import warnings import numpy as np from ..base import BaseEstimator, TransformerMixin, _fit_context from ..utils._param_validation import StrOptions from ..utils.metaestimators import available_if from ..utils.validation import ( _allclose_dense_sparse, _check_feature_names_in, check_array, ) 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 `. 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. 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) return self._transform(X, func=self.func, kw_args=self.kw_args) 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 - `None`: Transform configuration is unchanged Returns ------- self : estimator instance Estimator instance. """ if hasattr(super(), "set_output"): return super().set_output(transform=transform) if transform == "pandas" and self.feature_names_out is None: warnings.warn( 'With transform="pandas", `func` should return a DataFrame to follow' " the set_output API." ) return self