try offline

eda/smart_rank/tag3_update_user_portrait_offline.py

@@ -9,7 +9,7 @@ from pyspark import SparkConf
 from pyspark.sql import SparkSession
 
 from tool import (get_doris_prod, get_redis_client, get_tag3_user_log, get_user_portrait_tag3_with_score, send_email,
-                  write_user_portrait, write_user_portrait_by_event, write_user_portrait_doris)
+                  write_user_portrait)
 
 
 # [{'激光': 1.949194898204873}, {'手术': 1.949194898204873}, {'手术': 1.949194898204873}, {'手术': 1.949194898204873, '植发际线': 7.1}]
@@ -130,59 +130,59 @@ def _get_all_by_projects(name_lst):
     return None
 
 
-def update_tag3_user_portrait_by_event(cl_id):
-    user_df = get_tag3_user_log(cl_id)
-    if not user_df.empty:
-        user_df["first_solutions"] = list(zip(user_df["first_solutions"].apply(lambda x: x.split(",")), user_df["tag_score"]))
-        user_df["second_solutions"] = list(zip(user_df["second_solutions"].apply(lambda x: x.split(",")), user_df["tag_score"]))
-        user_df["first_demands"] = list(zip(user_df["first_demands"].apply(lambda x: x.split(",")), user_df["tag_score"]))
-        user_df["second_demands"] = list(zip(user_df["second_demands"].apply(lambda x: x.split(",")), user_df["tag_score"]))
-        user_df["first_positions"] = list(zip(user_df["first_positions"].apply(lambda x: x.split(",")), user_df["tag_score"]))
-        user_df["second_positions"] = list(zip(user_df["second_positions"].apply(lambda x: x.split(",")), user_df["tag_score"]))
-        user_df["projects"] = list(zip(user_df["projects"].apply(lambda x: x.split(",")), user_df["tag_score"]))
-
-        user_df["first_solutions_dict"] = user_df["first_solutions"].apply(lambda x: make_dict_from_pair(x))
-        user_df["second_solutions_dict"] = user_df["second_solutions"].apply(lambda x: make_dict_from_pair(x))
-        user_df["first_demands_dict"] = user_df["first_demands"].apply(lambda x: make_dict_from_pair(x))
-        user_df["second_demands_dict"] = user_df["second_demands"].apply(lambda x: make_dict_from_pair(x))
-        user_df["first_positions_dict"] = user_df["first_positions"].apply(lambda x: make_dict_from_pair(x))
-        user_df["second_positions_dict"] = user_df["second_positions"].apply(lambda x: make_dict_from_pair(x))
-        user_df["projects_dict"] = user_df["projects"].apply(lambda x: make_dict_from_pair(x))
-
-        user_df["first_solutions"] = list(zip(user_df["event_cn"], user_df["first_solutions_dict"]))
-        user_df["second_solutions"] = list(zip(user_df["event_cn"], user_df["second_solutions_dict"]))
-        user_df["first_demands"] = list(zip(user_df["event_cn"], user_df["first_demands_dict"]))
-        user_df["second_demands"] = list(zip(user_df["event_cn"], user_df["second_demands_dict"]))
-        user_df["first_positions"] = list(zip(user_df["event_cn"], user_df["first_positions_dict"]))
-        user_df["second_positions"] = list(zip(user_df["event_cn"], user_df["second_positions_dict"]))
-        user_df["projects"] = list(zip(user_df["event_cn"], user_df["projects_dict"]))
-
-        first_solutions_dict = merge_results_by_event(user_df["first_solutions"].tolist())
-        second_solutions_dict = merge_results_by_event(user_df["second_solutions"].tolist())
-        first_demands_dict = merge_results_by_event(user_df["first_demands"].tolist())
-        second_demands_dict = merge_results_by_event(user_df["second_demands"].tolist())
-        first_positions_dict = merge_results_by_event(user_df["first_positions"].tolist())
-        second_positions_dict = merge_results_by_event(user_df["second_positions"].tolist())
-        projects_dict = merge_results_by_event(user_df["projects"].tolist())
-
-        events = set(
-            list(first_solutions_dict.keys()) + list(second_solutions_dict.keys()) + list(first_demands_dict.keys()) +
-            list(second_demands_dict.keys()) + list(first_positions_dict.keys()) + list(second_positions_dict.keys()) +
-            list(projects_dict.keys()))
-
-        for e in events:
-            first_solutions = ",".join(first_solutions_dict.get(e, []))
-            second_solutions = ",".join(second_solutions_dict.get(e, []))
-            first_demands = ",".join(first_demands_dict.get(e, []))
-            second_demands = ",".join(second_demands_dict.get(e, []))
-            first_positions = ",".join(first_positions_dict.get(e, []))
-            second_positions = ",".join(second_positions_dict.get(e, []))
