content: """Test the validation module"""
  from __future__ import division
  
  import sys
  import warnings
  import tempfile
  import os
  from time import sleep
  
  import numpy as np
  from scipy.sparse import coo_matrix, csr_matrix
  
  from sklearn.utils.testing import assert_true
  from sklearn.utils.testing import assert_false
  from sklearn.utils.testing import assert_equal
  from sklearn.utils.testing import assert_almost_equal
  from sklearn.utils.testing import assert_raises
  from sklearn.utils.testing import assert_raise_message
  from sklearn.utils.testing import assert_greater
  from sklearn.utils.testing import assert_less
  from sklearn.utils.testing import assert_array_almost_equal
  from sklearn.utils.testing import assert_array_equal
  from sklearn.utils.testing import assert_warns
  from sklearn.utils.mocking import CheckingClassifier, MockDataFrame
  
  from sklearn.model_selection import cross_val_score
  from sklearn.model_selection import cross_val_predict
  from sklearn.model_selection import permutation_test_score
  from sklearn.model_selection import KFold
  from sklearn.model_selection import StratifiedKFold
  from sklearn.model_selection import LeaveOneOut
  from sklearn.model_selection import LeaveOneGroupOut
  from sklearn.model_selection import LeavePGroupsOut
  from sklearn.model_selection import GroupKFold
  from sklearn.model_selection import GroupShuffleSplit
  from sklearn.model_selection import learning_curve
  from sklearn.model_selection import validation_curve
  from sklearn.model_selection._validation import _check_is_permutation
  
  from sklearn.datasets import make_regression
  from sklearn.datasets import load_boston
  from sklearn.datasets import load_iris
  from sklearn.metrics import explained_variance_score
  from sklearn.metrics import make_scorer
  from sklearn.metrics import precision_score
  
  from sklearn.linear_model import Ridge, LogisticRegression
  from sklearn.linear_model import PassiveAggressiveClassifier
  from sklearn.neighbors import KNeighborsClassifier
  from sklearn.svm import SVC
  from sklearn.cluster import KMeans
  
  from sklearn.preprocessing import Imputer
  from sklearn.pipeline import Pipeline
  
  from sklearn.externals.six.moves import cStringIO as StringIO
  from sklearn.base import BaseEstimator
  from sklearn.multiclass import OneVsRestClassifier
  from sklearn.utils import shuffle
  from sklearn.datasets import make_classification
  from sklearn.datasets import make_multilabel_classification
  
  from sklearn.model_selection.tests.test_split import MockClassifier
  
  
  try:
      WindowsError
  except NameError:
      WindowsError = None
  
  
  class MockImprovingEstimator(BaseEstimator):
      """Dummy classifier to test the learning curve"""
      def __init__(self, n_max_train_sizes):
          self.n_max_train_sizes = n_max_train_sizes
          self.train_sizes = 0
          self.X_subset = None
  
      def fit(self, X_subset, y_subset=None):
          self.X_subset = X_subset
          self.train_sizes = X_subset.shape[0]
          return self
  
      def predict(self, X):
          raise NotImplementedError
  
      def score(self, X=None, Y=None):
          # training score becomes worse (2 -> 1), test error better (0 -> 1)
          if self._is_training_data(X):
              return 2. - float(self.train_sizes) / self.n_max_train_sizes
          else:
              return float(self.train_sizes) / self.n_max_train_sizes
  
      def _is_training_data(self, X):
          return X is self.X_subset
  
  
  class MockIncrementalImprovingEstimator(MockImprovingEstimator):
      """Dummy classifier that provides partial_fit"""
      def __init__(self, n_max_train_sizes):
          super(MockIncrementalImprovingEstimator,
                self).__init__(n_max_train_sizes)
          self.x = None
  
      def _is_training_data(self, X):
          return self.x in X
  
      def partial_fit(self, X, y=None, **params):
          self.train_sizes += X.shape[0]
          self.x = X[0]
  
  
  class MockEstimatorWithParameter(BaseEstimator):
      """Dummy classifier to test the validation curve"""
      def __init__(self, param=0.5):
          self.X_subset = None
          self.param = param
  
      def fit(self, X_subset, y_subset):
          self.X_subset = X_subset
          self.train_sizes = X_subset.shape[0]
          return self
  
