Source code for sklearn.pipeline

"""
The :mod:`sklearn.pipeline` module implements utilities to build a composite
estimator, as a chain of transforms and estimators.
"""
# Author: Edouard Duchesnay
#         Gael Varoquaux
#         Virgile Fritsch
#         Alexandre Gramfort
#         Lars Buitinck
# License: BSD

from collections import defaultdict
from itertools import islice
import warnings

import numpy as np
from scipy import sparse
from joblib import Parallel, delayed

from .base import clone, TransformerMixin
from .utils._estimator_html_repr import _VisualBlock
from .utils.metaestimators import if_delegate_has_method
from .utils import Bunch, _print_elapsed_time
from .utils.validation import check_memory
from .utils.validation import _deprecate_positional_args

from .utils.metaestimators import _BaseComposition

__all__ = ['Pipeline', 'FeatureUnion', 'make_pipeline', 'make_union']


class Pipeline(_BaseComposition):
    """
    Pipeline of transforms with a final estimator.

    Sequentially apply a list of transforms and a final estimator.
    Intermediate steps of the pipeline must be 'transforms', that is, they
    must implement fit and transform methods.
    The final estimator only needs to implement fit.
    The transformers in the pipeline can be cached using ``memory`` argument.

    The purpose of the pipeline is to assemble several steps that can be
    cross-validated together while setting different parameters.
    For this, it enables setting parameters of the various steps using their
    names and the parameter name separated by a '__', as in the example below.
    A step's estimator may be replaced entirely by setting the parameter
    with its name to another estimator, or a transformer removed by setting
    it to 'passthrough' or ``None``.

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

    .. versionadded:: 0.5

    Parameters
    ----------
    steps : list
        List of (name, transform) tuples (implementing fit/transform) that are
        chained, in the order in which they are chained, with the last object
        an estimator.

    memory : str or object with the joblib.Memory interface, default=None
        Used to cache the fitted transformers of the pipeline. By default,
        no caching is performed. If a string is given, it is the path to
        the caching directory. Enabling caching triggers a clone of
        the transformers before fitting. Therefore, the transformer
        instance given to the pipeline cannot be inspected
        directly. Use the attribute ``named_steps`` or ``steps`` to
        inspect estimators within the pipeline. Caching the
        transformers is advantageous when fitting is time consuming.

    verbose : bool, default=False
        If True, the time elapsed while fitting each step will be printed as it
        is completed.

    Attributes
    ----------
    named_steps : :class:`~sklearn.utils.Bunch`
        Dictionary-like object, with the following attributes.
        Read-only attribute to access any step parameter by user given name.
        Keys are step names and values are steps parameters.

    See Also
    --------
    sklearn.pipeline.make_pipeline : Convenience function for simplified
        pipeline construction.

    Examples
    --------
    >>> from sklearn.svm import SVC
    >>> from sklearn.preprocessing import StandardScaler
    >>> from sklearn.datasets import make_classification
    >>> from sklearn.model_selection import train_test_split
    >>> from sklearn.pipeline import Pipeline
    >>> X, y = make_classification(random_state=0)
    >>> X_train, X_test, y_train, y_test = train_test_split(X, y,
    ...                                                     random_state=0)
    >>> pipe = Pipeline([('scaler', StandardScaler()), ('svc', SVC())])
    >>> # The pipeline can be used as any other estimator
    >>> # and avoids leaking the test set into the train set
    >>> pipe.fit(X_train, y_train)
    Pipeline(steps=[('scaler', StandardScaler()), ('svc', SVC())])
    >>> pipe.score(X_test, y_test)
    0.88
    """

