feat: add robustscaler

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

@@ -10,6 +10,7 @@
     from ._label_encoder import LabelEncoder
     from ._one_hot_encoder import OneHotEncoder
     from ._range_scaler import RangeScaler
+    from ._robust_scaler import RobustScaler
     from ._simple_imputer import SimpleImputer
     from ._standard_scaler import StandardScaler
     from ._table_transformer import TableTransformer
@@ -22,6 +23,7 @@
         "LabelEncoder": "._label_encoder:LabelEncoder",
         "OneHotEncoder": "._one_hot_encoder:OneHotEncoder",
         "RangeScaler": "._range_scaler:RangeScaler",
+        "RobustScaler": "._robust_scaler:RobustScaler",
         "SimpleImputer": "._simple_imputer:SimpleImputer",
         "StandardScaler": "._standard_scaler:StandardScaler",
         "TableTransformer": "._table_transformer:TableTransformer",
@@ -34,6 +36,7 @@
     "LabelEncoder",
     "OneHotEncoder",
     "RangeScaler",
+    "RobustScaler",
     "SimpleImputer",
     "StandardScaler",
     "TableTransformer",

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

@@ -0,0 +1,199 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+from safeds._validation import _check_columns_exist
+from safeds._validation._check_columns_are_numeric import _check_columns_are_numeric
+from safeds.data.tabular.containers import Table
+from safeds.exceptions import TransformerNotFittedError
+
+from ._invertible_table_transformer import InvertibleTableTransformer
+
+if TYPE_CHECKING:
+    import polars as pl
+
+
+class RobustScaler(InvertibleTableTransformer):
+    """
+    The RobustScaler transforms column values to a range by removing the median and scaling to the interquartile range.
+
+    Currently, for columns with high stability (IQR == 0), it will only substract the median and not scale to avoid dividing by zero.
+
+    Parameters
+    ----------
+    column_names:
+        The list of columns used to fit the transformer. If `None`, all numeric columns are used.
+    """
+
+    # ------------------------------------------------------------------------------------------------------------------
+    # Dunder methods
+    # ------------------------------------------------------------------------------------------------------------------
+
+    def __init__(self, *, column_names: str | list[str] | None = None) -> None:
+        super().__init__(column_names)
+
+        # Internal state
+        self._data_median: pl.DataFrame | None = None
+        self._data_scale: pl.DataFrame | None = None
+
+    def __hash__(self) -> int:
+        # Leave out the internal state for faster hashing
+        return super().__hash__()
+
+    # ------------------------------------------------------------------------------------------------------------------
+    # Properties
+    # ------------------------------------------------------------------------------------------------------------------
+
+    @property
+    def is_fitted(self) -> bool:
+        """Whether the transformer is fitted."""
+        return self._data_median is not None and self._data_scale is not None
+
+    # ------------------------------------------------------------------------------------------------------------------
+    # Learning and transformation
+    # ------------------------------------------------------------------------------------------------------------------
+
+    def fit(self, table: Table) -> RobustScaler:
+        """
+        Learn a transformation for a set of columns in a table.
+
+        **Note:** This transformer is not modified.
+
+        Parameters
+        ----------
+        table:
+            The table used to fit the transformer.
+
+        Returns
+        -------
+        fitted_transformer:
+            The fitted transformer.
+
+        Raises
+        ------
+        ColumnNotFoundError
+            If column_names contain a column name that is missing in the table.
+        ColumnTypeError
+            If at least one of the specified columns in the table contains non-numerical data.
+        ValueError
+            If the table contains 0 rows.
+        """
+        import polars as pl
+
+        if self._column_names is None:
+            column_names = [name for name in table.column_names if table.get_column_type(name).is_numeric]
+        else:
+            column_names = self._column_names
+            _check_columns_exist(table, column_names)
+            _check_columns_are_numeric(table, column_names, operation="fit a RobustScaler")
+
+        if table.row_count == 0:
+            raise ValueError("The RobustScaler cannot be fitted because the table contains 0 rows")
+
+        _data_median = table._lazy_frame.select(column_names).median().collect()
+        q1 = table._lazy_frame.select(column_names).quantile(0.25).collect()
+        q3 = table._lazy_frame.select(column_names).quantile(0.75).collect()
+        _data_scale = q3 - q1
+
+        # To make sure there is no division by zero
+        for col_e in column_names:
+            _data_scale = _data_scale.with_columns(
+                pl.when(pl.col(col_e) == 0).then(1).otherwise(pl.col(col_e)).alias(col_e),
+            )
+
+        # Create a copy with the learned transformation
+        result = RobustScaler(column_names=column_names)
+        result._data_median = _data_median
+        result._data_scale = _data_scale
+
+        return result
+
+    def transform(self, table: Table) -> Table:
+        """
+        Apply the learned transformation to a table.
+
+        **Note:** The given table is not modified.
+
+        Parameters
+        ----------
+        table:
+            The table to which the learned transformation is applied.
+
+        Returns
+        -------
+        transformed_table:
+            The transformed table.
+
+        Raises
+        ------
+        TransformerNotFittedError
+            If the transformer has not been fitted yet.
+        ColumnNotFoundError
+            If the input table does not contain all columns used to fit the transformer.
+        ColumnTypeError
+            If at least one of the columns in the input table that is used to fit contains non-numerical data.
+        """
+        import polars as pl
+
+        # Used in favor of is_fitted, so the type checker is happy
+        if self._column_names is None or self._data_median is None or self._data_scale is None:
+            raise TransformerNotFittedError
+
+        _check_columns_exist(table, self._column_names)
+        _check_columns_are_numeric(table, self._column_names, operation="transform with a RobustScaler")
+
+        columns = [
+            (pl.col(name) - self._data_median.get_column(name)) / self._data_scale.get_column(name)
+            for name in self._column_names
+        ]
+
+        return Table._from_polars_lazy_frame(
+            table._lazy_frame.with_columns(columns),
+        )
+
+    def inverse_transform(self, transformed_table: Table) -> Table:
+        """
+        Undo the learned transformation.
+
+        **Note:** The given table is not modified.
+
+        Parameters
+        ----------
+        transformed_table:
+            The table to be transformed back to the original version.
+
+        Returns
+        -------
+        original_table:
+            The original table.
+
+        Raises
+        ------
+        TransformerNotFittedError
+            If the transformer has not been fitted yet.
+        ColumnNotFoundError
+            If the input table does not contain all columns used to fit the transformer.
+        ColumnTypeError
+            If the transformed columns of the input table contain non-numerical data.
+        """
+        import polars as pl
+
+        # Used in favor of is_fitted, so the type checker is happy
+        if self._column_names is None or self._data_median is None or self._data_scale is None:
+            raise TransformerNotFittedError
+
+        _check_columns_exist(transformed_table, self._column_names)
+        _check_columns_are_numeric(
+            transformed_table,
+            self._column_names,
+            operation="inverse-transform with a RobustScaler",
+        )
+
+        columns = [
+            pl.col(name) * self._data_scale.get_column(name) + self._data_median.get_column(name)
+            for name in self._column_names
+        ]
+
+        return Table._from_polars_lazy_frame(
+            transformed_table._lazy_frame.with_columns(columns),
+        )