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))
