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382 lines (351 loc) · 13.7 KB
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from torch.utils.data import DataLoader
from modules.data import tokenization
import torch
import pandas as pd
import numpy as np
from tqdm import tqdm
import json
class InputFeatures(object):
"""A single set of features of data."""
def __init__(
self,
# Bert data
bert_tokens, input_ids, input_mask, input_type_ids,
# Origin data
tokens, labels, labels_ids, labels_mask, tok_map, cls=None, cls_idx=None, meta=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, tokens meta info (if meta is not None)
...
data[-2]: list, labels mask
data[-1]: list, 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)
# Meta data
self.meta = meta
if meta is not None:
self.data.append(meta)
# Origin data
self.tokens = tokens
self.labels = labels
# Used for joint model
self.cls = cls
self.cls_idx = cls_idx
if cls is not None:
self.data.append(cls_idx)
# Labels data
self.labels_mask = labels_mask
self.data.append(labels_mask)
self.labels_ids = labels_ids
self.data.append(labels_ids)
self.tok_map = tok_map
class DataLoaderForTrain(DataLoader):
def __init__(self, data_set, shuffle, cuda, **kwargs):
super(DataLoaderForTrain, self).__init__(
dataset=data_set,
collate_fn=self.collate_fn,
shuffle=shuffle,
**kwargs
)
self.cuda = cuda
def collate_fn(self, data):
res = []
token_ml = max(map(lambda x_: sum(x_.data[1]), data))
label_ml = max(map(lambda x_: sum(x_.data[-2]), data))
sorted_idx = np.argsort(list(map(lambda x_: sum(x_.data[1]), data)))[::-1]
for idx in sorted_idx:
f = data[idx]
example = []
for idx_, x in enumerate(f.data[:-2]):
if isinstance(x, list):
x = x[:token_ml]
example.append(x)
example.append(f.data[-2][:label_ml])
example.append(f.data[-1][:label_ml])
res.append(example)
res_ = []
for idx, x in enumerate(zip(*res)):
if data[0].meta is not None and idx == 3:
res_.append(torch.FloatTensor(x))
else:
res_.append(torch.LongTensor(x))
if self.cuda:
res_ = [t.cuda() for t in res_]
return res_
class DataLoaderForPredict(DataLoader):
def __init__(self, data_set, cuda, **kwargs):
super(DataLoaderForPredict, self).__init__(
dataset=data_set,
collate_fn=self.collate_fn,
**kwargs
)
self.cuda = cuda
def collate_fn(self, data):
res = []
token_ml = max(map(lambda x_: sum(x_.data[1]), data))
label_ml = max(map(lambda x_: sum(x_.data[-2]), data))
sorted_idx = np.argsort(list(map(lambda x_: sum(x_.data[1]), data)))[::-1]
for idx in sorted_idx:
f = data[idx]
example = []
for x in f.data[:-2]:
if isinstance(x, list):
x = x[:token_ml]
example.append(x)
example.append(f.data[-2][:label_ml])
example.append(f.data[-1][:label_ml])
res.append(example)
res_ = []
for idx, x in enumerate(zip(*res)):
if data[0].meta is not None and idx == 3:
res_.append(torch.FloatTensor(x))
else:
res_.append(torch.LongTensor(x))
sorted_idx = torch.LongTensor(list(sorted_idx))
if self.cuda:
res_ = [t.cuda() for t in res_]
sorted_idx = sorted_idx.cuda()
return res_, sorted_idx
def get_data(
df, tokenizer, label2idx=None, max_seq_len=424, pad="<pad>", cls2idx=None,
is_cls=False, is_meta=False):
tqdm_notebook = tqdm
if label2idx is None:
label2idx = {pad: 0, '[CLS]': 1}
features = []
all_args = []
if is_cls:
# Use joint model
if cls2idx is None:
cls2idx = dict()
all_args.extend([df["1"].tolist(), df["0"].tolist(), df["2"].tolist()])
else:
all_args.extend([df["1"].tolist(), df["0"].tolist()])
if is_meta:
all_args.append(df["3"].tolist())
