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## Introduction | |||
This is the implementation of [Hierarchical Attention Networks for Document Classification](https://www.cs.cmu.edu/~diyiy/docs/naacl16.pdf) paper in PyTorch. | |||
* Dataset is 600k documents extracted from [Yelp 2018](https://www.yelp.com/dataset) customer reviews | |||
* Use [NLTK](http://www.nltk.org/) and [Stanford CoreNLP](https://stanfordnlp.github.io/CoreNLP/) to tokenize documents and sentences | |||
* Both CPU & GPU support | |||
* The best accuracy is 71%, reaching the same performance in the paper | |||
## Requirement | |||
* python 3.6 | |||
* pytorch = 0.3.0 | |||
* numpy | |||
* gensim | |||
* nltk | |||
* coreNLP | |||
## Parameters | |||
According to the paper and experiment, I set model parameters: | |||
|word embedding dimension|GRU hidden size|GRU layer|word/sentence context vector dimension| | |||
|---|---|---|---| | |||
|200|50|1|100| | |||
And the training parameters: | |||
|Epoch|learning rate|momentum|batch size| | |||
|---|---|---|---| | |||
|3|0.01|0.9|64| | |||
## Run | |||
1. Prepare dataset. Download the [data set](https://www.yelp.com/dataset), and unzip the custom reviews as a file. Use preprocess.py to transform file into data set foe model input. | |||
2. Train the model. Word enbedding of train data in 'yelp.word2vec'. The model will trained and autosaved in 'model.dict' | |||
``` | |||
python train | |||
``` | |||
3. Test the model. | |||
``` | |||
python evaluate | |||
``` |
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from model import * | |||
from train import * | |||
def evaluate(net, dataset, bactch_size=64, use_cuda=False): | |||
dataloader = DataLoader(dataset, batch_size=bactch_size, collate_fn=collate, num_workers=0) | |||
count = 0 | |||
if use_cuda: | |||
net.cuda() | |||
for i, batch_samples in enumerate(dataloader): | |||
x, y = batch_samples | |||
doc_list = [] | |||
for sample in x: | |||
doc = [] | |||
for sent_vec in sample: | |||
if use_cuda: | |||
sent_vec = sent_vec.cuda() | |||
doc.append(Variable(sent_vec, volatile=True)) | |||
doc_list.append(pack_sequence(doc)) | |||
if use_cuda: | |||
y = y.cuda() | |||
predicts = net(doc_list) | |||
p, idx = torch.max(predicts, dim=1) | |||
idx = idx.data | |||
count += torch.sum(torch.eq(idx, y)) | |||
return count | |||
if __name__ == '__main__': | |||
''' | |||
Evaluate the performance of model | |||
''' | |||
from gensim.models import Word2Vec | |||
import gensim | |||
from gensim import models | |||
embed_model = Word2Vec.load('yelp.word2vec') | |||
embedding = Embedding_layer(embed_model.wv, embed_model.wv.vector_size) | |||
del embed_model | |||
net = HAN(input_size=200, output_size=5, | |||
word_hidden_size=50, word_num_layers=1, word_context_size=100, | |||
sent_hidden_size=50, sent_num_layers=1, sent_context_size=100) | |||
net.load_state_dict(torch.load('model.dict')) | |||
test_dataset = YelpDocSet('reviews', 199, 4, embedding) | |||
correct = evaluate(net, test_dataset, True) | |||
print('accuracy {}'.format(correct/len(test_dataset))) |
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import torch | |||
import torch.nn as nn | |||
from torch.autograd import Variable | |||
import torch.nn.functional as F | |||
def pack_sequence(tensor_seq, padding_value=0.0): | |||
if len(tensor_seq) <= 0: | |||
return | |||
length = [v.size(0) for v in tensor_seq] | |||
max_len = max(length) | |||
size = [len(tensor_seq), max_len] | |||
