content: """
  The :mod:`sklearn.model_selection._split` module includes classes and
  functions to split the data based on a preset strategy.
  """
  
  # Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>,
  #         Gael Varoquaux <gael.varoquaux@normalesup.org>,
  #         Olivier Grisel <olivier.grisel@ensta.org>
  #         Raghav RV <rvraghav93@gmail.com>
  # License: BSD 3 clause
  
  
  from __future__ import print_function
  from __future__ import division
  
  import warnings
  from itertools import chain, combinations
  from collections import Iterable
  from math import ceil, floor
  import numbers
  from abc import ABCMeta, abstractmethod
  
  import numpy as np
  
  from scipy.misc import comb
  from ..utils import indexable, check_random_state, safe_indexing
  from ..utils.validation import _num_samples, column_or_1d
  from ..utils.validation import check_array
  from ..utils.multiclass import type_of_target
  from ..externals.six import with_metaclass
  from ..externals.six.moves import zip
  from ..utils.fixes import bincount
  from ..utils.fixes import signature
  from ..utils.random import choice
  from ..base import _pprint
  
  __all__ = ['BaseCrossValidator',
             'KFold',
             'GroupKFold',
             'LeaveOneGroupOut',
             'LeaveOneOut',
             'LeavePGroupsOut',
             'LeavePOut',
             'ShuffleSplit',
             'GroupShuffleSplit',
             'StratifiedKFold',
             'StratifiedShuffleSplit',
             'PredefinedSplit',
             'train_test_split',
             'check_cv']
  
  
  class BaseCrossValidator(with_metaclass(ABCMeta)):
      """Base class for all cross-validators
  
      Implementations must define `_iter_test_masks` or `_iter_test_indices`.
      """
  
      def __init__(self):
          # We need this for the build_repr to work properly in py2.7
          # see #6304
          pass
  
      def split(self, X, y=None, groups=None):
          """Generate indices to split data into training and test set.
  
          Parameters
          ----------
          X : array-like, shape (n_samples, n_features)
              Training data, where n_samples is the number of samples
              and n_features is the number of features.
  
          y : array-like, of length n_samples
              The target variable for supervised learning problems.
  
          groups : array-like, with shape (n_samples,), optional
              Group labels for the samples used while splitting the dataset into
              train/test set.
  
          Returns
          -------
          train : ndarray
              The training set indices for that split.
  
          test : ndarray
              The testing set indices for that split.
          """
          X, y, groups = indexable(X, y, groups)
          indices = np.arange(_num_samples(X))
          for test_index in self._iter_test_masks(X, y, groups):
              train_index = indices[np.logical_not(test_index)]
              test_index = indices[test_index]
              yield train_index, test_index
  
      # Since subclasses must implement either _iter_test_masks or
      # _iter_test_indices, neither can be abstract.
      def _iter_test_masks(self, X=None, y=None, groups=None):
          """Generates boolean masks corresponding to test sets.
  
          By default, delegates to _iter_test_indices(X, y, groups)
          """
          for test_index in self._iter_test_indices(X, y, groups):
              test_mask = np.zeros(_num_samples(X), dtype=np.bool)
              test_mask[test_index] = True
              yield test_mask
  
      def _iter_test_indices(self, X=None, y=None, groups=None):
          """Generates integer indices corresponding to test sets."""
          raise NotImplementedError
  
      @abstractmethod
      def get_n_splits(self, X=None, y=None, groups=None):
          """Returns the number of splitting iterations in the cross-validator"""
  
      def __repr__(self):
          return _build_repr(self)
  
  
  class LeaveOneOut(BaseCrossValidator):
      """Leave-One-Out cross-validator
  
      Provides train/test indices to split data in train/test sets. Each
      sample is used once as a test set (singleton) while the remaining
      samples form the training set.
  
      Note: ``LeaveOneOut()`` is equivalent to ``KFold(n_splits=n)`` and
      ``LeavePOut(p=1)`` where ``n`` is the number of samples.
  
      Due to the high number of test sets (which is the same as the
      number of samples) this cross-validation method can be very costly.
      For large datasets one should favor :class:`KFold`, :class:`ShuffleSplit`
      or :class:`StratifiedKFold`.
  
      Read more in the :ref:`User Guide <cross_validation>`.
  
      Examples
      --------
      >>> from sklearn.model_selection import LeaveOneOut
      >>> X = np.array([[1, 2], [3, 4]])
      >>> y = np.array([1, 2])
      >>> loo = LeaveOneOut()
      >>> loo.get_n_splits(X)
      2
      >>> print(loo)
      LeaveOneOut()
      >>> for train_index, test_index in loo.split(X):
      ...    print("TRAIN:", train_index, "TEST:", test_index)
      ...    X_train, X_test = X[train_index], X[test_index]
      ...    y_train, y_test = y[train_index], y[test_index]
      ...    print(X_train, X_test, y_train, y_test)
      TRAIN: [1] TEST: [0]
      [[3 4]] [[1 2]] [2] [1]
      TRAIN: [0] TEST: [1]
      [[1 2]] [[3 4]] [1] [2]
  
      See also
      --------
      LeaveOneGroupOut
          For splitting the data according to explicit, domain-specific
          stratification of the dataset.
  
      GroupKFold: K-fold iterator variant with non-overlapping groups.
      """
  
      def _iter_test_indices(self, X, y=None, groups=None):
          return range(_num_samples(X))
  
      def get_n_splits(self, X, y=None, groups=None):
          """Returns the number of splitting iterations in the cross-validator
  
          Parameters
          ----------
          X : array-like, shape (n_samples, n_features)
              Training data, where n_samples is the number of samples
              and n_features is the number of features.
  
          y : object
              Always ignored, exists for compatibility.
  
          groups : object
              Always ignored, exists for compatibility.
  
          Returns
          -------
          n_splits : int
              Returns the number of splitting iterations in the cross-validator.
          """
          if X is None:
              raise ValueError("The X parameter should not be None")
          return _num_samples(X)
  
  
  class LeavePOut(BaseCrossValidator):
      """Leave-P-Out cross-validator
  
      Provides train/test indices to split data in train/test sets. This results
      in testing on all distinct samples of size p, while the remaining n - p
      samples form the training set in each iteration.
  
      Note: ``LeavePOut(p)`` is NOT equivalent to
      ``KFold(n_splits=n_samples // p)`` which creates non-overlapping test sets.
  
      Due to the high number of iterations which grows combinatorically with the
      number of samples this cross-validation method can be very costly. For
      large datasets one should favor :class:`KFold`, :class:`StratifiedKFold`
      or :class:`ShuffleSplit`.
  
      Read more in the :ref:`User Guide <cross_validation>`.
  
      Parameters
      ----------
      p : int
          Size of the test sets.
  
