def test_frienemy_safe_region():
ds_test = DS(create_pool_classifiers(), safe_k=3)
ds_test.processed_dsel = dsel_processed_ex1
ds_test.DSEL_target = y_dsel_ex1
ds_test.DSEL_data = X_dsel_ex1
ds_test.neighbors = np.array([0, 1, 2, 6, 7, 8, 14])
result = ds_test._frienemy_pruning()
assert np.array_equal(result, np.array([[1, 1, 1]]))
def test_frienemy_not_all_classifiers_crosses():
ds_test = DS(create_pool_classifiers(), safe_k=3)
ds_test.processed_dsel = dsel_processed_ex1
ds_test.DSEL_target = y_dsel_ex1
ds_test.DSEL_data = X_dsel_ex1
ds_test.neighbors = neighbors_ex1[0, :]
result = ds_test._frienemy_pruning()
assert np.array_equal(result, np.array([[1, 1, 0]]))
def test_frienemy_no_classifier_crosses():
X = X_dsel_ex1
y = y_dsel_ex1
ds_test = DS(create_pool_classifiers())
ds_test.fit(X, y)
ds_test.neighbors = neighbors_ex1[0, :]
mask = ds_test._frienemy_pruning()
assert mask.shape == (1, 3) and np.allclose(mask, 1)
def test_frienemy_all_classifiers_crosses(index):
ds_test = DS(create_pool_classifiers())
ds_test.processed_dsel = dsel_processed_all_ones
ds_test.DSEL_target = y_dsel_ex1
ds_test.DSEL_data = X_dsel_ex1
ds_test.neighbors = neighbors_ex1[index, :]
result = ds_test._frienemy_pruning()
assert result.all() == 1.0
def test_frienemy_no_classifier_crosses():
X = X_dsel_ex1
y = y_dsel_ex1
ds_test = DS(create_pool_classifiers())
ds_test.fit(X, y)
ds_test.neighbors = neighbors_ex1[0, :]
mask = ds_test._frienemy_pruning()
assert mask.size == 3 and mask.all() == 1
def test_frienemy_not_all_classifiers_crosses_batch():
expected = np.array([[1, 1, 0], [0, 1, 0], [1, 1, 1]])
ds_test = DS(create_pool_classifiers(), safe_k=3)
ds_test.processed_dsel = dsel_processed_ex1
ds_test.DSEL_target = y_dsel_ex1
ds_test.DSEL_data = X_dsel_ex1
# passing three samples to compute the DFP at the same time
ds_test.neighbors = neighbors_ex1
result = ds_test._frienemy_pruning()
assert np.array_equal(result, expected)
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 = DS(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 = DS(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_instance = MagicMock(return_value=np.atleast_2d([0.25, 0.75]))
proba = ds_test.predict_proba(query)
assert np.isclose(proba, np.atleast_2d([0.25, 0.75])).all()
def test_DFP_is_used():
query = np.atleast_2d([1, 0])
ds_test = DS(create_pool_classifiers(), DFP=True, safe_k=3)
ds_test.fit(X_dsel_ex1, y_dsel_ex1)
ds_test.processed_dsel = dsel_processed_ex1
ds_test.DSEL_target = y_dsel_ex1
ds_test.DSEL_data = X_dsel_ex1
ds_test.neighbors = neighbors_ex1[0, :]
ds_test.distances = distances_ex1[0, :]
ds_test.classify_with_ds = MagicMock(return_value=0)
ds_test.predict(query)
assert np.array_equal(ds_test.DFP_mask, np.atleast_2d([1, 1, 0]))
def test_predict_proba_IH_knn(index):
query = np.atleast_2d([1, 1])
ds_test = DS(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.isclose(proba, np.atleast_2d([0.45, 0.55])).all()
def test_frienemy_safe_region_batch():
n_samples = 10
n_classifiers = 3
expected = np.ones((n_samples, n_classifiers))
ds_test = DS(create_pool_classifiers(), safe_k=3)
ds_test.processed_dsel = dsel_processed_ex1
ds_test.DSEL_target = y_dsel_ex1
ds_test.DSEL_data = X_dsel_ex1
ds_test.neighbors = np.tile(np.array([0, 1, 2, 6, 7, 8, 14]),
(n_samples, 1))
result = ds_test._frienemy_pruning()
assert np.array_equal(result, expected)
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 = DS(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'])
def test_predict_proba_DFP():
query = np.atleast_2d([1, 1])
ds_test = DS(create_pool_classifiers(), DFP=True, safe_k=3)
ds_test.fit(X_dsel_ex1, y_dsel_ex1)
# change the state of the system
ds_test.processed_dsel = dsel_processed_ex1
ds_test.DSEL_target = y_dsel_ex1
ds_test.DSEL_data = X_dsel_ex1
ds_test.neighbors = neighbors_ex1[0, :]
ds_test.distances = distances_ex1[0, :]
ds_test.predict_proba_instance = MagicMock(return_value=np.atleast_2d([0.25, 0.75]))
ds_test.predict_proba(query)
assert np.array_equal(ds_test.DFP_mask, np.array([1, 1, 0]))
def test_IH_is_used(index, expected):
query = np.atleast_2d([1, 0])
ds_test = DS(create_pool_classifiers(), with_IH=True, IH_rate=0.5)
ds_test.fit(X_dsel_ex1, y_dsel_ex1)
ds_test.processed_dsel = dsel_processed_ex1
ds_test.DSEL_target = y_dsel_ex1
ds_test.DSEL_data = X_dsel_ex1
ds_test.neighbors = neighbors_ex1[index, :]
ds_test.distances = distances_ex1[index, :]
predicted = ds_test.predict(query)
assert predicted == expected
def test_IH_is_used():
expected = [0, 0, 1]
query = np.ones((3, 2))
ds_test = DS(create_pool_classifiers(), with_IH=True, IH_rate=0.5)
ds_test.fit(X_dsel_ex1, y_dsel_ex1)
ds_test.processed_dsel = dsel_processed_ex1
ds_test.DSEL_target = y_dsel_ex1
ds_test.DSEL_data = X_dsel_ex1
ds_test.neighbors = neighbors_ex1
ds_test.distances = distances_ex1
predicted = ds_test.predict(query)
assert np.array_equal(predicted, expected)
