def process(self,
paths: Union[str, Dict[str, str]],
train_ds: Iterable[str] = None,
src_vocab_op: VocabularyOption = None,
tgt_vocab_op: VocabularyOption = None,
embed_opt: EmbeddingOption = None,
char_level_op=False):
paths = check_dataloader_paths(paths)
datasets = {}
info = DataInfo(datasets=self.load(paths))
src_vocab = Vocabulary() if src_vocab_op is None else Vocabulary(
**src_vocab_op)
tgt_vocab = Vocabulary(unknown=None, padding=None) \
if tgt_vocab_op is None else Vocabulary(**tgt_vocab_op)
_train_ds = [info.datasets[name] for name in train_ds
] if train_ds else info.datasets.values()
def wordtochar(words):
chars = []
for word in words:
word = word.lower()
for char in word:
chars.append(char)
chars.append('')
chars.pop()
return chars
input_name, target_name = 'words', 'target'
info.vocabs = {}
#就分隔为char形式
if char_level_op:
for dataset in info.datasets.values():
dataset.apply_field(wordtochar,
field_name="words",
new_field_name='chars')
# if embed_opt is not None:
# embed = EmbedLoader.load_with_vocab(**embed_opt, vocab=vocab)
# info.embeddings['words'] = embed
else:
src_vocab.from_dataset(*_train_ds, field_name=input_name)
src_vocab.index_dataset(*info.datasets.values(),
field_name=input_name,
new_field_name=input_name)
info.vocabs[input_name] = src_vocab
tgt_vocab.from_dataset(*_train_ds, field_name=target_name)
tgt_vocab.index_dataset(*info.datasets.values(),
field_name=target_name,
new_field_name=target_name)
info.vocabs[target_name] = tgt_vocab
info.datasets['train'], info.datasets['dev'] = info.datasets[
'train'].split(0.1, shuffle=False)
for name, dataset in info.datasets.items():
dataset.set_input("words")
dataset.set_target("target")
return info
def process(self,
paths: Union[str, Dict[str, str]],
src_vocab_opt: VocabularyOption = None,
tgt_vocab_opt: VocabularyOption = None,
src_embed_opt: EmbeddingOption = None,
char_level_op=False):
datasets = {}
info = DataBundle()
paths = check_dataloader_paths(paths)
for name, path in paths.items():
dataset = self.load(path)
datasets[name] = dataset
def wordtochar(words):
chars = []
for word in words:
word = word.lower()
for char in word:
chars.append(char)
chars.append('')
chars.pop()
return chars
if char_level_op:
for dataset in datasets.values():
dataset.apply_field(wordtochar,
field_name="words",
new_field_name='chars')
datasets["train"], datasets["dev"] = datasets["train"].split(
0.1, shuffle=False)
src_vocab = Vocabulary() if src_vocab_opt is None else Vocabulary(
**src_vocab_opt)
src_vocab.from_dataset(datasets['train'], field_name='words')
src_vocab.index_dataset(*datasets.values(), field_name='words')
tgt_vocab = Vocabulary(unknown=None, padding=None) \
if tgt_vocab_opt is None else Vocabulary(**tgt_vocab_opt)
tgt_vocab.from_dataset(datasets['train'], field_name='target')
tgt_vocab.index_dataset(*datasets.values(), field_name='target')
info.vocabs = {"words": src_vocab, "target": tgt_vocab}
info.datasets = datasets
if src_embed_opt is not None:
embed = EmbedLoader.load_with_vocab(**src_embed_opt,
vocab=src_vocab)
info.embeddings['words'] = embed
for name, dataset in info.datasets.items():
dataset.set_input("words")
dataset.set_target("target")
return info
def process(self,
paths: Union[str, Dict[str, str]],
src_vocab_opt: VocabularyOption = None,
tgt_vocab_opt: VocabularyOption = None,
src_embed_opt: EmbeddingOption = None):
paths = check_dataloader_paths(paths)
datasets = {}
info = DataBundle()
for name, path in paths.items():
dataset = self.load(path)
datasets[name] = dataset
src_vocab = Vocabulary() if src_vocab_opt is None else Vocabulary(**src_vocab_opt)
src_vocab.from_dataset(datasets['train'], field_name='words')
src_vocab.index_dataset(*datasets.values(), field_name='words')
tgt_vocab = Vocabulary(unknown=None, padding=None) \
if tgt_vocab_opt is None else Vocabulary(**tgt_vocab_opt)
tgt_vocab.from_dataset(datasets['train'], field_name='target')
