def test_all_classifiers_agree():
# 10 classifiers that return 1
pool_classifiers = [create_base_classifier(return_value=1)] * 10
ds = DS(pool_classifiers)
x = np.ones((1, 10))
assert ds._all_classifier_agree(x)
def test_predict_proba_all_agree():
query = np.atleast_2d([1, 1])
ds_test = DS(create_pool_classifiers())
ds_test.fit(X_dsel_ex1, y_dsel_ex1)
ds_test.dsel_scores = dsel_scores_ex1
ds_test._all_classifier_agree = MagicMock(return_value=True)
proba = ds_test.predict_proba(query)
assert np.isclose(proba, np.atleast_2d([0.61, 0.39])).all()
def test_not_all_classifiers_agree():
# 10 classifiers that return 1, and one that returns 2
pool_classifiers = [create_base_classifier(return_value=1)] * 10
pool_classifiers.append(create_base_classifier(return_value=2))
ds = DS(pool_classifiers)
x = np.ones((1, 10))
assert not ds._all_classifier_agree(x)
def test_all_classifiers_agree():
# 10 classifiers that return 1
predictions = np.ones((1, 10))
assert np.all(DS._all_classifier_agree(predictions))
def test_not_all_classifiers_agree():
# 10 classifiers that return 1, and one that returns 2
predictions = np.ones((10, 11))
predictions[:, -1] = 2
assert not np.all(DS._all_classifier_agree(predictions))
