def test_frob_norm(self):
"""
Check Frobenius norm is calculated correctly whether the full
or partial PCA is computed.
"""
true_frob_norm = np.linalg.norm(self.X_cent, ord='fro')
pca = PCA(n_components=None).fit(self.X)
self.assertTrue(np.allclose(pca.frob_norm_, true_frob_norm))
# TODO: this is failing, it could be a numerical issue.
pca = PCA(n_components=3).fit(self.X)
self.assertTrue(np.allclose(pca.frob_norm_, true_frob_norm))
def test_reconstruction(self):
"""
We can reconstruct the original data matrix exactly from the full
reconstruction.
"""
pca = PCA().fit(self.X)
self.assertTrue(np.allclose(self.X, pca.predict_reconstruction()))
def svals(data, joint):
init = 0
last = 0
previous_step = 0
for i in PCA().fit(data).svals_:
if last - i < 0.2 * previous_step:
init += 1
previous_step = last - i
last = i
return max(init, joint)
def test_centering(self):
"""
Make sure PCA computes the correct centers. Also check center=False
works correctly.
"""
self.assertTrue(np.allclose(self.pca.m_, self.X.mean(axis=0)))
# no centering
pca = PCA(n_components=4, center=False).fit(self.X)
self.assertTrue(pca.m_ is None)
Z = np.random.normal(size=(20, self.X.shape[1]))
V = pca.loadings_.values
self.assertTrue(np.allclose(pca.predict_scores(Z), np.dot(Z, V)))
def setUp(self):
n = 100
d = 20
n_components = 10
obs_names = ['sample_{}'.format(i) for i in range(n)]
var_names = ['var_{}'.format(i) for i in range(d)]
X = pd.DataFrame(np.random.normal(size=(n, d)),
index=obs_names,
columns=var_names)
X_cent = X - X.mean(axis=0)
pca = PCA(n_components=n_components).fit(X)
# store these for testing
self.n = n
self.d = d
self.n_components = n_components
self.obs_names = obs_names
self.var_names = var_names
self.X = X
self.X_cent = X_cent
self.pca = pca
from jive.AJIVE import AJIVE
from jive.PCA import PCA
from jive.ajive_fig2 import generate_data_ajive_fig2
from jive.viz.block_visualization import data_block_heatmaps, jive_full_estimate_heatmaps
import matplotlib.pyplot as plt
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')