tgt_vocab.index_dataset(*datasets.values(), field_name='target')
info.vocabs = {
"words": src_vocab,
"target": tgt_vocab
}
info.datasets = datasets
if src_embed_opt is not None:
embed = EmbedLoader.load_with_vocab(**src_embed_opt, vocab=src_vocab)
info.embeddings['words'] = embed
for name, dataset in info.datasets.items():
dataset.set_input("words")
dataset.set_target("target")
return info
def process(self,
paths: Union[str, Dict[str, str]],
word_vocab_opt: VocabularyOption = None,
lower: bool = False):
"""
读取并处理数据。数据中的'-DOCSTART-'开头的行会被忽略
:param paths:
:param word_vocab_opt: vocabulary的初始化值
:param lower: 是否将所有字母转为小写。
:return:
"""
# 读取数据
paths = check_dataloader_paths(paths)
data = DataInfo()
input_fields = [Const.TARGET, Const.INPUT, Const.INPUT_LEN]
target_fields = [Const.TARGET, Const.INPUT_LEN]
for name, path in paths.items():
dataset = self.load(path)
dataset.apply_field(lambda words: words,
field_name='raw_words',
new_field_name=Const.INPUT)
if lower:
dataset.words.lower()
data.datasets[name] = dataset
# 对construct vocab
word_vocab = Vocabulary(
min_freq=2) if word_vocab_opt is None else Vocabulary(
**word_vocab_opt)
word_vocab.from_dataset(data.datasets['train'],
field_name=Const.INPUT,
no_create_entry_dataset=[
dataset
for name, dataset in data.datasets.items()
if name != 'train'
])
word_vocab.index_dataset(*data.datasets.values(),
field_name=Const.INPUT,
new_field_name=Const.INPUT)
data.vocabs[Const.INPUT] = word_vocab
# cap words
cap_word_vocab = Vocabulary()
cap_word_vocab.from_dataset(
data.datasets['train'],
field_name='raw_words',
no_create_entry_dataset=[
dataset for name, dataset in data.datasets.items()
if name != 'train'
])
cap_word_vocab.index_dataset(*data.datasets.values(),
field_name='raw_words',
new_field_name='cap_words')
input_fields.append('cap_words')
data.vocabs['cap_words'] = cap_word_vocab
# 对target建vocab
target_vocab = Vocabulary(unknown=None, padding=None)
target_vocab.from_dataset(*data.datasets.values(),
field_name=Const.TARGET)
target_vocab.index_dataset(*data.datasets.values(),
field_name=Const.TARGET)
data.vocabs[Const.TARGET] = target_vocab
for name, dataset in data.datasets.items():
dataset.add_seq_len(Const.INPUT, new_field_name=Const.INPUT_LEN)
dataset.set_input(*input_fields)
dataset.set_target(*target_fields)
return data
def process(self,
paths: Union[str, Dict[str, str]],
word_vocab_opt: VocabularyOption = None,
lower: bool = True) -> DataBundle:
"""
读取并处理数据。返回的DataInfo包含以下的内容
vocabs:
word: Vocabulary
target: Vocabulary
datasets:
train: DataSet
words: List[int], 被设置为input
target: int. label，被同时设置为input和target
seq_len: int. 句子的长度，被同时设置为input和target
raw_words: List[str]
xxx(根据传入的paths可能有所变化)
:param paths:
:param word_vocab_opt: vocabulary的初始化值
:param lower: 是否使用小写
:return:
"""
paths = check_dataloader_paths(paths)
data = DataBundle()
input_fields = [Const.TARGET, Const.INPUT, Const.INPUT_LEN]
target_fields = [Const.TARGET, Const.INPUT_LEN]
for name, path in paths.items():
dataset = self.load(path)
dataset.apply_field(lambda words: words,
field_name='raw_words',
new_field_name=Const.INPUT)
if lower:
dataset.words.lower()
data.datasets[name] = dataset
# 对construct vocab
word_vocab = Vocabulary(
min_freq=2) if word_vocab_opt is None else Vocabulary(
**word_vocab_opt)
word_vocab.from_dataset(data.datasets['train'],
field_name=Const.INPUT,
no_create_entry_dataset=[
dataset
for name, dataset in data.datasets.items()
if name != 'train'
])
word_vocab.index_dataset(*data.datasets.values(),
field_name=Const.INPUT,
new_field_name=Const.INPUT)
data.vocabs[Const.INPUT] = word_vocab
# cap words
cap_word_vocab = Vocabulary()
cap_word_vocab.from_dataset(*data.datasets.values(),
field_name='raw_words')
cap_word_vocab.index_dataset(*data.datasets.values(),
field_name='raw_words',
new_field_name='cap_words')
input_fields.append('cap_words')
data.vocabs['cap_words'] = cap_word_vocab
