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.)
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_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_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_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(): 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(): 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)