def check_wrong(output_key, w2v_key='ntua_ek'):
mode = TEST
path = data_config.output_path(output_key, mode, LABEL_PREDICT)
pred = load_label_list(path)
path = data_config.path(mode, LABEL)
gold = load_label_list(path)
w2v_model_path = data_config.path(ALL, WORD2VEC, w2v_key)
vocab_train_path = data_config.path(TRAIN, VOCAB, 'ek')
# 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'] >= 2]
# 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)
tokens_0 = load_tokenized_list(data_config.path(mode, TURN, '0.ek'))
tokens_1 = load_tokenized_list(data_config.path(mode, TURN, '1.ek'))
tokens_2 = load_tokenized_list(data_config.path(mode, TURN, '2.ek'))
tid_list_0 = tokenized_to_tid_list(tokens_0, vocab_id_mapping)
tid_list_1 = tokenized_to_tid_list(tokens_1, vocab_id_mapping)
tid_list_2 = tokenized_to_tid_list(
load_tokenized_list(data_config.path(mode, TURN, '2.ek')),
vocab_id_mapping)
max_seq_len = 0
for p, g, tid_0, tid_1, tid_2, tk_0, tk_1, tk_2 in zip(
pred, gold, tid_list_0, tid_list_1, tid_list_2, tokens_0, tokens_1,
tokens_2):
if p != g and (len(tid_0) > 30 or len(tid_1) > 30 or len(tid_2) > 30):
print('pred: {}, gold: {}'.format(p, g))
print('turn0: {}'.format(' '.join(tk_0)))
print('turn1: {}'.format(' '.join(tk_1)))
print('turn2: {}'.format(' '.join(tk_2)))
if p != g:
max_seq_len = max(max_seq_len, len(tid_0), len(tid_1), len(tid_2))
print(max_seq_len)
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))