def test_predict_no_centroids1():
with pytest.raises(AttributeError):
kmeans = MultiviewKMeans()
kmeans.centroids_ = [None, None]
view1 = np.random.random((10, 11))
view2 = np.random.random((10, 10))
kmeans.predict([view1, view2])
def test_predict_no_centroids2():
kmeans = MultiviewKMeans()
with pytest.raises(ConvergenceWarning):
view1 = np.array([[0, 1], [1, 0]])
view2 = np.array([[1, 0], [0, 1]])
v1_centroids = np.array([[0, 1], [1, 0]])
v2_centroids = np.array([[0, 1], [1, 0]])
centroids = [v1_centroids, v2_centroids]
kmeans._final_centroids([view1, view2], centroids)
with pytest.raises(AttributeError):
kmeans.predict([view1, view2])
def test_predict_random_small(data_random):
kmeans = MultiviewKMeans()
input_data = [
data_random['fit_data'][0][:2], data_random['fit_data'][1][:2]
]
kmeans.fit(input_data)
cluster_pred = kmeans.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_predict_deterministic():
n_clusters = 2
v1_centroid = np.array([[0, 0], [1, 1]])
v2_centroid = np.array([[0, 0], [1, 1]])
centroids = [v1_centroid, v2_centroid]
v1_data = np.array([[0, 0], [0.3, 0.2], [0.5, 0.5], [0.7, 0.7], [1, 1]])
v2_data = np.array([[0, 0], [0.2, 0.4], [0.5, 0.5], [0.4, 0.7], [1, 1]])
data = [v1_data, v2_data]
kmeans = MultiviewKMeans(n_clusters=n_clusters)
kmeans.centroids_ = centroids
cluster_pred = kmeans.predict(data)
true_clusters = [0, 0, 0, 1, 1]
for ind in range(len(true_clusters)):
assert cluster_pred[ind] == true_clusters[ind]
def test_predict_not_fit():
with pytest.raises(NotFittedError):
kmeans = MultiviewKMeans()
view1 = np.random.random((10, 11))
view2 = np.random.random((10, 10))
kmeans.predict([view1, view2])