def test_centering(self):
xmean = self.X.mean(axis=0)
ymean = self.Y.mean(axis=0)
self.assertTrue(np.allclose(self.ajive.centers_["x"], xmean))
self.assertTrue(np.allclose(self.ajive.blocks_["x"].joint.m_, xmean))
self.assertTrue(
np.allclose(self.ajive.blocks_["x"].individual.m_, xmean))
self.assertTrue(np.allclose(self.ajive.centers_["y"], ymean))
self.assertTrue(np.allclose(self.ajive.blocks_["y"].joint.m_, ymean))
self.assertTrue(
np.allclose(self.ajive.blocks_["y"].individual.m_, ymean))
# no centering
ajive = AJIVE(init_signal_ranks=[2, 3], center=False)
ajive = ajive.fit(Xs=[self.X, self.Y], view_names=["x", "y"])
self.assertTrue(ajive.centers_["x"] is None)
self.assertTrue(ajive.centers_["y"] is None)
# only center x
ajive = AJIVE(init_signal_ranks=[2, 3], center=[True, False])
ajive = ajive.fit(Xs=[self.X, self.Y], view_names=["x", "y"])
self.assertTrue(np.allclose(ajive.centers_["x"], xmean))
self.assertTrue(ajive.centers_["y"] is None)
def test_list_input(self):
"""
Check AJIVE can take a list input.
"""
ajive = AJIVE(init_signal_ranks=[2, 3])
ajive.fit(Xs=[self.X, self.Y])
self.assertTrue(set(ajive.block_names) == set([0, 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_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_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_wrong_sig(data):
dat = data["diff_views"]
ajive = AJIVE(init_signal_ranks=[-1, -4])
try:
ajive.fit(Xs=dat)
j = 0
except:
j = 1
assert j == 1
def test_precomp_init_svd(data):
dat = data["same_views"]
precomp = []
for i in dat:
precomp.append(svd_wrapper(i))
ajive = AJIVE(init_signal_ranks=[2, 2], joint_rank=1)
ajive.fit(dat, precomp_init_svd=precomp)
p = 3
assert p == 3
def test_dont_store_full(self):
"""
Make sure setting store_full = False works
"""
ajive = AJIVE(init_signal_ranks=[2, 3], store_full=False)
ajive.fit(Xs=[self.X, self.Y])
self.assertTrue(ajive.blocks_[0].joint.full_ is None)
self.assertTrue(ajive.blocks_[0].individual.full_ is None)
self.assertTrue(ajive.blocks_[1].joint.full_ is None)
self.assertTrue(ajive.blocks_[1].individual.full_ is None)
def test_rank0(self):
"""
Check setting joint/individual rank to zero works
"""
ajive = AJIVE(init_signal_ranks=[2, 3], joint_rank=0)
ajive.fit(Xs=[self.X, self.Y])
self.assertTrue(ajive.common_.rank == 0)
self.assertTrue(ajive.blocks_[0].joint.rank == 0)
self.assertTrue(ajive.blocks_[0].joint.scores_ is None)
ajive = AJIVE(init_signal_ranks=[2, 3], indiv_ranks=[0, 1])
ajive.fit(Xs=[self.X, self.Y])
self.assertTrue(ajive.blocks_[0].individual.rank == 0)
self.assertTrue(ajive.blocks_[0].individual.scores_ is None)
def test_fit_elbows():
n = 10
elbows = 3
np.random.seed(1)
x = np.random.binomial(1, 0.6, (n**2)).reshape(n, n)
xorth = orth(x)
d = np.zeros(xorth.shape[0])
for i in range(0, len(d), int(len(d) / (elbows + 1))):
d[:i] += 10
X = xorth.T.dot(np.diag(d)).dot(xorth)
Xs = [X, X]
ajive = AJIVE(n_elbows=2)
ajive = ajive.fit(Xs)
np.testing.assert_equal(list(ajive.init_signal_ranks_.values())[0], 4)
def test_check_joint_rank_large(data):
with pytest.raises(ValueError):
dat = data["same_views"]
ajive = AJIVE(init_signal_ranks=[2, 2], joint_rank=5)
ajive.fit(Xs=dat)
def test_name_values_type(data):
with pytest.raises(ValueError):
x = data["same_views"]
ajive = AJIVE(init_signal_ranks=[2, 2])
ajive.fit(Xs=x, view_names={"jon": "first", "rich": "second"})
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_name_values(data):
with pytest.raises(ValueError):
x = data["same_views"]
ajive = AJIVE(init_signal_ranks=[2, 2])
ajive.fit(Xs=x, view_names=["1", "2", "3"])
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
def test_check_gen_lin_op_scipy(data):
with pytest.raises(TypeError):
dat = data["bad_views"]
ajive = AJIVE(init_signal_ranks=[2, 2])
ajive.fit(Xs=dat)
def test_indiv_rank(data):
dat = data["same_views"]
ajive = AJIVE(init_signal_ranks=[2, 2], indiv_ranks=[2, 1])
ajive.fit(Xs=dat)
assert ajive.indiv_ranks[0] == 2
def test_joint_rank(data):
dat = data["same_views"]
ajive = AJIVE(init_signal_ranks=[2, 2], joint_rank=2)
ajive.fit(Xs=dat)
assert ajive.joint_rank == 2
