def test_check_outlier_corruption(): # should raise AssertionError decision = np.array([0., 1., 1.5, 2.]) assert_raises(AssertionError, check_outlier_corruption, 1, 2, decision) # should pass decision = np.array([0., 1., 1., 2.]) check_outlier_corruption(1, 2, decision)
def test_check_outlier_corruption(): # should raise AssertionError decision = np.array([0.0, 1.0, 1.5, 2.0]) with raises(AssertionError): check_outlier_corruption(1, 2, decision) # should pass decision = np.array([0.0, 1.0, 1.0, 2.0]) check_outlier_corruption(1, 2, decision)
def test_predicted_outlier_number(expected_outliers): # the number of predicted outliers should be equal to the number of # expected outliers unless there are ties in the abnormality scores. X = iris.data n_samples = X.shape[0] contamination = float(expected_outliers) / n_samples clf = neighbors.LocalOutlierFactor(contamination=contamination) y_pred = clf.fit_predict(X) num_outliers = np.sum(y_pred != 1) if num_outliers != expected_outliers: y_dec = clf.negative_outlier_factor_ check_outlier_corruption(num_outliers, expected_outliers, y_dec)
def test_predicted_outlier_number(): # the number of predicted outliers should be equal to the number of # expected outliers unless there are ties in the abnormality scores. X = iris.data n_samples = X.shape[0] expected_outliers = 30 contamination = float(expected_outliers)/n_samples clf = neighbors.LocalOutlierFactor(contamination=contamination) y_pred = clf.fit_predict(X) num_outliers = np.sum(y_pred != 1) if num_outliers != expected_outliers: y_dec = clf.negative_outlier_factor_ check_outlier_corruption(num_outliers, expected_outliers, y_dec)