feat: Regressor.summarize_metrics and Classifier.summarize_metrics (

#729)

Closes #713

### Summary of Changes

Add `Regressor.summarize_metrics` and `Classifier.summarize_metrics` to
quickly check suitable metrics.

---------

Co-authored-by: megalinter-bot <[email protected]>

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

@@ -92,7 +92,43 @@ def _get_sklearn_classifier(self) -> ClassifierMixin:
             The sklearn Classifier.
         """
 
-    # noinspection PyProtectedMember
+    # ------------------------------------------------------------------------------------------------------------------
+    # Metrics
+    # ------------------------------------------------------------------------------------------------------------------
+
+    def summarize_metrics(self, validation_or_test_set: TabularDataset, positive_class: Any) -> Table:
+        """
+        Summarize the classifier's metrics on the given data.
+
+        Parameters
+        ----------
+        validation_or_test_set:
+            The validation or test set.
+        positive_class:
+            The class to be considered positive. All other classes are considered negative.
+
+        Returns
+        -------
+        metrics:
+            A table containing the classifier's metrics.
+
+        Raises
+        ------
+        TypeError
+            If a table is passed instead of a tabular dataset.
+        """
+        accuracy = self.accuracy(validation_or_test_set)
+        precision = self.precision(validation_or_test_set, positive_class)
+        recall = self.recall(validation_or_test_set, positive_class)
+        f1_score = self.f1_score(validation_or_test_set, positive_class)
+
+        return Table(
+            {
+                "metric": ["accuracy", "precision", "recall", "f1_score"],
+                "value": [accuracy, precision, recall, f1_score],
+            },
+        )
+
     def accuracy(self, validation_or_test_set: TabularDataset) -> float:
         """
         Compute the accuracy of the classifier on the given data.

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

@@ -90,10 +90,13 @@ def _get_sklearn_regressor(self) -> RegressorMixin:
             The sklearn Regressor.
         """
 
-    # noinspection PyProtectedMember
-    def mean_squared_error(self, validation_or_test_set: TabularDataset) -> float:
+    # ------------------------------------------------------------------------------------------------------------------
+    # Metrics
+    # ------------------------------------------------------------------------------------------------------------------
+
+    def summarize_metrics(self, validation_or_test_set: TabularDataset) -> Table:
         """
-        Compute the mean squared error (MSE) on the given data.
+        Summarize the regressor's metrics on the given data.
 
         Parameters
         ----------
@@ -102,28 +105,57 @@ def mean_squared_error(self, validation_or_test_set: TabularDataset) -> float:
 
         Returns
         -------
-        mean_squared_error:
-            The calculated mean squared error (the average of the distance of each individual row squared).
+        metrics:
+            A table containing the regressor's metrics.
 
         Raises
         ------
         TypeError
             If a table is passed instead of a tabular dataset.
         """
-        from sklearn.metrics import mean_squared_error as sk_mean_squared_error
+        mean_absolute_error = self.mean_absolute_error(validation_or_test_set)
+        mean_squared_error = self.mean_squared_error(validation_or_test_set)
+
+        return Table(
+            {
+                "metric": ["mean_absolute_error", "mean_squared_error"],
+                "value": [mean_absolute_error, mean_squared_error],
+            },
+        )
+
+    def mean_absolute_error(self, validation_or_test_set: TabularDataset) -> float:
+        """
+        Compute the mean absolute error (MAE) of the regressor on the given data.
+
+        Parameters
+        ----------
+        validation_or_test_set:
+            The validation or test set.
+
+        Returns
+        -------
+        mean_absolute_error:
+            The calculated mean absolute error (the average of the distance of each individual row).
+
+        Raises
+        ------
+        TypeError
+            If a table is passed instead of a tabular dataset.
+        """
+        from sklearn.metrics import mean_absolute_error as sk_mean_absolute_error
 
         if not isinstance(validation_or_test_set, TabularDataset) and isinstance(validation_or_test_set, Table):
             raise PlainTableError
         expected = validation_or_test_set.target
         predicted = self.predict(validation_or_test_set.features).target
 
         _check_metrics_preconditions(predicted, expected)
-        return sk_mean_squared_error(expected._data, predicted._data)
+        return sk_mean_absolute_error(expected._data, predicted._data)
 
     # noinspection PyProtectedMember
-    def mean_absolute_error(self, validation_or_test_set: TabularDataset) -> float:
+    def mean_squared_error(self, validation_or_test_set: TabularDataset) -> float:
         """
-        Compute the mean absolute error (MAE) of the regressor on the given data.
+        Compute the mean squared error (MSE) on the given data.
 
