def get_seed_main():
flag_dict = dict()
for name in classes:
flag_dict[name] = []
error = 0
for area in classes:
uidlist = readUidByArea(area)
ufriends = readFriendsByArea(area)
for uid in uidlist:
try:
for f in ufriends[uid]:
v_t = read_by_xapian(xs,f)
if v_t != 'other':
flag = user_domain_classifier_v2(v_t)
if flag != 'other':
flag_dict[name].append(f)
except:
error +=1
pass
flag_dict[area].append(uid)
protou=open('./protou_combine/protou.txt','w')
count = 0
for k,v in flag_dict.items():
if count == 0:
protou.write(k+':')
count = count + 1
else:
protou.write('\n'+k+':')
for uid in v:
protou.write(' '+str(uid))
def domain_classfiy(uid_weibo):#领域分类主函数
'''
用户领域分类主函数
输入数据示例：
uid_weibo:字典
{uid1:[weibo1,weibo2,weibo3,...]}
输出数据示例：
domain：标签字典
{uid1:[label1,label2,label3],uid2:[label1,label2,label3]...}
注：label1是根据粉丝结构分类的结果，label2是根据认证类型分类的结果，label3是根据用户文本分类的结果
re_label：推荐标签字典
{uid1:label,uid2:label2...}
'''
weibo_text = dict()
uidlist = []
for k,v in uid_weibo.items():
item = ''
for i in range(0,len(v)):
text = re_cut(v[i]['text'])
item = item + ',' + text
weibo_text[k] = item
uidlist.append(k)
users = get_user(uidlist)
print 'len(users):',len(users)
print len(uidlist)
domain = dict()
r_domain = dict()
text_result = dict()
user_result = dict()
for k,v in users.items():
uid = k
result_label = []
sorted_mbr = dict()
field1 = getFieldFromProtou(k, protou_dict=train_users)#判断uid是否在种子用户里面
if field1 != 'Null':#该用户在种子用户里面
result_label.append(field1)
else:
f= get_friends([k])#返回用户的粉丝列表
friends = f[str(uid)]
if len(friends):
field1,sorted_mbr = user_domain_classifier_v1(friends, fields_value=txt_labels, protou_dict=proto_users)
else:
field1 = 'other'
sorted_mbr = {'university':0, 'homeadmin':0, 'abroadadmin':0, 'homemedia':0, 'abroadmedia':0, 'folkorg':0, \
'lawyer':0, 'politician':0, 'mediaworker':0, 'activer':0, 'grassroot':0, 'other':0, 'business':0}
result_label.append(field1)
r = read_by_xapian(xs,uid)
if r == 'other':
field2 = 'other'
else:
field2 = user_domain_classifier_v2(r)
result_label.append(field2)
field_dict,result = domain_classfiy_by_text({k: weibo_text[k]})#根据用户文本进行分类
field3 = field_dict[k]
result_label.append(field3)
domain[str(uid)] = result_label
user_result[str(uid)] = sorted_mbr#有问题
text_result[str(uid)] = result[k]#有问题
if r == 'other':
re_label = get_recommend_result('other',result_label)#没有认证类型字段
else:
re_label = get_recommend_result(r['verified_type'],result_label)
r_domain[str(uid)] = re_label
return domain,re_label