解决冲突

databaseConn/__init__.py

@@ -58,8 +58,14 @@ mysql_info={
         "pwd":"123456",
         "host":"172.16.9.89",
         "port":"3306"
-    }
-    ,
+    },
+    "feature_aliyun":{
+        "host":"aliyun-test.mysql.rds.aliyuncs.com",
+        "pwd":"NaL7F7j522Kn8D5kJz",
+        "name":"tjzimu_test",
+        "port":"3306"
+
+    },
     "usergroup":{
         "name":"tjzimuali_test",
         "pwd":"bYut032QgkJ7ElMXwW",

databaseConn/mysqlOperation.py

@@ -15,17 +15,20 @@ def mysqlInfo(namespace,dbtype='mysql'):
     :param  dbtype:查询的类型，目前支持mysql和mongo
     :return: 获取namespace对应的mysql信息，对大数据的mysql不适用，此时需要自定义mysql_info
     """
-    url="https://holmes.liangkebang.com/k8s/service/detail?namespace={}" \
-        "&serviceType=base&serviceName={dbtype}".format(namespace,dbtype=dbtype)
+    url="https://doraemon.quantgroups.cn/ack/service/detail?namespace={}&serviceType=base&serviceName={dbtype}".format(namespace,dbtype=dbtype)
+    # url="https://holmes.liangkebang.com/k8s/service/detail?namespace={}" \
+    #     "&serviceType=base&serviceName={dbtype}".format(namespace,dbtype=dbtype)
     result=mysql_info.get(str(namespace)) or {}
     if result:
         return result
     try:
         resp=requests.get(url)
-        result['host']=jsonpath.jsonpath(resp.json(),'$..lanIp')[0]
+        #print(resp.json(),url)
+        result['host']=jsonpath.jsonpath(resp.json(),'$..nodeIp')[0]
         result['port']=jsonpath.jsonpath(resp.json(),'$..nodePort')[0]
         result['pwd']='qatest'
         result['name']='qa'
+        #print(result)
         return result
     except:
         raise Exception("该namespace[{}]找不到对应的mysql信息".format(namespace))
@@ -39,7 +42,7 @@ def mysql_universal(namespace):
     return temp
 
 def execmysl(namespace,sql):
-    if 'select' not in sql or 'from' not in sql:
+    if 'select' not in sql.strip() and 'alter' not in sql.strip() and 'update' not in sql:
         raise Exception('在mysql看来不是有效的sql',sql)
     try:
         print(sql)
@@ -73,5 +76,5 @@ def concatSql(sql,**kwargs):
     return sql+' where '+temp[:-4]
 
 if __name__ == '__main__':
-    print(mysqlInfo('test1'))
-    #print(execmysl('test1',"select key_word as word,key_word_url as jumpUrl,icon_url as iconUrl from t_app_config_search_keyword where status=1 and DATE_FORMAT(end_time,'%Y-%m-%d') >= DATE_FORMAT('2022-06-14 15:38:50','%Y-%m-%d') and DATE_FORMAT('2022-06-14 15:38:50','%Y-%m-%d') >= DATE_FORMAT(start_time,'%Y-%m-%d') order by sort"))
+    #print(mysqlInfo('test1'))
+    print(execmysl('bd',"select 1 "))
\ No newline at end of file

databaseConn/redisOperation.py

@@ -4,6 +4,7 @@ from collections import  defaultdict
 from urllib import parse
 
 REDIS_CONFIG = {
+    "119":{
     'REDIS_NODES': [
         {'host': '172.24.17.119', 'port': 6371},
         {'host': '172.24.17.119', 'port': 6372},
@@ -15,6 +16,21 @@ REDIS_CONFIG = {
     'REDIS_EXPIRE_TIME': 26 * 3600,
     'REDIS_MAX_CONNECTIONS': 50,
     'REDIS_PASSWD': 'redis',
+},"89":{
+        'REDIS_NODES': [
+            {'host': '172.16.9.89', 'port': 6371},
+            {'host': '172.16.9.89', 'port': 6372},
+            {'host': '172.16.9.89', 'port': 6373},
+            {'host': '172.16.9.89', 'port': 6374},
+            {'host': '172.16.9.89', 'port': 6375},
+            {'host': '172.16.9.89', 'port': 6376}
+        ],
+        'REDIS_EXPIRE_TIME': 6000,
+        'REDIS_MAX_CONNECTIONS': 50,
+        'REDIS_PASSWD':None
+
+    }
+
 }
 
 dapan_redis_config={
@@ -27,33 +43,25 @@ dapan_redis_config={
     'REDIS_MAX_CONNECTIONS': 50,
     'REDIS_PASSWD': '1234',
 }
-
 from rediscluster import RedisCluster
 
