f_log = '%s/output/example.siplda.log' % root_dir
f_out = '%s/output/example.siplda.out' % root_dir
f_ref = ''
# load graph
print('loading graph...')
graph = Graph(f_net,
'edge list',
directed=False,
weighted=False,
memory_control=True)
# generate corpus
print('generating training/testing corpus...')
corpus = Corpus()
corpus.generate_corpus_from_graph_using_SIP(graph, '012-SIP')
train_corpus, test_corpus = corpus_split(corpus)
# stochastic variational inference
hyper_params_svb = {}
hyper_params_svb['num_topics'] = K
hyper_params_svb['alpha'] = alpha # uniform [1/K, ..., 1/K]
hyper_params_svb['eta'] = eta # uniform [1/K, ..., 1/K]
hyper_params_svb['size_vocab'] = graph.n
hyper_params_svb['num_docs'] = train_corpus.num_docs
hyper_params_svb['tau0'] = tau0
hyper_params_svb['kappa'] = kappa
lda_svb = LDA(hyper_params_svb, 'SVB')
log_file = open(f_log, "w")
log_file.write("iteration time rthot held-out log-perplexity estimate\n")
