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main.py
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main.py
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import pandas as pd
import numpy as np
import re
import jieba
class Person:
def __init__(self,student):
#这里对应的是我们问卷中的一些数据
self.index = student["编号"]
self.name = student["1.姓名"]
self.gender = student["2.性别"]
self.birthday = student["出生年"]
self.college = student["5.所在学院"]
self.height = student["10.身高"]
self.weight = student["11.体重"]
self.mbti = student["12.mbti"].upper()
self.character = student["14.用2-3个词语形容自己性格"]
self.req_height = student["17.对对方的身高要求"]
self.req_mbti = student["18.对对方的mbti要求"].upper()
self.req_character = student["19.对对方的性格要求"]
class Girl(Person):
def __init__(self, student):
super().__init__(student)
pass
class Boy(Person):
def __init__(self, student):
super().__init__(student)
pass
class Friendship:
def __init__(self, boy, girl):
self.boy = boy
self.girl = girl
def age_score(self):
dif_age = abs(self.girl.birthday-self.boy.birthday)
if dif_age<=3:
return 1
else:
return 1/dif_age
def mbti_score(self,mbti,target_mbti):
if "/" in target_mbti:
req_mbti_list = target_mbti.split('/')
if mbti in req_mbti_list:
return 1
else:
return 0
else:
score = 0
for i in range(0,4):
c1 = target_mbti[i]
c2 = mbti[i]
if c1==c2 or c1 == 'X':
score+=1
if score==4:
return 1
else:
return 0
def height_score(self,height,target_height):
if type(target_height)==int:
if target_height==0:
low_height = 0
high_height = 300
else:
low_height = target_height
high_height = 300
else:
if '-' in target_height:
req_height_list = target_height.split('-')
low_height = int(req_height_list[0])
high_height = int(req_height_list[1])
elif target_height=='0':
low_height = 0
high_height = 300
else:
low_height = int(target_height)
high_height = 300
if height>=low_height and height<=high_height:
return 1
else:
return 0
def character_score(self,character,target_character):
if target_character=='无':
return 0.1
pattern = '[\s,、，；。|]+'
target_character = "|".join(jieba.cut(target_character, cut_all=False))
character = "|".join(jieba.cut(character, cut_all=False))
target_character_words = re.split(pattern, target_character)
character_words = re.split(pattern, character)
score = 0
for tc in target_character_words:
for c in character_words:
if c in tc:
score+=0.1
if score>=1:
score = 1
return score
def get_character_score(self):
self.boy_character_score = self.character_score(self.girl.character,self.boy.req_character)
self.girl_character_score = self.character_score(self.boy.character,self.girl.req_character)
return self.boy_character_score+self.girl_character_score
def get_mbti_score(self):
self.boy_mbti_score = self.mbti_score(self.girl.mbti,self.boy.req_mbti)
self.girl_mbti_score = self.mbti_score(self.boy.mbti,self.girl.req_mbti)
return self.boy_mbti_score+self.girl_mbti_score
def get_height_score(self):
self.boy_height_score = self.height_score(self.girl.height,self.boy.req_height)
self.girl_height_score = self.height_score(self.boy.height,self.girl.req_height)
return self.boy_height_score+self.girl_height_score
def get_score(self):
return self.get_height_score()*1000+self.age_score()*100+self.get_mbti_score()*10+self.get_character_score()
if __name__=='__main__':
#数据路径
file_path = './data/data_process.xlsx'#处理后的数据
save_excel_path = './data/match.xlsx'#匹配结果数据
#信息录入
df = pd.read_excel(file_path)
boy_map = {}
girl_map = {}
for _,item in df.iterrows():
if item["2.性别"]=='A.男':
b = Boy(item)
boy_map[b.name] = b
if item["2.性别"]=='B.女':
g = Girl(item)
girl_map[g.name] = g
#匹配得分计算
friend_map = {}
for boy in boy_map.values():
for girl in girl_map.values():
f = Friendship(boy,girl)
friend_map[(f.boy.name,f.girl.name)] = f.get_score()
#得分排序
threshold = 0#调整阈值过滤掉分数低的匹配结果
sorted_friend_map = sorted(friend_map.items(), key=lambda item: item[1], reverse=True)
sorted_friend_map = [item for item in sorted_friend_map if item[1]>=threshold]
sorted_friend_map = sorted(sorted_friend_map, key=lambda x: x[0][0])
#Kuhn-Munkras算法完成匹配
import networkx as nx
G = nx.Graph()
edges_with_weights = []
for friendship in sorted_friend_map:
tuple_friend_score = (friendship[0][0],friendship[0][1],friendship[1])
edges_with_weights.append(tuple_friend_score)
for u, v, weight in edges_with_weights:
G.add_edge(u, v, weight=weight)
# 使用最大权重匹配算法
matching = nx.algorithms.matching.max_weight_matching(G, maxcardinality=True)
#匹配结果导出
concatenated_data = []
is_match_list = []
for friend in sorted_friend_map:
boy_name = friend[0][0]
girl_name = friend[0][1]
boy = boy_map[boy_name]
girl = girl_map[girl_name]
score = friend[1]
is_match=0
if (boy_name,girl_name) in matching or (girl_name,boy_name) in matching:
is_match = 1
is_match_list.append(boy_name)
is_match_list.append(girl_name)
if is_match:
concatenated_data.append([boy.name,boy.birthday,boy.height,boy.mbti,boy.character,boy.req_height,boy.req_mbti,boy.req_character,girl.name,girl.birthday,girl.height,girl.mbti,girl.character,girl.req_height,girl.req_mbti,girl.req_character,score])
for girl in girl_map.values():
girl_name = girl.name
if girl_name not in is_match_list:
concatenated_data.append(['','','','','','','','',girl.name,girl.birthday,girl.height,girl.mbti,girl.character,girl.req_height,girl.req_mbti,girl.req_character,0])
# triples_df = pd.DataFrame(concatenated_data, columns=['boy name','boy brithday','boy height','boy mbti','boy character','boy height req','boy mbti req','boy character req','girl name','girl brithday','girl height','girl mbti','girl character','girl height req','girl mbti req','gril character req','score'])
triples_df = pd.DataFrame(concatenated_data, columns=['男生名字','男生生日','男生身高','男生mbti','男生性格','男生身高要求','男生mbti要求','男生性格要求','女生名字','女生生日','女生身高','女生mbti','女生性格','女生身高要求','女生mbti要求','女生性格要求','匹配分数'])
triples_df.to_excel(save_excel_path, index=False, engine='openpyxl')