastic_net_regression.py

@@ -1,39 +1,68 @@
 import pytest
 from safeds.data.tabular.containers import Table
-from safeds.ml.classical.regression._elastic_net_regression import ElasticNetRegression
+from safeds.ml.classical.regression import ElasticNetRegression
 
 
-def test_lasso_ratio_valid() -> None:
+def test_should_throw_value_error_alpha() -> None:
+    with pytest.raises(ValueError, match="alpha must be non-negative"):
+        ElasticNetRegression(alpha=-1.0)
+
+
+def test_should_throw_warning_alpha() -> None:
+    with pytest.warns(
+        UserWarning,
+        match=(
+            "Setting alpha to zero makes this model equivalent to LinearRegression. You "
+            "should use LinearRegression instead for better numerical stability."
+        ),
+    ):
+        ElasticNetRegression(alpha=0.0)
+
+
+def test_should_give_alpha_to_sklearn() -> None:
+    training_set = Table.from_dict({"col1": [1, 2, 3, 4], "col2": [1, 2, 3, 4]})
+    tagged_training_set = training_set.tag_columns(target_name="col1", feature_names=["col2"])
+
+    elastic_net_regression = ElasticNetRegression(alpha=1.0).fit(tagged_training_set)
+    assert elastic_net_regression._wrapped_regressor is not None
+    assert elastic_net_regression._wrapped_regressor.alpha == elastic_net_regression._alpha
+
+
+def test_should_give_lasso_ratio_to_sklearn() -> None:
     training_set = Table.from_dict({"col1": [1, 2, 3, 4], "col2": [1, 2, 3, 4]})
     tagged_training_set = training_set.tag_columns(target_name="col1", feature_names=["col2"])
     lasso_ratio = 0.3
 
-    elastic_net_regression = ElasticNetRegression(lasso_ratio).fit(tagged_training_set)
+    elastic_net_regression = ElasticNetRegression(lasso_ratio=lasso_ratio).fit(tagged_training_set)
     assert elastic_net_regression._wrapped_regressor is not None
     assert elastic_net_regression._wrapped_regressor.l1_ratio == lasso_ratio
 
 
-def test_lasso_ratio_invalid() -> None:
+def test_should_throw_value_error_lasso_ratio() -> None:
     with pytest.raises(ValueError, match="lasso_ratio must be between 0 and 1."):
-        ElasticNetRegression(-1)
+        ElasticNetRegression(lasso_ratio=-1.0)
 
 
-def test_lasso_ratio_zero() -> None:
+def test_should_throw_warning_lasso_ratio_zero() -> None:
     with pytest.warns(
         UserWarning,
-        match="ElasticNetRegression with lasso_ratio = 0 is essentially RidgeRegression."
-        " Use RidgeRegression instead for better numerical stability.",
+        match=(
+            "ElasticNetRegression with lasso_ratio = 0 is essentially RidgeRegression."
+            " Use RidgeRegression instead for better numerical stability."
+        ),
     ):
-        ElasticNetRegression(0)
+        ElasticNetRegression(lasso_ratio=0)
 
 
-def test_lasso_ratio_one() -> None:
+def test_should_throw_warning_lasso_ratio_one() -> None:
     with pytest.warns(
         UserWarning,
-        match="ElasticNetRegression with lasso_ratio = 0 is essentially LassoRegression."
-        " Use LassoRegression instead for better numerical stability.",
+        match=(
+            "ElasticNetRegression with lasso_ratio = 0 is essentially LassoRegression."
+            " Use LassoRegression instead for better numerical stability."
+        ),
     ):
-        ElasticNetRegression(1)
+        ElasticNetRegression(lasso_ratio=1)
 
 
 # (Default parameter is tested in `test_regressor.py`.)