-            projects = ",".join(projects_dict.get(e, []))
-
-            write_user_portrait_by_event(cl_id, first_solutions, second_solutions, first_demands, second_demands, first_positions,
-                                         second_positions, projects, e)
-
-        return cl_id
+# def update_tag3_user_portrait_by_event(cl_id):
+#     user_df = get_tag3_user_log(cl_id)
+#     if not user_df.empty:
+#         user_df["first_solutions"] = list(zip(user_df["first_solutions"].apply(lambda x: x.split(",")), user_df["tag_score"]))
+#         user_df["second_solutions"] = list(zip(user_df["second_solutions"].apply(lambda x: x.split(",")), user_df["tag_score"]))
+#         user_df["first_demands"] = list(zip(user_df["first_demands"].apply(lambda x: x.split(",")), user_df["tag_score"]))
+#         user_df["second_demands"] = list(zip(user_df["second_demands"].apply(lambda x: x.split(",")), user_df["tag_score"]))
+#         user_df["first_positions"] = list(zip(user_df["first_positions"].apply(lambda x: x.split(",")), user_df["tag_score"]))
+#         user_df["second_positions"] = list(zip(user_df["second_positions"].apply(lambda x: x.split(",")), user_df["tag_score"]))
+#         user_df["projects"] = list(zip(user_df["projects"].apply(lambda x: x.split(",")), user_df["tag_score"]))
+
+#         user_df["first_solutions_dict"] = user_df["first_solutions"].apply(lambda x: make_dict_from_pair(x))
+#         user_df["second_solutions_dict"] = user_df["second_solutions"].apply(lambda x: make_dict_from_pair(x))
+#         user_df["first_demands_dict"] = user_df["first_demands"].apply(lambda x: make_dict_from_pair(x))
+#         user_df["second_demands_dict"] = user_df["second_demands"].apply(lambda x: make_dict_from_pair(x))
+#         user_df["first_positions_dict"] = user_df["first_positions"].apply(lambda x: make_dict_from_pair(x))
+#         user_df["second_positions_dict"] = user_df["second_positions"].apply(lambda x: make_dict_from_pair(x))
+#         user_df["projects_dict"] = user_df["projects"].apply(lambda x: make_dict_from_pair(x))
+
+#         user_df["first_solutions"] = list(zip(user_df["event_cn"], user_df["first_solutions_dict"]))
+#         user_df["second_solutions"] = list(zip(user_df["event_cn"], user_df["second_solutions_dict"]))
+#         user_df["first_demands"] = list(zip(user_df["event_cn"], user_df["first_demands_dict"]))
+#         user_df["second_demands"] = list(zip(user_df["event_cn"], user_df["second_demands_dict"]))
+#         user_df["first_positions"] = list(zip(user_df["event_cn"], user_df["first_positions_dict"]))
+#         user_df["second_positions"] = list(zip(user_df["event_cn"], user_df["second_positions_dict"]))
+#         user_df["projects"] = list(zip(user_df["event_cn"], user_df["projects_dict"]))
+
+#         first_solutions_dict = merge_results_by_event(user_df["first_solutions"].tolist())
+#         second_solutions_dict = merge_results_by_event(user_df["second_solutions"].tolist())
+#         first_demands_dict = merge_results_by_event(user_df["first_demands"].tolist())
+#         second_demands_dict = merge_results_by_event(user_df["second_demands"].tolist())
+#         first_positions_dict = merge_results_by_event(user_df["first_positions"].tolist())
+#         second_positions_dict = merge_results_by_event(user_df["second_positions"].tolist())
+#         projects_dict = merge_results_by_event(user_df["projects"].tolist())
+
+#         events = set(
+#             list(first_solutions_dict.keys()) + list(second_solutions_dict.keys()) + list(first_demands_dict.keys()) +
+#             list(second_demands_dict.keys()) + list(first_positions_dict.keys()) + list(second_positions_dict.keys()) +
+#             list(projects_dict.keys()))
+
+#         for e in events:
+#             first_solutions = ",".join(first_solutions_dict.get(e, []))
+#             second_solutions = ",".join(second_solutions_dict.get(e, []))
+#             first_demands = ",".join(first_demands_dict.get(e, []))
+#             second_demands = ",".join(second_demands_dict.get(e, []))
+#             first_positions = ",".join(first_positions_dict.get(e, []))
+#             second_positions = ",".join(second_positions_dict.get(e, []))
+#             projects = ",".join(projects_dict.get(e, []))
+
+#             write_user_portrait_by_event(cl_id, first_solutions, second_solutions, first_demands, second_demands, first_positions,
+#                                          second_positions, projects, e)
+
+#         return cl_id
 
