def test_predict_proba_selection():
query = np.array([-1, 1])
pool_classifiers = create_pool_classifiers() + create_pool_classifiers()
des_test = DES(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_predict_proba_hybrid():
query = np.array([-1, 1])
pool_classifiers = create_pool_classifiers() + create_pool_classifiers()
des_test = DES(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_selection():
query = np.array([-1, 1])
pool_classifiers = create_pool_classifiers() + create_pool_classifiers()
des_test = DES(pool_classifiers, mode='selection')
selected_indices = [0, 1, 5]
des_test.select = MagicMock(return_value=selected_indices)
des_test.n_classes = 2
expected = np.array([0.61, 0.39])
predicted_proba = des_test.predict_proba_instance(query)
assert np.isclose(predicted_proba, expected, atol=0.01).all()
def test_classify_instance_weighting():
query = np.atleast_2d([-1, 1])
pool_classifiers = create_pool_classifiers() + create_pool_classifiers()
des_test = DES(pool_classifiers, mode='weighting')
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_instance(query, np.array(predictions))
assert predicted_label == 1.0
def test_classify_instance_selection():
query = np.array([-1, 1])
pool_classifiers = create_pool_classifiers() + create_pool_classifiers()
des_test = DES(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_classify_instance_selection():
query = np.atleast_2d([-1, 1])
pool_classifiers = create_pool_classifiers() + create_pool_classifiers()
des_test = DES(pool_classifiers, mode='selection')
# competences = [0.55, 1.0, 0.2, 0.65, 0.75, 0.8]
selected_index = [0, 1, 5]
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_instance(query, np.array(predictions))
assert predicted_label == 0.0
def test_classify_instance_selection_batch():
n_samples = 3
query = np.ones((n_samples, 2))
pool_classifiers = create_pool_classifiers() + create_pool_classifiers()
des_test = DES(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_predict_proba_hybrid():
query = np.array([-1, 1])
pool_classifiers = create_pool_classifiers() + create_pool_classifiers()
des_test = DES(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)
des_test.select = MagicMock(return_value=selected_indices)
predicted_proba = des_test.predict_proba_instance(query)
assert np.isclose(predicted_proba, expected, atol=0.01).all()
def test_classify_with_ds_diff_sizes():
query = np.ones((10, 2))
predictions = np.ones((5, 3))
des_test = DES(create_pool_classifiers())
with pytest.raises(ValueError):
des_test.classify_with_ds(query, predictions)
def test_diversity_metric_ratio():
test = DESClustering(create_pool_classifiers() * 10, metric='ratio')
# Mocking this method to avoid preprocessing the cluster information that
# is not required in this test.
test._preprocess_clusters = MagicMock(return_value=1)
test.fit(X_dsel_ex1, y_dsel_ex1)
assert test.diversity_func_ == ratio_errors
def test_classify_instance_weighting_batch():
n_samples = 3
query = np.ones((n_samples, 2))
pool_classifiers = create_pool_classifiers() + create_pool_classifiers()
des_test = DES(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(query, np.tile(predictions, (3, 1)))
assert np.allclose(predicted_label, 1) and predicted_label.size == 3
def test_classify_instance():
query = np.atleast_2d([1, -1])
clustering_test = DESClustering(create_pool_classifiers() * 4, k=2)
clustering_test.select = MagicMock(return_value=[0, 1, 2, 3, 5, 6, 7, 9])
predicted = clustering_test.classify_instance(query)
assert predicted == 0
def test_classify_with_ds_single_sample():
query = np.ones(2)
predictions = np.array([0, 1, 0])
desknn_test = DESClustering(create_pool_classifiers())
desknn_test.select = MagicMock(return_value=np.array([[0, 2]]))
result = desknn_test.classify_with_ds(query, predictions)
assert np.allclose(result, 0)
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_fit():
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)
assert single_best_test.best_clf_index == 0 or single_best_test.best_clf_index == 2
def test_predict():
X = X_dsel_ex1
y = y_dsel_ex1
oracle_test = Oracle(create_pool_classifiers())
predicted_labels = oracle_test.predict(X, y)
assert np.equal(predicted_labels, y).all()
assert oracle_test.score(X, y) == 1.0
def test_select():
query = np.atleast_2d([1, -1])
clustering_test = DESClustering(create_pool_classifiers() * 2, k=2)
clustering_test.roc_algorithm.predict = MagicMock(return_value=[0])
clustering_test.indices = np.array([[0, 2], [1, 4]])
assert np.array_equal(clustering_test.select(query), [0, 2])
clustering_test.roc_algorithm.predict = MagicMock(return_value=[1])
assert np.array_equal(clustering_test.select(query), [1, 4])
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_proba():
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_proba = single_best_test.predict_proba(X)
assert np.equal(predicted_proba, pool_classifiers[0].predict_proba(X)).all()
def test_predict_proba_weighting():
query = np.array([-1, 1])
pool_classifiers = create_pool_classifiers()
des_test = DES(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])
predicted_proba = des_test.predict_proba_instance(query)
assert np.isclose(predicted_proba, expected, atol=0.01).all()
def test_classify_instance_hybrid_batch():