      Examples
      --------
      >>> from sklearn.model_selection import LeavePOut
      >>> X = np.array([[1, 2], [3, 4], [5, 6], [7, 8]])
      >>> y = np.array([1, 2, 3, 4])
      >>> lpo = LeavePOut(2)
      >>> lpo.get_n_splits(X)
      6
      >>> print(lpo)
      LeavePOut(p=2)
      >>> for train_index, test_index in lpo.split(X):
      ...    print("TRAIN:", train_index, "TEST:", test_index)
      ...    X_train, X_test = X[train_index], X[test_index]
      ...    y_train, y_test = y[train_index], y[test_index]
      TRAIN: [2 3] TEST: [0 1]
      TRAIN: [1 3] TEST: [0 2]
      TRAIN: [1 2] TEST: [0 3]
      TRAIN: [0 3] TEST: [1 2]
      TRAIN: [0 2] TEST: [1 3]
      TRAIN: [0 1] TEST: [2 3]
      """
  
      def __init__(self, p):
          self.p = p
  
      def _iter_test_indices(self, X, y=None, groups=None):
          for combination in combinations(range(_num_samples(X)), self.p):
              yield np.array(combination)
  
      def get_n_splits(self, X, y=None, groups=None):
          """Returns the number of splitting iterations in the cross-validator
  
          Parameters
          ----------
          X : array-like, shape (n_samples, n_features)
              Training data, where n_samples is the number of samples
              and n_features is the number of features.
  
          y : object
              Always ignored, exists for compatibility.
  
          groups : object
              Always ignored, exists for compatibility.
          """
          if X is None:
              raise ValueError("The X parameter should not be None")
          return int(comb(_num_samples(X), self.p, exact=True))
  
  
  class _BaseKFold(with_metaclass(ABCMeta, BaseCrossValidator)):
      """Base class for KFold, GroupKFold, and StratifiedKFold"""
  
      @abstractmethod
      def __init__(self, n_splits, shuffle, random_state):
          if not isinstance(n_splits, numbers.Integral):
              raise ValueError('The number of folds must be of Integral type. '
                               '%s of type %s was passed.'
                               % (n_splits, type(n_splits)))
          n_splits = int(n_splits)
  
          if n_splits <= 1:
              raise ValueError(
                  "k-fold cross-validation requires at least one"
                  " train/test split by setting n_splits=2 or more,"
                  " got n_splits={0}.".format(n_splits))
  
          if not isinstance(shuffle, bool):
              raise TypeError("shuffle must be True or False;"
                              " got {0}".format(shuffle))
  
          self.n_splits = n_splits
          self.shuffle = shuffle
          self.random_state = random_state
  
      def split(self, X, y=None, groups=None):
          """Generate indices to split data into training and test set.
  
          Parameters
          ----------
          X : array-like, shape (n_samples, n_features)
              Training data, where n_samples is the number of samples
              and n_features is the number of features.
  
          y : array-like, shape (n_samples,)
              The target variable for supervised learning problems.
  
          groups : array-like, with shape (n_samples,), optional
              Group labels for the samples used while splitting the dataset into
              train/test set.
  
          Returns
          -------
          train : ndarray
              The training set indices for that split.
  
          test : ndarray
              The testing set indices for that split.
          """
          X, y, groups = indexable(X, y, groups)
          n_samples = _num_samples(X)
          if self.n_splits > n_samples:
              raise ValueError(
                  ("Cannot have number of splits n_splits={0} greater"
                   " than the number of samples: {1}.").format(self.n_splits,
                                                               n_samples))
  
          for train, test in super(_BaseKFold, self).split(X, y, groups):
              yield train, test
  
      def get_n_splits(self, X=None, y=None, groups=None):
          """Returns the number of splitting iterations in the cross-validator
  
          Parameters
          ----------
          X : object
              Always ignored, exists for compatibility.
  
          y : object
              Always ignored, exists for compatibility.
  
          groups : object
              Always ignored, exists for compatibility.
  
          Returns
          -------
          n_splits : int
              Returns the number of splitting iterations in the cross-validator.
          """
          return self.n_splits
  
  
  class KFold(_BaseKFold):
      """K-Folds cross-validator
  
      Provides train/test indices to split data in train/test sets. Split
      dataset into k consecutive folds (without shuffling by default).
  
      Each fold is then used once as a validation while the k - 1 remaining
      folds form the training set.
  
      Read more in the :ref:`User Guide <cross_validation>`.
  
      Parameters
      ----------
      n_splits : int, default=3
          Number of folds. Must be at least 2.
  
      shuffle : boolean, optional
          Whether to shuffle the data before splitting into batches.
  
      random_state : None, int or RandomState
          When shuffle=True, pseudo-random number generator state used for
          shuffling. If None, use default numpy RNG for shuffling.
  
      Examples
      --------
      >>> from sklearn.model_selection import KFold
      >>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]])
      >>> y = np.array([1, 2, 3, 4])
      >>> kf = KFold(n_splits=2)
      >>> kf.get_n_splits(X)
      2
      >>> print(kf)  # doctest: +NORMALIZE_WHITESPACE
      KFold(n_splits=2, random_state=None, shuffle=False)
      >>> for train_index, test_index in kf.split(X):
      ...    print("TRAIN:", train_index, "TEST:", test_index)
      ...    X_train, X_test = X[train_index], X[test_index]
      ...    y_train, y_test = y[train_index], y[test_index]
      TRAIN: [2 3] TEST: [0 1]
      TRAIN: [0 1] TEST: [2 3]
  
      Notes
      -----
      The first ``n_samples % n_splits`` folds have size
      ``n_samples // n_splits + 1``, other folds have size
      ``n_samples // n_splits``, where ``n_samples`` is the number of samples.
  
      See also
      --------
      StratifiedKFold
          Takes group information into account to avoid building folds with
          imbalanced class distributions (for binary or multiclass
          classification tasks).
  
      GroupKFold: K-fold iterator variant with non-overlapping groups.
      """
  
      def __init__(self, n_splits=3, shuffle=False,
                   random_state=None):
          super(KFold, self).__init__(n_splits, shuffle, random_state)
  
      def _iter_test_indices(self, X, y=None, groups=None):
          n_samples = _num_samples(X)
          indices = np.arange(n_samples)
          if self.shuffle:
              check_random_state(self.random_state).shuffle(indices)
  
          n_splits = self.n_splits
          fold_sizes = (n_samples // n_splits) * np.ones(n_splits, dtype=np.int)
          fold_sizes[:n_samples % n_splits] += 1
          current = 0
          for fold_size in fold_sizes:
              start, stop = current, current + fold_size
              yield indices[start:stop]
              current = stop
  
  
  class GroupKFold(_BaseKFold):
      """K-fold iterator variant with non-overlapping groups.
  
      The same group will not appear in two different folds (the number of
      distinct groups has to be at least equal to the number of folds).
  
      The folds are approximately balanced in the sense that the number of
      distinct groups is approximately the same in each fold.
  
      Parameters
      ----------
      n_splits : int, default=3
          Number of folds. Must be at least 2.
  
