def test_list_input(self):
"""
Check AJIVE can take a list input.
"""
ajive = AJIVE(init_signal_ranks=[2, 3])
ajive.fit(blocks=[self.X, self.Y])
self.assertTrue(set(ajive.block_names) == set([0, 1]))
def test_parallel_runs(self):
"""
Check wedin/random samples works with parallel processing.
"""
ajive = AJIVE(init_signal_ranks={'x': 2, 'y': 3}, n_jobs=-1)
ajive.fit(blocks={'x': self.X, 'y': self.Y})
self.assertTrue(hasattr(ajive, 'blocks'))
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(blocks=[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 setUp(self):
X, Y = generate_data_ajive_fig2()
obs_names = ['sample_{}'.format(i) for i in range(X.shape[0])]
var_names = {
'x': ['x_var_{}'.format(i) for i in range(X.shape[1])],
'y': ['y_var_{}'.format(i) for i in range(Y.shape[1])]
}
X = pd.DataFrame(X, index=obs_names, columns=var_names['x'])
Y = pd.DataFrame(Y, index=obs_names, columns=var_names['y'])
ajive = AJIVE(init_signal_ranks={
'x': 2,
'y': 3
}).fit(blocks={
'x': X,
'y': Y
})
self.ajive = ajive
self.X = X
self.Y = Y
self.obs_names = obs_names
self.var_names = var_names
def ajive(data, joint):
#Only overlapping cancer cell lines will be used
blocks = {j: data.loc[j] for j in data.index.levels[0]}
ccls = {k: v.index for k, v in blocks.items()}
overlap = reduce(np.intersect1d, ccls.values())
blocks = {k: v.loc[overlap] for k, v in blocks.items()}
#Calculates the number of singular values that should be used for the PCA
#projections of the blocks
init = {k: svals(v, joint) for k, v in blocks.items()}
model = AJIVE(init, joint_rank=joint)
model.fit(blocks)
result = ajive_predict(model, data)
return result, model
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={'x': 2, 'y': 3}, center=False)
ajive = ajive.fit(blocks={'x': self.X, 'y': self.Y})
self.assertTrue(ajive.centers_['x'] is None)
self.assertTrue(ajive.centers_['y'] is None)
# only center x
ajive = AJIVE(init_signal_ranks={
'x': 2,
'y': 3
},
center={
'x': True,
'y': False
})
ajive = ajive.fit(blocks={'x': self.X, 'y': self.Y})
self.assertTrue(np.allclose(ajive.centers_['x'], xmean))
self.assertTrue(ajive.centers_['y'] 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(blocks=[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(blocks=[self.X, self.Y])
self.assertTrue(ajive.blocks[0].individual.rank == 0)
self.assertTrue(ajive.blocks[0].individual.scores_ is None)
X, Y = generate_data_ajive_fig2()
plt.figure(figsize=[6.5, 3])
data_block_heatmaps({'x': X, 'y': Y})
plt.savefig('figures/data_heatmaps.png', bbox_inches='tight')
plt.close()
# determine initial signal ranks by inspecting scree plots
plt.figure(figsize=[8.4, 3])
plt.subplot(1, 2, 1)
PCA().fit(X).plot_scree()
plt.subplot(1, 2, 2)
PCA().fit(Y).plot_scree()
plt.savefig('figures/scree_plots.png', bbox_inches='tight')
plt.close()
ajive = AJIVE(init_signal_ranks={'x': 2, 'y': 3})
ajive.fit(blocks={'x': X, 'y': Y})
plt.figure(figsize=[6.5, 12])
jive_full_estimate_heatmaps(ajive.get_full_block_estimates(),
blocks={
'x': X,
'y': Y
})
plt.savefig('figures/jive_estimate_heatmaps.png', bbox_inches='tight')
plt.close()
plt.figure(figsize=[7, 7])
ajive.plot_joint_diagnostic()
plt.savefig('figures/jive_diagnostic.png', bbox_inches='tight')
plt.close()