      def predict(self, X):
          raise NotImplementedError
  
      def score(self, X=None, y=None):
          return self.param if self._is_training_data(X) else 1 - self.param
  
      def _is_training_data(self, X):
          return X is self.X_subset
  
  
  # XXX: use 2D array, since 1D X is being detected as a single sample in
  # check_consistent_length
  X = np.ones((10, 2))
  X_sparse = coo_matrix(X)
  y = np.array([0, 0, 1, 1, 2, 2, 3, 3, 4, 4])
  # The number of samples per class needs to be > n_splits,
  # for StratifiedKFold(n_splits=3)
  y2 = np.array([1, 1, 1, 2, 2, 2, 3, 3, 3, 3])
  
  
  def test_cross_val_score():
      clf = MockClassifier()
  
      for a in range(-10, 10):
          clf.a = a
          # Smoke test
          scores = cross_val_score(clf, X, y2)
          assert_array_equal(scores, clf.score(X, y2))
  
          # test with multioutput y
          multioutput_y = np.column_stack([y2, y2[::-1]])
          scores = cross_val_score(clf, X_sparse, multioutput_y)
          assert_array_equal(scores, clf.score(X_sparse, multioutput_y))
  
          scores = cross_val_score(clf, X_sparse, y2)
          assert_array_equal(scores, clf.score(X_sparse, y2))
  
          # test with multioutput y
          scores = cross_val_score(clf, X_sparse, multioutput_y)
          assert_array_equal(scores, clf.score(X_sparse, multioutput_y))
  
      # test with X and y as list
      list_check = lambda x: isinstance(x, list)
      clf = CheckingClassifier(check_X=list_check)
      scores = cross_val_score(clf, X.tolist(), y2.tolist())
  
      clf = CheckingClassifier(check_y=list_check)
      scores = cross_val_score(clf, X, y2.tolist())
  
      assert_raises(ValueError, cross_val_score, clf, X, y2, scoring="sklearn")
  
      # test with 3d X and
      X_3d = X[:, :, np.newaxis]
      clf = MockClassifier(allow_nd=True)
      scores = cross_val_score(clf, X_3d, y2)
  
      clf = MockClassifier(allow_nd=False)
      assert_raises(ValueError, cross_val_score, clf, X_3d, y2)
  
  
  def test_cross_val_score_predict_groups():
      # Check if ValueError (when groups is None) propagates to cross_val_score
      # and cross_val_predict
      # And also check if groups is correctly passed to the cv object
      X, y = make_classification(n_samples=20, n_classes=2, random_state=0)
  
      clf = SVC(kernel="linear")
  
      group_cvs = [LeaveOneGroupOut(), LeavePGroupsOut(2), GroupKFold(),
                   GroupShuffleSplit()]
      for cv in group_cvs:
          assert_raise_message(ValueError,
                               "The groups parameter should not be None",
                               cross_val_score, estimator=clf, X=X, y=y, cv=cv)
          assert_raise_message(ValueError,
                               "The groups parameter should not be None",
                               cross_val_predict, estimator=clf, X=X, y=y, cv=cv)
  
  
  def test_cross_val_score_pandas():
      # check cross_val_score doesn't destroy pandas dataframe
      types = [(MockDataFrame, MockDataFrame)]
      try:
          from pandas import Series, DataFrame
          types.append((Series, DataFrame))
      except ImportError:
          pass
      for TargetType, InputFeatureType in types:
          # X dataframe, y series
          # 3 fold cross val is used so we need atleast 3 samples per class
          X_df, y_ser = InputFeatureType(X), TargetType(y2)
          check_df = lambda x: isinstance(x, InputFeatureType)
          check_series = lambda x: isinstance(x, TargetType)
          clf = CheckingClassifier(check_X=check_df, check_y=check_series)
          cross_val_score(clf, X_df, y_ser)
  
  
  def test_cross_val_score_mask():
      # test that cross_val_score works with boolean masks
      svm = SVC(kernel="linear")
      iris = load_iris()
      X, y = iris.data, iris.target
      kfold = KFold(5)
      scores_indices = cross_val_score(svm, X, y, cv=kfold)
      kfold = KFold(5)
      cv_masks = []
      for train, test in kfold.split(X, y):
          mask_train = np.zeros(len(y), dtype=np.bool)
          mask_test = np.zeros(len(y), dtype=np.bool)
          mask_train[train] = 1
          mask_test[test] = 1
          cv_masks.append((train, test))
      scores_masks = cross_val_score(svm, X, y, cv=cv_masks)
      assert_array_equal(scores_indices, scores_masks)
  
  
  def test_cross_val_score_precomputed():
      # test for svm with precomputed kernel
      svm = SVC(kernel="precomputed")
      iris = load_iris()
      X, y = iris.data, iris.target
      linear_kernel = np.dot(X, X.T)
      score_precomputed = cross_val_score(svm, linear_kernel, y)
      svm = SVC(kernel="linear")
      score_linear = cross_val_score(svm, X, y)
      assert_array_equal(score_precomputed, score_linear)
  
      # Error raised for non-square X
      svm = SVC(kernel="precomputed")
      assert_raises(ValueError, cross_val_score, svm, X, y)
  
      # test error is raised when the precomputed kernel is not array-like
      # or sparse
      assert_raises(ValueError, cross_val_score, svm,
                    linear_kernel.tolist(), y)
  
  
  def test_cross_val_score_fit_params():
      clf = MockClassifier()
      n_samples = X.shape[0]
      n_classes = len(np.unique(y))
  
      W_sparse = coo_matrix((np.array([1]), (np.array([1]), np.array([0]))),
                            shape=(10, 1))
      P_sparse = coo_matrix(np.eye(5))
  
      DUMMY_INT = 42
      DUMMY_STR = '42'
      DUMMY_OBJ = object()
  
      def assert_fit_params(clf):
          # Function to test that the values are passed correctly to the
          # classifier arguments for non-array type
  
          assert_equal(clf.dummy_int, DUMMY_INT)
          assert_equal(clf.dummy_str, DUMMY_STR)
          assert_equal(clf.dummy_obj, DUMMY_OBJ)
  
      fit_params = {'sample_weight': np.ones(n_samples),
                    'class_prior': np.ones(n_classes) / n_classes,
                    'sparse_sample_weight': W_sparse,
                    'sparse_param': P_sparse,
                    'dummy_int': DUMMY_INT,
                    'dummy_str': DUMMY_STR,
                    'dummy_obj': DUMMY_OBJ,
                    'callback': assert_fit_params}
      cross_val_score(clf, X, y, fit_params=fit_params)
  
  
  def test_cross_val_score_score_func():
      clf = MockClassifier()
      _score_func_args = []
  
      def score_func(y_test, y_predict):
          _score_func_args.append((y_test, y_predict))
          return 1.0
  
      with warnings.catch_warnings(record=True):
          scoring = make_scorer(score_func)
          score = cross_val_score(clf, X, y, scoring=scoring)
      assert_array_equal(score, [1.0, 1.0, 1.0])
      assert len(_score_func_args) == 3
  
  
  def test_cross_val_score_errors():
      class BrokenEstimator:
          pass
  
      assert_raises(TypeError, cross_val_score, BrokenEstimator(), X)
  
  
  def test_cross_val_score_with_score_func_classification():
      iris = load_iris()
      clf = SVC(kernel='linear')
  
      # Default score (should be the accuracy score)
      scores = cross_val_score(clf, iris.data, iris.target, cv=5)
      assert_array_almost_equal(scores, [0.97, 1., 0.97, 0.97, 1.], 2)
  
      # Correct classification score (aka. zero / one score) - should be the
      # same as the default estimator score
      zo_scores = cross_val_score(clf, iris.data, iris.target,
                                  scoring="accuracy", cv=5)
      assert_array_almost_equal(zo_scores, [0.97, 1., 0.97, 0.97, 1.], 2)
  
      # F1 score (class are balanced so f1_score should be equal to zero/one
      # score
      f1_scores = cross_val_score(clf, iris.data, iris.target,
                                  scoring="f1_weighted", cv=5)
      assert_array_almost_equal(f1_scores, [0.97, 1., 0.97, 0.97, 1.], 2)
  
  
  def test_cross_val_score_with_score_func_regression():
      X, y = make_regression(n_samples=30, n_features=20, n_informative=5,
                             random_state=0)
      reg = Ridge()
  
      # Default score of the Ridge regression estimator
      scores = cross_val_score(reg, X, y, cv=5)
      assert_array_almost_equal(scores, [0.94, 0.97, 0.97, 0.99, 0.92], 2)
  
      # R2 score (aka. determination coefficient) - should be the
      # same as the default estimator score
      r2_scores = cross_val_score(reg, X, y, scoring="r2", cv=5)
      assert_array_almost_equal(r2_scores, [0.94, 0.97, 0.97, 0.99, 0.92], 2)
  
      # Mean squared error; this is a loss function, so "scores" are negative
      neg_mse_scores = cross_val_score(reg, X, y, cv=5,
                                       scoring="neg_mean_squared_error")
      expected_neg_mse = np.array([-763.07, -553.16, -274.38, -273.26, -1681.99])
      assert_array_almost_equal(neg_mse_scores, expected_neg_mse, 2)
  
      # Explained variance
      scoring = make_scorer(explained_variance_score)
      ev_scores = cross_val_score(reg, X, y, cv=5, scoring=scoring)
      assert_array_almost_equal(ev_scores, [0.94, 0.97, 0.97, 0.99, 0.92], 2)
  
  
  def test_permutation_score():
      iris = load_iris()
      X = iris.data
      X_sparse = coo_matrix(X)
      y = iris.target
      svm = SVC(kernel='linear')
      cv = StratifiedKFold(2)
  
      score, scores, pvalue = permutation_test_score(
          svm, X, y, n_permutations=30, cv=cv, scoring="accuracy")
      assert_greater(score, 0.9)
      assert_almost_equal(pvalue, 0.0, 1)
  
      score_group, _, pvalue_group = permutation_test_score(
          svm, X, y, n_permutations=30, cv=cv, scoring="accuracy",
          groups=np.ones(y.size), random_state=0)
      assert_true(score_group == score)
      assert_true(pvalue_group == pvalue)
  
      # check that we obtain the same results with a sparse representation
      svm_sparse = SVC(kernel='linear')
      cv_sparse = StratifiedKFold(2)
      score_group, _, pvalue_group = permutation_test_score(
          svm_sparse, X_sparse, y, n_permutations=30, cv=cv_sparse,
          scoring="accuracy", groups=np.ones(y.size), random_state=0)
  
      assert_true(score_group == score)
      assert_true(pvalue_group == pvalue)
  
      # test with custom scoring object
      def custom_score(y_true, y_pred):
          return (((y_true == y_pred).sum() - (y_true != y_pred).sum()) /
                  y_true.shape[0])
  
      scorer = make_scorer(custom_score)
      score, _, pvalue = permutation_test_score(
          svm, X, y, n_permutations=100, scoring=scorer, cv=cv, random_state=0)
      assert_almost_equal(score, .93, 2)
      assert_almost_equal(pvalue, 0.01, 3)
  
      # set random y
      y = np.mod(np.arange(len(y)), 3)
  
      score, scores, pvalue = permutation_test_score(
          svm, X, y, n_permutations=30, cv=cv, scoring="accuracy")
  
      assert_less(score, 0.5)
      assert_greater(pvalue, 0.2)
  
  
  def test_permutation_test_score_allow_nans():
      # Check that permutation_test_score allows input data with NaNs
      X = np.arange(200, dtype=np.float64).reshape(10, -1)
      X[2, :] = np.nan
      y = np.repeat([0, 1], X.shape[0] / 2)
      p = Pipeline([
          ('imputer', Imputer(strategy='mean', missing_values='NaN')),
          ('classifier', MockClassifier()),
      ])
      permutation_test_score(p, X, y, cv=5)
  
  
  def test_cross_val_score_allow_nans():
      # Check that cross_val_score allows input data with NaNs
      X = np.arange(200, dtype=np.float64).reshape(10, -1)
      X[2, :] = np.nan
      y = np.repeat([0, 1], X.shape[0] / 2)
      p = Pipeline([
          ('imputer', Imputer(strategy='mean', missing_values='NaN')),
          ('classifier', MockClassifier()),
      ])
      cross_val_score(p, X, y, cv=5)
  
