def wsim(self):
model = self.model
#y = model.generate(**expe)
theta, phi = model.get_params()
# NB mean/var
mean, var = model.get_nb_ss()
phi = mean
print(theta.shape, theta.min(), theta.max())
print(phi.shape, phi.min(), phi.max())
description = '/'.join(
(self.expe._refdir, os.path.basename(self.output_path)))
adjshow(theta, title='theta', colorbar=True)
adjshow(phi, title='Phi Mean', colorbar=True)
data = self.frontend.data
sim = np.random.poisson(theta.dot(phi).dot(theta.T))
print('data shape', data.shape)
print('sim shape', sim.shape)
# l2 norm
l2 = ((data - sim)**2).sum()**(0.5)
print('l2', l2)
def zipf(self, clusters_org='source'):
''' Zipf Analysis
Local/Global Preferential attachment effect analysis
Parameters
----------
clusters_org: str
cluster origin if from either ['source'|'model']
'''
expe = self.expe
frontend = FrontendManager.load(expe)
#
# Get the Class/Cluster and local degree information
# Reordering Adjacency Mmatrix based on Clusters/Class/Communities
#
clusters = None
K = None
if clusters_org == 'source':
clusters = frontend.get_clusters()
elif clusters_org == 'model':
model = ModelManager.from_expe(expe, load=True)
#clusters = model.get_clusters(K, skip=1)
#clusters = model.get_communities(K)
clusters = Louvain.get_clusters(frontend.to_directed(),
resolution=10)
if len(np.unique(clusters)) > 20 or False:
self.log.info('Using Annealing clustering')
clusters = Annealing(frontend.data,
iterations=200,
C_init=5,
grow_rate=0).search()
else:
self.log.info('Using Louvain clustering')
if clusters is None:
lgg.error('No clusters here...passing')
data_r = frontend.data
else:
block_hist = np.bincount(clusters)
K = (block_hist != 0).sum()
lgg.info('%d Clusters from `%s\':' % (K, clusters_org))
data_r = reorder_mat(frontend.data, clusters)
np.fill_diagonal(data_r, 0)
from pymake.util.math import dilate
dlt = lambda x: dilate(x) if x.sum() / x.shape[0]**2 < 0.1 else x
### Plot Adjacency matrix
fig, (ax1, ax2) = plt.subplots(1, 2)
fig.tight_layout(pad=1.6)
adjshow(dlt(data_r), title=self.specname(expe.corpus), ax=ax1)
#plt.figtext(.15, .1, homo_text, fontsize=12)
#plt.suptitle(self.specname(expe.corpus))
### Plot Degree
plot_degree_poly(data_r, ax=ax2)
if expe._write:
self.write_frames([fig], suffix='dd')
def clustering(self):
algo = 'Louvain'
algo = 'Annealing'
data = self.frontend.data
mat = data
#mat = phi
alg = getattr(A, algo)(mat)
clusters = alg.search()
mat = draw_boundary(alg.hi_phi(), alg.B)
#mat = draw_boundary(mat, clusters)
adjshow(mat, algo)
plt.colorbar()
def draw(self):
#if expe._mode == 'predictive':
# model = self.frontend
# y = model.data
# # move this in draw data
#elif expe._mode == 'generative':
# model = self.model
# y = model.generate(**expe)
model = self.model
y = model.generate(**expe)
#clustering = 'modularity'
#print('@heeere, push commmunities annalysis outside static method of frontendnetwork')
#comm = model.communities_analysis(y, clustering=clustering)
#clusters = comm['clusters']
#draw_graph_spring(y, clusters)
#draw_graph_spectral(y, clusters)
#draw_graph_circular(y, clusters)
description = '/'.join(
(self.expe._refdir, os.path.basename(self.output_path)))
adjshow(y, title=description)
def wsim(self):
expe = self.expe
frontend = self.frontend
model = self.load_model(load=True)
#y = model.generate(**expe)
theta, phi = model.get_params()
# NB mean/var
mean, var = model.get_nb_ss()
phi = mean
print(theta.shape, theta.min(), theta.max())
print(phi.shape, phi.min(), phi.max())
adjshow(theta, title='theta', colorbar=True)
adjshow(phi, title='Phi Mean', colorbar=True)
y = self.frontend.adj()
sim = np.random.poisson(theta.dot(phi).dot(theta.T))
# l2 norm
l2 = ((y - sim)**2).sum()**(0.5)
print('l2', l2)