_function_transformer.py

import warnings

import numpy as np

from ..base import BaseEstimator, TransformerMixin, _fit_context
from ..utils._param_validation import StrOptions
from ..utils._set_output import ADAPTERS_MANAGER, _get_output_config
from ..utils.metaestimators import available_if
from ..utils.validation import (
    _allclose_dense_sparse,
    _check_feature_names_in,
    _get_feature_names,
    _is_pandas_df,
    _is_polars_df,
    check_array,
)


def _get_adapter_from_container(container):
    """Get the adapter that nows how to handle such container.

    See :class:`sklearn.utils._set_output.ContainerAdapterProtocol` for more
    details.
    """
    module_name = container.__class__.__module__.split(".")[0]
    try:
        return ADAPTERS_MANAGER.adapters[module_name]
    except KeyError as exc:
        available_adapters = list(ADAPTERS_MANAGER.adapters.keys())
        raise ValueError(
            "The container does not have a registered adapter in scikit-learn. "
            f"Available adapters are: {available_adapters} while the container "
            f"provided is: {container!r}."
        ) from exc


def _identity(X):
    """The identity function."""
    return X


class FunctionTransformer(TransformerMixin, BaseEstimator):
    """Constructs a transformer from an arbitrary callable.

    A FunctionTransformer forwards its X (and optionally y) arguments to a
    user-defined function or function object and returns the result of this
    function. This is useful for stateless transformations such as taking the
    log of frequencies, doing custom scaling, etc.

    Note: If a lambda is used as the function, then the resulting
    transformer will not be pickleable.

    .. versionadded:: 0.17

    Read more in the :ref:`User Guide <function_transformer>`.

    Parameters
    ----------
    func : callable, default=None
        The callable to use for the transformation. This will be passed
        the same arguments as transform, with args and kwargs forwarded.
        If func is None, then func will be the identity function.

    inverse_func : callable, default=None
        The callable to use for the inverse transformation. This will be
        passed the same arguments as inverse transform, with args and
        kwargs forwarded. If inverse_func is None, then inverse_func
        will be the identity function.

    validate : bool, default=False
        Indicate that the input X array should be checked before calling
        ``func``. The possibilities are:

        - If False, there is no input validation.
        - If True, then X will be converted to a 2-dimensional NumPy array or
          sparse matrix. If the conversion is not possible an exception is
          raised.

        .. versionchanged:: 0.22
           The default of ``validate`` changed from True to False.

    accept_sparse : bool, default=False
        Indicate that func accepts a sparse matrix as input. If validate is
        False, this has no effect. Otherwise, if accept_sparse is false,
        sparse matrix inputs will cause an exception to be raised.

    check_inverse : bool, default=True
       Whether to check that or ``func`` followed by ``inverse_func`` leads to
       the original inputs. It can be used for a sanity check, raising a
       warning when the condition is not fulfilled.

       .. versionadded:: 0.20

    feature_names_out : callable, 'one-to-one' or None, default=None
        Determines the list of feature names that will be returned by the
        `get_feature_names_out` method. If it is 'one-to-one', then the output
        feature names will be equal to the input feature names. If it is a
        callable, then it must take two positional arguments: this
        `FunctionTransformer` (`self`) and an array-like of input feature names
        (`input_features`). It must return an array-like of output feature
        names. The `get_feature_names_out` method is only defined if
        `feature_names_out` is not None.

        See ``get_feature_names_out`` for more details.

        .. versionadded:: 1.1

    kw_args : dict, default=None
        Dictionary of additional keyword arguments to pass to func.

        .. versionadded:: 0.18

    inv_kw_args : dict, default=None
        Dictionary of additional keyword arguments to pass to inverse_func.

        .. versionadded:: 0.18

    Attributes
    ----------
    n_features_in_ : int
        Number of features seen during :term:`fit`.

        .. versionadded:: 0.24

    feature_names_in_ : ndarray of shape (`n_features_in_`,)
        Names of features seen during :term:`fit`. Defined only when `X` has feature
        names that are all strings.

        .. versionadded:: 1.0

    See Also
    --------
    MaxAbsScaler : Scale each feature by its maximum absolute value.
    StandardScaler : Standardize features by removing the mean and
        scaling to unit variance.
    LabelBinarizer : Binarize labels in a one-vs-all fashion.
    MultiLabelBinarizer : Transform between iterable of iterables
        and a multilabel format.

    Notes
    -----
    If `func` returns an output with a `columns` attribute, then the columns is enforced
    to be consistent with the output of `get_feature_names_out`.

