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:

funccallable, 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_funccallable, 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.

validatebool, 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_sparsebool, 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_inversebool, 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_outcallable, ‘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_argsdict, default=None

Dictionary of additional keyword arguments to pass to func.

New in version 0.18.

inv_kw_argsdict, 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.

yIgnored

Not used, present here for API consistency by convention.

Returns:

selfobject

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:

Xarray-like of shape (n_samples, n_features)

Input samples.

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

Target values (None for unsupervised transformations).

**fit_paramsdict

Additional fit parameters.

Returns:

X_newndarray 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_featuresarray-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_outndarray 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:

routingMetadataRequest

A MetadataRequest encapsulating routing information.

get_params(deep=True)

Get parameters for this estimator.

Parameters:

deepbool, default=True

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

Returns:

paramsdict

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_outarray-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:

selfestimator 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:

**paramsdict

Estimator parameters.

Returns:

selfestimator 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_outarray-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