feat: new method is_fitted to check whether a model is fitted (#130)

### Summary of Changes

Add a new method `is_fitted` to `Classifier`s and `Regressor`s to easily
check whether they have been fitted already.

---------

Co-authored-by: lars-reimann <lars-reimann@users.noreply.github.com>

src/safeds/ml/classification/_ada_boost.py

@@ -71,3 +71,14 @@ def predict(self, dataset: Table) -> TaggedTable:
             If prediction with the given dataset failed.
         """
         return predict(self._wrapped_classifier, dataset, self._feature_names, self._target_name)
+
+    def is_fitted(self) -> bool:
+        """
+        Checks if the classifier is fitted.
+
+        Returns
+        -------
+        is_fitted : bool
+            Whether the classifier is fitted.
+        """
+        return self._wrapped_classifier is not None

src/safeds/ml/classification/_classifier.py

@@ -54,6 +54,17 @@ def predict(self, dataset: Table) -> TaggedTable:
             If prediction with the given dataset failed.
         """
 
+    @abstractmethod
+    def is_fitted(self) -> bool:
+        """
+        Checks if the classifier is fitted.
+
+        Returns
+        -------
+        is_fitted : bool
+            Whether the classifier is fitted.
+        """
+
     # noinspection PyProtectedMember
     def accuracy(self, validation_or_test_set: TaggedTable) -> float:
         """

src/safeds/ml/classification/_decision_tree.py

@@ -71,3 +71,14 @@ def predict(self, dataset: Table) -> TaggedTable:
             If prediction with the given dataset failed.
         """
         return predict(self._wrapped_classifier, dataset, self._feature_names, self._target_name)
+
+    def is_fitted(self) -> bool:
+        """
+        Checks if the classifier is fitted.
+
+        Returns
+        -------
+        is_fitted : bool
+            Whether the classifier is fitted.
+        """
+        return self._wrapped_classifier is not None

src/safeds/ml/classification/_gradient_boosting_classification.py

@@ -72,3 +72,14 @@ def predict(self, dataset: Table) -> TaggedTable:
             If prediction with the given dataset failed.
         """
         return predict(self._wrapped_classifier, dataset, self._feature_names, self._target_name)
+
+    def is_fitted(self) -> bool:
+        """
+        Checks if the classifier is fitted.
+
+        Returns
+        -------
+        is_fitted : bool
+            Whether the classifier is fitted.
+        """
+        return self._wrapped_classifier is not None

src/safeds/ml/classification/_k_nearest_neighbors.py

@@ -76,3 +76,14 @@ def predict(self, dataset: Table) -> TaggedTable:
             If prediction with the given dataset failed.
         """
         return predict(self._wrapped_classifier, dataset, self._feature_names, self._target_name)
+
+    def is_fitted(self) -> bool:
+        """
+        Checks if the classifier is fitted.
+
+        Returns
+        -------
+        is_fitted : bool
+            Whether the classifier is fitted.
+        """
+        return self._wrapped_classifier is not None

src/safeds/ml/classification/_logistic_regression.py

@@ -71,3 +71,14 @@ def predict(self, dataset: Table) -> TaggedTable:
             If prediction with the given dataset failed.
         """
         return predict(self._wrapped_classifier, dataset, self._feature_names, self._target_name)
+
+    def is_fitted(self) -> bool:
+        """
+        Checks if the classifier is fitted.
+
+        Returns
+        -------
+        is_fitted : bool
+            Whether the classifier is fitted.
+        """
+        return self._wrapped_classifier is not None

src/safeds/ml/classification/_random_forest.py

@@ -70,3 +70,14 @@ def predict(self, dataset: Table) -> TaggedTable:
             If prediction with the given dataset failed.
         """
         return predict(self._wrapped_classifier, dataset, self._feature_names, self._target_name)
+
+    def is_fitted(self) -> bool:
+        """
+        Checks if the classifier is fitted.
+
+        Returns
+        -------
+        is_fitted : bool
+            Whether the classifier is fitted.
+        """
+        return self._wrapped_classifier is not None

src/safeds/ml/regression/_ada_boost.py

@@ -71,3 +71,14 @@ def predict(self, dataset: Table) -> TaggedTable:
             If prediction with the given dataset failed.
         """
         return predict(self._wrapped_regressor, dataset, self._feature_names, self._target_name)
+
+    def is_fitted(self) -> bool:
+        """
+        Check if the regressor is fitted.
+
+        Returns
+        -------
+        is_fitted : bool
+            Whether the regressor is fitted.
+        """
+        return self._wrapped_regressor is not None

