Amazon评论数据的预处理代码，用于情感分析，代码改自
https://github.com/PaddlePaddle/Paddle/tree/develop/demo/quick_start/data
Amazon商品评论数据网址：
http://jmcauley.ucsd.edu/data/amazon/
Bash脚本文件
get_data.sh：
#!/bin/bash # 1. size of pos : neg = 1:1.
# 2. size of testing set = min(25k, len(all_data) * 0.1), others is traning set.
# 3. distinct train set and test set. set -e # Download data
echo "Downloading Amazon Electronics reviews data..."
# http://jmcauley.ucsd.edu/data/amazon/
# wget http://snap.stanford.edu/data/amazon/productGraph/categoryFiles/reviews_Electronics_5.json.gz
# wget http://snap.stanford.edu/data/amazon/productGraph/categoryFiles/reviews_Digital_Music_5.json.gz
echo "Downloading mosesdecoder..."
# https://github.com/moses-smt/mosesdecoder
# wget https://github.com/moses-smt/mosesdecoder/archive/master.zip # unzip master.zip
# rm master.zip ##################
# Preprocess data
echo "Preprocess data..."
export LC_ALL=C
UNAME_STR=`uname` if [ ${UNAME_STR} == 'Linux' ]; then
SHUF_PROG='shuf'
else
SHUF_PROG='gshuf'
fi mkdir -p tmp
# python preprocess.py -i reviews_Electronics_5.json.gz
python preprocess.py -i reviews_Digital_Music_5.json.gz
# uniq and shuffle
cd tmp
echo 'Uniq and shuffle...'
cat pos_*|sort|uniq|${SHUF_PROG}> pos.shuffed
cat neg_*|sort|uniq|${SHUF_PROG}> neg.shuffed min_len=`sed -n '$=' neg.shuffed`
echo `sed -n '$=' neg.shuffed`
test_num=$((min_len/10))
if [ $test_num -gt 12500 ];then
test_num=12500
fi
train_num=$((min_len-test_num)) head -n$train_num pos.shuffed >train.pos
head -n$train_num neg.shuffed >train.neg
tail -n$test_num pos.shuffed >test.pos
tail -n$test_num neg.shuffed >test.neg cat train.pos train.neg | ${SHUF_PROG} >../train.txt
cat test.pos test.neg | ${SHUF_PROG} >../test.txt cd -
echo 'train.txt' > train.list
echo 'test.txt' > test.list # use 30k dict
# rm -rf tmp
mv dict.txt dict_all.txt
cat dict_all.txt | head -n 30001 > dict.txt
echo 'Done.'
数据处理文件：preprocess.py：
# -*- coding: UTF-8 -*- """
1. Tokenize the words and punctuation
Usage:
python preprocess.py -i data_file [random seed]
""" import sys
import os
import operator
import gzip
from subprocess import Popen, PIPE
from optparse import OptionParser
import json
from multiprocessing import Queue
from multiprocessing import Pool
import multiprocessing batch_size = 5000
word_count = {}
num_tokenize = max(1,
multiprocessing.cpu_count() - 2) # parse + tokenize + save
max_queue_size = 8
parse_queue = Queue(maxsize=max_queue_size + num_tokenize)
tokenize_queue = Queue(maxsize=max_queue_size + num_tokenize) def create_dict(data):
"""
Create dictionary based on data, and saved in data_dir/dict.txt.
The first line is unk \t -1.
data: list, input data by batch.
"""
for seq in data:
try:
for w in seq.lower().split():
if w not in word_count:
word_count[w] = 1
else:
word_count[w] += 1
except:
sys.stderr.write(seq + "\tERROR\n") def parse(path):
"""
Open .gz file.
"""
sys.stderr.write(path)
g = gzip.open(path, 'r')
for l in g:
yield json.loads(l)
g.close() def tokenize(sentences):
"""
Use tokenizer.perl to tokenize input sentences.
tokenizer.perl is tool of Moses.
sentences : a list of input sentences.
return: a list of processed text.
"""
dir = './mosesdecoder-master/scripts/tokenizer/tokenizer.perl'
if not os.path.exists(dir):
sys.exit(
"The ./mosesdecoder-master/scripts/tokenizer/tokenizer.perl does not exists."