# 对target建vocab
target_vocab = Vocabulary(unknown=None, padding=None)
target_vocab.from_dataset(*data.datasets.values(),
field_name=Const.TARGET)
target_vocab.index_dataset(*data.datasets.values(),
field_name=Const.TARGET)
data.vocabs[Const.TARGET] = target_vocab
for name, dataset in data.datasets.items():
dataset.add_seq_len(Const.INPUT, new_field_name=Const.INPUT_LEN)
dataset.set_input(*input_fields)
dataset.set_target(*target_fields)
return data
def process(self, paths):
"""
:param paths:
:return:
Dataset包含以下的field
chars:
bigrams:
trigrams:
pre_chars:
pre_bigrams:
pre_trigrams:
seg_targets:
seg_masks:
seq_lens:
char_labels:
char_heads:
gold_word_pairs:
seg_targets:
seg_masks:
char_labels:
char_heads:
pun_masks:
gold_label_word_pairs:
"""
paths = check_dataloader_paths(paths)
data = DataBundle()
for name, path in paths.items():
dataset = self.load(path)
data.datasets[name] = dataset
char_labels_vocab = Vocabulary(padding=None, unknown=None)
def process(dataset, char_label_vocab):
dataset.apply(add_word_lst, new_field_name='word_lst')
dataset.apply(lambda x: list(chain(*x['word_lst'])), new_field_name='chars')
dataset.apply(add_bigram, field_name='chars', new_field_name='bigrams')
dataset.apply(add_trigram, field_name='chars', new_field_name='trigrams')
dataset.apply(add_char_heads, new_field_name='char_heads')
dataset.apply(add_char_labels, new_field_name='char_labels')
dataset.apply(add_segs, new_field_name='seg_targets')
dataset.apply(add_mask, new_field_name='seg_masks')
dataset.add_seq_len('chars', new_field_name='seq_lens')
dataset.apply(add_pun_masks, new_field_name='pun_masks')
if len(char_label_vocab.word_count)==0:
char_label_vocab.from_dataset(dataset, field_name='char_labels')
char_label_vocab.index_dataset(dataset, field_name='char_labels')
new_dataset = add_root(dataset)
new_dataset.apply(add_word_pairs, new_field_name='gold_word_pairs', ignore_type=True)
global add_label_word_pairs
add_label_word_pairs = partial(add_label_word_pairs, label_vocab=char_label_vocab)
new_dataset.apply(add_label_word_pairs, new_field_name='gold_label_word_pairs', ignore_type=True)
new_dataset.set_pad_val('char_labels', -1)
new_dataset.set_pad_val('char_heads', -1)
return new_dataset
for name in list(paths.keys()):
dataset = data.datasets[name]
dataset = process(dataset, char_labels_vocab)
data.datasets[name] = dataset
data.vocabs['char_labels'] = char_labels_vocab
char_vocab = Vocabulary(min_freq=2).from_dataset(data.datasets['train'], field_name='chars')
bigram_vocab = Vocabulary(min_freq=5).from_dataset(data.datasets['train'], field_name='bigrams')
trigram_vocab = Vocabulary(min_freq=5).from_dataset(data.datasets['train'], field_name='trigrams')
for name in ['chars', 'bigrams', 'trigrams']:
vocab = Vocabulary().from_dataset(field_name=name, no_create_entry_dataset=list(data.datasets.values()))
vocab.index_dataset(*data.datasets.values(), field_name=name, new_field_name='pre_' + name)
data.vocabs['pre_{}'.format(name)] = vocab
for name, vocab in zip(['chars', 'bigrams', 'trigrams'],
[char_vocab, bigram_vocab, trigram_vocab]):
vocab.index_dataset(*data.datasets.values(), field_name=name, new_field_name=name)
data.vocabs[name] = vocab
for name, dataset in data.datasets.items():
dataset.set_input('chars', 'bigrams', 'trigrams', 'seq_lens', 'char_labels', 'char_heads', 'pre_chars',
'pre_bigrams', 'pre_trigrams')
dataset.set_target('gold_word_pairs', 'seq_lens', 'seg_targets', 'seg_masks', 'char_labels',
'char_heads',
'pun_masks', 'gold_label_word_pairs')
return data
def process(self,
paths: Union[str, Dict[str, str]],
char_vocab_opt: VocabularyOption = None,
char_embed_opt: EmbeddingOption = None,
bigram_vocab_opt: VocabularyOption = None,
bigram_embed_opt: EmbeddingOption = None,
L: int = 4):
"""
支持的数据格式为一行一个sample，并且用空格隔开不同的词语。例如
Option::
共同 创造 美好 的 新 世纪 —— 二○○一年 新年 贺词
（ 二○○○年 十二月 三十一日 ） （ 附 图片 1 张 ）
女士 们 ， 先生 们 ， 同志 们 ， 朋友 们 ：
paths支持两种格式，第一种是str，第二种是Dict[str, str].
