def test_tde_on_gunpoint():
# 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)
# train tde
tde = TemporalDictionaryEnsemble(random_state=0)
tde.fit(X_train.iloc[indices], y_train[indices])
# assert probabilities are the same
probas = tde.predict_proba(X_test.iloc[indices])
testing.assert_array_equal(probas, tde_gunpoint_probas)
def test_contracted_tde_on_unit_test_data():
"""Test of contracted TDE on unit test data."""
# load unit test data
X_train, y_train = load_unit_test(split="train")
# train contracted TDE
tde = TemporalDictionaryEnsemble(
time_limit_in_minutes=0.25,
contract_max_n_parameter_samples=10,
max_ensemble_size=5,
randomly_selected_params=5,
random_state=0,
)
tde.fit(X_train, y_train)
assert len(tde.estimators_) > 1
def test_contracted_tde_on_unit_test_data():
"""Test of contracted TDE 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 TDE
tde = TemporalDictionaryEnsemble(
time_limit_in_minutes=0.025,
max_ensemble_size=5,
randomly_selected_params=5,
random_state=0,
)
tde.fit(X_train, y_train)
assert len(tde.estimators_) > 1
assert accuracy_score(y_test, tde.predict(X_test)) >= 0.8
def test_tde_on_power_demand():
# load power demand data
X_train, y_train = load_italy_power_demand(split="train", return_X_y=True)
X_test, y_test = load_italy_power_demand(split="test", return_X_y=True)
indices = np.random.RandomState(0).permutation(100)
# train TDE
tde = TemporalDictionaryEnsemble(
n_parameter_samples=50,
max_ensemble_size=10,
randomly_selected_params=40,
random_state=0,
)
tde.fit(X_train, y_train)
score = tde.score(X_test.iloc[indices], y_test[indices])
assert score >= 0.92
def test_tde_on_unit_test_data():
"""Test of TDE 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 TDE
tde = TemporalDictionaryEnsemble(
n_parameter_samples=10,
max_ensemble_size=5,
randomly_selected_params=5,
random_state=0,
)
tde.fit(X_train, y_train)
# assert probabilities are the same
probas = tde.predict_proba(X_test.iloc[indices]).round(6)
testing.assert_array_equal(probas, tde_unit_test_probas)
# test loocv train estimate
train_probas = tde._get_train_probs(X_train, y_train)
train_preds = tde.classes_[np.argmax(train_probas, axis=1)]
assert accuracy_score(y_train, train_preds) >= 0.85
# test oob estimate
train_probas = tde._get_train_probs(X_train,
y_train,
train_estimate_method="oob")
train_preds = tde.classes_[np.argmax(train_probas, axis=1)]
assert accuracy_score(y_train, train_preds) >= 0.8
def test_tde_on_basic_motions():
# 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(0).permutation(20)
# train TDE
tde = TemporalDictionaryEnsemble(
n_parameter_samples=50,
max_ensemble_size=10,
randomly_selected_params=40,
random_state=0,
)
tde.fit(X_train.iloc[indices], y_train[indices])
# assert probabilities are the same
probas = tde.predict_proba(X_test.iloc[indices])
testing.assert_array_equal(probas, tde_basic_motions_probas)
def test_tde_on_basic_motions():
"""Test of TDE 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 TDE
tde = TemporalDictionaryEnsemble(
n_parameter_samples=10,
max_ensemble_size=5,
randomly_selected_params=5,
random_state=0,
)
tde.fit(X_train.iloc[indices], y_train[indices])
# assert probabilities are the same
probas = tde.predict_proba(X_test.iloc[indices]).round(6)
testing.assert_array_equal(probas, tde_basic_motions_probas)
def test_tde_train_estimate():
"""Test of TDE train estimate on unit test data."""
# load unit test data
X_train, y_train = load_unit_test(split="train")
# train TDE
tde = TemporalDictionaryEnsemble(
n_parameter_samples=5,
max_ensemble_size=2,
randomly_selected_params=3,
random_state=0,
save_train_predictions=True,
)
tde.fit(X_train, y_train)
# test loocv train estimate
train_probas = tde._get_train_probs(X_train, y_train)
assert train_probas.shape == (20, 2)
train_preds = tde.classes_[np.argmax(train_probas, axis=1)]
assert accuracy_score(y_train, train_preds) >= 0.6
# test oob estimate
train_probas = tde._get_train_probs(X_train,
y_train,
train_estimate_method="oob")
assert train_probas.shape == (20, 2)
train_preds = tde.classes_[np.argmax(train_probas, axis=1)]
assert accuracy_score(y_train, train_preds) >= 0.6