feat: set kernel of support vector machine (#350)

Closes #172.

A new parameter kernel: SupportVectorMachineKernel was added to the
initializer of SupportVectorMachine classes of both the classifier and
the regressor alongside getters for the parameters. It was later passed
as the kernel of the wrapped scikit-learn model in the fit method.
The SupportVectorMachineKernel was created as an abstract base class
from which the signatures of the methods in the nested subclasses of
Kernel were the same.
Tests were adjusted to test the functionality of the kernel parameter.

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

@@ -1,19 +1,34 @@
 from __future__ import annotations
 
+from abc import ABC, abstractmethod
 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
+from safeds.ml.classical.classification import Classifier
 
 if TYPE_CHECKING:
     from sklearn.base import ClassifierMixin
 
     from safeds.data.tabular.containers import Table, TaggedTable
 
 
+class SupportVectorMachineKernel(ABC):
+    """The abstract base class of the different subclasses supported by the `Kernel`."""
+
+    @abstractmethod
+    def get_sklearn_kernel(self) -> object:
+        """
+        Get the kernel of the given SupportVectorMachine.
+
+        Returns
+        -------
+        object
+        The kernel of the SupportVectorMachine.
+        """
+
+
 class SupportVectorMachine(Classifier):
     """
     Support vector machine.
@@ -22,14 +37,15 @@ class SupportVectorMachine(Classifier):
     ----------
     c: float
         The strength of regularization. Must be strictly positive.
+    kernel: The type of kernel to be used. Defaults to None.
 
     Raises
     ------
     ValueError
         If `c` is less than or equal to 0.
     """
 
-    def __init__(self, *, c: float = 1.0) -> None:
+    def __init__(self, *, c: float = 1.0, kernel: SupportVectorMachineKernel | None = None) -> None:
         # Internal state
         self._wrapped_classifier: sk_SVC | None = None
         self._feature_names: list[str] | None = None
@@ -39,11 +55,50 @@ def __init__(self, *, c: float = 1.0) -> None:
         if c <= 0:
             raise ValueError("The parameter 'c' has to be strictly positive.")
         self._c = c
+        self._kernel = kernel
 
     @property
     def c(self) -> float:
         return self._c
 
+    @property
+    def kernel(self) -> SupportVectorMachineKernel | None:
+        return self._kernel
+
+    class Kernel:
+        class Linear(SupportVectorMachineKernel):
+            def get_sklearn_kernel(self) -> str:
+                return "linear"
+
+        class Polynomial(SupportVectorMachineKernel):
+            def __init__(self, degree: int):
+                if degree < 1:
+                    raise ValueError("The parameter 'degree' has to be greater than or equal to 1.")
+                self._degree = degree
+
+            def get_sklearn_kernel(self) -> str:
+                return "poly"
+
+        class Sigmoid(SupportVectorMachineKernel):
+            def get_sklearn_kernel(self) -> str:
+                return "sigmoid"
+
+        class RadialBasisFunction(SupportVectorMachineKernel):
+            def get_sklearn_kernel(self) -> str:
+                return "rbf"
+
+    def _get_kernel_name(self) -> str:
+        if isinstance(self.kernel, SupportVectorMachine.Kernel.Linear):
+            return "linear"
+        elif isinstance(self.kernel, SupportVectorMachine.Kernel.Polynomial):
+            return "poly"
+        elif isinstance(self.kernel, SupportVectorMachine.Kernel.Sigmoid):
+            return "sigmoid"
+        elif isinstance(self.kernel, SupportVectorMachine.Kernel.RadialBasisFunction):
+            return "rbf"
+        else:
+            raise TypeError("Invalid kernel type.")
+
     def fit(self, training_set: TaggedTable) -> SupportVectorMachine:
         """
         Create a copy of this classifier and fit it with the given training data.
@@ -68,7 +123,7 @@ def fit(self, training_set: TaggedTable) -> SupportVectorMachine:
         wrapped_classifier = self._get_sklearn_classifier()
         fit(wrapped_classifier, training_set)
 
