def main():
# Read already finished
already_finished = []
if os.path.exists("file/already_finished.txt"):
already_finished = mytools.load_from_txt("file/already_finished.txt")
# Read data
data = np.array(
pd.read_csv("../2_text_maching/baseline/file/submission.csv"))
data = data.tolist()
refine_data = []
if os.path.exists("file/bleu_after_optim.csv"):
refine_data = getSavedCsv()
for idx, d in tqdm(enumerate(data)):
if idx % 1000 == 999:
refine_data, all_bleu = save_to_csv(refine_data,
default_path="after_optim")
print("lens of refine_data:", len(refine_data))
print("now bleu is: ", all_bleu)
if " ".join(d) + "\n" in already_finished:
continue
tmp_data = compute_column_score(d)
for t in tmp_data:
refine_data.append(t)
data = np.array(pd.read_csv("file/bleu_after_optim.csv"))
print("After Optim:", np.sum(np.transpose(data)[0]))
def randomGetTarget():
root_source = "../data/processData/it/"
source_dir = root_source + random.choice(os.listdir(root_source)) + "/"
source_file = source_dir + random.choice(os.listdir(source_dir))
source_ctx = mytools.load_from_txt(source_file)
try:
itemPath, start2end, text = source_ctx[random.randint(
0, len(source_ctx))].split(" | ")
itemPath = itemPath.replace("../sourceData/", "")
return itemPath, start2end, text
except:
return None, None, None
def readDict(path="new_dict.txt"):
print("Reading Directory ...")
# 读取中译日词典
ctxs = mytools.load_from_txt(path)
# 解析每一条并保转换为json形式
directory = {}
for ctx in ctxs:
try:
source, target = ctx.replace("\n", "").split(" | ")
directory[source] = target
except:
pass
print("Directory load completed .")
return directory
def __init__(self, train="train", transform=None):
print("Reading data...")
self.img_path = "../data/argumented_data/"
if train == "train":
data_path = config.train_path
elif train == "val":
data_path = config.val_path
else:
data_path = config.test_path
#读取数据
self.data = mytools.load_from_txt(data_path)
self.transform = transform
self.train = train
ctx = replace_all_blank(ctx)
# if len(ctx.split())<=4 and lang=='zh':
# return None
# word_tokens = word_tokenize(ctx) #分词
word_tokens = jieba.lcut(ctx)
# filtered_sentence = [w for w in word_tokens if w not in stop_words] #去停用词
# stem_words = [ps.stem(w) for w in filtered_sentence]
# return stem_words
return word_tokens
if __name__ == "__main__":
file_name = "sorted_data/sorted_" + lang + ".txt"
ctxs = mytools.load_from_txt(file_name)
filted_results = []
for ctx in tqdm(ctxs):
try:
html, location, text = ctx.split(" | ")
# 决定要保存与否 以及保存的内容
text = save_decision(text, lang)
if text is not None:
filted_results.append({
"html":
html.replace("data/it\\",
"../zhit-0825/it/2020-04-17/").replace(
"zh/2020-04-18/",
"../zhit-0825/zh/2020-04-18/"),
data = pd.DataFrame(results[:, 1:],
index=None,
columns=[
"file_source", "location_source", "file_target",
"location_target"
])
data.to_csv("file/" + default_path + ".csv", index=None)
print("Save finished")
if return_min:
return results.tolist(), all_bleu, float(results[-1][0])
else:
return results.tolist(), all_bleu
ctxs = mytools.load_from_txt("new_dict_lower.txt")
DICT = {}
for ctx in ctxs:
try:
zh_, it_ = ctx.replace("\n", "").split(" | ")
except:
items = ctx.replace("\n", "").split(" | ")
zh_, it_ = items[0], items[1]
DICT[zh_] = it_
csv_path = "bleu_submission.csv"
data = np.array(pd.read_csv(csv_path)).tolist()
goodwords = mytools.load_from_json("good_words.json")
for goodword in goodwords:
去除value中的所有非字母内容，包括标点符号、空格、换行、下划线等
:param value: 需要处理的内容
:return: 返回处理后的内容
"""
# \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
import mytools,os
from tqdm import tqdm
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)
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")
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, os
from tqdm import tqdm
root_path = "../filter_data/sorted_data/zh/"
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")
"""
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import os, sys
from ots_python3_demo.ots_python3_demo import WebOTS
import time
import mytools
host = "ntrans.xfyun.cn"
# 初始化类
gClass = WebOTS.get_result(host)
# 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")
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")