"""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.)