def test_silhouette_score_batched_non_monotonic():
vecs = np.array([[0.0, 0.0, 0.0], [1.0, 1.0, 1.0], [2.0, 2.0, 2.0],
[10.0, 10.0, 10.0]])
labels = np.array([0, 0, 1, 3])
cuml_score = cu_silhouette_score(X=vecs, labels=labels)
sk_score = sk_silhouette_score(X=vecs, labels=labels)
assert_almost_equal(cuml_score, sk_score, decimal=2)
vecs = np.array([[0.0, 0.0, 0.0], [1.0, 1.0, 1.0], [10.0, 10.0, 10.0]])
labels = np.array([1, 1, 3])
cuml_score = cu_silhouette_score(X=vecs, labels=labels)
sk_score = sk_silhouette_score(X=vecs, labels=labels)
assert_almost_equal(cuml_score, sk_score, decimal=2)
def test_silhouette_score_batched(metric, chunk_divider, labeled_clusters):
X, labels = labeled_clusters
cuml_score = cu_silhouette_score(X,
labels,
metric=metric,
chunksize=int(X.shape[0] / chunk_divider))
sk_score = sk_silhouette_score(X, labels, metric=metric)
assert_almost_equal(cuml_score, sk_score, decimal=2)
def test_silhouette_score(metric, labeled_clusters):
X, labels = labeled_clusters
cuml_score = cu_silhouette_score(X, labels, metric=metric)
sk_score = sk_silhouette_score(X, labels, metric=metric)
assert_almost_equal(cuml_score, sk_score)