    # BaseEstimator interface
    _required_parameters = ['steps']

[docs] @_deprecate_positional_args def __init__(self, steps, *, memory=None, verbose=False): self.steps = steps self.memory = memory self.verbose = verbose self._validate_steps()
[docs] def get_params(self, 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 : mapping of string to any Parameter names mapped to their values. """ return self._get_params('steps', deep=deep)
[docs] def set_params(self, **kwargs): """Set the parameters of this estimator. Valid parameter keys can be listed with ``get_params()``. Returns ------- self """ self._set_params('steps', **kwargs) return self
def _validate_steps(self): names, estimators = zip(*self.steps) # validate names self._validate_names(names) # validate estimators transformers = estimators[:-1] estimator = estimators[-1] for t in transformers: if t is None or t == 'passthrough': continue if (not (hasattr(t, "fit") or hasattr(t, "fit_transform")) or not hasattr(t, "transform")): raise TypeError("All intermediate steps should be " "transformers and implement fit and transform " "or be the string 'passthrough' " "'%s' (type %s) doesn't" % (t, type(t))) # We allow last estimator to be None as an identity transformation if (estimator is not None and estimator != 'passthrough' and not hasattr(estimator, "fit")): raise TypeError( "Last step of Pipeline should implement fit " "or be the string 'passthrough'. " "'%s' (type %s) doesn't" % (estimator, type(estimator))) def _iter(self, with_final=True, filter_passthrough=True): """ Generate (idx, (name, trans)) tuples from self.steps When filter_passthrough is True, 'passthrough' and None transformers are filtered out. """ stop = len(self.steps) if not with_final: stop -= 1 for idx, (name, trans) in enumerate(islice(self.steps, 0, stop)): if not filter_passthrough: yield idx, name, trans elif trans is not None and trans != 'passthrough': yield idx, name, trans def __len__(self): """ Returns the length of the Pipeline """ return len(self.steps) def __getitem__(self, ind): """Returns a sub-pipeline or a single esimtator in the pipeline Indexing with an integer will return an estimator; using a slice returns another Pipeline instance which copies a slice of this Pipeline. This copy is shallow: modifying (or fitting) estimators in the sub-pipeline will affect the larger pipeline and vice-versa. However, replacing a value in `step` will not affect a copy. """ if isinstance(ind, slice): if ind.step not in (1, None): raise ValueError('Pipeline slicing only supports a step of 1') return self.__class__(self.steps[ind]) try: name, est = self.steps[ind] except TypeError: # Not an int, try get step by name return self.named_steps[ind] return est @property def _estimator_type(self): return self.steps[-1][1]._estimator_type @property def named_steps(self): # Use Bunch object to improve autocomplete return Bunch(**dict(self.steps)) @property def _final_estimator(self): estimator = self.steps[-1][1] return 'passthrough' if estimator is None else estimator def _log_message(self, step_idx): if not self.verbose: return None name, step = self.steps[step_idx] return '(step %d of %d) Processing %s' % (step_idx + 1, len(self.steps), name) def _check_fit_params(self, **fit_params): fit_params_steps = {name: {} for name, step in self.steps if step is not None} for pname, pval in fit_params.items(): if '__' not in pname: raise ValueError( "Pipeline.fit does not accept the {} parameter. " "You can pass parameters to specific steps of your " "pipeline using the stepname__parameter format, e.g. " "`Pipeline.fit(X, y, logisticregression__sample_weight" "=sample_weight)`.".format(pname)) step, param = pname.split('__', 1) fit_params_steps[step][param] = pval return fit_params_steps # Estimator interface def _fit(self, X, y=None, **fit_params_steps): # shallow copy of steps - this should really be steps_ self.steps = list(self.steps) self._validate_steps() # Setup the memory memory = check_memory(self.memory) fit_transform_one_cached = memory.cache(_fit_transform_one) for (step_idx, name, transformer) in self._iter(with_final=False, filter_passthrough=False): if (transformer is None or transformer == 'passthrough'): with _print_elapsed_time('Pipeline', self._log_message(step_idx)): continue if hasattr(memory, 'location'): # joblib >= 0.12 if memory.location is None: # we do not clone when caching is disabled to # preserve backward compatibility