total = len(df["0"].tolist())
cls = None
meta = None
for args in tqdm_notebook(enumerate(zip(*all_args)), total=total, leave=False):
if is_cls:
if is_meta:
idx, (text, labels, cls, meta) = args
else:
idx, (text, labels, cls) = args
else:
if is_meta:
idx, (text, labels, meta) = args
else:
idx, (text, labels) = args
tok_map = []
meta_tokens = []
if is_meta:
meta = json.loads(meta)
meta_tokens.append([0] * len(meta[0]))
bert_tokens = []
bert_labels = []
bert_tokens.append("[CLS]")
bert_labels.append("[CLS]")
orig_tokens = []
orig_tokens.extend(str(text).split())
labels = str(labels).split()
pad_idx = label2idx[pad]
assert len(orig_tokens) == len(labels)
# prev_label = ""
for idx_, (orig_token, label) in enumerate(zip(orig_tokens, labels)):
# Fix BIO to IO as BERT proposed https://arxiv.org/pdf/1810.04805.pdf
prefix = "I_"
if label != "O":
label = label.split("_")[1]
# prev_label = label
# else:
# prev_label = label
cur_tokens = tokenizer.tokenize(orig_token)
if max_seq_len - 1 < len(bert_tokens) + len(cur_tokens):
break
tok_map.append(len(bert_tokens))
if is_meta:
meta_tokens.extend([meta[idx_]] * len(cur_tokens))
bert_tokens.extend(cur_tokens)
# ["I_" + label] * (len(cur_tokens) - 1)
bert_label = [prefix + label] + ["X"] * (len(cur_tokens) - 1)
bert_labels.extend(bert_label)
# bert_tokens.append("[SEP]")
# bert_labels.append("[SEP]")
if is_meta:
meta_tokens.append([0] * len(meta[0]))
# + ["[SEP]"]
orig_tokens = ["[CLS]"] + orig_tokens
input_ids = tokenizer.convert_tokens_to_ids(bert_tokens)
labels = bert_labels
for l in labels:
if l not in label2idx:
label2idx[l] = len(label2idx)
labels_ids = [label2idx[l] for l in 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)
labels_mask = [1] * len(labels_ids)
# Zero-pad up to the sequence length.
while len(input_ids) < max_seq_len:
input_ids.append(0)
input_mask.append(0)
labels_ids.append(pad_idx)
labels_mask.append(0)
tok_map.append(-1)
if is_meta:
meta_tokens.append([0] * len(meta[0]))
# assert len(input_ids) == len(bert_labels_ids)
input_type_ids = [0] * len(input_ids)
# For joint model
cls_idx = None
if is_cls:
if cls not in cls2idx:
cls2idx[cls] = len(cls2idx)
cls_idx = cls2idx[cls]
if is_meta:
meta = meta_tokens
features.append(InputFeatures(
# Bert data
bert_tokens=bert_tokens,
input_ids=input_ids,
input_mask=input_mask,
input_type_ids=input_type_ids,
# Origin data
tokens=orig_tokens,
labels=labels,
labels_ids=labels_ids,
labels_mask=labels_mask,
tok_map=tok_map,
# Joint data
cls=cls,
cls_idx=cls_idx,
# Meta data
meta=meta
))
assert len(input_ids) == len(input_mask)
assert len(input_ids) == len(input_type_ids)
assert len(input_ids) == len(labels_ids)
assert len(input_ids) == len(labels_mask)
if is_cls:
return features, (label2idx, cls2idx)
return features, label2idx
def get_bert_data_loaders(train, valid, vocab_file, batch_size=16, cuda=True, is_cls=False,
do_lower_case=False, max_seq_len=424, is_meta=False, label2idx=None, cls2idx=None):
train = pd.read_csv(train)
valid = pd.read_csv(valid)
tokenizer = tokenization.FullTokenizer(vocab_file=vocab_file, do_lower_case=do_lower_case)
train_f, label2idx = get_data(
train, tokenizer, label2idx, cls2idx=cls2idx, is_cls=is_cls, max_seq_len=max_seq_len, is_meta=is_meta)
if is_cls:
label2idx, cls2idx = label2idx
train_dl = DataLoaderForTrain(
train_f, batch_size=batch_size, shuffle=True, cuda=cuda)
valid_f, label2idx = get_data(
valid, tokenizer, label2idx, cls2idx=cls2idx, is_cls=is_cls, max_seq_len=max_seq_len, is_meta=is_meta)
if is_cls:
label2idx, cls2idx = label2idx
valid_dl = DataLoaderForTrain(
valid_f, batch_size=batch_size, cuda=cuda, shuffle=False)
if is_cls:
return train_dl, valid_dl, tokenizer, label2idx, max_seq_len, cls2idx
return train_dl, valid_dl, tokenizer, label2idx, max_seq_len
def get_bert_data_loader_for_predict(path, learner):
df = pd.read_csv(path)
f, _ = get_data(df, tokenizer=learner.data.tokenizer,
label2idx=learner.data.label2idx, cls2idx=learner.data.cls2idx,
is_cls=learner.data.is_cls,
max_seq_len=learner.data.max_seq_len, is_meta=learner.data.is_meta)
dl = DataLoaderForPredict(
f, batch_size=learner.data.batch_size, shuffle=False,
cuda=True)
return dl
class BertNerData(object):
@property
def config(self):
config = {
"train_path": self.train_path,
"valid_path": self.valid_path,
"vocab_file": self.vocab_file,
"data_type": self.data_type,
"max_seq_len": self.max_seq_len,
"batch_size": self.batch_size,
"is_cls": self.is_cls,
"cuda": self.cuda,
"is_meta": self.is_meta,
"label2idx": self.label2idx,
"cls2idx": self.cls2idx
}
return config
def __init__(self, train_path, valid_path, vocab_file, data_type,
train_dl=None, valid_dl=None, tokenizer=None,
label2idx=None, max_seq_len=424,
cls2idx=None, batch_size=16, cuda=True, is_meta=False):
self.train_path = train_path
self.valid_path = valid_path
self.data_type = data_type
self.vocab_file = vocab_file
self.train_dl = train_dl
self.valid_dl = valid_dl
self.tokenizer = tokenizer
self.label2idx = label2idx
self.cls2idx = cls2idx
self.batch_size = batch_size
self.is_meta = is_meta
self.cuda = cuda
self.id2label = sorted(label2idx.keys(), key=lambda x: label2idx[x])
self.is_cls = False
self.max_seq_len = max_seq_len
if cls2idx is not None:
self.is_cls = True
self.id2cls = sorted(cls2idx.keys(), key=lambda x: cls2idx[x])
# TODO: write docs
@classmethod
def from_config(cls, config, for_train=True):
if config["data_type"] == "bert_cased":
do_lower_case = False
fn = get_bert_data_loaders
elif config["data_type"] == "bert_uncased":
do_lower_case = True
fn = get_bert_data_loaders
else:
raise NotImplementedError("No requested mode :(.")
if config["train_path"] and config["valid_path"] and for_train:
fn_res = fn(config["train_path"], config["valid_path"], config["vocab_file"], config["batch_size"],
config["cuda"], config["is_cls"], do_lower_case, config["max_seq_len"], config["is_meta"],
label2idx=config["label2idx"], cls2idx=config["cls2idx"])
else:
fn_res = (None, None, tokenization.FullTokenizer(
vocab_file=config["vocab_file"], do_lower_case=do_lower_case), config["label2idx"],
config["max_seq_len"], config["cls2idx"])
return cls(
config["train_path"], config["valid_path"], config["vocab_file"], config["data_type"],
*fn_res, batch_size=config["batch_size"], cuda=config["cuda"], is_meta=config["is_meta"])
# with open(config_path, "w") as f:
# json.dump(config, f)
@classmethod
def create(cls,
train_path, valid_path, vocab_file, batch_size=16, cuda=True, is_cls=False,
data_type="bert_cased", max_seq_len=424, is_meta=False):
if data_type == "bert_cased":
do_lower_case = False
fn = get_bert_data_loaders
elif data_type == "bert_uncased":
do_lower_case = True
fn = get_bert_data_loaders
else:
raise NotImplementedError("No requested mode :(.")
return cls(train_path, valid_path, vocab_file, data_type, *fn(
train_path, valid_path, vocab_file, batch_size, cuda, is_cls, do_lower_case, max_seq_len, is_meta),
batch_size=batch_size, cuda=cuda, is_meta=is_meta)