fix: mark various API elements as internal (#587)

Closes #582
Closes #585

### Summary of Changes

Mark the following API elements as internal:

* Static method `Schema.merge_multiple_schemas`
* Instance method `SupportVectorMachineKernel.get_sklearn_kernel`
* Module `config`

---------

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

src/safeds/data/image/containers/_image.py

@@ -1,7 +1,7 @@
 from __future__ import annotations
 
-import sys
 import io
+import sys
 import warnings
 from pathlib import Path
 from typing import TYPE_CHECKING
@@ -12,7 +12,7 @@
 from PIL.Image import open as pil_image_open
 from torch import Tensor
 
-from safeds.config import _get_device
+from safeds._config import _get_device
 
 if TYPE_CHECKING:
     from torch.types import Device

src/safeds/data/tabular/containers/_table.py

@@ -337,7 +337,7 @@ def from_rows(rows: list[Row]) -> Table:
         dataframe: DataFrame = pd.concat(row_array, ignore_index=True)
         dataframe.columns = column_names_compare
 
-        schema = Schema.merge_multiple_schemas([row.schema for row in rows])
+        schema = Schema._merge_multiple_schemas([row.schema for row in rows])
 
         return Table._from_pandas_dataframe(dataframe, schema)
 
@@ -1012,7 +1012,7 @@ def add_row(self, row: Row) -> Table:
 
         new_df = pd.concat([self._data, row._data]).infer_objects()
         new_df.columns = self.column_names
-        schema = Schema.merge_multiple_schemas([self.schema, row.schema])
+        schema = Schema._merge_multiple_schemas([self.schema, row.schema])
         result = Table._from_pandas_dataframe(new_df, schema)
 
         for column in int_columns:

src/safeds/data/tabular/typing/_schema.py

@@ -111,7 +111,11 @@ def __sizeof__(self) -> int:
         -------
         Size of this object in bytes.
         """
-        return sum(map(sys.getsizeof, self._schema.keys())) + sum(map(sys.getsizeof, self._schema.values())) + sys.getsizeof(self._schema)
+        return (
+            sum(map(sys.getsizeof, self._schema.keys()))
+            + sum(map(sys.getsizeof, self._schema.values()))
+            + sys.getsizeof(self._schema)
+        )
 
     def __str__(self) -> str:
         """
@@ -237,7 +241,7 @@ def to_dict(self) -> dict[str, ColumnType]:
         return dict(self._schema)  # defensive copy
 
     @staticmethod
-    def merge_multiple_schemas(schemas: list[Schema]) -> Schema:
+    def _merge_multiple_schemas(schemas: list[Schema]) -> Schema:
         """
         Merge multiple schemas into one.
 

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

@@ -19,7 +19,7 @@ class SupportVectorMachineKernel(ABC):
     """The abstract base class of the different subclasses supported by the `Kernel`."""
 
     @abstractmethod
-    def get_sklearn_kernel(self) -> object:
+    def _get_sklearn_kernel(self) -> object:
         """
         Get the kernel of the given SupportVectorMachine.
 
@@ -85,7 +85,7 @@ def kernel(self) -> SupportVectorMachineKernel | None:
 
     class Kernel:
         class Linear(SupportVectorMachineKernel):
-            def get_sklearn_kernel(self) -> str:
+            def _get_sklearn_kernel(self) -> str:
                 """
                 Get the name of the linear kernel.
 
@@ -102,7 +102,7 @@ def __init__(self, degree: int):
                     raise OutOfBoundsError(degree, name="degree", lower_bound=ClosedBound(1))
                 self._degree = degree
 
-            def get_sklearn_kernel(self) -> str:
+            def _get_sklearn_kernel(self) -> str:
                 """
                 Get the name of the polynomial kernel.
 
@@ -114,7 +114,7 @@ def get_sklearn_kernel(self) -> str:
                 return "poly"
 
         class Sigmoid(SupportVectorMachineKernel):
-            def get_sklearn_kernel(self) -> str:
+            def _get_sklearn_kernel(self) -> str:
                 """
                 Get the name of the sigmoid kernel.
 
@@ -126,7 +126,7 @@ def get_sklearn_kernel(self) -> str:
                 return "sigmoid"
 
         class RadialBasisFunction(SupportVectorMachineKernel):
-            def get_sklearn_kernel(self) -> str:
+            def _get_sklearn_kernel(self) -> str:
                 """
                 Get the name of the radial basis function (RBF) kernel.
 