      Examples
      --------
      >>> from sklearn.model_selection import GroupKFold
      >>> X = np.array([[1, 2], [3, 4], [5, 6], [7, 8]])
      >>> y = np.array([1, 2, 3, 4])
      >>> groups = np.array([0, 0, 2, 2])
      >>> group_kfold = GroupKFold(n_splits=2)
      >>> group_kfold.get_n_splits(X, y, groups)
      2
      >>> print(group_kfold)
      GroupKFold(n_splits=2)
      >>> for train_index, test_index in group_kfold.split(X, y, groups):
      ...     print("TRAIN:", train_index, "TEST:", test_index)
      ...     X_train, X_test = X[train_index], X[test_index]
      ...     y_train, y_test = y[train_index], y[test_index]
      ...     print(X_train, X_test, y_train, y_test)
      ...
      TRAIN: [0 1] TEST: [2 3]
      [[1 2]
       [3 4]] [[5 6]
       [7 8]] [1 2] [3 4]
      TRAIN: [2 3] TEST: [0 1]
      [[5 6]
       [7 8]] [[1 2]
       [3 4]] [3 4] [1 2]
  
      See also
      --------
      LeaveOneGroupOut
          For splitting the data according to explicit domain-specific
          stratification of the dataset.
      """
      def __init__(self, n_splits=3):
          super(GroupKFold, self).__init__(n_splits, shuffle=False,
                                           random_state=None)
  
      def _iter_test_indices(self, X, y, groups):
          if groups is None:
              raise ValueError("The groups parameter should not be None")
          groups = check_array(groups, ensure_2d=False, dtype=None)
  
          unique_groups, groups = np.unique(groups, return_inverse=True)
          n_groups = len(unique_groups)
  
          if self.n_splits > n_groups:
              raise ValueError("Cannot have number of splits n_splits=%d greater"
                               " than the number of groups: %d."
                               % (self.n_splits, n_groups))
  
          # Weight groups by their number of occurrences
          n_samples_per_group = np.bincount(groups)
  
          # Distribute the most frequent groups first
          indices = np.argsort(n_samples_per_group)[::-1]
          n_samples_per_group = n_samples_per_group[indices]
  
          # Total weight of each fold
          n_samples_per_fold = np.zeros(self.n_splits)
  
          # Mapping from group index to fold index
          group_to_fold = np.zeros(len(unique_groups))
  
          # Distribute samples by adding the largest weight to the lightest fold
          for group_index, weight in enumerate(n_samples_per_group):
              lightest_fold = np.argmin(n_samples_per_fold)
              n_samples_per_fold[lightest_fold] += weight
              group_to_fold[indices[group_index]] = lightest_fold
  
          indices = group_to_fold[groups]
  
          for f in range(self.n_splits):
              yield np.where(indices == f)[0]
  
  
  class StratifiedKFold(_BaseKFold):
      """Stratified K-Folds cross-validator
  
      Provides train/test indices to split data in train/test sets.
  
      This cross-validation object is a variation of KFold that returns
      stratified folds. The folds are made by preserving the percentage of
      samples for each class.
  
      Read more in the :ref:`User Guide <cross_validation>`.
  
      Parameters
      ----------
      n_splits : int, default=3
          Number of folds. Must be at least 2.
  
      shuffle : boolean, optional
          Whether to shuffle each stratification of the data before splitting
          into batches.
  
      random_state : None, int or RandomState
          When shuffle=True, pseudo-random number generator state used for
          shuffling. If None, use default numpy RNG for shuffling.
  
      Examples
      --------
      >>> from sklearn.model_selection import StratifiedKFold
      >>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]])
      >>> y = np.array([0, 0, 1, 1])
      >>> skf = StratifiedKFold(n_splits=2)
      >>> skf.get_n_splits(X, y)
      2
      >>> print(skf)  # doctest: +NORMALIZE_WHITESPACE
      StratifiedKFold(n_splits=2, random_state=None, shuffle=False)
      >>> for train_index, test_index in skf.split(X, y):
      ...    print("TRAIN:", train_index, "TEST:", test_index)
      ...    X_train, X_test = X[train_index], X[test_index]
      ...    y_train, y_test = y[train_index], y[test_index]
      TRAIN: [1 3] TEST: [0 2]
      TRAIN: [0 2] TEST: [1 3]
  
      Notes
      -----
      All the folds have size ``trunc(n_samples / n_splits)``, the last one has
      the complementary.
  
      """
  
      def __init__(self, n_splits=3, shuffle=False, random_state=None):
          super(StratifiedKFold, self).__init__(n_splits, shuffle, random_state)
  
      def _make_test_folds(self, X, y=None, groups=None):
          if self.shuffle:
              rng = check_random_state(self.random_state)
          else:
              rng = self.random_state
          y = np.asarray(y)
          n_samples = y.shape[0]
          unique_y, y_inversed = np.unique(y, return_inverse=True)
          y_counts = bincount(y_inversed)
          min_groups = np.min(y_counts)
          if np.all(self.n_splits > y_counts):
              raise ValueError("All the n_groups for individual classes"
                               " are less than n_splits=%d."
                               % (self.n_splits))
          if self.n_splits > min_groups:
              warnings.warn(("The least populated class in y has only %d"
                             " members, which is too few. The minimum"
                             " number of groups for any class cannot"
                             " be less than n_splits=%d."
                             % (min_groups, self.n_splits)), Warning)
  
          # pre-assign each sample to a test fold index using individual KFold
          # splitting strategies for each class so as to respect the balance of
          # classes
          # NOTE: Passing the data corresponding to ith class say X[y==class_i]
          # will break when the data is not 100% stratifiable for all classes.
          # So we pass np.zeroes(max(c, n_splits)) as data to the KFold
          per_cls_cvs = [
              KFold(self.n_splits, shuffle=self.shuffle,
                    random_state=rng).split(np.zeros(max(count, self.n_splits)))
              for count in y_counts]
  
          test_folds = np.zeros(n_samples, dtype=np.int)
          for test_fold_indices, per_cls_splits in enumerate(zip(*per_cls_cvs)):
              for cls, (_, test_split) in zip(unique_y, per_cls_splits):
                  cls_test_folds = test_folds[y == cls]
                  # the test split can be too big because we used
                  # KFold(...).split(X[:max(c, n_splits)]) when data is not 100%
                  # stratifiable for all the classes
                  # (we use a warning instead of raising an exception)
                  # If this is the case, let's trim it:
                  test_split = test_split[test_split < len(cls_test_folds)]
                  cls_test_folds[test_split] = test_fold_indices
                  test_folds[y == cls] = cls_test_folds
  
          return test_folds
  
      def _iter_test_masks(self, X, y=None, groups=None):
          test_folds = self._make_test_folds(X, y)
          for i in range(self.n_splits):
              yield test_folds == i
  
      def split(self, X, y, groups=None):
          """Generate indices to split data into training and test set.
  
          Parameters
          ----------
          X : array-like, shape (n_samples, n_features)
              Training data, where n_samples is the number of samples
              and n_features is the number of features.
  
              Note that providing ``y`` is sufficient to generate the splits and
              hence ``np.zeros(n_samples)`` may be used as a placeholder for
              ``X`` instead of actual training data.
  
          y : array-like, shape (n_samples,)
              The target variable for supervised learning problems.
              Stratification is done based on the y labels.
  
          groups : object
              Always ignored, exists for compatibility.
  
          Returns
          -------
          train : ndarray
              The training set indices for that split.
  
          test : ndarray
              The testing set indices for that split.
          """
          y = check_array(y, ensure_2d=False, dtype=None)
          return super(StratifiedKFold, self).split(X, y, groups)
  
  
  class TimeSeriesSplit(_BaseKFold):
      """Time Series cross-validator
  
      Provides train/test indices to split time series data samples
      that are observed at fixed time intervals, in train/test sets.
      In each split, test indices must be higher than before, and thus shuffling
      in cross validator is inappropriate.
  
      This cross-validation object is a variation of :class:`KFold`.
      In the kth split, it returns first k folds as train set and the
      (k+1)th fold as test set.
  
