# \W 表示匹配非数字字母下划线
result = re.sub('\W+', ' ', value).replace("_", ' ').replace(r"\u", " ")
result = re.sub(pat1, '', result)
if (len(value) - len(result)) <= 0.3 * len(value):
return result
else:
return "_"
ctxs = mytools.load_from_txt("../filter_data/sorted_data/sorted_it.txt")
counts = {}
for ctx in tqdm(ctxs):
html, location, text = ctx.replace("\n", "").split(" | ")
text = replace_all_blank(text)
word_tokens = word_tokenize(text) #分词
# filtered_sentence = [w for w in word_tokens if w not in stop_words] #去停用词
# filtered_sentence = [word.lower() for word in filtered_sentence]
# stem_words = [ps.stem(w) for w in filtered_sentence]
stem_words = [word.lower() for word in word_tokens]
for w in stem_words:
counts[w] = counts.get(w, 0) + 1
cw = sorted([(count, w) for w, count in counts.items()], reverse=True)
cw = [w[1] + " | " + str(w[0]) + "\n" for w in cw]
mytools.log_to_txt(cw, "all_it_vocab.txt")
def compute_column_score(item):
"""
给定一行数据，返回至多1000行结果
:param item:data中的一行
:return: 相似度数组
"""
zh_route = "../0_data/zhit-0825/" + item[0]
ja_route = "../0_data/zhit-0825/" + item[2]
# 读取文本所在行数
zh_start, zh_end = int(item[1].split(":")[0]), int(item[1].split(":")[1])
ja_start, ja_end = int(item[3].split(":")[0]), int(item[3].split(":")[1])
# search
searched_results = []
zh_all = read_text_from_file(zh_route, zh_start, zh_end)
zh_all_filter = replace_all_blank(zh_all)
ja_all = read_text_from_file(ja_route, ja_start, ja_end)
ja_translate = utils.querySentence(Dict, ja_all)
score = utils.compute_item_score(ja_translate, zh_all_filter)
if len(zh_all_filter) == 5:
for ja_windows_lens in range(len(ja_all), 5, -1):
for start_bias in range(ja_start, ja_end - ja_windows_lens):
ja_tmp = ja_all[start_bias - ja_start:start_bias - ja_start +
ja_windows_lens]
ja_translate = utils.querySentence(Dict, ja_tmp)
score = utils.compute_item_score(ja_translate, zh_all_filter)
if score > 0.5:
searched_results.append([
score, item[0], item[1], item[2],
str(start_bias) + ":" +
str(start_bias + ja_windows_lens)
])
log_info = " ".join(item) + "\n"
mytools.log_to_txt(log_info, "file/already_finished.txt")
return searched_results
else:
if score > 0.5:
searched_results.append(
[score, item[0], item[1], item[2], item[3]])
for ja_windows_lens in range(len(ja_all), 5, -1):
for start_bias in range(ja_start, ja_end - ja_windows_lens):
ja_tmp = ja_all[start_bias - ja_start:start_bias - ja_start +
ja_windows_lens]
ja_translate = utils.querySentence(Dict, ja_tmp)
for zh_windows_lens in range(len(zh_all), 4, -1):
for start_bias_zh in range(zh_start,
zh_end - zh_windows_lens):
zh_tmp = zh_all[start_bias_zh -
zh_start:start_bias_zh - zh_start +
zh_windows_lens]
zh_tmp_filter = replace_all_blank(zh_tmp)
if len(zh_tmp_filter) <= 4:
continue
score = utils.compute_item_score(
ja_translate, zh_tmp_filter)
if score > 0.5:
searched_results.append([
score, item[0],
str(start_bias_zh) + ":" +
str(start_bias_zh + zh_windows_lens), item[2],
str(start_bias) + ":" +
str(start_bias + ja_windows_lens)
])
# 记录已完成
log_info = " ".join(item) + "\n"
mytools.log_to_txt(log_info, "file/already_finished.txt")
return searched_results
root = "../../data/processedData/zh/"
for file in tqdm(os.listdir(root)):
ctxs = mytools.load_from_txt(root+file)
new_ctxs = []
for ctx in ctxs:
# print("-----------------------------------------")
# print(ctx)
try:
html, location, texts = ctx.replace("\n","").split(" | ")
except:
continue
start,end = int(location.split(":")[0]),int(location.split(":")[1])
while len(texts) > 11:
new_texts = texts[:11]
new_ctxs.append(
html + " | " + str(start)+":"+str(start+11) + " | " +new_texts + "\n"
)
# print(html + " | " + str(start)+":"+str(start+11) + " | " +new_texts)
texts = texts[7:]
start = start + 7
new_ctxs.append(html + " | " + str(start)+":"+str(end) + " | " + texts + "\n")
# print(html + " | " + str(start)+":"+str(end) + " | " + texts)
# exit(0)
mytools.log_to_txt(new_ctxs, root.replace("zh","optim_zh/")+file)
new_ctxs = []
return respData["data"]["result"]["trans_result"]['dst']
def translate_init(from_lang='zh', to_lang='en'):
gClass = get_result("ntrans.xfyun.cn",
from_lang=from_lang,
to_lang=to_lang)
return gClass
import jieba
time.sleep(1)
import nltk
