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)