      Note that unlike standard cross-validation methods, successive
      training sets are supersets of those that come before them.
  
      Read more in the :ref:`User Guide <cross_validation>`.
  
      Parameters
      ----------
      n_splits : int, default=3
          Number of splits. Must be at least 1.
  
      Examples
      --------
      >>> from sklearn.model_selection import TimeSeriesSplit
      >>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]])
      >>> y = np.array([1, 2, 3, 4])
      >>> tscv = TimeSeriesSplit(n_splits=3)
      >>> print(tscv)  # doctest: +NORMALIZE_WHITESPACE
      TimeSeriesSplit(n_splits=3)
      >>> for train_index, test_index in tscv.split(X):
      ...    print("TRAIN:", train_index, "TEST:", test_index)
      ...    X_train, X_test = X[train_index], X[test_index]
      ...    y_train, y_test = y[train_index], y[test_index]
      TRAIN: [0] TEST: [1]
      TRAIN: [0 1] TEST: [2]
      TRAIN: [0 1 2] TEST: [3]
  
      Notes
      -----
      The training set has size ``i * n_samples // (n_splits + 1)
      + n_samples % (n_splits + 1)`` in the ``i``th split,
      with a test set of size ``n_samples//(n_splits + 1)``,
      where ``n_samples`` is the number of samples.
      """
      def __init__(self, n_splits=3):
          super(TimeSeriesSplit, self).__init__(n_splits,
                                                shuffle=False,
                                                random_state=None)
  
      def split(self, X, y=None, groups=None):
          """Generate indices to split data into training and test set.
  
          Parameters
          ----------
          X : array-like, shape (n_samples, n_features)
              Training data, where n_samples is the number of samples
              and n_features is the number of features.
  
          y : array-like, shape (n_samples,)
              Always ignored, exists for compatibility.
  
          groups : array-like, with shape (n_samples,), optional
              Always ignored, exists for compatibility.
  
          Returns
          -------
          train : ndarray
              The training set indices for that split.
  
          test : ndarray
              The testing set indices for that split.
          """
          X, y, groups = indexable(X, y, groups)
          n_samples = _num_samples(X)
          n_splits = self.n_splits
          n_folds = n_splits + 1
          if n_folds > n_samples:
              raise ValueError(
                  ("Cannot have number of folds ={0} greater"
                   " than the number of samples: {1}.").format(n_folds,
                                                               n_samples))
          indices = np.arange(n_samples)
          test_size = (n_samples // n_folds)
          test_starts = range(test_size + n_samples % n_folds,
                              n_samples, test_size)
          for test_start in test_starts:
              yield (indices[:test_start],
                     indices[test_start:test_start + test_size])
  
  
  class LeaveOneGroupOut(BaseCrossValidator):
      """Leave One Group Out cross-validator
  
      Provides train/test indices to split data according to a third-party
      provided group. This group information can be used to encode arbitrary
      domain specific stratifications of the samples as integers.
  
      For instance the groups could be the year of collection of the samples
      and thus allow for cross-validation against time-based splits.
  
      Read more in the :ref:`User Guide <cross_validation>`.
  
      Examples
      --------
      >>> from sklearn.model_selection import LeaveOneGroupOut
      >>> X = np.array([[1, 2], [3, 4], [5, 6], [7, 8]])
      >>> y = np.array([1, 2, 1, 2])
      >>> groups = np.array([1, 1, 2, 2])
      >>> logo = LeaveOneGroupOut()
      >>> logo.get_n_splits(X, y, groups)
      2
      >>> print(logo)
      LeaveOneGroupOut()
      >>> for train_index, test_index in logo.split(X, y, groups):
      ...    print("TRAIN:", train_index, "TEST:", test_index)
      ...    X_train, X_test = X[train_index], X[test_index]
      ...    y_train, y_test = y[train_index], y[test_index]
      ...    print(X_train, X_test, y_train, y_test)
      TRAIN: [2 3] TEST: [0 1]
      [[5 6]
       [7 8]] [[1 2]
       [3 4]] [1 2] [1 2]
      TRAIN: [0 1] TEST: [2 3]
      [[1 2]
       [3 4]] [[5 6]
       [7 8]] [1 2] [1 2]
  
      """
  
      def _iter_test_masks(self, X, y, groups):
          if groups is None:
              raise ValueError("The groups parameter should not be None")
          # We make a copy of groups to avoid side-effects during iteration
          groups = check_array(groups, copy=True, ensure_2d=False, dtype=None)
          unique_groups = np.unique(groups)
          if len(unique_groups) <= 1:
              raise ValueError(
                  "The groups parameter contains fewer than 2 unique groups "
                  "(%s). LeaveOneGroupOut expects at least 2." % unique_groups)
          for i in unique_groups:
              yield groups == i
  
      def get_n_splits(self, X, y, groups):
          """Returns the number of splitting iterations in the cross-validator
  
          Parameters
          ----------
          X : object
              Always ignored, exists for compatibility.
  
          y : object
              Always ignored, exists for compatibility.
  
          groups : array-like, with shape (n_samples,), optional
              Group labels for the samples used while splitting the dataset into
              train/test set.
  
          Returns
          -------
          n_splits : int
              Returns the number of splitting iterations in the cross-validator.
          """
          if groups is None:
              raise ValueError("The groups parameter should not be None")
          return len(np.unique(groups))
  
  
  class LeavePGroupsOut(BaseCrossValidator):
      """Leave P Group(s) Out cross-validator
  
      Provides train/test indices to split data according to a third-party
      provided group. This group information can be used to encode arbitrary
      domain specific stratifications of the samples as integers.
  
      For instance the groups could be the year of collection of the samples
      and thus allow for cross-validation against time-based splits.
  
      The difference between LeavePGroupsOut and LeaveOneGroupOut is that
      the former builds the test sets with all the samples assigned to
      ``p`` different values of the groups while the latter uses samples
      all assigned the same groups.
  
      Read more in the :ref:`User Guide <cross_validation>`.
  
      Parameters
      ----------
      n_groups : int
          Number of groups (``p``) to leave out in the test split.
  
      Examples
      --------
      >>> from sklearn.model_selection import LeavePGroupsOut
      >>> X = np.array([[1, 2], [3, 4], [5, 6]])
      >>> y = np.array([1, 2, 1])
      >>> groups = np.array([1, 2, 3])
      >>> lpgo = LeavePGroupsOut(n_groups=2)
      >>> lpgo.get_n_splits(X, y, groups)
      3
      >>> print(lpgo)
      LeavePGroupsOut(n_groups=2)
      >>> for train_index, test_index in lpgo.split(X, y, groups):
      ...    print("TRAIN:", train_index, "TEST:", test_index)
      ...    X_train, X_test = X[train_index], X[test_index]
      ...    y_train, y_test = y[train_index], y[test_index]
      ...    print(X_train, X_test, y_train, y_test)
      TRAIN: [2] TEST: [0 1]
      [[5 6]] [[1 2]
       [3 4]] [1] [1 2]
      TRAIN: [1] TEST: [0 2]
      [[3 4]] [[1 2]
       [5 6]] [2] [1 1]
      TRAIN: [0] TEST: [1 2]
      [[1 2]] [[3 4]
       [5 6]] [1] [2 1]
  