    Examples
    --------
    >>> import numpy as np
    >>> from sklearn.preprocessing import FunctionTransformer
    >>> transformer = FunctionTransformer(np.log1p)
    >>> X = np.array([[0, 1], [2, 3]])
    >>> transformer.transform(X)
    array([[0.       , 0.6931...],
           [1.0986..., 1.3862...]])
    """

    _parameter_constraints: dict = {
        "func": [callable, None],
        "inverse_func": [callable, None],
        "validate": ["boolean"],
        "accept_sparse": ["boolean"],
        "check_inverse": ["boolean"],
        "feature_names_out": [callable, StrOptions({"one-to-one"}), None],
        "kw_args": [dict, None],
        "inv_kw_args": [dict, None],
    }

    def __init__(
        self,
        func=None,
        inverse_func=None,
        *,
        validate=False,
        accept_sparse=False,
        check_inverse=True,
        feature_names_out=None,
        kw_args=None,
        inv_kw_args=None,
    ):
        self.func = func
        self.inverse_func = inverse_func
        self.validate = validate
        self.accept_sparse = accept_sparse
        self.check_inverse = check_inverse
        self.feature_names_out = feature_names_out
        self.kw_args = kw_args
        self.inv_kw_args = inv_kw_args

    def _check_input(self, X, *, reset):
        if self.validate:
            return self._validate_data(X, accept_sparse=self.accept_sparse, reset=reset)
        elif reset:
            # Set feature_names_in_ and n_features_in_ even if validate=False
            # We run this only when reset==True to store the attributes but not
            # validate them, because validate=False
            self._check_n_features(X, reset=reset)
            self._check_feature_names(X, reset=reset)
        return X

    def _check_inverse_transform(self, X):
        """Check that func and inverse_func are the inverse."""
        idx_selected = slice(None, None, max(1, X.shape[0] // 100))
        X_round_trip = self.inverse_transform(self.transform(X[idx_selected]))

        if hasattr(X, "dtype"):
            dtypes = [X.dtype]
        elif hasattr(X, "dtypes"):
            # Dataframes can have multiple dtypes
            dtypes = X.dtypes

        if not all(np.issubdtype(d, np.number) for d in dtypes):
            raise ValueError(
                "'check_inverse' is only supported when all the elements in `X` is"
                " numerical."
            )

        if not _allclose_dense_sparse(X[idx_selected], X_round_trip):
            warnings.warn(
                (
                    "The provided functions are not strictly"
                    " inverse of each other. If you are sure you"
                    " want to proceed regardless, set"
                    " 'check_inverse=False'."
                ),
                UserWarning,
            )

    @_fit_context(prefer_skip_nested_validation=True)
    def fit(self, X, y=None):
        """Fit transformer by checking X.

        If ``validate`` is ``True``, ``X`` will be checked.

        Parameters
        ----------
        X : {array-like, sparse-matrix} of shape (n_samples, n_features) \
                if `validate=True` else any object that `func` can handle
            Input array.

        y : Ignored
            Not used, present here for API consistency by convention.

        Returns
        -------
        self : object
            FunctionTransformer class instance.
        """
        X = self._check_input(X, reset=True)
        if self.check_inverse and not (self.func is None or self.inverse_func is None):
            self._check_inverse_transform(X)
        return self

    def transform(self, X):
        """Transform X using the forward function.

        Parameters
        ----------
        X : {array-like, sparse-matrix} of shape (n_samples, n_features) \
                if `validate=True` else any object that `func` can handle
            Input array.

        Returns
        -------
        X_out : array-like, shape (n_samples, n_features)
            Transformed input.
        """
        X = self._check_input(X, reset=False)
        out = self._transform(X, func=self.func, kw_args=self.kw_args)
        output_config = _get_output_config("transform", self)["dense"]

        if hasattr(out, "columns") and self.feature_names_out is not None:
            # check the consistency between the column provided by `transform` and
            # the the column names provided by `get_feature_names_out`.
            feature_names_out = self.get_feature_names_out()
            if list(out.columns) != list(feature_names_out):
                # we can override the column names of the output if it is inconsistent
                # with the column names provided by `get_feature_names_out` in the
                # following cases:
                # * `func` preserved the column names between the input and the output
                # * the input column names are all numbers
                # * the output is requested to be a DataFrame (pandas or polars)
                feature_names_in = getattr(
                    X, "feature_names_in_", _get_feature_names(X)
                )
                same_feature_names_in_out = feature_names_in is not None and list(
                    feature_names_in
                ) == list(out.columns)
                not_all_str_columns = not all(
                    isinstance(col, str) for col in out.columns
                )
                if same_feature_names_in_out or not_all_str_columns:
                    adapter = _get_adapter_from_container(out)
                    out = adapter.create_container(
                        X_output=out,
                        X_original=out,
                        columns=feature_names_out,
                        inplace=False,
                    )
                else:
                    raise ValueError(
                        "The output generated by `func` have different column names "
                        "than the ones provided by `get_feature_names_out`. "
                        f"Got output with columns names: {list(out.columns)} and "
                        "`get_feature_names_out` returned: "
                        f"{list(self.get_feature_names_out())}. "
                        "The column names can be overridden by setting "
                        "`set_output(transform='pandas')` or "
                        "`set_output(transform='polars')` such that the column names "
                        "are set to the names provided by `get_feature_names_out`."
                    )

