def test_UCImultifeature_dataloader_select_views():
# load data
views = [4, 5, 1]
o_data, o_labels = load_UCImultifeature()
data, labels = load_UCImultifeature(views=views)
assert len(data) == len(views)
assert labels.shape[0] == 2000
# Check the shape of the data
for i in range(len(views)):
assert data[i].shape == o_data[views[i]].shape
def test_UCImultifeature_dataloader():
# load data
data, labels = load_UCImultifeature()
assert len(data) == 6
assert labels.shape[0] == 2000
# check size of data
for i in range(6):
assert data[i].shape[0] == 2000
data1, labels1 = load_UCImultifeature()
# check data and labels are same
assert (np.allclose(data[0], data1[0]))
assert (np.allclose(labels, labels1))
def test_UCImultifeature_dataloader():
# load data
data, labels = load_UCImultifeature()
assert len(data) == 6
assert labels.shape[0] == 2000
# check size of data
for i in range(6):
assert data[i].shape[0] == 2000
def test_UCImultifeature_randomstate_sameordifferent():
# load data
data, labels = load_UCImultifeature(shuffle=True, random_state=2)
data1, labels1 = load_UCImultifeature(shuffle=True, random_state=5)
data2, labels2 = load_UCImultifeature(shuffle=True, random_state=2)
data3, labels3 = load_UCImultifeature(shuffle=False)
assert len(data) == 6
assert labels.shape[0] == 2000
# check size of data
for i in range(6):
assert data[i].shape[0] == 2000
# check data is same
for idx in range(6):
assert (np.allclose(data[idx], data2[idx]))
assert (not np.allclose(data[idx], data1[idx]))
assert (not np.allclose(data[idx], data3[idx]))
assert (not np.allclose(data1[idx], data3[idx]))
def test_UCImultifeature_dataloader_select():
# load data
lab = [0, 1, 2]
data, labels = load_UCImultifeature(select_labeled=lab)
assert len(data) == 6
assert labels.shape[0] == 600
labels_set = list(set(labels))
assert len(labels_set) == len(lab)
for j, lab_in_set in enumerate(labels_set):
assert lab_in_set == lab[j]
# check size of data
for i in range(6):
assert data[i].shape[0] == 600
def test_UCImultifeature_dataloader_badselect3():
bad_list = [0, 2, 4, -2]
with pytest.raises(ValueError):
data, labels = load_UCImultifeature(select_labeled=bad_list)
def test_UCImultifeature_dataloader_badselect2():
long_list = [0, 0, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
with pytest.raises(ValueError):
data, labels = load_UCImultifeature(select_labeled=long_list)
def test_UCImultifeature_dataloader_badselect():
with pytest.raises(ValueError):
data, labels = load_UCImultifeature(select_labeled=[])
def test_dataloader_badviews1():
v_list = []
with pytest.raises(ValueError):
data, labels = load_UCImultifeature(views=v_list)
alpha=0.15)
plt.xlabel("Component 1", fontsize=20)
plt.ylabel("Component 2", fontsize=20)
plt.tight_layout()
ax.set_title('Latent Positions from Omnibus Embedding', fontsize=20)
plt.show()
###############################################################################
# UCI Digits Dataset
# ------------------
#
# Finally, we run Omnibus on the UCI Multiple Features Digits
# Dataset. We use the Fourier coefficient and profile correlation
# views (View 1 and 2 respectively) as a 2-view dataset.
full_data, full_labels = load_UCImultifeature()
view_1 = full_data[0]
view_2 = full_data[1]
Xs = [view_1, view_2]
# Running omnibus
embedder = omnibus.Omnibus()
embeddings = embedder.fit_transform(Xs)
###############################################################################
# Visualizing the Results
# ^^^^^^^^^^^^^^^^^^^^^^^
#
# This time, the points in the plot are colored by digit (0-9). The marker
# symbols denote which view each sample is from. We randomly plot 500 samples