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(dataset_key,
label_version=None,
config_path='config_svm.yaml',
kernel='rbf'):
"""
python algo/svm.py main semeval2018_task3 A
:param dataset_key: string
:param label_version: string or None
:param config_path: string
:param kernel: string
:return:
"""
pos_label = None
if dataset_key == 'semeval2018_task3' and label_version == 'A':
pos_label = 1
config_data = yaml.load(open(config_path))
train_config = Config(data=config_data)
data_config = getattr(
importlib.import_module('dataset.{}.config'.format(dataset_key)),
'config')
datasets = load_dataset(data_config, train_config, label_version)
if train_config.use_class_weights:
class_weight = 'balanced'
else:
class_weight = None
clf = svm.SVC(class_weight=class_weight, kernel=kernel)
#clf = LogisticRegression(C=1., random_state=0, class_weight='balanced')
X = datasets[TRAIN][FEATS]
clf.fit(X=X, y=datasets[TRAIN][LABEL_GOLD])
if kernel == 'linear':
coef = sorted(list(enumerate(clf.coef_.ravel())),
key=lambda _item: math.fabs(_item[1]))
coef = list(map(lambda _item: _item[0], coef))
print(coef)
for mode in [TRAIN, TEST]:
X = datasets[mode][FEATS]
labels_predict = clf.predict(X=X)
labels_gold = datasets[mode][LABEL_GOLD]
res = basic_evaluate(gold=labels_gold,
pred=labels_predict,
pos_label=pos_label)
print(mode)
print_evaluation(res)
print()
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 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 export_error(filename):
pred = load_label_list(filename)
dataset = Processor.load_origin_test('B')
wrong = defaultdict(lambda: defaultdict(lambda: list()))
gold = list(map(lambda _item: _item[0], dataset))
res = basic_evaluate(gold=gold, pred=pred)
print(res)
for p, sample in zip(pred, dataset):
g = sample[0]
if p != g:
wrong[g][p].append(sample[1])
for _g in range(4):
for _p in range(4):
print('{}->{}'.format(_g, _p))
for sample in wrong[_g][_p]:
print('\t{}'.format(sample))
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))
def logistic_regression(dataset_key,
text_version,
label_version=None,
use_class_weights=True):
"""
python algo/svm.py lr semeval2018_task3 -t ek -l A
"""
from sklearn.preprocessing import Normalizer
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer, HashingVectorizer
data_config = getattr(
importlib.import_module('dataset.{}.config'.format(dataset_key)),
'config')
pos_label = None
if dataset_key == 'semeval2018_task3' and label_version == 'A':
pos_label = 1
datasets = dict()
for mode in [TRAIN, TEST]:
datasets[mode] = {
TEXT: load_text_list(data_config.path(mode, TEXT, text_version)),
LABEL: load_label_list(data_config.path(mode, LABEL,
label_version))
}
max_features = 10000
'''vectorizer = TfidfVectorizer(
ngram_range=(1, 1),
#tokenizer=lambda x: x.split(' '),
#tokenizer=lambda x: x.split(' '),
analyzer='word',
min_df=5,
# max_df=0.9,
lowercase=False,
use_idf=True,
smooth_idf=True,
max_features=max_features,
sublinear_tf=True
)'''
vectorizer = TfidfVectorizer(
ngram_range=(1, 6),
analyzer='char',
lowercase=False,
smooth_idf=True,
#sublinear_tf=True,
max_features=50000)
clf = LogisticRegression(C=1., random_state=0, class_weight='balanced')
#clf = svm.SVC(C=0.6, random_state=0, kernel='linear', class_weight='balanced')
#clf = svm.SVC(C=0.6, random_state=0, kernel='rbf', class_weight='balanced')
pipeline = Pipeline([
('vectorizer', vectorizer),
#('normalizer', Normalizer(norm='l2')),
('clf', clf)
])
pipeline.fit(datasets[TRAIN][TEXT], datasets[TRAIN][LABEL])
for mode in [TRAIN, TEST]:
labels_predict = pipeline.predict(datasets[mode][TEXT])
#print(labels_predict)
labels_gold = datasets[mode][LABEL]
res = basic_evaluate(gold=labels_gold,
pred=labels_predict,
pos_label=pos_label)
print(mode)
print_evaluation(res)
print()
def tf_idf(dataset_key,
text_version,
label_version=None,
use_class_weights=True):
"""
python algo/svm.py tf_idf semeval2018_task3 -t ek -l A
"""
from sklearn import preprocessing
from sklearn.preprocessing import Normalizer
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer, HashingVectorizer
data_config = getattr(
importlib.import_module('dataset.{}.config'.format(dataset_key)),
'config')
pos_label = None
if dataset_key == 'semeval2018_task3' and label_version == 'A':
pos_label = 1
datasets = dict()
for mode in [TRAIN, TEST]:
datasets[mode] = {
TEXT: load_text_list(data_config.path(mode, TEXT, text_version)),
