SklearnScaler¶

class SklearnScaler(SklearnTransformer):
    """Wrapper to integrate sklearn scalers into the Yohou pipeline.

    Preserves the polars DataFrame structure and "time" column while applying
    sklearn scaling transformations to all non-time columns.

    This class can be used to:

    1. Wrap any sklearn-compatible scaler for use in yohou pipelines
    2. Serve as a base class for creating yohou scaler extensions

    Parameters
    ----------
    scaler : type, default=None
        The sklearn scaler class to wrap. Must be a subclass of
        ``sklearn.base.TransformerMixin``. If not provided,
        ``_estimator_default_class`` is used (subclasses define this).

    **params : dict
        Parameters passed to the underlying sklearn scaler constructor.
        See the documentation of the specific scaler for available parameters.

    Attributes
    ----------
    instance_ : TransformerMixin
        The fitted sklearn scaler instance (created by ``BaseClassWrapper``).

    Examples
    --------
    >>> import polars as pl
    >>> from datetime import datetime
    >>> from sklearn.preprocessing import StandardScaler as SklearnStandardScaler
    >>> from yohou.preprocessing import SklearnScaler
    >>> X = pl.DataFrame({
    ...     "time": [datetime(2024, 1, i) for i in range(1, 6)],
    ...     "value": [10.0, 20.0, 30.0, 40.0, 50.0],
    ... })
    >>> scaler = SklearnScaler(scaler=SklearnStandardScaler, with_mean=True)
    >>> scaler.fit(X)  # doctest: +ELLIPSIS
    SklearnScaler(...)
    >>> X_scaled = scaler.transform(X)
    >>> "time" in X_scaled.columns
    True

    See Also
    --------
    - [`StandardScaler`][yohou.preprocessing.sklearn_wrappers.StandardScaler] : Pre-configured wrapper for sklearn's StandardScaler.
    - [`MinMaxScaler`][yohou.preprocessing.sklearn_wrappers.MinMaxScaler] : Pre-configured wrapper for sklearn's MinMaxScaler.
    - [`RobustScaler`][yohou.preprocessing.sklearn_wrappers.RobustScaler] : Pre-configured wrapper for sklearn's RobustScaler.
    - [`MaxAbsScaler`][yohou.preprocessing.sklearn_wrappers.MaxAbsScaler] : Pre-configured wrapper for sklearn's MaxAbsScaler.

    """

    _estimator_name = "scaler"
    _estimator_base_class = TransformerMixin
    _estimator_default_class: type | None = None

    _parameter_constraints: dict = {
        "scaler": [HasMethods(["fit", "transform"]), None],
    }

    _tags = {"stateful": False}

    def __init__(self, scaler=None, **params):
        if scaler is not None:
            super().__init__(scaler=scaler, **params)
        else:
            super().__init__(**params)