feat: new method is_fitted to check whether a transformer is fitted

src/safeds/data/tabular/transformation/_imputer.py

@@ -161,3 +161,14 @@ def transform(self, table: Table) -> Table:
         indices = [table.schema._get_column_index_by_name(name) for name in self._column_names]
         data[indices] = pd.DataFrame(self._wrapped_transformer.transform(data[indices]), columns=indices)
         return Table(data, table.schema)
+
+    def is_fitted(self) -> bool:
+        """
+        Check if the transformer is fitted.
+
+        Returns
+        -------
+        is_fitted : bool
+            Whether the transformer is fitted.
+        """
+        return self._wrapped_transformer is not None

src/safeds/data/tabular/transformation/_label_encoder.py

@@ -124,3 +124,14 @@ def inverse_transform(self, transformed_table: Table) -> Table:
         data.columns = transformed_table.get_column_names()
         data[self._column_names] = self._wrapped_transformer.inverse_transform(data[self._column_names])
         return Table(data)
+
+    def is_fitted(self) -> bool:
+        """
+        Check if the transformer is fitted.
+
+        Returns
+        -------
+        is_fitted : bool
+            Whether the transformer is fitted.
+        """
+        return self._wrapped_transformer is not None

src/safeds/data/tabular/transformation/_one_hot_encoder.py

@@ -129,3 +129,14 @@ def inverse_transform(self, transformed_table: Table) -> Table:
         unchanged = data.drop(self._wrapped_transformer.get_feature_names_out(), axis=1)
 
         return Table(pd.concat([unchanged, decoded], axis=1))
+
+    def is_fitted(self) -> bool:
+        """
+        Check if the transformer is fitted.
+
+        Returns
+        -------
+        is_fitted : bool
+            Whether the transformer is fitted.
+        """
+        return self._wrapped_transformer is not None

src/safeds/data/tabular/transformation/_table_transformer.py

@@ -52,6 +52,17 @@ def transform(self, table: Table) -> Table:
             If the transformer has not been fitted yet.
         """
 
+    @abstractmethod
+    def is_fitted(self) -> bool:
+        """
+        Check if the transformer is fitted.
+
+        Returns
+        -------
+        is_fitted : bool
+            Whether the transformer is fitted.
+        """
+
     def fit_and_transform(self, table: Table, column_names: Optional[list[str]] = None) -> Table:
         """
         Learn a transformation for a set of columns in a table and apply the learned transformation to the same table.

tests/safeds/data/tabular/transformation/test_imputer.py

@@ -63,6 +63,23 @@ def test_should_raise_if_not_fitted(self) -> None:
             transformer.transform(table)
 
 
+class TestIsFitted:
+    def test_should_return_false_before_fitting(self) -> None:
+        transformer = Imputer(Imputer.Strategy.Mean())
+        assert not transformer.is_fitted()
+
+    def test_should_return_true_after_fitting(self) -> None:
+        table = Table.from_columns(
+            [
+                Column("a", [1, 3, None]),
+            ]
+        )
+
+        transformer = Imputer(Imputer.Strategy.Mean())
+        fitted_transformer = transformer.fit(table)
+        assert fitted_transformer.is_fitted()
+
+
 class TestFitAndTransform:
     @pytest.mark.parametrize(
         ("table", "column_names", "strategy", "expected"),

tests/safeds/data/tabular/transformation/test_label_encoder.py

@@ -63,6 +63,23 @@ def test_should_raise_if_not_fitted(self) -> None:
             transformer.transform(table)
 
 
+class TestIsFitted:
+    def test_should_return_false_before_fitting(self) -> None:
+        transformer = LabelEncoder()
+        assert not transformer.is_fitted()
+
+    def test_should_return_true_after_fitting(self) -> None:
+        table = Table.from_columns(
+            [
+                Column("col1", ["a", "b", "c"]),
+            ]
+        )
+
+        transformer = LabelEncoder()
+        fitted_transformer = transformer.fit(table)
+        assert fitted_transformer.is_fitted()
+
+
 class TestFitAndTransform:
     @pytest.mark.parametrize(
         ("table", "column_names", "expected"),

tests/safeds/data/tabular/transformation/test_one_hot_encoder.py

@@ -63,6 +63,23 @@ def test_should_raise_if_not_fitted(self) -> None:
             transformer.transform(table)
 
 
+class TestIsFitted:
+    def test_should_return_false_before_fitting(self) -> None:
+        transformer = OneHotEncoder()
+        assert not transformer.is_fitted()
+
+    def test_should_return_true_after_fitting(self) -> None:
+        table = Table.from_columns(
+            [
+                Column("col1", ["a", "b", "c"]),
+            ]
+        )
+
+        transformer = OneHotEncoder()
+        fitted_transformer = transformer.fit(table)
+        assert fitted_transformer.is_fitted()
+
+
 class TestFitAndTransform:
     @pytest.mark.parametrize(
         ("table", "column_names", "expected"),