feat: add alpha parameter to lasso_regression (#232)

Closes #163.

### Summary of Changes

Add alpha parameter to lasso regression

---------

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/_lasso_regression.py

@@ -1,6 +1,7 @@
 from __future__ import annotations
 
 from typing import TYPE_CHECKING
+from warnings import warn
 
 from sklearn.linear_model import Lasso as sk_Lasso
 
@@ -13,9 +14,32 @@
 
 
 class LassoRegression(Regressor):
-    """Lasso regression."""
-
-    def __init__(self) -> None:
+    """Lasso regression.
+
+    Parameters
+    ----------
+    alpha : float
+        Controls the regularization of the model. The higher the value, the more regularized it becomes.
+
+    Raises
+    ------
+    ValueError
+        If alpha is negative.
+    """
+
+    def __init__(self, alpha: float = 1.0) -> 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
         self._wrapped_regressor: sk_Lasso | None = None
         self._feature_names: list[str] | None = None
         self._target_name: str | None = None
@@ -41,10 +65,10 @@ def fit(self, training_set: TaggedTable) -> LassoRegression:
         LearningError
             If the training data contains invalid values or if the training failed.
         """
-        wrapped_regressor = sk_Lasso()
+        wrapped_regressor = sk_Lasso(alpha=self._alpha)
         fit(wrapped_regressor, training_set)
 
-        result = LassoRegression()
+        result = LassoRegression(alpha=self._alpha)
         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_lasso_regression.py

@@ -0,0 +1,28 @@
+import pytest
+from safeds.data.tabular.containers import Table
+from safeds.ml.classical.regression import LassoRegression
+
+
+def test_should_throw_value_error() -> None:
+    with pytest.raises(ValueError, match="alpha must be non-negative"):
+        LassoRegression(alpha=-1)
+
+
+def test_should_throw_warning() -> 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."
+        ),
+    ):
+        LassoRegression(alpha=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_table = training_set.tag_columns("col1")
+
+    regressor = LassoRegression(alpha=1).fit(tagged_table)
+    assert regressor._wrapped_regressor is not None
+    assert regressor._wrapped_regressor.alpha == regressor._alpha