def process(self, paths, **kwargs):
data_info = DataBundle()
for name in ['train', 'test', 'dev']:
data_info.datasets[name] = self.load(paths[name])
config = Config()
vocab = Vocabulary().from_dataset(*data_info.datasets.values(),
field_name='sentences')
vocab.build_vocab()
word2id = vocab.word2idx
char_dict = preprocess.get_char_dict(config.char_path)
data_info.vocabs = vocab
genres = {
g: i
for i, g in enumerate(["bc", "bn", "mz", "nw", "pt", "tc", "wb"])
}
for name, ds in data_info.datasets.items():
ds.apply(
lambda x: preprocess.doc2numpy(x['sentences'],
word2id,
char_dict,
max(config.filter),
config.max_sentences,
is_train=name == 'train')[0],
new_field_name='doc_np')
ds.apply(
lambda x: preprocess.doc2numpy(x['sentences'],
word2id,
char_dict,
max(config.filter),
config.max_sentences,
is_train=name == 'train')[1],
new_field_name='char_index')
ds.apply(
lambda x: preprocess.doc2numpy(x['sentences'],
word2id,
char_dict,
max(config.filter),
config.max_sentences,
is_train=name == 'train')[2],
new_field_name='seq_len')
ds.apply(lambda x: preprocess.speaker2numpy(
x["speakers"], config.max_sentences, is_train=name == 'train'),
new_field_name='speaker_ids_np')
ds.apply(lambda x: genres[x["doc_key"][:2]],
new_field_name='genre')
ds.set_ignore_type('clusters')
ds.set_padder('clusters', None)
ds.set_input("sentences", "doc_np", "speaker_ids_np", "genre",
"char_index", "seq_len")
ds.set_target("clusters")
# train_dev, test = self.ds.split(348 / (2802 + 343 + 348), shuffle=False)
# train, dev = train_dev.split(343 / (2802 + 343), shuffle=False)
return data_info
class VocabProcessor(Processor):
def __init__(self, field_name):
super(VocabProcessor, self).__init__(field_name, None)
self.vocab = Vocabulary()
def process(self, *datasets):
for dataset in datasets:
assert isinstance(dataset, DataSet), "Only Dataset class is allowed, not {}.".format(type(dataset))
for ins in dataset:
tokens = ins[self.field_name]
self.vocab.update(tokens)
def get_vocab(self):
self.vocab.build_vocab()
return self.vocab
class VocabProcessor(Processor):
def __init__(self, field_name, min_freq=1, max_size=None):
super(VocabProcessor, self).__init__(field_name, None)
self.vocab = Vocabulary(min_freq=min_freq, max_size=max_size)
def process(self, *datasets):
for dataset in datasets:
assert isinstance(dataset, DataSet), "Only Dataset class is allowed, not {}.".format(type(dataset))
dataset.apply(lambda ins: self.vocab.update(ins[self.field_name]))
def get_vocab(self):
self.vocab.build_vocab()
return self.vocab
def get_vocab_size(self):
return len(self.vocab)
def process(self, data_bundle: DataBundle):
r"""
对load进来的数据进一步处理原始数据包含：raw_key,raw_speaker,raw_words,raw_clusters
.. csv-table::
:header: "raw_key", "raw_speaker","raw_words","raw_clusters"
"bc/cctv/00/cctv_0000_0", "[[Speaker#1, Speaker#1],[]]","[['I','am'],[]]","[[[2,3],[6,7]],[[10,12],[20,22]]]"
"bc/cctv/00/cctv_0000_1", "[['Speaker#1', 'peaker#1'],[]]","[['He','is'],[]]","[[[2,3],[6,7]],[[10,12],[20,22]]]"
"[...]", "[...]","[...]","[...]"