 
 def update_tag3_user_portrait(cl_id):
@@ -195,6 +195,7 @@ def update_tag3_user_portrait(cl_id):
         user_df["first_positions"] = list(zip(user_df["first_positions"].apply(lambda x: x.split(",")), user_df["tag_score"]))
         user_df["second_positions"] = list(zip(user_df["second_positions"].apply(lambda x: x.split(",")), user_df["tag_score"]))
         user_df["projects"] = list(zip(user_df["projects"].apply(lambda x: x.split(",")), user_df["tag_score"]))
+        user_df["business_tags"] = list(zip(user_df["business_tags"].apply(lambda x: x.split(",")), user_df["tag_score"]))
 
         user_df["first_solutions_dict"] = user_df["first_solutions"].apply(lambda x: make_dict_from_pair(x))
         user_df["second_solutions_dict"] = user_df["second_solutions"].apply(lambda x: make_dict_from_pair(x))
@@ -203,6 +204,7 @@ def update_tag3_user_portrait(cl_id):
         user_df["first_positions_dict"] = user_df["first_positions"].apply(lambda x: make_dict_from_pair(x))
         user_df["second_positions_dict"] = user_df["second_positions"].apply(lambda x: make_dict_from_pair(x))
         user_df["projects_dict"] = user_df["projects"].apply(lambda x: make_dict_from_pair(x))
+        user_df["business_tags_dict"] = user_df["business_tags"].apply(lambda x: make_dict_from_pair(x))
 
         first_solutions_list = user_df["first_solutions_dict"].tolist()
         second_solutions_list = user_df["second_solutions_dict"].tolist()
@@ -211,6 +213,7 @@ def update_tag3_user_portrait(cl_id):
         first_positions_list = user_df["first_positions_dict"].tolist()
         second_positions_list = user_df["second_positions_dict"].tolist()
         projects_list = user_df["projects_dict"].tolist()
+        business_tags_list = user_df["business_tags_dict"].tolist()
 
         first_demands_score = merge_values(first_demands_list)
         second_demands_score = merge_values(second_demands_list)
@@ -220,6 +223,7 @@ def update_tag3_user_portrait(cl_id):
         second_positions_score = merge_values(second_positions_list)
         # projects_score = merge_values(projects_list)
         projects_score_tmp = merge_values(projects_list)
+        business_tags_score = merge_values(business_tags_list)
 
         tmp = [
             "不感兴趣", "没有想法", "八卦来了", "颜值速报", "医美审美干货", "其他项目", "网红颜值", "少年之名", "郑人予", "热点课代表", "私密", "陈瑞泽", "符仁杰", "祖力亚尔", "刘泽旭",
@@ -248,7 +252,8 @@ def update_tag3_user_portrait(cl_id):
             "second_solutions": second_solutions_score,
             "first_positions": first_positions_score,
             "second_positions": second_positions_score,
-            "projects": projects_score
+            "projects": projects_score,
+            "business_tags": business_tags_score
         }
         if tmp_res:
             res.update(tmp_res)
@@ -262,14 +267,21 @@ def update_tag3_user_portrait(cl_id):
         if (len(first_demands_score.keys()) > 0) or (len(second_demands_score.keys()) > 0) or \
            (len(first_solutions_score.keys()) > 0) or (len(second_solutions_score.keys()) > 0) or \
            (len(first_positions_score.keys()) > 0) or (len(second_positions_score.keys()) > 0) or \
-           (len(projects_score.keys()) > 0):
+           (len(projects_score.keys()) > 0) or (len(business_tags_score.keys()) > 0):
             redis_client.set(key, json.dumps(res))
             redis_client.expire(key, 60 * 60 * 24 * 180)
 
-            write_user_portrait(cl_id, ",".join(first_solutions_score.keys()), ",".join(second_solutions_score.keys()),
-                                ",".join(first_demands_score.keys()), ",".join(second_demands_score.keys()),
-                                ",".join(first_positions_score.keys()), ",".join(second_positions_score.keys()),
-                                ",".join(projects_score.keys()))
+            write_user_portrait(
+                cl_id,
+                ",".join(first_solutions_score.keys()),
+                ",".join(second_solutions_score.keys()),
+                ",".join(first_demands_score.keys()),
+                ",".join(second_demands_score.keys()),
+                ",".join(first_positions_score.keys()),
+                ",".join(second_positions_score.keys()),
+                ",".join(projects_score.keys()),
+                ",".join(business_tags_score.keys()),
+            )
 