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

@@ -19,7 +19,7 @@ class SupportVectorMachineKernel(ABC):
     """The abstract base class of the different subclasses supported by the `Kernel`."""
 
     @abstractmethod
-    def get_sklearn_kernel(self) -> object:
+    def _get_sklearn_kernel(self) -> object:
         """
         Get the kernel of the given SupportVectorMachine.
 
@@ -85,7 +85,7 @@ def kernel(self) -> SupportVectorMachineKernel | None:
 
     class Kernel:
         class Linear(SupportVectorMachineKernel):
-            def get_sklearn_kernel(self) -> str:
+            def _get_sklearn_kernel(self) -> str:
                 """
                 Get the name of the linear kernel.
 
@@ -102,7 +102,7 @@ def __init__(self, degree: int):
                     raise OutOfBoundsError(degree, name="degree", lower_bound=ClosedBound(1))
                 self._degree = degree
 
-            def get_sklearn_kernel(self) -> str:
+            def _get_sklearn_kernel(self) -> str:
                 """
                 Get the name of the polynomial kernel.
 
@@ -114,7 +114,7 @@ def get_sklearn_kernel(self) -> str:
                 return "poly"
 
         class Sigmoid(SupportVectorMachineKernel):
-            def get_sklearn_kernel(self) -> str:
+            def _get_sklearn_kernel(self) -> str:
                 """
                 Get the name of the sigmoid kernel.
 
@@ -126,7 +126,7 @@ def get_sklearn_kernel(self) -> str:
                 return "sigmoid"
 
         class RadialBasisFunction(SupportVectorMachineKernel):
-            def get_sklearn_kernel(self) -> str:
+            def _get_sklearn_kernel(self) -> str:
                 """
                 Get the name of the radial basis function (RBF) kernel.
 

tests/safeds/config/test_device.py → tests/safeds/_config/test_device.py

@@ -1,5 +1,5 @@
 import torch
-from safeds.config import _get_device
+from safeds._config import _get_device
 
 
 def test_device() -> None:

tests/safeds/data/tabular/typing/test_schema.py

@@ -280,7 +280,7 @@ class TestMergeMultipleSchemas:
     )
     def test_should_raise_if_column_names_are_different(self, schemas: list[Schema], error_msg_regex: str) -> None:
         with pytest.raises(UnknownColumnNameError, match=error_msg_regex):
-            Schema.merge_multiple_schemas(schemas)
+            Schema._merge_multiple_schemas(schemas)
 
     @pytest.mark.parametrize(
         ("schemas", "expected"),
@@ -470,10 +470,10 @@ def test_should_raise_if_column_names_are_different(self, schemas: list[Schema],
         ],
     )
     def test_should_return_merged_schema(self, schemas: list[Schema], expected: Schema) -> None:
-        assert Schema.merge_multiple_schemas(schemas) == expected
+        assert Schema._merge_multiple_schemas(schemas) == expected
         schemas.reverse()
         assert (
-            Schema.merge_multiple_schemas(schemas) == expected
+            Schema._merge_multiple_schemas(schemas) == expected
         )  # test the reversed list because the first parameter is handled differently
 
 

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

@@ -41,7 +41,7 @@ def test_should_be_passed_to_sklearn(self, training_set: TaggedTable) -> None:
     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()
+        linear_kernel = svm.kernel._get_sklearn_kernel()
         assert linear_kernel == "linear"
 
     @pytest.mark.parametrize("degree", [-1, 0], ids=["minus_one", "zero"])
@@ -52,19 +52,19 @@ def test_should_raise_if_degree_less_than_1(self, degree: int) -> None:
     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()
+        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()
+        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()
+        rbf_kernel = svm.kernel._get_sklearn_kernel()
         assert rbf_kernel == "rbf"
 
     def test_should_get_kernel_name(self) -> None:

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

@@ -41,7 +41,7 @@ def test_should_be_passed_to_sklearn(self, training_set: TaggedTable) -> None:
     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()
+        linear_kernel = svm.kernel._get_sklearn_kernel()
         assert linear_kernel == "linear"
 
     @pytest.mark.parametrize("degree", [-1, 0], ids=["minus_one", "zero"])
@@ -52,19 +52,19 @@ def test_should_raise_if_degree_less_than_1(self, degree: int) -> None:
     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()
+        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()
+        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()
+        rbf_kernel = svm.kernel._get_sklearn_kernel()
         assert rbf_kernel == "rbf"
 
     def test_should_get_kernel_name(self) -> None: