def test_predict():
X = X_dsel_ex1
y = y_dsel_ex1
pool_classifiers = create_pool_classifiers()
single_best_test = SingleBest(pool_classifiers)
single_best_test.fit(X, y)
predicted_labels = single_best_test.predict(X)
assert np.equal(predicted_labels, 0).all()
def test_predict(create_X_y, create_pool_classifiers):
X, y = create_X_y
pool_classifiers = create_pool_classifiers
single_best_test = SingleBest(pool_classifiers=pool_classifiers)
single_best_test.fit(X, y)
predicted_labels = single_best_test.predict(X)
assert np.equal(predicted_labels, 0).all()
def test_label_encoder_base_ensemble():
from sklearn.ensemble import RandomForestClassifier
X, y = make_classification()
y[y == 1] = 2
y = y.astype(np.float)
pool = RandomForestClassifier().fit(X, y)
sb = SingleBest(pool)
sb.fit(X, y)
pred = sb.predict(X)
assert np.isin(sb.classes_, pred).all()
def test_not_fitted():
single_best_test = SingleBest(create_pool_classifiers())
with pytest.raises(NotFittedError):
single_best_test.predict(np.array([1, -1]))
def test_not_fitted():
single_best_test = SingleBest()
with pytest.raises(NotFittedError):
single_best_test.predict(np.array([[1, -1]]))
def test_label_encoder(create_label_encoder_test):
X, y, pool = create_label_encoder_test
sb = SingleBest(pool).fit(X, y)
pred = sb.predict(X)
assert np.array_equal(pred, y)