size.extend(list(tensor_seq[0].size()[1:])) | |||
ans = torch.Tensor(*size).fill_(padding_value) | |||
if tensor_seq[0].data.is_cuda: | |||
ans = ans.cuda() | |||
ans = Variable(ans) | |||
for i, v in enumerate(tensor_seq): | |||
ans[i, :length[i], :] = v | |||
return ans | |||
class HAN(nn.Module): | |||
def __init__(self, input_size, output_size, | |||
word_hidden_size, word_num_layers, word_context_size, | |||
sent_hidden_size, sent_num_layers, sent_context_size): | |||
super(HAN, self).__init__() | |||
self.word_layer = AttentionNet(input_size, | |||
word_hidden_size, | |||
word_num_layers, | |||
word_context_size) | |||
self.sent_layer = AttentionNet(2* word_hidden_size, | |||
sent_hidden_size, | |||
sent_num_layers, | |||
sent_context_size) | |||
self.output_layer = nn.Linear(2* sent_hidden_size, output_size) | |||
self.softmax = nn.LogSoftmax(dim=1) | |||
def forward(self, batch_doc): | |||
# input is a sequence of matrix | |||
doc_vec_list = [] | |||
for doc in batch_doc: | |||
sent_mat = self.word_layer(doc) # doc's dim (num_sent, seq_len, word_dim) | |||
doc_vec_list.append(sent_mat) # sent_mat's dim (num_sent, vec_dim) | |||
doc_vec = self.sent_layer(pack_sequence(doc_vec_list)) | |||
output = self.softmax(self.output_layer(doc_vec)) | |||
return output | |||
class AttentionNet(nn.Module): | |||
def __init__(self, input_size, gru_hidden_size, gru_num_layers, context_vec_size): | |||
super(AttentionNet, self).__init__() | |||
self.input_size = input_size | |||
self.gru_hidden_size = gru_hidden_size | |||
self.gru_num_layers = gru_num_layers | |||
self.context_vec_size = context_vec_size | |||
# Encoder | |||
self.gru = nn.GRU(input_size=input_size, | |||
hidden_size=gru_hidden_size, | |||
num_layers=gru_num_layers, | |||
batch_first=True, | |||
bidirectional=True) | |||
# Attention | |||
self.fc = nn.Linear(2* gru_hidden_size, context_vec_size) | |||
self.tanh = nn.Tanh() | |||
self.softmax = nn.Softmax(dim=1) | |||
# context vector | |||
self.context_vec = nn.Parameter(torch.Tensor(context_vec_size, 1)) | |||
self.context_vec.data.uniform_(-0.1, 0.1) | |||
def forward(self, inputs): | |||
# GRU part | |||
h_t, hidden = self.gru(inputs) # inputs's dim (batch_size, seq_len, word_dim) | |||
u = self.tanh(self.fc(h_t)) | |||
# Attention part | |||
alpha = self.softmax(torch.matmul(u, self.context_vec)) # u's dim (batch_size, seq_len, context_vec_size) | |||
output = torch.bmm(torch.transpose(h_t, 1, 2), alpha) # alpha's dim (batch_size, seq_len, 1) | |||
return torch.squeeze(output, dim=2) # output's dim (batch_size, 2*hidden_size, 1) | |||
if __name__ == '__main__': | |||
''' | |||
Test the model correctness | |||
''' | |||
import numpy as np | |||
use_cuda = True | |||
net = HAN(input_size=200, output_size=5, | |||
word_hidden_size=50, word_num_layers=1, word_context_size=100, | |||
sent_hidden_size=50, sent_num_layers=1, sent_context_size=100) | |||
optimizer = torch.optim.SGD(net.parameters(), lr=0.01, momentum=0.9) | |||
criterion = nn.NLLLoss() | |||
test_time = 10 | |||
batch_size = 64 | |||
if use_cuda: | |||
net.cuda() | |||
print('test training') | |||
for step in range(test_time): | |||
x_data = [torch.randn(np.random.randint(1,10), 200, 200) for i in range(batch_size)] | |||
y_data = torch.LongTensor([np.random.randint(0, 5) for i in range(batch_size)]) | |||
if use_cuda: | |||
x_data = [x_i.cuda() for x_i in x_data] | |||
y_data = y_data.cuda() | |||