      See also
      --------
      GroupKFold: K-fold iterator variant with non-overlapping groups.
      """
  
      def __init__(self, n_groups):
          self.n_groups = n_groups
  
      def _iter_test_masks(self, X, y, groups):
          if groups is None:
              raise ValueError("The groups parameter should not be None")
          groups = check_array(groups, copy=True, ensure_2d=False, dtype=None)
          unique_groups = np.unique(groups)
          if self.n_groups >= len(unique_groups):
              raise ValueError(
                  "The groups parameter contains fewer than (or equal to) "
                  "n_groups (%d) numbers of unique groups (%s). LeavePGroupsOut "
                  "expects that at least n_groups + 1 (%d) unique groups be "
                  "present" % (self.n_groups, unique_groups, self.n_groups + 1))
          combi = combinations(range(len(unique_groups)), self.n_groups)
          for indices in combi:
              test_index = np.zeros(_num_samples(X), dtype=np.bool)
              for l in unique_groups[np.array(indices)]:
                  test_index[groups == l] = True
              yield test_index
  
      def get_n_splits(self, X, y, groups):
          """Returns the number of splitting iterations in the cross-validator
  
          Parameters
          ----------
          X : object
              Always ignored, exists for compatibility.
              ``np.zeros(n_samples)`` may be used as a placeholder.
  
          y : object
              Always ignored, exists for compatibility.
              ``np.zeros(n_samples)`` may be used as a placeholder.
  
          groups : array-like, with shape (n_samples,), optional
              Group labels for the samples used while splitting the dataset into
              train/test set.
  
          Returns
          -------
          n_splits : int
              Returns the number of splitting iterations in the cross-validator.
          """
          if groups is None:
              raise ValueError("The groups parameter should not be None")
          groups = check_array(groups, ensure_2d=False, dtype=None)
          X, y, groups = indexable(X, y, groups)
          return int(comb(len(np.unique(groups)), self.n_groups, exact=True))
  
  
  class BaseShuffleSplit(with_metaclass(ABCMeta)):
      """Base class for ShuffleSplit and StratifiedShuffleSplit"""
  
      def __init__(self, n_splits=10, test_size=0.1, train_size=None,
                   random_state=None):
          _validate_shuffle_split_init(test_size, train_size)
          self.n_splits = n_splits
          self.test_size = test_size
          self.train_size = train_size
          self.random_state = random_state
  
      def split(self, X, y=None, groups=None):
          """Generate indices to split data into training and test set.
  
          Parameters
          ----------
          X : array-like, shape (n_samples, n_features)
              Training data, where n_samples is the number of samples
              and n_features is the number of features.
  
          y : array-like, shape (n_samples,)
              The target variable for supervised learning problems.
  
          groups : array-like, with shape (n_samples,), optional
              Group labels for the samples used while splitting the dataset into
              train/test set.
  
          Returns
          -------
          train : ndarray
              The training set indices for that split.
  
          test : ndarray
              The testing set indices for that split.
          """
          X, y, groups = indexable(X, y, groups)
          for train, test in self._iter_indices(X, y, groups):
              yield train, test
  
      @abstractmethod
      def _iter_indices(self, X, y=None, groups=None):
          """Generate (train, test) indices"""
  
      def get_n_splits(self, X=None, y=None, groups=None):
          """Returns the number of splitting iterations in the cross-validator
  
          Parameters
          ----------
          X : object
              Always ignored, exists for compatibility.
  
          y : object
              Always ignored, exists for compatibility.
  
          groups : object
              Always ignored, exists for compatibility.
  
          Returns
          -------
          n_splits : int
              Returns the number of splitting iterations in the cross-validator.
          """
          return self.n_splits
  
      def __repr__(self):
          return _build_repr(self)
  
  
  class ShuffleSplit(BaseShuffleSplit):
      """Random permutation cross-validator
  
      Yields indices to split data into training and test sets.
  
      Note: contrary to other cross-validation strategies, random splits
      do not guarantee that all folds will be different, although this is
      still very likely for sizeable datasets.
  
      Read more in the :ref:`User Guide <cross_validation>`.
  
      Parameters
      ----------
      n_splits : int (default 10)
          Number of re-shuffling & splitting iterations.
  
      test_size : float, int, or None, default 0.1
          If float, should be between 0.0 and 1.0 and represent the
          proportion of the dataset to include in the test split. If
          int, represents the absolute number of test samples. If None,
          the value is automatically set to the complement of the train size.
  
      train_size : float, int, or None (default is None)
          If float, should be between 0.0 and 1.0 and represent the
          proportion of the dataset to include in the train split. If
          int, represents the absolute number of train samples. If None,
          the value is automatically set to the complement of the test size.
  
      random_state : int or RandomState
          Pseudo-random number generator state used for random sampling.
  
      Examples
      --------
      >>> from sklearn.model_selection import ShuffleSplit
      >>> X = np.array([[1, 2], [3, 4], [5, 6], [7, 8]])
      >>> y = np.array([1, 2, 1, 2])
      >>> rs = ShuffleSplit(n_splits=3, test_size=.25, random_state=0)
      >>> rs.get_n_splits(X)
      3
      >>> print(rs)
      ShuffleSplit(n_splits=3, random_state=0, test_size=0.25, train_size=None)
      >>> for train_index, test_index in rs.split(X):
      ...    print("TRAIN:", train_index, "TEST:", test_index)
      ...  # doctest: +ELLIPSIS
      TRAIN: [3 1 0] TEST: [2]
      TRAIN: [2 1 3] TEST: [0]
      TRAIN: [0 2 1] TEST: [3]
      >>> rs = ShuffleSplit(n_splits=3, train_size=0.5, test_size=.25,
      ...                   random_state=0)
      >>> for train_index, test_index in rs.split(X):
      ...    print("TRAIN:", train_index, "TEST:", test_index)
      ...  # doctest: +ELLIPSIS
      TRAIN: [3 1] TEST: [2]
      TRAIN: [2 1] TEST: [0]
      TRAIN: [0 2] TEST: [3]
      """
  
      def _iter_indices(self, X, y=None, groups=None):
          n_samples = _num_samples(X)
          n_train, n_test = _validate_shuffle_split(n_samples, self.test_size,
                                                    self.train_size)
          rng = check_random_state(self.random_state)
          for i in range(self.n_splits):
              # random partition
              permutation = rng.permutation(n_samples)
              ind_test = permutation[:n_test]
              ind_train = permutation[n_test:(n_test + n_train)]
              yield ind_train, ind_test
  
  
  class GroupShuffleSplit(ShuffleSplit):
      '''Shuffle-Group(s)-Out cross-validation iterator
  
      Provides randomized train/test indices to split data according to a
      third-party provided group. This group information can be used to encode
      arbitrary domain specific stratifications of the samples as integers.
  
      For instance the groups could be the year of collection of the samples
      and thus allow for cross-validation against time-based splits.
  
      The difference between LeavePGroupsOut and GroupShuffleSplit is that
      the former generates splits using all subsets of size ``p`` unique groups,
      whereas GroupShuffleSplit generates a user-determined number of random
      test splits, each with a user-determined fraction of unique groups.
  
      For example, a less computationally intensive alternative to
      ``LeavePGroupsOut(p=10)`` would be
      ``GroupShuffleSplit(test_size=10, n_splits=100)``.
  
