"""
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

import numpy as np
from scipy import sparse

from .base import clone, TransformerMixin
from .utils import Parallel, delayed
from .externals import six
from .utils.metaestimators import if_delegate_has_method
from .utils import Bunch
from .utils.validation import check_memory

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
    to None.

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

    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 : None, str or object with the joblib.Memory interface, optional
        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.

    Attributes
    ----------
    named_steps : bunch object, a dictionary with attribute access
        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 import svm
    >>> from sklearn.datasets import samples_generator
    >>> from sklearn.feature_selection import SelectKBest
    >>> from sklearn.feature_selection import f_regression
    >>> from sklearn.pipeline import Pipeline
    >>> # generate some data to play with
    >>> X, y = samples_generator.make_classification(
    ...     n_informative=5, n_redundant=0, random_state=42)
    >>> # ANOVA SVM-C
    >>> anova_filter = SelectKBest(f_regression, k=5)
    >>> clf = svm.SVC(kernel='linear')
    >>> anova_svm = Pipeline([('anova', anova_filter), ('svc', clf)])
    >>> # You can set the parameters using the names issued
    >>> # For instance, fit using a k of 10 in the SelectKBest
    >>> # and a parameter 'C' of the svm
    >>> anova_svm.set_params(anova__k=10, svc__C=.1).fit(X, y)
    ...                      # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
    Pipeline(memory=None,
             steps=[('anova', SelectKBest(...)),
                    ('svc', SVC(...))])
    >>> prediction = anova_svm.predict(X)
    >>> anova_svm.score(X, y)                        # doctest: +ELLIPSIS
    0.83
    >>> # getting the selected features chosen by anova_filter
    >>> anova_svm.named_steps['anova'].get_support()
    ... # doctest: +NORMALIZE_WHITESPACE
    array([False, False,  True,  True, False, False, True,  True, False,
           True,  False,  True,  True, False, True,  False, True, True,
           False, False])
    >>> # Another way to get selected features chosen by anova_filter
    >>> anova_svm.named_steps.anova.get_support()
    ... # doctest: +NORMALIZE_WHITESPACE
    array([False, False,  True,  True, False, False, True,  True, False,
           True,  False,  True,  True, False, True,  False, True, True,
           False, False])
    """

    # BaseEstimator interface

    def __init__(self, steps, memory=None):
        self.steps = steps
        self._validate_steps()
        self.memory = memory

    def get_params(self, deep=True):
        """Get parameters for this estimator.

        Parameters
        ----------
        deep : boolean, optional
            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)

    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:
                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."
                                " '%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 not hasattr(estimator, "fit"):
            raise TypeError("Last step of Pipeline should implement fit. "
                            "'%s' (type %s) doesn't"
                            % (estimator, type(estimator)))

    @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):
        return self.steps[-1][1]

    # Estimator interface

    def _fit(self, X, y=None, **fit_params):
        # 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)

        fit_params_steps = dict((name, {}) for name, step in self.steps
                                if step is not None)
        for pname, pval in six.iteritems(fit_params):
            step, param = pname.split('__', 1)
            fit_params_steps[step][param] = pval
        Xt = X
        for step_idx, (name, transformer) in enumerate(self.steps[:-1]):
            if transformer is None:
                pass
            else:
                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 transfomer
                Xt, fitted_transformer = fit_transform_one_cached(
                    cloned_transformer, Xt, y, None,
                    **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)
        if self._final_estimator is None:
            return Xt, {}
        return Xt, fit_params_steps[self.steps[-1][0]]

    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
        """
        Xt, fit_params = self._fit(X, y, **fit_params)
        if self._final_estimator is not None:
            self._final_estimator.fit(Xt, y, **fit_params)
        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, shape = [n_samples, n_transformed_features]
            Transformed samples
        """
        last_step = self._final_estimator
        Xt, fit_params = self._fit(X, y, **fit_params)
        if hasattr(last_step, 'fit_transform'):
            return last_step.fit_transform(Xt, y, **fit_params)
        elif last_step is None:
            return Xt
        else:
            return last_step.fit(Xt, y, **fit_params).transform(Xt)

    @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.

