def perform_clustering(seed, m_data, labels, n_clusters):
# Singleview spherical kmeans clustering
# Cluster each view separately
s_kmeans = SphericalKMeans(n_clusters=n_clusters,
random_state=seed,
n_init=100)
s_clusters_v1 = s_kmeans.fit_predict(m_data[0])
s_clusters_v2 = s_kmeans.fit_predict(m_data[1])
# Concatenate the multiple views into a single view
s_data = np.hstack(m_data)
s_clusters = s_kmeans.fit_predict(s_data)
# Compute nmi between true class labels and singleview cluster labels
s_nmi_v1 = nmi_score(labels, s_clusters_v1)
s_nmi_v2 = nmi_score(labels, s_clusters_v2)
s_nmi = nmi_score(labels, s_clusters)
print('Singleview View 1 NMI Score: {0:.3f}\n'.format(s_nmi_v1))
print('Singleview View 2 NMI Score: {0:.3f}\n'.format(s_nmi_v2))
print('Singleview Concatenated NMI Score: {0:.3f}\n'.format(s_nmi))
# Multiview spherical kmeans clustering
# Use the MultiviewKMeans instance to cluster the data
m_kmeans = MultiviewSphericalKMeans(n_clusters=n_clusters,
n_init=100,
random_state=seed)
m_clusters = m_kmeans.fit_predict(m_data)
# Compute nmi between true class labels and multiview cluster labels
m_nmi = nmi_score(labels, m_clusters)
print('Multiview NMI Score: {0:.3f}\n'.format(m_nmi))
return m_clusters
def test_fit_predict_n_jobs_all(data_random):
n_clusters = data_random['n_clusters']
kmeans = MultiviewSphericalKMeans(n_clusters=n_clusters, n_jobs=-1)
cluster_pred = kmeans.fit_predict(data_random['test_data'])
assert(data_random['n_test'] == cluster_pred.shape[0])
for cl in cluster_pred:
assert(cl >= 0 and cl < data_random['n_clusters'])
def test_fit_predict_init_random(data_random):
n_clusters = data_random['n_clusters']
init = 'random'
kmeans = MultiviewSphericalKMeans(n_clusters=n_clusters, init='random')
cluster_pred = kmeans.fit_predict(data_random['test_data'])
assert(data_random['n_test'] == cluster_pred.shape[0])
for cl in cluster_pred:
assert(cl >= 0 and cl < data_random['n_clusters'])
def test_fit_predict_patience(data_random):
n_clusters = data_random['n_clusters']
patience=10
kmeans = MultiviewSphericalKMeans(n_clusters=n_clusters, patience=patience)
cluster_pred = kmeans.fit_predict(data_random['test_data'])
assert(data_random['n_test'] == cluster_pred.shape[0])
for cl in cluster_pred:
assert(cl >= 0 and cl < data_random['n_clusters'])
def test_fit_predict_max_iter(data_random):
n_clusters = data_random['n_clusters']
max_iter = 5
kmeans = MultiviewSphericalKMeans(n_clusters=n_clusters, max_iter=max_iter)
cluster_pred = kmeans.fit_predict(data_random['test_data'])
assert (data_random['n_test'] == cluster_pred.shape[0])
for cl in cluster_pred:
assert (cl >= 0 and cl < data_random['n_clusters'])
def test_fit_predict_init_predefined():
n_clusters = 2
v1_centroid = np.array([[-1, -1],[1, 1]])
v2_centroid = np.array([[-1, -1],[1, 1]])
centroids = [v1_centroid, v2_centroid]
v1_data = np.array([[-1, -1],[-2, -2],[0.5, 0.5],[0.7, 0.7],[1, 1]])
v2_data = np.array([[-1, -1],[-2, -2],[0.5, 0.5],[0.4, 0.7],[1, 1]])
data = [v1_data, v2_data]
kmeans = MultiviewSphericalKMeans(n_clusters=n_clusters, init=centroids)
cluster_pred = kmeans.fit_predict(data)