+def redis_conn(key):
+    REDIS_CONFIG_temp=REDIS_CONFIG.get(key)
+    return RedisCluster(startup_nodes=REDIS_CONFIG_temp.get('REDIS_NODES'),
+                 max_connections=REDIS_CONFIG_temp.get('REDIS_MAX_CONNECTIONS'),
+                 password=REDIS_CONFIG_temp.get('REDIS_PASSWD'), decode_responses=True)
 
-redis_db_conn_119 = RedisCluster(startup_nodes=REDIS_CONFIG.get('REDIS_NODES'),
-                                 max_connections=REDIS_CONFIG.get('REDIS_MAX_CONNECTIONS'),
-                                 password=REDIS_CONFIG.get('REDIS_PASSWD'), decode_responses=True)
-
-# dapan_redis_db_conn = RedisCluster(startup_nodes=dapan_redis_config.get('REDIS_NODES'),
-#                                       max_connections=dapan_redis_config.get('REDIS_MAX_CONNECTIONS'),
-#                                       password=dapan_redis_config.get('REDIS_PASSWD'),
-#                                        decode_responses=True,
-#                                        skip_full_coverage_check=True)
-def getRedisValue(key,conntype=None,oper='select'):
+def getRedisValue(redis_key,key,conntype=None,oper='select'):
     '''
     :param key:
     :param conntype: 区分不同的redis连接
     :param oper:如果是delete，则做删除操作。目前只支持delete，select
     :return:select->获取所有匹配到key的值
     '''
+    redis_db_conn=redis_conn(redis_key)
     if 'select' in key or 'from' in key:
         raise Exception('在redis看来不是有效的key',key)
     redis_value = defaultdict()
-    if conntype == 'dapan':
-        redis_db_conn=dapan_redis_db_conn
-    else:
-        redis_db_conn=redis_db_conn_119
     uuid_redis_result=redis_db_conn.keys(key.strip())
     print('模糊匹配到的所有key:',uuid_redis_result)
     if uuid_redis_result and oper=='delete':
@@ -85,7 +93,7 @@ def getRedisValue(key,conntype=None,oper='select'):
 if __name__ == '__main__':
     userUuid='00003a93-2a32-4501-b338-755b6cb1ec49'
     #t=getRedisValue("order_product_graph:179240378044417")
-    t=getRedisValue("pay_product_graph:179240378044417")
+    t=getRedisValue('89',"*_same_product_batch_uuid*")
     print(t)
 
     ## search_fm_offline_feature:{md5(搜索词)}_query_offline

feature/__init__.py

+from databaseConn import *
+from tools import *
+from tools.publicFun import *
+from tools.httprequest import *
+from tools.listOperation import *
+from databaseConn.mysqlOperation import *
+#from databaseConn.redisOperation import *
+from feature.publicSql import *
+feature_path=os.path.dirname(__file__)
+feature_report=os.path.join(feature_path,'tempFile')
+
+##贷前域名
+loan_url="https://finance-feature-calc-test.tjzimu.com" ##贷前域名
+mid_url="https://finance-feature-box-test.tjzimu.com" ##贷中域名
+pre_loan_url=loan_url+"/calc/features"
+mid_loan_url=mid_url+'/feature/query'

feature/publicSql.py

+##消费地图白名单,phone本身就是md5加密的
+map_white_sql="""
+select phone from finance_offline_feature.del_20220627 
+"""
+##贷前贷中用户,未加密
+feature_user_sql="""
+select phone_no,uuid from xyqb_user.user order by rand() limit {num}
+"""