query = np.ones((3, 2))
expected = 1
pool_classifiers = create_pool_classifiers() + create_pool_classifiers()
des_test = DES(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_classify_instance_hybrid():
query = np.array([-1, 1])
expected = 1
pool_classifiers = create_pool_classifiers() + create_pool_classifiers()
des_test = DES(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():
query = np.atleast_2d([1, -1])
clustering_test = DESClustering(create_pool_classifiers() * 4, k=2)
clustering_test.select = MagicMock(return_value=[0, 1, 2, 3, 5, 6, 7, 9])
predictions = []
for clf in clustering_test.pool_classifiers:
predictions.append(clf.predict(query)[0])
predicted = clustering_test.classify_instance(query, np.array(predictions))
assert predicted == 0
def test_classify_instance():
query = np.ones((1, 2))
clustering_test = DESClustering(create_pool_classifiers() * 4,
clustering=KMeans(n_clusters=2))
clustering_test.select = MagicMock(return_value=[0, 1, 2, 3, 5, 6, 7, 9])
predictions = []
for clf in clustering_test.pool_classifiers:
predictions.append(clf.predict(query)[0])
predicted = clustering_test.classify_with_ds(query, np.array(predictions))
assert predicted == 0
def test_fit_homogeneous_clusters():
clustering_test = DESClustering(create_pool_classifiers()*2, k=2, pct_accuracy=0.5, pct_diversity=0.33)
clustering_test.roc_algorithm.fit_predict = MagicMock(return_value=return_cluster_index_ex1)
clustering_test.DFP_mask = np.ones(clustering_test.n_classifiers)
clustering_test.fit(X_dsel_ex1, y_dsel_ex1)
assert clustering_test.accuracy_cluster[0, 1] == 0.0 and clustering_test.accuracy_cluster[0, [0, 2]].all() == 1.0
assert clustering_test.accuracy_cluster[1, 1] == 1.0 and clustering_test.accuracy_cluster[1, [0, 2]].all() == 0.0
for idx in clustering_test.indices[0, :]:
assert idx in (0, 2, 3, 5)
def test_select():
query = np.atleast_2d([1, -1])
clustering_test = DESClustering(create_pool_classifiers() * 2,
clustering=KMeans(n_clusters=2))
clustering_test.clustering_ = KMeans()
clustering_test.clustering_.predict = MagicMock(return_value=[0])
clustering_test.indices_ = np.array([[0, 2], [1, 4]])
assert np.array_equal(clustering_test.select(query), [[0, 2]])
clustering_test.clustering_.predict = MagicMock(return_value=[1])
assert np.array_equal(clustering_test.select(query), [[1, 4]])
def test_fit_heterogeneous_clusters():
clustering_test = DESClustering(create_pool_classifiers(), k=2, pct_accuracy=0.5, pct_diversity=0.33)
clustering_test.roc_algorithm.fit_predict = MagicMock(return_value=return_cluster_index_ex2)
clustering_test.DFP_mask = np.ones(clustering_test.n_classifiers)
clustering_test.fit(X_dsel_ex1, y_dsel_ex1)
# Index selected should be of any classifier that predicts the class label 0
assert np.isclose(clustering_test.accuracy_cluster[:, 1], [0.428, 0.375], atol=0.01).all()
assert np.isclose(clustering_test.accuracy_cluster[:, 0], [0.572, 0.625], atol=0.01).all()
assert clustering_test.indices[0, 0] == 0 or clustering_test.indices[0, 0] == 2
assert clustering_test.indices[1, 0] == 0 or clustering_test.indices[1, 0] == 2
def test_estimate_competence():
query = np.atleast_2d([1, 1])
clustering_test = DESClustering(create_pool_classifiers(), k=2, pct_accuracy=0.5, pct_diversity=0.33)
clustering_test.roc_algorithm.fit_predict = MagicMock(return_value=return_cluster_index_ex2)
clustering_test.DFP_mask = np.ones(clustering_test.n_classifiers)
clustering_test.fit(X_dsel_ex1, y_dsel_ex1)
clustering_test.roc_algorithm.predict = MagicMock(return_value=0)
competences = clustering_test.estimate_competence(query)
assert np.array_equal(competences, clustering_test.accuracy_cluster[0, :])
clustering_test.roc_algorithm.predict = MagicMock(return_value=1)
competences = clustering_test.estimate_competence(query)
assert np.array_equal(competences, clustering_test.accuracy_cluster[1, :])
def test_fit_clusters_less_diverse():
clustering_test = DESClustering(create_pool_classifiers() * 2,
clustering=KMeans(n_clusters=2),
pct_accuracy=0.5,
pct_diversity=0.33,
more_diverse=False)
clustering_test.clustering.predict = MagicMock(
return_value=return_cluster_index_ex1)
clustering_test.fit(X_dsel_ex1, y_dsel_ex1)
assert (clustering_test.accuracy_cluster_[0, 1] == 0.0
and clustering_test.accuracy_cluster_[0, [0, 2]].all() == 1.0)
assert (clustering_test.accuracy_cluster_[1, 1] == 1.0
and clustering_test.accuracy_cluster_[1, [0, 2]].all() == 0.0)
for idx in clustering_test.indices_[0, :]:
assert idx in (1, 3, 4, 5)
def test_estimate_competence():
query = np.atleast_2d([1, 1])
clustering_test = DESClustering(create_pool_classifiers() * 2,
clustering=KMeans(n_clusters=2),
pct_accuracy=0.5,
pct_diversity=0.33)
# Keep the original predict method to change after
clustering_test.clustering.predict = MagicMock(
return_value=return_cluster_index_ex2)
clustering_test.fit(X_dsel_ex1, y_dsel_ex1)
clustering_test.clustering_.predict = MagicMock(return_value=0)
competences = clustering_test.estimate_competence(query)
assert np.array_equal(competences, clustering_test.accuracy_cluster_[0, :])
clustering_test.clustering_.predict = MagicMock(return_value=1)
competences = clustering_test.estimate_competence(query)
assert np.array_equal(competences, clustering_test.accuracy_cluster_[1, :])