LABEL: load_label_list(data_config.path(mode, LABEL,
label_version))
}
vectorizers = {
#TF_IDF: TfidfVectorizer(ngram_range=(1, 3), min_df=0.01),
TF:
CountVectorizer(
#tokenizer=lambda x: filter(lambda _t: not _t.startswith('</'), x.split(' ')),
tokenizer=lambda x: x.split(' '),
ngram_range=(1, 3),
min_df=0.02,
max_features=1000),
TF_C:
TfidfVectorizer(ngram_range=(1, 1),
analyzer='char',
lowercase=False,
smooth_idf=True,
sublinear_tf=True)
}
for key, vectorizer in vectorizers.items():
feat = vectorizer.fit_transform(datasets[TRAIN][TEXT])
datasets[TRAIN][key] = feat
feat = vectorizer.transform(datasets[TEST][TEXT])
datasets[TEST][key] = feat
if use_class_weights:
class_weight = 'balanced'
else:
class_weight = None
clf = svm.SVC(class_weight=class_weight)
#clf = LogisticRegression(C=1., random_state=0, class_weight='balanced')
X = hstack([datasets[TRAIN][k] for k in vectorizers.keys()])
#scaler = preprocessing.StandardScaler()
#X = scaler.fit_transform(X=X.todense())
clf.fit(X=X, y=datasets[TRAIN][LABEL])
for mode in [TRAIN, TEST]:
X = hstack([datasets[mode][k] for k in vectorizers.keys()])
#X = scaler.transform(X=X.todense())
labels_predict = clf.predict(X=X)
labels_gold = datasets[mode][LABEL]
res = basic_evaluate(gold=labels_gold,
pred=labels_predict,
pos_label=pos_label)
print(mode)
print_evaluation(res)
print()
def main(label_version,
ensemble_mode='mv',
config_path='e83.yaml',
build_analysis=False):
"""
[Usage]
python algo/ensemble.py main -d semeval2018_task3 -l A -e mv
python algo/ensemble.py main -d semeval2018_task3 -l A -e mv --build-analysis
:param dataset_key:
:param label_version:
:param ensemble_mode:
:param config_path:
:param build_analysis: bool
:return:
"""
dataset_key = 'semeval2018_task3'
output_key = 'ensemble_{}_{}'.format(ensemble_mode, int(time.time()))
pos_label = None
if dataset_key == 'semeval2018_task3' and label_version == 'A':
pos_label = 1
config_data = yaml.load(open(config_path))
config = Config(data=config_data)
data_config = getattr(
importlib.import_module('dataset.{}.config'.format(dataset_key)),
'config')
labels_predict = dict()
labels_gold = dict()
n_sample = dict()
for mode in [TRAIN, TEST]:
label_path = data_config.path(mode, LABEL, label_version)
labels_gold[mode] = load_label_list(label_path)
n_sample[mode] = len(labels_gold[mode])
output_dim = max(labels_gold[TEST]) + 1
if ensemble_mode == SOFT_VOTING:
for mode in [TRAIN, TEST]:
components = dict()
for output_key in config.components:
path = data_config.output_path(output_key, mode, PROB_PREDICT)
prob_list = list()
with open(path) as file_obj:
for line in file_obj:
line = line.strip()
if line == '':
continue
prob = list(map(float, line.split('\t')))
prob_list.append(prob)
components[output_key] = prob_list
labels = list()
for i in range(n_sample[mode]):
prob = np.zeros((output_dim, ))
for output_key, prob_list in components.items():
prob += np.asarray(prob_list[i])
labels.append(np.argmax(prob))
labels_predict[mode] = labels
elif ensemble_mode == MAJORITY_VOTING:
components = dict()
for mode in [TRAIN, TEST]:
for output_key in config.components:
path = data_config.output_path(output_key, mode, LABEL_PREDICT)
label_list = list()
with open(path) as file_obj:
for line in file_obj:
line = line.strip()
if line == '':
continue
label = int(line)
label_list.append(label)
components[output_key] = label_list
labels = list()
for i in range(n_sample[mode]):
prob = np.zeros((output_dim, ))
for output_key, label_list in components.items():
label = label_list[i]
prob[label] += 1
labels.append(np.argmax(prob))
labels_predict[mode] = labels
elif ensemble_mode == WEIGHTED_MAJORITY_VOTE:
raise NotImplementedError
else:
raise ValueError('unknown mode: {}'.format(ensemble_mode))
for mode in [TRAIN, TEST]:
res = basic_evaluate(gold=labels_gold[mode],
pred=labels_predict[mode],
pos_label=pos_label)
print(mode)
print_evaluation(res)
print()
if build_analysis:
output_path = data_config.path(mode, ANALYSIS, WRONG_PREDICT)
text_list = load_text_list(data_config.path(mode, TEXT))
res = generate_wrong_prediction_report(
labels_gold=labels_gold[mode],
labels_predict=labels_predict[mode],
text_list=text_list)
with open(output_path, 'w') as file_obj:
file_obj.write('gold\tpredict\ttext')
for l_gold, l_predict, t in res:
file_obj.write('{} {} {}\n'.format(l_gold, l_predict, t))