feature/white_list_hit_apply.py

+import pandas as pd
+import requests
+
+from feature import *
+
+#codes=["user_consumption_map_whitelist"]
+
+def devResult(uuid,codes,codetype='pre'):
+    headers = {
+        'Content-Type': 'application/x-www-form-urlencoded',
+        'rc_auth_key': 'rc_offline',
+        'rc_auth_secret': '9d9}bc24!e1z1x3`(x~4r29d$+45n3)\'zb696b$85e>_]p2&4f{,a3~8b3e_ldt^'
+    }
+    temp={'uuid':uuid}
+    body={
+        "codes":codes
+    }
+    if codetype=='pre':
+        body["user_uuid"]=uuid
+        url=pre_loan_url
+    else:
+        url=mid_loan_url
+        body['tradeNo']=0
+        body['userUuid']=uuid
+    t=requests.post(url,data=body,headers=headers)
+    print(t.json())
+
+    temp[code+'_dev']=jsonpath.jsonpath(t.json(),'$..{}.value'.format(code))[0]
+    return temp
+
+def baseinfo(num=10):
+    feature_user_df=execmysl('feature_aliyun',feature_user_sql.format(num=num))
+    feature_user_df=feature_user_df.astype(str)
+    feature_user_df['phone_no']=feature_user_df['phone_no'].apply(lambda x:md5jiami(x.encode()))
+    return feature_user_df
+
+def getwhite(code,num=10):
+    feature_user_df=baseinfo(num)
+    try:
+        #return feature_user_df.head(1)
+        map_white_change_sql=concatSql(map_white_sql,**{"phone":feature_user_df['phone_no'].to_list()})
+        map_white_df=execmysl('feature_aliyun',map_white_change_sql)
+        #print(map_white_df['phone'].to_list)
+        #feature_user_df['iswhite']=feature_user_df[~(feature_user_df['phone_no'].isin(map_white_df['phone'].to_list()))]
+        newcolumes=code+'_test'
+        feature_user_df[newcolumes]=feature_user_df['phone_no'].apply(lambda x:1 if x in map_white_df['phone'].to_list() else 0)
+        feature_user_df[newcolumes]=feature_user_df['phone_no'].apply(lambda x:1 if x in map_white_df['phone'].to_list() else 0)
+        reportname=filePath(feature_report,newcolumes)
+        feature_user_df[['uuid',newcolumes]].to_excel(reportname,index=0,encoding = 'utf-8',float_format = str)
+        #devResult(code,reportname)
+        #return feature_user_df
+        return feature_user_df
+    except:
+        traceback.print_exc()
+
+def dev_compare(code,codetype='pre',filepath=None):
+    temp=[]
+    filepath=os.path.join(feature_path,'tempFile','map_white_result_1656582391.xlsx')
+    test_df=readFile(filepath)
+    test_df=test_df.rename(columns={'user_consumption_map_whitelist_test':'userConsumptionMapWhitelistCalc#isWhite_test'})
+    # print(test_df.columns)
+    # return 0
+    for i in test_df['uuid'].to_list():
+        t=devResult(i,code,codetype)
+        temp.append(t)
+    dev_df=pd.DataFrame(temp)
+    df=pd.merge(test_df,dev_df,on='uuid',how='left')
+    #return df.head(2)
+    print(df.columns)
+    df['结果是否正确']=df[code+'_test']==df[code+'_dev']
+    df=df.astype(str)
+    reportname=filePath(feature_report,code)
+    df.to_excel(reportname,index=0,encoding = 'utf-8',float_format = str)
+
+
+
+
+
+
+if __name__ == '__main__':
+    code='user_consumption_map_whitelist'
+    code='userConsumptionMapWhitelistCalc#isWhite'
+    print(dev_compare(code,codetype='mid'))
+    #print(getwhite(code,1000))
+
+
+    #print(devResult('58f23a26-df3e-4d3e-80c2-8a4fece16c0e'))
+
+
+
+

recommend/__init__.py

@@ -10,4 +10,7 @@ from recommend.publicSql import *
 cur_dir=os.path.dirname(os.path.abspath(__file__))
 file_path=os.path.join(cur_dir,'tempFile')
 nearline_url_aws="http://aws-online-recommend-parent.ec-test.qg-rc.net"
-nearline_url_aliyun="http://172.16.9.90:19999"
+nearline_url_aliyun="http://172.16.9.90:19999"
\ No newline at end of file
+search_url_aliyun="http://172.16.9.90:8091"
+
+nearline_url_bd="https://online-recommend-bd.liangkebang.net"
\ No newline at end of file

recommend/cold_boot.py

+from recommend import *
\ No newline at end of file

recommend/product_result_page_less.py

+import json
+
 import requests
 
 from recommend import *
@@ -24,10 +26,10 @@ def diwenci(uuid=None,gid=None):
     word=jsonpath.jsonpath(temp.json(),'$..word')
     return word
 
-def searchTopic(content):
+def searchTopic(uuid,gid,content):
     #uuid,gid=genUuidDeviceid()
-    uuid=""
-    gid="4205df9318c69521"
+    #uuid=""
+    #gid="58A9A9D2-6445-4550-9715-72151CC1A726"
     print("uuid:",uuid,"====>","gid",gid)
     #diwenci_result=diwenci(uuid,gid)
     result_skus=preconditions(content,uuid,gid)
@@ -35,7 +37,7 @@ def searchTopic(content):
     print("搜索结果的skus,",result_skus)
     #query+similar_query
     similar_temp=[]
-    similar_change_sql=recommend_hot_query_similar_info_sql+" where hot_query="+"'"+str(content)+"'" +" order by rank asc"
+    similar_change_sql=recommend_hot_query_similar_info_sql+" where hot_query="+"'"+str(content)+"'" +" order by rank asc limit 5"
     similar_df=execmysl('89',similar_change_sql)
     similar_query_word=similar_df['hot_query_similar'].to_list() if   not similar_df.empty else []
     similar_temp+=similar_query_word
@@ -44,40 +46,85 @@ def searchTopic(content):
     ##sku+query
     if not result_skus:
 
