feat: Getters for hyperparameters of Regression and Classification models

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

@@ -58,6 +58,18 @@ def __init__(
         self._feature_names: list[str] | None = None
         self._target_name: str | None = None
 
+    @property
+    def learner(self) -> Classifier | None:
+        return self._learner
+
+    @property
+    def maximum_number_of_learners(self) -> int:
+        return self._maximum_number_of_learners
+
+    @property
+    def learning_rate(self) -> float:
+        return self._learning_rate
+
     def fit(self, training_set: TaggedTable) -> AdaBoost:
         """
         Create a copy of this classifier and fit it with the given training data.
@@ -83,8 +95,8 @@ def fit(self, training_set: TaggedTable) -> AdaBoost:
         fit(wrapped_classifier, training_set)
 
         result = AdaBoost(
-            learner=self._learner,
-            maximum_number_of_learners=self._maximum_number_of_learners,
+            learner=self.learner,
+            maximum_number_of_learners=self.maximum_number_of_learners,
             learning_rate=self._learning_rate,
         )
         result._wrapped_classifier = wrapped_classifier
@@ -140,9 +152,9 @@ def _get_sklearn_classifier(self) -> ClassifierMixin:
         wrapped_classifier: ClassifierMixin
             The sklearn Classifier.
         """
-        learner = self._learner._get_sklearn_classifier() if self._learner is not None else None
+        learner = self.learner._get_sklearn_classifier() if self.learner is not None else None
         return sk_AdaBoostClassifier(
             estimator=learner,
-            n_estimators=self._maximum_number_of_learners,
+            n_estimators=self.maximum_number_of_learners,
             learning_rate=self._learning_rate,
         )

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

@@ -49,6 +49,14 @@ def __init__(self, *, number_of_trees: int = 100, learning_rate: float = 0.1) ->
         self._feature_names: list[str] | None = None
         self._target_name: str | None = None
 
+    @property
+    def number_of_trees(self) -> int:
+        return self._number_of_trees
+
+    @property
+    def learning_rate(self) -> float:
+        return self._learning_rate
+
     def fit(self, training_set: TaggedTable) -> GradientBoosting:
         """
         Create a copy of this classifier and fit it with the given training data.

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

@@ -43,6 +43,10 @@ def __init__(self, number_of_neighbors: int) -> None:
         self._feature_names: list[str] | None = None
         self._target_name: str | None = None
 
+    @property
+    def number_of_neighbors(self) -> int:
+        return self._number_of_neighbors
+
     def fit(self, training_set: TaggedTable) -> KNearestNeighbors:
         """
         Create a copy of this classifier and fit it with the given training data.

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

@@ -41,6 +41,10 @@ def __init__(self, *, number_of_trees: int = 100) -> None:
         self._feature_names: list[str] | None = None
         self._target_name: str | None = None
 
+    @property
+    def number_of_trees(self) -> int:
+        return self._number_of_trees
+
     def fit(self, training_set: TaggedTable) -> RandomForest:
         """
         Create a copy of this classifier and fit it with the given training data.

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

@@ -35,10 +35,15 @@ def __init__(self, *, c: float = 1.0) -> None:
         self._feature_names: list[str] | None = None
         self._target_name: str | None = None
 
+        # Hyperparameters
         if c <= 0:
             raise ValueError("The parameter 'c' has to be strictly positive.")
         self._c = c
 
+    @property
+    def c(self) -> float:
+        return self._c
+
     def fit(self, training_set: TaggedTable) -> SupportVectorMachine:
         """
         Create a copy of this classifier and fit it with the given training data.

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

@@ -58,6 +58,18 @@ def __init__(
         self._feature_names: list[str] | None = None
         self._target_name: str | None = None
 
+    @property
+    def learner(self) -> Regressor | None:
+        return self._learner
+
+    @property
+    def maximum_number_of_learners(self) -> int:
+        return self._maximum_number_of_learners
+
+    @property
+    def learning_rate(self) -> float:
+        return self._learning_rate
+
     def fit(self, training_set: TaggedTable) -> AdaBoost:
         """
         Create a copy of this regressor and fit it with the given training data.

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

@@ -74,6 +74,14 @@ def __init__(self, *, alpha: float = 1.0, lasso_ratio: float = 0.5) -> None:
         self._feature_names: list[str] | None = None
         self._target_name: str | None = None
 
+    @property
+    def alpha(self) -> float:
+        return self._alpha
+
+    @property
+    def lasso_ratio(self) -> float:
+        return self._lasso_ratio
+
     def fit(self, training_set: TaggedTable) -> ElasticNetRegression:
         """
         Create a copy of this regressor and fit it with the given training data.