cloned_transformer = transformer else: cloned_transformer = clone(transformer) elif hasattr(memory, 'cachedir'): # joblib < 0.11 if memory.cachedir is None: # we do not clone when caching is disabled to # preserve backward compatibility cloned_transformer = transformer else: cloned_transformer = clone(transformer) else: cloned_transformer = clone(transformer) # Fit or load from cache the current transformer X, fitted_transformer = fit_transform_one_cached( cloned_transformer, X, y, None, message_clsname='Pipeline', message=self._log_message(step_idx), **fit_params_steps[name]) # Replace the transformer of the step with the fitted # transformer. This is necessary when loading the transformer # from the cache. self.steps[step_idx] = (name, fitted_transformer) return X def fit(self, X, y=None, **fit_params): """Fit the model Fit all the transforms one after the other and transform the data, then fit the transformed data using the final estimator. Parameters ---------- X : iterable Training data. Must fulfill input requirements of first step of the pipeline. y : iterable, default=None Training targets. Must fulfill label requirements for all steps of the pipeline. **fit_params : dict of string -> object Parameters passed to the ``fit`` method of each step, where each parameter name is prefixed such that parameter ``p`` for step ``s`` has key ``s__p``. Returns ------- self : Pipeline This estimator """ fit_params_steps = self._check_fit_params(**fit_params) Xt = self._fit(X, y, **fit_params_steps) with _print_elapsed_time('Pipeline', self._log_message(len(self.steps) - 1)): if self._final_estimator != 'passthrough': fit_params_last_step = fit_params_steps[self.steps[-1][0]] self._final_estimator.fit(Xt, y, **fit_params_last_step) return self def fit_transform(self, X, y=None, **fit_params): """Fit the model and transform with the final estimator Fits all the transforms one after the other and transforms the data, then uses fit_transform on transformed data with the final estimator. Parameters ---------- X : iterable Training data. Must fulfill input requirements of first step of the pipeline. y : iterable, default=None Training targets. Must fulfill label requirements for all steps of the pipeline. **fit_params : dict of string -> object Parameters passed to the ``fit`` method of each step, where each parameter name is prefixed such that parameter ``p`` for step ``s`` has key ``s__p``. Returns ------- Xt : array-like of shape (n_samples, n_transformed_features) Transformed samples """ fit_params_steps = self._check_fit_params(**fit_params) Xt = self._fit(X, y, **fit_params_steps) last_step = self._final_estimator with _print_elapsed_time('Pipeline', self._log_message(len(self.steps) - 1)): if last_step == 'passthrough': return Xt fit_params_last_step = fit_params_steps[self.steps[-1][0]] if hasattr(last_step, 'fit_transform'): return last_step.fit_transform(Xt, y, **fit_params_last_step) else: return last_step.fit(Xt, y, **fit_params_last_step).transform(Xt)
[docs] @if_delegate_has_method(delegate='_final_estimator') def predict(self, X, **predict_params): """Apply transforms to the data, and predict with the final estimator Parameters ---------- X : iterable Data to predict on. Must fulfill input requirements of first step of the pipeline. **predict_params : dict of string -> object Parameters to the ``predict`` called at the end of all transformations in the pipeline. Note that while this may be used to return uncertainties from some models with return_std or return_cov, uncertainties that are generated by the transformations in the pipeline are not propagated to the final estimator. .. versionadded:: 0.20 Returns ------- y_pred : array-like """ Xt = X for _, name, transform in self._iter(with_final=False): Xt = transform.transform(Xt) return self.steps[-1][-1].predict(Xt, **predict_params)
@if_delegate_has_method(delegate='_final_estimator') def fit_predict(self, X, y=None, **fit_params): """Applies fit_predict of last step in pipeline after transforms. Applies fit_transforms of a pipeline to the data, followed by the fit_predict method of the final estimator in the pipeline. Valid only if the final estimator implements fit_predict. Parameters ---------- X : iterable Training data. Must fulfill input requirements of first step of the pipeline. y : iterable, default=None Training targets. Must fulfill label requirements for all steps of the pipeline. **fit_params : dict of string -> object Parameters passed to the ``fit`` method of each step, where each parameter name is prefixed such that parameter ``p`` for step ``s`` has key ``s__p``. Returns ------- y_pred : array-like """ fit_params_steps = self._check_fit_params(**fit_params) Xt = self._fit(X, y, **fit_params_steps) fit_params_last_step = fit_params_steps[self.steps[-1][0]] with _print_elapsed_time('Pipeline', self._log_message(len(self.steps) - 1)): y_pred = self.steps[-1][-1].fit_predict(Xt, y, **fit_params_last_step) return y_pred