:param data_bundle:
:return:
"""
genres = {g: i for i, g in enumerate(["bc", "bn", "mz", "nw", "pt", "tc", "wb"])}
vocab = Vocabulary().from_dataset(*data_bundle.datasets.values(), field_name= Const.RAW_WORDS(3))
vocab.build_vocab()
word2id = vocab.word2idx
data_bundle.set_vocab(vocab, Const.INPUTS(0))
if self.config.char_path:
char_dict = get_char_dict(self.config.char_path)
else:
char_set = set()
for i,w in enumerate(word2id):
if i < 2:
continue
for c in w:
char_set.add(c)
char_dict = collections.defaultdict(int)
char_dict.update({c: i for i, c in enumerate(char_set)})
for name, ds in data_bundle.datasets.items():
# genre
ds.apply(lambda x: genres[x[Const.RAW_WORDS(0)][:2]], new_field_name=Const.INPUTS(0))
# speaker_ids_np
ds.apply(lambda x: speaker2numpy(x[Const.RAW_WORDS(1)], self.config.max_sentences, is_train=name == 'train'),
new_field_name=Const.INPUTS(1))
# sentences
ds.rename_field(Const.RAW_WORDS(3),Const.INPUTS(2))
# doc_np
ds.apply(lambda x: doc2numpy(x[Const.INPUTS(2)], word2id, char_dict, max(self.config.filter),
self.config.max_sentences, is_train=name == 'train')[0],
new_field_name=Const.INPUTS(3))
# char_index
ds.apply(lambda x: doc2numpy(x[Const.INPUTS(2)], word2id, char_dict, max(self.config.filter),
self.config.max_sentences, is_train=name == 'train')[1],
new_field_name=Const.CHAR_INPUT)
# seq len
ds.apply(lambda x: doc2numpy(x[Const.INPUTS(2)], word2id, char_dict, max(self.config.filter),
self.config.max_sentences, is_train=name == 'train')[2],
new_field_name=Const.INPUT_LEN)
# clusters
ds.rename_field(Const.RAW_WORDS(2), Const.TARGET)
ds.set_ignore_type(Const.TARGET)
ds.set_padder(Const.TARGET, None)
ds.set_input(Const.INPUTS(0), Const.INPUTS(1), Const.INPUTS(2), Const.INPUTS(3), Const.CHAR_INPUT, Const.INPUT_LEN)
ds.set_target(Const.TARGET)
return data_bundle
def process(self,
paths,
vocab_size,
vocab_path,
sent_max_len,
doc_max_timesteps,
domain=False,
tag=False,
load_vocab=True):
"""
:param paths: dict path for each dataset
:param vocab_size: int max_size for vocab
:param vocab_path: str vocab path
:param sent_max_len: int max token number of the sentence
:param doc_max_timesteps: int max sentence number of the document
:param domain: bool build vocab for publication, use 'X' for unknown
:param tag: bool build vocab for tag, use 'X' for unknown
:param load_vocab: bool build vocab (False) or load vocab (True)
:return: DataInfo
datasets: dict keys correspond to the paths dict
vocabs: dict key: vocab(if "train" in paths), domain(if domain=True), tag(if tag=True)
embeddings: optional
"""
def _pad_sent(text_wd):
pad_text_wd = []
for sent_wd in text_wd:
if len(sent_wd) < sent_max_len:
pad_num = sent_max_len - len(sent_wd)
sent_wd.extend([WORD_PAD] * pad_num)
else:
sent_wd = sent_wd[:sent_max_len]
pad_text_wd.append(sent_wd)
return pad_text_wd
def _token_mask(text_wd):
token_mask_list = []
for sent_wd in text_wd:
token_num = len(sent_wd)
if token_num < sent_max_len:
mask = [1] * token_num + [0] * (sent_max_len - token_num)
else:
mask = [1] * sent_max_len
token_mask_list.append(mask)
return token_mask_list
def _pad_label(label):
text_len = len(label)
if text_len < doc_max_timesteps:
pad_label = label + [0] * (doc_max_timesteps - text_len)
else:
pad_label = label[:doc_max_timesteps]
return pad_label
def _pad_doc(text_wd):
text_len = len(text_wd)
if text_len < doc_max_timesteps:
padding = [WORD_PAD] * sent_max_len
pad_text = text_wd + [padding] * (doc_max_timesteps - text_len)
else:
pad_text = text_wd[:doc_max_timesteps]
return pad_text
def _sent_mask(text_wd):
text_len = len(text_wd)
if text_len < doc_max_timesteps:
sent_mask = [1] * text_len + [0] * (doc_max_timesteps -
text_len)
else:
sent_mask = [1] * doc_max_timesteps
return sent_mask
datasets = {}
train_ds = None
for key, value in paths.items():
ds = self.load(value)
# pad sent
ds.apply(lambda x: _pad_sent(x["text_wd"]),
new_field_name="pad_text_wd")
ds.apply(lambda x: _token_mask(x["text_wd"]),
new_field_name="pad_token_mask")
# pad document
ds.apply(lambda x: _pad_doc(x["pad_text_wd"]),
new_field_name="pad_text")
ds.apply(lambda x: _sent_mask(x["pad_text_wd"]),
new_field_name="seq_len")
ds.apply(lambda x: _pad_label(x["flatten_label"]),
new_field_name="pad_label")
# rename field
ds.rename_field("pad_text", Const.INPUT)
ds.rename_field("seq_len", Const.INPUT_LEN)
ds.rename_field("pad_label", Const.TARGET)
# set input and target
ds.set_input(Const.INPUT, Const.INPUT_LEN)
ds.set_target(Const.TARGET, Const.INPUT_LEN)
datasets[key] = ds
if "train" in key:
train_ds = datasets[key]
vocab_dict = {}
if load_vocab == False:
logger.info("[INFO] Build new vocab from training dataset!")