  
  def test_cross_val_score_multilabel():
      X = np.array([[-3, 4], [2, 4], [3, 3], [0, 2], [-3, 1],
                    [-2, 1], [0, 0], [-2, -1], [-1, -2], [1, -2]])
      y = np.array([[1, 1], [0, 1], [0, 1], [0, 1], [1, 1],
                    [0, 1], [1, 0], [1, 1], [1, 0], [0, 0]])
      clf = KNeighborsClassifier(n_neighbors=1)
      scoring_micro = make_scorer(precision_score, average='micro')
      scoring_macro = make_scorer(precision_score, average='macro')
      scoring_samples = make_scorer(precision_score, average='samples')
      score_micro = cross_val_score(clf, X, y, scoring=scoring_micro, cv=5)
      score_macro = cross_val_score(clf, X, y, scoring=scoring_macro, cv=5)
      score_samples = cross_val_score(clf, X, y, scoring=scoring_samples, cv=5)
      assert_almost_equal(score_micro, [1, 1 / 2, 3 / 4, 1 / 2, 1 / 3])
      assert_almost_equal(score_macro, [1, 1 / 2, 3 / 4, 1 / 2, 1 / 4])
      assert_almost_equal(score_samples, [1, 1 / 2, 3 / 4, 1 / 2, 1 / 4])
  
  
  def test_cross_val_predict():
      boston = load_boston()
      X, y = boston.data, boston.target
      cv = KFold()
  
      est = Ridge()
  
      # Naive loop (should be same as cross_val_predict):
      preds2 = np.zeros_like(y)
      for train, test in cv.split(X, y):
          est.fit(X[train], y[train])
          preds2[test] = est.predict(X[test])
  
      preds = cross_val_predict(est, X, y, cv=cv)
      assert_array_almost_equal(preds, preds2)
  
      preds = cross_val_predict(est, X, y)
      assert_equal(len(preds), len(y))
  
      cv = LeaveOneOut()
      preds = cross_val_predict(est, X, y, cv=cv)
      assert_equal(len(preds), len(y))
  
      Xsp = X.copy()
      Xsp *= (Xsp > np.median(Xsp))
      Xsp = coo_matrix(Xsp)
      preds = cross_val_predict(est, Xsp, y)
      assert_array_almost_equal(len(preds), len(y))
  
      preds = cross_val_predict(KMeans(), X)
      assert_equal(len(preds), len(y))
  
      class BadCV():
          def split(self, X, y=None, groups=None):
              for i in range(4):
                  yield np.array([0, 1, 2, 3]), np.array([4, 5, 6, 7, 8])
  
      assert_raises(ValueError, cross_val_predict, est, X, y, cv=BadCV())
  
  
  def test_cross_val_predict_input_types():
      iris = load_iris()
      X, y = iris.data, iris.target
      X_sparse = coo_matrix(X)
      multioutput_y = np.column_stack([y, y[::-1]])
  
      clf = Ridge(fit_intercept=False, random_state=0)
      # 3 fold cv is used --> atleast 3 samples per class
      # Smoke test
      predictions = cross_val_predict(clf, X, y)
      assert_equal(predictions.shape, (150,))
  
      # test with multioutput y
      predictions = cross_val_predict(clf, X_sparse, multioutput_y)
      assert_equal(predictions.shape, (150, 2))
  
      predictions = cross_val_predict(clf, X_sparse, y)
      assert_array_equal(predictions.shape, (150,))
  
      # test with multioutput y
      predictions = cross_val_predict(clf, X_sparse, multioutput_y)
      assert_array_equal(predictions.shape, (150, 2))
  
      # test with X and y as list
      list_check = lambda x: isinstance(x, list)
      clf = CheckingClassifier(check_X=list_check)
      predictions = cross_val_predict(clf, X.tolist(), y.tolist())
  
      clf = CheckingClassifier(check_y=list_check)
      predictions = cross_val_predict(clf, X, y.tolist())
  
