349 lines
12 KiB
Python
349 lines
12 KiB
Python
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import warnings
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import numpy as np
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from ..base import BaseEstimator, TransformerMixin
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from ..utils.metaestimators import available_if
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from ..utils.validation import (
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_allclose_dense_sparse,
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_check_feature_names_in,
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check_array,
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)
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from ..utils._param_validation import StrOptions
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def _identity(X):
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"""The identity function."""
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return X
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class FunctionTransformer(TransformerMixin, BaseEstimator):
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"""Constructs a transformer from an arbitrary callable.
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A FunctionTransformer forwards its X (and optionally y) arguments to a
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user-defined function or function object and returns the result of this
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function. This is useful for stateless transformations such as taking the
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log of frequencies, doing custom scaling, etc.
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Note: If a lambda is used as the function, then the resulting
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transformer will not be pickleable.
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.. versionadded:: 0.17
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Read more in the :ref:`User Guide <function_transformer>`.
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Parameters
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----------
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func : callable, default=None
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The callable to use for the transformation. This will be passed
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the same arguments as transform, with args and kwargs forwarded.
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If func is None, then func will be the identity function.
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inverse_func : callable, default=None
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The callable to use for the inverse transformation. This will be
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passed the same arguments as inverse transform, with args and
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kwargs forwarded. If inverse_func is None, then inverse_func
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will be the identity function.
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validate : bool, default=False
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Indicate that the input X array should be checked before calling
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``func``. The possibilities are:
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- If False, there is no input validation.
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- If True, then X will be converted to a 2-dimensional NumPy array or
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sparse matrix. If the conversion is not possible an exception is
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raised.
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.. versionchanged:: 0.22
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The default of ``validate`` changed from True to False.
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accept_sparse : bool, default=False
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Indicate that func accepts a sparse matrix as input. If validate is
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False, this has no effect. Otherwise, if accept_sparse is false,
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sparse matrix inputs will cause an exception to be raised.
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check_inverse : bool, default=True
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Whether to check that or ``func`` followed by ``inverse_func`` leads to
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the original inputs. It can be used for a sanity check, raising a
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warning when the condition is not fulfilled.
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.. versionadded:: 0.20
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feature_names_out : callable, 'one-to-one' or None, default=None
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Determines the list of feature names that will be returned by the
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`get_feature_names_out` method. If it is 'one-to-one', then the output
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feature names will be equal to the input feature names. If it is a
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callable, then it must take two positional arguments: this
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`FunctionTransformer` (`self`) and an array-like of input feature names
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(`input_features`). It must return an array-like of output feature
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names. The `get_feature_names_out` method is only defined if
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`feature_names_out` is not None.
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See ``get_feature_names_out`` for more details.
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.. versionadded:: 1.1
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kw_args : dict, default=None
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Dictionary of additional keyword arguments to pass to func.
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.. versionadded:: 0.18
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inv_kw_args : dict, default=None
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Dictionary of additional keyword arguments to pass to inverse_func.
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.. versionadded:: 0.18
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Attributes
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----------
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n_features_in_ : int
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Number of features seen during :term:`fit`.
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.. versionadded:: 0.24
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feature_names_in_ : ndarray of shape (`n_features_in_`,)
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Names of features seen during :term:`fit`. Defined only when `X` has feature
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names that are all strings.
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.. versionadded:: 1.0
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See Also
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--------
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MaxAbsScaler : Scale each feature by its maximum absolute value.
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StandardScaler : Standardize features by removing the mean and
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scaling to unit variance.
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LabelBinarizer : Binarize labels in a one-vs-all fashion.
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MultiLabelBinarizer : Transform between iterable of iterables
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and a multilabel format.
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Examples
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--------
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>>> import numpy as np
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>>> from sklearn.preprocessing import FunctionTransformer
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>>> transformer = FunctionTransformer(np.log1p)
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>>> X = np.array([[0, 1], [2, 3]])
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>>> transformer.transform(X)
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array([[0. , 0.6931...],
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[1.0986..., 1.3862...]])
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"""
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_parameter_constraints: dict = {
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"func": [callable, None],
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"inverse_func": [callable, None],
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"validate": ["boolean"],
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"accept_sparse": ["boolean"],
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"check_inverse": ["boolean"],
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"feature_names_out": [callable, StrOptions({"one-to-one"}), None],
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"kw_args": [dict, None],
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"inv_kw_args": [dict, None],
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}
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def __init__(
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self,
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func=None,
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inverse_func=None,
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*,
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validate=False,
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accept_sparse=False,
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check_inverse=True,
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feature_names_out=None,
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kw_args=None,
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inv_kw_args=None,
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):
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self.func = func
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self.inverse_func = inverse_func
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self.validate = validate
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self.accept_sparse = accept_sparse
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self.check_inverse = check_inverse
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self.feature_names_out = feature_names_out
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self.kw_args = kw_args
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self.inv_kw_args = inv_kw_args
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def _check_input(self, X, *, reset):
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if self.validate:
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return self._validate_data(X, accept_sparse=self.accept_sparse, reset=reset)
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elif reset:
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# Set feature_names_in_ and n_features_in_ even if validate=False
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# We run this only when reset==True to store the attributes but not
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# validate them, because validate=False
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self._check_n_features(X, reset=reset)
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self._check_feature_names(X, reset=reset)
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return X
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def _check_inverse_transform(self, X):
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"""Check that func and inverse_func are the inverse."""
