""" sbm graph classification """
from Esme.dgms.fil import nodefeat
from Esme.graph.function import fil_strategy
from Esme.graph.generativemodel import sbms
from networkx.linalg.algebraicconnectivity import fiedler_vector
if __name__ == '__main__':
n = 1
p, q = 0.5, 0.1
gs = sbms(n=n, n1=100, n2=50, p=p, q=q)
for i in range(len(gs)):
g = gs[i]
# lapfeat = nodefeat(g, 'fiedler', norm=True)
nodefeat = fiedler_vector(g, normalized=False) # np.ndarray
nodefeat = nodefeat.reshape(len(g), 1)
gs[i] = fil_strategy(g, nodefeat, method='node', viz_flag=False)
print('Finish computing lapfeat')
from Esme.graph.function import fil_strategy
from Esme.graph.generativemodel import sbm2
from Esme.ml.svm import classifier
if __name__ == '__main__':
radius, zigzag, fil, n1, n2 = 1, True, 'deg', 150, 150
fil_method = 'combined'
g, labels = sbm2(n1=n1, n2=n2, p=0.5, q=0.2)
lp = LaplacianEigenmaps(d=1)
lp.learn_embedding(g, weight='weight')
lapfeat = lp.get_embedding()
lapdist = cdist(lapfeat, lapfeat, metric='euclidean')
kwargs = {'h': 0.3}
g = fil_strategy(g, lapfeat, method=fil_method, viz_flag=False, **kwargs)
ego = egograph(g,
radius=radius,
n=len(g),
recompute_flag=True,
norm_flag=True,
print_flag=False)
egographs = ego.egographs(method='serial')
dgms = alldgms(egographs,
radius=radius,
dataset='',
recompute_flag=True,
method='serial',
n=n1 + n2,
zigzag=zigzag) # compute dgms in parallel
plt.title('p: %s, q: %s' % (p, q))
for i in range(len(gs)):
g = gs[i]
lp = LaplacianEigenmaps(d=1)
lp.learn_embedding(g, weight='weight')
lapfeat = lp.get_embedding() # lapfeat is an array
# viz = True if i%100==0 else False
# plt.subplot(2, 1, 1+i//100)
# plt.plot(lapfeat)
# plt.show()
gs[i] = fil_strategy(g,
lapfeat,
method=fil_method,
viz_flag=False,
**edge_kwargs)
# plt.show()
# sys.exit()
# plot node fv value
# viz fv value
# val = dict(nx.get_node_attributes(gs[i], 'fv')).values()
# plt.plot(val)
# plt.title('q: %s, i: %s'%(q, i))
# plt.show()
# sys.exit()
print('Finish computing lapfeat')
dgms = alldgms(gs,