-        result = SupportVectorMachine(c=self._c)
+        result = SupportVectorMachine(c=self._c, kernel=self._kernel)
         result._wrapped_classifier = wrapped_classifier
         result._feature_names = training_set.features.column_names
         result._target_name = training_set.target.name

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

@@ -1,19 +1,34 @@
 from __future__ import annotations
 
+from abc import ABC, abstractmethod
 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
+from safeds.ml.classical.regression import Regressor
 
 if TYPE_CHECKING:
     from sklearn.base import RegressorMixin
 
     from safeds.data.tabular.containers import Table, TaggedTable
 
 
+class SupportVectorMachineKernel(ABC):
+    """The abstract base class of the different subclasses supported by the `Kernel`."""
+
+    @abstractmethod
+    def get_sklearn_kernel(self) -> object:
+        """
+        Get the kernel of the given SupportVectorMachine.
+
+        Returns
+        -------
+        object
+        The kernel of the SupportVectorMachine.
+        """
+
+
 class SupportVectorMachine(Regressor):
     """
     Support vector machine.
@@ -22,14 +37,15 @@ class SupportVectorMachine(Regressor):
     ----------
     c: float
         The strength of regularization. Must be strictly positive.
+    kernel: The type of kernel to be used. Defaults to None.
 
     Raises
     ------
     ValueError
         If `c` is less than or equal to 0.
     """
 
-    def __init__(self, *, c: float = 1.0) -> None:
+    def __init__(self, *, c: float = 1.0, kernel: SupportVectorMachineKernel | None = None) -> None:
         # Internal state
         self._wrapped_regressor: sk_SVR | None = None
         self._feature_names: list[str] | None = None
@@ -39,11 +55,50 @@ def __init__(self, *, c: float = 1.0) -> None:
         if c <= 0:
             raise ValueError("The parameter 'c' has to be strictly positive.")
         self._c = c
+        self._kernel = kernel
 
     @property
     def c(self) -> float:
         return self._c
 
+    @property
+    def kernel(self) -> SupportVectorMachineKernel | None:
+        return self._kernel
+
+    class Kernel:
+        class Linear(SupportVectorMachineKernel):
+            def get_sklearn_kernel(self) -> str:
+                return "linear"
+
+        class Polynomial(SupportVectorMachineKernel):
+            def __init__(self, degree: int):
+                if degree < 1:
+                    raise ValueError("The parameter 'degree' has to be greater than or equal to 1.")
+                self._degree = degree
+
+            def get_sklearn_kernel(self) -> str:
+                return "poly"
+
+        class Sigmoid(SupportVectorMachineKernel):
+            def get_sklearn_kernel(self) -> str:
+                return "sigmoid"
+
+        class RadialBasisFunction(SupportVectorMachineKernel):
+            def get_sklearn_kernel(self) -> str:
+                return "rbf"
+
+    def _get_kernel_name(self) -> str:
+        if isinstance(self.kernel, SupportVectorMachine.Kernel.Linear):
+            return "linear"
+        elif isinstance(self.kernel, SupportVectorMachine.Kernel.Polynomial):
+            return "poly"
+        elif isinstance(self.kernel, SupportVectorMachine.Kernel.Sigmoid):
+            return "sigmoid"
+        elif isinstance(self.kernel, SupportVectorMachine.Kernel.RadialBasisFunction):
+            return "rbf"
+        else:
+            raise TypeError("Invalid kernel type.")
+
     def fit(self, training_set: TaggedTable) -> SupportVectorMachine:
         """
         Create a copy of this regressor and fit it with the given training data.
@@ -68,7 +123,7 @@ def fit(self, training_set: TaggedTable) -> SupportVectorMachine:
         wrapped_regressor = self._get_sklearn_regressor()
         fit(wrapped_regressor, training_set)
 
-        result = SupportVectorMachine(c=self._c)
+        result = SupportVectorMachine(c=self._c, kernel=self._kernel)
         result._wrapped_regressor = wrapped_regressor
         result._feature_names = training_set.features.column_names
         result._target_name = training_set.target.name