-        ##如果搜索没有商品数，则结果=相似搜索词+底纹词,并去重
-        return removeRepeat(similar_temp+diwenci_result[:3])
+        #print("底纹词,",diwenci_result)
 
+        ##如果搜索没有商品数，则结果=相似搜索词+底纹词,并去重
+        return removeRepeat(similar_temp+diwenci_result)[:8],result_skus
 
     sku_match_change_sql=concatSql(recommend_sku_query_match_info_sql,**{"sku_no":result_skus}) +'order by rank asc'
     sku_match_df=execmysl('89',sku_match_change_sql)
-    sku_match_change_df=sku_match_df.astype(str)[['sku_no','query']]. \
-            groupby(by='query')['sku_no'].count().reset_index(name='count') \
+    sku_match_change_df=sku_match_df.astype(str)[['sku_no','query','rank']]. \
+            groupby(by=['query'])['sku_no'].count().reset_index(name='count') \
         if not  sku_match_df.empty else []
     ##通过query分组拿到skuno，如果3个元素的值都小于1，则空列表
     try:
-        sku_match_temp=sku_match_change_df[sku_match_change_df['count']>1].sort_values(by='count',ascending=False)['query'].to_list()
+        if len(result_skus)==1:
+            sku_match_temp=sku_match_change_df['query'].to_list()
+        else:
+            sku_match_temp=sku_match_change_df[sku_match_change_df['count']>1].sort_values(by=['count'],ascending=0)['query'].to_list()
     except:
         sku_match_temp=[]
 
     finally:
-        if not (similar_temp and sku_match_temp):
-            return removeRepeat(diwenci_result)
         print("商品相关词:",sku_match_temp)
+        if not similar_temp and not sku_match_temp:
+            return removeRepeat(diwenci_result),result_skus
         ##从sku的query结果中，去掉相似query，然后2边交叉排序
         sku_match_temp_remove_from_similar=bingji(sku_match_temp,similar_temp)
         ##因为随机取不好弄，所以拿到所有数据交叉的
-
         ttemp=listCross(similar_temp,sku_match_temp_remove_from_similar)
         if len(ttemp)<8:
             return removeRepeat(ttemp+diwenci_result)[:8]
-        return ttemp
+
+        return removeRepeat(listCross(similar_temp,sku_match_temp_remove_from_similar)+diwenci_result)[:8],result_skus
+
+def supplementSearch(uuid,gid,searchword):
+    temp=[]
+    url=search_url_aliyun+'/searchReplenishment'
+    headers={
+        "XQG-USER-UUID":uuid if uuid else "",
+        "Content-Type":"application/json"
+    }
+    body={
+        "deviceId":gid if gid else "" ,
+        "userUuid": uuid if uuid else "",
+        "extraParams": {
+            "terminalType": 1
+        },
+        "searchContent": "",
+        "sortType": 1
+    }
+    for i in searchword[:3]:
+        body['searchContent']=i
+        t=requests.post(url,headers=headers,data=json.dumps(body))
+        #print(body)
+        #print(t.json())
+        skus=jsonpath.jsonpath(t.json(),'$..skuNo')
+        temp+=skus
+    return temp
+
 
 
 
 
+def main(uuid,gid,searchword):
+    words,search_skus=searchTopic(uuid,gid,searchword)
+    recommend_supplement_result=supplementSearch(uuid,gid,words)
+    print("补量的推荐商品数据如下:",len(recommend_supplement_result),recommend_supplement_result)
+    print("补量的搜索商品数据:",search_skus)
+    if search_skus:
+        temp=[i for i in search_skus if i  in recommend_supplement_result]
+        print("以上数据是否有重复:",temp if len(temp)>=1 else 0)
+    print("补量的推荐词如下:",words)
+
+    return words,recommend_supplement_result
+
 
