@@ -1,42 +0,0 @@ | |||
import pickle | |||
import json | |||
import nltk | |||
from nltk.tokenize import stanford | |||
# f = open('dataset/review.json', 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('review/samples%d.pkl'%j, 'wb')) | |||
# j += 1 | |||
# samples = [] | |||
# pickle.dump(samples, open('review/samples%d.pkl'%j, 'wb')) | |||
samples = pickle.load(open('review/samples0.pkl', 'rb')) | |||
# print(samples[0]) | |||
import os | |||
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() | |||
dirname = 'review' | |||
dirname1 = 'reviews' | |||
for fn in os.listdir(dirname): | |||
print(fn) | |||
precessed = [] | |||
for stars, text in pickle.load(open(os.path.join(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(dirname1, fn), 'wb')) | |||
@@ -1,2 +0,0 @@ | |||
# Implementation of the model in | |||
Hierarchical Attention Networks for Document Classification |
@@ -0,0 +1,36 @@ | |||
## 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. The model will trained and autosaved in 'model.dict' | |||
``` | |||
python train | |||
``` | |||
3. Test the model. | |||
``` | |||
python evaluate | |||
``` |
@@ -12,7 +12,6 @@ def evaluate(net, dataset, bactch_size=64, use_cuda=False): | |||
for sample in x: | |||
doc = [] | |||
for sent_vec in sample: | |||
# print(sent_vec.size()) | |||
if use_cuda: | |||
sent_vec = sent_vec.cuda() | |||
doc.append(Variable(sent_vec, volatile=True)) | |||
@@ -20,10 +19,6 @@ def evaluate(net, dataset, bactch_size=64, use_cuda=False): | |||
if use_cuda: | |||
y = y.cuda() | |||
predicts = net(doc_list) | |||
# idx = [] | |||
# for p in predicts.data: | |||
# idx.append(np.random.choice(5, p=torch.exp(p).numpy())) | |||
# idx = torch.LongTensor(idx) | |||
p, idx = torch.max(predicts, dim=1) | |||
idx = idx.data | |||
count += torch.sum(torch.eq(idx, y)) |
@@ -38,11 +38,9 @@ class HAN(nn.Module): | |||
def forward(self, batch_doc): | |||
# input is a sequence of matrix | |||
doc_vec_list = [] | |||
for doc in batch_doc: | |||
# doc's dim (num_sent, seq_len, word_dim) | |||
sent_mat = self.word_layer(doc) | |||
# sent_mat's dim (num_sent, vec_dim) | |||
doc_vec_list.append(sent_mat) | |||
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 | |||
@@ -55,7 +53,6 @@ class AttentionNet(nn.Module): | |||
self.gru_hidden_size = gru_hidden_size | |||
self.gru_num_layers = gru_num_layers | |||
self.context_vec_size = context_vec_size | |||
self.last_alpha = None | |||
# Encoder | |||
self.gru = nn.GRU(input_size=input_size, | |||
@@ -72,18 +69,13 @@ class AttentionNet(nn.Module): | |||
self.context_vec.data.uniform_(-0.1, 0.1) | |||
def forward(self, inputs): | |||
# inputs's dim (batch_size, seq_len, word_dim) | |||
# GRU part | |||
h_t, hidden = self.gru(inputs) | |||
h_t, hidden = self.gru(inputs) # inputs's dim (batch_size, seq_len, word_dim) | |||
u = self.tanh(self.fc(h_t)) | |||
# Attention part | |||
# u's dim (batch_size, seq_len, context_vec_size) | |||
alpha = self.softmax(torch.matmul(u, self.context_vec)) | |||
self.last_alpha = alpha.data | |||
# alpha's dim (batch_size, seq_len, 1) | |||
output = torch.bmm(torch.transpose(h_t, 1, 2), alpha) | |||
# output's dim (batch_size, 2*hidden_size, 1) | |||
return torch.squeeze(output, dim=2) | |||
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__': |
@@ -0,0 +1,51 @@ | |||
'''' | |||
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')) | |||
@@ -1,9 +1,6 @@ | |||
import os | |||
import pickle | |||
import matplotlib.pyplot as plt | |||
import matplotlib.ticker as ticker | |||
import nltk | |||
import numpy as np | |||
import torch | |||
@@ -60,7 +57,6 @@ class YelpDocSet(Dataset): | |||
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] | |||
@@ -90,7 +86,6 @@ class YelpDocSet(Dataset): | |||
vec = self.embedding.get_vec(word) | |||
sent_vec.append(vec.tolist()) | |||
sent_vec = torch.Tensor(sent_vec) | |||
# print(sent_vec.size()) | |||
doc.append(sent_vec) | |||
if len(doc) == 0: | |||
doc = [torch.zeros(1,200)] | |||
@@ -124,7 +119,6 @@ def train(net, dataset, num_epoch, batch_size, print_size=10, use_cuda=False): | |||
for sample in x: | |||
doc = [] | |||
for sent_vec in sample: | |||
# print(sent_vec.size()) | |||
if use_cuda: | |||
sent_vec = sent_vec.cuda() | |||
doc.append(Variable(sent_vec)) |