[docs] @if_delegate_has_method(delegate='_final_estimator') def predict_proba(self, X): """Apply transforms, and predict_proba of the final estimator Parameters ---------- X : iterable Data to predict on. Must fulfill input requirements of first step of the pipeline. Returns ------- y_proba : array-like of shape (n_samples, n_classes) """ Xt = X for _, name, transform in self._iter(with_final=False): Xt = transform.transform(Xt) return self.steps[-1][-1].predict_proba(Xt)
[docs] @if_delegate_has_method(delegate='_final_estimator') def decision_function(self, X): """Apply transforms, and decision_function of the final estimator Parameters ---------- X : iterable Data to predict on. Must fulfill input requirements of first step of the pipeline. Returns ------- y_score : array-like of shape (n_samples, n_classes) """ Xt = X for _, name, transform in self._iter(with_final=False): Xt = transform.transform(Xt) return self.steps[-1][-1].decision_function(Xt)
[docs] @if_delegate_has_method(delegate='_final_estimator') def score_samples(self, X): """Apply transforms, and score_samples of the final estimator. Parameters ---------- X : iterable Data to predict on. Must fulfill input requirements of first step of the pipeline. Returns ------- y_score : ndarray of shape (n_samples,) """ Xt = X for _, _, transformer in self._iter(with_final=False): Xt = transformer.transform(Xt) return self.steps[-1][-1].score_samples(Xt)
[docs] @if_delegate_has_method(delegate='_final_estimator') def predict_log_proba(self, X): """Apply transforms, and predict_log_proba of the final estimator Parameters ---------- X : iterable Data to predict on. Must fulfill input requirements of first step of the pipeline. Returns ------- y_score : array-like of shape (n_samples, n_classes) """ Xt = X for _, name, transform in self._iter(with_final=False): Xt = transform.transform(Xt) return self.steps[-1][-1].predict_log_proba(Xt)
@property def transform(self): """Apply transforms, and transform with the final estimator This also works where final estimator is ``None``: all prior transformations are applied. Parameters ---------- X : iterable Data to transform. Must fulfill input requirements of first step of the pipeline. Returns ------- Xt : array-like of shape (n_samples, n_transformed_features) """ # _final_estimator is None or has transform, otherwise attribute error # XXX: Handling the None case means we can't use if_delegate_has_method if self._final_estimator != 'passthrough': self._final_estimator.transform return self._transform def _transform(self, X): Xt = X for _, _, transform in self._iter(): Xt = transform.transform(Xt) return Xt @property def inverse_transform(self): """Apply inverse transformations in reverse order All estimators in the pipeline must support ``inverse_transform``. Parameters ---------- Xt : array-like of shape (n_samples, n_transformed_features) Data samples, where ``n_samples`` is the number of samples and ``n_features`` is the number of features. Must fulfill input requirements of last step of pipeline's ``inverse_transform`` method. Returns ------- Xt : array-like of shape (n_samples, n_features) """ # raise AttributeError if necessary for hasattr behaviour # XXX: Handling the None case means we can't use if_delegate_has_method for _, _, transform in self._iter(): transform.inverse_transform return self._inverse_transform def _inverse_transform(self, X): Xt = X reverse_iter = reversed(list(self._iter())) for _, _, transform in reverse_iter: Xt = transform.inverse_transform(Xt) return Xt @if_delegate_has_method(delegate='_final_estimator') def score(self, X, y=None, sample_weight=None): """Apply transforms, and score with the final estimator Parameters ---------- X : iterable Data to predict on. Must fulfill input requirements of first step of the pipeline. y : iterable, default=None Targets used for scoring. Must fulfill label