      # test with 3d X and
      X_3d = X[:, :, np.newaxis]
      check_3d = lambda x: x.ndim == 3
      clf = CheckingClassifier(check_X=check_3d)
      predictions = cross_val_predict(clf, X_3d, y)
      assert_array_equal(predictions.shape, (150,))
  
  
  def test_cross_val_predict_pandas():
      # check cross_val_score doesn't destroy pandas dataframe
      types = [(MockDataFrame, MockDataFrame)]
      try:
          from pandas import Series, DataFrame
          types.append((Series, DataFrame))
      except ImportError:
          pass
      for TargetType, InputFeatureType in types:
          # X dataframe, y series
          X_df, y_ser = InputFeatureType(X), TargetType(y2)
          check_df = lambda x: isinstance(x, InputFeatureType)
          check_series = lambda x: isinstance(x, TargetType)
          clf = CheckingClassifier(check_X=check_df, check_y=check_series)
          cross_val_predict(clf, X_df, y_ser)
  
  
  def test_cross_val_score_sparse_fit_params():
      iris = load_iris()
      X, y = iris.data, iris.target
      clf = MockClassifier()
      fit_params = {'sparse_sample_weight': coo_matrix(np.eye(X.shape[0]))}
      a = cross_val_score(clf, X, y, fit_params=fit_params)
      assert_array_equal(a, np.ones(3))
  
  
  def test_learning_curve():
      X, y = make_classification(n_samples=30, n_features=1, n_informative=1,
                                 n_redundant=0, n_classes=2,
                                 n_clusters_per_class=1, random_state=0)
      estimator = MockImprovingEstimator(20)
      with warnings.catch_warnings(record=True) as w:
          train_sizes, train_scores, test_scores = learning_curve(
              estimator, X, y, cv=3, train_sizes=np.linspace(0.1, 1.0, 10))
      if len(w) > 0:
          raise RuntimeError("Unexpected warning: %r" % w[0].message)
      assert_equal(train_scores.shape, (10, 3))
      assert_equal(test_scores.shape, (10, 3))
      assert_array_equal(train_sizes, np.linspace(2, 20, 10))
      assert_array_almost_equal(train_scores.mean(axis=1),
                                np.linspace(1.9, 1.0, 10))
      assert_array_almost_equal(test_scores.mean(axis=1),
                                np.linspace(0.1, 1.0, 10))
  
  
  def test_learning_curve_unsupervised():
      X, _ = make_classification(n_samples=30, n_features=1, n_informative=1,
                                 n_redundant=0, n_classes=2,
                                 n_clusters_per_class=1, random_state=0)
      estimator = MockImprovingEstimator(20)
      train_sizes, train_scores, test_scores = learning_curve(
          estimator, X, y=None, cv=3, train_sizes=np.linspace(0.1, 1.0, 10))
      assert_array_equal(train_sizes, np.linspace(2, 20, 10))
      assert_array_almost_equal(train_scores.mean(axis=1),
                                np.linspace(1.9, 1.0, 10))
      assert_array_almost_equal(test_scores.mean(axis=1),
                                np.linspace(0.1, 1.0, 10))
  
  
  def test_learning_curve_verbose():
      X, y = make_classification(n_samples=30, n_features=1, n_informative=1,
                                 n_redundant=0, n_classes=2,
                                 n_clusters_per_class=1, random_state=0)
      estimator = MockImprovingEstimator(20)
  
      old_stdout = sys.stdout
      sys.stdout = StringIO()
      try:
          train_sizes, train_scores, test_scores = \
              learning_curve(estimator, X, y, cv=3, verbose=1)
      finally:
          out = sys.stdout.getvalue()
          sys.stdout.close()
          sys.stdout = old_stdout
  
      assert("[learning_curve]" in out)
  
  
  def test_learning_curve_incremental_learning_not_possible():
      X, y = make_classification(n_samples=2, n_features=1, n_informative=1,
                                 n_redundant=0, n_classes=2,
                                 n_clusters_per_class=1, random_state=0)
      # The mockup does not have partial_fit()
      estimator = MockImprovingEstimator(1)
      assert_raises(ValueError, learning_curve, estimator, X, y,
                    exploit_incremental_learning=True)
  
