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

import numpy as np
from scipy import sparse

from .base import BaseEstimator, TransformerMixin
from .externals.joblib import Parallel, delayed

__all__ = ['Pipeline', 'FeatureUnion']


# One round of beers on me if someone finds out why the backslash
# is needed in the Attributes section so as not to upset sphinx.

class Pipeline(BaseEstimator):
    """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 implements fit and transform methods.
    The final estimator needs only implements fit.

    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.

    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.

    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 svn
    >>> anova_svm.set_params(anova__k=10, svc__C=.1).fit(X, y)
    ...                                              # doctest: +ELLIPSIS
    Pipeline(steps=[...])

    >>> prediction = anova_svm.predict(X)
    >>> anova_svm.score(X, y)
    0.75
    """

    # BaseEstimator interface

    def __init__(self, steps):
        self.named_steps = dict(steps)
        names, estimators = zip(*steps)
        if len(self.named_steps) != len(steps):
            raise ValueError("Names provided are not unique: %s" % names)

        self.steps = zip(names, estimators)     # shallow copy of steps
        transforms = estimators[:-1]
        estimator = estimators[-1]

        for t in transforms:
            if (not (hasattr(t, "fit") or hasattr(t, "fit_transform")) or not
                    hasattr(t, "transform")):
                raise TypeError("All intermediate steps a the chain should "
                                "be transforms and implement fit and transform"
                                "'%s' (type %s) doesn't)" % (t, type(t)))

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

    def get_params(self, deep=True):
        if not deep:
            return super(Pipeline, self).get_params(deep=False)
        else:
            out = self.named_steps.copy()
            for name, step in self.named_steps.iteritems():
                for key, value in step.get_params(deep=True).iteritems():
                    out['%s__%s' % (name, key)] = value
            return out

    # Estimator interface

    def _pre_transform(self, X, y=None, **fit_params):
        fit_params_steps = dict((step, {}) for step, _ in self.steps)
        for pname, pval in fit_params.iteritems():
            step, param = pname.split('__', 1)
            fit_params_steps[step][param] = pval
        Xt = X
        for name, transform in self.steps[:-1]:
            if hasattr(transform, "fit_transform"):
                Xt = transform.fit_transform(Xt, y, **fit_params_steps[name])
            else:
                Xt = transform.fit(Xt, y, **fit_params_steps[name]) \
                              .transform(Xt)
        return Xt, fit_params_steps[self.steps[-1][0]]

    def fit(self, X, y=None, **fit_params):
        """Fit all the transforms one after the other and transform the
        data, then fit the transformed data using the final estimator.
        """
        Xt, fit_params = self._pre_transform(X, y, **fit_params)
        self.steps[-1][-1].fit(Xt, y, **fit_params)
        return self

    def fit_transform(self, X, y=None, **fit_params):
        """Fit all the transforms one after the other and transform the
        data, then use fit_transform on transformed data using the final
        estimator."""
        Xt, fit_params = self._pre_transform(X, y, **fit_params)
        if hasattr(self.steps[-1][-1], 'fit_transform'):
            return self.steps[-1][-1].fit_transform(Xt, y, **fit_params)
        else:
            return self.steps[-1][-1].fit(Xt, y, **fit_params).transform(Xt)

    def predict(self, X):
        """Applies transforms to the data, and the predict method of the
        final estimator. Valid only if the final estimator implements
        predict."""
        Xt = X
        for name, transform in self.steps[:-1]:
            Xt = transform.transform(Xt)
        return self.steps[-1][-1].predict(Xt)

    def predict_proba(self, X):
        """Applies transforms to the data, and the predict_proba method of the
        final estimator. Valid only if the final estimator implements
        predict_proba."""
        Xt = X
        for name, transform in self.steps[:-1]:
            Xt = transform.transform(Xt)
        return self.steps[-1][-1].predict_proba(Xt)

    def decision_function(self, X):
        """Applies transforms to the data, and the decision_function method of
        the final estimator. Valid only if the final estimator implements
        decision_function."""
        Xt = X
        for name, transform in self.steps[:-1]:
            Xt = transform.transform(Xt)
        return self.steps[-1][-1].decision_function(Xt)

