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# !/usr/bin/env python
# -*- coding:utf-8 -*-
import os
import time
import jieba
import jsonlines
import codecs
import random
import fasttext
stopwords_set = set()
basedir = './stopwords/'
# 停用词文件
with open(basedir + 'baidu_stopwords.txt', 'r', encoding='utf-8') as infile:
for line in infile:
stopwords_set.add(line.strip())
with open(basedir + 'cn_stopwords.txt', 'r', encoding='utf-8') as infile:
for line in infile:
stopwords_set.add(line.strip())
with open(basedir + 'hit_stopwords.txt', 'r', encoding='utf-8') as infile:
for line in infile:
stopwords_set.add(line.strip())
with open(basedir + 'scu_stopwords.txt', 'r', encoding='utf-8') as infile:
for line in infile:
stopwords_set.add(line.strip())
def segment(text):
# 结巴分词
seg_text = jieba.cut(text.replace("\t", " ").replace("\n", " "))
outline = " ".join(seg_text)
outline = " ".join(outline.split())
# 去停用词与HTML标签
outline_list = outline.split(" ")
outline_list_filter = [item for item in outline_list if item not in stopwords_set]
outline = " ".join(outline_list_filter)
return outline
def predict_score(preds):
score_dict = {
'__label__': 0,
'__label__0': 0,
'__label__1': 1,
}
score_list = []
for l, s in zip(*preds):
score = 0
for _l, _s in zip(l, s):
score += score_dict[_l] * _s
score_list.append(float(score))
return score_list
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--fasttext-model-path', type=str, default="", help="file path", required=True)
parser.add_argument('--input-file-path', type=str, default="", help="file path", required=True)
parser.add_argument('--output-file-path', type=str, default="", help="file path", required=True)
parser.add_argument('--text-key', type=str, default="text", help="file path", required=False)
parser.add_argument('--output-key', type=str, default="score", help="file path", required=False)
parser.add_argument('--do-score-filter', action='store_true', default=False, help='do score filter or not', dest='do_score_filter')
parser.add_argument('--score-thres', type=float, default=0.1, help="score thres", required=False)
args = parser.parse_args()
model_dir = args.fasttext_model_path
model = fasttext.load_model(model_dir)
import jsonlines
file_path = args.input_file_path
output_file_path = args.output_file_path
writer = jsonlines.open(output_file_path, mode='w')
dir_path = None
if os.path.isdir(file_path):
dir_path = os.listdir(file_path)
else:
dir_path = [file_path]
lines = 0
filtered = 0
start_time = time.time()
for file_path in dir_path:
input_file = os.path.join(args.input_file_path, file_path)
with jsonlines.open(input_file) as reader:
for line in reader:
lines += 1
if lines % 1000 == 0:
end_time = time.time()
elapsed_time = end_time - start_time
samples_per_second = lines / elapsed_time
print(f"Processed {lines} lines in {elapsed_time:.2f} seconds.", flush=True)
print(f"Samples per second: {samples_per_second:.2f}.", flush=True)
if args.text_key not in line:
filtered += 1
continue
sentecnce = line[args.text_key]
outline = segment(sentecnce)
preds = model.predict([outline], k=-1)
score = predict_score(preds)
#print(preds, score)
line[args.output_key] = score[0]
# do filter
if args.do_score_filter and line[args.output_key] < args.score_thres:
filtered += 1
continue
writer.write(line)
end_time = time.time()
elapsed_time = end_time - start_time
samples_per_second = lines / elapsed_time
print(f"Processed {lines} lines in {elapsed_time:.2f} seconds, Filtered {filtered} samples.", flush=True)
print(f"Samples per second: {samples_per_second:.2f}.", flush=True)
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