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

@@ -25,3 +25,62 @@ def test_should_raise_if_less_than_or_equal_to_0(self) -> None:
             match="The parameter 'c' has to be strictly positive.",
         ):
             SupportVectorMachine(c=-1)
+
+
+class TestKernel:
+    def test_should_be_passed_to_fitted_model(self, training_set: TaggedTable) -> None:
+        kernel = SupportVectorMachine.Kernel.Linear()
+        fitted_model = SupportVectorMachine(c=2, kernel=kernel).fit(training_set=training_set)
+        assert isinstance(fitted_model.kernel, SupportVectorMachine.Kernel.Linear)
+
+    def test_should_be_passed_to_sklearn(self, training_set: TaggedTable) -> None:
+        kernel = SupportVectorMachine.Kernel.Linear()
+        fitted_model = SupportVectorMachine(c=2, kernel=kernel).fit(training_set)
+        assert fitted_model._wrapped_classifier is not None
+        assert isinstance(fitted_model.kernel, SupportVectorMachine.Kernel.Linear)
+
+    def test_should_get_sklearn_kernel_linear(self) -> None:
+        svm = SupportVectorMachine(c=2, kernel=SupportVectorMachine.Kernel.Linear())
+        assert isinstance(svm.kernel, SupportVectorMachine.Kernel.Linear)
+        linear_kernel = svm.kernel.get_sklearn_kernel()
+        assert linear_kernel == "linear"
+
+    def test_should_raise_if_degree_less_than_1(self) -> None:
+        with pytest.raises(ValueError, match="The parameter 'degree' has to be greater than or equal to 1."):
+            SupportVectorMachine.Kernel.Polynomial(degree=0)
+
+    def test_should_get_sklearn_kernel_polynomial(self) -> None:
+        svm = SupportVectorMachine(c=2, kernel=SupportVectorMachine.Kernel.Polynomial(degree=2))
+        assert isinstance(svm.kernel, SupportVectorMachine.Kernel.Polynomial)
+        poly_kernel = svm.kernel.get_sklearn_kernel()
+        assert poly_kernel == "poly"
+
+    def test_should_get_sklearn_kernel_sigmoid(self) -> None:
+        svm = SupportVectorMachine(c=2, kernel=SupportVectorMachine.Kernel.Sigmoid())
+        assert isinstance(svm.kernel, SupportVectorMachine.Kernel.Sigmoid)
+        sigmoid_kernel = svm.kernel.get_sklearn_kernel()
+        assert sigmoid_kernel == "sigmoid"
+
+    def test_should_get_sklearn_kernel_rbf(self) -> None:
+        svm = SupportVectorMachine(c=2, kernel=SupportVectorMachine.Kernel.RadialBasisFunction())
+        assert isinstance(svm.kernel, SupportVectorMachine.Kernel.RadialBasisFunction)
+        rbf_kernel = svm.kernel.get_sklearn_kernel()
+        assert rbf_kernel == "rbf"
+
+    def test_should_get_kernel_name(self) -> None:
+        svm = SupportVectorMachine(c=2, kernel=SupportVectorMachine.Kernel.Linear())
+        assert svm._get_kernel_name() == "linear"
+
+        svm = SupportVectorMachine(c=2, kernel=SupportVectorMachine.Kernel.Polynomial(degree=2))
+        assert svm._get_kernel_name() == "poly"
+
+        svm = SupportVectorMachine(c=2, kernel=SupportVectorMachine.Kernel.Sigmoid())
+        assert svm._get_kernel_name() == "sigmoid"
+
+        svm = SupportVectorMachine(c=2, kernel=SupportVectorMachine.Kernel.RadialBasisFunction())
+        assert svm._get_kernel_name() == "rbf"
+
+    def test_should_get_kernel_name_invalid_kernel_type(self) -> None:
+        svm = SupportVectorMachine(c=2)
+        with pytest.raises(TypeError, match="Invalid kernel type."):
+            svm._get_kernel_name()

