def test_samples_not_n_views():
with pytest.raises(ValueError):
view1 = np.random.random((10, 11))
view2 = np.random.random((10, 10))
spectral = MultiviewSpectralClustering(n_views=3,
random_state=RANDOM_STATE)
spectral.fit_predict([view1, view2])
def test_n_init_not_positive_int(small_data):
with pytest.raises(ValueError):
spectral = MultiviewSpectralClustering(n_init=-1)
spectral.fit_predict(small_data)
with pytest.raises(ValueError):
spectral = MultiviewSpectralClustering(n_init=0)
spectral.fit_predict(small_data)
def test_not_valid_affinity(small_data):
with pytest.raises(ValueError):
spectral = MultiviewSpectralClustering(affinity='What')
spectral.fit_predict(small_data)
with pytest.raises(ValueError):
spectral = MultiviewSpectralClustering(affinity=None)
spectral.fit_predict(small_data)
def test_info_view_not_valid(small_data):
with pytest.raises(ValueError):
spectral = MultiviewSpectralClustering(n_clusters=2, info_view=-1)
spectral.fit_predict(small_data)
with pytest.raises(ValueError):
spectral = MultiviewSpectralClustering(n_clusters=2, info_view=6)
spectral.fit_predict(small_data)
def test_gamma_not_positive_float(small_data):
with pytest.raises(ValueError):
spectral = MultiviewSpectralClustering(gamma=-1.5)
spectral.fit_predict(small_data)
with pytest.raises(ValueError):
spectral = MultiviewSpectralClustering(gamma=0)
spectral.fit_predict(small_data)
def test_n_neighbors_not_positive_int(small_data):
with pytest.raises(ValueError):
spectral = MultiviewSpectralClustering(affinity='nearest_neighbors',
n_neighbors=-1)
spectral.fit_predict(small_data)
with pytest.raises(ValueError):
spectral = MultiviewSpectralClustering(affinity='nearest_neighbors',
n_neighbors=0)
spectral.fit_predict(small_data)
def perform_clustering(seed, m_data, labels, n_clusters):
# Singleview spectral clustering
# Cluster each view separately
s_spectral = SpectralClustering(n_clusters=n_clusters,
random_state=RANDOM_SEED,
n_init=100)
s_clusters_v1 = s_spectral.fit_predict(m_data[0])
s_clusters_v2 = s_spectral.fit_predict(m_data[1])
# Concatenate the multiple views into a single view
s_data = np.hstack(m_data)
s_clusters = s_spectral.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 spectral clustering
# Use the MultiviewSpectralClustering instance to cluster the data
m_spectral = MultiviewSpectralClustering(n_clusters=n_clusters,
random_state=RANDOM_SEED,
n_init=100)
m_clusters = m_spectral.fit_predict(m_data)
# Compute nmi between true class labels and multiview cluster labels
m_nmi = nmi_score(labels, m_clusters)
print('Multiview Concatenated NMI Score: {0:.3f}\n'.format(m_nmi))
return m_clusters
def test_fit_predict_default(data):
v_data = data['fit_data'][:2]
spectral = MultiviewSpectralClustering(2, random_state=RANDOM_STATE)
predictions = spectral.fit_predict(v_data)
n_clusts = data['n_clusters']
assert (predictions.shape[0] == data['n_fit'])
for clust in predictions:
assert (clust >= 0 and clust < n_clusts)
def test_fit_predict_info_view(data):
v_data = data['fit_data']
info_view = np.random.randint(len(v_data))
n_clusts = data['n_clusters']
spectral = MultiviewSpectralClustering(n_clusts,
random_state=RANDOM_STATE,
info_view=info_view)
predictions = spectral.fit_predict(v_data)
assert (predictions.shape[0] == data['n_fit'])
for clust in predictions:
assert (clust >= 0 and clust < n_clusts)
def test_fit_predict_max_iter(data):
v_data = data['fit_data']
max_iter = 5
n_clusts = data['n_clusters']
spectral = MultiviewSpectralClustering(n_clusts,
random_state=RANDOM_STATE,
max_iter=max_iter)
predictions = spectral.fit_predict(v_data)
assert (predictions.shape[0] == data['n_fit'])
for clust in predictions:
assert (clust >= 0 and clust < n_clusts)
def test_fit_predict_n_iter(data):
v_data = data['fit_data']
n_views = data['n_views']
n_iter = 5
n_clusts = data['n_clusters']
spectral = MultiviewSpectralClustering(n_clusts,
n_views=n_views,
n_iter=n_iter)
predictions = spectral.fit_predict(v_data)
assert (predictions.shape[0] == data['n_fit'])
for clust in predictions:
assert (clust >= 0 and clust < n_clusts)
def test_fit_predict_info_view(data):
v_data = data['fit_data']
n_views = data['n_views']
info_view = np.random.randint(n_views)
print('n_views is ' + str(n_views))
print('info_views is ' + str(info_view))
n_clusts = data['n_clusters']
spectral = MultiviewSpectralClustering(n_clusts,
n_views=n_views,
info_view=info_view)
predictions = spectral.fit_predict(v_data)
assert (predictions.shape[0] == data['n_fit'])
for clust in predictions:
assert (clust >= 0 and clust < n_clusts)
def test_samples_not_2D_1():
with pytest.raises(ValueError):
view1 = np.random.random((5, 8, 7))
view2 = np.random.random((5, 9, 7))
spectral = MultiviewSpectralClustering(random_state=RANDOM_STATE)
spectral.fit_predict([view1, view2])
def test_samples_not_list():
with pytest.raises(ValueError):
view1 = 1
view2 = 3
spectral = MultiviewSpectralClustering(random_state=RANDOM_STATE)
spectral.fit_predict([view1, view2])
def test_samples_not_2D_2():
with pytest.raises(ValueError):
view1 = np.random.random((10, ))
view2 = np.random.random((10, ))
spectral = MultiviewSpectralClustering(2)
spectral.fit_predict([view1, view2])
def test_samples_not_list():
with pytest.raises(ValueError):
view1 = 1
view2 = 3
spectral = MultiviewSpectralClustering(2)
spectral.fit_predict([view1, view2])
def test_n_views_too_small2(small_data):
with pytest.raises(ValueError):
spectral = MultiviewSpectralClustering(random_state=RANDOM_STATE)
spectral.fit_predict([])
def test_random_state_not_convertible(small_data):
with pytest.raises(ValueError):
spectral = MultiviewSpectralClustering(n_clusters=5, random_state='ab')
spectral.fit_predict(small_data)
