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142 lines (110 loc) · 4.97 KB
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import random
from collections import defaultdict
import pandas as pd
import torch
from sklearn.preprocessing import LabelEncoder
from torch.utils.data import DataLoader
from transformers import AutoTokenizer
from src import logger
class Dataset(torch.utils.data.Dataset):
def __init__(self, args, tokenizer_str, split):
logger.info('loading data')
random.seed(args.seed)
self.batch_size = args.batch_size
self.split = split
self.data_dir = args.data_dir
self.dataname = args.dataname
self.max_len = args.max_len
self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_str)
self.pad_id = 0
def collate(self, batch):
inputs = [b['input_ids'] for b in batch]
lengths = torch.LongTensor([len(b['input_ids']) for b in batch])
lengths = torch.clamp(lengths, max=512)
if self.max_len != -1:
max_length = self.max_len
else:
max_length = min(lengths.max(), 512)
for i in range(len(inputs)):
if len(inputs[i]) < max_length:
inputs[i] = torch.cat(
[inputs[i],
torch.zeros(max_length - len(inputs[i])).long() + self.pad_id],
dim=0) # 0 is fine as pad since it's masked out
else:
inputs[i] = inputs[i][:max_length]
inputs = torch.stack(inputs, dim=0)
labels = [b['label'] for b in batch]
labels = torch.LongTensor(labels)
return inputs, lengths, labels
class SST2Dataset(Dataset):
def __init__(self, args, tokenizer_str, pad_token, split):
super().__init__(args, tokenizer_str, split)
self.tokenizer.add_special_tokens({'pad_token': pad_token})
self.pad_id = 0
print(self.data_dir)
print(self.dataname)
self.df = pd.read_csv(self.data_dir + self.dataname + "/" + split + ".csv")
self.class_num = 2
self.le_name_mapping = {0: "negative", 1: "positive"}
logger.info('done loading data')
logger.info('split {} size: {}'.format(split, len(self.df)))
def __getitem__(self, index):
raw_sentence = self.df.iloc[index]["sentence"]
label = int(self.df.iloc[index]["label"])
encoded = self.tokenizer.encode(raw_sentence, return_tensors='pt')[0]
return {'input_ids': encoded, 'length': len(encoded), 'label': label}
def __len__(self):
return len(self.df)
class FoodDataset(Dataset):
def __init__(self, args, label_col, tokenizer_str, pad_token, split):
super().__init__(args, tokenizer_str, split)
self.label_col = label_col
self.tokenizer.add_special_tokens({'pad_token': pad_token})
self.pad_id = 0
# self.pad_id = self.tokenizer.encode(pad_token)[0]
self.vocab = defaultdict(lambda: 0)
self.splits = []
self.split_labels = []
self.labels_vocab = set()
self.label_encoder = LabelEncoder()
with open(self.data_dir + self.dataname + "_data/src1_" + split + ".txt") as f:
for line in f:
text = line.split("||")[1].strip()
properties = line.split("||")[0].strip()
properties = {s.split(":")[0].strip(): s.split(":")[1].strip() for s in
properties.split("|")}
if self.label_col not in properties:
continue
for word in text.strip().split(' '):
self.vocab[word] += 1
self.split_labels.append(properties[self.label_col])
self.labels_vocab.add(properties[self.label_col])
self.splits.append(text)
self.class_num = len(self.labels_vocab)
self.label_encoder.fit(list(self.labels_vocab))
self.le_name_mapping = dict(
zip(self.label_encoder.transform(self.label_encoder.classes_),
self.label_encoder.classes_))
logger.info(self.le_name_mapping)
self.split_labels = self.label_encoder.transform(self.split_labels)
self.splits = tuple(zip(self.splits, self.split_labels))
logger.info('done loading data')
logger.info('split {} size: {}'.format(split, len(self.splits)))
def __getitem__(self, index):
raw_sentence = self.splits[index][0]
label = self.splits[index][1]
encoded = self.tokenizer.encode(raw_sentence, return_tensors='pt')[0]
return {'input_ids': encoded, 'length': len(encoded), 'label': label}
def __len__(self):
return len(self.splits)
def loader(args, label_col, tokenizer_str, pad_token, split):
dataset = None
if args.dataname == "e2e":
dataset = FoodDataset(args, label_col, tokenizer_str, pad_token, split)
elif args.dataname == "sst2":
dataset = SST2Dataset(args, tokenizer_str, pad_token, split)
return DataLoader(dataset,
batch_size=args.batch_size,
collate_fn=dataset.collate,
shuffle=True)