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402 lines (369 loc) · 13.9 KB
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from torch.utils.data import DataLoader
import torch
from transformers import BertTokenizer
from modules.utils import read_config, if_none
from modules import tqdm
import pandas as pd
from copy import deepcopy
class InputFeature(object):
"""A single set of features of data."""
def __init__(
self,
# Bert data
bert_tokens, input_ids, input_mask, input_type_ids,
# Ner data
bert_labels, labels_ids, labels,
# Origin data
tokens, tok_map,
# Cls data
cls=None, id_cls=None):
"""
Data has the following structure.
data[0]: list, tokens ids
data[1]: list, tokens mask
data[2]: list, tokens type ids (for bert)
data[3]: list, bert labels ids
"""
self.data = []
# Bert data
self.bert_tokens = bert_tokens
self.input_ids = input_ids
self.data.append(input_ids)
self.input_mask = input_mask
self.data.append(input_mask)
self.input_type_ids = input_type_ids
self.data.append(input_type_ids)
# Ner data
self.bert_labels = bert_labels
self.labels_ids = labels_ids
self.data.append(labels_ids)
# Classification data
self.cls = cls
self.id_cls = id_cls
if id_cls is not None:
self.data.append(id_cls)
# Origin data
self.tokens = tokens
self.tok_map = tok_map
self.labels = labels
def __iter__(self):
return iter(self.data)
class TextDataLoader(DataLoader):
def __init__(self, data_set, device,shuffle=False, batch_size=16):
super(TextDataLoader, self).__init__(
dataset=data_set,
collate_fn=self.collate_fn,
shuffle=shuffle,
batch_size=batch_size
)
self.device = device
def collate_fn(self, data):
res = []
token_ml = max(map(lambda x_: sum(x_.data[1]), data))
for sample in data:
example = []
for x in sample:
if isinstance(x, list):
x = x[:token_ml]
example.append(x)
res.append(example)
res_ = []
for x in zip(*res):
res_.append(torch.LongTensor(x))
return [t.to(self.device) for t in res_]
class TextDataSet(object):
@classmethod
def from_config(cls, config, clear_cache=False, df=None):
return cls.create(**read_config(config), clear_cache=clear_cache, df=df)
@classmethod
def create(cls,
idx2labels_path,
df_path=None,
idx2labels=None,
idx2cls=None,
idx2cls_path=None,
min_char_len=1,
model_name="bert-base-multilingual-cased",
max_sequence_length=424,
pad_idx=0,
clear_cache=False,
is_cls=False,
markup="IO",
df=None, tokenizer=None):
if tokenizer is None:
tokenizer = BertTokenizer.from_pretrained(model_name)
config = {
"min_char_len": min_char_len,
"model_name": model_name,
"max_sequence_length": max_sequence_length,
"clear_cache": clear_cache,
"df_path": df_path,
"pad_idx": pad_idx,
"is_cls": is_cls,
"idx2labels_path": idx2labels_path,
"idx2cls_path": idx2cls_path,
"markup": markup
}
if df is None and df_path is not None:
df = pd.read_csv(df_path, sep='\t')
elif df is None:
if is_cls:
df = pd.DataFrame(columns=["labels", "text", "clf"])
else:
df = pd.DataFrame(columns=["labels", "text"])
if clear_cache:
_ = cls.create_vocabs(
df, tokenizer, idx2labels_path, markup, idx2cls_path, pad_idx, is_cls, idx2labels, idx2cls)
self = cls(tokenizer, df=df, config=config, is_cls=is_cls)
self.load(df=df)
return self
@staticmethod
def create_vocabs(
df, tokenizer, idx2labels_path, markup="IO",
idx2cls_path=None, pad_idx=0, is_cls=False, idx2labels=None, idx2cls=None):
if idx2labels is None:
label2idx = {"[PAD]": pad_idx, '[CLS]': 1, '[SEP]': 2, "X": 3}
idx2label = ["[PAD]", '[CLS]', '[SEP]', "X"]
else:
label2idx = {label: idx for idx, label in enumerate(idx2labels)}
idx2label = idx2labels
idx2cls = idx2cls
cls2idx = None
if is_cls:
idx2cls = []
cls2idx = {label: idx for idx, label in enumerate(idx2cls)}
