def test_y_proba_on_gunpoint(): X, y = load_gunpoint(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.1, random_state=42 ) stsf = SupervisedTimeSeriesForest(random_state=42, n_estimators=20) stsf.fit(X_train, y_train) actual = stsf.predict_proba(X_test) np.testing.assert_array_equal(actual, expected)
def test_stsf_on_gunpoint(): """Test of STSF on gun point.""" # load gunpoint data X_train, y_train = load_gunpoint(split="train", return_X_y=True) X_test, y_test = load_gunpoint(split="test", return_X_y=True) indices = np.random.RandomState(0).permutation(10) stsf = SupervisedTimeSeriesForest(n_estimators=20, random_state=0) stsf.fit(X_train.iloc[indices], y_train[indices]) # assert probabilities are the same probas = stsf.predict_proba(X_test.iloc[indices]) testing.assert_array_equal(probas, stsf_gunpoint_probas)
def test_stsf_on_unit_test_data(): """Test of SupervisedTimeSeriesForest on unit test data.""" # load unit test data X_train, y_train = load_unit_test(split="train", return_X_y=True) X_test, y_test = load_unit_test(split="test", return_X_y=True) indices = np.random.RandomState(0).choice(len(y_train), 10, replace=False) # train STSF stsf = SupervisedTimeSeriesForest(n_estimators=10, random_state=0) stsf.fit(X_train, y_train) # assert probabilities are the same probas = stsf.predict_proba(X_test.iloc[indices]) testing.assert_array_equal(probas, stsf_unit_test_probas)