def test_contracted_drcif_on_unit_test_data():
"""Test of contracted DrCIF 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)
# train contracted DrCIF
drcif = DrCIF(time_limit_in_minutes=0.025, random_state=0)
drcif.fit(X_train, y_train)
assert len(drcif.estimators_) > 1
assert accuracy_score(y_test, drcif.predict(X_test)) >= 0.8
def test_drcif_on_basic_motions():
"""Test of DrCIF on basic motions data."""
# load basic motions data
X_train, y_train = load_basic_motions(split="train", return_X_y=True)
X_test, y_test = load_basic_motions(split="test", return_X_y=True)
indices = np.random.RandomState(4).choice(len(y_train), 10, replace=False)
# train DrCIF
drcif = DrCIF(n_estimators=10, random_state=0)
drcif.fit(X_train.iloc[indices], y_train[indices])
# assert probabilities are the same
probas = drcif.predict_proba(X_test.iloc[indices])
testing.assert_array_equal(probas, drcif_basic_motions_probas)
def test_contracted_drcif_on_unit_test_data():
"""Test of contracted DrCIF on unit test data."""
# load unit test data
X_train, y_train = load_unit_test(split="train")
# train contracted DrCIF
drcif = DrCIF(
time_limit_in_minutes=0.25,
contract_max_n_estimators=10,
random_state=0,
)
drcif.fit(X_train, y_train)
assert len(drcif.estimators_) > 1
def test_drcif_on_unit_test_data():
"""Test of DrCIF 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 DrCIF
drcif = DrCIF(n_estimators=10, random_state=0, save_transformed_data=True)
drcif.fit(X_train, y_train)
# assert probabilities are the same
probas = drcif.predict_proba(X_test.iloc[indices])
testing.assert_array_equal(probas, drcif_unit_test_probas)
# test train estimate
train_probas = drcif._get_train_probs(X_train, y_train)
train_preds = drcif.classes_[np.argmax(train_probas, axis=1)]
assert accuracy_score(y_train, train_preds) >= 0.85