def ensemble(): f_train = "../data/train.txt" # f_test = "data/test_attr2.txt" if args.w2v == "merge": f_w2v = "../embedding/embedding_all_merge_300.txt" elif args.w2v == "fasttext2": f_w2v = "../embedding/embedding_all_fasttext2_300.txt" elif args.w2v == "tencent": f_w2v = "../embedding/embedding_all_tencent_200.txt" else: print("error, no embedding") exit(-1) f_dict = "../dataset/attribute.json" print(f_train) print(f_w2v) if not os.path.exists("%s" % args.check_dir): os.mkdir("%s" % args.check_dir) raw_texts, raw_labels = load_attr_data(filename=f_train) W, word2index2 = load_w2v(f_w2v) word2index = pickle.load(open("../data/vocabulary.pkl", 'rb')) assert word2index == word2index2 attr_list, attr_dict = parse_json(f_dict) kf = 0 for train_index, test_index in kfold_split(len(raw_texts), args.folds): kf += 1 print("FOLD:", kf) print("TRAIN:", str(len(train_index)), '\n', "TEST:", str(len(test_index))) # train_index, test_index = train_index.tolist(), test_index.tolist() test_texts, test_labels = [raw_texts[i] for i in test_index ], [raw_labels[i] for i in test_index] train_texts, train_labels = [raw_texts[i] for i in train_index ], [raw_labels[i] for i in train_index] print(len(train_texts)) print(len(test_labels)) model = AttributeClassifier() print(attr_list) print(attr_dict) # exit(-1) # print(train_texts) model.train_from_data((train_texts, train_labels), (test_texts, test_labels), W, word2index, attr_dict, args, kf) pass
def main(): f_train = "../data/train.txt" # f_test = "data/test_attr2.txt" if args.w2v == "merge": f_w2v = "../embedding/embedding_all_merge_300.txt" elif args.w2v == "fasttext": f_w2v = "../embedding/embedding_all_fasttext_300.txt" elif args.w2v == "fasttext2": f_w2v = "../embedding/embedding_all_fasttext2_300.txt" elif args.w2v == "tencent": f_w2v = "../embedding/embedding_all_tencent_200.txt" else: print("error, no embedding") exit(-1) f_dict = "../dataset/attribute.json" print(f_w2v) train_texts, train_labels = load_attr_data(filename=f_train) train_texts, train_labels, test_texts, test_labels = split_dev( train_texts, train_labels) print(len(train_texts)) print(len(test_labels)) # train_texts2, train_labels2, test_texts, test_labels = split_dev(train_texts, train_labels) if not os.path.exists("%s" % args.check_dir): os.mkdir("%s" % args.check_dir) # test_texts, test_labels = load_attr_data(filename=f_test) W, word2index2 = load_w2v(f_w2v) word2index = pickle.load(open("../data/vocabulary.pkl", 'rb')) assert word2index == word2index2 attr_list, attr_dict = parse_json(f_dict) print(list(attr_dict.keys())) model = AttributeClassifier() print(attr_list) print(attr_dict) # exit(-1) # print(train_texts) model.train_from_data((train_texts, train_labels), (test_texts, test_labels), W, word2index, attr_dict, args)
def dev(): model = AttributeClassifier() check_point = "checkpoints5/checkpoint_AttA3_0.8666.pt" model.load_model(check_point) f_train = "data/attribute_data.txt" # f_test = "data/test_attr2.txt" f_w2v = "../embedding/embedding_all_merge_300.txt" f_dict = "../dataset/attribute.json" print(f_w2v) raw_texts, raw_labels = load_attr_data(filename=f_train) W, word2index = load_w2v(f_w2v) attr_list, attr_dict = parse_json(f_dict) kf = 0 _, test_index = kfold_split(len(raw_texts), args.folds)[2] test_texts, test_labels = [raw_texts[i] for i in test_index ], [raw_labels[i] for i in test_index] test_data = Data((test_texts, test_labels), word2index, attr_dict, args) test_predict = train.predict(model.classifier, test_data, args) pred_acc_t = score(test_predict, test_data.labels) print(pred_acc_t)
def stacking(): saved = True if args.saved != 0 else False f_train = "../data/train.txt" test_file = "../data/test.txt" test_texts = load_test_data(test_file) raw_texts, raw_labels = load_attr_data(filename=f_train) word2index = pickle.load(open("../data/vocabulary.pkl", 'rb')) f_dict = "../dataset/attribute.json" attr_list, attr_dict = parse_json(f_dict) paths = args.test_dir.split('#') models_files = [] for path in paths: models_files.append([ os.path.join(path, f) for f in os.listdir(path) if os.path.isfile(os.path.join(path, f)) ]) test_data = Data((test_texts, None), word2index) if args.use_elmo != 0: test_elmo = load_elmo(test_texts) test_data.add_feature(test_elmo) x_train = [] y_train = [] # TODO replace x_test = [] for dir, checkpoints_per_model in zip(paths, models_files): print(dir, checkpoints_per_model) if saved == 1 and os.path.isfile( os.path.join(dir, 'npy', "oof_train.npy")): oof_train, oof_train_y, oof_test = load_oof(dir) else: NFOLDS = len(checkpoints_per_model) print(NFOLDS) assert NFOLDS == args.folds clfs = [None for i in range(NFOLDS)] for cp in checkpoints_per_model: fold = int(cp.replace('_', '.').split('.')[-2]) print(fold) clfs[fold - 1] = cp oof_train, oof_train_y, oof_test = get_oof(clfs, raw_texts, raw_labels, test_data, word2index, attr_dict) x_train.append(oof_train) if y_train == []: y_train = oof_train_y else: assert (y_train == oof_train_y).all() x_test.append(oof_test) x_train = np.stack(x_train, axis=2) x_test = np.stack(x_test, axis=2) print(x_train.shape) num_train = x_train.shape[0] num_test = x_test.shape[0] test_predict = [] for c in range(x_train.shape[1]): x_train_c = x_train[:, c, :].reshape(num_train, -1) x_test_c = x_test[:, c, :].reshape(num_test, -1) meta_clf_c = LogisticRegression() y_train_c = y_train[:, c] meta_clf_c.fit(x_train_c, y_train_c) test_predict_c = meta_clf_c.predict_proba(x_test_c)[:, 1] test_predict.append(test_predict_c) test_predict = np.stack(test_predict, axis=1) print(test_predict.shape) fw = codecs.open("../data/test_predict_aspect_ensemble.txt", 'w', encoding='utf-8') for prob in test_predict: attributes = [] voted = [0 for a in range(len(attr_list))] for i in range(len(prob)): p = prob[i] # print(p) if p > args.threshold: voted[i] = 1 # categories.append(attrC[i]) if sum(voted) == 0: voted[prob.argmax()] = 1 for i, l in enumerate(voted): if l != 0: attributes.append(attr_list[i]) fw.write('|'.join(attributes) + '\n') time_stamp = time.asctime().replace(':', '_').split() fw.close() shutil.copy2( "../data/test_predict_aspect_ensemble.txt", "../data/backup/test_predict_aspect_ensemble_%s.txt" % time_stamp)