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Tree_show.py
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Tree_show.py
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from graphviz import Digraph
#树型语料库构建与展示
# # 创建树状结构
# dot = Digraph(comment='pidancode.com') # 创建图形对象
# dot.node('dir1')
# dot.node('dir2')
# dot.edge('pidancode.com', 'dir1')
# dot.edge('pidancode.com', 'dir2')
# dot.node('file1')
# dot.node('file2')
# dot.node('file3')
# dot.edge('dir1', 'file1')
# dot.edge('dir1', 'file2')
# dot.edge('dir2', 'file3')
#
# # 生成图形
# dot.render('pidancode_tree', view=True)
import numpy as np
import nltk
nltk.download()
from nltk.tokenize import word_tokenize
import os, re
import pandas as pd
def show_tree_dir(father_name, children_list):
dir_str ="├──"
dir_str += father_name+"\n"
for c in children_list:
dir_str += " └──"+c+"\n"
dot = Digraph(comment='pidancode.com')
dot.node(father_name)
for s in range(len(children_list)):
dot.node(children_list[s])
dot.edge(father_name, children_list[s])
dot.render("../CS_A_Tree_show/CS-A_"+father_name, view=True)
return dir_str
def get_word_vector(s1, s2): # 将文本转换为词向量
cut1 = word_tokenize(s1)
cut2 = word_tokenize(s2)
#列出所有的词,取并集
key_word = list(set(cut1+cut2))
# print(key_word)
# 给定形状和类型的用0填充的矩阵存储向量 初始化矩阵
word_vector1 = np.zeros(len(key_word))
word_vector2 = np.zeros(len(key_word))
# 计算词频
# 依次确定向量的每个位置的值
for i in range(len(key_word)):
# 遍历key_word中每个词在句子中的出现次数
for j in range(len(cut1)):
if key_word[i] == cut1[j]:
word_vector1[i] += 1
for k in range(len(cut2)):
if key_word[i] == cut2[k]:
word_vector2[i] += 1
# 输出向量
# print("s1词向量矩阵:")
# print(word_vector1)
# print("s2词向量矩阵:")
# print(word_vector2)
return word_vector1, word_vector2
def cos_dist(vec1, vec2):
dist1 = float(np.dot(vec1, vec2) / (np.linalg.norm(vec1) * np.linalg.norm(vec2)))
return dist1
if __name__ == '__main__':
base_path1 = "F:/小论文1实验数据/corpus_final/corpus_annotation_wang_v3/"
acu_list = []
cyber_syndrome_list = []
treats_count = 0
for file_name in os.listdir(base_path1):
if re.search(r'\d', file_name):
cyber_syndrome_list.append(file_name)
else:
acu_list.append(file_name)
# disease_symtom = []
for i in range(len(cyber_syndrome_list)):
children_acu_list = []
dist1 = 0
max = 0.8
url1 = base_path1 + cyber_syndrome_list[i]
print(url1)
father_name = cyber_syndrome_list[i]
disease_symtom = []
ann1 = pd.read_csv(url1, header=None, sep='\t', engine='python', error_bad_lines=False, encoding='utf-8')
ann1_label = ann1[ann1[1].str.startswith('S')]
for item in ann1_label[2]:
disease_symtom.append(item.lower())
disease_symtom = list(set(disease_symtom))
print(disease_symtom)
cyber_label = ann1[ann1[1].str.startswith('Cyber')]
search_cyber_syndrome = []
for element in cyber_label[2]:
search_cyber_syndrome.append(element.lower())
search_cyber_syndrome = set(search_cyber_syndrome)
# print(search_cyber_syndrome)
for j in range(len(acu_list)):
search_acu = []
search_cyber_acu = []
acu_url = base_path1 + acu_list[j]
acu_data = pd.read_csv(acu_url, header=None, sep='\t', engine='python', error_bad_lines=False,
encoding='utf-8')
acu_label = acu_data[acu_data[1].str.startswith('S')]
cyber_acu_label = acu_data[acu_data[1].str.startswith('Cyber')]
for temp in acu_label[2]:
search_acu.append(temp.lower())
search_acu = list(set(search_acu))
# print(search_acu)
for item in cyber_acu_label[2]:
search_cyber_acu.append(item.lower())
search_cyber_acu = list(set(search_cyber_syndrome))
for cyber_item in search_cyber_syndrome:
if cyber_item in search_acu:
children_acu_list.append(acu_list[i])
for m in range(len(disease_symtom)):
for n in range(len(search_acu)):
vec1, vec2 = get_word_vector(disease_symtom[m], search_acu[n])
dist1 = cos_dist(vec1, vec2) # 将矩阵传入
if dist1 >= max:
children_acu_list.append(acu_list[j])
print(disease_symtom[m], search_acu[n], dist1, acu_list[j])
children_acu_list = list(set(children_acu_list))
treats_count = treats_count + len(children_acu_list)
dir_str = show_tree_dir(father_name, children_acu_list)
write_file = open('output.txt', mode='a+')
write_file.write(dir_str)
write_file.write('\n')
print(dir_str)
write_file.close()
print(treats_count)