x = [Variable(x_i) for x_i in x_data] | |||
y = Variable(y_data) | |||
predict = net(x) | |||
loss = criterion(predict, y) | |||
optimizer.zero_grad() | |||
loss.backward() | |||
optimizer.step() | |||
print(loss.data[0]) |
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'''' | |||
Tokenize yelp dataset's documents using stanford core nlp | |||
''' | |||
import pickle | |||
import json | |||
import nltk | |||
from nltk.tokenize import stanford | |||
import os | |||
input_filename = 'review.json' | |||
# config for stanford core nlp | |||
os.environ['JAVAHOME'] = 'D:\\java\\bin\\java.exe' | |||
path_to_jar = 'E:\\College\\fudanNLP\\stanford-corenlp-full-2018-02-27\\stanford-corenlp-3.9.1.jar' | |||
tokenizer = stanford.CoreNLPTokenizer() | |||
in_dirname = 'review' | |||
out_dirname = 'reviews' | |||
f = open(input_filename, encoding='utf-8') | |||
samples = [] | |||
j = 0 | |||
for i, line in enumerate(f.readlines()): | |||
review = json.loads(line) | |||
samples.append((review['stars'], review['text'])) | |||
if (i+1) % 5000 == 0: | |||
print(i) | |||
pickle.dump(samples, open(in_dirname + '/samples%d.pkl'%j, 'wb')) | |||
j += 1 | |||
samples = [] | |||
pickle.dump(samples, open(in_dirname + '/samples%d.pkl'%j, 'wb')) | |||
# samples = pickle.load(open(out_dirname + '/samples0.pkl', 'rb')) | |||
# print(samples[0]) | |||
for fn in os.listdir(in_dirname): | |||
print(fn) | |||
precessed = [] | |||
for stars, text in pickle.load(open(os.path.join(in_dirname, fn), 'rb')): | |||
tokens = [] | |||
sents = nltk.tokenize.sent_tokenize(text) | |||
for s in sents: | |||
tokens.append(tokenizer.tokenize(s)) | |||
precessed.append((stars, tokens)) | |||
# print(tokens) | |||
if len(precessed) % 100 == 0: | |||
print(len(precessed)) | |||
pickle.dump(precessed, open(os.path.join(out_dirname, fn), 'wb')) | |||
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import os | |||
import pickle | |||
import nltk | |||
import numpy as np | |||
import torch | |||
from model import * | |||
class SentIter: | |||
def __init__(self, dirname, count): | |||
self.dirname = dirname | |||
self.count = int(count) | |||
def __iter__(self): | |||
for f in os.listdir(self.dirname)[:self.count]: | |||
with open(os.path.join(self.dirname, f), 'rb') as f: | |||
for y, x in pickle.load(f): | |||
for sent in x: | |||
yield sent | |||
def train_word_vec(): | |||
# load data | |||
dirname = 'reviews' | |||
sents = SentIter(dirname, 238) | |||
# define model and train | |||
model = models.Word2Vec(size=200, sg=0, workers=4, min_count=5) | |||
model.build_vocab(sents) | |||
model.train(sents, total_examples=model.corpus_count, epochs=10) | |||
model.save('yelp.word2vec') | |||
print(model.wv.similarity('woman', 'man')) | |||
print(model.wv.similarity('nice', 'awful')) | |||
class Embedding_layer: | |||
def __init__(self, wv, vector_size): | |||
self.wv = wv | |||
self.vector_size = vector_size | |||
def get_vec(self, w): | |||
try: | |||
v = self.wv[w] | |||
except KeyError as e: | |||
v = np.random.randn(self.vector_size) | |||
return v | |||
from torch.utils.data import DataLoader, Dataset | |||
class YelpDocSet(Dataset): | |||
def __init__(self, dirname, start_file, num_files, embedding): | |||
self.dirname = dirname | |||
self.num_files = num_files | |||
self._files = os.listdir(dirname)[start_file:start_file + num_files] | |||
self.embedding = embedding | |||
self._cache = [(-1, None) for i in range(5)] | |||
def get_doc(self, n): | |||