      Note: The parameters ``test_size`` and ``train_size`` refer to groups, and
      not to samples, as in ShuffleSplit.
  
  
      Parameters
      ----------
      n_splits : int (default 5)
          Number of re-shuffling & splitting iterations.
  
      test_size : float (default 0.2), int, or None
          If float, should be between 0.0 and 1.0 and represent the
          proportion of the groups to include in the test split. If
          int, represents the absolute number of test groups. If None,
          the value is automatically set to the complement of the train size.
  
      train_size : float, int, or None (default is None)
          If float, should be between 0.0 and 1.0 and represent the
          proportion of the groups to include in the train split. If
          int, represents the absolute number of train groups. If None,
          the value is automatically set to the complement of the test size.
  
      random_state : int or RandomState
          Pseudo-random number generator state used for random sampling.
      '''
  
      def __init__(self, n_splits=5, test_size=0.2, train_size=None,
                   random_state=None):
          super(GroupShuffleSplit, self).__init__(
              n_splits=n_splits,
              test_size=test_size,
              train_size=train_size,
              random_state=random_state)
  
      def _iter_indices(self, X, y, groups):
          if groups is None:
              raise ValueError("The groups parameter should not be None")
          groups = check_array(groups, ensure_2d=False, dtype=None)
          classes, group_indices = np.unique(groups, return_inverse=True)
          for group_train, group_test in super(
                  GroupShuffleSplit, self)._iter_indices(X=classes):
              # these are the indices of classes in the partition
              # invert them into data indices
  
              train = np.flatnonzero(np.in1d(group_indices, group_train))
              test = np.flatnonzero(np.in1d(group_indices, group_test))
  
              yield train, test
  
  
  def _approximate_mode(class_counts, n_draws, rng):
      """Computes approximate mode of multivariate hypergeometric.
  
      This is an approximation to the mode of the multivariate
      hypergeometric given by class_counts and n_draws.
      It shouldn't be off by more than one.
  
      It is the mostly likely outcome of drawing n_draws many
      samples from the population given by class_counts.
  
      Parameters
      ----------
      class_counts : ndarray of int
          Population per class.
      n_draws : int
          Number of draws (samples to draw) from the overall population.
      rng : random state
          Used to break ties.
  
      Returns
      -------
      sampled_classes : ndarray of int
          Number of samples drawn from each class.
          np.sum(sampled_classes) == n_draws
  
      Examples
      --------
      >>> from sklearn.model_selection._split import _approximate_mode
      >>> _approximate_mode(class_counts=np.array([4, 2]), n_draws=3, rng=0)
      array([2, 1])
      >>> _approximate_mode(class_counts=np.array([5, 2]), n_draws=4, rng=0)
      array([3, 1])
      >>> _approximate_mode(class_counts=np.array([2, 2, 2, 1]),
      ...                   n_draws=2, rng=0)
      array([0, 1, 1, 0])
      >>> _approximate_mode(class_counts=np.array([2, 2, 2, 1]),
      ...                   n_draws=2, rng=42)
      array([1, 1, 0, 0])
      """
      # this computes a bad approximation to the mode of the
      # multivariate hypergeometric given by class_counts and n_draws
      continuous = n_draws * class_counts / class_counts.sum()
      # floored means we don't overshoot n_samples, but probably undershoot
      floored = np.floor(continuous)
      # we add samples according to how much "left over" probability
      # they had, until we arrive at n_samples
      need_to_add = int(n_draws - floored.sum())
      if need_to_add > 0:
          remainder = continuous - floored
          values = np.sort(np.unique(remainder))[::-1]
          # add according to remainder, but break ties
          # randomly to avoid biases
          for value in values:
              inds, = np.where(remainder == value)
              # if we need_to_add less than what's in inds
              # we draw randomly from them.
              # if we need to add more, we add them all and
              # go to the next value
              add_now = min(len(inds), need_to_add)
              inds = choice(inds, size=add_now, replace=False, random_state=rng)
              floored[inds] += 1
              need_to_add -= add_now
              if need_to_add == 0:
                  break
      return floored.astype(np.int)
  
  
  class StratifiedShuffleSplit(BaseShuffleSplit):
      """Stratified ShuffleSplit cross-validator
  
      Provides train/test indices to split data in train/test sets.
  
      This cross-validation object is a merge of StratifiedKFold and
      ShuffleSplit, which returns stratified randomized folds. The folds
      are made by preserving the percentage of samples for each class.
  
      Note: like the ShuffleSplit strategy, stratified random splits
      do not guarantee that all folds will be different, although this is
      still very likely for sizeable datasets.
  
      Read more in the :ref:`User Guide <cross_validation>`.
  
      Parameters
      ----------
      n_splits : int (default 10)
          Number of re-shuffling & splitting iterations.
  
      test_size : float (default 0.1), int, or None
          If float, should be between 0.0 and 1.0 and represent the
          proportion of the dataset to include in the test split. If
          int, represents the absolute number of test samples. If None,
          the value is automatically set to the complement of the train size.
  
      train_size : float, int, or None (default is None)
          If float, should be between 0.0 and 1.0 and represent the
          proportion of the dataset to include in the train split. If
          int, represents the absolute number of train samples. If None,
          the value is automatically set to the complement of the test size.
  
      random_state : int or RandomState
          Pseudo-random number generator state used for random sampling.
  
      Examples
      --------
      >>> from sklearn.model_selection import StratifiedShuffleSplit
      >>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]])
      >>> y = np.array([0, 0, 1, 1])
      >>> sss = StratifiedShuffleSplit(n_splits=3, test_size=0.5, random_state=0)
      >>> sss.get_n_splits(X, y)
      3
      >>> print(sss)       # doctest: +ELLIPSIS
      StratifiedShuffleSplit(n_splits=3, random_state=0, ...)
      >>> for train_index, test_index in sss.split(X, y):
      ...    print("TRAIN:", train_index, "TEST:", test_index)
      ...    X_train, X_test = X[train_index], X[test_index]
      ...    y_train, y_test = y[train_index], y[test_index]
      TRAIN: [1 2] TEST: [3 0]
      TRAIN: [0 2] TEST: [1 3]
      TRAIN: [0 2] TEST: [3 1]
      """
  
      def __init__(self, n_splits=10, test_size=0.1, train_size=None,
                   random_state=None):
          super(StratifiedShuffleSplit, self).__init__(
              n_splits, test_size, train_size, random_state)
  
      def _iter_indices(self, X, y, groups=None):
          n_samples = _num_samples(X)
          y = check_array(y, ensure_2d=False, dtype=None)
          n_train, n_test = _validate_shuffle_split(n_samples, self.test_size,
                                                    self.train_size)
          classes, y_indices = np.unique(y, return_inverse=True)
          n_classes = classes.shape[0]
  
          class_counts = bincount(y_indices)
          if np.min(class_counts) < 2:
              raise ValueError("The least populated class in y has only 1"
                               " member, which is too few. The minimum"
                               " number of groups for any class cannot"
                               " be less than 2.")
  
          if n_train < n_classes:
              raise ValueError('The train_size = %d should be greater or '
                               'equal to the number of classes = %d' %
                               (n_train, n_classes))
          if n_test < n_classes:
              raise ValueError('The test_size = %d should be greater or '
                               'equal to the number of classes = %d' %
                               (n_test, n_classes))
  
          rng = check_random_state(self.random_state)
  
          for _ in range(self.n_splits):
              # if there are ties in the class-counts, we want
              # to make sure to break them anew in each iteration
              n_i = _approximate_mode(class_counts, n_train, rng)
              class_counts_remaining = class_counts - n_i
              t_i = _approximate_mode(class_counts_remaining, n_test, rng)
  
              train = []
              test = []
  
              for i, class_i in enumerate(classes):
                  permutation = rng.permutation(class_counts[i])
                  perm_indices_class_i = np.where((y == class_i))[0][permutation]
  
                  train.extend(perm_indices_class_i[:n_i[i]])
                  test.extend(perm_indices_class_i[n_i[i]:n_i[i] + t_i[i]])
              train = rng.permutation(train)
              test = rng.permutation(test)
  
              yield train, test
  
      def split(self, X, y, groups=None):
          """Generate indices to split data into training and test set.
  
