def load_dataset(mode, vocab_id_mapping, max_seq_len, sampling=False, with_label=True, label_version=None): dataset = dict() tid_list = tokenized_to_tid_list( load_tokenized_list(data_config.path(mode, TEXT, EK)), vocab_id_mapping) dataset[TID] = tid_list print('{}: {}'.format(mode, max(list(map(lambda _item: len(_item), tid_list))))) if with_label: label_path = data_config.path(mode, LABEL, label_version) label_list = load_label_list(label_path) dataset[LABEL_GOLD] = np.asarray(label_list) if sampling: dataset = custom_sampling(dataset) dataset[TID], dataset[SEQ_LEN] = to_nn_input(dataset[TID], max_seq_len=max_seq_len) if with_label: output_dim = max(dataset[LABEL_GOLD]) + 1 return dataset, output_dim else: return dataset
def build_text_label(): for key, func in {TRAIN: Processor.load_origin_train, TEST: Processor.load_origin_test}.items(): text_path = config.path(key, TEXT) label_A_path = config.path(key, LABEL, 'A') label_B_path = config.path(key, LABEL, 'B') labels_A = list() with open(text_path, 'w') as text_obj, open(label_A_path, 'w') as label_A_obj, open(label_B_path, 'w') as label_B_obj: for label, text in func('B'): text = re.sub('\s+', ' ', text) text_obj.write(text + '\n') label_B_obj.write(str(label) + '\n') label_A_obj.write(str(0 if label == 0 else 1) + '\n') labels_A.append(0 if label == 0 else 1) mismatch = 0 for i, res in enumerate(func('A')): if not res[0] == labels_A[i]: mismatch += 1 print(key, mismatch)
def m3(config_path='e83.yaml'): """ [Usage] python3 -m algo.ensemble93 main -e mv --build-analysis :param config_path: :return: """ config_data = yaml.load(open(config_path)) config = Config(data=config_data) for mode in [TEST, ]: labels_gold = load_label_list(data_config.path(mode, LABEL, 'B')) b_result = combine(output_keys=config.components(), mode=mode) b_vote = list(map(lambda _item: _item[0], b_result)) b0_result = dict() b0_vote = dict() last_vote = b_vote res = basic_evaluate(gold=labels_gold, pred=last_vote) print('{}'.format(mode)) print_evaluation(res) for col in res[CONFUSION_MATRIX]: print(','.join(map(str, col))) for i in [1, 2, 3]: key = 'b0{}'.format(i) thr = config.thr(key) b0_result[i] = combine(output_keys=config.components(key), mode=mode) new_vote = list() for l_v, b0_res in zip(last_vote, b0_result[i]): this_vote = 0 if b0_res[0] == 0 else i if l_v in {0, i} and b0_res[1] >= thr: new_vote.append(this_vote) else: new_vote.append(l_v) last_vote = new_vote res = basic_evaluate(gold=labels_gold, pred=new_vote) print('{} - {}'.format(mode, i)) print_evaluation(res) for col in res[CONFUSION_MATRIX]: print(','.join(map(str, col))) open('latest_ef83.label', 'w').write('\n'.join(list(map(str, last_vote))))
def main(config_path='e83.yaml'): """ [Usage] python3 -m algo.ensemble93 main -e mv --build-analysis :param config_path: :return: """ config_data = yaml.load(open(config_path)) config = Config(data=config_data) for mode in [TRAIN, TEST]: b_result = combine(output_keys=config.components('b'), mode=mode) b_vote = list(map(lambda _item: _item[0], b_result)) b2_result = combine(output_keys=config.components('b2'), mode=mode) b2_vote = list(map(lambda _item: _item[0], b2_result)) last_vote = list() for b_v, b2_v in zip(b_vote, b2_vote): if b_v == 0: label = 0 elif b2_v == 0: label = 1 else: label = 2 last_vote.append(label) b3_result = combine(output_keys=config.components('b3'), mode=mode) b3_vote = list(map(lambda _item: _item[0], b3_result)) labels_predict = list() for last_v, b3_v in zip(last_vote, b3_vote): if last_v != 2: label = last_v elif b3_v == 0: label = 2 else: label = 3 labels_predict.append(label) labels_gold = load_label_list(data_config.path(mode, LABEL, 'B')) res = basic_evaluate(gold=labels_gold, pred=labels_predict) print(mode) print_evaluation(res) for col in res[CONFUSION_MATRIX]: print(','.join(map(str, col)))
