https://github.com/palash1992/DynamicGEM
Tip revision: 911fe36dc8fa5f85deb0d931210cac823955f643 authored by Palash Goyal on 07 April 2020, 19:10:13 UTC
Modified metrics defn
Modified metrics defn
Tip revision: 911fe36
test_ae_static.py
import os
from dynamicgem.evaluation import visualize_embedding as viz
from dynamicgem.embedding.ae_static import AE
from dynamicgem.graph_generation import dynamic_SBM_graph as sbm
from time import time
def main():
# Parameters for Stochastic block model graph
# Todal of 1000 nodes
node_num = 1000
# Test with two communities
community_num = 2
# At each iteration migrate 10 nodes from one community to the another
node_change_num = 10
# Length of total time steps the graph will dynamically change
length = 7
# output directory for result
outdir = './output'
intr = './intermediate'
if not os.path.exists(outdir):
os.mkdir(outdir)
if not os.path.exists(intr):
os.mkdir(intr)
testDataType = 'sbm_cd'
# Generate the dynamic graph
dynamic_sbm_series = list(sbm.get_community_diminish_series_v2(node_num,
community_num,
length,
1, # comminity ID to perturb
node_change_num))
graphs = [g[0] for g in dynamic_sbm_series]
# parameters for the dynamic embedding
# dimension of the embedding
dim_emb = 8
lookback = 2
# AE Static
embedding = AE(d=dim_emb,
beta=5,
nu1=1e-6,
nu2=1e-6,
K=3,
n_units=[500, 300],
n_iter=5,
xeta=1e-4,
n_batch=100,
modelfile=['./intermediate/enc_modelsbm.json',
'./intermediate/dec_modelsbm.json'],
weightfile=['./intermediate/enc_weightssbm.hdf5',
'./intermediate/dec_weightssbm.hdf5'])
embs = []
t1 = time()
# ae static
for temp_var in range(length):
emb, _ = embedding.learn_embeddings(graphs[temp_var])
embs.append(emb)
print(embedding._method_name + ':\n\tTraining time: %f' % (time() - t1))
viz.plot_static_sbm_embedding(embs[-4:], dynamic_sbm_series[-4:])
if __name__ == '__main__':
main()