def test_fresh_prince_on_unit_test_data():
"""Test of FreshPRINCE on unit test data."""
# load unit test data
X_train, y_train = load_unit_test(split="train")
X_test, y_test = load_unit_test(split="test")
indices = np.random.RandomState(0).choice(len(y_train), 10, replace=False)
# train FreshPRINCE classifier
fp = FreshPRINCE(
random_state=0,
default_fc_parameters="minimal",
n_estimators=10,
save_transformed_data=True,
)
fp.fit(X_train, y_train)
# assert probabilities are the same
probas = fp.predict_proba(X_test.iloc[indices])
testing.assert_array_almost_equal(probas,
fp_classifier_unit_test_probas,
decimal=2)
# test train estimate
train_probas = fp._get_train_probs(X_train, y_train)
train_preds = fp.classes_[np.argmax(train_probas, axis=1)]
assert accuracy_score(y_train, train_preds) >= 0.75
def test_fresh_prince_on_unit_test_data():
"""Test of FreshPRINCE on unit test data."""
# load unit test data
X_train, y_train = load_unit_test(split="train")
X_test, y_test = load_unit_test(split="test")
indices = np.random.RandomState(0).choice(len(y_train), 10, replace=False)
# train FreshPRINCE classifier
fp = FreshPRINCE(
random_state=0,
default_fc_parameters="minimal",
n_estimators=10,
save_transformed_data=True,
)
fp.fit(X_train, y_train)
score = fp.score(X_test.iloc[indices], y_test[indices])
assert score >= 0.8
def test_fresh_prince_train_estimate():
"""Test of FreshPRINCE train estimate on unit test data."""
# load unit test data
X_train, y_train = load_unit_test(split="train")
# train FreshPRINCE classifier
fp = FreshPRINCE(
n_estimators=2,
default_fc_parameters="minimal",
random_state=0,
save_transformed_data=True,
)
fp.fit(X_train, y_train)
# test train estimate
train_probas = fp._get_train_probs(X_train, y_train)
assert train_probas.shape == (20, 2)
train_preds = fp.classes_[np.argmax(train_probas, axis=1)]
assert accuracy_score(y_train, train_preds) >= 0.6