def test_classify_with_ds_diff_sizes(create_pool_classifiers):
query = np.ones((10, 2))
predictions = np.ones((5, 3))
des_test = BaseDES(create_pool_classifiers)
with pytest.raises(ValueError):
des_test.classify_with_ds(query, predictions)
def test_predict_proba_selection(create_pool_classifiers):
query = np.array([-1, 1])
pool_classifiers = create_pool_classifiers + create_pool_classifiers
des_test = BaseDES(pool_classifiers, mode='selection')
selected_indices = np.array([0, 1, 5])
selected_classifiers = np.zeros((1, 6), dtype=bool)
selected_classifiers[0, selected_indices] = 1
des_test.select = MagicMock(return_value=selected_classifiers)
des_test.n_classes_ = 2
expected = np.array([0.61, 0.39])
predictions = []
probabilities = []
for clf in des_test.pool_classifiers:
predictions.append(clf.predict(query)[0])
probabilities.append(clf.predict_proba(query)[0])
query = np.atleast_2d(query)
predictions = np.atleast_2d(predictions)
probabilities = np.array(probabilities)
probabilities = np.expand_dims(probabilities, axis=0)
predicted_proba = des_test.predict_proba_with_ds(query, predictions,
probabilities)
assert np.isclose(predicted_proba, expected, atol=0.01).all()
def test_proba_with_ds_diff_sizes():
query = np.ones((10, 2))
predictions = np.ones((5, 3))
probabilities = np.ones((5, 3, 2))
des_test = BaseDES(create_pool_classifiers())
with pytest.raises(ValueError):
des_test.predict_proba_with_ds(query, predictions, probabilities)
def test_predict_proba_weighting_hard_voting(create_pool_classifiers):
query = np.array([[-1, 1]])
expected = np.array([0.4117, 0.5883])
competences = np.array([[0.5, 1.0, 0.2]])
predictions = np.array([[0, 1, 0]])
pool_classifiers = create_pool_classifiers
des_test = BaseDES(pool_classifiers, mode='weighting', voting='hard')
des_test.estimate_competence = MagicMock(return_value=competences)
des_test.n_classes_ = 2
predicted_proba = des_test.predict_proba_with_ds(predictions)
assert np.isclose(predicted_proba, expected, atol=0.01).all()
def test_predict_proba_selection_hard_voting(create_pool_classifiers):
query = np.array([[-1, 1]])
expected = np.array([0.66, 0.33])
predictions = np.array([[0, 1, 0, 0, 1, 0]])
pool_classifiers = create_pool_classifiers + create_pool_classifiers
selected_indices = np.array([[True, True, False, False, False, True]])
des_test = BaseDES(pool_classifiers, mode='selection', voting='hard')
des_test.n_classes_ = 2
des_test.select = MagicMock(return_value=selected_indices)
predicted_proba = des_test.predict_proba_with_ds(predictions)
assert np.isclose(predicted_proba, expected, atol=0.01).all()
def test_classify_instance_selection(create_pool_classifiers):
query = np.array([-1, 1])
pool_classifiers = create_pool_classifiers + create_pool_classifiers
des_test = BaseDES(pool_classifiers, mode='selection')
selected_index = np.array([[True, True, False, False, False, True]])
des_test.select = MagicMock(return_value=selected_index)
predictions = []
for clf in des_test.pool_classifiers:
predictions.append(clf.predict(query)[0])
predicted_label = des_test.classify_with_ds(query, np.array(predictions))
assert predicted_label == 0.0
def test_predict_proba_weighting_soft_voting(create_pool_classifiers):
query = np.array([[-1, 1]])
expected = np.array([0.5769, 0.4231])
competences = np.array([[0.5, 1.0, 0.2]])
predictions = np.array([[0, 1, 0]])
probabilities = np.array([[[0.5, 0.5], [1., 0.], [0.33, 0.67]]])
pool_classifiers = create_pool_classifiers
des_test = BaseDES(pool_classifiers, mode='weighting', voting='soft')
des_test.estimate_competence = MagicMock(return_value=competences)
des_test.n_classes_ = 2
predicted_proba = des_test.predict_proba_with_ds(predictions,
probabilities)
assert np.isclose(predicted_proba, expected, atol=0.01).all()
