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# `sklearn.preprocessing`.FunctionTransformer

class sklearn.preprocessing.FunctionTransformer(func=None, inverse_func=None, *, validate=False, accept_sparse=False, check_inverse=True, feature_names_out=None, kw_args=None, inv_kw_args=None)

  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.

  New in version 0.17.

  Read more in the User Guide.

  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.

      Changed in version 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.

      New in version 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.

      New in version 1.1.

    kw_args dict, default=None

      Dictionary of additional keyword arguments to pass to func.

      New in version 0.18.

    inv_kw_args dict, default=None

      Dictionary of additional keyword arguments to pass to inverse_func.

      New in version 0.18.

  Attributes:
    n_features_in_int

      Number of features seen during fit.

      New in version 0.24.

    feature_names_in_ndarray of shape (`n_features_in_`,)

      Names of features seen during fit. Defined only when `X` has feature names that are all strings.

      New in version 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.

  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...]])

  Methods

  `fit`(X[, y]) | Fit transformer by checking X.
  `fit_transform`(X[, y]) | Fit to data, then transform it.
  `get_feature_names_out`([input_features]) | Get output feature names for transformation.
  `get_metadata_routing`() | Get metadata routing of this object.
  `get_params`([deep]) | Get parameters for this estimator.
  `inverse_transform`(X) | Transform X using the inverse function.
  `set_output`(*[, transform]) | Set output container.
  `set_params`(**params) | Set the parameters of this estimator.
  `transform`(X) | Transform X using the forward function.

  fit(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.

  fit_transform(X, y=None, **fit_params)

    Fit to data, then transform it.

    Fits transformer to `X` and `y` with optional parameters `fit_params` and returns a transformed version of `X`.

    Parameters:
      X array-like of shape (n_samples, n_features)

        Input samples.

      y array-like of shape (n_samples,) or (n_samples, n_outputs), default=None

        Target values (None for unsupervised transformations).

      **fit_params dict

        Additional fit parameters.

    Returns:
      X_new ndarray array of shape (n_samples, n_features_new)

        Transformed array.

  get_feature_names_out(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.

  get_metadata_routing()

    Get metadata routing of this object.

    Please check User Guide on how the routing mechanism works.

    Returns:
      routing MetadataRequest

        A `MetadataRequest` encapsulating routing information.

  get_params(deep=True)

    Get parameters for this estimator.

    Parameters:
      deep bool, default=True

        If True, will return the parameters for this estimator and contained subobjects that are estimators.

    Returns:
      params dict

        Parameter names mapped to their values.

  inverse_transform(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.

  set_output(*, transform=None)

    Set output container.

    See Introducing the set_output API 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

        - `None`: Transform configuration is unchanged

    Returns:
      self estimator instance

        Estimator instance.

  set_params(**params)

    Set the parameters of this estimator.

    The method works on simple estimators as well as on nested objects (such as `Pipeline`). The latter have parameters of the form `<component>__<parameter>` so that it’s possible to update each component of a nested object.

    Parameters:
      **params dict

        Estimator parameters.

    Returns:
      self estimator instance

        Estimator instance.

  transform(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.

## Examples using `sklearn.preprocessing.FunctionTransformer`

Feature transformations with ensembles of trees

Feature transformations with ensembles of trees

Time-related feature engineering

Time-related feature engineering

Poisson regression and non-normal loss

Poisson regression and non-normal loss

Tweedie regression on insurance claims

Tweedie regression on insurance claims

Column Transformer with Heterogeneous Data Sources

Column Transformer with Heterogeneous Data Sources

Semi-supervised Classification on a Text Dataset

Semi-supervised Classification on a Text Dataset