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data.py
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import torch
from transformers import AutoTokenizer
import random
import os
import numpy as np
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
import csv
class Data:
def __init__(self, args):
self.set_seed(args.seed)
max_seq_lengths = {'clinc':30, 'stackoverflow':45, 'banking':55}
train_epochs = {'clinc':20, 'stackoverflow':10, 'banking':20}
args.max_seq_length = max_seq_lengths[args.dataset]
args.num_train_epochs = train_epochs[args.dataset]
args.pretrain_dir = 'premodel/model_' + args.dataset + '_' + str(args.seed)
processor = DatasetProcessor()
self.data_dir = os.path.join(args.data_dir, args.dataset)
self.all_label_list = processor.get_labels(self.data_dir)
self.num_known = round(len(self.all_label_list) * args.known_cls_ratio)
self.known_label_list = list(np.random.choice(np.array(self.all_label_list), self.num_known, replace=False))
self.known_lab = [int(np.where(self.all_label_list== a)[0]) for a in self.known_label_list]
self.num_labels = int(len(self.all_label_list) * args.cluster_num_factor)
self.train_labeled_examples, self.train_unlabeled_examples = self.get_examples(processor, args, 'train')
print('num_labeled_samples', len(self.train_labeled_examples))
print('num_unlabeled_samples', len(self.train_unlabeled_examples))
self.eval_examples = self.get_examples(processor, args, 'eval')
self.test_examples = self.get_examples(processor, args, 'test')
self.semi_input_ids, self.semi_input_mask, self.semi_segment_ids, self.semi_label_ids = self.get_semi(self.train_labeled_examples, self.train_unlabeled_examples, args)
self.train_labeled_dataloader = self.get_loader(self.train_labeled_examples, args, 'train')
self.pretrain_labeled_dataloader = self.get_loader(self.train_labeled_examples, args, 'pretrain')
self.train_semi_dataloader = self.get_semi_loader(self.semi_input_ids, self.semi_input_mask, self.semi_segment_ids, self.semi_label_ids, args)
self.pretrain_semi_dataloader = self.get_semi_loader(self.semi_input_ids, self.semi_input_mask, self.semi_segment_ids, self.semi_label_ids, args, 'pretrain')
self.eval_dataloader = self.get_loader(self.eval_examples, args, 'eval')
self.test_dataloader = self.get_loader(self.test_examples, args, 'test')
def set_seed(self, seed):
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
random.seed(seed)
torch.backends.cudnn.deterministic = True
def get_examples(self, processor, args, mode = 'train'):
ori_examples = processor.get_examples(self.data_dir, mode)
if mode == 'train':
train_labels = np.array([example.label for example in ori_examples])
train_labeled_ids = []
for label in self.known_label_list:
num = round(len(train_labels[train_labels == label]) * args.labeled_ratio)
pos = list(np.where(train_labels == label)[0])
train_labeled_ids.extend(random.sample(pos, num))
train_labeled_examples, train_unlabeled_examples = [], []
for idx, example in enumerate(ori_examples):
if idx in train_labeled_ids:
train_labeled_examples.append(example)
else:
train_unlabeled_examples.append(example)
return train_labeled_examples, train_unlabeled_examples
elif mode == 'eval':
eval_examples = []
for example in ori_examples:
if example.label in self.known_label_list:
eval_examples.append(example)
return eval_examples
elif mode == 'test':
return ori_examples
def get_semi(self, labeled_examples, unlabeled_examples, args):
tokenizer = AutoTokenizer.from_pretrained(args.bert_model, do_lower_case=True)
labeled_features = self.convert_examples_to_features(labeled_examples, self.known_label_list, args.max_seq_length, tokenizer)
unlabeled_features = self.convert_examples_to_features(unlabeled_examples, self.all_label_list, args.max_seq_length, tokenizer)
labeled_input_ids = torch.tensor([f.input_ids for f in labeled_features], dtype=torch.long)
labeled_input_mask = torch.tensor([f.input_mask for f in labeled_features], dtype=torch.long)
labeled_segment_ids = torch.tensor([f.segment_ids for f in labeled_features], dtype=torch.long)
labeled_label_ids = torch.tensor([f.label_id for f in labeled_features], dtype=torch.long)
unlabeled_input_ids = torch.tensor([f.input_ids for f in unlabeled_features], dtype=torch.long)
unlabeled_input_mask = torch.tensor([f.input_mask for f in unlabeled_features], dtype=torch.long)
unlabeled_segment_ids = torch.tensor([f.segment_ids for f in unlabeled_features], dtype=torch.long)
unlabeled_label_ids = torch.tensor([f.label_id for f in unlabeled_features], dtype=torch.long)
