args.pairStartIndex) + "." + str(args.pairEndIndex) + ".txt" # print("loading dictionary/embedding") dictionary = helper.load_object(args.save_path + 'gene_dictionary.p') embeddings_index = helper.load_word_embeddings(args.word_vectors_directory, args.word_vectors_file, dictionary.word2idx) print("loading model") # print (args) model = SentenceClassifier(dictionary, embeddings_index, args, select_method='max') if args.cuda: model = model.cuda() helper.load_model_states_from_checkpoint( model, args.save_path + 'model_best.pth.tar', 'state_dict', args.cuda) print('vocabulary size = ', len(dictionary)) annotationBP = pickle.load( open(args.goAnnotationFile + "goBP.cPickle", "rb")) annotationCC = pickle.load( open(args.goAnnotationFile + "goCC.cPickle", "rb")) annotationMF = pickle.load( open(args.goAnnotationFile + "goMF.cPickle", "rb")) annotationAll3 = pickle.load( open(args.goAnnotationFile + "go3ontology.cPickle", "rb")) f = open(args.pairs2test, 'r') ## load genes to be tested. [[gene1,gene2],...] pairs = [] for line in f:
0, di + 1].data].data.cpu().numpy()[0] return gen_prob if __name__ == "__main__": dictionary = helper.load_object(args.save_path + 'dictionary.p') embeddings_index = helper.load_word_embeddings(args.word_vectors_directory, args.word_vectors_file, dictionary.word2idx) model = Seq2Seq(dictionary, embeddings_index, args) print(model) if args.cuda: model = model.cuda() helper.load_model_states_from_checkpoint( model, os.path.join(args.save_path, 'model_best.pth.tar'), 'state_dict', args.cuda) print('model, embedding index and dictionary loaded.') model.eval() # load the test dataset test_corpus = data.Corpus(args.tokenize, args.max_query_length) test_corpus.parse(args.data + 'dev.txt', args.max_example, False) print('test set size = ', len(test_corpus.data)) candidate_map = dict() with open('../data/anchor_candidates.txt', 'r') as f: for line in f: tokens = line.strip().split(':::') candidate_map[tokens[0]] = [] for i in range(1, len(tokens)):