def _generate_structure_K(self, X):
lasso = GraphLassoCV(alphas=20)
lasso.fit(X.T)
K_structure = lasso.get_precision()
if (hasattr(lasso, 'alpha_')):
print('alpha=', lasso.alpha_)
return K_structure
def computePartialCorrelationsCV(coupling_data):
# standardize
coupling_data -= coupling_data.mean(axis=0)
coupling_data /= coupling_data.std(axis=0)
estimator = GraphLassoCV(alphas=10)
estimator.fit(coupling_data)
prec = estimator.get_precision()
reg_alpha = estimator.alpha_
#### partial correlations: rho_ij = - p_ij/ sqrt(p_ii * p_jj)
#diagonal of precision matrix
prec_diag = np.diag(prec)
partial_correlations = -prec / np.sqrt(np.outer(prec_diag, prec_diag))
# set lower half to zero
partial_correlations[np.tril_indices(400)] = 0
return estimator.get_precision(), partial_correlations, reg_alpha
def _generate_structure_K(self, X):
# lasso = GraphLasso(alpha=0.012)
lasso = GraphLassoCV(alphas=20)
lasso.fit(X.T)
K_structure = lasso.get_precision()
if (hasattr(lasso, 'alpha_')):
print('alpha=', lasso.alpha_)
M = (np.abs(K_structure) > 1e-10)
if (M == np.eye(M.shape[0], dtype=bool)).all():
print('Got identity structure')
# K_structure = np.ones(K_lasso.shape)
return K_structure
def matrix_analysis(argv):
x = pd.read_csv(argv[1], sep=',', header=None)
features = x.iloc[:, 2:66].astype(np.float64)
features = features.subtract(features.mean(axis=0), axis=1)
features = features.divide(features.std(axis=0), axis=1)
model = GraphLassoCV()
model.fit(features)
prec = model.get_precision()
corr = features.corr()
norm = SymLogNorm(linthresh=0.03, linscale=0.03, vmin=-1.0, vmax=1.0)
cmap = plt.get_cmap('PuOr')
plt.imshow(corr, cmap=cmap, norm=norm)
plt.colorbar()
plt.xticks([])
plt.yticks([])
plt.show()
# plot precision matrix
norm = SymLogNorm(linthresh=0.03, linscale=0.03, vmin=-1.0, vmax=1.0)
cmap = plt.get_cmap('PuOr')
plt.imshow(prec, cmap=cmap, norm=norm)
plt.colorbar()
plt.xticks([])
plt.yticks([])
plt.show()
g = nx.Graph(prec)
nx.draw_circular(g, with_labels=True)
plt.show()
##########################################
# Rearrange the colombs and plot again
perm = [5, 6, 7, 8, 9]
perm += [25, 26, 29, 30, 31, 34, 35, 36, 39]
perm += [27, 28, 32, 33, 37, 38]
perm += [61, 62, 63]
perm += [20, 24, 40, 41]
perm += [i for i in xrange(64) if i not in perm]
features = features.iloc[:, perm]
# print perm[27]
model = GraphLassoCV()
model.fit(features)
prec = model.get_precision()
corr = features.corr()
#########cov
features = features.iloc[:, perm]
norm = SymLogNorm(linthresh=0.03, linscale=0.03, vmin=-1.0, vmax=1.0)
cmap = plt.get_cmap('PuOr')
plt.imshow(corr, cmap=cmap, norm=norm)
plt.colorbar()
plt.xticks([])
plt.yticks([])
plt.show()
############prec
features = features.iloc[:, perm]
norm = SymLogNorm(linthresh=0.03, linscale=0.03, vmin=-1.0, vmax=1.0)
cmap = plt.get_cmap('PuOr')
plt.imshow(prec, cmap=cmap, norm=norm)
plt.colorbar()
plt.xticks([])
plt.show()
