def test_indiv_rank_0(data):
dat = data["diff_views"]
ajive = AJIVE(init_signal_ranks=[2, 2], individual_ranks=[0, 0])
_ = ajive.fit_transform(dat)
Is = ajive.individual_mats_
assert_allclose(Is[0], 0)
assert_allclose(Is[1], 0)
def test_same_joint(data, init_signal_ranks, individual_ranks, joint_rank):
# Test same joint input result across varying inputs
dat = data["same_views"]
ajive = AJIVE(
init_signal_ranks=init_signal_ranks,
joint_rank=joint_rank,
individual_ranks=individual_ranks,
)
Js = ajive.fit_transform(Xs=dat)
for i in np.arange(20):
j = np.sum(Js[0][i] == Js[1][i])
assert_equal(j, 100)
U, S, V = np.linalg.svd(X2)
vars2 = S**2 / np.sum(S**2)
axes[0].plot(np.arange(10) + 1, vars1[:10], 'ro-', linewidth=2)
axes[1].plot(np.arange(10) + 1, vars2[:10], 'ro-', linewidth=2)
axes[0].set_title('Scree Plot View 1')
axes[1].set_title('Scree Plot View 2')
axes[0].set_xlabel('Number of top singular values')
axes[1].set_xlabel('Number of top singular values')
axes[0].set_ylabel('Percent variance explained')
plt.show()
# Based on the scree plots, we fit AJIVE with both initial signal ranks set to
# 2.
ajive1 = AJIVE(init_signal_ranks=[2, 2])
Js_1 = ajive1.fit_transform(Xs_same)
ajive2 = AJIVE(init_signal_ranks=[2, 2])
Js_2 = ajive2.fit_transform(Xs_diff)
###############################################################################
# Heatmap Visualizations
# ----------------------
#
# Here we are using heatmaps to visualize the decomposition of our views. As we
# can see when we use two of the same views there is no Individualized
# Variation displayed. When we create two different views, the algorithm finds
# different decompositions where common and individual structural artifacts
# can be seen in their corresponding heatmaps.
axes[0].set_title('Multiview Kmeans Clusters')
plt.tight_layout()
plt.show()
###############################################################################
# Decomposition using AJIVE
# -------------------------
#
# We can also apply joint decomposition tools to find features across views
# that are jointly related. Using :class:`mvlearn.decomposition.AJIVE`, we can
# find genes and lipids that are jointly related.
from mvlearn.decomposition import AJIVE # noqa: E402
ajive = AJIVE()
Xs_joint = ajive.fit_transform(Xs)
f, axes = plt.subplots(2, 1, figsize=(5, 5))
sort_idx = np.hstack((np.argsort(y[:20, 1]), np.argsort(y[20:, 1]) + 20))
y_ticks = [
diet_names[j] + f' ({genotype_names[i]})' if idx % 4 == 0 else ''
for idx, (i, j) in enumerate(y[sort_idx])
]
gene_ticks = [
n if i in [31, 36, 76, 94] else ''
for i, n in enumerate(dataset['gene_feature_names'])
]
g = sns.heatmap(Xs_joint[0][sort_idx],
yticklabels=y_ticks,
cmap="RdBu_r",
def ajive(data):
aj = AJIVE(init_signal_ranks=[3, 2], random_state=0)
joint_mats = aj.fit_transform(Xs=data['diff_views'])
aj.joint_mats = joint_mats
return aj
def test_zero_rank_warn(data):
# warn returning rank 0 joint
dat = data["diff_views"]
ajive = AJIVE(init_signal_ranks=[2, 2], joint_rank=0)
with pytest.warns(RuntimeWarning):
_ = ajive.fit_transform(dat)
def test_joint_rank_0(data):
dat = data["diff_views"]
ajive = AJIVE(init_signal_ranks=[2, 2], joint_rank=0)
Js = ajive.fit_transform(dat)
assert_allclose(Js[0], 0)
assert_allclose(Js[1], 0)
def test_same_joint_indiv_length(data):
dat = data["same_views"]
ajive = AJIVE(init_signal_ranks=[2, 2])
Js = ajive.fit_transform(Xs=dat)
Is = ajive.individual_mats_
assert_equal(Js[0].shape, Is[0].shape)