def m3a(target=0, thr=1, config_path='e83a.yaml'): target = int(target) thr = int(thr) config_data = yaml.load(open(config_path)) config = Config(data=config_data) for mode in [TEST, ]: labels_gold = load_label_list(data_config.path(mode, LABEL, 'A')) b_result = combine(output_keys=config.components(), mode=mode) new_vote = list() for r in b_result: if r[0] == target and r[1] >= thr: new_vote.append(target) else: new_vote.append(1 - target) res = basic_evaluate(gold=labels_gold, pred=new_vote) print('{}'.format(mode)) print_evaluation(res) for col in res[CONFUSION_MATRIX]: print(','.join(map(str, col))) last_vote = new_vote output_keys = config.components('b') b_result, counts = combine(output_keys=output_keys, mode=mode, full_output=True) new_vote = list() for count, l_v in zip(counts, last_vote): if count[0] <= 1: new_vote.append(0) else: new_vote.append(l_v) res = basic_evaluate(gold=labels_gold, pred=new_vote) print('{}'.format(mode)) print_evaluation(res) for col in res[CONFUSION_MATRIX]: print(','.join(map(str, col)))
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))
def train(dataset_key, text_version, label_version=None, config_path='config.yaml'): """ python algo/main.py train semeval2018_task3 -l A -t ek python algo/main.py train semeval2018_task3 -l A -t ek -c config_ntua.yaml python algo/main.py train semeval2018_task3 -l A -t raw -c config_ntua_char.yaml python algo/main.py train semeval2019_task3_dev -t ek python algo/main.py train semeval2018_task1 -l love python algo/main.py train semeval2014_task9 :param dataset_key: string :param text_version: string :param label_version: string :param config_path: string :return: """ pos_label = None if dataset_key == 'semeval2018_task3' and label_version == 'A': pos_label = 1 config_data = yaml.load(open(config_path)) data_config = getattr( importlib.import_module('dataset.{}.config'.format(dataset_key)), 'config') output_key = '{}_{}_{}'.format(config_data['module'].rsplit('.', 1)[1], 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)) # 根据配置加载模块 module_relative_path = config_data['module'] NNModel = getattr(importlib.import_module(module_relative_path), 'NNModel') NNConfig = getattr(importlib.import_module(module_relative_path), 'NNConfig') if config_data['analyzer'] == WORD: 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) vocab_meta_list += load_vocab_list( semeval2018_task3_date_config.path(TRAIN, VOCAB, text_version)) 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_table( 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')) max_seq_len = MAX_WORD_SEQ_LEN elif config_data['analyzer'] == CHAR: texts = load_text_list(data_config.path(TRAIN, TEXT)) char_set = set() for text in texts: char_set |= set(text) lookup_table, vocab_id_mapping, embedding_dim = build_random_lookup_table( vocabs=char_set, dim=config_data['char']['embedding_dim']) max_seq_len = MAX_CHAR_SEQ_LEN else: raise ValueError('invalid analyzer: {}'.format( config_data['analyzer'])) # 加载训练数据 datasets, output_dim = load_dataset(data_config=data_config, analyzer=config_data['analyzer'], vocab_id_mapping=vocab_id_mapping, seq_len=max_seq_len, with_label=True, label_version=label_version, text_version=text_version) # 加载配置 nn_config = NNConfig(config_data) train_config = TrainConfig(config_data['train']) # 初始化数据集的检索 index_iterators = { mode: IndexIterator(datasets[mode][LABEL_GOLD]) for mode in [TRAIN, TEST] } # 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) nn_config.set_seq_len(max_seq_len) # 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] } last_eval = {TRAIN: None, VALID: None, TEST: None} 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 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)) # 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] 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) last_eval[TRAIN] = res print_evaluation(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[F1_SCORE] > best_res[TRAIN][ F1_SCORE]: 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[F1_SCORE] > best_res[TRAIN][ F1_SCORE]: 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) last_eval[VALID] = res print_evaluation(res) # Early Stop if best_res[VALID] is None or res[F1_SCORE] > best_res[VALID][ F1_SCORE]: 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 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 =========================' ) 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]: dataset = datasets[mode] index_iterator = index_iterators[mode] 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() 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) json.dump( res, open(data_config.output_path(output_key, mode, EVALUATION), 'w')) print() print('OUTPUT_KEY: {}'.format(output_key))