def match(title, shorten_content, answer, question):
if utils.is_zh_or_en(title):
title_list = utils.split_word_zh(title) + ['。']
else:
title_list = utils.split_word_en(title) + ['.']
if utils.is_zh_or_en(shorten_content):
content_list = utils.split_word_zh(shorten_content)
else:
content_list = utils.split_word_en(shorten_content)
merge_list = title_list + content_list
merge_len = len(merge_list)
if utils.is_zh_or_en(question):
answer_list = utils.split_word_zh(answer)
question_list = utils.split_word_zh(question)
question_str = ' '.join(question_list)
else:
answer_list = utils.split_word_en(answer)
question_list = utils.split_word_en(question)
question_str = ' '.join(question_list)
answer_len = len(answer_list)
start = []
end = []
if answer == '':
return -1, -1
for i in range(0, merge_len - answer_len + 1):
if merge_list[i:i + answer_len] == answer_list:
start.append(i)
end.append(i + answer_len - 1)
if len(start) == 0:
return -1, -1
elif len(start) == 1:
return start[0], end[0]
else:
scores = []
# 前后扩展5个词
for s, e in zip(start, end):
s = max(s - 5, 0)
answer_can = ' '.join(merge_list[s:e + 5])
score = rouge.get_scores(answer_can, question_str,
avg=True)['rouge-l']['r']
scores.append(score)
max_idx = np.argmax(scores)
return start[max_idx], end[max_idx]
def build_vocab_embedding(list_df, vocab_path, embedding_in_zh,
embedding_in_en, embedding_out):
data = []
for df in list_df:
if 'answer' in df:
data += df[['title', 'content', 'question',
'answer']].values.flatten().tolist()
else:
data += df[['title', 'content',
'question']].values.flatten().tolist()
vocab = set()
for d in data:
if utils.is_zh_or_en(d):
d_list = utils.split_word_zh(d)
else:
d_list = utils.split_word_en(d)
for dd in d_list:
vocab.add(dd)
print('data, word_num:%d' % len(vocab))
# zh
model_zh = gensim.models.KeyedVectors.load_word2vec_format(embedding_in_zh)
# en
model_en = gensim.models.KeyedVectors.load_word2vec_format(embedding_in_en)
tmp = set()
for word in vocab:
if word in model_zh or word in model_en:
tmp.add(word)
print('word_nums in pre-embedding:%d/%d, radio:%.4f' %
(len(tmp), len(vocab), len(tmp) / len(vocab)))
w2i = {'<pad>': 0}
i2w = {0: '<pad>'}
c = 1
embedding = np.zeros([len(tmp) + 3, model_zh.vector_size])
for word in tmp:
w2i[word] = c
i2w[c] = word
if word in model_zh:
embedding[c] = model_zh[word]
elif word in model_en:
embedding[c] = model_en[word]
c += 1
w2i['<unk>'] = len(tmp) + 1
i2w[len(tmp) + 1] = '<unk>'
w2i[' '] = len(tmp) + 2
i2w[len(tmp) + 2] = ' '
lang = {'w2i': w2i, 'i2w': i2w}
print('vocab size:%d' % (c + 2))
print('embedding size:', embedding.shape)
# save
with open(vocab_path, 'wb') as file:
pickle.dump(lang, file)
np.save(embedding_out, embedding)
def gen_tag_index(df):
df = df[['title', 'content', 'question']]
data = df.values.flatten().tolist()
tag2i = {'<pad>': 0, '<unk>': 1}
cc = 2
for d in data:
if utils.is_zh_or_en(d):
_, tags = utils.split_word_zh(d, have_tag=True)
else:
_, tags = utils.split_word_en(d, have_tag=True)
for t in tags:
if t not in tag2i:
tag2i[t] = cc
cc += 1
with open(config.tag_path, 'wb') as file:
pickle.dump(tag2i, file)
print('word flag num:%d' % len(tag2i)) # 98个
def shorten_content_all(df, max_len):
"""
:param df:
:param max_len:
:return: df
"""
sys.setrecursionlimit(1000000)
rouge = Rouge(metrics=['rouge-l'])
def match(title, content, question, max_len):
title_is_zh = utils.is_zh_or_en(title)
content_is_zh = utils.is_zh_or_en(content)
if title_is_zh:
title_list = utils.split_word_zh(title)
else:
title_list = utils.split_word_en(title)
def count(flag, content_list):
""" 查数 """
number = 0
for i in range(len(flag)):
if flag[i] != 0:
number += len(content_list[i])+1
return number
# 过滤
if content_is_zh:
title_number = len(title_list)
content_number = len(utils.split_word_zh(content))
if (title_number + content_number + 1) <= max_len:
return content
if content_is_zh:
if '。' not in content:
c_list = utils.split_word_zh(content)
c_list = c_list[: config.max_len-len(title_list)-1]
return ''.join(c_list)
content_list = content.split('。')
temp = []
for c in content_list:
if c not in ['', ' ', ' ']:
temp.append(c)
content_list = temp
content_list = [utils.split_word_zh(c) for c in content_list]
content_list = [title_list] + content_list
content_len = len(content_list)
if utils.is_zh_or_en(question):
question_list = utils.split_word_zh(question)
else:
question_list = utils.split_word_en(question)
question_str = ' '.join(question_list)
# 相似性得分: rouge-l
scores = []
for c in content_list:
if ''.join(c) in question:
scores.append(-5)
continue
c_str = ' '.join(c)
score = rouge.get_scores(c_str, question_str, avg=True)['rouge-l']['r']
scores.append(score)
# 标记类型
flag = np.zeros(content_len)
# title_number = len(utils.split_word_zh(title))
# max_len = max_len - title_number
# 核心句:
max_score = max(scores)
for i in range(content_len):
if scores[i] == max_score:
flag[i] = -1
# 核心句下一句
for i in range(content_len):
if (flag[i] == -1) and (i+1 < content_len) and (flag[i+1] == 0):
flag[i+1] = -2
# 最后一句
if flag[-1] == 0:
flag[-1] = -3
# 第一句
if flag[1] == 0:
flag[1] = -4
# 蕴含句（上+中+下）
for i in range(content_len):
if scores[i] == -5:
if (i-1 >= 0) and (flag[i-1] == 0):
flag[i-1] = -5
if flag[i] == 0:
flag[i] = -5
if (i+1 < content_len) and (flag[i+1] == 0):
flag[i+1] = -5
# 核心句下下句
for i in range(content_len):
if (flag[i] == -1) and (i+2 < content_len) and (flag[i+2] == 0):
flag[i+2] = -6
# 核心句上一句
for i in range(content_len):
if (flag[i] == -1) and (i-1 >= 0) and (flag[i-1] == 0):
flag[i-1] = -7
# 核心句下下下句
for i in range(content_len):
if (flag[i] == -1) and (i+3 < content_len) and (flag[i+3] == 0):
flag[i+3] = -8
# 核心句上上句
for i in range(content_len):
if (flag[i] == -1) and (i-2 >= 0) and (flag[i-2] == 0):
flag[i-2] = -9
# 倒数第二句
if(len(flag) >= 3) and (flag[-2] == 0):
flag[-2] = -10
# 第二句
if (len(flag) >= 3) and (flag[2] == 0):
flag[2] = -11
flag[0] = 0
number = count(flag, content_list)
max_len = max_len - len(title_list)
result = []
if number <= max_len:
for i in range(content_len):
if flag[i] != 0:
result.append(''.join(content_list[i]))
else:
flag_copy = np.zeros(content_len)
c_count = 0
xxx = True
for i in range(-1, -12, -1):
for j in range(len(flag)):
if flag[j] == i:
c_count = c_count + len(content_list[j]) + 1
if c_count <= max_len:
flag_copy[j] = -1
else:
xxx = False
break
if xxx is False:
break
for i in range(content_len):
if flag_copy[i] == -1:
result.append(''.join(content_list[i]))
if len(result) == 0:
for j in range(len(flag)):
if flag[j] == -1:
result = [''.join(content_list[j][: max_len-1])]
break
# 过滤重复
temp = []
for r in result:
if r not in temp:
temp.append(r)
result = temp
return '。'.join(result)
else:
www = content
words = utils.split_word_en(www)
if (len(words) + len(title_list) + 1) <= config.max_len:
return content
else:
index = 0
for i in words[: config.max_len-len(title_list)-1]:
index = index + len(i)
while content[index] != ' ':
index = index + 1
return content[: index]
titles = df['title'].values
contents = df['content'].values
questions = df['question'].values
shorten_content = [match(t, c, q, max_len) for t, c, q in zip(titles, contents, questions)]
df['shorten_content'] = shorten_content
# 评估数据集构建效果
if 'answer' in df:
answers = df['answer'].values
is_in = [True if (a in c) or (a in t) else False for c, t, a in zip(contents, titles, answers)]
r1 = sum(is_in)/len(df)
print('答案存在比例：%.4f' % r1)
is_in = [True if (a in t) or (a in m) else False for t, m, a in zip(titles, shorten_content, answers)]
df['is_in'] = is_in
r2 = sum(is_in)/len(df)
print('截取比例：%.4f' % r2)
print('截取准确率：%.4f' % (r2/r1))
merge_len = []
for t, s in zip(titles, shorten_content):
if utils.is_zh_or_en(t):
len_t = len(utils.split_word_zh(t))
else:
len_t = len(utils.split_word_en(t))
if utils.is_zh_or_en(s):
len_s = len(utils.split_word_zh(s))
else:
len_s = len(utils.split_word_en(s))
len_m = len_t + len_s + 1
merge_len.append(len_m)
df['len'] = merge_len
print('max length: %d' % max(merge_len))
print('min length: %d' % min(merge_len))
print('mean length:%d' % df['len'].mean())
print('median length:%d' % df['len'].median())
return df
def match(title, content, question, max_len):
title_is_zh = utils.is_zh_or_en(title)
content_is_zh = utils.is_zh_or_en(content)
if title_is_zh:
title_list = utils.split_word_zh(title)
else:
title_list = utils.split_word_en(title)
def count(flag, content_list):
""" 查数 """
number = 0
for i in range(len(flag)):
if flag[i] != 0:
number += len(content_list[i])+1
return number
# 过滤
if content_is_zh:
title_number = len(title_list)
content_number = len(utils.split_word_zh(content))
if (title_number + content_number + 1) <= max_len:
return content
if content_is_zh:
if '。' not in content:
c_list = utils.split_word_zh(content)
c_list = c_list[: config.max_len-len(title_list)-1]
return ''.join(c_list)
content_list = content.split('。')
temp = []
for c in content_list:
if c not in ['', ' ', ' ']:
temp.append(c)
content_list = temp
content_list = [utils.split_word_zh(c) for c in content_list]
content_list = [title_list] + content_list
content_len = len(content_list)
if utils.is_zh_or_en(question):
question_list = utils.split_word_zh(question)
else:
question_list = utils.split_word_en(question)
question_str = ' '.join(question_list)
# 相似性得分: rouge-l
scores = []
for c in content_list:
if ''.join(c) in question:
scores.append(-5)
continue
c_str = ' '.join(c)
score = rouge.get_scores(c_str, question_str, avg=True)['rouge-l']['r']
scores.append(score)
# 标记类型
flag = np.zeros(content_len)
# title_number = len(utils.split_word_zh(title))
# max_len = max_len - title_number
# 核心句:
max_score = max(scores)
for i in range(content_len):
if scores[i] == max_score:
flag[i] = -1
# 核心句下一句
for i in range(content_len):
if (flag[i] == -1) and (i+1 < content_len) and (flag[i+1] == 0):
flag[i+1] = -2
# 最后一句
if flag[-1] == 0:
flag[-1] = -3
# 第一句
if flag[1] == 0:
flag[1] = -4
# 蕴含句（上+中+下）
for i in range(content_len):
if scores[i] == -5:
if (i-1 >= 0) and (flag[i-1] == 0):
flag[i-1] = -5
if flag[i] == 0:
flag[i] = -5
if (i+1 < content_len) and (flag[i+1] == 0):
flag[i+1] = -5
# 核心句下下句
for i in range(content_len):
if (flag[i] == -1) and (i+2 < content_len) and (flag[i+2] == 0):
flag[i+2] = -6
# 核心句上一句
for i in range(content_len):
if (flag[i] == -1) and (i-1 >= 0) and (flag[i-1] == 0):
flag[i-1] = -7
# 核心句下下下句
for i in range(content_len):
if (flag[i] == -1) and (i+3 < content_len) and (flag[i+3] == 0):
flag[i+3] = -8
# 核心句上上句
for i in range(content_len):
if (flag[i] == -1) and (i-2 >= 0) and (flag[i-2] == 0):
flag[i-2] = -9
# 倒数第二句
if(len(flag) >= 3) and (flag[-2] == 0):
flag[-2] = -10
# 第二句
if (len(flag) >= 3) and (flag[2] == 0):
flag[2] = -11
flag[0] = 0
number = count(flag, content_list)
max_len = max_len - len(title_list)
result = []
if number <= max_len:
for i in range(content_len):
if flag[i] != 0:
result.append(''.join(content_list[i]))
else:
flag_copy = np.zeros(content_len)
c_count = 0
xxx = True
for i in range(-1, -12, -1):
for j in range(len(flag)):
if flag[j] == i:
c_count = c_count + len(content_list[j]) + 1
if c_count <= max_len:
flag_copy[j] = -1
else:
xxx = False
break
if xxx is False:
break
for i in range(content_len):
if flag_copy[i] == -1:
result.append(''.join(content_list[i]))
if len(result) == 0:
for j in range(len(flag)):
if flag[j] == -1:
result = [''.join(content_list[j][: max_len-1])]
break
# 过滤重复
temp = []
for r in result:
if r not in temp:
temp.append(r)
result = temp
return '。'.join(result)
else:
www = content
words = utils.split_word_en(www)
if (len(words) + len(title_list) + 1) <= config.max_len:
return content
else:
index = 0
for i in words[: config.max_len-len(title_list)-1]:
index = index + len(i)
while content[index] != ' ':
index = index + 1
return content[: index]