        if self.feature_names_out is None:
            warn_msg = (
                "When `set_output` is configured to be '{0}', `func` should return "
                "a {0} DataFrame to follow the `set_output` API  or `feature_names_out`"
                " should be defined."
            )
            if output_config == "pandas" and not _is_pandas_df(out):
                warnings.warn(warn_msg.format("pandas"))
            elif output_config == "polars" and not _is_polars_df(out):
                warnings.warn(warn_msg.format("polars"))

        return out

    def inverse_transform(self, X):
        """Transform X using the inverse function.

        Parameters
        ----------
        X : {array-like, sparse-matrix} of shape (n_samples, n_features) \
                if `validate=True` else any object that `inverse_func` can handle
            Input array.

        Returns
        -------
        X_out : array-like, shape (n_samples, n_features)
            Transformed input.
        """
        if self.validate:
            X = check_array(X, accept_sparse=self.accept_sparse)
        return self._transform(X, func=self.inverse_func, kw_args=self.inv_kw_args)

    @available_if(lambda self: self.feature_names_out is not None)
    def get_feature_names_out(self, input_features=None):
        """Get output feature names for transformation.

        This method is only defined if `feature_names_out` is not None.

        Parameters
        ----------
        input_features : array-like of str or None, default=None
            Input feature names.

            - If `input_features` is None, then `feature_names_in_` is
              used as the input feature names. If `feature_names_in_` is not
              defined, then names are generated:
              `[x0, x1, ..., x(n_features_in_ - 1)]`.
            - If `input_features` is array-like, then `input_features` must
              match `feature_names_in_` if `feature_names_in_` is defined.

        Returns
        -------
        feature_names_out : ndarray of str objects
            Transformed feature names.

            - If `feature_names_out` is 'one-to-one', the input feature names
              are returned (see `input_features` above). This requires
              `feature_names_in_` and/or `n_features_in_` to be defined, which
              is done automatically if `validate=True`. Alternatively, you can
              set them in `func`.
            - If `feature_names_out` is a callable, then it is called with two
              arguments, `self` and `input_features`, and its return value is
              returned by this method.
        """
        if hasattr(self, "n_features_in_") or input_features is not None:
            input_features = _check_feature_names_in(self, input_features)
        if self.feature_names_out == "one-to-one":
            names_out = input_features
        elif callable(self.feature_names_out):
            names_out = self.feature_names_out(self, input_features)
        else:
            raise ValueError(
                f"feature_names_out={self.feature_names_out!r} is invalid. "
                'It must either be "one-to-one" or a callable with two '
                "arguments: the function transformer and an array-like of "
                "input feature names. The callable must return an array-like "
                "of output feature names."
            )
        return np.asarray(names_out, dtype=object)

    def _transform(self, X, func=None, kw_args=None):
        if func is None:
            func = _identity

        return func(X, **(kw_args if kw_args else {}))

    def __sklearn_is_fitted__(self):
        """Return True since FunctionTransfomer is stateless."""
        return True

    def _more_tags(self):
        return {"no_validation": not self.validate, "stateless": True}

    def set_output(self, *, transform=None):
        """Set output container.

        See :ref:`sphx_glr_auto_examples_miscellaneous_plot_set_output.py`
        for an example on how to use the API.

        Parameters
        ----------
        transform : {"default", "pandas"}, default=None
            Configure output of `transform` and `fit_transform`.

            - `"default"`: Default output format of a transformer
            - `"pandas"`: DataFrame output
            - `"polars"`: Polars output
            - `None`: Transform configuration is unchanged

            .. versionadded:: 1.4
                `"polars"` option was added.

        Returns
        -------
        self : estimator instance
            Estimator instance.
        """
        if not hasattr(self, "_sklearn_output_config"):
            self._sklearn_output_config = {}

        self._sklearn_output_config["transform"] = transform
        return self