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encode_amazon.py
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encode_amazon.py
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import numpy as np
import os
import skipthoughts
import sqlite3
from nltk.tokenize import TweetTokenizer
import nltk
import cPickle
from training import tools
import time
import pdb
DATA_PATH = '/home/shunan/Code/Data/'
def get_data(word_set):
'''
Get all the data and return it as a list.
'''
conn = sqlite3.connect(os.path.join(DATA_PATH, 'amazon_food/database.sqlite'))
c = conn.cursor()
all_data = []
tokenizer = TweetTokenizer()
for row in c.execute('SELECT Score, Text FROM Reviews'):
sen = row[1].strip().lower()
tmp = nltk.word_tokenize(' '.join(tokenizer.tokenize(sen)))
new_sen = []
for tok in tmp:
if tok in word_set:
new_sen.append(tok)
all_data.append(' '.join(new_sen))
return all_data
def preprocess(data, word_set):
'''
Preprocess list of documents.
'''
preprocessed = []
tokenizer = TweetTokenizer()
for elem in data:
sen = elem.strip().lower()
tmp = nltk.word_tokenize(' '.join(tokenizer.tokenize(sen)))
new_sen = [tok for tok in tmp if tok in word_set]
preprocessed.append(' '.join(new_sen))
return preprocessed
def get_pretrained_encodings(pretrained=False):
'''
Get encodings using the pre-trained models.
'''
word_set = set()
dict_f = open(os.path.join(DATA_PATH, 'word2vec/dict.txt'), 'r')
for line in dict_f:
word_set.add(line.strip())
dict_f.close()
# Getting the data.
with open(os.path.join(DATA_PATH, 'amazon_food/train_data.pkl'), 'r') as f:
train_data = cPickle.load(f)
train_preprocessed = preprocess(train_data[0], word_set)
with open(os.path.join(DATA_PATH, 'amazon_food/test_data.pkl'), 'r') as f:
test_data = cPickle.load(f)
test_preprocessed = preprocess(test_data[0], word_set)
if pretrained:
model = skipthoughts.load_model()
encoder = skipthoughts.Encoder(model)
test_save_path = os.path.join(DATA_PATH, 'amazon_food/skip_thought_vecs/skip_thought_vecs_test_pretrained.npy')
train_save_path = os.path.join(DATA_PATH,
'amazon_food/skip_thought_vecs/skip_thought_vecs_train_pretrained.npy')
print('Encoding training vectors')
train_vectors = encoder.encode(train_preprocessed)
print('Encoding test vectors')
test_vectors = encoder.encode(test_preprocessed)
else:
model = tools.load_model(None)
test_save_path = os.path.join(DATA_PATH, 'amazon_food/skip_thought_vecs/skip_thought_vecs_test_bi.npy')
train_save_path = os.path.join(DATA_PATH, 'amazon_food/skip_thought_vecs/skip_thought_vecs_train_bi.npy')
print('Encoding training vectors')
train_vectors = tools.encode(model, train_preprocessed)
print('Encoding test vectors')
test_vectors = tools.encode(model, test_preprocessed)
np.save(train_save_path, train_vectors)
np.save(test_save_path, test_vectors)
if __name__ == '__main__':
get_pretrained_encodings(True)