def test_select_less_diverse():
"""
Test case: 10 base classifiers; select 5 based on accuracy,
then the 3 less diverse
accuracies (/10): 4 6 1 2 9 8 7 9 3 2
(should select indices_ 1, 4, 5, 6, 7)
diversity: 0 8 0 0 1 6 7 2 0 0
(should select indices_ 4, 5, 7 as most diverse)
"""
pool_classifiers = [create_base_classifier(1) for _ in range(10)]
accuracies = np.array([[4, 6, 1, 2, 9, 8, 7, 9, 3, 2]]) / 10.
diversity = np.array([[0, 8, 0, 0, 1, 6, 7, 2, 0, 0]])
target = DESKNN(pool_classifiers,
k=7,
pct_accuracy=5. / 10,
pct_diversity=3. / 10,
more_diverse=False)
target.N_ = 5
target.J_ = 3
selected_classifiers = target.select(accuracies, diversity)
expected = np.array([[4, 5, 7]])
assert np.array_equal(np.unique(selected_classifiers), np.unique(expected))
def test_select_batch():
"""
Test case: 10 base classifiers; select 5 based on accuracy,
then the 3 most diverse.
accuracies (/10): 4 6 1 2 9 8 7 9 3 2
(should select indices_ 1, 4, 5, 6, 7)
diversity: 0 8 0 0 1 6 7 2 0 0
(should select indices_ 1, 5, 6 as most diverse)
"""
n_samples = 10
pool_classifiers = [create_base_classifier(1) for _ in range(10)]
accuracies = np.tile([4, 6, 1, 2, 9, 8, 7, 9, 3, 2], (n_samples, 1)) / 10.
diversity = np.tile([0, 8, 0, 0, 1, 6, 7, 2, 0, 0], (n_samples, 1))
target = DESKNN(pool_classifiers,
k=7,
pct_accuracy=5. / 10,
pct_diversity=3. / 10)
target.N_ = 5
target.J_ = 3
selected_classifiers = target.select(accuracies, diversity)
expected = np.tile([1, 5, 6], (n_samples, 1))
assert np.array_equal(np.unique(selected_classifiers), np.unique(expected))
def test_classify_instance():
query = np.atleast_2d([1, -1])
des_knn_test = DESKNN(create_pool_classifiers() * 4, k=2)
des_knn_test.select = MagicMock(return_value=[0, 1, 2, 3, 5, 6, 7, 9])
predicted = des_knn_test.classify_instance(query)
assert predicted == 0
def test_classify_instance():
query = np.atleast_2d([1, -1])
des_knn_test = DESKNN(create_pool_classifiers() * 4, k=2)
des_knn_test.select = MagicMock(return_value=[0, 1, 2, 3, 5, 6, 7, 9])
predictions = []
for clf in des_knn_test.pool_classifiers:
predictions.append(clf.predict(query)[0])
predicted = des_knn_test.classify_instance(query, predictions=np.array(predictions))
assert predicted == 0
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)
def test_select():
"""
Test case: 10 base classifiers; select 5 based on accuracy, then the 3 most diverse
accuracies (/10): 4 6 1 2 9 8 7 9 3 2 (should select indices 1, 4, 5, 6, 7)
diversity: 0 8 0 0 1 6 7 2 0 0 (should select indices 1, 5, 6 as most diverse)
"""
pool_classifiers = [create_base_classifier(1) for _ in range(10)]
accuracies = np.array([4, 6, 1, 2, 9, 8, 7, 9, 3, 2]) / 10.
diversity = np.array([0, 8, 0, 0, 1, 6, 7, 2, 0, 0])
target = DESKNN(pool_classifiers, k=7, pct_accuracy=5./10, pct_diversity=3./10)
target.estimate_competence = lambda x: (accuracies, diversity)
selected_indices = target.select(2)
assert set(selected_indices) == {1, 5, 6}
