def test_valid_selection_method(selection_method):
X = np.random.rand(10, 2)
y = np.ones(10)
with pytest.raises(ValueError):
dcs = BaseDCS(create_pool_classifiers(),
selection_method=selection_method)
dcs.fit(X, y)
def test_selection_method_type(selection_method, create_pool_classifiers):
X = np.random.rand(10, 2)
y = np.ones(10)
with pytest.raises(TypeError):
dcs = BaseDCS(create_pool_classifiers,
selection_method=selection_method)
dcs.fit(X, y)
def test_valid_diff_threshold_value(diff_thresh):
X = np.random.rand(10, 2)
y = np.ones(10)
with pytest.raises(ValueError):
dcs = BaseDCS(create_pool_classifiers(),
selection_method='diff',
diff_thresh=diff_thresh)
dcs.fit(X, y)
def test_select_random(competences, expected):
rng = np.random.RandomState(123456)
pool_classifiers = create_pool_classifiers()
dcs_test = BaseDCS(pool_classifiers, selection_method='random', random_state=rng)
dcs_test.fit(X_dsel_ex1, y_dsel_ex1)
selected_clf = dcs_test.select(np.array(competences))
assert np.allclose(selected_clf, expected)
def test_select_random_batch():
competences = np.array([[0.5, 0.5, 0.5], [0.8, 0.9, 1.0], [0.0, 0.10, 0.0]])
expected = np.array([1, 2, 1])
rng = np.random.RandomState(123456)
pool_classifiers = create_pool_classifiers()
dcs_test = BaseDCS(pool_classifiers, selection_method='random', random_state=rng)
dcs_test.fit(X_dsel_ex1, y_dsel_ex1)
selected_clf = dcs_test.select(competences)
assert np.array_equal(selected_clf, expected)