for _, row in tqdm(df.iterrows(), total=len(df), leave=False, desc="Creating labels vocabs"):
labels = row.labels.split()
origin_tokens = row.text.split()
if is_cls and row.cls not in cls2idx:
cls2idx[row.cls] = len(cls2idx)
idx2cls.append(row.cls)
prev_label = ""
for origin_token, label in zip(origin_tokens, labels):
if markup == "BIO":
prefix = "B_"
else:
prefix = "I_"
if label != "O":
label = label.split("_")[1]
if label == prev_label:
prefix = "I_"
prev_label = label
else:
prev_label = label
cur_tokens = tokenizer.tokenize(origin_token)
bert_label = [prefix + label] + ["X"] * (len(cur_tokens) - 1)
for label_ in bert_label:
if label_ not in label2idx:
label2idx[label_] = len(label2idx)
idx2label.append(label_)
with open(idx2labels_path, "w", encoding="utf-8") as f:
for label in idx2label:
f.write("{}\n".format(label))
if is_cls:
with open(idx2cls_path, "w", encoding="utf-8") as f:
for label in idx2cls:
f.write("{}\n".format(label))
return label2idx, idx2label, cls2idx, idx2cls
def load(self, df_path=None, df=None):
df_path = if_none(df_path, self.config["df_path"])
if df is None:
self.df = pd.read_csv(df_path, sep='\t')
self.label2idx = {}
self.idx2label = []
with open(self.config["idx2labels_path"], "r", encoding="utf-8") as f:
for idx, label in enumerate(f.readlines()):
label = label.strip()
self.label2idx[label] = idx
self.idx2label.append(label)
if self.config["is_cls"]:
self.idx2cls = []
self.cls2idx = {}
with open(self.config["idx2cls_path"], "r", encoding="utf-8") as f:
for idx, label in enumerate(f.readlines()):
label = label.strip()
self.cls2idx[label] = idx
self.idx2cls.append(label)
def create_feature(self, row):
bert_tokens = []
bert_labels = []
orig_tokens = row.text.split()
origin_labels = row.labels.split()
tok_map = []
prev_label = ""
for orig_token, label in zip(orig_tokens, origin_labels):
cur_tokens = self.tokenizer.tokenize(orig_token)
if self.config["max_sequence_length"] - 2 < len(bert_tokens) + len(cur_tokens):
break
if self.config["markup"] == "BIO":
prefix = "B_"
else:
prefix = "I_"
if label != "O":
label = label.split("_")[1]
if label == prev_label:
prefix = "I_"
prev_label = label
else:
prev_label = label
cur_tokens = self.tokenizer.tokenize(orig_token)
bert_label = [prefix + label] + ["X"] * (len(cur_tokens) - 1)
tok_map.append(len(bert_tokens))
bert_tokens.extend(cur_tokens)
bert_labels.extend(bert_label)
orig_tokens = ["[CLS]"] + orig_tokens + ["[SEP]"]
bert_labels = ["[CLS]"] + bert_labels + ["[SEP]"]
if self.config["markup"] == "BIO":
O_label = self.label2idx.get("B_O")
else:
O_label = self.label2idx.get("I_O")
input_ids = self.tokenizer.convert_tokens_to_ids(['[CLS]'] + bert_tokens + ['[SEP]'])
labels_ids = [self.label2idx.get(l, O_label) for l in bert_labels]
# The mask has 1 for real tokens and 0 for padding tokens. Only real
# tokens are attended to.
input_mask = [1] * len(input_ids)
# Zero-pad up to the sequence length.
while len(input_ids) < self.config["max_sequence_length"]:
input_ids.append(self.config["pad_idx"])
labels_ids.append(self.config["pad_idx"])
input_mask.append(0)
tok_map.append(-1)
input_type_ids = [0] * len(input_ids)
cls = None
id_cls = None
if self.is_cls:
cls = row.cls
try:
id_cls = self.cls2idx[cls]
except KeyError:
id_cls = self.cls2idx[str(cls)]
return InputFeature(
# Bert data
bert_tokens=bert_tokens,
input_ids=input_ids,
input_mask=input_mask,
input_type_ids=input_type_ids,
bert_labels=bert_labels, labels_ids=labels_ids, labels=origin_labels,
# Origin data
tokens=orig_tokens,
tok_map=tok_map,
# Cls
cls=cls, id_cls=id_cls
)
def __getitem__(self, item):
if self.config["df_path"] is None and self.df is None:
raise ValueError("Should setup df_path or df.")
