def predict_final(output_key, output_labels): config_path = data_config.output_path(output_key, ALL, CONFIG) config_data = yaml.load(open(config_path)) nn_config = NNConfig(config_data) vocab_id_mapping = json.load(open(data_config.output_path(output_key, ALL, VOCAB_ID_MAPPING), 'r')) dataset = load_dataset( mode=FINAL, vocab_id_mapping=vocab_id_mapping, max_seq_len=nn_config.seq_len, sampling=False, with_label=False ) index_iterator = SimpleIndexIterator.from_dataset(dataset) n_sample = index_iterator.n_sample() with tf.Session() as sess: prefix_checkpoint = tf.train.latest_checkpoint(data_config.model_path(key=output_key)) saver = tf.train.import_meta_graph('{}.meta'.format(prefix_checkpoint)) saver.restore(sess, prefix_checkpoint) nn = BaseNNModel(config=None) nn.set_graph(tf.get_default_graph()) fetches = {_key: nn.var(_key) for _key in [LABEL_PREDICT]} labels_predict = list() for batch_index in index_iterator.iterate(nn_config.batch_size, shuffle=False): feed_dict = {nn.var(_key): dataset[_key][batch_index] for _key in feed_key[TEST]} feed_dict[nn.var(TEST_MODE)] = 1 res = sess.run(fetches=fetches[TEST], feed_dict=feed_dict) labels_predict += res[LABEL_PREDICT].tolist() labels_predict = labels_predict[:n_sample] with open(output_labels, 'w') as file_obj: for i, label in enumerate(labels_predict): file_obj.write('{},{},{}'.format(i, label, label_str[label]))
def train(text_version='ek', label_version=None, config_path='c83.yaml'): """ python -m algo.main93_v2 train python3 -m algo.main93_v2 train -c config_ntua93.yaml :param text_version: string :param label_version: string :param config_path: string :return: """ pos_label = 1 if label_version == 'A' else None config_data = yaml.load(open(config_path)) output_key = '{}_{}_{}'.format(NNModel.name, text_version, int(time.time())) if label_version is not None: output_key = '{}_{}'.format(label_version, output_key) print('OUTPUT_KEY: {}'.format(output_key)) # 准备输出路径的文件夹 data_config.prepare_output_folder(output_key=output_key) data_config.prepare_model_folder(output_key=output_key) shutil.copy(config_path, data_config.output_path(output_key, ALL, CONFIG)) w2v_key = '{}_{}'.format(config_data['word']['w2v_version'], text_version) w2v_model_path = data_config.path(ALL, WORD2VEC, w2v_key) vocab_train_path = data_config.path(TRAIN, VOCAB, text_version) # 加载字典集 # 在模型中会采用所有模型中支持的词向量, 并为有足够出现次数的单词随机生成词向量 vocab_meta_list = load_vocab_list(vocab_train_path) vocabs = [ _meta['t'] for _meta in vocab_meta_list if _meta['tf'] >= config_data['word']['min_tf'] ] # 加载词向量与相关数据 lookup_table, vocab_id_mapping, embedding_dim = load_lookup_table2( w2v_model_path=w2v_model_path, vocabs=vocabs) json.dump( vocab_id_mapping, open(data_config.output_path(output_key, ALL, VOCAB_ID_MAPPING), 'w')) # 加载配置 nn_config = NNConfig(config_data) train_config = TrainConfig(config_data['train']) early_stop_metric = train_config.early_stop_metric # 加载训练数据 datasets = dict() datasets[TRAIN], output_dim = load_dataset( mode=TRAIN, vocab_id_mapping=vocab_id_mapping, max_seq_len=nn_config.seq_len, sampling=train_config.train_sampling, label_version=label_version) datasets[TEST], _ = load_dataset(mode=TEST, vocab_id_mapping=vocab_id_mapping, max_seq_len=nn_config.seq_len, label_version=label_version) # 初始化数据集的检索 index_iterators = { TRAIN: IndexIterator.from_dataset(datasets[TRAIN]), } # 按配置将训练数据切割成训练集和验证集 index_iterators[TRAIN].split_train_valid(train_config.valid_rate) # 计算各个类的权重 if train_config.use_class_weights: label_weight = { # 参考 sklearn 中 