        Returns
        -------
        y_pred : array-like
        """
        Xt = X
        for name, transform in self.steps[:-1]:
            if transform is not None:
                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
        """
        Xt, fit_params = self._fit(X, y, **fit_params)
        return self.steps[-1][-1].fit_predict(Xt, y, **fit_params)

    @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, shape = [n_samples, n_classes]
        """
        Xt = X
        for name, transform in self.steps[:-1]:
            if transform is not None:
                Xt = transform.transform(Xt)
        return self.steps[-1][-1].predict_proba(Xt)

    @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, shape = [n_samples, n_classes]
        """
        Xt = X
        for name, transform in self.steps[:-1]:
            if transform is not None:
                Xt = transform.transform(Xt)
        return self.steps[-1][-1].decision_function(Xt)

    @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, shape = [n_samples, n_classes]
        """
        Xt = X
        for name, transform in self.steps[:-1]:
            if transform is not None:
                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, 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 is not None:
            self._final_estimator.transform
        return self._transform

    def _transform(self, X):
        Xt = X
        for name, transform in self.steps:
            if transform is not None:
                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, 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, 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 name, transform in self.steps:
            if transform is not None:
                transform.inverse_transform
        return self._inverse_transform

    def _inverse_transform(self, X):
        Xt = X
        for name, transform in self.steps[::-1]:
            if transform is not None:
                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.steps[:-1]:
            if transform is not None:
                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)


def _name_estimators(estimators):
    """Generate names for estimators."""

    names = [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(six.iteritems(namecount)):
        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 : None, str or object with the joblib.Memory interface, optional
        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.

    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))
    ...     # doctest: +NORMALIZE_WHITESPACE
    Pipeline(memory=None,
             steps=[('standardscaler',
                     StandardScaler(copy=True, with_mean=True, with_std=True)),
                    ('gaussiannb',
                     GaussianNB(priors=None, var_smoothing=1e-09))])

    Returns
    -------
    p : Pipeline
    """
    memory = kwargs.pop('memory', None)
    if kwargs:
        raise TypeError('Unknown keyword arguments: "{}"'
                        .format(list(kwargs.keys())[0]))
    return Pipeline(_name_estimators(steps), memory=memory)


# weight and fit_params are not used but it allows _fit_one_transformer,
# _transform_one and _fit_transform_one to have the same signature to
#  factorize the code in ColumnTransformer
def _fit_one_transformer(transformer, X, y, weight=None, **fit_params):
    return transformer.fit(X, y)


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, **fit_params):
    if hasattr(transformer, 'fit_transform'):
        res = transformer.fit_transform(X, y, **fit_params)
    else:
        res = transformer.fit(X, y, **fit_params).transform(X)
    # if we have a weight for this transformer, multiply output
    if weight is None:
        return res, transformer
    return res * weight, transformer


class FeatureUnion(_BaseComposition, TransformerMixin):
    """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' or ``None``.

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

    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.

    n_jobs : int or None, optional (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.

    transformer_weights : dict, optional
        Multiplicative weights for features per transformer.
        Keys are transformer names, values the weights.

    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)    # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS
    array([[ 1.5       ,  3.0...,  0.8...],
           [-1.5       ,  5.7..., -0.4...]])
    """
    def __init__(self, transformer_list, n_jobs=None,
                 transformer_weights=None):
        self.transformer_list = transformer_list
        self.n_jobs = n_jobs
        self.transformer_weights = transformer_weights
        self._validate_transformers()

    def get_params(self, deep=True):
        """Get parameters for this estimator.

        Parameters
        ----------
        deep : boolean, optional
            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:
            if t is None or 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 all transformers using X.

        Parameters
        ----------
        X : iterable or array-like, depending on transformers
            Input data, used to fit transformers.

        y : array-like, shape (n_samples, ...), optional
            Targets for supervised learning.

        Returns
        -------
        self : FeatureUnion
            This estimator
        """
        self.transformer_list = list(self.transformer_list)
        self._validate_transformers()
        transformers = Parallel(n_jobs=self.n_jobs)(
            delayed(_fit_one_transformer)(trans, X, y)
            for _, trans, _ in self._iter())
        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, shape (n_samples, ...), optional
            Targets for supervised learning.

        Returns
        -------
        X_t : array-like or sparse matrix, shape (n_samples, sum_n_components)
            hstack of results of transformers. sum_n_components is the
            sum of n_components (output dimension) over transformers.
        """
        self._validate_transformers()
        result = Parallel(n_jobs=self.n_jobs)(
            delayed(_fit_transform_one)(trans, X, y, weight,
                                        **fit_params)
            for name, trans, weight in self._iter())

        if not result:
            # All transformers are None
            return np.zeros((X.shape[0], 0))
        Xs, transformers = zip(*result)
        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 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, shape (n_samples, sum_n_components)
            hstack of results of transformers. sum_n_components is the
            sum of n_components (output dimension) over transformers.
        """
        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]


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 or None, optional (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.

    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())    # doctest: +NORMALIZE_WHITESPACE
    FeatureUnion(n_jobs=None,
           transformer_list=[('pca',
                              PCA(copy=True, iterated_power='auto',
                                  n_components=None, random_state=None,
                                  svd_solver='auto', tol=0.0, whiten=False)),
                             ('truncatedsvd',
                              TruncatedSVD(algorithm='randomized',
                              n_components=2, n_iter=5,
                              random_state=None, tol=0.0))],
           transformer_weights=None)
    """
    n_jobs = kwargs.pop('n_jobs', None)
    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)