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

@@ -49,6 +49,14 @@ def __init__(self, *, number_of_trees: int = 100, learning_rate: float = 0.1) ->
         self._feature_names: list[str] | None = None
         self._target_name: str | None = None
 
+    @property
+    def number_of_trees(self) -> int:
+        return self._number_of_trees
+
+    @property
+    def learning_rate(self) -> float:
+        return self._learning_rate
+
     def fit(self, training_set: TaggedTable) -> GradientBoosting:
         """
         Create a copy of this regressor and fit it with the given training data.

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

@@ -43,6 +43,10 @@ def __init__(self, number_of_neighbors: int) -> None:
         self._feature_names: list[str] | None = None
         self._target_name: str | None = None
 
+    @property
+    def number_of_neighbors(self) -> int:
+        return self._number_of_neighbors
+
     def fit(self, training_set: TaggedTable) -> KNearestNeighbors:
         """
         Create a copy of this regressor and fit it with the given training data.

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

@@ -51,6 +51,10 @@ def __init__(self, *, alpha: float = 1.0) -> None:
         self._feature_names: list[str] | None = None
         self._target_name: str | None = None
 
+    @property
+    def alpha(self) -> float:
+        return self._alpha
+
     def fit(self, training_set: TaggedTable) -> LassoRegression:
         """
         Create a copy of this regressor and fit it with the given training data.

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

@@ -41,6 +41,10 @@ def __init__(self, *, number_of_trees: int = 100) -> None:
         self._feature_names: list[str] | None = None
         self._target_name: str | None = None
 
+    @property
+    def number_of_trees(self) -> int:
+        return self._number_of_trees
+
     def fit(self, training_set: TaggedTable) -> RandomForest:
         """
         Create a copy of this regressor and fit it with the given training data.

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

@@ -52,6 +52,10 @@ def __init__(self, *, alpha: float = 1.0) -> None:
         self._feature_names: list[str] | None = None
         self._target_name: str | None = None
 
+    @property
+    def alpha(self) -> float:
+        return self._alpha
+
     def fit(self, training_set: TaggedTable) -> RidgeRegression:
         """
         Create a copy of this regressor and fit it with the given training data.

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

@@ -35,10 +35,15 @@ def __init__(self, *, c: float = 1.0) -> None:
         self._feature_names: list[str] | None = None
         self._target_name: str | None = None
 
+        # Hyperparameters
         if c <= 0:
             raise ValueError("The parameter 'c' has to be strictly positive.")
         self._c = c
 
+    @property
+    def c(self) -> float:
+        return self._c
+
     def fit(self, training_set: TaggedTable) -> SupportVectorMachine:
         """
         Create a copy of this regressor and fit it with the given training data.

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

@@ -13,7 +13,7 @@ class TestLearner:
     def test_should_be_passed_to_fitted_model(self, training_set: TaggedTable) -> None:
         learner = AdaBoost()
         fitted_model = AdaBoost(learner=learner).fit(training_set)
-        assert fitted_model._learner == learner
+        assert fitted_model.learner == learner
 
     def test_should_be_passed_to_sklearn(self, training_set: TaggedTable) -> None:
         learner = AdaBoost()
@@ -25,7 +25,7 @@ def test_should_be_passed_to_sklearn(self, training_set: TaggedTable) -> None:
 class TestMaximumNumberOfLearners:
     def test_should_be_passed_to_fitted_model(self, training_set: TaggedTable) -> None:
         fitted_model = AdaBoost(maximum_number_of_learners=2).fit(training_set)
-        assert fitted_model._maximum_number_of_learners == 2
+        assert fitted_model.maximum_number_of_learners == 2
 
     def test_should_be_passed_to_sklearn(self, training_set: TaggedTable) -> None:
         fitted_model = AdaBoost(maximum_number_of_learners=2).fit(training_set)
@@ -40,7 +40,7 @@ def test_should_raise_if_less_than_or_equal_to_0(self) -> None:
 class TestLearningRate:
     def test_should_be_passed_to_fitted_model(self, training_set: TaggedTable) -> None:
         fitted_model = AdaBoost(learning_rate=2).fit(training_set)
-        assert fitted_model._learning_rate == 2
+        assert fitted_model.learning_rate == 2
 
     def test_should_be_passed_to_sklearn(self, training_set: TaggedTable) -> None:
         fitted_model = AdaBoost(learning_rate=2).fit(training_set)

