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))