  
  def test_learning_curve_incremental_learning():
      X, y = make_classification(n_samples=30, n_features=1, n_informative=1,
                                 n_redundant=0, n_classes=2,
                                 n_clusters_per_class=1, random_state=0)
      estimator = MockIncrementalImprovingEstimator(20)
      train_sizes, train_scores, test_scores = learning_curve(
          estimator, X, y, cv=3, exploit_incremental_learning=True,
          train_sizes=np.linspace(0.1, 1.0, 10))
      assert_array_equal(train_sizes, np.linspace(2, 20, 10))
      assert_array_almost_equal(train_scores.mean(axis=1),
                                np.linspace(1.9, 1.0, 10))
      assert_array_almost_equal(test_scores.mean(axis=1),
                                np.linspace(0.1, 1.0, 10))
  
  
  def test_learning_curve_incremental_learning_unsupervised():
      X, _ = make_classification(n_samples=30, n_features=1, n_informative=1,
                                 n_redundant=0, n_classes=2,
                                 n_clusters_per_class=1, random_state=0)
      estimator = MockIncrementalImprovingEstimator(20)
      train_sizes, train_scores, test_scores = learning_curve(
          estimator, X, y=None, cv=3, exploit_incremental_learning=True,
          train_sizes=np.linspace(0.1, 1.0, 10))
      assert_array_equal(train_sizes, np.linspace(2, 20, 10))
      assert_array_almost_equal(train_scores.mean(axis=1),
                                np.linspace(1.9, 1.0, 10))
      assert_array_almost_equal(test_scores.mean(axis=1),
                                np.linspace(0.1, 1.0, 10))
  
  
  def test_learning_curve_batch_and_incremental_learning_are_equal():
      X, y = make_classification(n_samples=30, n_features=1, n_informative=1,
                                 n_redundant=0, n_classes=2,
                                 n_clusters_per_class=1, random_state=0)
      train_sizes = np.linspace(0.2, 1.0, 5)
      estimator = PassiveAggressiveClassifier(n_iter=1, shuffle=False)
  
      train_sizes_inc, train_scores_inc, test_scores_inc = \
          learning_curve(
              estimator, X, y, train_sizes=train_sizes,
              cv=3, exploit_incremental_learning=True)
      train_sizes_batch, train_scores_batch, test_scores_batch = \
          learning_curve(
              estimator, X, y, cv=3, train_sizes=train_sizes,
              exploit_incremental_learning=False)
  
      assert_array_equal(train_sizes_inc, train_sizes_batch)
      assert_array_almost_equal(train_scores_inc.mean(axis=1),
                                train_scores_batch.mean(axis=1))
      assert_array_almost_equal(test_scores_inc.mean(axis=1),
                                test_scores_batch.mean(axis=1))
  
  
  def test_learning_curve_n_sample_range_out_of_bounds():
      X, y = make_classification(n_samples=30, n_features=1, n_informative=1,
                                 n_redundant=0, n_classes=2,
                                 n_clusters_per_class=1, random_state=0)
      estimator = MockImprovingEstimator(20)
      assert_raises(ValueError, learning_curve, estimator, X, y, cv=3,
                    train_sizes=[0, 1])
      assert_raises(ValueError, learning_curve, estimator, X, y, cv=3,
                    train_sizes=[0.0, 1.0])
      assert_raises(ValueError, learning_curve, estimator, X, y, cv=3,
                    train_sizes=[0.1, 1.1])
      assert_raises(ValueError, learning_curve, estimator, X, y, cv=3,
                    train_sizes=[0, 20])
      assert_raises(ValueError, learning_curve, estimator, X, y, cv=3,
                    train_sizes=[1, 21])
  
  
  def test_learning_curve_remove_duplicate_sample_sizes():
      X, y = make_classification(n_samples=3, n_features=1, n_informative=1,
                                 n_redundant=0, n_classes=2,
                                 n_clusters_per_class=1, random_state=0)
      estimator = MockImprovingEstimator(2)
      train_sizes, _, _ = assert_warns(
          RuntimeWarning, learning_curve, estimator, X, y, cv=3,
          train_sizes=np.linspace(0.33, 1.0, 3))
      assert_array_equal(train_sizes, [1, 2])
  
