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ner_input_data.py
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# Copyright 2020 trueto
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re
import json
import chardet
import pandas as pd
from pathlib import Path
str_type = {
"疾病和诊断": "Dis",
"解剖部位": "Body",
"实验室检验": "Test",
"影像检查": "CT",
"药物": "Drug",
"手术": "Sur"
}
def C_trans_to_E(string):
E_pun = u',.!?[]()<>"\'"\':;'
C_pun = u'，。！？【】（）《》“‘”’：；'
table= {ord(f): ord(t) for f, t in zip(C_pun, E_pun)}
string = string.translate(table)
return re.sub("[ |\t|\r|\n|\\\]", "_", string)
def strQ2B(ustr):
"全角转半角"
rstr = ""
for uchar in ustr:
inside_code = ord(uchar)
# 全角空格直接转换
if inside_code == 12288:
inside_code = 32
# 全角字符（除空格）根据关系转化
elif (inside_code >= 65281 and inside_code <= 65374):
inside_code -= 65248
rstr += chr(inside_code)
return rstr
def get_X_y(in_file, out_file, max_len=500):
X = []
y = []
entity_data = []
with open(in_file, 'r', encoding='utf8') as f:
for line in f:
tempObj = json.loads(line)
originalText = tempObj['originalText']
text = C_trans_to_E(strQ2B(originalText))
entities = tempObj['entities']
print("Processing text:{}".format(text))
if len(text) <= max_len:
X_ = list(text)
y_ = ["O"] * len(X_)
for entity in entities:
start_pos = entity["start_pos"]
end_pos = entity["end_pos"]
label_type = entity["label_type"]
if "clinical" in in_file:
tag = str_type[label_type]
else:
tag = label_type
# for i in range(start_pos, end_pos):
# y_[i] = tag
entity_data.append([text[start_pos : end_pos], tag])
y_[start_pos] = 'B-'+tag
for i in range(start_pos+1, end_pos):
y_[i] = 'I-' + tag
assert len(X_) == len(y_)
X.append(X_)
y.append(y_)
else:
# 分句
dot_index_list = []
text_ = text
flag = 0
while(len(text_) > max_len):
text_ = text_[:max_len]
index_list = []
for match in re.finditer(',', text_):
index = match.span()[0]
index_list.append(index)
# last_dot = index_list.pop()
if len(index_list) > 1:
last_dot = index_list.pop()
else:
index_list_ = []
for match in re.finditer('.', text_):
index = match.span()[0]
index_list_.append(index)
if len(index_list_) > 1:
last_dot = index_list_.pop()
else:
last_dot = len(text_)
dot_index_list.append(last_dot + flag)
text_ = text[last_dot+flag:]
flag += last_dot
print(dot_index_list)
flag = 0
dot_index_list.append(len(text))
for i, dot_index in enumerate(dot_index_list):
short_text = text[flag: dot_index+1]
X_ = list(short_text)
print("Short text:{}".format(short_text))
y_ = ["O"] * len(X_)
for entity in entities:
start_pos = entity["start_pos"]
end_pos = entity["end_pos"]
label_type = entity["label_type"]
if "clinical" in in_file:
tag = str_type[label_type]
else:
tag = label_type
#for j in range(start_pos, end_pos):
# j = j - flag
# if j >= 0 and j < len(y_):
# y_[j] = tag
en_list = []
k = start_pos - flag
if k >= 0 and k < len(y_):
y_[k] = 'B-' + tag
en_list.append(X_[k])
for j in range(start_pos+1, end_pos):
j = j - flag
if j >= 0 and j < len(y_):
y_[j] = 'I-' + tag
en_list.append(X_[j])
if len(en_list) > 0:
entity_data.append(["".join(en_list), tag])
# if start_pos - flag > 0:
# print(short_text[start_pos - flag : end_pos - flag])
assert len(X_) == len(y_)
X.append(X_)
y.append(y_)
flag = dot_index + 1
assert len(X) == len(y)
data_obj = (X, y, entity_data)
pd.to_pickle(data_obj, out_file)
def get_X(in_file, out_file, max_len=500):
X = []
cut_his = {}
originalTexts = []
texts = []
with open(in_file, 'rb') as f:
encoding = chardet.detect(f.read())['encoding']
with open(in_file, 'r', encoding="utf8") as f:
for text_id, line in enumerate(f):
tempObj = json.loads(line, encoding=encoding)
originalText = tempObj['originalText']
originalTexts.append(originalText)
text = C_trans_to_E(strQ2B(originalText))
texts.append(text)
print("Processing text:{}".format(text))
if len(text) <= max_len:
X_ = list(text)
X.append(X_)
cut_his[text_id] = len(X) - 1
else:
# 分句
dot_index_list = []
text_ = text
flag = 0
while(len(text_) > max_len):
text_ = text_[:max_len]
index_list = []
for match in re.finditer(',', text_):
index = match.span()[0]
index_list.append(index)
# last_dot = index_list.pop()
if len(index_list) > 1:
last_dot = index_list.pop()
else:
index_list_ = []
for match in re.finditer('.', text_):
index = match.span()[0]
index_list_.append(index)
if len(index_list_) > 1:
last_dot = index_list_.pop()
else:
last_dot = len(text_)
dot_index_list.append(last_dot + flag)
text_ = text[last_dot+flag:]
flag += last_dot
print(dot_index_list)
flag = 0
dot_index_list.append(len(text))
text_id_list = []
for i, dot_index in enumerate(dot_index_list):
short_text = text[flag: dot_index+1]
X_ = list(short_text)
X.append(X_)
text_id_list.append(len(X)-1)
flag = dot_index + 1
cut_his[text_id] = text_id_list
# assert len(X) == len(ids)
data_obj = (X, cut_his, originalTexts, texts)
pd.to_pickle(data_obj, out_file)
def get_vocab_csv(input_file, name):
_, _, entity_data = pd.read_pickle(input_file)
tmp_df = pd.DataFrame(data=entity_data, columns=['entity', 'label_type'])
tmp_df.drop_duplicates(inplace=True)
for label_type, entity_df in tmp_df.groupby(by='label_type', sort=False):
entity_df.to_csv("data/{}_{}_vocab.csv".format(name, label_type), index=None)
if __name__ == '__main__':
# get_X_y("data/clinical_ner_train.txt", "data/clinical_ner_train.pkl", max_len=125)
# get_X("data/clinical_ner_dev.txt", "data/clinical_ner_dev.pkl", max_len=125)
# get_X("data/clinical_ner_test.txt", "data/clinical_ner_test.pkl", max_len=125)
# get_vocab_csv("data/clinical_ner_train.pkl", name="clinical")
# get_X_y("data/diabetes_ner_train.txt", "data/diabetes_ner_train.pkl", max_len=125)
get_X("data/diabetes_ner_dev.txt", "data/diabetes_ner_dev.pkl", max_len=125)
get_X("data/diabetes_ner_test.txt", "data/diabetes_ner_test.pkl", max_len=125)
# get_vocab_csv("data/diabetes_ner_train.pkl", name="diabetes")