             # body = {}
             # for (k, v) in res.items():
@@ -333,6 +345,11 @@ if __name__ == "__main__":
     # update_tag3_user_portrait("862460044588666")
     # update_tag3_user_portrait("androidid_a25a1129c0b38f7b")
 
+    # cl_id = "864350041167473"
+    # df = get_tag3_user_log(cl_id)
+    # df[["projects", "business_tags"]]
+    # update_tag3_user_portrait(cl_id)
+
     consume_kafka()
     end = datetime.datetime.now()
     print(end - start)

eda/smart_rank/tool.py

 # coding: utf-8
 
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-import pymysql
+from __future__ import absolute_import, division, print_function
+
+import datetime
+import json
 import smtplib
+import time
+import traceback
+from email.mime.application import MIMEApplication
+from email.mime.multipart import MIMEMultipart
 from email.mime.text import MIMEText
 from email.utils import formataddr
-from email.mime.multipart import MIMEMultipart
-from email.mime.application import MIMEApplication
-import redis
-import datetime
-import time
-import json
+
 import numpy as np
 import pandas as pd
-import traceback
+import pymysql
+import redis
 
 
-def send_email(app,id,e):
+def send_email(app, id, e):
     # 第三方 SMTP 服务
-    mail_host = 'smtp.exmail.qq.com'  # 设置服务器
+    mail_host = "smtp.exmail.qq.com"  # 设置服务器
     mail_user = "zhaowei@igengmei.com"  # 用户名
     mail_pass = "Gengmei1234"  # 口令
 
-    sender = 'zhaowei@igengmei.com'
-    receivers = ['zhaowei@igengmei.com']  # 接收邮件，可设置为你的QQ邮箱或者其他邮箱
+    sender = "zhaowei@igengmei.com"
+    receivers = ["zhaowei@igengmei.com"]  # 接收邮件，可设置为你的QQ邮箱或者其他邮箱
     e = str(e)
     msg = MIMEMultipart()
-    part = MIMEText('app_id:'+id+':fail' + "\n" + e, 'plain', 'utf-8')
+    part = MIMEText("app_id:" + id + ":fail" + "\n" + e, "plain", "utf-8")
     msg.attach(part)
-    msg['From'] = formataddr(["gaoyazhe", sender])
+    msg["From"] = formataddr(["gaoyazhe", sender])
     # 括号里的对应收件人邮箱昵称、收件人邮箱账号
-    msg['To'] = ";".join(receivers)
+    msg["To"] = ";".join(receivers)
     # message['Cc'] = ";".join(cc_reciver)
-    msg['Subject'] = 'spark streaming:app_name:'+app
+    msg["Subject"] = "spark streaming:app_name:" + app
 
     try:
-        with open('error.txt','w') as f:
+        with open("error.txt", "w") as f:
             f.write(e)
             f.close()
-        part = MIMEApplication(open('error.txt', 'r').read())
-        part.add_header('Content-Disposition', 'attachment', filename="error.txt")
+        part = MIMEApplication(open("error.txt", "r").read())
+        part.add_header("Content-Disposition", "attachment", filename="error.txt")
         msg.attach(part)
     except Exception as e:
         print(e)
@@ -51,7 +51,7 @@ def send_email(app,id,e):
         smtpObj.login(mail_user, mail_pass)
         smtpObj.sendmail(sender, receivers, msg.as_string())
     except smtplib.SMTPException:
-        print('error')
+        print("error")
 
