feat: add SequentialTableTransformer

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

@@ -12,6 +12,7 @@
     from ._one_hot_encoder import OneHotEncoder
     from ._range_scaler import RangeScaler
     from ._robust_scaler import RobustScaler
+    from ._sequential_table_transformer import SequentialTableTransformer
     from ._simple_imputer import SimpleImputer
     from ._standard_scaler import StandardScaler
     from ._table_transformer import TableTransformer
@@ -24,6 +25,7 @@
         "LabelEncoder": "._label_encoder:LabelEncoder",
         "OneHotEncoder": "._one_hot_encoder:OneHotEncoder",
         "RangeScaler": "._range_scaler:RangeScaler",
+        "SequentialTableTransformer": "._sequential_table_transformer:SequentialTableTransformer",
         "RobustScaler": "._robust_scaler:RobustScaler",
         "SimpleImputer": "._simple_imputer:SimpleImputer",
         "StandardScaler": "._standard_scaler:StandardScaler",
@@ -38,6 +40,7 @@
     "LabelEncoder",
     "OneHotEncoder",
     "RangeScaler",
+    "SequentialTableTransformer",
     "RobustScaler",
     "SimpleImputer",
     "StandardScaler",

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

@@ -0,0 +1,166 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+from safeds._utils import _structural_hash
+from safeds.exceptions import TransformerNotFittedError, TransformerNotInvertibleError
+
+from ._invertible_table_transformer import InvertibleTableTransformer
+
+if TYPE_CHECKING:
+    from safeds.data.tabular.containers import Table
+
+    from ._table_transformer import TableTransformer
+
+
+class SequentialTableTransformer(InvertibleTableTransformer):
+    """
+    The SequentialTableTransforrmer transforms a table using multiple transformers in sequence.
+
+    Parameters
+    ----------
+    transformers:
+        The list of transformers used to transform the table. Used in the order as they are supplied in the list.
+
+    Raises
+    ------
+    ValueError:
+        Raises a ValueError if the list of Transformers is None or contains no transformers.
+    """
+
+    def __init__(
+        self,
+        transformers: list[TableTransformer],
+        *,
+        column_names: str | list[str] | None = None,  # noqa: ARG002
+    ) -> None:
+        super().__init__(None)
+
+        # Check if transformers actually contains any transformers.
+        if transformers is None or len(transformers) == 0:
+            raise ValueError("transformers must contain at least 1 transformer")
+
+        # Parameters
+        self._transformers: list[TableTransformer] = transformers
+
+        # Internal State
+        self._is_fitted: bool = False
+
+    def __hash__(self) -> int:
+        return _structural_hash(
+            super().__hash__(),
+            self._transformers,
+            self._is_fitted,
+        )
+
+    @property
+    def is_fitted(self) -> bool:
+        """
+        Whether the transformer is fitted.
+
+        Returns
+        -------
+        True, if the transformer is fitted, False otherwise.
+        """
+        return self._is_fitted
+
+    def fit(self, table: Table) -> SequentialTableTransformer:
+        """
+        Fits all of the transformers in order.
+
+        Parameters
+        ----------
+        table:
+            The table used to fit the transformers.
+
+        Returns
+        -------
+        The fitted transformer.
+
+        Raises
+        ------
+        ValueError:
+            Raises a ValueError if the table has no rows.
+        """
+        if table.row_count == 0:
+            raise ValueError("The SequentialTableTransformer cannot be fitted because the table contains 0 rows.")
+
+        current_table: Table = table
+        fitted_transformers: list[TableTransformer] = []
+
+        for transformer in self._transformers:
+            fitted_transformer = transformer.fit(current_table)
+            fitted_transformers.append(fitted_transformer)
+            current_table = fitted_transformer.transform(current_table)
+
+        result: SequentialTableTransformer = SequentialTableTransformer(
+            transformers=fitted_transformers,
+            column_names=self._column_names,
+        )
+
+        result._is_fitted = True
+        return result
+
+    def transform(self, table: Table) -> Table:
+        """
+        Transform the table using all the transformers sequentially.
+
+        Might change the order and type of columns base on the transformers used.
+
+        Parameters
+        ----------
+        table:
+            The table to be transformed.
+
+        Returns
+        -------
+        The transformed table.
+
+        Raises
+        ------
+        TransformerNotFittedError:
+            Raises a TransformerNotFittedError if the transformer isn't fitted.
+        """
+        if not self._is_fitted:
+            raise TransformerNotFittedError
+
+        current_table: Table = table
+        for transformer in self._transformers:
+            current_table = transformer.transform(current_table)
+
+        return current_table
+
+    def inverse_transform(self, transformed_table: Table) -> Table:
+        """
+        Inversely transforms the table using all the transformers sequentially in inverse order.
+
+        Might change the order and type of columns base on the transformers used.
+
+        Parameters
+        ----------
+        transformed_table:
+            The table to be transformed back.
+
+        Returns
+        -------
+        The untranformed table.
+
+        Raises
+        ------
+        TransformerNotFittedError:
+            Raises a TransformerNotFittedError if the transformer isn't fitted.
+        TransformerNotInvertibleError:
+            Raises a TransformerNotInvertibleError if one of the transformers isn't invertible.
+        """
+        if not self._is_fitted:
+            raise TransformerNotFittedError
+
+        # sequentially inverse transform the table with all transformers, working from the back of the list forwards.
+        current_table: Table = transformed_table
+        for transformer in reversed(self._transformers):
+            # check if transformer is invertable
+            if not (isinstance(transformer, InvertibleTableTransformer)):
+                raise TransformerNotInvertibleError(str(type(transformer)))
+            current_table = transformer.inverse_transform(current_table)
+
+        return current_table

