feat: regularization strength for logistic classifier (#866)

Closes #750 

### Summary of Changes

Added an optional, keyword-only constructor parameter c: float = 1.0 and
passed it to the wrapped scikit-learn estimator.

<!-- Please provide a summary of changes in this pull request, ensuring
all changes are explained. -->

---------

Co-authored-by: grefrathc <s23cgref@uni-bonn.de>
Co-authored-by: megalinter-bot <129584137+megalinter-bot@users.noreply.github.com>
Co-authored-by: Lars Reimann <mail@larsreimann.com>

src/safeds/ml/classical/classification/_logistic_classifier.py

@@ -3,6 +3,7 @@
 from typing import TYPE_CHECKING
 
 from safeds._utils import _get_random_seed, _structural_hash
+from safeds._validation import _check_bounds, _OpenBound
 
 from ._classifier import Classifier
 
@@ -11,31 +12,54 @@
 
 
 class LogisticClassifier(Classifier):
-    """Regularized logistic regression for classification."""
+    """
+    Regularized logistic regression for classification.
+
+    Parameters
+    ----------
+    c:
+        The regularization strength. Lower values imply stronger regularization. Must be greater than 0.
+    """
 
     # ------------------------------------------------------------------------------------------------------------------
     # Dunder methods
     # ------------------------------------------------------------------------------------------------------------------
 
-    def __init__(self) -> None:
+    def __init__(self, *, c: float = 1.0) -> None:
         super().__init__()
 
+        # Validation
+        _check_bounds("c", c, lower_bound=_OpenBound(0))
+
+        # Hyperparameters
+        self._c: float = c
+
     def __hash__(self) -> int:
         return _structural_hash(
             super().__hash__(),
         )
 
+    # ------------------------------------------------------------------------------------------------------------------
+    # Properties
+    # ------------------------------------------------------------------------------------------------------------------
+
+    @property
+    def c(self) -> float:
+        """The regularization strength. Lower values imply stronger regularization."""
+        return self._c
+
     # ------------------------------------------------------------------------------------------------------------------
     # Template methods
     # ------------------------------------------------------------------------------------------------------------------
 
     def _clone(self) -> LogisticClassifier:
-        return LogisticClassifier()
+        return LogisticClassifier(c=self.c)
 
     def _get_sklearn_model(self) -> ClassifierMixin:
         from sklearn.linear_model import LogisticRegression as SklearnLogisticRegression
 
         return SklearnLogisticRegression(
             random_state=_get_random_seed(),
             n_jobs=-1,
+            C=self.c,
         )

tests/safeds/ml/classical/classification/test_logistic_classifier.py

@@ -0,0 +1,33 @@
+import pytest
+from safeds.data.labeled.containers import TabularDataset
+from safeds.data.tabular.containers import Table
+from safeds.exceptions import OutOfBoundsError
+from safeds.ml.classical.classification import LogisticClassifier
+
+
+@pytest.fixture()
+def training_set() -> TabularDataset:
+    table = Table({"col1": [1, 2, 3, 4], "col2": [1, 2, 3, 4]})
+    return table.to_tabular_dataset(target_name="col1")
+
+
+class TestC:
+    def test_should_be_passed_to_fitted_model(self, training_set: TabularDataset) -> None:
+        fitted_model = LogisticClassifier(c=2).fit(training_set)
+        assert fitted_model.c == 2
+
+    def test_should_be_passed_to_sklearn(self, training_set: TabularDataset) -> None:
+        fitted_model = LogisticClassifier(c=2).fit(training_set)
+        assert fitted_model._wrapped_model is not None
+        assert fitted_model._wrapped_model.C == 2
+
+    def test_clone(self, training_set: TabularDataset) -> None:
+        fitted_model = LogisticClassifier(c=2).fit(training_set)
+        cloned_classifier = fitted_model._clone()
+        assert isinstance(cloned_classifier, LogisticClassifier)
+        assert cloned_classifier.c == fitted_model.c
+
+    @pytest.mark.parametrize("c", [-1.0, 0.0], ids=["minus_one", "zero"])
+    def test_should_raise_if_less_than_or_equal_to_0(self, c: float) -> None:
+        with pytest.raises(OutOfBoundsError):
+            LogisticClassifier(c=c)