 
 
 if __name__ == '__main__':
-    print(searchTopic('苹果耳机'))
+    uuid=""
+    gid="65E052A9-7C04-433A-80B9-927964707456"
+    searchword='beats耳机'
+    print("最终推荐结果:",main(uuid,gid,searchword))
 

recommend/searchTopic.py

@@ -19,7 +19,7 @@ def getseed(top10_sku):
     result['sku_nos']=top10_sku
     sql=concatSql(skuinfo_sql,**{'sku_no':top10_sku})
     print(sql)
-    df=execmysl(119,sql)
+    df=execmysl(89,sql)
     df['cid3']=df['cid3'].astype('string')
     df['brand_id']=df['brand_id'].astype('string')
     cid3=df.groupby(by=['cid3']).groups.__repr__()#['cid3']#.max()
@@ -69,14 +69,16 @@ def seedSkus(top10_sku):
 if __name__ == '__main__':
     # top10_sku=[1,2,3,4]
     # print(similarskus(top10_sku))
-    aa={}
-    skus={'skuinfo': [{'id': 13573, 'sku_no': '10982072256513', 'price': 139.9,'cid1': 1320, 'cid2': 1583, 'cid3': '1592', 'brand_name': '蜀道香','brand_id': 'nan', 'source_type': 6},
-                      {'id': 229065184, 'sku_no': '275043759168001', 'price': 25.0, 'cid1': 1320, 'cid2': 1583, 'cid3': '1590', 'brand_name': '口水娃','brand_id': 'nan', 'source_type': 1},
-                      {'id': 154060057, 'sku_no': '37993171924993', 'price': 39.9, 'cid1': 1320, 'cid2': 1583, 'cid3': '1590', 'brand_name': '口水娃', 'brand_id': 10590.0, 'source_type': 2},
-                      {'id': 154058134, 'sku_no': '37993180305409', 'price': 36.0, 'cid1': 1320, 'cid2': 1583, 'cid3': '1590', 'brand_name': '口水娃', 'brand_id': 10590.0, 'source_type': 2},
-                      {'id': 55340, 'sku_no': '37993239032321', 'price': 21.9, 'cid1': 1320, 'cid2': 1583,'cid3': '1590', 'brand_name': '口水娃', 'brand_id': 10590.0, 'source_type': 2},
-                      {'id': 534359815, 'sku_no': '37993297751553', 'price': 69.9, 'cid1': 1320, 'cid2': 1583, 'cid3': '1590', 'brand_name': '盐津铺子', 'brand_id': 2574.0, 'source_type': 2},
-                      {'id': 337773081, 'sku_no': '37993473904641', 'price': 35.9, 'cid1': 1320, 'cid2': 1583, 'cid3': '1590', 'brand_name': '口水娃', 'brand_id': 10590.0, 'source_type': 2},
-                      {'id': 154060077, 'sku_no': '37993633292801', 'price': 13.9, 'cid1': 1320, 'cid2': 1583, 'cid3': '1590', 'brand_name': '口水娃', 'brand_id': 10590.0, 'source_type': 2},
-                      {'id': 56569, 'sku_no': '37993926891009', 'price': 19.0, 'cid1': 1320, 'cid2': 1583, 'cid3': '1590', 'brand_name': '口水娃', 'brand_id': 10590.0, 'source_type': 2}]}
-    print('最终结果：',seedSkus(skus))
+    # aa={}
+    # skus={'skuinfo': [{'id': 13573, 'sku_no': '10982072256513', 'price': 139.9,'cid1': 1320, 'cid2': 1583, 'cid3': '1592', 'brand_name': '蜀道香','brand_id': 'nan', 'source_type': 6},
+    #                   {'id': 229065184, 'sku_no': '275043759168001', 'price': 25.0, 'cid1': 1320, 'cid2': 1583, 'cid3': '1590', 'brand_name': '口水娃','brand_id': 'nan', 'source_type': 1},
+    #                   {'id': 154060057, 'sku_no': '37993171924993', 'price': 39.9, 'cid1': 1320, 'cid2': 1583, 'cid3': '1590', 'brand_name': '口水娃', 'brand_id': 10590.0, 'source_type': 2},
+    #                   {'id': 154058134, 'sku_no': '37993180305409', 'price': 36.0, 'cid1': 1320, 'cid2': 1583, 'cid3': '1590', 'brand_name': '口水娃', 'brand_id': 10590.0, 'source_type': 2},
+    #                   {'id': 55340, 'sku_no': '37993239032321', 'price': 21.9, 'cid1': 1320, 'cid2': 1583,'cid3': '1590', 'brand_name': '口水娃', 'brand_id': 10590.0, 'source_type': 2},
+    #                   {'id': 534359815, 'sku_no': '37993297751553', 'price': 69.9, 'cid1': 1320, 'cid2': 1583, 'cid3': '1590', 'brand_name': '盐津铺子', 'brand_id': 2574.0, 'source_type': 2},
+    #                   {'id': 337773081, 'sku_no': '37993473904641', 'price': 35.9, 'cid1': 1320, 'cid2': 1583, 'cid3': '1590', 'brand_name': '口水娃', 'brand_id': 10590.0, 'source_type': 2},
+    #                   {'id': 154060077, 'sku_no': '37993633292801', 'price': 13.9, 'cid1': 1320, 'cid2': 1583, 'cid3': '1590', 'brand_name': '口水娃', 'brand_id': 10590.0, 'source_type': 2},
+    #                   {'id': 56569, 'sku_no': '37993926891009', 'price': 19.0, 'cid1': 1320, 'cid2': 1583, 'cid3': '1590', 'brand_name': '口水娃', 'brand_id': 10590.0, 'source_type': 2}]}
+    # print('最终结果：',seedSkus(skus))
\ No newline at end of file
+    top10_sku=['327855415236097', ' 323546673847809', ' 266105822909441', ' 437347360180225', '265358221515265', '265347593669633', '46676152944641', '294644949195265', '256787949822465', '277221256729601']
+    print(getseed(top10_sku))
\ No newline at end of file