  
  def test_learning_curve_with_boolean_indices():
      X, y = make_classification(n_samples=30, n_features=1, n_informative=1,
                                 n_redundant=0, n_classes=2,
                                 n_clusters_per_class=1, random_state=0)
      estimator = MockImprovingEstimator(20)
      cv = KFold(n_splits=3)
      train_sizes, train_scores, test_scores = learning_curve(
          estimator, X, y, cv=cv, train_sizes=np.linspace(0.1, 1.0, 10))
      assert_array_equal(train_sizes, np.linspace(2, 20, 10))
      assert_array_almost_equal(train_scores.mean(axis=1),
                                np.linspace(1.9, 1.0, 10))
      assert_array_almost_equal(test_scores.mean(axis=1),
                                np.linspace(0.1, 1.0, 10))
  
  
  def test_validation_curve():
      X, y = make_classification(n_samples=2, n_features=1, n_informative=1,
                                 n_redundant=0, n_classes=2,
                                 n_clusters_per_class=1, random_state=0)
      param_range = np.linspace(0, 1, 10)
      with warnings.catch_warnings(record=True) as w:
          train_scores, test_scores = validation_curve(
              MockEstimatorWithParameter(), X, y, param_name="param",
              param_range=param_range, cv=2
          )
      if len(w) > 0:
          raise RuntimeError("Unexpected warning: %r" % w[0].message)
  
      assert_array_almost_equal(train_scores.mean(axis=1), param_range)
      assert_array_almost_equal(test_scores.mean(axis=1), 1 - param_range)
  
  
  def test_check_is_permutation():
      rng = np.random.RandomState(0)
      p = np.arange(100)
      rng.shuffle(p)
      assert_true(_check_is_permutation(p, 100))
      assert_false(_check_is_permutation(np.delete(p, 23), 100))
  
      p[0] = 23
      assert_false(_check_is_permutation(p, 100))
  
      # Check if the additional duplicate indices are caught
      assert_false(_check_is_permutation(np.hstack((p, 0)), 100))
  
  
  def test_cross_val_predict_sparse_prediction():
      # check that cross_val_predict gives same result for sparse and dense input
      X, y = make_multilabel_classification(n_classes=2, n_labels=1,
                                            allow_unlabeled=False,
                                            return_indicator=True,
                                            random_state=1)
      X_sparse = csr_matrix(X)
      y_sparse = csr_matrix(y)
      classif = OneVsRestClassifier(SVC(kernel='linear'))
      preds = cross_val_predict(classif, X, y, cv=10)
      preds_sparse = cross_val_predict(classif, X_sparse, y_sparse, cv=10)
      preds_sparse = preds_sparse.toarray()
      assert_array_almost_equal(preds_sparse, preds)
  
  
  def test_cross_val_predict_with_method():
      iris = load_iris()
      X, y = iris.data, iris.target
      X, y = shuffle(X, y, random_state=0)
      classes = len(set(y))
  
      kfold = KFold(len(iris.target))
  
      methods = ['decision_function', 'predict_proba', 'predict_log_proba']
      for method in methods:
          est = LogisticRegression()
  
          predictions = cross_val_predict(est, X, y, method=method)
          assert_equal(len(predictions), len(y))
  
          expected_predictions = np.zeros([len(y), classes])
          func = getattr(est, method)
  
          # Naive loop (should be same as cross_val_predict):
          for train, test in kfold.split(X, y):
              est.fit(X[train], y[train])
              expected_predictions[test] = func(X[test])
  
          predictions = cross_val_predict(est, X, y, method=method,
                                          cv=kfold)
          assert_array_almost_equal(expected_predictions, predictions)
  
  
  def test_score_memmap():
      # Ensure a scalar score of memmap type is accepted
      iris = load_iris()
      X, y = iris.data, iris.target
      clf = MockClassifier()
      tf = tempfile.NamedTemporaryFile(mode='wb', delete=False)
      tf.write(b'Hello world!!!!!')
      tf.close()
      scores = np.memmap(tf.name, dtype=np.float64)
      score = np.memmap(tf.name, shape=(), mode='r', dtype=np.float64)
      try:
          cross_val_score(clf, X, y, scoring=lambda est, X, y: score)
          # non-scalar should still fail
          assert_raises(ValueError, cross_val_score, clf, X, y,
                        scoring=lambda est, X, y: scores)
      finally:
          # Best effort to release the mmap file handles before deleting the
          # backing file under Windows
          scores, score = None, None
          for _ in range(3):
              try:
                  os.unlink(tf.name)
                  break
              except WindowsError:
                  sleep(1.)
  