if train_ds == None:
raise ValueError("Lack train file to build vocabulary!")
vocabs = Vocabulary(max_size=vocab_size,
padding=WORD_PAD,
unknown=WORD_UNK)
vocabs.from_dataset(train_ds, field_name=["text_wd", "summary_wd"])
vocab_dict["vocab"] = vocabs
else:
logger.info("[INFO] Load existing vocab from %s!" % vocab_path)
word_list = []
with open(vocab_path, 'r', encoding='utf8') as vocab_f:
cnt = 2 # pad and unk
for line in vocab_f:
pieces = line.split("\t")
word_list.append(pieces[0])
cnt += 1
if cnt > vocab_size:
break
vocabs = Vocabulary(max_size=vocab_size,
padding=WORD_PAD,
unknown=WORD_UNK)
vocabs.add_word_lst(word_list)
vocabs.build_vocab()
vocab_dict["vocab"] = vocabs
if domain == True:
domaindict = Vocabulary(padding=None, unknown=DOMAIN_UNK)
domaindict.from_dataset(train_ds, field_name="publication")
vocab_dict["domain"] = domaindict
if tag == True:
tagdict = Vocabulary(padding=None, unknown=TAG_UNK)
tagdict.from_dataset(train_ds, field_name="tag")
vocab_dict["tag"] = tagdict
for ds in datasets.values():
vocab_dict["vocab"].index_dataset(ds,
field_name=Const.INPUT,
new_field_name=Const.INPUT)
return DataInfo(vocabs=vocab_dict, datasets=datasets)
class VocabIndexerProcessor(Processor):
"""
根据DataSet创建Vocabulary，并将其用数字index。新生成的index的field会被放在new_added_filed_name, 如果没有提供
new_added_field_name, 则覆盖原有的field_name.
"""
def __init__(self, field_name, new_added_filed_name=None, min_freq=1, max_size=None,
verbose=0, is_input=True):
"""
:param field_name: 从哪个field_name创建词表，以及对哪个field_name进行index操作
:param new_added_filed_name: index时，生成的index field的名称，如果不传入，则覆盖field_name.
:param min_freq: 创建的Vocabulary允许的单词最少出现次数.
:param max_size: 创建的Vocabulary允许的最大的单词数量
:param verbose: 0, 不输出任何信息；1，输出信息
:param bool is_input:
"""
super(VocabIndexerProcessor, self).__init__(field_name, new_added_filed_name)
self.min_freq = min_freq
self.max_size = max_size
self.verbose =verbose
self.is_input = is_input
def construct_vocab(self, *datasets):
"""
使用传入的DataSet创建vocabulary
:param datasets: DataSet类型的数据，用于构建vocabulary
:return:
"""
self.vocab = Vocabulary(min_freq=self.min_freq, max_size=self.max_size)
for dataset in datasets:
assert isinstance(dataset, DataSet), "Only Dataset class is allowed, not {}.".format(type(dataset))
dataset.apply(lambda ins: self.vocab.update(ins[self.field_name]))
self.vocab.build_vocab()
if self.verbose:
print("Vocabulary Constructed, has {} items.".format(len(self.vocab)))
def process(self, *datasets, only_index_dataset=None):
"""
若还未建立Vocabulary，则使用dataset中的DataSet建立vocabulary；若已经有了vocabulary则使用已有的vocabulary。得到vocabulary
后，则会index datasets与only_index_dataset。
:param datasets: DataSet类型的数据
:param only_index_dataset: DataSet, or list of DataSet. 该参数中的内容只会被用于index，不会被用于生成vocabulary。
:return:
"""
if len(datasets)==0 and not hasattr(self,'vocab'):
raise RuntimeError("You have to construct vocabulary first. Or you have to pass datasets to construct it.")