 
 def get_data_by_mysql(host, port, user, passwd, db, sql):
@@ -90,13 +90,13 @@ def get_all_search_word_synonym_tags():
               "left join api_wordrelsynonym b on a.id = b.wordrel_id  " \
               "left join api_tag c  on b.word=c.name " \
               "where c.tag_type+0<'4'+0 and c.is_online=1"
-        mysql_results = get_data_by_mysql('172.16.30.141', 3306, 'zx_str', 'ZXueX58pStrage', 'zhengxing', sql)
+        mysql_results = get_data_by_mysql("172.16.30.141", 3306, "zx_str", "ZXueX58pStrage", "zhengxing", sql)
         result_dict = dict()
         for data in mysql_results:
-            if data['keyword'] not in result_dict:
-                result_dict[data['keyword']] = [data['id']]
+            if data["keyword"] not in result_dict:
+                result_dict[data["keyword"]] = [data["id"]]
             else:
-                result_dict[data['keyword']].append(data['id'])
+                result_dict[data["keyword"]].append(data["id"])
         return result_dict
     except Exception as e:
         print(e)
@@ -106,13 +106,13 @@ def get_all_word_synonym_words():
     try:
         sql = "select a.keyword, b.word from api_wordrel a  " \
               "left join api_wordrelsynonym b on a.id = b.wordrel_id "
-        mysql_results = get_data_by_mysql('172.16.30.141', 3306, 'zx_str', 'ZXueX58pStrage', 'zhengxing', sql)
+        mysql_results = get_data_by_mysql("172.16.30.141", 3306, "zx_str", "ZXueX58pStrage", "zhengxing", sql)
         result_dict = dict()
         for data in mysql_results:
-            if data['keyword'] not in result_dict:
-                result_dict[data['keyword']] = [data['word']]
+            if data["keyword"] not in result_dict:
+                result_dict[data["keyword"]] = [data["word"]]
             else:
-                result_dict[data['keyword']].append(data['word'])
+                result_dict[data["keyword"]].append(data["word"])
         return result_dict
     except Exception as e:
         print(e)
@@ -125,13 +125,13 @@ def get_all_synonym_tags():
     try:
         sql = "select a.word, b.id from api_wordrelsynonym a left join api_tag b " \
               "on a.word=b.name where b.tag_type+0<'4'+0 and b.is_online=1"
-        mysql_results = get_data_by_mysql('172.16.30.141', 3306, 'zx_str', 'ZXueX58pStrage', 'zhengxing', sql)
+        mysql_results = get_data_by_mysql("172.16.30.141", 3306, "zx_str", "ZXueX58pStrage", "zhengxing", sql)
         result_dict = dict()
         for data in mysql_results:
-            if data['word'] not in result_dict:
-                result_dict[data['word']] = [data['id']]
+            if data["word"] not in result_dict:
+                result_dict[data["word"]] = [data["id"]]
             else:
-                result_dict[data['word']].append(data['id'])
+                result_dict[data["word"]].append(data["id"])
         return result_dict
     except Exception as e:
         print(e)
@@ -143,13 +143,13 @@ def get_all_api_tags():
     """
     try:
         sql = "select name, id from api_tag  where tag_type in ('1', '2', '3', '5') and is_online=1"
-        mysql_results = get_data_by_mysql('172.16.30.141', 3306, 'zx_str', 'ZXueX58pStrage', 'zhengxing', sql)
+        mysql_results = get_data_by_mysql("172.16.30.141", 3306, "zx_str", "ZXueX58pStrage", "zhengxing", sql)
         result_dict = dict()
         for data in mysql_results:
-            if data['name'] not in result_dict:
-                result_dict[data['name']] = [data['id']]
+            if data["name"] not in result_dict:
+                result_dict[data["name"]] = [data["id"]]
             else:
-                result_dict[data['name']].append(data['id'])
+                result_dict[data["name"]].append(data["id"])
         return result_dict
     except Exception as e:
         print(e)
@@ -171,10 +171,10 @@ def get_all_tag_tag_type():
     """
     try:
         sql = "select id,tag_type from api_tag where tag_type+0<'4'+0 and is_online=1"
-        mysql_results = get_data_by_mysql('172.16.30.141', 3306, 'zx_str', 'ZXueX58pStrage', 'zhengxing', sql)
+        mysql_results = get_data_by_mysql("172.16.30.141", 3306, "zx_str", "ZXueX58pStrage", "zhengxing", sql)
         result_dict = dict()
         for data in mysql_results:
-            result_dict[data['id']] = data['tag_type']
+            result_dict[data["id"]] = data["tag_type"]
         return result_dict
     except Exception as e:
         print(e)
@@ -186,13 +186,13 @@ def get_all_3tag_2tag():
               " left join api_tag b on a.parent_id=b.id " \
               "where a.child_id in (select id from api_tag where tag_type='3' and is_online=1) " \
               "and b.tag_type='2' and b.is_online=1"
-        mysql_results = get_data_by_mysql('172.16.30.141', 3306, 'zx_str', 'ZXueX58pStrage', 'zhengxing', sql)
+        mysql_results = get_data_by_mysql("172.16.30.141", 3306, "zx_str", "ZXueX58pStrage", "zhengxing", sql)
         result_dict = dict()
         for data in mysql_results:
-            if data['child_id'] not in result_dict:
-                result_dict[data['child_id']] = [data['parent_id']]