from nltk.tokenize import word_tokenize #分词
from nltk.translate.bleu_score import sentence_bleu, SmoothingFunction, corpus_bleu
sf7 = SmoothingFunction().method7
import mytools
from nltk.corpus import stopwords #停用词
from nltk.stem import PorterStemmer # 词干化
if __name__ == '__main__':
import mytools
from tqdm import tqdm
goodwords = mytools.load_from_json("good_words.json")[584:]
for goodword in tqdm(goodwords):
gClass = translate_init(from_lang='zh', to_lang='it')
trans_text = translate(goodword, gClass)
ctx = goodword + " | " + trans_text + "\n"
mytools.log_to_txt(ctx, "goodwords_dict.txt")
import mytools, os
from tqdm import tqdm
ctxs = mytools.load_from_txt("sorted_data/zh.txt")
for ctx in tqdm(ctxs):
text, pos = ctx.split(" ||| ")
html, location = pos.replace("\n", "").split(" || ")[0].split(" | ")
ctx = html + " | " + location + " | " + text + "\n"
mytools.log_to_txt(ctx, "sorted_data/sorted_zh.txt")
import mytools
import os
ctxs = mytools.load_from_txt("all_it_vocab.txt")
remove_words = "0123456789,./;'[]\\-=<>?:\"{}|~!@#$%^&*()_+"
smooth = []
for ctx in ctxs:
try:
word, freq = ctx.replace("\n", "").split(" | ")
flag = True
for tmp in word:
if tmp in remove_words:
flag = False
break
if len(word) > 18:
flag = False
if flag:
smooth.append(word + " | " + freq + "\n")
except:
print("error")
mytools.log_to_txt(smooth, "smooth_it_vocab.txt")
saved_results = {}
all_idx = 0
for file in tqdm(os.listdir(root_path)):
try:
ctxs = mytools.load_from_txt(root_path + file)
# try:
for ctx in ctxs:
all_idx += 1
html, location, text = ctx.replace("\n", "").split(" | ")
if text not in saved_results:
saved_results[text] = [html + " | " + location]
else:
saved_results[text].append(html + " | " + location)
except:
pass
print(all_idx)
print(len(saved_results.keys()))
for k in tqdm(saved_results.keys()):
mytools.log_to_txt(k + " ||| " + " || ".join(saved_results[k]) + "\n",
"saved_results/zh.txt")
"""
1202420
582393
3215128
1162976
"""
for rootpath in rootpaths:
for idx, item in enumerate(os.listdir(rootpath)):
try:
with open(rootpath + item, encoding='UTF-8') as f:
s = f.read()
for line in s.split("\n"):
# 分割每一行
texts = utilize.filterForLine(line)
if texts != []:
for text in texts:
if text != "":
# 计算每一个文本的起始位置和终止位置
start, end = utilize.calcIndexForPiece(text, s)
# 生成item 写入文件
ctx = rootpath + item + " | " + str(
start) + ":" + str(end) + " | " + text
mytools.log_to_txt(
ctx,
rootpath.replace("zhit-0825", "processedData")
+ str(int(idx / 1000)) +
rootpath.replace("/", "") + ".txt",
encoding="UTF-8")
except:
pass
print(rootpath +
" Finished:{}/{}".format(idx, len(os.listdir(rootpath))))
import os
import mytools
import numpy as np
import random
root_path = "../../data/train/"
paths = os.listdir(root_path)
items = []
for path in paths:
sub_path = root_path + path +"/"
for file in os.listdir(sub_path):
item = "_ | "+"train/"+path+"/"+file+" | "+path+"\n"
items.append(item)
mytools.log_to_txt(items,"trainB.txt")
root_path = "../../../data/val/"
items = []
for file in os.listdir(root_path):
item = "_ | "+"val/"+file+" | "+file[9]+"\n"
items.append(item)
mytools.log_to_txt(items,"valB.txt")
# text = "hello world"
# respData = gClass.call_url(text=text)
# print(respData["data"]["result"]["trans_result"]['dst'])
ctxs = mytools.load_from_txt("smooth_it_vocab.txt")
i = 0
for ctx in ctxs:
try:
# print(ctx.replace("\n","").split(" | ")[0])
# ans = translate(fromLang='zh', toLang='it', q=ctx.replace("\n","").split(" | ")[0])
ans = gClass.call_url(text=ctx.replace("\n", "").split(" | ")
[0])["data"]["result"]["trans_result"]['dst']
# print(ans)
if ans == None:
ans = "unknown"
except:
ans = "unknown"
tmp = ctx.replace("\n", "").split(" | ")[0] + " | " + ans + "\n"
mytools.log_to_txt(tmp, "smooth_xunfei_dict.txt")
i += 1
print(i, "/", len(ctxs))
# time.sleep(0.01)
if i % 500 == 499:
# 初始化类
gClass = WebOTS.get_result(host)
import mytools
ctxs = mytools.load_from_txt("goodwords_dict.txt")
remove_ = "|»,# ?."
for ctx in ctxs:
try:
zh_, it_ = ctx.replace("\n", "").split(" | ")
except:
items = ctx.replace("\n", "").split(" | ")
zh_, it_ = items[0], items[1]
it_ = it_.lower()
for i in remove_:
it_ = it_.replace(i, "")
mytools.log_to_txt(zh_ + " | " + it_ + "\n", "new_dict_lower_.txt")
