def test_ajive_plot_list(data):
x = data["same_views"]
ajive = AJIVE(init_signal_ranks=[2, 2])
ajive.fit(Xs=x)
blocks = ajive.predict(return_dict=False)
ajive_full_estimate_heatmaps(x, blocks, names=["x1", "x2"])
p = 1
assert p == 1
def test_ajive_plot(data):
x = data["same_views"]
ajive = AJIVE(init_signal_ranks=[2, 2])
ajive.fit(Xs=x)
blocks = ajive.predict(return_dict=True)
ajive_full_estimate_heatmaps(x, blocks)
p = 1
assert p == 1
def test_indiv(data):
dat = data["same_views"]
ajive = AJIVE(init_signal_ranks=[2, 2])
ajive.fit(Xs=dat)
blocks = ajive.predict(return_dict=True)
for i in np.arange(100):
j = np.sum(blocks[0]["individual"][i] == blocks[1]["individual"][i])
assert j == 20
def test_check_sparse(data):
dat = data["sparse_views"]
spar_mat = dat[0]
assert np.sum(spar_mat == 0) > np.sum(spar_mat != 0)
ajive = AJIVE(init_signal_ranks=[2, 2])
ajive.fit(Xs=dat)
blocks = ajive.predict(return_dict=True)
assert np.sum(np.sum(blocks[0]["individual"] == 0)) > np.sum(
np.sum(blocks[0]["individual"] != 0))
def test_traditional_output(data):
x = data["same_views"]
ajive = AJIVE(init_signal_ranks=[2, 2])
ajive.fit(Xs=x, view_names=["x", "y"])
ajive.predict(return_dict=False)
def test_joint_noise_length(data):
dat = data["same_views"]
ajive = AJIVE(init_signal_ranks=[2, 2])
ajive.fit(Xs=dat)
blocks = ajive.predict(return_dict=True)
assert blocks[0]["joint"].shape == blocks[0]["noise"].shape