recommend/temp.py

 # -*- coding: utf-8 -*-
 from recommend import *
 tempresult=defaultdict(list)
-a=[{"a":1,"b":11},{"a":2,"b":22},{"a":3,"b":33}]
+a=[{"a":1,"b":11},{"a":1,"b":22},{"a":3,"b":33}]
 df=pd.DataFrame(a)
-temp=df.fillna('invalid').astype(str).to_dict(orient='list')
-#print(temp['a'].remove('invalid'))
-print(temp)
-for i in temp['a']:
-    print(i,type(i))
+print(df)
+t=df.groupby(by=['a']).groups#.__repr__()
+print(t,type(t))
 
 #print(temp)
\ No newline at end of file

search/__init__.py

@@ -2,7 +2,7 @@ from databaseConn import *
 from tools import *
 from tools.fileOperation import *
 from tools.listOperation import *
-from databaseConn.redisOperation import *
+#from databaseConn.redisOperation import *
 from tools.publicFun import *
 cur_dir=os.path.dirname(os.path.abspath(__file__))
 file_path=os.path.join(cur_dir,'tempFile')

search/abSearch.py

@@ -17,6 +17,8 @@ def requestsend(uuid,deviceid,searchContent,selectedActivitie,page=1,searchtype=
         raise Exception('env[{}]只能:test|pro'.format(env))
     if env=='online':
         baseurl="http://caesar-gateway.q-gp.com"
+    elif env=="test":
+        baseurl="http://172.16.9.90:8091"
     else:
         baseurl="http://open-search-engine.ec-{env}.qg-rc.net".format(env=env) if env=='test' \
             else "http://open-search-engine.qg-{env}.qgwebservice.com".format(env=env)

tools/__init__.py

 from databaseConn.mysqlOperation import *
 from recommend.publicSql import *
 from databaseConn import *
+git_url="http://git.quantgroup.cn/"
+git_token='zubux2fMyyp8s8Cys3T6'
+
+git_headers={
+    "PRIVATE-TOKEN":git_token
+}
 
