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
def domain_classfiy(uid_list,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_list:uid列表 [uid1,uid2,uid3,...]
uid_weibo:分词之后的词频字典 {uid1:{'key1':f1,'key2':f2...}...}
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...}
'''
users = get_user(uid_list)
frineds = get_friends(uid_list)
domain = dict()
r_domain = dict()
text_result = dict()
user_result = dict()
for k,v in users.iteritems():
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= frineds[k]#返回用户的粉丝列表
if len(f):
field1,sorted_mbr = user_domain_classifier_v1(f, 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 = v
if r == 'other':
field2 = 'other'
else:
field2 = user_domain_classifier_v2(r)
result_label.append(field2)
if uid_weibo.has_key(k):
field_dict,result = domain_classfiy_by_text({k: uid_weibo[k]})#根据用户文本进行分类
field3 = field_dict[k]
else:
field3 = 'other'
result_label.append(field3)
domain[str(uid)] = result_label
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,r_domain
def domain_classfiy(uid_list, 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_list:uid列表 [uid1,uid2,uid3,...]
uid_weibo:分词之后的词频字典 {uid1:{'key1':f1,'key2':f2...}...}
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...}
'''
if not len(uid_weibo) and len(uid_list):
domain = dict()
r_domain = dict()
for uid in uid_list:
domain[uid] = ['other']
r_domain[uid] = ['other']
return domain, r_domain
elif len(uid_weibo) and not len(uid_list):
uid_list = uid_weibo.keys()
elif not len(uid_weibo) and not len(uid_list):
domain = dict()
r_domain = dict()
return domain, r_domain
else:
pass
users = get_user(uid_list)
frineds = get_friends(uid_list)
domain = dict()
r_domain = dict()
text_result = dict()
user_result = dict()
for k, v in users.iteritems():
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 = frineds[k] #返回用户的粉丝列表
if len(f):
field1, sorted_mbr = user_domain_classifier_v1(
f, 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 = v
if r == 'other':
field2 = 'other'
else:
field2 = user_domain_classifier_v2(r)
result_label.append(field2)
if uid_weibo.has_key(k) and len(uid_weibo[k]):
field_dict, result = domain_classfiy_by_text({k: uid_weibo[k]
}) #根据用户文本进行分类
field3 = field_dict[k]
else:
field3 = 'other'
result_label.append(field3)
domain[str(uid)] = result_label
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, r_domain