requirements for all steps of the pipeline. sample_weight : array-like, default=None If not None, this argument is passed as ``sample_weight`` keyword argument to the ``score`` method of the final estimator. Returns ------- score : float """ Xt = X for _, name, transform in self._iter(with_final=False): Xt = transform.transform(Xt) score_params = {} if sample_weight is not None: score_params['sample_weight'] = sample_weight return self.steps[-1][-1].score(Xt, y, **score_params) @property def classes_(self): return self.steps[-1][-1].classes_ @property def _pairwise(self): # check if first estimator expects pairwise input return getattr(self.steps[0][1], '_pairwise', False) @property def n_features_in_(self): # delegate to first step (which will call _check_is_fitted) return self.steps[0][1].n_features_in_ def _sk_visual_block_(self): _, estimators = zip(*self.steps) def _get_name(name, est): if est is None or est == 'passthrough': return f'{name}: passthrough' # Is an estimator return f'{name}: {est.__class__.__name__}' names = [_get_name(name, est) for name, est in self.steps] name_details = [str(est) for est in estimators] return _VisualBlock('serial', estimators, names=names, name_details=name_details, dash_wrapped=False) def _name_estimators(estimators): """Generate names for estimators.""" names = [ estimator if isinstance(estimator, str) else type(estimator).__name__.lower() for estimator in estimators ] namecount = defaultdict(int) for est, name in zip(estimators, names): namecount[name] += 1 for k, v in list(namecount.items()): if v == 1: del namecount[k] for i in reversed(range(len(estimators))): name = names[i] if name in namecount: names[i] += "-%d" % namecount[name] namecount[name] -= 1 return list(zip(names, estimators)) def make_pipeline(*steps, **kwargs): """Construct a Pipeline from the given estimators. This is a shorthand for the Pipeline constructor; it does not require, and does not permit, naming the estimators. Instead, their names will be set to the lowercase of their types automatically. Parameters ---------- *steps : list of estimators. memory : str or object with the joblib.Memory interface, default=None Used to cache the fitted transformers of the pipeline. By default, no caching is performed. If a string is given, it is the path to the caching directory. Enabling caching triggers a clone of the transformers before fitting. Therefore, the transformer instance given to the pipeline cannot be inspected directly. Use the attribute ``named_steps`` or ``steps`` to inspect estimators within the pipeline. Caching the transformers is advantageous when fitting is time consuming. verbose : bool, default=False If True, the time elapsed while fitting each step will be printed as it is completed. See Also -------- sklearn.pipeline.Pipeline : Class for creating a pipeline of transforms with a final estimator. Examples -------- >>> from sklearn.naive_bayes import GaussianNB >>> from sklearn.preprocessing import StandardScaler >>> make_pipeline(StandardScaler(), GaussianNB(priors=None)) Pipeline(steps=[('standardscaler', StandardScaler()), ('gaussiannb', GaussianNB())]) Returns ------- p : Pipeline """ memory = kwargs.pop('memory', None) verbose = kwargs.pop('verbose', False) if kwargs: raise TypeError('Unknown keyword arguments: "{}"' .format(list(kwargs.keys())[0])) return Pipeline(_name_estimators(steps), memory=memory, verbose=verbose) def _transform_one(transformer, X, y, weight, **fit_params): res = transformer.transform(X) # if we have a weight for this transformer, multiply output if weight is None: return res return res * weight def _fit_transform_one(transformer, X, y, weight, message_clsname='', message=None, **fit_params): """ Fits ``transformer`` to ``X`` and ``y``. The transformed result is returned with the fitted transformer. If ``weight`` is not ``None``, the result will be multiplied by ``weight``. """ with _print_elapsed_time(message_clsname, message): if hasattr(transformer, 'fit_transform'): res = transformer.fit_transform(X, y, **fit_params) else: res = transformer.fit(X, y, **fit_params).transform(X) if weight is None: return res, transformer return res * weight, transformer def _fit_one(transformer, X, y, weight, message_clsname='', message=None, **fit_params): """ Fits ``transformer`` to ``X`` and ``y``. """ with _print_elapsed_time(message_clsname, message): return transformer.fit(X, y, **fit_params) class