src/safeds/exceptions/__init__.py

@@ -11,6 +11,7 @@
     NonNumericColumnError,
     OutputLengthMismatchError,
     TransformerNotFittedError,
+    TransformerNotInvertibleError,
     ValueNotPresentWhenFittedError,
 )
 from ._ml import (
@@ -66,6 +67,7 @@ class OutOfBoundsError(SafeDsError):
     "NonNumericColumnError",
     "OutputLengthMismatchError",
     "TransformerNotFittedError",
+    "TransformerNotInvertibleError",
     "ValueNotPresentWhenFittedError",
     # ML exceptions
     "DatasetMissesDataError",

src/safeds/exceptions/_data.py

@@ -111,6 +111,13 @@ def __init__(self) -> None:
         super().__init__("The transformer has not been fitted yet.")
 
 
+class TransformerNotInvertibleError(Exception):
+    """Raised when a function tries to invert a non-invertible transformer."""
+
+    def __init__(self, transformer_type: str) -> None:
+        super().__init__(f"{transformer_type} is not invertible.")
+
+
 class ValueNotPresentWhenFittedError(Exception):
     """Exception raised when attempting to one-hot-encode a table containing values not present in the fitting phase."""
 

tests/helpers/_assertions.py

@@ -6,7 +6,15 @@
 from safeds.data.tabular.containers import Cell, Column, Table
 
 
-def assert_tables_equal(table1: Table, table2: Table) -> None:
+def assert_tables_equal(
+    table1: Table,
+    table2: Table,
+    *,
+    ignore_column_order: bool = False,
+    ignore_row_order: bool = False,
+    ignore_types: bool = False,
+    check_exact: bool = False,
+) -> None:
     """
     Assert that two tables are almost equal.
 
@@ -16,8 +24,23 @@ def assert_tables_equal(table1: Table, table2: Table) -> None:
         The first table.
     table2:
         The table to compare the first table to.
+    ignore_column_order:
+        Ignore the column order when True. Will return true, even when the column order is different.
+    ignore_row_order:
+        Ignore the column order when True. Will return true, even when the row order is different.
+    ignore_types:
+        Ignore differing data Types. Will return true, even when columns have differing data types.
+    check_exact:
+        If True, check, if floating point values match EXACTLY.
     """
-    assert_frame_equal(table1._data_frame, table2._data_frame)
+    assert_frame_equal(
+        table1._data_frame,
+        table2._data_frame,
+        check_row_order=not ignore_row_order,
+        check_column_order=not ignore_column_order,
+        check_dtypes=not ignore_types,
+        check_exact=check_exact,
+    )
 
 
 def assert_that_tabular_datasets_are_equal(table1: TabularDataset, table2: TabularDataset) -> None: