def test_majority_voting_single_sample():
query = np.array([1, -1])
ensemble_classifiers = create_pool_all_agree(return_value=0, size=10)
ensemble_classifiers += create_pool_all_agree(return_value=2, size=9)
ensemble_classifiers += create_pool_all_agree(return_value=1, size=20)
predicted = majority_voting(ensemble_classifiers, query)
assert predicted.all() == 1 and predicted.size == 1
def test_weighted_majority_voting():
query = np.array([[1, -1], [0, 0], [3, -1]])
ensemble_classifiers = create_pool_all_agree(return_value=0, size=10)
ensemble_classifiers += create_pool_all_agree(return_value=2, size=9)
weights = np.array([([0.5] * 10) + ([0.8] * 9), ([0.5] * 10) + ([0.8] * 9),
([0.5] * 10) + ([0.8] * 9)])
predicted = weighted_majority_voting(ensemble_classifiers, weights, query)
assert predicted.all() == 1 and predicted.size == 3
def test_predict_all_same():
X = X_dsel_ex1
y = np.copy(y_dsel_ex1)
expected = y
oracle_test = Oracle(create_pool_all_agree(return_value=0, size=10))
expected[expected == 1] = -1
predicted_labels = oracle_test.predict(X, y)
assert np.equal(predicted_labels, expected).all()
def create_pool_classifiers_score(prediction, size, score):
pool = create_pool_all_agree(return_value=prediction, size=size)
for clf in pool:
clf.score = MagicMock(return_value=score)
return pool
def test_majority_voting():
query = np.array([[1, -1], [0, 0], [3, -1]])
ensemble_classifiers = create_pool_all_agree(return_value=0, size=10)
ensemble_classifiers += create_pool_all_agree(return_value=2, size=9)
predicted = majority_voting(ensemble_classifiers, query)
assert predicted.all() == 0 and predicted.size == 3