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classifier.py
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classifier.py
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import time, random, numpy as np, argparse, sys, re, os
from types import SimpleNamespace
import csv
import torch
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader
from sklearn.metrics import classification_report, f1_score, recall_score, accuracy_score
# change it with respect to the original model
from tokenizer import BertTokenizer
from bert import BertModel
from optimizer import AdamW
from tqdm import tqdm
TQDM_DISABLE=False
# fix the random seed
def seed_everything(seed=11711):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
class BertSentimentClassifier(torch.nn.Module):
'''
This module performs sentiment classification using BERT embeddings on the SST dataset.
In the SST dataset, there are 5 sentiment categories (from 0 - "negative" to 4 - "positive").
Thus, your forward() should return one logit for each of the 5 classes.
'''
def __init__(self, config):
super(BertSentimentClassifier, self).__init__()
self.num_labels = config.num_labels
self.bert = BertModel.from_pretrained('bert-base-uncased', local_files_only=args.local_files_only)
# Pretrain mode does not require updating bert paramters.
for param in self.bert.parameters():
if config.option == 'pretrain':
param.requires_grad = False
elif config.option == 'finetune':
param.requires_grad = True
self.drop = torch.nn.Dropout(p=0.3)
self.linear = torch.nn.Linear(self.bert.config.hidden_size, self.num_labels)
def forward(self, input_ids, attention_mask):
'''Takes a batch of sentences and returns logits for sentiment classes'''
# The final BERT contextualized embedding is the hidden state of [CLS] token (the first token).
# HINT: you should consider what is the appropriate output to return given that
# the training loop currently uses F.cross_entropy as the loss function.
bert_out = self.bert(input_ids, attention_mask)
dropped = self.drop(bert_out['pooler_output'])
out = self.linear(dropped)
return out
class SentimentDataset(Dataset):
def __init__(self, dataset, args):
self.dataset = dataset
self.p = args
self.tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', local_files_only=args.local_files_only)
def __len__(self):
return len(self.dataset)
def __getitem__(self, idx):
return self.dataset[idx]
def pad_data(self, data):
sents = [x[0] for x in data]
labels = [x[1] for x in data]
sent_ids = [x[2] for x in data]
encoding = self.tokenizer(sents, return_tensors='pt', padding=True, truncation=True)
token_ids = torch.LongTensor(encoding['input_ids'])
attention_mask = torch.LongTensor(encoding['attention_mask'])
labels = torch.LongTensor(labels)
return token_ids, attention_mask, labels, sents, sent_ids
def collate_fn(self, all_data):
token_ids, attention_mask, labels, sents, sent_ids= self.pad_data(all_data)
batched_data = {
'token_ids': token_ids,
'attention_mask': attention_mask,
'labels': labels,
'sents': sents,
'sent_ids': sent_ids
}
return batched_data
class SentimentTestDataset(Dataset):
def __init__(self, dataset, args):
self.dataset = dataset
self.p = args
self.tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', local_files_only=args.local_files_only)
def __len__(self):
return len(self.dataset)
def __getitem__(self, idx):
return self.dataset[idx]
def pad_data(self, data):
sents = [x[0] for x in data]
sent_ids = [x[1] for x in data]
encoding = self.tokenizer(sents, return_tensors='pt', padding=True, truncation=True)
token_ids = torch.LongTensor(encoding['input_ids'])
attention_mask = torch.LongTensor(encoding['attention_mask'])
return token_ids, attention_mask, sents, sent_ids
def collate_fn(self, all_data):
token_ids, attention_mask, sents, sent_ids= self.pad_data(all_data)
batched_data = {
'token_ids': token_ids,
'attention_mask': attention_mask,
'sents': sents,
'sent_ids': sent_ids
}
return batched_data
# Load the data: a list of (sentence, label)
def load_data(filename, flag='train'):
num_labels = {}
data = []
if flag == 'test':
with open(filename, 'r') as fp:
for record in csv.DictReader(fp,delimiter = '\t'):
sent = record['sentence'].lower().strip()
sent_id = record['id'].lower().strip()
data.append((sent,sent_id))
else:
with open(filename, 'r') as fp:
for record in csv.DictReader(fp,delimiter = '\t'):
sent = record['sentence'].lower().strip()
sent_id = record['id'].lower().strip()
label = int(record['sentiment'].strip())
if label not in num_labels:
num_labels[label] = len(num_labels)
data.append((sent, label,sent_id))
print(f"load {len(data)} data from {filename}")
if flag == 'train':
return data, len(num_labels)
else:
