feat: add parameter lasso_ratio to ElasticNetRegression (#237)

Closes #166.

### Summary of Changes

Added parameter `lasso_ratio` to `ElasticNetRegression` and tests for
edge cases 0, 1, invalid and default.

---------

Co-authored-by: zzril <>
Co-authored-by: megalinter-bot <[email protected]>

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

@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import warnings
 from typing import TYPE_CHECKING
 
 from sklearn.linear_model import ElasticNet as sk_ElasticNet
@@ -15,7 +16,22 @@
 class ElasticNetRegression(Regressor):
     """Elastic net regression."""
 
-    def __init__(self) -> None:
+    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,
+            )
+        elif lasso_ratio == 1:
+            warnings.warn(
+                "ElasticNetRegression with lasso_ratio = 0 is essentially LassoRegression."
+                " Use LassoRegression instead for better numerical stability.",
+                stacklevel=1,
+            )
+        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
@@ -41,10 +57,10 @@ def fit(self, training_set: TaggedTable) -> ElasticNetRegression:
         LearningError
             If the training data contains invalid values or if the training failed.
         """
-        wrapped_regressor = sk_ElasticNet()
+        wrapped_regressor = sk_ElasticNet(l1_ratio=self.lasso_ratio)
         fit(wrapped_regressor, training_set)
 
-        result = ElasticNetRegression()
+        result = ElasticNetRegression(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

@@ -0,0 +1,39 @@
+import pytest
+from safeds.data.tabular.containers import Table
+from safeds.ml.classical.regression._elastic_net_regression import ElasticNetRegression
+
+
+def test_lasso_ratio_valid() -> 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)
+    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:
+    with pytest.raises(ValueError, match="lasso_ratio must be between 0 and 1."):
+        ElasticNetRegression(-1)
+
+
+def test_lasso_ratio_zero() -> None:
+    with pytest.warns(
+        UserWarning,
+        match="ElasticNetRegression with lasso_ratio = 0 is essentially RidgeRegression."
+        " Use RidgeRegression instead for better numerical stability.",
+    ):
+        ElasticNetRegression(0)
+
+
+def test_lasso_ratio_one() -> None:
+    with pytest.warns(
+        UserWarning,
+        match="ElasticNetRegression with lasso_ratio = 0 is essentially LassoRegression."
+        " Use LassoRegression instead for better numerical stability.",
+    ):
+        ElasticNetRegression(1)
+
+
+# (Default parameter is tested in `test_regressor.py`.)