class_weight='balanced'的公式, 实验显示效果显着 _label: float(index_iterators[TRAIN].n_sample()) / (index_iterators[TRAIN].dim * len(_index)) for _label, _index in index_iterators[TRAIN].label_index.items() } else: label_weight = { _label: 1. for _label in range(index_iterators[TRAIN].dim) } # 基于加载的数据更新配置 nn_config.set_embedding_dim(embedding_dim) nn_config.set_output_dim(output_dim) # 搭建神经网络 nn = NNModel(config=nn_config) nn.build_neural_network(lookup_table=lookup_table) batch_size = train_config.batch_size fetches = { mode: {_key: nn.var(_key) for _key in fetch_key[mode]} for mode in [TRAIN, TEST] } model_output_prefix = data_config.model_path(key=output_key) + '/model' best_res = {mode: None for mode in [TRAIN, VALID]} no_update_count = {mode: 0 for mode in [TRAIN, VALID]} max_no_update_count = 10 eval_history = {TRAIN: list(), VALID: list(), TEST: list()} with tf.Session() as sess: sess.run(tf.global_variables_initializer()) saver = tf.train.Saver(tf.global_variables()) dataset = datasets[TRAIN] index_iterator = index_iterators[TRAIN] # 训练开始 ########################################################################## for epoch in range(train_config.epoch): print('== epoch {} = {} ='.format(epoch, output_key)) # 利用训练集进行训练 print('TRAIN') n_sample = index_iterator.n_sample(TRAIN) labels_predict = list() labels_gold = list() for batch_index in index_iterator.iterate(batch_size, mode=TRAIN, shuffle=True): feed_dict = { nn.var(_key): dataset[_key][batch_index] for _key in feed_key[TRAIN] } feed_dict[nn.var(SAMPLE_WEIGHTS)] = list( map(label_weight.get, feed_dict[nn.var(LABEL_GOLD)])) feed_dict[nn.var(TEST_MODE)] = 0 res = sess.run(fetches=fetches[TRAIN], feed_dict=feed_dict) labels_predict += res[LABEL_PREDICT].tolist() labels_gold += dataset[LABEL_GOLD][batch_index].tolist() labels_predict, labels_gold = labels_predict[: n_sample], labels_gold[: n_sample] res = basic_evaluate(gold=labels_gold, pred=labels_predict, pos_label=pos_label) print_evaluation(res) eval_history[TRAIN].append(res) global_step = tf.train.global_step(sess, nn.var(GLOBAL_STEP)) if train_config.valid_rate == 0.: if best_res[TRAIN] is None or res[ early_stop_metric] > best_res[TRAIN][early_stop_metric]: best_res[TRAIN] = res no_update_count[TRAIN] = 0 saver.save(sess, save_path=model_output_prefix, global_step=global_step) else: no_update_count[TRAIN] += 1 else: if best_res[TRAIN] is None or res[ early_stop_metric] > best_res[TRAIN][early_stop_metric]: best_res[TRAIN] = res no_update_count[TRAIN] = 0 else: no_update_count[TRAIN] += 1 # 计算在验证集上的表现, 不更新模型参数 print('VALID') n_sample = index_iterator.n_sample(VALID) labels_predict = list() labels_gold = list() for batch_index in index_iterator.iterate(batch_size, mode=VALID, shuffle=False): feed_dict = { nn.var(_key): dataset[_key][batch_index] for _key in feed_key[TEST] } feed_dict[nn.var(TEST_MODE)] = 1 res = sess.run(fetches=fetches[TEST], feed_dict=feed_dict) labels_predict += res[LABEL_PREDICT].tolist() labels_gold += dataset[LABEL_GOLD][batch_index].tolist() labels_predict, labels_gold = labels_predict[: n_sample], labels_gold[: n_sample] res = basic_evaluate(gold=labels_gold, pred=labels_predict, pos_label=pos_label) eval_history[VALID].append(res) print_evaluation(res) # Early Stop if best_res[VALID] is None or res[ early_stop_metric] > best_res[VALID][early_stop_metric]: saver.save(sess, save_path=model_output_prefix, global_step=global_step) best_res[VALID] = res no_update_count[VALID] = 0 else: no_update_count[VALID] += 1 # eval test _mode = TEST _dataset = datasets[_mode] _index_iterator = SimpleIndexIterator.from_dataset(_dataset) _n_sample = _index_iterator.n_sample() labels_predict = list() labels_gold = list() for batch_index in _index_iterator.iterate(batch_size, shuffle=False): feed_dict = { nn.var(_key): _dataset[_key][batch_index] for _key in feed_key[TEST] } feed_dict[nn.var(TEST_MODE)] = 1 res = sess.run(fetches=fetches[TEST], feed_dict=feed_dict) labels_predict += res[LABEL_PREDICT].tolist() labels_gold += _dataset[LABEL_GOLD][batch_index].tolist() labels_predict, labels_gold = labels_predict[: _n_sample], labels_gold[: _n_sample] res = basic_evaluate(gold=labels_gold, pred=labels_predict, pos_label=pos_label) eval_history[TEST].append(res) print('TEST') print_evaluation(res) if no_update_count[TRAIN] >= max_no_update_count: break # 训练结束 ########################################################################## # 确保输出文件夹存在 print( '========================= BEST ROUND EVALUATION =========================' ) json.dump(eval_history, open(data_config.output_path(output_key, 'eval', 'json'), 'w')) with tf.Session() as sess: prefix_checkpoint = tf.train.latest_checkpoint( data_config.model_path(key=output_key)) saver = tf.train.import_meta_graph('{}.meta'.format(prefix_checkpoint)) saver.restore(sess, prefix_checkpoint) nn = BaseNNModel(config=None) nn.set_graph(tf.get_default_graph()) for mode in [TRAIN, TEST]: if mode == TRAIN and train_config.train_sampling: dataset, _ = load_dataset(mode=TRAIN, vocab_id_mapping=vocab_id_mapping, max_seq_len=nn_config.seq_len, sampling=False, label_version=label_version) else: dataset = datasets[mode] index_iterator = SimpleIndexIterator.from_dataset(dataset) n_sample = index_iterator.n_sample() prob_predict = list() labels_predict = list() labels_gold = list() hidden_feats = list() for batch_index in index_iterator.iterate(batch_size, shuffle=False): feed_dict = { nn.var(_key): dataset[_key][batch_index] for _key in feed_key[TEST] } feed_dict[nn.var(TEST_MODE)] = 1 res = sess.run(fetches=fetches[TEST], feed_dict=feed_dict) prob_predict += res[PROB_PREDICT].tolist() labels_predict += res[LABEL_PREDICT].tolist() hidden_feats += res[HIDDEN_FEAT].tolist() if LABEL_GOLD in dataset: labels_gold += dataset[LABEL_GOLD][batch_index].tolist() prob_predict = prob_predict[:n_sample] labels_predict = labels_predict[:n_sample] labels_gold = labels_gold[:n_sample] hidden_feats = hidden_feats[:n_sample] if mode == TEST: res = basic_evaluate(gold=labels_gold, pred=labels_predict, pos_label=pos_label) best_res[TEST] = res # 导出隐藏层 with open(data_config.output_path(output_key, mode, HIDDEN_FEAT), 'w') as file_obj: for _feat in hidden_feats: file_obj.write('\t'.join(map(str, _feat)) + '\n') # 导出预测的label with open(data_config.output_path(output_key, mode, LABEL_PREDICT), 'w') as file_obj: for _label in labels_predict: file_obj.write('{}\n'.format(_label)) with open(data_config.output_path(output_key, mode, PROB_PREDICT), 'w') as file_obj: for _prob in prob_predict: file_obj.write('\t'.join(map(str, _prob)) + '\n') for mode in [TRAIN, VALID, TEST]: if mode == VALID and train_config.valid_rate == 0.: continue res = best_res[mode] print(mode) print_evaluation(res) for col in res[CONFUSION_MATRIX]: print(','.join(map(str, col))) json.dump( res, open(data_config.output_path(output_key, mode, EVALUATION), 'w')) print() test_score_list = map(lambda _item: _item['f1'], eval_history[TEST]) print('best test f1 reached: {}'.format(max(test_score_list))) print('OUTPUT_KEY: {}'.format(output_key))