Option::
# 1. str类型
# 1.1 传入具体的文件路径
data = SigHanLoader('bmes').process('/path/to/cws/data.txt') # 将读取data.txt的内容
# 包含以下的内容data.vocabs['chars']:Vocabulary对象，
# data.vocabs['target']: Vocabulary对象，根据encoding_type可能会没有该值
# data.embeddings['chars']: Embedding对象. 只有提供了预训练的词向量的路径才有该项
# data.datasets['train']: DataSet对象
# 包含的field有:
# raw_chars: list[str], 每个元素是一个汉字
# chars: list[int], 每个元素是汉字对应的index
# target: list[int], 根据encoding_type有对应的变化
# 1.2 传入一个目录, 里面必须包含train.txt文件
data = SigHanLoader('bmes').process('path/to/cws/') #将尝试在该目录下读取 train.txt, test.txt以及dev.txt
# 包含以下的内容data.vocabs['chars']: Vocabulary对象
# data.vocabs['target']:Vocabulary对象
# data.embeddings['chars']: 仅在提供了预训练embedding路径的情况下，为Embedding对象;
# data.datasets['train']: DataSet对象
# 包含的field有:
# raw_chars: list[str], 每个元素是一个汉字
# chars: list[int], 每个元素是汉字对应的index
# target: list[int], 根据encoding_type有对应的变化
# data.datasets['dev']: DataSet对象，如果文件夹下包含了dev.txt；内容与data.datasets['train']一样
# 2. dict类型, key是文件的名称，value是对应的读取路径. 必须包含'train'这个key
paths = {'train': '/path/to/train/train.txt', 'test':'/path/to/test/test.txt', 'dev':'/path/to/dev/dev.txt'}
data = SigHanLoader(paths).process(paths)
# 结果与传入目录时是一致的，但是可以传入多个数据集。data.datasets中的key将与这里传入的一致
:param paths: 支持传入目录，文件路径，以及dict。
:param char_vocab_opt: 用于构建chars的vocabulary参数，默认为min_freq=2
:param char_embed_opt: 用于读取chars的Embedding的参数，默认不读取pretrained的embedding
:param bigram_vocab_opt: 用于构建bigram的vocabulary参数，默认不使用bigram, 仅在指定该参数的情况下会带有bigrams这个field。
为List[int], 每个instance长度与chars一样, abcde的bigram为ab bc cd de e<eos>
:param bigram_embed_opt: 用于读取预训练bigram的参数，仅在传入bigram_vocab_opt有效
:param L: 当target_type为shift_relay时传入的segment长度
:return:
"""
# 推荐大家使用这个check_data_loader_paths进行paths的验证
paths = check_dataloader_paths(paths)
datasets = {}
data = DataBundle()
bigram = bigram_vocab_opt is not None
for name, path in paths.items():
dataset = self.load(path, bigram=bigram)
datasets[name] = dataset
input_fields = []
target_fields = []
# 创建vocab
char_vocab = Vocabulary(
min_freq=2) if char_vocab_opt is None else Vocabulary(
**char_vocab_opt)
char_vocab.from_dataset(datasets['train'], field_name='raw_chars')
char_vocab.index_dataset(*datasets.values(),
field_name='raw_chars',
new_field_name='chars')
data.vocabs[Const.CHAR_INPUT] = char_vocab
input_fields.extend([Const.CHAR_INPUT, Const.INPUT_LEN, Const.TARGET])
target_fields.append(Const.TARGET)
# 创建target
if self.target_type == 'bmes':
target_vocab = Vocabulary(unknown=None, padding=None)
target_vocab.add_word_lst(['B'] * 4 + ['M'] * 3 + ['E'] * 2 +
['S'])
target_vocab.index_dataset(*datasets.values(), field_name='target')
data.vocabs[Const.TARGET] = target_vocab
if char_embed_opt is not None:
char_embed = EmbedLoader.load_with_vocab(**char_embed_opt,
vocab=char_vocab)
data.embeddings['chars'] = char_embed
if bigram:
bigram_vocab = Vocabulary(**bigram_vocab_opt)
bigram_vocab.from_dataset(datasets['train'], field_name='bigrams')
bigram_vocab.index_dataset(*datasets.values(),
field_name='bigrams')
data.vocabs['bigrams'] = bigram_vocab
if bigram_embed_opt is not None:
bigram_embed = EmbedLoader.load_with_vocab(**bigram_embed_opt,
vocab=bigram_vocab)
data.embeddings['bigrams'] = bigram_embed
input_fields.append('bigrams')
if self.target_type == 'shift_relay':