    def predict_log_proba(self, X):
        Xt = X
        for name, transform in self.steps[:-1]:
            Xt = transform.transform(Xt)
        return self.steps[-1][-1].predict_log_proba(Xt)

    def transform(self, X):
        """Applies transforms to the data, and the transform method of the
        final estimator. Valid only if the final estimator implements
        transform."""
        Xt = X
        for name, transform in self.steps:
            Xt = transform.transform(Xt)
        return Xt

    def inverse_transform(self, X):
        if X.ndim == 1:
            X = X[None, :]
        Xt = X
        for name, step in self.steps[::-1]:
            Xt = step.inverse_transform(Xt)
        return Xt

    def score(self, X, y=None):
        """Applies transforms to the data, and the score method of the
        final estimator. Valid only if the final estimator implements
        score."""
        Xt = X
        for name, transform in self.steps[:-1]:
            Xt = transform.transform(Xt)
        return self.steps[-1][-1].score(Xt, y)

    @property
    def _pairwise(self):
        # check if first estimator expects pairwise input
        return getattr(self.steps[0][1], '_pairwise', False)


def _fit_one_transformer(transformer, X, y):
    transformer.fit(X, y)


def _transform_one(transformer, name, X, transformer_weights):
    if transformer_weights is not None and name in transformer_weights:
        # if we have a weight for this transformer, muliply output
        return transformer.transform(X) * transformer_weights[name]
    return transformer.transform(X)


def _fit_transform_one(transformer, name, X, y, transformer_weights,
                       **fit_params):
    if transformer_weights is not None and name in transformer_weights:
        # if we have a weight for this transformer, muliply output
        if hasattr(transformer, 'fit_transform'):
            return (transformer.fit_transform(X, y, **fit_params)
                    * transformer_weights[name])
        else:
            return (transformer.fit(X, y, **fit_params).transform(X)
                    * transformer_weights[name])
    if hasattr(transformer, 'fit_transform'):
        return transformer.fit_transform(X, y, **fit_params)
    else:
        return transformer.fit(X, y, **fit_params).transform(X)


class FeatureUnion(BaseEstimator, 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
    ----------
    transformers: list of (name, transformer)
        List of transformer objects to be applied to the data.

    n_jobs: int, optional
        Number of jobs to run in parallel (default 1).

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

    """
    def __init__(self, transformer_list, n_jobs=1, transformer_weights=None):
        self.transformer_list = transformer_list
        self.n_jobs = n_jobs
        self.transformer_weights = transformer_weights

    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 in self.transformer_list:
            if not hasattr(trans, 'get_feature_names'):
                raise AttributeError("Transformer %s does not provide"
                                     " get_feature_names." % str(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 : array-like or sparse matrix, shape (n_samples, n_features)
            Input data, used to fit transformers.
        """
        Parallel(n_jobs=self.n_jobs)(
            delayed(_fit_one_transformer)(trans, X, y)
            for name, trans in self.transformer_list)
        return self

    def fit_transform(self, X, y=None, **fit_params):
        """Fit all tranformers using X, transform the data and concatenate
        results.

        Parameters
        ----------
        X : array-like or sparse matrix, shape (n_samples, n_features)
            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(_fit_transform_one)(trans, name, X, y,
                                        self.transformer_weights, **fit_params)
            for name, trans in self.transformer_list)
        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 : array-like or sparse matrix, shape (n_samples, n_features)
            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, name, X, self.transformer_weights)
            for name, trans in self.transformer_list)
        if any(sparse.issparse(f) for f in Xs):
            Xs = sparse.hstack(Xs).tocsr()
        else:
            Xs = np.hstack(Xs)
        return Xs

    def get_params(self, deep=True):
        if not deep:
            return super(FeatureUnion, self).get_params(deep=False)
        else:
            out = dict(self.transformer_list)
            for name, trans in self.transformer_list:
                for key, value in trans.get_params(deep=True).iteritems():
                    out['%s__%s' % (name, key)] = value
            return out