-dapan_sql
\ No newline at end of file

tools/build_git.py

+from tools import *
+import jenkins, requests, re, os
+import pandas as pd
+
+base_url = "http://jenkins.quantgroups.cn"
+# java_url="/TKE/job/tke-java/"
+jk = jenkins.Jenkins(base_url, username="qiuyue.gui", password="1234567890Yue@")
+header = {"Host": "jenkins.quantgroups.cn"}
+
+
+def buildJob(project, branch, env, jobname="java",prefix_job='ACK'):
+    '''
+    :param project:
+    :param branch:
+    :param env:
+    :param jobname: 只支持tke的构建
+    :return:
+    '''
+    jobname=jobname.upper()
+    jobs = jk.get_all_jobs()
+    #print("jobs:",jobs)
+    jobnames = []
+    joburls = []
+    projects_branch = get_project_branch(project)
+    #projects_branch={"kdsp":["master11"]}
+    temp_branchs=projects_branch.get(project)
+    if branch not in temp_branchs:
+        raise  Exception("branch({0})项目不存在，确定一下".format(branch), temp_branchs)
+
+    jobname = prefix_job+'-' + jobname if prefix_job not in jobname else jobname
+    for kv in jobs:
+        jobnames.append(kv.get('name'))
+        joburls.append(kv.get('url'))
+    #jobs_name_url = dict(zip(jobnames, joburls))
+    if jobname not in jobnames:
+        print("jobname({0})错误，需要在其中一个:{1}".format(jobname, ','.join(jobnames)))
+        return 0
+    # jk.get_build_info()
+    # jk.build_job(jobname,branch,env)
+    #job_config = jk.get_job_config(jobname)
+    params = {"GIT_REPO": project, "DEPLOY": 'true', 'NAMESPACE': env, "BRANCH_NAME": branch}
+    # branchs_url=requests.get("")
+    t = jk.build_job(jobname, params)
+    print("project({0}-{1}-{2}) build succ".format(project, branch, env), t)
+
+def get_project_branch(project):
+    """
+    :param project:
+    :return: 根据project获取分支信息
+    """
+    try:
+        temp_branchs=[]
+        num=1
+        project_url =git_url+'/api/v4/projects?search='+project
+        temp=requests.get(project_url,headers=git_headers)
+        project_name=jsonpath.jsonpath(temp.json(),'$[*].name')
+        project_id=jsonpath.jsonpath(temp.json(),'$[*].id')
+        project_info=dict(zip(project_name,project_id))
+        for i in range(10):
+            #brand_url=git_url+"/api/v4/projects/{project_id}/repository/branches?per_page=100".format(project_id=project_info.get(project))
+            brand_url=git_url+"/api/v4/projects/{project_id}/repository/branches?per_page=100&page={num}".format(project_id=project_info.get(project),num=i)
+            temp=requests.get(brand_url,headers=git_headers)
+            #print(temp.json())
+            brands_name=jsonpath.jsonpath(temp.json(),'$[*].name')
+            if brands_name:
+                temp_branchs+=brands_name
+            else:
+                break
+        project_brands={project:temp_branchs}
+        return project_brands
+    except:
+        print("project[{}]不存在".format(project))
+        traceback.print_exc(3)
+
+
+def main(project,prefix_job='ACK'):
+    #="nearline-recommend-parent"
+    project_branch={
+        "kdsp":{
+            "branch":"feature-test1-test-202206081010",
+            "env":'bd',
+            "jobname":"java"},
+
+        "nearline-recommend-parent":{
+            "branch":"master",
+            "env":'bd',
+            "jobname":"java"
+        },
+        "online-recommend":{
+            "branch":"master",
+            "env":'bd',
+            "jobname":"java"
+        }
+    }
+    branch=project_branch.get(project).get('branch')
+    env=project_branch.get(project).get('env')
+    jobname=project_branch.get(project).get('jobname')
+    if not (branch and env and jobname):
+        raise Exception('项目错误，无法构建')
+    buildJob(project, branch, env, jobname=jobname,prefix_job=prefix_job)
+
+
+if __name__ == '__main__':
+    project='online-recommend'
+    main(project)

tools/changeLXQpassword.py

@@ -28,4 +28,6 @@ def modifyPasseord(name,namespace):
         print("this is update")
 
 if __name__ == '__main__':
-    modifyPasseord('haiyuan.wen','vcc2')
+
\ No newline at end of file
+    modifyPasseord('haiyuan.wen','yxm')
+

tools/fileOperation.py

@@ -58,8 +58,9 @@ def readRenameColums(filename,fcolums,sheetname=0):
     return df
 
 
+
 if __name__ == '__main__':
-    filename="/Users/dm/Downloads/量化派/测试用例/召回测试耗时的数据.xlsx"
-    df=readRenameColums(filename,['id'])
-    print(df)
+    filename="/Users/dm/Downloads/量化派/特征/百行贷前特征code(1).xlsx"
+    df=readFile(filename)
+    print(df['指标英文'].to_list())
 

tools/listOperation.py

@@ -65,6 +65,33 @@ def listSubgroup(tt,size=6):
         temp.append(tt[i:i+size])
     return temp
 
+def divlist(a,num=0):
+    if not num:
+        return a
+    divnum=int(len(a)/num)
+    temp=[]
+    flag=0
+    for i in range(divnum):
+        temp.append(a[flag:flag+divnum])
+        flag+=divnum
+        if divnum==i+1:
+            temp.append(a[flag:])
+
+    return temp
+
+def split_by_length(init_list, children_list_len):
+    """
+    按照长度拆分给定数组
+    :param init_list:
+    :param children_list_len:
+    :return:
+    """
+    #print(next(tt[0]))
+    list_of_groups = zip(*(iter(init_list),) * children_list_len)
+    end_list = [list(i) for i in list_of_groups]
+    count = len(init_list) % children_list_len
+    end_list.append(init_list[-count:]) if count != 0 else end_list
+    return end_list
 
 def countlist(a,num=0):
     from collections import Counter
@@ -92,7 +119,8 @@ def bingji(a,b):
 if __name__ == '__main__':
     a=[2,2,4,3,5,6,7,8]
     b=[10,2,4,3,9]
-    print(bingji(b,a))
+    print(split_by_length(a,5))
+    #print(int(11/2))
 
