def test_parallel_clustering_metric_affinity_precomputed_not_implemented():
class DummyClusterer(sk.base.BaseEstimator, sk.base.ClusterMixin):
def __init__(self, metric="precomputed", affinity="precomputed"):
self.metric = metric
self.affinity = affinity
pc = ParallelClustering(DummyClusterer())
X = [np.random.random((5, 4)), np.ones((5, 4), dtype=bool)]
with pytest.raises(NotImplementedError,
match="Behaviour when metric and affinity"):
pc.fit(X)
def test_parallel_clustering_kmeans(sample_weight):
kmeans = sk.cluster.KMeans(n_clusters=2, random_state=0)
pc = ParallelClustering(kmeans)
X = [np.random.random((5, 4)), np.ones((5, 4), dtype=bool)]
single_labels = kmeans.fit_predict(X[0], sample_weight=sample_weight)
unique_labels, inverse = np.unique(single_labels, return_inverse=True)
res = pc.fit_predict(X, sample_weight=sample_weight)
res = [[(i, label, list(indices)) for [i, label, indices] in sublist]
for sublist in res]
exp = [[(i, label, list(np.flatnonzero(inverse == label)))
for label in unique_labels] for i in range(X[1].shape[1])]
assert res == exp
def test_parallel_clustering_kmeans(n_jobs, sample_weight):
kmeans = sk.cluster.KMeans(n_clusters=2, random_state=0)
pc = ParallelClustering(kmeans)
X = [np.random.random((5, 4)), np.ones((5, 4), dtype=bool)]
single_labels = kmeans.fit_predict(X[0], sample_weight=sample_weight)
_, inverse = np.unique(single_labels, return_inverse=True)
res = pc.fit_predict(X, sample_weight=sample_weight)
exp = np.empty(5, dtype=object)
exp[:] = [tuple([])] * 5
for i in range(4):
labels_i = np.empty(len(single_labels), dtype=object)
labels_i[:] = [((i, rel_label), ) for rel_label in inverse]
exp[:] += labels_i
assert np.array_equal(res, exp)
def test_parallel_clustering_precomputed(n_jobs):
pc = ParallelClustering(sk.cluster.DBSCAN())
masks = np.random.choice([True, False], size=20).reshape((10, 2))
X = [np.random.random((10, 4)), masks]
pc_precomp = ParallelClustering(sk.cluster.DBSCAN(metric="precomputed"))
X_precomp = [sk.metrics.pairwise_distances(X[0]), masks]
res = pc.fit_predict(X)
res_precomp = pc_precomp.fit_predict(X_precomp)
assert np.array_equal(res, res_precomp)
def test_parallel_clustering_bad_input():
pc = ParallelClustering(sk.cluster.DBSCAN())
X = [np.random.random((5, 4)), np.random.random((5, 4))]
with pytest.raises(TypeError, match="`masks` must be a boolean array."):
pc.fit(X)
X[1] = np.ones((6, 4), dtype=bool)
with pytest.raises(ValueError,
match="`X_tot` and `masks` must have the same number"):
pc.fit(X)
def test_parallel_clustering_precomputed():
pc = ParallelClustering(sk.cluster.DBSCAN())
masks = np.random.choice([True, False], size=20).reshape((10, 2))
X = [np.random.random((10, 4)), masks]
pc_precomp = ParallelClustering(sk.cluster.DBSCAN(metric="precomputed"))
X_precomp = [sk.metrics.pairwise_distances(X[0]), masks]
res = pc.fit_predict(X)
res_precomp = pc_precomp.fit_predict(X_precomp)
res = [[(i, label, list(indices)) for [i, label, indices] in sublist]
for sublist in res]
res_precomp = [[(i, label, list(indices))
for [i, label, indices] in sublist]
for sublist in res_precomp]
assert res == res_precomp
def test_parallel_clustering_transform_not_implemented():
pc = ParallelClustering(sk.cluster.DBSCAN())
X = [np.random.random((5, 4)), np.ones((5, 4), dtype=bool)]
with pytest.raises(NotImplementedError):
pc.transform(X)
def test_parallel_clustering_bad_clusterer():
pc = ParallelClustering(sk.decomposition.PCA())
X = [np.random.random((5, 4)), np.ones((5, 4), dtype=bool)]
with pytest.raises(TypeError, match="`clusterer` must be an instance of"):
pc.fit(X)