if self.df is None:
self.load()
return self.create_feature(self.df.iloc[item])
def __len__(self):
return len(self.df) if self.df is not None else 0
def save(self, df_path=None):
df_path = if_none(df_path, self.config["df_path"])
self.df.to_csv(df_path, sep='\t', index=False)
def __init__(
self, tokenizer,
df=None,
config=None,
idx2label=None,
idx2cls=None,
is_cls=False):
self.df = df
self.tokenizer = tokenizer
self.config = config
self.idx2label = idx2label
self.label2idx = None
if idx2label is not None:
self.label2idx = {label: idx for idx, label in enumerate(idx2label)}
self.idx2cls = idx2cls
if idx2cls is not None:
self.cls2idx = {label: idx for idx, label in enumerate(idx2cls)}
self.is_cls = is_cls
class LearnData(object):
def __init__(self, train_ds=None, train_dl=None, valid_ds=None, valid_dl=None):
self.train_ds = train_ds
self.train_dl = train_dl
self.valid_ds = valid_ds
self.valid_dl = valid_dl
@classmethod
def create(cls,
# DataSet params
train_df_path,
valid_df_path,
idx2labels_path,
device,
idx2labels=None,
idx2cls=None,
idx2cls_path=None,
min_char_len=1,
model_name="bert-base-multilingual-cased",
max_sequence_length=424,
pad_idx=0,
clear_cache=False,
is_cls=False,
markup="IO",
train_df=None,
valid_df=None,
# DataLoader params
batch_size=16):
train_ds = None
train_dl = None
valid_ds = None
valid_dl = None
if idx2labels_path is not None:
train_ds = TextDataSet.create(
idx2labels_path,
train_df_path,
idx2labels=idx2labels,
idx2cls=idx2cls,
idx2cls_path=idx2cls_path,
min_char_len=min_char_len,
model_name=model_name,
max_sequence_length=max_sequence_length,
pad_idx=pad_idx,
clear_cache=clear_cache,
is_cls=is_cls,
markup=markup,
df=train_df)
if len(train_ds):
train_dl = TextDataLoader(train_ds, device=device, shuffle=True, batch_size=batch_size)
if valid_df_path is not None:
valid_ds = TextDataSet.create(
idx2labels_path,
valid_df_path,
idx2labels=train_ds.idx2label,
idx2cls=train_ds.idx2cls,
idx2cls_path=idx2cls_path,
min_char_len=min_char_len,
model_name=model_name,
max_sequence_length=max_sequence_length,
pad_idx=pad_idx,
clear_cache=False,
is_cls=is_cls,
markup=markup,
df=valid_df, tokenizer=train_ds.tokenizer)
valid_dl = TextDataLoader(valid_ds, device=device, batch_size=batch_size)
self = cls(train_ds, train_dl, valid_ds, valid_dl)
self.device = device
self.batch_size = batch_size
return self
def load(self):
if self.train_ds is not None:
self.train_ds.load()
if self.valid_ds is not None:
self.valid_ds.load()
def save(self):
if self.train_ds is not None:
self.train_ds.save()
if self.valid_ds is not None:
self.valid_ds.save()
def get_data_loader_for_predict(data, df_path=None, df=None):
config = deepcopy(data.train_ds.config)
config["df_path"] = df_path
config["clear_cache"] = False
ds = TextDataSet.create(
idx2labels=data.train_ds.idx2label,
idx2cls=data.train_ds.idx2cls,
df=df, tokenizer=data.train_ds.tokenizer, **config)
return TextDataLoader(
ds, device=data.device, batch_size=data.batch_size, shuffle=False)