)
tokenizer_cmd = [dir, '-l', 'en', '-q', '-']
assert isinstance(sentences, list)
text = "\n".join(sentences)
tokenizer = Popen(tokenizer_cmd, stdin=PIPE, stdout=PIPE)
tok_text, _ = tokenizer.communicate(text)
toks = tok_text.split('\n')[:-1]
return toks def save_data(instance, data_dir, pre_fix, batch_num):
"""
save data by batch
"""
label = ['1' if pre_fix == 'pos' else '0' for i in range(len(instance))]
lines = ['%s\t%s' % (label[i], instance[i]) for i in range(len(label))]
file_name = os.path.join(data_dir, "%s_%s.txt" % (pre_fix, batch_num))
file(file_name, 'w').write('\n'.join(lines) + '\n') def tokenize_batch(id):
"""
tokenize data by batch
"""
while True:
num_batch, instance, pre_fix = parse_queue.get()
if num_batch == -1: ### parse_queue finished
tokenize_queue.put((-1, None, None))
sys.stderr.write("Thread %s finish\n" % (id))
break
tokenize_instance = tokenize(instance)
tokenize_queue.put((num_batch, tokenize_instance, pre_fix))
sys.stderr.write('.') def save_batch(data_dir, num_tokenize, data_dir_dict):
"""
save data by batch
build dict.txt
"""
token_count = 0
while True:
num_batch, instance, pre_fix = tokenize_queue.get()
if num_batch == -1:
token_count += 1
if token_count == num_tokenize: #### tokenize finished.
break
else:
continue
save_data(instance, data_dir, pre_fix, num_batch)
create_dict(instance) ## update dict sys.stderr.write("save file finish\n")
f = open(data_dir_dict, 'w')
f.write('%s\t%s\n' % ('unk', '-1'))
for k, v in sorted(word_count.items(), key=operator.itemgetter(1), \
reverse=True):
f.write('%s\t%s\n' % (k, v))
f.close()
sys.stderr.write("build dict finish\n") def parse_batch(data, num_tokenize):
"""
parse data by batch
parse -> tokenize -> save
"""
raw_txt = parse(data)
neg, pos = [], []
count = 0
sys.stderr.write("extract raw data\n")
for l in raw_txt:
rating = l["overall"]
text = l["reviewText"].lower() # # convert words to lower case
if rating == 5.0 and text:
pos.append(text)
if rating < 3.0 and text:
neg.append(text)
if len(pos) == batch_size or len(neg) == batch_size:
if len(pos) == batch_size:
batch = pos
pre_fix = 'pos'
else:
batch = neg
pre_fix = 'neg' parse_queue.put((count, batch, pre_fix))
count += 1
if pre_fix == 'pos':
pos = []
else:
neg = [] if len(pos) > 0:
parse_queue.put((count, pos, 'pos'))
count += 1
if len(neg) > 0:
parse_queue.put((count, neg, 'neg'))
count += 1
for i in range(num_tokenize):
parse_queue.put((-1, None, None)) #### for tokenize's input finished
sys.stderr.write("parsing finish\n") def option_parser():
parser = OptionParser(usage="usage: python preprcoess.py "\
"-i data_path [options]")
parser.add_option(
"-i", "--data", action="store", dest="input", help="Input data path.")
parser.add_option(
"-s",
"--seed",
action="store",
dest="seed",
default=1024,
help="Set random seed.")
return parser.parse_args() def main():
reload(sys)
sys.setdefaultencoding('utf-8')
options, args = option_parser()
data = options.input
seed = options.seed
data_dir_dict = os.path.join(os.path.dirname(data), 'dict.txt')
data_dir = os.path.join(os.path.dirname(data), 'tmp')
pool = Pool(processes=num_tokenize + 2)
pool.apply_async(parse_batch, args=(data, num_tokenize))
for i in range(num_tokenize):
pool.apply_async(tokenize_batch, args=(str(i), ))
pool.apply_async(save_batch, args=(data_dir, num_tokenize, data_dir_dict))
pool.close()
pool.join() file(os.path.join(os.path.dirname(data), 'labels.list'),
'w').write('neg\t0\npos\t1\n') if __name__ == '__main__':
main()