semi_input_ids = torch.cat([labeled_input_ids, unlabeled_input_ids])
semi_input_mask = torch.cat([labeled_input_mask, unlabeled_input_mask])
semi_segment_ids = torch.cat([labeled_segment_ids, unlabeled_segment_ids])
semi_label_ids = torch.cat([labeled_label_ids, unlabeled_label_ids])
return semi_input_ids, semi_input_mask, semi_segment_ids, semi_label_ids
def get_semi_loader(self, semi_input_ids, semi_input_mask, semi_segment_ids, semi_label_ids, args, mode='train'):
semi_data = TensorDataset(semi_input_ids, semi_input_mask, semi_segment_ids, semi_label_ids)
semi_sampler = SequentialSampler(semi_data)
semi_dataloader = DataLoader(semi_data, sampler=semi_sampler, batch_size = args.train_batch_size)
if mode == 'pretrain':
semi_dataloader = DataLoader(semi_data, sampler=semi_sampler, batch_size = args.pretrain_batch_size)
return semi_dataloader
def get_loader(self, examples, args, mode = 'train'):
tokenizer = AutoTokenizer.from_pretrained(args.bert_model, do_lower_case=True)
if mode == 'train' or mode == 'eval' or mode == 'pretrain':
features = self.convert_examples_to_features(examples, self.known_label_list, args.max_seq_length, tokenizer)
elif mode == 'test':
features = self.convert_examples_to_features(examples, self.all_label_list, args.max_seq_length, tokenizer)
input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)
input_mask = torch.tensor([f.input_mask for f in features], dtype=torch.long)
segment_ids = torch.tensor([f.segment_ids for f in features], dtype=torch.long)
label_ids = torch.tensor([f.label_id for f in features], dtype=torch.long)
data = TensorDataset(input_ids, input_mask, segment_ids, label_ids)
if mode == 'train':
sampler = RandomSampler(data)
dataloader = DataLoader(data, sampler=sampler, batch_size = args.train_batch_size)
elif mode == 'eval' or mode == 'test':
sampler = SequentialSampler(data)
dataloader = DataLoader(data, sampler=sampler, batch_size = args.eval_batch_size)
elif mode == 'pretrain':
sampler = RandomSampler(data)
dataloader = DataLoader(data, sampler=sampler, batch_size = args.pretrain_batch_size)
return dataloader
def convert_examples_to_features(self, examples, label_list, max_seq_length, tokenizer):
label_map = {}
for i, label in enumerate(label_list):
label_map[label] = i
features = []
for (index, example) in enumerate(examples):
tokens = tokenizer(example.text, padding='max_length', max_length=max_seq_length, truncation=True)
input_ids = tokens['input_ids']
input_mask = tokens['attention_mask']
segment_ids = tokens['token_type_ids']
assert len(input_ids) == max_seq_length
assert len(input_mask) == max_seq_length
assert len(segment_ids) == max_seq_length
label_id = label_map[example.label]
features.append(
InputFeatures(input_ids=input_ids,
input_mask=input_mask,
segment_ids=segment_ids,
label_id=label_id))
return features
class InputExample(object):
"""A single training/test example for simple sequence classification."""
def __init__(self, guid, text, label=None):
self.guid = guid
self.text = text
self.label = label
class InputFeatures(object):
"""A single set of features of data."""
def __init__(self, input_ids, input_mask, segment_ids, label_id):
self.input_ids = input_ids
self.input_mask = input_mask
self.segment_ids = segment_ids
self.label_id = label_id
class DatasetProcessor():
def get_examples(self, data_dir, mode):
if mode == 'train':
return self._create_examples(
self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
elif mode == 'eval':
return self._create_examples(
self._read_tsv(os.path.join(data_dir, "dev.tsv")), "train")
elif mode == 'test':
return self._create_examples(
self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
def get_labels(self, data_dir):
"""See base class."""
import pandas as pd
test = pd.read_csv(os.path.join(data_dir, "train.tsv"), sep="\t")
labels = np.unique(np.array(test['label'], dtype=str))
return labels
def _create_examples(self, lines, set_type):
"""Creates examples for the training and dev sets."""
examples = []
for (i, line) in enumerate(lines):
if i == 0:
continue
if len(line) != 2:
continue
guid = "%s-%s" % (set_type, i)
text = line[0]
label = line[1]
examples.append(
InputExample(guid=guid, text=text, label=label))
return examples
def _read_tsv(self, input_file, quotechar=None):
"""Reads a tab separated value file."""
with open(input_file, "r") as f:
reader = csv.reader(f, delimiter="\t", quotechar=quotechar)
lines = []
for line in reader:
lines.append(line)
return lines