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train_esim.py
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from data_help_new import DatasetReader
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from tensorboardX import SummaryWriter
import argparse
from models.Double_ESIM import Double_ESIM
import numpy as np
import pandas as pd
from sklearn.metrics import accuracy_score,precision_score, recall_score, f1_score
class Instructor:
def __init__(self, opt):
self.opt = opt
dataset = DatasetReader(embed_dim=opt.embed_dim, char_embed_dim=opt.char_dim, max_seq_len=opt.max_seq_len, max_char_len=opt.max_char_len)
self.train_data_loader = DataLoader(dataset=dataset.train_data, batch_size=opt.batch_size, shuffle=False)
self.test_data_loader = DataLoader(dataset=dataset.test_data, batch_size=opt.batch_size, shuffle=False)
self.val_data_loader = DataLoader(dataset=dataset.val_data, batch_size=len(dataset.val_data), shuffle=False)
self.model = Double_ESIM(opt, dataset.embedding_matrix, dataset.char_embedding_matrix).to(self.opt.device)
# print(dataset.embedding_matrix[1:10])
def _train(self, criterion, optimizer):
# writer = SummaryWriter(log_dir=self.opt.logdir)
max_val_acc = 0
max_val_epoch = 0
global_step = 0
for epoch in range(self.opt.num_epoch):
print('>'*50)
print('epoch:', epoch)
n_correct, n_total = 0, 0
for i_batch, sample_batched in enumerate(self.train_data_loader):
global_step += 1
self.model.train()
optimizer.zero_grad()
# print(sample_batched['p'].size())
if self.opt.use_char_emb:
inputs = [sample_batched['p'].to(self.opt.device), sample_batched['h'].to(self.opt.device),
sample_batched['p_char'].to(self.opt.device), sample_batched['h_char'].to(self.opt.device)]
else:
inputs = [sample_batched['p'].to(self.opt.device), sample_batched['h'].to(self.opt.device)]
outputs = self.model(inputs)
# print(outputs.size())
label = sample_batched['label'].to(self.opt.device)
loss = criterion(outputs, label)
loss.backward()
optimizer.step()
val_acc_,_,_,val_f1 = self._evaluate_acc()
if float(val_acc_) >= 0.845:
self._test(epoch, i_batch)
print('----epoch: ' + str(epoch)+ '----batch: ' + str(i_batch)
+ '---val_acc: ' + str(val_acc_) + '---val_f1: ' + str(val_f1))
if global_step % self.opt.log_step == 0:
# pred = torch.argmax(outputs, dim=1).item()
# acc = accuracy_score(label, outputs)
# recall = recall_score(label, outputs)
# f1 = f1_score(label, outputs)
n_correct += (torch.argmax(outputs, -1) == label).sum().item()
n_total += len(outputs)
train_acc = n_correct / n_total
val_acc, val_p, val_r, val_f = self._evaluate_acc()
if val_acc > max_val_acc:
max_val_acc = val_acc
max_val_epoch = epoch
print('loss: {:.4f}, train_acc:{:.4f}, val_acc:{:.4f}, val_p:{:.4f}, '
'val_r:{:.4f}, val_f:{:.4f}'.format(loss.item(), train_acc, val_acc, val_p, val_r, val_f))
self._test(epoch)
return max_val_acc, max_val_epoch
def _evaluate_acc(self):
self.model.eval()
n_val_correct, n_val_total = 0, 0
with torch.no_grad():
for t_batch, t_sample_batched in enumerate(self.val_data_loader):
if self.opt.use_char_emb:
t_inputs = [t_sample_batched['p'].to(self.opt.device), t_sample_batched['h'].to(self.opt.device),
t_sample_batched['p_char'].to(self.opt.device), t_sample_batched['h_char'].to(self.opt.device)]
else:
t_inputs = [t_sample_batched['p'].to(self.opt.device), t_sample_batched['h'].to(self.opt.device)]
# t_inputs = [t_sample_batched['p'].to(self.opt.device), t_sample_batched['h'].to(self.opt.device)]
t_label = t_sample_batched['label'].to(self.opt.device)
t_outputs = self.model(t_inputs)
n_val_correct += (torch.argmax(t_outputs, -1) == t_label).sum().item()
n_val_total += len(t_outputs)
val_p = precision_score(t_label, torch.argmax(t_outputs, -1))
val_r = recall_score(t_label, torch.argmax(t_outputs, -1))
val_f = f1_score(t_label, torch.argmax(t_outputs, -1))
val_acc = n_val_correct / n_val_total
return val_acc, val_p, val_r, val_f
def _test(self, epoch, batch='last'):
self.model.eval()
output = []
for t_batch, t_sample_batched in enumerate(self.test_data_loader):
if self.opt.use_char_emb:
t_inputs = [t_sample_batched['p'].to(self.opt.device), t_sample_batched['h'].to(self.opt.device),
t_sample_batched['p_char'].to(self.opt.device),
t_sample_batched['h_char'].to(self.opt.device)]
else:
t_inputs = [t_sample_batched['p'].to(self.opt.device), t_sample_batched['h'].to(self.opt.device)]
# t_inputs = [t_sample_batched['p'].to(self.opt.device), t_sample_batched['h'].to(self.opt.device)]
t_outputs = self.model(t_inputs)
t_outputs = torch.argmax(t_outputs, -1)
output.extend(t_outputs.cpu().numpy())
# print (len(output))
df_test = pd.read_csv('data/new_test.csv', header=None, sep=',', names=['qid1','qid2','wid_1','wid_2','cid_1','cid_2'])
submission = pd.DataFrame({
'qid1': df_test['qid1'],
'qid2': df_test['qid2']
})
submission['label'] = pd.Series(output)
submission.to_csv('results/Double_ESIM_epoch'+str(epoch)+'batch'+str(batch)+'.csv', index=False)
def run(self):
criterion = nn.CrossEntropyLoss()
_params = filter(lambda p: p.requires_grad, self.model.parameters())
optimizer = self.opt.optimizer(_params, lr=self.opt.learning_rate)
max_val_acc, max_val_epoch = self._train(criterion, optimizer)
print("max_val_acc: {0}".format(max_val_acc))
print('max_val_epoch: {0}'.format(max_val_epoch))
return max_val_acc
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--optimizer', default='adam', type=str)
parser.add_argument('--learning_rate', default=0.0003, type=float)
parser.add_argument('--dropout', default=0.5, type=float)
parser.add_argument('--num_epoch', default=100, type=int)
parser.add_argument('--batch_size', default=128, type=int)
parser.add_argument('--log_step', default=10, type=int)
parser.add_argument('--embed_dim', default=300, type=int)
parser.add_argument('--hidden_size', default=100, type=int)
parser.add_argument('--max_seq_len', default=22, type=int)
parser.add_argument('--class_size', default=2, type=int)
parser.add_argument('--device', default=0, type=int)
parser.add_argument('--use_char_emb', default=True, type=bool)
parser.add_argument('--char_dim', default=300, type=int)
parser.add_argument('--char_hidden_size', default=100, type=int)
parser.add_argument('--max_char_len', default=22, type=int)
opt = parser.parse_args()
optimizers = {
'adadelta': torch.optim.Adadelta,
'adam':torch.optim.Adam,
'sgd': torch.optim.SGD
}
opt.optimizer = optimizers[opt.optimizer]
opt.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\
if opt.device is None else torch.device(opt.device)
ins = Instructor(opt)
ins.run()