src/safeds/ml/regression/_decision_tree.py

@@ -71,3 +71,14 @@ def predict(self, dataset: Table) -> TaggedTable:
             If prediction with the given dataset failed.
         """
         return predict(self._wrapped_regressor, dataset, self._feature_names, self._target_name)
+
+    def is_fitted(self) -> bool:
+        """
+        Check if the regressor is fitted.
+
+        Returns
+        -------
+        is_fitted : bool
+            Whether the regressor is fitted.
+        """
+        return self._wrapped_regressor is not None

src/safeds/ml/regression/_elastic_net_regression.py

@@ -71,3 +71,14 @@ def predict(self, dataset: Table) -> TaggedTable:
             If prediction with the given dataset failed
         """
         return predict(self._wrapped_regressor, dataset, self._feature_names, self._target_name)
+
+    def is_fitted(self) -> bool:
+        """
+        Check if the regressor is fitted.
+
+        Returns
+        -------
+        is_fitted : bool
+            Whether the regressor is fitted.
+        """
+        return self._wrapped_regressor is not None

src/safeds/ml/regression/_gradient_boosting_regression.py

@@ -71,3 +71,14 @@ def predict(self, dataset: Table) -> TaggedTable:
             If prediction with the given dataset failed.
         """
         return predict(self._wrapped_regressor, dataset, self._feature_names, self._target_name)
+
+    def is_fitted(self) -> bool:
+        """
+        Check if the regressor is fitted.
+
+        Returns
+        -------
+        is_fitted : bool
+            Whether the regressor is fitted.
+        """
+        return self._wrapped_regressor is not None

src/safeds/ml/regression/_k_nearest_neighbors.py

@@ -77,3 +77,14 @@ def predict(self, dataset: Table) -> TaggedTable:
             If prediction with the given dataset failed.
         """
         return predict(self._wrapped_regressor, dataset, self._feature_names, self._target_name)
+
+    def is_fitted(self) -> bool:
+        """
+        Check if the regressor is fitted.
+
+        Returns
+        -------
+        is_fitted : bool
+            Whether the regressor is fitted.
+        """
+        return self._wrapped_regressor is not None

src/safeds/ml/regression/_lasso_regression.py

@@ -71,3 +71,14 @@ def predict(self, dataset: Table) -> TaggedTable:
             If prediction with the given dataset failed.
         """
         return predict(self._wrapped_regressor, dataset, self._feature_names, self._target_name)
+
+    def is_fitted(self) -> bool:
+        """
+        Check if the regressor is fitted.
+
+        Returns
+        -------
+        is_fitted : bool
+            Whether the regressor is fitted.
+        """
+        return self._wrapped_regressor is not None

src/safeds/ml/regression/_linear_regression.py

@@ -71,3 +71,14 @@ def predict(self, dataset: Table) -> TaggedTable:
             If prediction with the given dataset failed.
         """
         return predict(self._wrapped_regressor, dataset, self._feature_names, self._target_name)
+
+    def is_fitted(self) -> bool:
+        """
+        Check if the regressor is fitted.
+
+        Returns
+        -------
+        is_fitted : bool
+            Whether the regressor is fitted.
+        """
+        return self._wrapped_regressor is not None

src/safeds/ml/regression/_random_forest.py

@@ -70,3 +70,14 @@ def predict(self, dataset: Table) -> TaggedTable:
             If prediction with the given dataset failed.
         """
         return predict(self._wrapped_regressor, dataset, self._feature_names, self._target_name)
+
+    def is_fitted(self) -> bool:
+        """
+        Check if the regressor is fitted.
+
+        Returns
+        -------
+        is_fitted : bool
+            Whether the regressor is fitted.
+        """
+        return self._wrapped_regressor is not None

src/safeds/ml/regression/_regressor.py

@@ -56,6 +56,17 @@ def predict(self, dataset: Table) -> TaggedTable:
             If prediction with the given dataset failed.
         """
 