return data
# Evaluate the model for accuracy.
def model_eval(dataloader, model, device):
model.eval() # switch to eval model, will turn off randomness like dropout
y_true = []
y_pred = []
sents = []
sent_ids = []
for step, batch in enumerate(tqdm(dataloader, desc=f'eval', disable=TQDM_DISABLE)):
b_ids, b_mask, b_labels, b_sents, b_sent_ids = batch['token_ids'],batch['attention_mask'], \
batch['labels'], batch['sents'], batch['sent_ids']
b_ids = b_ids.to(device)
b_mask = b_mask.to(device)
logits = model(b_ids, b_mask)
logits = logits.detach().cpu().numpy()
preds = np.argmax(logits, axis=1).flatten()
b_labels = b_labels.flatten()
y_true.extend(b_labels)
y_pred.extend(preds)
sents.extend(b_sents)
sent_ids.extend(b_sent_ids)
f1 = f1_score(y_true, y_pred, average='macro')
acc = accuracy_score(y_true, y_pred)
return acc, f1, y_pred, y_true, sents, sent_ids
def model_test_eval(dataloader, model, device):
model.eval() # switch to eval model, will turn off randomness like dropout
y_pred = []
sents = []
sent_ids = []
for step, batch in enumerate(tqdm(dataloader, desc=f'eval', disable=TQDM_DISABLE)):
b_ids, b_mask, b_sents, b_sent_ids = batch['token_ids'],batch['attention_mask'], \
batch['sents'], batch['sent_ids']
b_ids = b_ids.to(device)
b_mask = b_mask.to(device)
logits = model(b_ids, b_mask)
logits = logits.detach().cpu().numpy()
preds = np.argmax(logits, axis=1).flatten()
y_pred.extend(preds)
sents.extend(b_sents)
sent_ids.extend(b_sent_ids)
return y_pred, sents, sent_ids
def save_model(model, optimizer, args, config, filepath):
save_info = {
'model': model.state_dict(),
'optim': optimizer.state_dict(),
'args': args,
'model_config': config,
'system_rng': random.getstate(),
'numpy_rng': np.random.get_state(),
'torch_rng': torch.random.get_rng_state(),
}
torch.save(save_info, filepath)
print(f"save the model to {filepath}")
def train(args):
device = torch.device('cuda') if args.use_gpu else torch.device('cpu')
# Load data
# Create the data and its corresponding datasets and dataloader
train_data, num_labels = load_data(args.train, 'train')
dev_data = load_data(args.dev, 'valid')
train_dataset = SentimentDataset(train_data, args)
dev_dataset = SentimentDataset(dev_data, args)
train_dataloader = DataLoader(train_dataset, shuffle=True, batch_size=args.batch_size,
collate_fn=train_dataset.collate_fn)
dev_dataloader = DataLoader(dev_dataset, shuffle=False, batch_size=args.batch_size,
collate_fn=dev_dataset.collate_fn)
# Init model
config = {'hidden_dropout_prob': args.hidden_dropout_prob,
'num_labels': num_labels,
'hidden_size': 768,
'data_dir': '.',
'option': args.option,
'local_files_only': args.local_files_only}
config = SimpleNamespace(**config)
model = BertSentimentClassifier(config)
model = model.to(device)
lr = args.lr
optimizer = AdamW(model.parameters(), lr=lr)
best_dev_acc = 0
# Run for the specified number of epochs
for epoch in range(args.epochs):
model.train()
train_loss = 0
num_batches = 0
for batch in tqdm(train_dataloader, desc=f'train-{epoch}', disable=TQDM_DISABLE):
b_ids, b_mask, b_labels = (batch['token_ids'],
batch['attention_mask'], batch['labels'])
b_ids = b_ids.to(device)
b_mask = b_mask.to(device)
b_labels = b_labels.to(device)
optimizer.zero_grad()
logits = model(b_ids, b_mask)
loss = F.cross_entropy(logits, b_labels.view(-1), reduction='sum') / args.batch_size
loss.backward()