if not hasattr(self, 'vocab'):
self.construct_vocab(*datasets)
else:
if self.verbose:
print("Using constructed vocabulary with {} items.".format(len(self.vocab)))
to_index_datasets = []
if len(datasets)!=0:
for dataset in datasets:
assert isinstance(dataset, DataSet), "Only DataSet class is allowed, not {}.".format(type(dataset))
to_index_datasets.append(dataset)
if not (only_index_dataset is None):
if isinstance(only_index_dataset, list):
for dataset in only_index_dataset:
assert isinstance(dataset, DataSet), "Only DataSet class is allowed, not {}.".format(type(dataset))
to_index_datasets.append(dataset)
elif isinstance(only_index_dataset, DataSet):
to_index_datasets.append(only_index_dataset)
else:
raise TypeError('Only DataSet or list of DataSet is allowed, not {}.'.format(type(only_index_dataset)))
for dataset in to_index_datasets:
assert isinstance(dataset, DataSet), "Only DataSet class is allowed, not {}.".format(type(dataset))
dataset.apply(lambda ins: [self.vocab.to_index(token) for token in ins[self.field_name]],
new_field_name=self.new_added_field_name, is_input=self.is_input)
# 只返回一个，infer时为了跟其他processor保持一致
if len(to_index_datasets) == 1:
return to_index_datasets[0]
def set_vocab(self, vocab):
assert isinstance(vocab, Vocabulary), "Only fastNLP.core.Vocabulary is allowed, not {}.".format(type(vocab))
self.vocab = vocab
def delete_vocab(self):
del self.vocab
def get_vocab_size(self):
return len(self.vocab)
def set_verbose(self, verbose):
"""
设置processor verbose状态。
:param verbose: int, 0，不输出任何信息；1，输出vocab 信息。
:return:
"""
self.verbose = verbose
def process(
self,
paths: Union[str, Dict[str, str]],
dataset_name: str = None,
to_lower=False,
seq_len_type: str = None,
bert_tokenizer: str = None,
cut_text: int = None,
get_index=True,
auto_pad_length: int = None,
auto_pad_token: str = '<pad>',
set_input: Union[list, str, bool] = True,
set_target: Union[list, str, bool] = True,
concat: Union[str, list, bool] = None,
) -> DataBundle:
"""
:param paths: str或者Dict[str, str]。如果是str，则为数据集所在的文件夹或者是全路径文件名：如果是文件夹，
则会从self.paths里面找对应的数据集名称与文件名。如果是Dict，则为数据集名称（如train、dev、test）和
对应的全路径文件名。
:param str dataset_name: 如果在paths里传入的是一个数据集的全路径文件名，那么可以用dataset_name来定义
这个数据集的名字，如果不定义则默认为train。
:param bool to_lower: 是否将文本自动转为小写。默认值为False。
:param str seq_len_type: 提供的seq_len类型，支持 ``seq_len`` ：提供一个数字作为句子长度； ``mask`` :
提供一个0/1的mask矩阵作为句子长度； ``bert`` ：提供segment_type_id（第一个句子为0，第二个句子为1）和
attention mask矩阵（0/1的mask矩阵）。默认值为None，即不提供seq_len
:param str bert_tokenizer: bert tokenizer所使用的词表所在的文件夹路径
:param int cut_text: 将长于cut_text的内容截掉。默认为None，即不截。
:param bool get_index: 是否需要根据词表将文本转为index
:param int auto_pad_length: 是否需要将文本自动pad到一定长度（超过这个长度的文本将会被截掉），默认为不会自动pad
:param str auto_pad_token: 自动pad的内容
:param set_input: 如果为True，则会自动将相关的field（名字里含有Const.INPUT的）设置为input，如果为False
则不会将任何field设置为input。如果传入str或者List[str]，则会根据传入的内容将相对应的field设置为input，
于此同时其他field不会被设置为input。默认值为True。
:param set_target: set_target将控制哪些field可以被设置为target，用法与set_input一致。默认值为True。
:param concat: 是否需要将两个句子拼接起来。如果为False则不会拼接。如果为True则会在两个句子之间插入一个<sep>。
如果传入一个长度为4的list，则分别表示插在第一句开始前、第一句结束后、第二句开始前、第二句结束后的标识符。如果
传入字符串 ``bert`` ，则会采用bert的拼接方式，等价于['[CLS]', '[SEP]', '', '[SEP]'].