def test_classify_instance_selection_batch(create_pool_classifiers):
n_samples = 3
query = np.ones((n_samples, 2))
pool_classifiers = create_pool_classifiers + create_pool_classifiers
des_test = BaseDES(pool_classifiers, mode='selection')
selected_index = np.array(
[[True, True, False, False, False, True] * n_samples])
des_test.select = MagicMock(return_value=selected_index)
predictions = []
for clf in des_test.pool_classifiers:
predictions.append(clf.predict(query)[0])
predicted_label = des_test.classify_with_ds(query, np.tile(predictions,
(n_samples, 1)))
assert np.allclose(predicted_label, 0) and predicted_label.size == 3
def test_soft_voting_no_proba(create_X_y):
from sklearn.linear_model import Perceptron
X, y = create_X_y
clf = Perceptron()
clf.fit(X, y)
with pytest.raises(ValueError):
BaseDES([clf, clf], voting='soft').fit(X, y)
def test_predict_proba_selection_soft_voting(create_pool_classifiers):
query = np.array([[-1, 1]])
expected = np.array([0.61, 0.39])
predictions = np.array([[0, 1, 0, 0, 1, 0]])
probabilities = np.array([[[0.5, 0.5], [1., 0.], [0.33, 0.67],
[0.5, 0.5], [1., 0.], [0.33, 0.67]]])
selected_indices = np.array([[True, True, False, False, False, True]])
pool_classifiers = create_pool_classifiers + create_pool_classifiers
des_test = BaseDES(pool_classifiers, mode='selection', voting='soft')
des_test.select = MagicMock(return_value=selected_indices)
des_test.n_classes_ = 2
predicted_proba = des_test.predict_proba_with_ds(predictions,
probabilities)
assert np.isclose(predicted_proba, expected, atol=0.01).all()
def test_predict_proba_hybrid(create_pool_classifiers):
query = np.array([-1, 1])
pool_classifiers = create_pool_classifiers + create_pool_classifiers
des_test = BaseDES(pool_classifiers, mode='hybrid')
des_test.n_classes_ = 2
selected_indices = [0, 1, 5]
competences = np.array([[0.55, 1.0, 0.2, 0.60, 0.75, 0.3]])
expected = np.array([0.5744, 0.4256])
des_test.estimate_competence = MagicMock(return_value=competences)
selected_classifiers = np.zeros((1, 6), dtype=bool)
selected_classifiers[0, selected_indices] = 1
des_test.select = MagicMock(return_value=selected_classifiers)
predictions = []
probabilities = []
for clf in des_test.pool_classifiers:
predictions.append(clf.predict(query)[0])
probabilities.append(clf.predict_proba(query)[0])
query = np.atleast_2d(query)
predictions = np.atleast_2d(predictions)
probabilities = np.array(probabilities)
probabilities = np.expand_dims(probabilities, axis=0)
predicted_proba = des_test.predict_proba_with_ds(query, predictions,
probabilities)
assert np.isclose(predicted_proba, expected, atol=0.01).all()
def test_predict_proba_weighting(create_pool_classifiers):
query = np.array([-1, 1])
pool_classifiers = create_pool_classifiers
des_test = BaseDES(pool_classifiers, mode='weighting')
competences = np.array([[0.5, 1.0, 0.2]])
des_test.estimate_competence = MagicMock(return_value=competences)
des_test.n_classes_ = 2
expected = np.array([0.5769, 0.4231])
predictions = []
probabilities = []
for clf in des_test.pool_classifiers:
predictions.append(clf.predict(query)[0])
probabilities.append(clf.predict_proba(query)[0])
query = np.atleast_2d(query)
predictions = np.atleast_2d(predictions)
probabilities = np.array(probabilities)
probabilities = np.expand_dims(probabilities, axis=0)
predicted_proba = des_test.predict_proba_with_ds(query, predictions,
probabilities)
assert np.isclose(predicted_proba, expected, atol=0.01).all()
def test_predict_proba_hybrid_hard_voting(create_pool_classifiers):
query = np.array([[-1, 1]])
expected = np.array([0.4594, 0.5405])
competences = np.array([[0.55, 1.0, 0.2, 0.60, 0.75, 0.3]])
predictions = np.array([[0, 1, 0, 0, 1, 0]])
selected_indices = np.array([[True, True, False, False, False, True]])