file_id = n // 5000 | |||
idx = file_id % 5 | |||
if self._cache[idx][0] != file_id: | |||
with open(os.path.join(self.dirname, self._files[file_id]), 'rb') as f: | |||
self._cache[idx] = (file_id, pickle.load(f)) | |||
y, x = self._cache[idx][1][n % 5000] | |||
sents = [] | |||
for s_list in x: | |||
sents.append(' '.join(s_list)) | |||
x = '\n'.join(sents) | |||
return x, y-1 | |||
def __len__(self): | |||
return len(self._files)*5000 | |||
def __getitem__(self, n): | |||
file_id = n // 5000 | |||
idx = file_id % 5 | |||
if self._cache[idx][0] != file_id: | |||
print('load {} to {}'.format(file_id, idx)) | |||
with open(os.path.join(self.dirname, self._files[file_id]), 'rb') as f: | |||
self._cache[idx] = (file_id, pickle.load(f)) | |||
y, x = self._cache[idx][1][n % 5000] | |||
doc = [] | |||
for sent in x: | |||
if len(sent) == 0: | |||
continue | |||
sent_vec = [] | |||
for word in sent: | |||
vec = self.embedding.get_vec(word) | |||
sent_vec.append(vec.tolist()) | |||
sent_vec = torch.Tensor(sent_vec) | |||
doc.append(sent_vec) | |||
if len(doc) == 0: | |||
doc = [torch.zeros(1,200)] | |||
return doc, y-1 | |||
def collate(iterable): | |||
y_list = [] | |||
x_list = [] | |||
for x, y in iterable: | |||
y_list.append(y) | |||
x_list.append(x) | |||
return x_list, torch.LongTensor(y_list) | |||
def train(net, dataset, num_epoch, batch_size, print_size=10, use_cuda=False): | |||
optimizer = torch.optim.SGD(net.parameters(), lr=0.01, momentum=0.9) | |||
criterion = nn.NLLLoss() | |||
dataloader = DataLoader(dataset, | |||
batch_size=batch_size, | |||
collate_fn=collate, | |||
num_workers=0) | |||
running_loss = 0.0 | |||
if use_cuda: | |||
net.cuda() | |||
print('start training') | |||
for epoch in range(num_epoch): | |||
for i, batch_samples in enumerate(dataloader): | |||
x, y = batch_samples | |||
doc_list = [] | |||
for sample in x: | |||
doc = [] | |||
for sent_vec in sample: | |||
if use_cuda: | |||
sent_vec = sent_vec.cuda() | |||
doc.append(Variable(sent_vec)) | |||
doc_list.append(pack_sequence(doc)) | |||
if use_cuda: | |||
y = y.cuda() | |||
y = Variable(y) | |||
predict = net(doc_list) | |||
loss = criterion(predict, y) | |||
optimizer.zero_grad() | |||
loss.backward() | |||
optimizer.step() | |||
running_loss += loss.data[0] | |||
if i % print_size == print_size-1: | |||
print('{}, {}'.format(i+1, running_loss/print_size)) | |||
running_loss = 0.0 | |||
torch.save(net.state_dict(), 'model.dict') | |||
torch.save(net.state_dict(), 'model.dict') | |||
if __name__ == '__main__': | |||
''' | |||
Train process | |||
''' | |||
from gensim.models import Word2Vec | |||
import gensim | |||
from gensim import models | |||
train_word_vec() | |||
embed_model = Word2Vec.load('yelp.word2vec') | |||
embedding = Embedding_layer(embed_model.wv, embed_model.wv.vector_size) | |||
del embed_model | |||
start_file = 0 | |||
dataset = YelpDocSet('reviews', start_file, 120-start_file, embedding) | |||
print('training data size {}'.format(len(dataset))) | |||
net = HAN(input_size=200, output_size=5, | |||
word_hidden_size=50, word_num_layers=1, word_context_size=100, | |||
sent_hidden_size=50, sent_num_layers=1, sent_context_size=100) | |||
try: | |||
net.load_state_dict(torch.load('model.dict')) | |||
print("last time trained model has loaded") | |||
except Exception: | |||
print("cannot load model, train the inital model") | |||
train(net, dataset, num_epoch=5, batch_size=64, use_cuda=True) |