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")