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StandardScaler

Bases: InvertibleTableTransformer

The StandardScaler transforms column values to a range by removing the mean and scaling to unit variance.

Parameters:

Name Type Description Default
column_names str | list[str] | None

The list of columns used to fit the transformer. If None, all numeric columns are used.

 None
Source code in src/safeds/data/tabular/transformation/_standard_scaler.py 
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class StandardScaler(InvertibleTableTransformer):
    """
    The StandardScaler transforms column values to a range by removing the mean and scaling to unit variance.

    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_mean: pl.DataFrame | None = None
        self._data_standard_deviation: 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_mean is not None and self._data_standard_deviation is not None

    # ------------------------------------------------------------------------------------------------------------------
    # Learning and transformation
    # ------------------------------------------------------------------------------------------------------------------

    def fit(self, table: Table) -> StandardScaler:
        """
        Learn a transformation for a set of columns in a table.

        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.
        """
        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 StandardScaler")

        if table.row_count == 0:
            raise ValueError("The StandardScaler cannot be fitted because the table contains 0 rows")

        # Learn the transformation (ddof=0 is used to match the behavior of scikit-learn)
        _data_mean = table._lazy_frame.select(column_names).mean().collect()
        _data_standard_deviation = table._lazy_frame.select(column_names).std(ddof=0).collect()

        # Create a copy with the learned transformation
        result = StandardScaler(column_names=column_names)
        result._data_mean = _data_mean
        result._data_standard_deviation = _data_standard_deviation

        return result

    def transform(self, table: Table) -> Table:
        """
        Apply the learned transformation to a table.

        The 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_mean is None or self._data_standard_deviation is None:
            raise TransformerNotFittedError

        _check_columns_exist(table, self._column_names)
        _check_columns_are_numeric(table, self._column_names, operation="transform with a StandardScaler")

        columns = [
            (pl.col(name) - self._data_mean.get_column(name)) / self._data_standard_deviation.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.

        The 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_mean is None or self._data_standard_deviation 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 StandardScaler",
        )

        columns = [
            pl.col(name) * self._data_standard_deviation.get_column(name) + self._data_mean.get_column(name)
            for name in self._column_names
        ]

        return Table._from_polars_lazy_frame(
            transformed_table._lazy_frame.with_columns(columns),
        )

is_fitted: bool

Whether the transformer is fitted.

fit

Learn a transformation for a set of columns in a table.

This transformer is not modified.

Parameters:

Name Type Description Default
table Table

The table used to fit the transformer.

 required

Returns:

Name Type Description
fitted_transformer StandardScaler

The fitted transformer.

Raises:

Type Description
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.
Source code in src/safeds/data/tabular/transformation/_standard_scaler.py 
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def fit(self, table: Table) -> StandardScaler:
    """
    Learn a transformation for a set of columns in a table.

    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.
    """
    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 StandardScaler")

    if table.row_count == 0:
        raise ValueError("The StandardScaler cannot be fitted because the table contains 0 rows")

    # Learn the transformation (ddof=0 is used to match the behavior of scikit-learn)
    _data_mean = table._lazy_frame.select(column_names).mean().collect()
    _data_standard_deviation = table._lazy_frame.select(column_names).std(ddof=0).collect()

    # Create a copy with the learned transformation
    result = StandardScaler(column_names=column_names)
    result._data_mean = _data_mean
    result._data_standard_deviation = _data_standard_deviation

    return result

fit_and_transform

Learn a transformation for a set of columns in a table and apply the learned transformation to the same table.

Note: Neither this transformer nor the given table are modified.

Parameters:

Name Type Description Default
table Table

The table used to fit the transformer. The transformer is then applied to this table.

 required

Returns:

Name Type Description
fitted_transformer Self

The fitted transformer.
transformed_table Table

The transformed table.
Source code in src/safeds/data/tabular/transformation/_table_transformer.py 
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def fit_and_transform(self, table: Table) -> tuple[Self, Table]:
    """
    Learn a transformation for a set of columns in a table and apply the learned transformation to the same table.

    **Note:** Neither this transformer nor the given table are modified.

    Parameters
    ----------
    table:
        The table used to fit the transformer. The transformer is then applied to this table.

    Returns
    -------
    fitted_transformer:
        The fitted transformer.
    transformed_table:
        The transformed table.
    """
    fitted_transformer = self.fit(table)
    transformed_table = fitted_transformer.transform(table)
    return fitted_transformer, transformed_table

inverse_transform

Undo the learned transformation.

The table is not modified.

Parameters:

Name Type Description Default
transformed_table Table

The table to be transformed back to the original version.

 required

Returns:

Name Type Description
original_table Table

The original table.

Raises:

Type Description
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.
Source code in src/safeds/data/tabular/transformation/_standard_scaler.py 
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def inverse_transform(self, transformed_table: Table) -> Table:
    """
    Undo the learned transformation.

    The 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_mean is None or self._data_standard_deviation 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 StandardScaler",
    )

    columns = [
        pl.col(name) * self._data_standard_deviation.get_column(name) + self._data_mean.get_column(name)
        for name in self._column_names
    ]

    return Table._from_polars_lazy_frame(
        transformed_table._lazy_frame.with_columns(columns),
    )

transform

Apply the learned transformation to a table.

The table is not modified.

Parameters:

Name Type Description Default
table Table

The table to which the learned transformation is applied.

 required

Returns:

Name Type Description
transformed_table Table

The transformed table.

Raises:

Type Description
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.
Source code in src/safeds/data/tabular/transformation/_standard_scaler.py 
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def transform(self, table: Table) -> Table:
    """
    Apply the learned transformation to a table.

    The 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_mean is None or self._data_standard_deviation is None:
        raise TransformerNotFittedError

    _check_columns_exist(table, self._column_names)
    _check_columns_are_numeric(table, self._column_names, operation="transform with a StandardScaler")

    columns = [
        (pl.col(name) - self._data_mean.get_column(name)) / self._data_standard_deviation.get_column(name)
        for name in self._column_names
    ]

    return Table._from_polars_lazy_frame(
        table._lazy_frame.with_columns(columns),
    )