def test_label_encoder_only_dsel_allagree():
X_dsel_ex1 = np.array([[-1, 1], [-0.75, 0.5], [-1.5, 1.5]])
y_dsel_ex1 = np.array(['cat', 'dog', 'plane'])
query = np.atleast_2d([[1, 0], [-1, -1]])
ds_test = BaseDS(create_pool_classifiers_dog(), k=2)
ds_test.fit(X_dsel_ex1, y_dsel_ex1)
ds_test.neighbors = neighbors_ex1[0, :]
ds_test.distances = distances_ex1[0, :]
predictions = ds_test.predict(query)
assert np.array_equal(predictions, ['dog', 'dog'])
def test_predict_proba_instance_called(index):
query = np.atleast_2d([1, 1])
ds_test = BaseDS(create_pool_classifiers(), with_IH=True, IH_rate=0.10)
ds_test.fit(X_dsel_ex1, y_dsel_ex1)
ds_test.neighbors = neighbors_ex1[index, :]
ds_test.distances = distances_ex1[index, :]
ds_test.predict_proba_with_ds = MagicMock(return_value=np.atleast_2d([0.25, 0.75]))
proba = ds_test.predict_proba(query)
assert np.allclose(proba, np.atleast_2d([0.25, 0.75]))
def test_predict_proba_IH_knn(index):
query = np.atleast_2d([1, 1])
ds_test = BaseDS(create_pool_classifiers(), with_IH=True, IH_rate=0.5)
ds_test.fit(X_dsel_ex1, y_dsel_ex1)
ds_test.DSEL_scores = dsel_scores_ex1
ds_test.neighbors = neighbors_ex1[index, :]
ds_test.distances = distances_ex1[index, :]
ds_test.roc_algorithm_.predict_proba = MagicMock(return_value=np.atleast_2d([0.45, 0.55]))
proba = ds_test.predict_proba(query)
assert np.allclose(proba, np.atleast_2d([0.45, 0.55]))
def test_label_encoder_only_dsel():
X_dsel_ex1 = np.array([[-1, 1], [-0.75, 0.5], [-1.5, 1.5]])
y_dsel_ex1 = np.array(['cat', 'dog', 'plane'])
query = np.atleast_2d([[1, 0], [-1, -1]])
ds_test = BaseDS(create_pool_classifiers_dog_cat_plane(), k=2)
ds_test.fit(X_dsel_ex1, y_dsel_ex1)
ds_test.neighbors = neighbors_ex1[0, :]
ds_test.distances = distances_ex1[0, :]
ds_test.classify_with_ds = Mock()
ds_test.classify_with_ds.return_value = [1, 0] # changed here due to batch processing
predictions = ds_test.predict(query)
assert np.array_equal(predictions, ['dog', 'cat'])