func = partial(self._clip_target, L=L)
for name, dataset in datasets.items():
res = dataset.apply_field(func, field_name='target')
relay_target = [res_i[0] for res_i in res]
relay_mask = [res_i[1] for res_i in res]
dataset.add_field('relay_target',
relay_target,
is_input=True,
is_target=False,
ignore_type=False)
dataset.add_field('relay_mask',
relay_mask,
is_input=True,
is_target=False,
ignore_type=False)
if self.target_type == 'shift_relay':
input_fields.extend(['end_seg_mask'])
target_fields.append('start_seg_mask')
# 将dataset加入DataInfo
for name, dataset in datasets.items():
dataset.set_input(*input_fields)
dataset.set_target(*target_fields)
data.datasets[name] = dataset
return data
def process(self, paths, bigrams=False, trigrams=False):
"""
:param paths:
:param bool, bigrams: 是否包含生成bigram feature, [a, b, c, d] -> [ab, bc, cd, d<eos>]
:param bool, trigrams: 是否包含trigram feature，[a, b, c, d] -> [abc, bcd, cd<eos>, d<eos><eos>]
:return: DataBundle
包含以下的fields
raw_chars: List[str]
chars: List[int]
seq_len: int, 字的长度
bigrams: List[int], optional
trigrams: List[int], optional
target: List[int]
"""
paths = check_dataloader_paths(paths)
data = DataBundle()
input_fields = [Const.CHAR_INPUT, Const.INPUT_LEN, Const.TARGET]
target_fields = [Const.TARGET, Const.INPUT_LEN]
for name, path in paths.items():
dataset = self.load(path)
if bigrams:
dataset.apply_field(lambda raw_chars: [
c1 + c2
for c1, c2 in zip(raw_chars, raw_chars[1:] + ['<eos>'])
],
field_name='raw_chars',
new_field_name='bigrams')
if trigrams:
dataset.apply_field(lambda raw_chars: [
c1 + c2 + c3
for c1, c2, c3 in zip(raw_chars, raw_chars[1:] + ['<eos>'],
raw_chars[2:] + ['<eos>'] * 2)
],
field_name='raw_chars',
new_field_name='trigrams')
data.datasets[name] = dataset
char_vocab = Vocabulary().from_dataset(
data.datasets['train'],
field_name='raw_chars',
no_create_entry_dataset=[
dataset for name, dataset in data.datasets.items()
if name != 'train'
])
char_vocab.index_dataset(*data.datasets.values(),
field_name='raw_chars',
new_field_name=Const.CHAR_INPUT)
data.vocabs[Const.CHAR_INPUT] = char_vocab
target_vocab = Vocabulary(unknown=None, padding=None).from_dataset(
data.datasets['train'], field_name=Const.TARGET)
target_vocab.index_dataset(*data.datasets.values(),
field_name=Const.TARGET)
data.vocabs[Const.TARGET] = target_vocab
if bigrams:
bigram_vocab = Vocabulary().from_dataset(
data.datasets['train'],
field_name='bigrams',
no_create_entry_dataset=[
dataset for name, dataset in data.datasets.items()
if name != 'train'
])
bigram_vocab.index_dataset(*data.datasets.values(),
field_name='bigrams',
new_field_name='bigrams')
data.vocabs['bigrams'] = bigram_vocab
input_fields.append('bigrams')
if trigrams:
trigram_vocab = Vocabulary().from_dataset(
data.datasets['train'],
field_name='trigrams',
no_create_entry_dataset=[
dataset for name, dataset in data.datasets.items()
if name != 'train'
])
trigram_vocab.index_dataset(*data.datasets.values(),
field_name='trigrams',
new_field_name='trigrams')
data.vocabs['trigrams'] = trigram_vocab
input_fields.append('trigrams')
for name, dataset in data.datasets.items():
dataset.add_seq_len(Const.CHAR_INPUT)
dataset.set_input(*input_fields)
dataset.set_target(*target_fields)
return data