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

@@ -25,3 +25,62 @@ def test_should_raise_if_less_than_or_equal_to_0(self) -> None:
             match="The parameter 'c' has to be strictly positive.",
         ):
             SupportVectorMachine(c=-1)
+
+
+class TestKernel:
+    def test_should_be_passed_to_fitted_model(self, training_set: TaggedTable) -> None:
+        kernel = SupportVectorMachine.Kernel.Linear()
+        fitted_model = SupportVectorMachine(c=2, kernel=kernel).fit(training_set=training_set)
+        assert isinstance(fitted_model.kernel, SupportVectorMachine.Kernel.Linear)
+
+    def test_should_be_passed_to_sklearn(self, training_set: TaggedTable) -> None:
+        kernel = SupportVectorMachine.Kernel.Linear()
+        fitted_model = SupportVectorMachine(c=2, kernel=kernel).fit(training_set)
+        assert fitted_model._wrapped_regressor is not None
+        assert isinstance(fitted_model.kernel, SupportVectorMachine.Kernel.Linear)
+
+    def test_should_get_sklearn_kernel_linear(self) -> None:
+        svm = SupportVectorMachine(c=2, kernel=SupportVectorMachine.Kernel.Linear())
+        assert isinstance(svm.kernel, SupportVectorMachine.Kernel.Linear)
+        linear_kernel = svm.kernel.get_sklearn_kernel()
+        assert linear_kernel == "linear"
+
+    def test_should_raise_if_degree_less_than_1(self) -> None:
+        with pytest.raises(ValueError, match="The parameter 'degree' has to be greater than or equal to 1."):
+            SupportVectorMachine.Kernel.Polynomial(degree=0)
+
+    def test_should_get_sklearn_kernel_polynomial(self) -> None:
+        svm = SupportVectorMachine(c=2, kernel=SupportVectorMachine.Kernel.Polynomial(degree=2))
+        assert isinstance(svm.kernel, SupportVectorMachine.Kernel.Polynomial)
+        poly_kernel = svm.kernel.get_sklearn_kernel()
+        assert poly_kernel == "poly"
+
+    def test_should_get_sklearn_kernel_sigmoid(self) -> None:
+        svm = SupportVectorMachine(c=2, kernel=SupportVectorMachine.Kernel.Sigmoid())
+        assert isinstance(svm.kernel, SupportVectorMachine.Kernel.Sigmoid)
+        sigmoid_kernel = svm.kernel.get_sklearn_kernel()
+        assert sigmoid_kernel == "sigmoid"
+
+    def test_should_get_sklearn_kernel_rbf(self) -> None:
+        svm = SupportVectorMachine(c=2, kernel=SupportVectorMachine.Kernel.RadialBasisFunction())
+        assert isinstance(svm.kernel, SupportVectorMachine.Kernel.RadialBasisFunction)
+        rbf_kernel = svm.kernel.get_sklearn_kernel()
+        assert rbf_kernel == "rbf"
+
+    def test_should_get_kernel_name(self) -> None:
+        svm = SupportVectorMachine(c=2, kernel=SupportVectorMachine.Kernel.Linear())
+        assert svm._get_kernel_name() == "linear"
+
+        svm = SupportVectorMachine(c=2, kernel=SupportVectorMachine.Kernel.Polynomial(degree=2))
+        assert svm._get_kernel_name() == "poly"
+
+        svm = SupportVectorMachine(c=2, kernel=SupportVectorMachine.Kernel.Sigmoid())
+        assert svm._get_kernel_name() == "sigmoid"
+
+        svm = SupportVectorMachine(c=2, kernel=SupportVectorMachine.Kernel.RadialBasisFunction())
+        assert svm._get_kernel_name() == "rbf"
+
+    def test_should_get_kernel_name_invalid_kernel_type(self) -> None:
+        svm = SupportVectorMachine(c=2)
+        with pytest.raises(TypeError, match="Invalid kernel type."):
+            svm._get_kernel_name()