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_select():
query = np.atleast_2d([1, -1])
clustering_test = DESClustering()
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_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_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(create_pool_classifiers):
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