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rnn_character.py
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import numpy as np
import torch
from gru import LSTMClassifier
import torch.optim as optim
import torch.nn as nn
from torch.autograd import Variable
import torch_preprocess as function
import math
import read_files as read
np.random.seed(123)
torch.manual_seed(123)
use_gpu = torch.cuda.is_available()
def iterate_minibatches(inputs, targets, sentence_len,batchsize, shuffle=False):
assert len(inputs) == len(targets)
indices = np.arange(len(inputs))
if shuffle:
np.random.shuffle(indices)
for start_idx in range(0, len(inputs) - batchsize + 1, batchsize):
if shuffle:
excerpt = indices[start_idx:start_idx + batchsize]
else:
excerpt = indices[start_idx:start_idx + batchsize]
sentence_len_batch = sentence_len[excerpt]
max_len = max(sentence_len_batch)
input_batch = np.zeros((batchsize,max_len))
for i,text_id in enumerate(excerpt):
input_batch[i][:len(inputs[text_id])] = inputs[text_id]
yield input_batch, targets[excerpt],sentence_len_batch
### parameter setting
### training procedure
def train(vocab_dict, label_dict, train_x,train_y,train_sentence_len,valid_x,valid_y , valid_sentence_len, dataset,folder):
embedding_dim =4096
hidden_dim = 1024
epochs = 100
batch_size = 64
learning_rate = 1.0
model = LSTMClassifier(embedding_dim=embedding_dim, hidden_dim=hidden_dim,
vocab_size=len(vocab_dict), label_size=len(label_dict), batch_size=batch_size)
if use_gpu:
model = model.cuda()
train_acc_ = []
valid_acc_ = []
optimizer = optim.Adadelta(model.parameters(), lr=learning_rate, weight_decay=1e-6)
loss_function = nn.CrossEntropyLoss()
train_loss_ = []
valid_loss_ = []
val_acc = -math.inf
for epoch in range(epochs):
## training epoch
total_acc = 0.0
total_loss = 0.0
total = 0.0
batch_count = 0
for batch in iterate_minibatches(train_x, train_y,train_sentence_len, batchsize=batch_size, shuffle=True):
input_batch, target_batch, seq_lens_batch = batch
train_input_batch = torch.LongTensor(input_batch)
train_target_batch = torch.LongTensor(target_batch)
train_seq_lens_batch = torch.LongTensor(seq_lens_batch)
train_seq_lens_batch, perm_index = train_seq_lens_batch.sort(0, descending=True)
if use_gpu:
train_x_batch = Variable(train_input_batch[perm_index]).cuda()
train_y_batch = Variable(train_target_batch[perm_index]).cuda()
train_seq_lens_batch = train_seq_lens_batch.cuda()
else:
train_x_batch = Variable(train_input_batch[perm_index])
train_y_batch = Variable(train_target_batch[perm_index])
model.zero_grad()
model.batch_size = len(train_x_batch)
model.hidden = model.init_hidden()
output = model(train_x_batch,train_seq_lens_batch)
loss = loss_function(output, train_y_batch)
loss.backward()
optimizer.step()
# calc training acc
_, predicted = torch.max(output.data, 1)
batch_acc = np.float((predicted == train_y_batch).sum().item())
batch_count +=1
#print("Batch "+ str(batch_count) +" Loss & Acc: " + str(loss.data.cpu().numpy()) + " " +str(batch_acc))
total_acc += batch_acc
total += len(train_y_batch)
total_loss += loss.item()
train_loss_.append(total_loss / total)
train_acc_.append(total_acc / total)
## validing epoch
total_acc = 0.0
total_loss = 0.0
total = 0.0
for batch in iterate_minibatches(valid_x, valid_y,valid_sentence_len, batchsize=batch_size, shuffle=False):
input_batch, target_batch, seq_lens_batch= batch
valid_input = torch.LongTensor(input_batch)
valid_label = torch.LongTensor(target_batch)
valid_seq_lens_batch = torch.LongTensor(seq_lens_batch)
valid_seq_lens_batch, perm_index = valid_seq_lens_batch.sort(0, descending=True)
if use_gpu:
valid_x_batch = Variable(valid_input[perm_index]).cuda()
valid_y_batch = Variable(valid_label[perm_index]).cuda()
valid_seq_lens_batch = valid_seq_lens_batch.cuda()
else:
valid_x_batch = Variable(valid_input[perm_index])
valid_y_batch = Variable(valid_label[perm_index])
model.batch_size = len(valid_x_batch)
model.hidden = model.init_hidden()
output = model(valid_x_batch,valid_seq_lens_batch)
loss = loss_function(output, valid_y_batch)
# calc testing acc
_, predicted = torch.max(output.data, 1)