FeatureUnion(TransformerMixin, _BaseComposition): """Concatenates results of multiple transformer objects. This estimator applies a list of transformer objects in parallel to the input data, then concatenates the results. This is useful to combine several feature extraction mechanisms into a single transformer. Parameters of the transformers may be set using its name and the parameter name separated by a '__'. A transformer may be replaced entirely by setting the parameter with its name to another transformer, or removed by setting to 'drop'. Read more in the :ref:`User Guide <feature_union>`. .. versionadded:: 0.13 Parameters ---------- transformer_list : list of (string, transformer) tuples List of transformer objects to be applied to the data. The first half of each tuple is the name of the transformer. .. versionchanged:: 0.22 Deprecated `None` as a transformer in favor of 'drop'. n_jobs : int, default=None Number of jobs to run in parallel. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary <n_jobs>` for more details. .. versionchanged:: v0.20 `n_jobs` default changed from 1 to None transformer_weights : dict, default=None Multiplicative weights for features per transformer. Keys are transformer names, values the weights. verbose : bool, default=False If True, the time elapsed while fitting each transformer will be printed as it is completed. See Also -------- sklearn.pipeline.make_union : Convenience function for simplified feature union construction. Examples -------- >>> from sklearn.pipeline import FeatureUnion >>> from sklearn.decomposition import PCA, TruncatedSVD >>> union = FeatureUnion([("pca", PCA(n_components=1)), ... ("svd", TruncatedSVD(n_components=2))]) >>> X = [[0., 1., 3], [2., 2., 5]] >>> union.fit_transform(X) array([[ 1.5 , 3.0..., 0.8...], [-1.5 , 5.7..., -0.4...]]) """ _required_parameters = ["transformer_list"] @_deprecate_positional_args def __init__(self, transformer_list, *, n_jobs=None, transformer_weights=None, verbose=False): self.transformer_list = transformer_list self.n_jobs = n_jobs self.transformer_weights = transformer_weights self.verbose = verbose self._validate_transformers() def get_params(self, 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 : mapping of string to any Parameter names mapped to their values. """ return self._get_params('transformer_list', deep=deep) def set_params(self, **kwargs): """Set the parameters of this estimator. Valid parameter keys can be listed with ``get_params()``. Returns ------- self """ self._set_params('transformer_list', **kwargs) return self def _validate_transformers(self): names, transformers = zip(*self.transformer_list) # validate names self._validate_names(names) # validate estimators for t in transformers: # TODO: Remove in 0.24 when None is removed if t is None: warnings.warn("Using None as a transformer is deprecated " "in version 0.22 and will be removed in " "version 0.24. Please use 'drop' instead.", FutureWarning) continue if t == 'drop': continue if (not (hasattr(t, "fit") or hasattr(t, "fit_transform")) or not hasattr(t, "transform")): raise TypeError("All estimators should implement fit and " "transform. '%s' (type %s) doesn't" % (t, type(t))) def _iter(self): """ Generate (name, trans, weight) tuples excluding None and 'drop' transformers. """ get_weight = (self.transformer_weights or {}).get return ((name, trans, get_weight(name)) for name, trans in self.transformer_list if trans is not None and trans != 'drop') def get_feature_names(self): """Get feature names from all transformers. Returns ------- feature_names : list of strings Names of the features produced by transform. """ feature_names = [] for name, trans, weight in self._iter(): if not hasattr(trans, 'get_feature_names'): raise AttributeError("Transformer %s (type %s) does not " "provide get_feature_names." % (str(name), type(trans).