pool_classifiers = create_pool_classifiers + create_pool_classifiers
des_test = BaseDES(pool_classifiers, mode='hybrid', voting='hard')
des_test.n_classes_ = 2
des_test.estimate_competence = MagicMock(return_value=competences)
des_test.select = MagicMock(return_value=selected_indices)
predicted_proba = des_test.predict_proba_with_ds(predictions)
assert np.isclose(predicted_proba, expected, atol=0.01).all()
def test_classify_instance_hybrid(create_pool_classifiers):
query = np.array([-1, 1])
expected = 1
pool_classifiers = create_pool_classifiers + create_pool_classifiers
des_test = BaseDES(pool_classifiers, mode='hybrid')
des_test.classes_ = np.array([0, 1])
des_test.n_classes_ = 2
selected_indices = np.array([[True, True, False, False, False, True]])
competences = np.array([[0.55, 1.0, 0.2, 0.60, 0.75, 0.3]])
des_test.estimate_competence = MagicMock(return_value=competences)
des_test.select = MagicMock(return_value=selected_indices)
predictions = []
for clf in des_test.pool_classifiers:
predictions.append(clf.predict(query)[0])
predicted_label = des_test.classify_with_ds(query, np.array(predictions))
assert expected == predicted_label
def test_classify_instance_weighting(create_pool_classifiers):
query = np.array([-1, 1])
pool_classifiers = create_pool_classifiers + create_pool_classifiers
des_test = BaseDES(pool_classifiers, mode='weighting')
des_test.classes_ = np.array([0, 1])
des_test.n_classes_ = 2
competences = np.array([0.55, 1.0, 0.2, 0.60, 0.75, 0.3])
des_test.estimate_competence = MagicMock(return_value=competences)
predictions = []
for clf in des_test.pool_classifiers:
predictions.append(clf.predict(query)[0])
predicted_label = des_test.classify_with_ds(query, np.array(predictions))
assert predicted_label == 1.0
def test_classify_instance_weighting(create_pool_classifiers):
n_samples = 3
query = np.ones((n_samples, 2))
pool_classifiers = create_pool_classifiers + create_pool_classifiers
des_test = BaseDES(pool_classifiers, mode='weighting')
des_test.classes_ = np.array([0, 1])
des_test.n_classes_ = 2
competences = np.tile([0.55, 1.0, 0.2, 0.60, 0.75, 0.3], (3, 1))
des_test.estimate_competence = MagicMock(return_value=competences)
predictions = []
for clf in des_test.pool_classifiers:
predictions.append(clf.predict(query)[0])
predicted_label = des_test.classify_with_ds(np.tile(predictions, (3, 1)))
assert np.allclose(predicted_label, 1) and predicted_label.size == 3
def test_classify_instance_hybrid_batch():
query = np.ones((3, 2))
expected = 1
pool_classifiers = create_pool_classifiers() + create_pool_classifiers()
des_test = BaseDES(pool_classifiers, mode='hybrid')
des_test.classes_ = np.array([0, 1])
des_test.n_classes_ = 2
selected_indices = np.tile([True, True, False, False, False, True], (3, 1))
competences = np.tile([0.55, 1.0, 0.2, 0.60, 0.75, 0.3], (3, 1))
des_test.estimate_competence = MagicMock(return_value=competences)
des_test.select = MagicMock(return_value=selected_indices)
predictions = []
for clf in des_test.pool_classifiers:
predictions.append(clf.predict(query)[0])
predicted_label = des_test.classify_with_ds(query,
np.tile(predictions, (3, 1)))
assert np.allclose(predicted_label, expected)
def test_wrong_voting_value(voting, create_X_y, create_pool_classifiers):
X, y = create_X_y
pool = create_pool_classifiers
with pytest.raises(ValueError):
BaseDES(pool, voting=voting).fit(X, y)
def test_valid_selection_mode(mode, create_pool_classifiers):
X = np.random.rand(10, 2)
y = np.ones(10)
with pytest.raises(ValueError):
des = BaseDES(create_pool_classifiers, mode=mode)
des.fit(X, y)
def test_selection_method_type(mode):
X = np.random.rand(10, 2)
y = np.ones(10)
with pytest.raises(TypeError):
des = BaseDES(create_pool_classifiers(), mode=mode)
des.fit(X, y)