def test_classify_with_ds_diff_sizes():
query = np.ones((10, 2))
predictions = np.ones((5, 3))
clf1 = create_base_classifier(np.array([1, 0, 1, 0, 0, 0, 0]))
clf2 = create_base_classifier(np.array([1, 0, 0, 0, 1, 0, 0]))
clf3 = create_base_classifier(np.array([0, 0, 1, 0, 1, 1, 0]))
pool_classifiers = [clf1, clf2, clf3]
desknn_test = DESKNN(pool_classifiers)
with pytest.raises(ValueError):
desknn_test.classify_with_ds(query, predictions)
def test_classify_with_ds_single_sample():
query = np.ones(2)
predictions = np.array([0, 1, 0])
clf1 = create_base_classifier(np.array([1, 0, 1, 0, 0, 0, 0]))
clf2 = create_base_classifier(np.array([1, 0, 0, 0, 1, 0, 0]))
clf3 = create_base_classifier(np.array([0, 0, 1, 0, 1, 1, 0]))
pool_classifiers = [clf1, clf2, clf3]
desknn_test = DESKNN(pool_classifiers)
desknn_test.estimate_competence = MagicMock(
return_value=(np.ones(3), np.ones(3)))
desknn_test.select = MagicMock(return_value=np.array([[0, 2]]))
result = desknn_test.classify_with_ds(query, predictions)
assert np.allclose(result, 0)
