feat: Support vector machine for regression and for classification (#236

)

Closes #154.

### Summary of Changes
Added a new svm class with a wrapped svc of scikit-learn to both the ml
classification- and regression packages and tested the new class when
added to the test_classifier.py and test_regressor.py.
More than half of the time was spent at the level of testing due to
error configurations with poetry and pyproject.toml, git rebase and
merges.

---------

Co-authored-by: Junior Atemebang Ashu <jay@juniors-mbp.localdomain>
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/__init__.py

@@ -7,6 +7,7 @@
 from ._k_nearest_neighbors import KNearestNeighbors
 from ._logistic_regression import LogisticRegression
 from ._random_forest import RandomForest
+from ._support_vector_machine import SupportVectorMachine
 
 __all__ = [
     "AdaBoost",
@@ -16,4 +17,5 @@
     "KNearestNeighbors",
     "LogisticRegression",
     "RandomForest",
+    "SupportVectorMachine",
 ]

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

@@ -0,0 +1,90 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+from sklearn.svm import SVC as sk_SVC  # noqa: N811
+
+from safeds.ml.classical._util_sklearn import fit, predict
+
+from ._classifier import Classifier
+
+if TYPE_CHECKING:
+    from safeds.data.tabular.containers import Table, TaggedTable
+
+
+class SupportVectorMachine(Classifier):
+    """Support vector machine."""
+
+    def __init__(self) -> None:
+        self._wrapped_classifier: sk_SVC | None = None
+        self._feature_names: list[str] | None = None
+        self._target_name: str | None = None
+
+    def fit(self, training_set: TaggedTable) -> SupportVectorMachine:
+        """
+        Create a copy of this classifier and fit it with the given training data.
+
+        This classifier is not modified.
+
+        Parameters
+        ----------
+        training_set : TaggedTable
+            The training data containing the feature and target vectors.
+
+        Returns
+        -------
+        fitted_classifier : SupportVectorMachine
+            The fitted classifier.
+
+        Raises
+        ------
+        LearningError
+            If the training data contains invalid values or if the training failed.
+        """
+        wrapped_classifier = sk_SVC()
+        fit(wrapped_classifier, training_set)
+
+        result = SupportVectorMachine()
+        result._wrapped_classifier = wrapped_classifier
+        result._feature_names = training_set.features.column_names
+        result._target_name = training_set.target.name
+
+        return result
+
+    def predict(self, dataset: Table) -> TaggedTable:
+        """
+        Predict a target vector using a dataset containing feature vectors. The model has to be trained first.
+
+        Parameters
+        ----------
+        dataset : Table
+            The dataset containing the feature vectors.
+
+        Returns
+        -------
+        table : TaggedTable
+            A dataset containing the given feature vectors and the predicted target vector.
+
+        Raises
+        ------
+        ModelNotFittedError
+            If the model has not been fitted yet.
+        DatasetContainsTargetError
+            If the dataset contains the target column already.
+        DatasetMissesFeaturesError
+            If the dataset misses feature columns.
+        PredictionError
+            If predicting with the given dataset failed.
+        """
+        return predict(self._wrapped_classifier, dataset, self._feature_names, self._target_name)
+
+    def is_fitted(self) -> bool:
+        """
+        Check if the classifier is fitted.
+
+        Returns
+        -------
+        is_fitted : bool
+            Whether the classifier is fitted.
+        """
+        return self._wrapped_classifier is not None

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

@@ -10,6 +10,7 @@
 from ._random_forest import RandomForest
 from ._regressor import Regressor
 from ._ridge_regression import RidgeRegression
+from ._support_vector_machine import SupportVectorMachine
 
 __all__ = [
     "AdaBoost",
@@ -22,4 +23,5 @@
     "RandomForest",
     "Regressor",
     "RidgeRegression",
+    "SupportVectorMachine",
 ]

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

@@ -0,0 +1,90 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+from sklearn.svm import SVR as sk_SVR  # noqa: N811
+
+from safeds.ml.classical._util_sklearn import fit, predict
+
+from ._regressor import Regressor
+
+if TYPE_CHECKING:
+    from safeds.data.tabular.containers import Table, TaggedTable
+
+
+class SupportVectorMachine(Regressor):
+    """Support vector machine."""
+
+    def __init__(self) -> None:
+        self._wrapped_regressor: sk_SVR | None = None
+        self._feature_names: list[str] | None = None
+        self._target_name: str | None = None
+
+    def fit(self, training_set: TaggedTable) -> SupportVectorMachine:
+        """
+        Create a copy of this regressor and fit it with the given training data.
+
+        This regressor is not modified.
+
+        Parameters
+        ----------
+        training_set : TaggedTable
+            The training data containing the feature and target vectors.
+
+        Returns
+        -------
+        fitted_regressor : SupportVectorMachine
+            The fitted regressor.
+
+        Raises
+        ------
+        LearningError
+            If the training data contains invalid values or if the training failed.
+        """
+        wrapped_regressor = sk_SVR()
+        fit(wrapped_regressor, training_set)
+
+        result = SupportVectorMachine()
+        result._wrapped_regressor = wrapped_regressor
+        result._feature_names = training_set.features.column_names
+        result._target_name = training_set.target.name
+
+        return result
+
+    def predict(self, dataset: Table) -> TaggedTable:
+        """
+        Predict a target vector using a dataset containing feature vectors. The model has to be trained first.
+
+        Parameters
+        ----------
+        dataset : Table
+            The dataset containing the feature vectors.
+
+        Returns
+        -------
+        table : TaggedTable
+            A dataset containing the given feature vectors and the predicted target vector.
+
+        Raises
+        ------
+        ModelNotFittedError
+            If the model has not been fitted yet.
+        DatasetContainsTargetError
+            If the dataset contains the target column already.
+        DatasetMissesFeaturesError
+            If the dataset misses feature columns.
+        PredictionError
+            If predicting 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/classical/classification/test_classifier.py

@@ -12,6 +12,7 @@
     KNearestNeighbors,
     LogisticRegression,
     RandomForest,
+    SupportVectorMachine,
 )
 from safeds.ml.exceptions import (
     DatasetContainsTargetError,
@@ -37,7 +38,15 @@ def classifiers() -> list[Classifier]:
     classifiers : list[Classifier]
         The list of classifiers to test.
     """
-    return [AdaBoost(), DecisionTree(), GradientBoosting(), KNearestNeighbors(2), LogisticRegression(), RandomForest()]
+    return [
+        AdaBoost(),
+        DecisionTree(),
+        GradientBoosting(),
+        KNearestNeighbors(2),
+        LogisticRegression(),
+        RandomForest(),
+        SupportVectorMachine(),
+    ]
 
 
 @pytest.fixture()

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

@@ -17,6 +17,7 @@
     RandomForest,
     Regressor,
     RidgeRegression,
+    SupportVectorMachine,
 )
 
 # noinspection PyProtectedMember
@@ -55,6 +56,7 @@ def regressors() -> list[Regressor]:
         LinearRegression(),
         RandomForest(),
         RidgeRegression(),
+        SupportVectorMachine(),
     ]