:return:
"""
if isinstance(set_input, str):
set_input = [set_input]
if isinstance(set_target, str):
set_target = [set_target]
if isinstance(set_input, bool):
auto_set_input = set_input
else:
auto_set_input = False
if isinstance(set_target, bool):
auto_set_target = set_target
else:
auto_set_target = False
if isinstance(paths, str):
if os.path.isdir(paths):
path = {
n: os.path.join(paths, self.paths[n])
for n in self.paths.keys()
}
else:
path = {
dataset_name if dataset_name is not None else 'train':
paths
}
else:
path = paths
data_info = DataBundle()
for data_name in path.keys():
data_info.datasets[data_name] = self._load(path[data_name])
for data_name, data_set in data_info.datasets.items():
if auto_set_input:
data_set.set_input(Const.INPUTS(0), Const.INPUTS(1))
if auto_set_target:
if Const.TARGET in data_set.get_field_names():
data_set.set_target(Const.TARGET)
if to_lower:
for data_name, data_set in data_info.datasets.items():
data_set.apply(
lambda x: [w.lower() for w in x[Const.INPUTS(0)]],
new_field_name=Const.INPUTS(0),
is_input=auto_set_input)
data_set.apply(
lambda x: [w.lower() for w in x[Const.INPUTS(1)]],
new_field_name=Const.INPUTS(1),
is_input=auto_set_input)
if bert_tokenizer is not None:
if bert_tokenizer.lower() in PRETRAINED_BERT_MODEL_DIR:
PRETRAIN_URL = _get_base_url('bert')
model_name = PRETRAINED_BERT_MODEL_DIR[bert_tokenizer]
model_url = PRETRAIN_URL + model_name
model_dir = cached_path(model_url)
# 检查是否存在
elif os.path.isdir(bert_tokenizer):
model_dir = bert_tokenizer
else:
raise ValueError(
f"Cannot recognize BERT tokenizer from {bert_tokenizer}.")
words_vocab = Vocabulary(padding='[PAD]', unknown='[UNK]')
with open(os.path.join(model_dir, 'vocab.txt'), 'r') as f:
lines = f.readlines()
lines = [line.strip() for line in lines]
words_vocab.add_word_lst(lines)
words_vocab.build_vocab()
tokenizer = BertTokenizer.from_pretrained(model_dir)
for data_name, data_set in data_info.datasets.items():
for fields in data_set.get_field_names():
if Const.INPUT in fields:
data_set.apply(
lambda x: tokenizer.tokenize(' '.join(x[fields])),
new_field_name=fields,
is_input=auto_set_input)
if isinstance(concat, bool):
concat = 'default' if concat else None
if concat is not None:
if isinstance(concat, str):
CONCAT_MAP = {
'bert': ['[CLS]', '[SEP]', '', '[SEP]'],
'default': ['', '<sep>', '', '']
}
if concat.lower() in CONCAT_MAP:
concat = CONCAT_MAP[concat]
else:
concat = 4 * [concat]
assert len(concat) == 4, \
f'Please choose a list with 4 symbols which at the beginning of first sentence ' \
f'the end of first sentence, the begin of second sentence, and the end of second' \
f'sentence. Your input is {concat}'
for data_name, data_set in data_info.datasets.items():
data_set.apply(
lambda x: [concat[0]] + x[Const.INPUTS(0)] + [concat[
1]] + [concat[2]] + x[Const.INPUTS(1)] + [concat[3]],
new_field_name=Const.INPUT)
data_set.apply(
lambda x: [w for w in x[Const.INPUT] if len(w) > 0],
new_field_name=Const.INPUT,
is_input=auto_set_input)
if seq_len_type is not None:
if seq_len_type == 'seq_len': #
for data_name, data_set in data_info.datasets.items():
for fields in data_set.get_field_names():
if Const.INPUT in fields:
data_set.apply(lambda x: len(x[fields]),
new_field_name=fields.replace(
Const.INPUT, Const.INPUT_LEN),
is_input=auto_set_input)
elif seq_len_type == 'mask':
for data_name, data_set in data_info.datasets.items():