         Parameters
         ----------
@@ -132,23 +164,23 @@ def mean_absolute_error(self, validation_or_test_set: TabularDataset) -> float:
 
         Returns
         -------
-        mean_absolute_error:
-            The calculated mean absolute error (the average of the distance of each individual row).
+        mean_squared_error:
+            The calculated mean squared error (the average of the distance of each individual row squared).
 
         Raises
         ------
         TypeError
             If a table is passed instead of a tabular dataset.
         """
-        from sklearn.metrics import mean_absolute_error as sk_mean_absolute_error
+        from sklearn.metrics import mean_squared_error as sk_mean_squared_error
 
         if not isinstance(validation_or_test_set, TabularDataset) and isinstance(validation_or_test_set, Table):
             raise PlainTableError
         expected = validation_or_test_set.target
         predicted = self.predict(validation_or_test_set.features).target
 
         _check_metrics_preconditions(predicted, expected)
-        return sk_mean_absolute_error(expected._data, predicted._data)
+        return sk_mean_squared_error(expected._data, predicted._data)
 
 
 # noinspection PyProtectedMember

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

@@ -336,6 +336,52 @@ def _get_sklearn_classifier(self) -> ClassifierMixin:
         pass
 
 
+class TestSummarizeMetrics:
+    @pytest.mark.parametrize(
+        ("predicted", "expected", "result"),
+        [
+            (
+                [1, 2],
+                [1, 2],
+                Table(
+                    {
+                        "metric": ["accuracy", "precision", "recall", "f1_score"],
+                        "value": [1.0, 1.0, 1.0, 1.0],
+                    },
+                ),
+            ),
+        ],
+    )
+    def test_valid_data(self, predicted: list[float], expected: list[float], result: Table) -> None:
+        table = Table(
+            {
+                "predicted": predicted,
+                "expected": expected,
+            },
+        ).to_tabular_dataset(
+            target_name="expected",
+        )
+
+        assert DummyClassifier().summarize_metrics(table, 1) == result
+
+    @pytest.mark.parametrize(
+        "table",
+        [
+            Table(
+                {
+                    "a": [1.0, 0.0, 0.0, 0.0],
+                    "b": [0.0, 1.0, 1.0, 0.0],
+                    "c": [0.0, 0.0, 0.0, 1.0],
+                },
+            ),
+        ],
+        ids=["table"],
+    )
+    def test_should_raise_if_given_normal_table(self, table: Table) -> None:
+        with pytest.raises(PlainTableError):
+            DummyClassifier().summarize_metrics(table, 1)  # type: ignore[arg-type]
+
+
 class TestAccuracy:
     def test_with_same_type(self) -> None:
         table = Table(

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

@@ -343,6 +343,52 @@ def _get_sklearn_regressor(self) -> RegressorMixin:
         pass
 
 
+class TestSummarizeMetrics:
+    @pytest.mark.parametrize(
+        ("predicted", "expected", "result"),
+        [
+            (
+                [1, 2],
+                [1, 2],
+                Table(
+                    {
+                        "metric": ["mean_absolute_error", "mean_squared_error"],
+                        "value": [0.0, 0.0],
+                    },
+                ),
+            ),
+        ],
+    )
+    def test_valid_data(self, predicted: list[float], expected: list[float], result: Table) -> None:
+        table = Table(
+            {
+                "predicted": predicted,
+                "expected": expected,
+            },
+        ).to_tabular_dataset(
+            target_name="expected",
+        )
+
+        assert DummyRegressor().summarize_metrics(table) == result
+
+    @pytest.mark.parametrize(
+        "table",
+        [
+            Table(
+                {
+                    "a": [1.0, 0.0, 0.0, 0.0],
+                    "b": [0.0, 1.0, 1.0, 0.0],
+                    "c": [0.0, 0.0, 0.0, 1.0],
+                },
+            ),
+        ],
+        ids=["table"],
+    )
+    def test_should_raise_if_given_normal_table(self, table: Table) -> None:
+        with pytest.raises(PlainTableError):
+            DummyRegressor().summarize_metrics(table)  # type: ignore[arg-type]
+
+
 class TestMeanAbsoluteError:
     @pytest.mark.parametrize(
         ("predicted", "expected", "result"),