          Parameters
          ----------
          X : array-like, shape (n_samples, n_features)
              Training data, where n_samples is the number of samples
              and n_features is the number of features.
  
              Note that providing ``y`` is sufficient to generate the splits and
              hence ``np.zeros(n_samples)`` may be used as a placeholder for
              ``X`` instead of actual training data.
  
          y : array-like, shape (n_samples,)
              The target variable for supervised learning problems.
              Stratification is done based on the y labels.
  
          groups : object
              Always ignored, exists for compatibility.
  
          Returns
          -------
          train : ndarray
              The training set indices for that split.
  
          test : ndarray
              The testing set indices for that split.
          """
          y = check_array(y, ensure_2d=False, dtype=None)
          return super(StratifiedShuffleSplit, self).split(X, y, groups)
  
  
  def _validate_shuffle_split_init(test_size, train_size):
      """Validation helper to check the test_size and train_size at init
  
      NOTE This does not take into account the number of samples which is known
      only at split
      """
      if test_size is None and train_size is None:
          raise ValueError('test_size and train_size can not both be None')
  
      if test_size is not None:
          if np.asarray(test_size).dtype.kind == 'f':
              if test_size >= 1.:
                  raise ValueError(
                      'test_size=%f should be smaller '
                      'than 1.0 or be an integer' % test_size)
          elif np.asarray(test_size).dtype.kind != 'i':
              # int values are checked during split based on the input
              raise ValueError("Invalid value for test_size: %r" % test_size)
  
      if train_size is not None:
          if np.asarray(train_size).dtype.kind == 'f':
              if train_size >= 1.:
                  raise ValueError("train_size=%f should be smaller "
                                   "than 1.0 or be an integer" % train_size)
              elif (np.asarray(test_size).dtype.kind == 'f' and
                      (train_size + test_size) > 1.):
                  raise ValueError('The sum of test_size and train_size = %f, '
                                   'should be smaller than 1.0. Reduce '
                                   'test_size and/or train_size.' %
                                   (train_size + test_size))
          elif np.asarray(train_size).dtype.kind != 'i':
              # int values are checked during split based on the input
              raise ValueError("Invalid value for train_size: %r" % train_size)
  
  
  def _validate_shuffle_split(n_samples, test_size, train_size):
      """
      Validation helper to check if the test/test sizes are meaningful wrt to the
      size of the data (n_samples)
      """
      if (test_size is not None and np.asarray(test_size).dtype.kind == 'i' and
              test_size >= n_samples):
          raise ValueError('test_size=%d should be smaller than the number of '
                           'samples %d' % (test_size, n_samples))
  
      if (train_size is not None and np.asarray(train_size).dtype.kind == 'i' and
              train_size >= n_samples):
          raise ValueError("train_size=%d should be smaller than the number of"
                           " samples %d" % (train_size, n_samples))
  
      if np.asarray(test_size).dtype.kind == 'f':
          n_test = ceil(test_size * n_samples)
      elif np.asarray(test_size).dtype.kind == 'i':
          n_test = float(test_size)
  
      if train_size is None:
          n_train = n_samples - n_test
      elif np.asarray(train_size).dtype.kind == 'f':
          n_train = floor(train_size * n_samples)
      else:
          n_train = float(train_size)
  
      if test_size is None:
          n_test = n_samples - n_train
  
      if n_train + n_test > n_samples:
          raise ValueError('The sum of train_size and test_size = %d, '
                           'should be smaller than the number of '
                           'samples %d. Reduce test_size and/or '
                           'train_size.' % (n_train + n_test, n_samples))
  
      return int(n_train), int(n_test)
  
  
  class PredefinedSplit(BaseCrossValidator):
      """Predefined split cross-validator
  
      Splits the data into training/test set folds according to a predefined
      scheme. Each sample can be assigned to at most one test set fold, as
      specified by the user through the ``test_fold`` parameter.
  
      Read more in the :ref:`User Guide <cross_validation>`.
  
      Examples
      --------
      >>> from sklearn.model_selection import PredefinedSplit
      >>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]])
      >>> y = np.array([0, 0, 1, 1])
      >>> test_fold = [0, 1, -1, 1]
      >>> ps = PredefinedSplit(test_fold)
      >>> ps.get_n_splits()
      2
      >>> print(ps)       # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS
      PredefinedSplit(test_fold=array([ 0,  1, -1,  1]))
      >>> for train_index, test_index in ps.split():
      ...    print("TRAIN:", train_index, "TEST:", test_index)
      ...    X_train, X_test = X[train_index], X[test_index]
      ...    y_train, y_test = y[train_index], y[test_index]
      TRAIN: [1 2 3] TEST: [0]
      TRAIN: [0 2] TEST: [1 3]
      """
  
      def __init__(self, test_fold):
          self.test_fold = np.array(test_fold, dtype=np.int)
          self.test_fold = column_or_1d(self.test_fold)
          self.unique_folds = np.unique(self.test_fold)
          self.unique_folds = self.unique_folds[self.unique_folds != -1]
  
      def split(self, X=None, y=None, groups=None):
          """Generate indices to split data into training and test set.
  
          Parameters
          ----------
          X : object
              Always ignored, exists for compatibility.
  
          y : object
              Always ignored, exists for compatibility.
  
          groups : object
              Always ignored, exists for compatibility.
  
          Returns
          -------
          train : ndarray
              The training set indices for that split.
  
          test : ndarray
              The testing set indices for that split.
          """
          ind = np.arange(len(self.test_fold))
          for test_index in self._iter_test_masks():
              train_index = ind[np.logical_not(test_index)]
              test_index = ind[test_index]
              yield train_index, test_index
  
      def _iter_test_masks(self):
          """Generates boolean masks corresponding to test sets."""
          for f in self.unique_folds:
              test_index = np.where(self.test_fold == f)[0]
              test_mask = np.zeros(len(self.test_fold), dtype=np.bool)
              test_mask[test_index] = True
              yield test_mask
  
      def get_n_splits(self, X=None, y=None, groups=None):
          """Returns the number of splitting iterations in the cross-validator
  
          Parameters
          ----------
          X : object
              Always ignored, exists for compatibility.
  
          y : object
              Always ignored, exists for compatibility.
  
          groups : object
              Always ignored, exists for compatibility.
  
