def test_n_init_not_positive_int():
with pytest.raises(ValueError):
kmeans = MultiviewSphericalKMeans(n_init=-1)
kmeans.fit(data_small)
with pytest.raises(ValueError):
kmeans = MultiviewSphericalKMeans(n_init=0)
kmeans.fit(data_small)
def test_init_not_2_views(data_small):
with pytest.raises(ValueError):
view1 = np.random.random((2,8))
view2 = np.random.random((2,9))
view3 = np.random.random((2,9))
kmeans = MultiviewSphericalKMeans(init=[view1, view2, view3])
kmeans.fit(data_small)
def test_not_2_views():
with pytest.raises(ValueError):
view1 = np.random.random((10,))
view2 = np.random.random((10,))
view3 = np.random.random((10,))
kmeans = MultiviewSphericalKMeans()
kmeans.fit([view1, view2, view3])
def test_final_centroids_less_than_n_clusters():
with pytest.warns(ConvergenceWarning):
kmeans = MultiviewSphericalKMeans(n_clusters=3,
random_state=RANDOM_SEED)
view1 = np.random.random((2, 11))
view2 = np.random.random((2, 10))
kmeans.fit([view1, view2])
def test_max_iter_not_positive_int(data_small):
with pytest.raises(ValueError):
kmeans = MultiviewSphericalKMeans(max_iter=-1)
kmeans.fit(data_small)
with pytest.raises(ValueError):
kmeans = MultiviewSphericalKMeans(max_iter=0)
kmeans.fit(data_small)
def test_predict_random_small(data_random):
kmeans = MultiviewSphericalKMeans()
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_patience_not_nonnegative_int(data_small):
with pytest.raises(ValueError):
kmeans = MultiviewSphericalKMeans(patience=-1)
kmeans.fit(data_small)
def test_samples_not_2D_2():
with pytest.raises(ValueError):
view1 = np.random.random((10,))
view2 = np.random.random((10,))
kmeans = MultiviewSphericalKMeans()
kmeans.fit([view1, view2])
def test_samples_not_list():
with pytest.raises(ValueError):
view1 = 1
view2 = 3
kmeans = MultiviewSphericalKMeans()
kmeans.fit([view1, view2])
def test_random_state_not_convertible(data_small):
with pytest.raises(ValueError):
kmeans = MultiviewSphericalKMeans(random_state='ab')
kmeans.fit(data_small)
def test_tol_not_nonnegative_float(data_small):
with pytest.raises(ValueError):
kmeans = MultiviewSphericalKMeans(tol=-1)
kmeans.fit(data_small)
def test_init_not_feat_dimensions(data_small):
with pytest.raises(ValueError):
view1 = np.random.random((2, 9))
view2 = np.random.random((2, 9))
kmeans = MultiviewSphericalKMeans(init=[view1, view2])
kmeans.fit(data_small)
def test_init_not_n_clusters(data_small):
with pytest.raises(ValueError):
view1 = np.random.random((3, 8))
view2 = np.random.random((3, 9))
kmeans = MultiviewSphericalKMeans(init=[view1, view2])
kmeans.fit(data_small)
def test_init_samples_not_2D_2(data_small):
with pytest.raises(ValueError):
view1 = np.random.random((2,))
view2 = np.random.random((2,))
kmeans = MultiviewSphericalKMeans(init=[view1, view2])
kmeans.fit(data_small)
def test_init_samples_not_list(data_small):
with pytest.raises(ValueError):
view1 = 1
view2 = 3
kmeans = MultiviewSphericalKMeans(init=[view1, view2])
kmeans.fit(data_small)
def test_not_init1(data_small):
with pytest.raises(ValueError):
kmeans = MultiviewSphericalKMeans(init='Not_Init')
kmeans.fit(data_small)