for fields in data_set.get_field_names():
if Const.INPUT in fields:
data_set.apply(lambda x: [1] * len(x[fields]),
new_field_name=fields.replace(
Const.INPUT, Const.INPUT_LEN),
is_input=auto_set_input)
elif seq_len_type == 'bert':
for data_name, data_set in data_info.datasets.items():
if Const.INPUT not in data_set.get_field_names():
raise KeyError(
f'Field ``{Const.INPUT}`` not in {data_name} data set: '
f'got {data_set.get_field_names()}')
data_set.apply(lambda x: [0] *
(len(x[Const.INPUTS(0)]) + 2) + [1] *
(len(x[Const.INPUTS(1)]) + 1),
new_field_name=Const.INPUT_LENS(0),
is_input=auto_set_input)
data_set.apply(lambda x: [1] * len(x[Const.INPUT_LENS(0)]),
new_field_name=Const.INPUT_LENS(1),
is_input=auto_set_input)
if auto_pad_length is not None:
cut_text = min(
auto_pad_length,
cut_text if cut_text is not None else auto_pad_length)
if cut_text is not None:
for data_name, data_set in data_info.datasets.items():
for fields in data_set.get_field_names():
if (Const.INPUT
in fields) or ((Const.INPUT_LEN in fields) and
(seq_len_type != 'seq_len')):
data_set.apply(lambda x: x[fields][:cut_text],
new_field_name=fields,
is_input=auto_set_input)
data_set_list = [d for n, d in data_info.datasets.items()]
assert len(data_set_list) > 0, f'There are NO data sets in data info!'
if bert_tokenizer is None:
words_vocab = Vocabulary(padding=auto_pad_token)
words_vocab = words_vocab.from_dataset(
*[d for n, d in data_info.datasets.items() if 'train' in n],
field_name=[
n for n in data_set_list[0].get_field_names()
if (Const.INPUT in n)
],
no_create_entry_dataset=[
d for n, d in data_info.datasets.items()
if 'train' not in n
])
target_vocab = Vocabulary(padding=None, unknown=None)
target_vocab = target_vocab.from_dataset(
*[d for n, d in data_info.datasets.items() if 'train' in n],
field_name=Const.TARGET)
data_info.vocabs = {
Const.INPUT: words_vocab,
Const.TARGET: target_vocab
}
if get_index:
for data_name, data_set in data_info.datasets.items():
for fields in data_set.get_field_names():
if Const.INPUT in fields:
data_set.apply(
lambda x:
[words_vocab.to_index(w) for w in x[fields]],
new_field_name=fields,
is_input=auto_set_input)
if Const.TARGET in data_set.get_field_names():
data_set.apply(
lambda x: target_vocab.to_index(x[Const.TARGET]),
new_field_name=Const.TARGET,
is_input=auto_set_input,
is_target=auto_set_target)
if auto_pad_length is not None:
if seq_len_type == 'seq_len':
raise RuntimeError(
f'the sequence will be padded with the length {auto_pad_length}, '
f'so the seq_len_type cannot be `{seq_len_type}`!')
for data_name, data_set in data_info.datasets.items():
for fields in data_set.get_field_names():
if Const.INPUT in fields:
data_set.apply(
lambda x: x[fields] +
[words_vocab.to_index(words_vocab.padding)] *
(auto_pad_length - len(x[fields])),
new_field_name=fields,
is_input=auto_set_input)
elif (Const.INPUT_LEN
in fields) and (seq_len_type != 'seq_len'):
data_set.apply(lambda x: x[fields] + [0] *
(auto_pad_length - len(x[fields])),
new_field_name=fields,
is_input=auto_set_input)
for data_name, data_set in data_info.datasets.items():
if isinstance(set_input, list):
data_set.set_input(*[
inputs for inputs in set_input
if inputs in data_set.get_field_names()
])
if isinstance(set_target, list):
data_set.set_target(*[
target for target in set_target
if target in data_set.get_field_names()
])
return data_info