__name__)) feature_names.extend([name + "__" + f for f in trans.get_feature_names()]) return feature_names def fit(self, X, y=None, **fit_params): """Fit all transformers using X. Parameters ---------- X : iterable or array-like, depending on transformers Input data, used to fit transformers. y : array-like of shape (n_samples, n_outputs), default=None Targets for supervised learning. Returns ------- self : FeatureUnion This estimator """ transformers = self._parallel_func(X, y, fit_params, _fit_one) if not transformers: # All transformers are None return self self._update_transformer_list(transformers) return self def fit_transform(self, X, y=None, **fit_params): """Fit all transformers, transform the data and concatenate results. Parameters ---------- X : iterable or array-like, depending on transformers Input data to be transformed. y : array-like of shape (n_samples, n_outputs), default=None Targets for supervised learning. Returns ------- X_t : array-like or sparse matrix of \ shape (n_samples, sum_n_components) hstack of results of transformers. sum_n_components is the sum of n_components (output dimension) over transformers. """ results = self._parallel_func(X, y, fit_params, _fit_transform_one) if not results: # All transformers are None return np.zeros((X.shape[0], 0)) Xs, transformers = zip(*results) self._update_transformer_list(transformers) if any(sparse.issparse(f) for f in Xs): Xs = sparse.hstack(Xs).tocsr() else: Xs = np.hstack(Xs) return Xs def _log_message(self, name, idx, total): if not self.verbose: return None return '(step %d of %d) Processing %s' % (idx, total, name) def _parallel_func(self, X, y, fit_params, func): """Runs func in parallel on X and y""" self.transformer_list = list(self.transformer_list) self._validate_transformers() transformers = list(self._iter()) return Parallel(n_jobs=self.n_jobs)(delayed(func)( transformer, X, y, weight, message_clsname='FeatureUnion', message=self._log_message(name, idx, len(transformers)), **fit_params) for idx, (name, transformer, weight) in enumerate(transformers, 1)) def transform(self, X): """Transform X separately by each transformer, concatenate results. Parameters ---------- X : iterable or array-like, depending on transformers Input data to be transformed. Returns ------- X_t : array-like or sparse matrix of \ shape (n_samples, sum_n_components) hstack of results of transformers. sum_n_components is the sum of n_components (output dimension) over transformers. """ for _, t in self.transformer_list: # TODO: Remove in 0.24 when None is removed if t is None: warnings.warn("Using None as a transformer is deprecated " "in version 0.22 and will be removed in " "version 0.24. Please use 'drop' instead.", FutureWarning) continue Xs = Parallel(n_jobs=self.n_jobs)( delayed(_transform_one)(trans, X, None, weight) for name, trans, weight in self._iter()) if not Xs: # All transformers are None return np.zeros((X.shape[0], 0)) if any(sparse.issparse(f) for f in Xs): Xs = sparse.hstack(Xs).tocsr() else: Xs = np.hstack(Xs) return Xs def _update_transformer_list(self, transformers): transformers = iter(transformers) self.transformer_list[:] = [(name, old if old is None or old == 'drop' else next(transformers)) for name, old in self.transformer_list] @property def n_features_in_(self): # X is passed to all transformers so we just delegate to the first one return self.transformer_list[0][1].n_features_in_ def _sk_visual_block_(self): names, transformers = zip(*self.transformer_list) return _VisualBlock('parallel', transformers, names=names) def make_union(*transformers, **kwargs): """ Construct a FeatureUnion from the given transformers. This is a shorthand for the FeatureUnion constructor; it does not require, and does not permit, naming the transformers. Instead, they will be given names automatically based on their types. It also does not allow weighting. Parameters ---------- *transformers : list of estimators n_jobs : int, default=None Number of jobs to run in parallel. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary <n_jobs>` for more details. .. versionchanged:: v0.20 `n_jobs` default changed from 1 to None verbose : bool, default=False If True, the time elapsed while fitting each transformer will be printed as it is completed. Returns ------- f : FeatureUnion See Also -------- sklearn.pipeline.FeatureUnion : Class for concatenating the results of multiple transformer objects. Examples -------- >>> from sklearn.decomposition import PCA, TruncatedSVD >>> from sklearn.pipeline import make_union >>> make_union(PCA(), TruncatedSVD()) FeatureUnion(transformer_list=[('pca', PCA()), ('truncatedsvd', TruncatedSVD())]) """ n_jobs = kwargs.pop('n_jobs', None) verbose = kwargs.pop('verbose', False) if kwargs: # We do not currently support `transformer_weights` as we may want to # change its type spec in make_union raise TypeError('Unknown keyword arguments: "{}"' .format(list(kwargs.keys())[0])) return FeatureUnion( _name_estimators(transformers), n_jobs=n_jobs, verbose=verbose)