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idx_selected = slice(None, None, max(1, X.shape[0] // 100))
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X_round_trip = self.inverse_transform(self.transform(X[idx_selected]))
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if hasattr(X, "dtype"):
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dtypes = [X.dtype]
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elif hasattr(X, "dtypes"):
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# Dataframes can have multiple dtypes
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dtypes = X.dtypes
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if not all(np.issubdtype(d, np.number) for d in dtypes):
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raise ValueError(
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"'check_inverse' is only supported when all the elements in `X` is"
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" numerical."
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)
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if not _allclose_dense_sparse(X[idx_selected], X_round_trip):
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warnings.warn(
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"The provided functions are not strictly"
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" inverse of each other. If you are sure you"
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" want to proceed regardless, set"
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" 'check_inverse=False'.",
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UserWarning,
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)
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def fit(self, X, y=None):
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"""Fit transformer by checking X.
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If ``validate`` is ``True``, ``X`` will be checked.
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Parameters
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----------
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X : {array-like, sparse-matrix} of shape (n_samples, n_features) \
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if `validate=True` else any object that `func` can handle
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Input array.
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y : Ignored
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Not used, present here for API consistency by convention.
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Returns
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-------
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self : object
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FunctionTransformer class instance.
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"""
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self._validate_params()
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X = self._check_input(X, reset=True)
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if self.check_inverse and not (self.func is None or self.inverse_func is None):
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self._check_inverse_transform(X)
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return self
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def transform(self, X):
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"""Transform X using the forward function.
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Parameters
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----------
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X : {array-like, sparse-matrix} of shape (n_samples, n_features) \
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if `validate=True` else any object that `func` can handle
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Input array.
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Returns
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-------
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X_out : array-like, shape (n_samples, n_features)
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Transformed input.
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"""
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X = self._check_input(X, reset=False)
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return self._transform(X, func=self.func, kw_args=self.kw_args)
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def inverse_transform(self, X):
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"""Transform X using the inverse function.
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Parameters
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----------
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X : {array-like, sparse-matrix} of shape (n_samples, n_features) \
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if `validate=True` else any object that `inverse_func` can handle
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Input array.
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Returns
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-------
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X_out : array-like, shape (n_samples, n_features)
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Transformed input.
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"""
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if self.validate:
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X = check_array(X, accept_sparse=self.accept_sparse)
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return self._transform(X, func=self.inverse_func, kw_args=self.inv_kw_args)
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@available_if(lambda self: self.feature_names_out is not None)
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def get_feature_names_out(self, input_features=None):
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"""Get output feature names for transformation.
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This method is only defined if `feature_names_out` is not None.
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Parameters
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----------
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input_features : array-like of str or None, default=None
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Input feature names.
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- If `input_features` is None, then `feature_names_in_` is
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used as the input feature names. If `feature_names_in_` is not
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defined, then names are generated:
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`[x0, x1, ..., x(n_features_in_ - 1)]`.
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- If `input_features` is array-like, then `input_features` must
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match `feature_names_in_` if `feature_names_in_` is defined.
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Returns
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-------
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feature_names_out : ndarray of str objects
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Transformed feature names.
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- If `feature_names_out` is 'one-to-one', the input feature names
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are returned (see `input_features` above). This requires
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`feature_names_in_` and/or `n_features_in_` to be defined, which
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is done automatically if `validate=True`. Alternatively, you can
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set them in `func`.
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- If `feature_names_out` is a callable, then it is called with two
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arguments, `self` and `input_features`, and its return value is
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returned by this method.
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"""
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if hasattr(self, "n_features_in_") or input_features is not None:
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input_features = _check_feature_names_in(self, input_features)
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if self.feature_names_out == "one-to-one":
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names_out = input_features
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elif callable(self.feature_names_out):
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names_out = self.feature_names_out(self, input_features)
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else:
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raise ValueError(
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f"feature_names_out={self.feature_names_out!r} is invalid. "
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'It must either be "one-to-one" or a callable with two '
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"arguments: the function transformer and an array-like of "
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"input feature names. The callable must return an array-like "
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"of output feature names."
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)
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return np.asarray(names_out, dtype=object)
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def _transform(self, X, func=None, kw_args=None):
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if func is None:
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func = _identity
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return func(X, **(kw_args if kw_args else {}))
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def __sklearn_is_fitted__(self):
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"""Return True since FunctionTransfomer is stateless."""
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return True
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def _more_tags(self):
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return {"no_validation": not self.validate, "stateless": True}
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def set_output(self, *, transform=None):
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"""Set output container.
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See :ref:`sphx_glr_auto_examples_miscellaneous_plot_set_output.py`
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for an example on how to use the API.
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Parameters
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----------
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transform : {"default", "pandas"}, default=None
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Configure output of `transform` and `fit_transform`.
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- `"default"`: Default output format of a transformer
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- `"pandas"`: DataFrame output
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- `None`: Transform configuration is unchanged
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Returns
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-------
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self : estimator instance
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Estimator instance.
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"""
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if hasattr(super(), "set_output"):
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return super().set_output(transform=transform)
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if transform == "pandas" and self.feature_names_out is None:
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warnings.warn(
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'With transform="pandas", `func` should return a DataFrame to follow'
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" the set_output API."
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)
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return self
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