def test_diversity_metric_Q(create_X_y):
X, y = create_X_y
test = DESClustering(metric_diversity='Q')
# Mocking this method to avoid preprocessing the cluster information
# that is not required in this test.
test._preprocess_clusters = MagicMock(return_value=1)
test.fit(X, y)
assert test.diversity_func_ == Q_statistic
def test_diversity_metric_DF(create_X_y):
X, y = create_X_y
test = DESClustering(metric_diversity='DF')
# Mocking this method to avoid preprocessing the cluster
# information that is not required in this test.
test._preprocess_clusters = MagicMock(return_value=1)
test.fit(X, y)
assert test.diversity_func_ == negative_double_fault
def test_diversity_metric_ratio():
test = DESClustering(create_pool_classifiers() * 10, metric='ratio')
# Mocking this method to avoid preprocessing the cluster information that is not required in this test.
test._preprocess_clusters = MagicMock(return_value=1)
test.fit(X_dsel_ex1, y_dsel_ex1)
assert test.diversity_func_ == ratio_errors