optimizer.step()
train_loss += loss.item()
num_batches += 1
train_loss = train_loss / (num_batches)
train_acc, train_f1, *_ = model_eval(train_dataloader, model, device)
dev_acc, dev_f1, *_ = model_eval(dev_dataloader, model, device)
if dev_acc > best_dev_acc:
best_dev_acc = dev_acc
save_model(model, optimizer, args, config, args.filepath)
print(f"Epoch {epoch}: train loss :: {train_loss :.3f}, train acc :: {train_acc :.3f}, dev acc :: {dev_acc :.3f}")
def test(args):
with torch.no_grad():
device = torch.device('cuda') if args.use_gpu else torch.device('cpu')
saved = torch.load(args.filepath)
config = saved['model_config']
model = BertSentimentClassifier(config)
model.load_state_dict(saved['model'])
model = model.to(device)
print(f"load model from {args.filepath}")
dev_data = load_data(args.dev, 'valid')
dev_dataset = SentimentDataset(dev_data, args)
dev_dataloader = DataLoader(dev_dataset, shuffle=False, batch_size=args.batch_size, collate_fn=dev_dataset.collate_fn)
test_data = load_data(args.test, 'test')
test_dataset = SentimentTestDataset(test_data, args)
test_dataloader = DataLoader(test_dataset, shuffle=False, batch_size=args.batch_size, collate_fn=test_dataset.collate_fn)
dev_acc, dev_f1, dev_pred, dev_true, dev_sents, dev_sent_ids = model_eval(dev_dataloader, model, device)
print('DONE DEV')
test_pred, test_sents, test_sent_ids = model_test_eval(test_dataloader, model, device)
print('DONE Test')
with open(args.dev_out, "w+") as f:
print(f"dev acc :: {dev_acc :.3f}")
f.write(f"id \t Predicted_Sentiment \n")
for p, s in zip(dev_sent_ids,dev_pred ):
f.write(f"{p} , {s} \n")
with open(args.test_out, "w+") as f:
f.write(f"id \t Predicted_Sentiment \n")
for p, s in zip(test_sent_ids,test_pred ):
f.write(f"{p} , {s} \n")
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument("--seed", type=int, default=11711)
parser.add_argument("--epochs", type=int, default=10)
parser.add_argument("--option", type=str,
help='pretrain: the BERT parameters are frozen; finetune: BERT parameters are updated',
choices=('pretrain', 'finetune'), default="pretrain")
parser.add_argument("--use_gpu", action='store_true')
parser.add_argument("--dev_out", type=str, default="sst-dev-out.csv")
parser.add_argument("--test_out", type=str, default="sst-test-out.csv")
parser.add_argument("--batch_size", help='sst: 64 can fit a 12GB GPU', type=int, default=64)
parser.add_argument("--hidden_dropout_prob", type=float, default=0.3)
parser.add_argument("--lr", type=float, help="learning rate, default lr for 'pretrain': 1e-3, 'finetune': 1e-5",
default=1e-3)
parser.add_argument("--local_files_only", action='store_true')
args = parser.parse_args()
return args
if __name__ == "__main__":
args = get_args()
seed_everything(args.seed)
#args.filepath = f'{args.option}-{args.epochs}-{args.lr}.pt'
print('Training Sentiment Classifier on SST...')
config = SimpleNamespace(
filepath='sst-classifier.pt',
lr=args.lr,
use_gpu=args.use_gpu,
epochs=args.epochs,
batch_size=args.batch_size,
hidden_dropout_prob=args.hidden_dropout_prob,
train='data/ids-sst-train.csv',
dev='data/ids-sst-dev.csv',
test='data/ids-sst-test-student.csv',
option=args.option,
dev_out = 'predictions/'+args.option+'-sst-dev-out.csv',
test_out = 'predictions/'+args.option+'-sst-test-out.csv',
local_files_only=args.local_files_only
)
train(config)
print('Evaluating on SST...')
test(config)