total_acc += np.float((predicted == valid_y_batch).sum().item())
total += len(valid_y_batch)
total_loss += loss.item()
if (total_acc/total > val_acc):
torch.save(model, "data/model_character/model_" + dataset + "_folder_" + str(folder) + ".pkl")
val_acc = total_acc/total
valid_loss_.append(total_loss / total)
valid_acc_.append(total_acc / total)
print(
'[Epoch: %3d/%3d] Training Loss: %.3f, Validating Loss: %.3f, Training Acc: %.3f, Validing Acc: %.3f'
% (epoch, epochs, train_loss_[epoch], valid_loss_[epoch], train_acc_[epoch],
valid_acc_[epoch]))
#train()
def eval(folder, model,test_x,test_y , test_sentence_len,mode):
## testing epoch
test_loss_ = []
test_acc_ = []
total_acc = 0.0
total_loss = 0.0
total = 0.0
batch_size = 64
for batch in iterate_minibatches(test_x, test_y,test_sentence_len, batchsize=batch_size, shuffle=False):
input_batch, target_batch, seq_lens_batch= batch
test_input = torch.LongTensor(input_batch)
test_label = torch.LongTensor(target_batch)
test_seq_lens_batch = torch.LongTensor(seq_lens_batch)
test_seq_lens_batch, perm_index = test_seq_lens_batch.sort(0, descending=True)
if use_gpu:
test_x_batch = Variable(test_input[perm_index]).cuda()
test_y_batch = Variable(test_label[perm_index]).cuda()
test_seq_lens_batch = test_seq_lens_batch.cuda()
else:
test_x_batch = Variable(test_input[perm_index])
test_y_batch = Variable(test_label[perm_index])
model.batch_size = len(test_x_batch)
model.hidden = model.init_hidden()
output = model(test_x_batch,test_seq_lens_batch)
loss_function = nn.CrossEntropyLoss()
loss = loss_function(output,test_y_batch)
# calc testing acc
_, predicted = torch.max(output.data, 1)
total_acc += np.float((predicted == test_y_batch).sum().item())
total += len(test_y_batch)
total_loss += loss.item()
test_loss_.append(total_loss / total)
test_acc_.append(total_acc / total)
# print('%s Loss for folder %s: %.3f, %s Acc: %.3f'
# % (mode ,str(folder),test_loss_[0], mode, test_acc_[0]))
return test_acc_[0]
#test()
def rnn_character(dataset,train_model):
avg_test_acc = 0.0
avg_dev_acc = 0.0
print("Run RNN Character for %s: ..." %(dataset))
# lm_f = LanguageModel.load_language_model('/home/u25/dongfangxu9/.flair/embeddings/lm-news-english-forward-v0.2rc.pt')
# dictionary = lm_f.dictionary
# vocab_dict = dict()
# for i, j in dictionary.item2idx.items():
# vocab_dict[i.decode('utf-8')] = j
vocab_dict = read.readfrom_json("data/config/char2int")
label_dict = read.readfrom_json("data/config/label_dict_"+dataset)
label_texts_dict = read.readfrom_json("data/config/label_texts_dict_"+dataset)
folder = 0
for i in range(folder, folder+1):
texts, label_texts, labels = function.load_data("data/"+dataset + "/"+dataset+".fold-"+ str(i) +".train.txt",
"data/"+dataset + "/"+dataset+".fold-"+ str(i) +".validation.txt",
"data/"+dataset + "/"+dataset+".fold-"+ str(i) +".test.txt")
train_x,train_y,train_sentence_len = function.dataset_preprocess_character(texts[0],labels[0], vocab_dict,label_dict)
valid_x,valid_y , valid_sentence_len = function.dataset_preprocess_character(texts[1],labels[1], vocab_dict,label_dict)
test_x,test_y , test_sentence_len = function.dataset_preprocess_character(texts[2],labels[2], vocab_dict,label_dict)
if train_model == True:
train(vocab_dict, label_dict, train_x, train_y, train_sentence_len, valid_x, valid_y, valid_sentence_len,dataset,i)
model = torch.load("data/model_character/model_" + dataset + "_folder_" + str(i) + ".pkl")
dev_acc = eval(i,model, valid_x,valid_y , valid_sentence_len,mode = "Dev")
test_acc = eval(i,model, test_x, test_y, test_sentence_len,mode = "Test")
else:
model = torch.load("data/model_character/model_" + dataset + "_folder_" + str(i) + ".pkl")
dev_acc = eval(i, model, valid_x, valid_y, valid_sentence_len, mode="Dev")
test_acc = eval(i, model, test_x, test_y, test_sentence_len, mode = "Test")
avg_test_acc += test_acc
avg_dev_acc +=dev_acc
print('Average Dev Acc for %s: %.3f'
% (dataset, avg_dev_acc/float(1)))
print('Average Testing Acc for %s: %.3f'
% (dataset, avg_test_acc/float(1)))
#import term_matching_baseline
# from flair.embeddings import CharLMEmbeddings
# from flair.data import Sentence