+            if data["child_id"] not in result_dict:
+                result_dict[data["child_id"]] = [data["parent_id"]]
             else:
-                result_dict[data['child_id']].append(data['parent_id'])
+                result_dict[data["child_id"]].append(data["parent_id"])
         return result_dict
     except Exception as e:
         print(e)
@@ -204,13 +204,13 @@ def get_all_tag_parent_tag():
               " left join api_tag b on a.parent_id=b.id " \
               "where a.child_id in (select id from api_tag where tag_type+0<'4'+0 and is_online=1) " \
               "and b.tag_type+0<'4'+0 and b.is_online=1"
-        mysql_results = get_data_by_mysql('172.16.30.141', 3306, 'zx_str', 'ZXueX58pStrage', 'zhengxing', sql)
+        mysql_results = get_data_by_mysql("172.16.30.141", 3306, "zx_str", "ZXueX58pStrage", "zhengxing", sql)
         result_dict = dict()
         for data in mysql_results:
-            if data['child_id'] not in result_dict:
-                result_dict[data['child_id']] = [data['parent_id']]
+            if data["child_id"] not in result_dict:
+                result_dict[data["child_id"]] = [data["parent_id"]]
             else:
-                result_dict[data['child_id']].append(data['parent_id'])
+                result_dict[data["child_id"]].append(data["parent_id"])
         return result_dict
     except Exception as e:
         print(e)
@@ -219,10 +219,10 @@ def get_all_tag_parent_tag():
 def get_all_tags_name():
     try:
         sql = "select id, name from api_tag where tag_type+0<'4'+0 and is_online=1"
-        mysql_results = get_data_by_mysql('172.16.30.141', 3306, 'zx_str', 'ZXueX58pStrage', 'zhengxing', sql)
+        mysql_results = get_data_by_mysql("172.16.30.141", 3306, "zx_str", "ZXueX58pStrage", "zhengxing", sql)
         result_dict = dict()
         for data in mysql_results:
-            result_dict[data['id']] = data['name']
+            result_dict[data["id"]] = data["name"]
         return result_dict
     except Exception as e:
         print(e)
@@ -244,57 +244,67 @@ def get_tag2_from_tag3(tag3, all_3tag_2tag, user_log_df_tag2_list):
 def compute_henqiang(x, decay_days=30, exponential=0, action_tag_count=1):
     if exponential:
         alpha = exponential_decay(x, decay_days)
-        score = 15/action_tag_count - 1.1**alpha * ((15-0.5)/decay_days)
+        score = 15 / action_tag_count - 1.1**alpha * ((15 - 0.5) / decay_days)
     else:
-        score = 15/action_tag_count - x * ((15-0.5)/decay_days)
+        score = 15 / action_tag_count - x * ((15 - 0.5) / decay_days)
     if score > 0.5:
         return score
     else:
         return 0.5
+
+
 def compute_jiaoqiang(x, decay_days=30, exponential=0, action_tag_count=1):
     if exponential:
         alpha = exponential_decay(x, decay_days)
-        score = 12/action_tag_count - 1.1**alpha * ((12-0.5)/decay_days)
+        score = 12 / action_tag_count - 1.1**alpha * ((12 - 0.5) / decay_days)
     else:
-        score = 12/action_tag_count - x * ((12-0.5)/decay_days)
+        score = 12 / action_tag_count - x * ((12 - 0.5) / decay_days)
     if score > 0.5:
         return score
     else:
         return 0.5
+
+
 def compute_ruoyixiang(x, decay_days=30, exponential=0, action_tag_count=1):
     if exponential:
         alpha = exponential_decay(x, decay_days)
-        score = 5/action_tag_count - 1.1**alpha * ((5-0.5)/decay_days)
+        score = 5 / action_tag_count - 1.1**alpha * ((5 - 0.5) / decay_days)
     else:
-        score = 5/action_tag_count - x * ((5-0.5)/decay_days)
+        score = 5 / action_tag_count - x * ((5 - 0.5) / decay_days)
     if score > 0.5:
         return score
     else:
         return 0.5
+
+
 def compute_validate(x, decay_days=30, exponential=0, action_tag_count=1):
     if exponential:
         alpha = exponential_decay(x, decay_days)
-        score = 10/action_tag_count - 1.1**alpha * ((10-0.5)/decay_days)
+        score = 10 / action_tag_count - 1.1**alpha * ((10 - 0.5) / decay_days)
     else:
-        score = 10/action_tag_count - x * ((10-0.5)/decay_days)
+        score = 10 / action_tag_count - x * ((10 - 0.5) / decay_days)
     if score > 0.5:
         return score
     else:
         return 0.5
+
+
 def compute_ai_scan(x, decay_days=30, exponential=0, action_tag_count=1):
     if exponential:
         alpha = exponential_decay(x, decay_days)
-        score = 2/action_tag_count - 1.1**alpha * ((2-0.5)/decay_days)
+        score = 2 / action_tag_count - 1.1**alpha * ((2 - 0.5) / decay_days)
     else:
-        score = 2/action_tag_count - x * ((2-0.5)/decay_days)
+        score = 2 / action_tag_count - x * ((2 - 0.5) / decay_days)
     if score > 0.5:
         return score
     else:
         return 0.5
+
+
 def get_action_tag_count(df, action_time):
     try:
-        if not df[df['time'] == action_time].empty:
-            return len(df[df['time'] == action_time])
+        if not df[df["time"] == action_time].empty:
+            return len(df[df["time"] == action_time])
         else:
             return 1
     except Exception as e:
@@ -303,7 +313,7 @@ def get_action_tag_count(df, action_time):
 
 def exponential_decay(days_diff, decay_days=30):
     # 天数差归一化到[0, decay_days]
-    x = np.arange(1, 180+1, 1)
+    x = np.arange(1, 180 + 1, 1)
     a = (decay_days - 0) * (days_diff - min(x)) / (max(x) - min(x))
     return a
 
@@ -315,8 +325,12 @@ def args_test(x):
 def get_user_log(cl_id, all_word_tags, pay_time=0, debug=0):
     user_df_service = pd.DataFrame(columns=["time", "cl_id", "score_type", "tag_id", "tag_referrer", "action"])
     try:
-        db_jerry_test = pymysql.connect(host='172.16.40.158', port=4000, user='st_user', passwd='aqpuBLYzEV7tML5RPsN1pntUzFy',
-                                        db='jerry_test', charset='utf8')
+        db_jerry_test = pymysql.connect(host="172.16.40.158",
+                                        port=4000,
+                                        user="st_user",
+                                        passwd="aqpuBLYzEV7tML5RPsN1pntUzFy",
+                                        db="jerry_test",
+                                        charset="utf8")
         cur_jerry_test = db_jerry_test.cursor()
         if pay_time == 0:
             user_df_service_sql = "select time,cl_id,score_type,tag_id,tag_referrer,action from user_new_tag_log " \
@@ -335,19 +349,19 @@ def get_user_log(cl_id, all_word_tags, pay_time=0, debug=0):
         user_df_search = user_df_service[user_df_service["action"] == "do_search"]
         if debug:
             # 用户的非搜索、支付行为
-            user_df_service = user_df_service.loc[
-                ~user_df_service["action"].isin(["do_search", "api/settlement/alipay_callback"])]
+            user_df_service = user_df_service.loc[~user_df_service["action"].isin(["do_search", "api/settlement/alipay_callback"]
+                                                                                  )]
         else:
             # 用户的非搜索行为
             user_df_service = user_df_service.loc[~user_df_service["action"].isin(["do_search"])]
         # 搜索词转成tag，合并用户日志
         user_df_search_dict = dict()
         for index, row in user_df_search.iterrows():
-            if row['tag_referrer'] in all_word_tags:
-                word_tag_list = all_word_tags[row['tag_referrer']]
-                row['tag_id'] = int(word_tag_list[0]) if word_tag_list else -1
+            if row["tag_referrer"] in all_word_tags:
+                word_tag_list = all_word_tags[row["tag_referrer"]]
+                row["tag_id"] = int(word_tag_list[0]) if word_tag_list else -1
             else:
-                row['tag_id'] = -1
+                row["tag_id"] = -1
         user_df_service = user_df_service.append(user_df_search)
         return user_df_service[user_df_service["tag_id"] != -1]
     except:
@@ -366,12 +380,7 @@ def get_jerry_test():
 
 
 def get_doris_prod():
-    db = pymysql.connect(host="172.16.30.136",
-                         port=3306,
-                         user="doris",
-                         passwd="o5gbA27hXHHm",
-                         db="doris_prod",
-                         charset="utf8")
+    db = pymysql.connect(host="172.16.30.136", port=3306, user="doris", passwd="o5gbA27hXHHm", db="doris_prod", charset="utf8")
     return db, db.cursor()
 
 
@@ -391,11 +400,11 @@ def compute_tag3_score(x):
 def get_tag3_user_log(cl_id):
     columns = [
         "log_time", "score_type", "event_cn", "first_solutions", "second_solutions", "first_demands", "second_demands",
-        "first_positions", "second_positions", "projects"
+        "first_positions", "second_positions", "projects", "business_tags"
     ]
     try:
         sql = """select log_time, score_type, event_cn, first_solutions, second_solutions, first_demands,
-                 second_demands, first_positions, second_positions, projects
+                 second_demands, first_positions, second_positions, projects, business_tags
                  from kafka_tag3_log where cl_id = '{}'""".format(cl_id)
 
         db, cursor = get_doris_prod()
@@ -412,6 +421,7 @@ def get_tag3_user_log(cl_id):
 
         user_df["days_diff_now"] = round((int(time.time()) - user_df["log_time"].astype(float)) / (24 * 60 * 60))
         user_df["tag_score"] = user_df.apply(lambda x: compute_tag3_score(x), axis=1)
+        user_df["business_tags"] = user_df["business_tags"].fillna("")
         return user_df
     except Exception as e:
         print(e)
@@ -431,15 +441,16 @@ def get_tag3_user_log(cl_id):
 #   `projects` text NOT NULL,
 #    PRIMARY KEY(`id`)
 # )
-def write_user_portrait(cl_id, first_solutions, second_solutions, first_demands, second_demands,
-                        first_positions, second_positions, projects):
+# ALTER TABLE `user_tag3_portrait` ADD COLUMN business_tags text COMMENT '商业标签';
+def write_user_portrait(cl_id, first_solutions, second_solutions, first_demands, second_demands, first_positions,
+                        second_positions, projects, business_tags):
     try:
         today = datetime.date.today()
         oneday = datetime.timedelta(days=1)
         yesterday = today - oneday
-        sql = """insert into user_tag3_portrait values(null, '{}', '{}', '{}', '{}', '{}', '{}', '{}', '{}', '{}')""".format(
-            yesterday, cl_id, first_solutions, second_solutions, first_demands, second_demands, first_positions,
-            second_positions, projects)
+        sql = """insert into user_tag3_portrait values(null, '{}', '{}', '{}', '{}', '{}', '{}', '{}', '{}', '{}', '{}')""".format(
+            yesterday, cl_id, first_solutions, second_solutions, first_demands, second_demands, first_positions, second_positions,
+            projects, business_tags)
 
         db, cursor = get_jerry_test()
         cursor.execute(sql)
@@ -451,24 +462,24 @@ def write_user_portrait(cl_id, first_solutions, second_solutions, first_demands,
         print(e)
 
 
-def write_user_portrait_doris(cl_id, first_solutions, second_solutions, first_demands, second_demands, first_positions,
-                              second_positions, projects):
-    try:
-        today = datetime.date.today()
-        oneday = datetime.timedelta(days=1)
-        yesterday = today - oneday
-        sql = """insert into user_tag3_portrait values(null, '{}', '{}', '{}', '{}', '{}', '{}', '{}', '{}', '{}')""".format(
-            yesterday, cl_id, first_solutions, second_solutions, first_demands, second_demands, first_positions, second_positions,
-            projects)
+# def write_user_portrait_doris(cl_id, first_solutions, second_solutions, first_demands, second_demands, first_positions,
+#                               second_positions, projects):
+#     try:
+#         today = datetime.date.today()
+#         oneday = datetime.timedelta(days=1)
+#         yesterday = today - oneday
+#         sql = """insert into user_tag3_portrait values(null, '{}', '{}', '{}', '{}', '{}', '{}', '{}', '{}', '{}')""".format(
+#             yesterday, cl_id, first_solutions, second_solutions, first_demands, second_demands, first_positions, second_positions,
+#             projects)
 
-        db, cursor = get_doris_prod()
-        cursor.execute(sql)
-        db.commit()
-        db.close()
-        cursor.close()
-    except Exception as e:
-        print("write db error")
-        print(e)
+#         db, cursor = get_doris_prod()
+#         cursor.execute(sql)
+#         db.commit()
+#         db.close()
+#         cursor.close()
+#     except Exception as e:
+#         print("write db error")
+#         print(e)
 
 
 # CREATE TABLE `user_tag3_event_portrait` (
@@ -506,7 +517,7 @@ def write_user_portrait_by_event(cl_id, first_solutions, second_solutions, first
 
 
 def get_redis_client():
-    return redis.StrictRedis.from_url('redis://:ReDis!GmTx*0aN9@172.16.40.173:6379')
+    return redis.StrictRedis.from_url("redis://:ReDis!GmTx*0aN9@172.16.40.173:6379")
 
 
 def get_user_portrait_tag3_from_redis(device_id, limit_score=0):
@@ -514,6 +525,7 @@ def get_user_portrait_tag3_from_redis(device_id, limit_score=0):
         new_d = dict(sorted(d.items(), key=lambda x: x[1], reverse=True))
         res = {tag: float(score) for tag, score in new_d.items() if float(score) >= limit_score}
         return list(res.keys())
+
     portrait_key = "doris:user_portrait:tag3:device_id:" + str(device_id)
     redis_client = get_redis_client()
     if redis_client.exists(portrait_key):