          Returns
          -------
          n_splits : int
              Returns the number of splitting iterations in the cross-validator.
          """
          return len(self.unique_folds)
  
  
  class _CVIterableWrapper(BaseCrossValidator):
      """Wrapper class for old style cv objects and iterables."""
      def __init__(self, cv):
          self.cv = list(cv)
  
      def get_n_splits(self, X=None, y=None, groups=None):
          """Returns the number of splitting iterations in the cross-validator
  
          Parameters
          ----------
          X : object
              Always ignored, exists for compatibility.
  
          y : object
              Always ignored, exists for compatibility.
  
          groups : object
              Always ignored, exists for compatibility.
  
          Returns
          -------
          n_splits : int
              Returns the number of splitting iterations in the cross-validator.
          """
          return len(self.cv)
  
      def split(self, X=None, y=None, groups=None):
          """Generate indices to split data into training and test set.
  
          Parameters
          ----------
          X : object
              Always ignored, exists for compatibility.
  
          y : object
              Always ignored, exists for compatibility.
  
          groups : object
              Always ignored, exists for compatibility.
  
          Returns
          -------
          train : ndarray
              The training set indices for that split.
  
          test : ndarray
              The testing set indices for that split.
          """
          for train, test in self.cv:
              yield train, test
  
  
  def check_cv(cv=3, y=None, classifier=False):
      """Input checker utility for building a cross-validator
  
      Parameters
      ----------
      cv : int, cross-validation generator or an iterable, optional
          Determines the cross-validation splitting strategy.
          Possible inputs for cv are:
            - None, to use the default 3-fold cross-validation,
            - integer, to specify the number of folds.
            - An object to be used as a cross-validation generator.
            - An iterable yielding train/test splits.
  
          For integer/None inputs, if classifier is True and ``y`` is either
          binary or multiclass, :class:`StratifiedKFold` is used. In all other
          cases, :class:`KFold` is used.
  
          Refer :ref:`User Guide <cross_validation>` for the various
          cross-validation strategies that can be used here.
  
      y : array-like, optional
          The target variable for supervised learning problems.
  
      classifier : boolean, optional, default False
          Whether the task is a classification task, in which case
          stratified KFold will be used.
  
      Returns
      -------
      checked_cv : a cross-validator instance.
          The return value is a cross-validator which generates the train/test
          splits via the ``split`` method.
      """
      if cv is None:
          cv = 3
  
      if isinstance(cv, numbers.Integral):
          if (classifier and (y is not None) and
                  (type_of_target(y) in ('binary', 'multiclass'))):
              return StratifiedKFold(cv)
          else:
              return KFold(cv)
  
      if not hasattr(cv, 'split') or isinstance(cv, str):
          if not isinstance(cv, Iterable) or isinstance(cv, str):
              raise ValueError("Expected cv as an integer, cross-validation "
                               "object (from sklearn.model_selection) "
                               "or an iterable. Got %s." % cv)
          return _CVIterableWrapper(cv)
  
      return cv  # New style cv objects are passed without any modification
  
  
  def train_test_split(*arrays, **options):
      """Split arrays or matrices into random train and test subsets
  
      Quick utility that wraps input validation and
      ``next(ShuffleSplit().split(X, y))`` and application to input data
      into a single call for splitting (and optionally subsampling) data in a
      oneliner.
  
      Read more in the :ref:`User Guide <cross_validation>`.
  
      Parameters
      ----------
      *arrays : sequence of indexables with same length / shape[0]
          Allowed inputs are lists, numpy arrays, scipy-sparse
          matrices or pandas dataframes.
  
      test_size : float, int, or None (default is None)
          If float, should be between 0.0 and 1.0 and represent the
          proportion of the dataset to include in the test split. If
          int, represents the absolute number of test samples. If None,
          the value is automatically set to the complement of the train size.
          If train size is also None, test size is set to 0.25.
  
      train_size : float, int, or None (default is None)
          If float, should be between 0.0 and 1.0 and represent the
          proportion of the dataset to include in the train split. If
          int, represents the absolute number of train samples. If None,
          the value is automatically set to the complement of the test size.
  
      random_state : int or RandomState
          Pseudo-random number generator state used for random sampling.
  
      stratify : array-like or None (default is None)
          If not None, data is split in a stratified fashion, using this as
          the class labels.
  
      Returns
      -------
      splitting : list, length=2 * len(arrays)
          List containing train-test split of inputs.
  
          .. versionadded:: 0.16
              If the input is sparse, the output will be a
              ``scipy.sparse.csr_matrix``. Else, output type is the same as the
              input type.
  
      Examples
      --------
      >>> import numpy as np
      >>> from sklearn.model_selection import train_test_split
      >>> X, y = np.arange(10).reshape((5, 2)), range(5)
      >>> X
      array([[0, 1],
             [2, 3],
             [4, 5],
             [6, 7],
             [8, 9]])
      >>> list(y)
      [0, 1, 2, 3, 4]
  
      >>> X_train, X_test, y_train, y_test = train_test_split(
      ...     X, y, test_size=0.33, random_state=42)
      ...
      >>> X_train
      array([[4, 5],
             [0, 1],
             [6, 7]])
      >>> y_train
      [2, 0, 3]
      >>> X_test
      array([[2, 3],
             [8, 9]])
      >>> y_test
      [1, 4]
  
      """
      n_arrays = len(arrays)
      if n_arrays == 0:
          raise ValueError("At least one array required as input")
      test_size = options.pop('test_size', None)
      train_size = options.pop('train_size', None)
      random_state = options.pop('random_state', None)
      stratify = options.pop('stratify', None)
  
      if options:
          raise TypeError("Invalid parameters passed: %s" % str(options))
  
      if test_size is None and train_size is None:
          test_size = 0.25
  
      arrays = indexable(*arrays)
  
      if stratify is not None:
          CVClass = StratifiedShuffleSplit
      else:
          CVClass = ShuffleSplit
  
      cv = CVClass(test_size=test_size,
                   train_size=train_size,
                   random_state=random_state)
  
      train, test = next(cv.split(X=arrays[0], y=stratify))
      return list(chain.from_iterable((safe_indexing(a, train),
                                       safe_indexing(a, test)) for a in arrays))
  
  
  train_test_split.__test__ = False  # to avoid a pb with nosetests
  
  def _build_repr(self):
      # XXX This is copied from BaseEstimator's get_params
      cls = self.__class__
      init = getattr(cls.__init__, 'deprecated_original', cls.__init__)
      # Ignore varargs, kw and default values and pop self
      init_signature = signature(init)
      # Consider the constructor parameters excluding 'self'
      if init is object.__init__:
          args = []
      else:
          args = sorted([p.name for p in init_signature.parameters.values()
                         if p.name != 'self' and p.kind != p.VAR_KEYWORD])
      class_name = self.__class__.__name__
      params = dict()
      for key in args:
          # We need deprecation warnings to always be on in order to
          # catch deprecated param values.
          # This is set in utils/__init__.py but it gets overwritten
          # when running under python3 somehow.
          warnings.simplefilter("always", DeprecationWarning)
          try:
              with warnings.catch_warnings(record=True) as w:
                  value = getattr(self, key, None)
              if len(w) and w[0].category == DeprecationWarning:
                  # if the parameter is deprecated, don't show it
                  continue
          finally:
              warnings.filters.pop(0)
          params[key] = value
  
      return '%s(%s)' % (class_name, _pprint(params, offset=len(class_name)))
  