feat: Add StandardScaler transformer

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

@@ -4,6 +4,7 @@
 from ._label_encoder import LabelEncoder
 from ._one_hot_encoder import OneHotEncoder
 from ._range_scaler import RangeScaler
+from ._standard_scaler import StandardScaler
 from ._table_transformer import InvertibleTableTransformer, TableTransformer
 
 __all__ = [
@@ -13,4 +14,5 @@
     "InvertibleTableTransformer",
     "TableTransformer",
     "RangeScaler",
+    "StandardScaler",
 ]

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

@@ -0,0 +1,180 @@
+from __future__ import annotations
+
+from sklearn.preprocessing import StandardScaler as sk_StandardScaler
+
+from safeds.data.tabular.containers import Table
+from safeds.data.tabular.transformation._table_transformer import InvertibleTableTransformer
+from safeds.exceptions import TransformerNotFittedError, UnknownColumnNameError
+
+
+class StandardScaler(InvertibleTableTransformer):
+    """The StandardScaler transforms column values by scaling each value to a given range."""
+
+    def __init__(self) -> None:
+        self._column_names: list[str] | None = None
+        self._wrapped_transformer: sk_StandardScaler | None = None
+
+    def fit(self, table: Table, column_names: list[str] | None) -> StandardScaler:
+        """
+        Learn a transformation for a set of columns in a table.
+
+        This transformer is not modified.
+
+        Parameters
+        ----------
+        table : Table
+            The table used to fit the transformer.
+        column_names : Optional[list[str]]
+            The list of columns from the table used to fit the transformer. If `None`, all columns are used.
+
+        Returns
+        -------
+        fitted_transformer : TableTransformer
+            The fitted transformer.
+        """
+        if column_names is None:
+            column_names = table.column_names
+        else:
+            missing_columns = set(column_names) - set(table.column_names)
+            if len(missing_columns) > 0:
+                raise UnknownColumnNameError(list(missing_columns))
+
+        wrapped_transformer = sk_StandardScaler()
+        wrapped_transformer.fit(table._data[column_names])
+
+        result = StandardScaler()
+        result._wrapped_transformer = wrapped_transformer
+        result._column_names = column_names
+
+        return result
+
+    def transform(self, table: Table) -> Table:
+        """
+        Apply the learned transformation to a table.
+
+        The table is not modified.
+
+        Parameters
+        ----------
+        table : Table
+            The table to which the learned transformation is applied.
+
+        Returns
+        -------
+        transformed_table : Table
+            The transformed table.
+
+        Raises
+        ------
+        TransformerNotFittedError
+            If the transformer has not been fitted yet.
+        """
+        # Transformer has not been fitted yet
+        if self._wrapped_transformer is None or self._column_names is None:
+            raise TransformerNotFittedError
+
+        # Input table does not contain all columns used to fit the transformer
+        missing_columns = set(self._column_names) - set(table.column_names)
+        if len(missing_columns) > 0:
+            raise UnknownColumnNameError(list(missing_columns))
+
+        data = table._data.copy()
+        data.columns = table.column_names
+        data[self._column_names] = self._wrapped_transformer.transform(data[self._column_names])
+        return Table._from_pandas_dataframe(data)
+
+    def inverse_transform(self, transformed_table: Table) -> Table:
+        """
+        Undo the learned transformation.
+
+        The table is not modified.
+
+        Parameters
+        ----------
+        transformed_table : Table
+            The table to be transformed back to the original version.
+
+        Returns
+        -------
+        table : Table
+            The original table.
+
+        Raises
+        ------
+        TransformerNotFittedError
+            If the transformer has not been fitted yet.
+        """
+        # Transformer has not been fitted yet
+        if self._wrapped_transformer is None or self._column_names is None:
+            raise TransformerNotFittedError
+
+        data = transformed_table._data.copy()
+        data.columns = transformed_table.column_names
+        data[self._column_names] = self._wrapped_transformer.inverse_transform(data[self._column_names])
+        return Table._from_pandas_dataframe(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
+
+    def get_names_of_added_columns(self) -> list[str]:
+        """
+        Get the names of all new columns that have been added by the StandardScaler.
+
+        Returns
+        -------
+        added_columns : list[str]
+            A list of names of the added columns, ordered as they will appear in the table.
+
+        Raises
+        ------
+        TransformerNotFittedError
+            If the transformer has not been fitted yet.
+        """
+        if not self.is_fitted():
+            raise TransformerNotFittedError
+        return []
+
+    # (Must implement abstract method, cannot instantiate class otherwise.)
+    def get_names_of_changed_columns(self) -> list[str]:
+        """
+         Get the names of all columns that may have been changed by the StandardScaler.
+
+        Returns
+        -------
+        changed_columns : list[str]
+             The list of (potentially) changed column names, as passed to fit.
+
+        Raises
+        ------
+        TransformerNotFittedError
+            If the transformer has not been fitted yet.
+        """
+        if self._column_names is None:
+            raise TransformerNotFittedError
+        return self._column_names
+
+    def get_names_of_removed_columns(self) -> list[str]:
+        """
+        Get the names of all columns that have been removed by the StandardScaler.
+
+        Returns
+        -------
+        removed_columns : list[str]
+            A list of names of the removed columns, ordered as they appear in the table the StandardScaler was fitted on.
+
+        Raises
+        ------
+        TransformerNotFittedError
+            If the transformer has not been fitted yet.
+        """
+        if not self.is_fitted():
+            raise TransformerNotFittedError
+        return []

tests/helpers/__init__.py

@@ -1,3 +1,4 @@
+from ._assertions import assert_that_tables_are_close
 from ._resources import resolve_resource_path
 
-__all__ = ["resolve_resource_path"]
+__all__ = ["assert_that_tables_are_close", "resolve_resource_path"]

tests/helpers/_assertions.py

@@ -0,0 +1,24 @@
+import pytest
+from safeds.data.tabular.containers import Table
+
+
+def assert_that_tables_are_close(table1: Table, table2: Table) -> None:
+    """
+    Assert that two tables are almost equal.
+
+    Parameters
+    ----------
+    table1: Table
+        The first table.
+    table2: Table
+        The table to compare the first table to.
+    """
+    assert table1.schema == table2.schema
+    for column_name in table1.column_names:
+        assert table1.get_column(column_name).type == table2.get_column(column_name).type
+        assert table1.get_column(column_name).type.is_numeric()
+        assert table2.get_column(column_name).type.is_numeric()
+        for i in range(table1.number_of_rows):
+            entry_1 = table1.get_column(column_name).get_value(i)
+            entry_2 = table2.get_column(column_name).get_value(i)
+            assert entry_1 == pytest.approx(entry_2)