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

@@ -12,7 +12,7 @@ def training_set() -> TaggedTable:
 class TestNumberOfTrees:
     def test_should_be_passed_to_fitted_model(self, training_set: TaggedTable) -> None:
         fitted_model = GradientBoosting(number_of_trees=2).fit(training_set)
-        assert fitted_model._number_of_trees == 2
+        assert fitted_model.number_of_trees == 2
 
     def test_should_be_passed_to_sklearn(self, training_set: TaggedTable) -> None:
         fitted_model = GradientBoosting(number_of_trees=2).fit(training_set)
@@ -27,7 +27,7 @@ def test_should_raise_if_less_than_1(self) -> None:
 class TestLearningRate:
     def test_should_be_passed_to_fitted_model(self, training_set: TaggedTable) -> None:
         fitted_model = GradientBoosting(learning_rate=2).fit(training_set)
-        assert fitted_model._learning_rate == 2
+        assert fitted_model.learning_rate == 2
 
     def test_should_be_passed_to_sklearn(self, training_set: TaggedTable) -> None:
         fitted_model = GradientBoosting(learning_rate=2).fit(training_set)

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

@@ -12,7 +12,7 @@ def training_set() -> TaggedTable:
 class TestNumberOfNeighbors:
     def test_should_be_passed_to_fitted_model(self, training_set: TaggedTable) -> None:
         fitted_model = KNearestNeighbors(number_of_neighbors=2).fit(training_set)
-        assert fitted_model._number_of_neighbors == 2
+        assert fitted_model.number_of_neighbors == 2
 
     def test_should_be_passed_to_sklearn(self, training_set: TaggedTable) -> None:
         fitted_model = KNearestNeighbors(number_of_neighbors=2).fit(training_set)

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

@@ -12,7 +12,7 @@ def training_set() -> TaggedTable:
 class TestNumberOfTrees:
     def test_should_be_passed_to_fitted_model(self, training_set: TaggedTable) -> None:
         fitted_model = RandomForest(number_of_trees=2).fit(training_set)
-        assert fitted_model._number_of_trees == 2
+        assert fitted_model.number_of_trees == 2
 
     def test_should_be_passed_to_sklearn(self, training_set: TaggedTable) -> None:
         fitted_model = RandomForest(number_of_trees=2).fit(training_set)

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

@@ -12,7 +12,7 @@ def training_set() -> TaggedTable:
 class TestC:
     def test_should_be_passed_to_fitted_model(self, training_set: TaggedTable) -> None:
         fitted_model = SupportVectorMachine(c=2).fit(training_set=training_set)
-        assert fitted_model._c == 2
+        assert fitted_model.c == 2
 
     def test_should_be_passed_to_sklearn(self, training_set: TaggedTable) -> None:
         fitted_model = SupportVectorMachine(c=2).fit(training_set)

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

@@ -13,7 +13,7 @@ class TestLearner:
     def test_should_be_passed_to_fitted_model(self, training_set: TaggedTable) -> None:
         learner = AdaBoost()
         fitted_model = AdaBoost(learner=learner).fit(training_set)
-        assert fitted_model._learner == learner
+        assert fitted_model.learner == learner
 
     def test_should_be_passed_to_sklearn(self, training_set: TaggedTable) -> None:
         learner = AdaBoost()
@@ -25,7 +25,7 @@ def test_should_be_passed_to_sklearn(self, training_set: TaggedTable) -> None:
 class TestMaximumNumberOfLearners:
     def test_should_be_passed_to_fitted_model(self, training_set: TaggedTable) -> None:
         fitted_model = AdaBoost(maximum_number_of_learners=2).fit(training_set)
-        assert fitted_model._maximum_number_of_learners == 2
+        assert fitted_model.maximum_number_of_learners == 2
 
     def test_should_be_passed_to_sklearn(self, training_set: TaggedTable) -> None:
         fitted_model = AdaBoost(maximum_number_of_learners=2).fit(training_set)
@@ -40,7 +40,7 @@ def test_should_raise_if_less_than_or_equal_to_0(self) -> None:
 class TestLearningRate:
     def test_should_be_passed_to_fitted_model(self, training_set: TaggedTable) -> None:
         fitted_model = AdaBoost(learning_rate=2).fit(training_set)
-        assert fitted_model._learning_rate == 2
+        assert fitted_model.learning_rate == 2
 
     def test_should_be_passed_to_sklearn(self, training_set: TaggedTable) -> None:
         fitted_model = AdaBoost(learning_rate=2).fit(training_set)

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

@@ -12,7 +12,7 @@ def training_set() -> TaggedTable:
 class TestAlpha:
     def test_should_be_passed_to_fitted_model(self, training_set: TaggedTable) -> None:
         fitted_model = ElasticNetRegression(alpha=1).fit(training_set)
-        assert fitted_model._alpha == 1
+        assert fitted_model.alpha == 1
 
     def test_should_be_passed_to_sklearn(self, training_set: TaggedTable) -> None:
         fitted_model = ElasticNetRegression(alpha=1).fit(training_set)
@@ -37,7 +37,7 @@ def test_should_warn_if_equal_to_0(self) -> None:
 class TestLassoRatio:
     def test_should_be_passed_to_fitted_model(self, training_set: TaggedTable) -> None:
         fitted_model = ElasticNetRegression(lasso_ratio=0.3).fit(training_set)
-        assert fitted_model._lasso_ratio == 0.3
+        assert fitted_model.lasso_ratio == 0.3
 
     def test_should_be_passed_to_sklearn(self, training_set: TaggedTable) -> None:
         fitted_model = ElasticNetRegression(lasso_ratio=0.3).fit(training_set)

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

@@ -12,7 +12,7 @@ def training_set() -> TaggedTable:
 class TestNumberOfTrees:
     def test_should_be_passed_to_fitted_model(self, training_set: TaggedTable) -> None:
         fitted_model = GradientBoosting(number_of_trees=2).fit(training_set)
-        assert fitted_model._number_of_trees == 2
+        assert fitted_model.number_of_trees == 2
 
     def test_should_be_passed_to_sklearn(self, training_set: TaggedTable) -> None:
         fitted_model = GradientBoosting(number_of_trees=2).fit(training_set)
@@ -27,7 +27,7 @@ def test_should_raise_if_less_than_1(self) -> None:
 class TestLearningRate:
     def test_should_be_passed_to_fitted_model(self, training_set: TaggedTable) -> None:
         fitted_model = GradientBoosting(learning_rate=2).fit(training_set)
-        assert fitted_model._learning_rate == 2
+        assert fitted_model.learning_rate == 2
 
     def test_should_be_passed_to_sklearn(self, training_set: TaggedTable) -> None:
         fitted_model = GradientBoosting(learning_rate=2).fit(training_set)

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

@@ -12,7 +12,7 @@ def training_set() -> TaggedTable:
 class TestNumberOfNeighbors:
     def test_should_be_passed_to_fitted_model(self, training_set: TaggedTable) -> None:
         fitted_model = KNearestNeighbors(number_of_neighbors=2).fit(training_set)
-        assert fitted_model._number_of_neighbors == 2
+        assert fitted_model.number_of_neighbors == 2
 
     def test_should_be_passed_to_sklearn(self, training_set: TaggedTable) -> None:
         fitted_model = KNearestNeighbors(number_of_neighbors=2).fit(training_set)

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

@@ -12,7 +12,7 @@ def training_set() -> TaggedTable:
 class TestAlpha:
     def test_should_be_passed_to_fitted_model(self, training_set: TaggedTable) -> None:
         fitted_model = LassoRegression(alpha=1).fit(training_set)
-        assert fitted_model._alpha == 1
+        assert fitted_model.alpha == 1
 
     def test_should_be_passed_to_sklearn(self, training_set: TaggedTable) -> None:
         fitted_model = LassoRegression(alpha=1).fit(training_set)

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

@@ -12,7 +12,7 @@ def training_set() -> TaggedTable:
 class TestNumberOfTrees:
     def test_should_be_passed_to_fitted_model(self, training_set: TaggedTable) -> None:
         fitted_model = RandomForest(number_of_trees=2).fit(training_set)
-        assert fitted_model._number_of_trees == 2
+        assert fitted_model.number_of_trees == 2
 
     def test_should_be_passed_to_sklearn(self, training_set: TaggedTable) -> None:
         fitted_model = RandomForest(number_of_trees=2).fit(training_set)

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

@@ -12,7 +12,7 @@ def training_set() -> TaggedTable:
 class TestAlpha:
     def test_should_be_passed_to_fitted_model(self, training_set: TaggedTable) -> None:
         fitted_model = RidgeRegression(alpha=1).fit(training_set)
-        assert fitted_model._alpha == 1
+        assert fitted_model.alpha == 1
 
     def test_should_be_passed_to_sklearn(self, training_set: TaggedTable) -> None:
         fitted_model = RidgeRegression(alpha=1).fit(training_set)

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

@@ -12,7 +12,7 @@ def training_set() -> TaggedTable:
 class TestC:
     def test_should_be_passed_to_fitted_model(self, training_set: TaggedTable) -> None:
         fitted_model = SupportVectorMachine(c=2).fit(training_set=training_set)
-        assert fitted_model._c == 2
+        assert fitted_model.c == 2
 
     def test_should_be_passed_to_sklearn(self, training_set: TaggedTable) -> None:
         fitted_model = SupportVectorMachine(c=2).fit(training_set)