+    @abstractmethod
+    def is_fitted(self) -> bool:
+        """
+        Checks if the regressor is fitted.
+
+        Returns
+        -------
+        is_fitted : bool
+            Whether the regressor is fitted.
+        """
+
     # noinspection PyProtectedMember
     def mean_squared_error(self, validation_or_test_set: TaggedTable) -> float:
         """
@@ -110,7 +121,5 @@ def _check_metrics_preconditions(actual: Column, expected: Column) -> None:
 
     if actual._data.size != expected._data.size:
         raise ColumnLengthMismatchError(
-            "\n".join(
-                [f"{column.name}: {column._data.size}" for column in [actual, expected]]
-            )
+            "\n".join([f"{column.name}: {column._data.size}" for column in [actual, expected]])
         )

src/safeds/ml/regression/_ridge_regression.py

@@ -71,3 +71,14 @@ def predict(self, dataset: Table) -> TaggedTable:
             If prediction with the given dataset failed.
         """
         return predict(self._wrapped_regressor, dataset, self._feature_names, self._target_name)
+
+    def is_fitted(self) -> bool:
+        """
+        Check if the regressor is fitted.
+
+        Returns
+        -------
+        is_fitted : bool
+            Whether the regressor is fitted.
+        """
+        return self._wrapped_regressor is not None

tests/safeds/ml/classification/test_classifier.py

@@ -62,6 +62,10 @@ def test_should_succeed_on_valid_data(self, classifier: Classifier, valid_data:
         classifier.fit(valid_data)
         assert True  # This asserts that the fit method succeeds
 
+    def test_should_not_change_input_classifier(self, classifier: Classifier, valid_data: TaggedTable) -> None:
+        classifier.fit(valid_data)
+        assert not classifier.is_fitted()
+
     def test_should_not_change_input_table(self, classifier: Classifier, request: FixtureRequest) -> None:
         valid_data = request.getfixturevalue("valid_data")
         valid_data_copy = request.getfixturevalue("valid_data")
@@ -109,6 +113,16 @@ def test_should_raise_on_invalid_data(
             fitted_classifier.predict(invalid_data.features)
 
 
+@pytest.mark.parametrize("classifier", classifiers(), ids=lambda x: x.__class__.__name__)
+class TestIsFitted:
+    def test_should_return_false_before_fitting(self, classifier: Classifier) -> None:
+        assert not classifier.is_fitted()
+
+    def test_should_return_true_after_fitting(self, classifier: Classifier, valid_data: TaggedTable) -> None:
+        fitted_classifier = classifier.fit(valid_data)
+        assert fitted_classifier.is_fitted()
+
+
 class DummyClassifier(Classifier):
     """
     Dummy classifier to test metrics.
@@ -133,6 +147,9 @@ def predict(self, dataset: Table) -> TaggedTable:
 
         return dataset.tag_columns(target_name="predicted")
 
+    def is_fitted(self) -> bool:
+        return True
+
 
 class TestAccuracy:
     def test_with_same_type(self) -> None:

tests/safeds/ml/regression/test_regressor.py

@@ -78,6 +78,10 @@ def test_should_succeed_on_valid_data(self, regressor: Regressor, valid_data: Ta
         regressor.fit(valid_data)
         assert True  # This asserts that the fit method succeeds
 
+    def test_should_not_change_input_regressor(self, regressor: Regressor, valid_data: TaggedTable) -> None:
+        regressor.fit(valid_data)
+        assert not regressor.is_fitted()
+
     def test_should_not_change_input_table(self, regressor: Regressor, request: FixtureRequest) -> None:
         valid_data = request.getfixturevalue("valid_data")
         valid_data_copy = request.getfixturevalue("valid_data")
@@ -125,6 +129,16 @@ def test_should_raise_on_invalid_data(
             fitted_regressor.predict(invalid_data.features)
 
 
+@pytest.mark.parametrize("regressor", regressors(), ids=lambda x: x.__class__.__name__)
+class TestIsFitted:
+    def test_should_return_false_before_fitting(self, regressor: Regressor) -> None:
+        assert not regressor.is_fitted()
+
+    def test_should_return_true_after_fitting(self, regressor: Regressor, valid_data: TaggedTable) -> None:
+        fitted_regressor = regressor.fit(valid_data)
+        assert fitted_regressor.is_fitted()
+
+
 class DummyRegressor(Regressor):
     """
     Dummy regressor to test metrics.
@@ -149,6 +163,9 @@ def predict(self, dataset: Table) -> TaggedTable:
 
         return dataset.tag_columns(target_name="predicted")
 
+    def is_fitted(self) -> bool:
+        return True
+
 
 class TestMeanAbsoluteError:
     @pytest.mark.parametrize(