     #a=[[1,2],[2,3]]
     #print(mergelist(a))

tools/mulThread.py

+import threading,time
+from tools.listOperation import *
+
+
+class MyThread(threading.Thread):
+    def __init__(self,func,divlist):
+        super(MyThread,self).__init__()
+        self.func=func
+        self.divlist=divlist
+    def run(self):
+        #time.sleep(1)
+        #print(threading.currentThread().name)
+        self.func(self.divlist)
+        #print('over')
+
+def func(divlist):
+    for i in divlist:
+        print(i)
+
+def main(thnum):
+    num=list(range(100))
+    #thnum=5
+    divnum=int(len(num)/thnum)
+    divlists=split_by_length(num,divnum)
+    #print(divlists)
+    temp=[]
+    for i in range(thnum):
+        t=MyThread(func,divlists[i])
+        temp.append(t)
+        t.start()
+    for i in temp:
+        i.join()
+
+
+if __name__ == '__main__':
+     t=time.time()
+     main(5)
+     tt=time.time()
+     print(tt-t)
+     # print("=="*50)
+     # t=time.time()
+     # for i in list(range(100)):
+     #     print(i)
+     # tt=time.time()
+     # print(tt-t)
+
+
+
+
+
+
+

tools/publicFun.py

 from tools import *
+import hashlib
+
+def md5jiami(mess):
+    t=hashlib.md5(mess)
+    return t.hexdigest()
+
 def genUuidDeviceid():
     """
     deviced必须随机生成，不然无法统计spu总数。故就算uuid是相同的，deviceid也不能相同

tools/temp.py

+import requests
+
+codes=['baihang_tile_info#homeInfoFirstTime', 'baihang_tile_info#homeInfoLastTime', 'baihang_tile_info#homeInfoNum', 'baihang_tile_info#homeInfoNumDrop', 'baihang_tile_info#workInfoFirstTime', 'baihang_tile_info#workInfoLastTime', 'baihang_tile_info#workInfoNum', 'baihang_tile_info#workInfoNumDrop', 'baihang_tile_info#firstQueryTime', 'baihang_tile_info#lastQueryTime', 'baihang_tile_info#queryNumHis', 'baihang_tile_info#queryNumHisApply', 'baihang_tile_info#queryCategoryHisApply', 'baihang_tile_info#queryHistoryReason1And90dNum', 'baihang_tile_info#queryHistoryReason1And365dNum', 'baihang_tile_info#queryHistory90dAnd365dRatio', 'baihang_tile_info#queryHistory30dNum', 'baihang_tile_info#diffQueryReason1Days', 'baihang_tile_info#diffFirstQueryDays', 'baihang_tile_info#newLoan90dNum', 'baihang_tile_info#newLoan360dNum', 'baihang_tile_info#newLoan90dAnd360dRatio', 'baihang_tile_info#revolvingLoanNoValidAccountRatio', 'baihang_tile_info#revolvingLoanNoValidAccountNum', 'baihang_tile_info#allSummaryLoanNums', 'baihang_tile_info#newLoanAmount180d', 'baihang_tile_info#newLoanAmount360d', 'baihang_tile_info#newLoanAmount180dAnd360dRatio', 'baihang_tile_info#newLoanTenant180dAnd360dRatio', 'third_data_source#baihang_homeInfo', 'third_data_source#baihang_nonRevolvingLoan_D180_applyTenantCount', 'third_data_source#baihang_nonRevolvingLoan_D180_averageLoanAmount', 'third_data_source#baihang_nonRevolvingLoan_D180_loanAmount', 'third_data_source#baihang_nonRevolvingLoan_D180_loanCount', 'third_data_source#baihang_nonRevolvingLoan_D180_loanTenantCount', 'third_data_source#baihang_nonRevolvingLoan_D180_maxLoanAmount', 'third_data_source#baihang_nonRevolvingLoan_D180_overdueLoanCount', 'third_data_source#baihang_nonRevolvingLoan_D30_applyTenantCount', 'third_data_source#baihang_nonRevolvingLoan_D30_averageLoanAmount', 'third_data_source#baihang_nonRevolvingLoan_D30_loanAmount', 'third_data_source#baihang_nonRevolvingLoan_D30_loanCount', 'third_data_source#baihang_nonRevolvingLoan_D30_loanTenantCount', 'third_data_source#baihang_nonRevolvingLoan_D30_maxLoanAmount', 'third_data_source#baihang_nonRevolvingLoan_D30_overdueLoanCount', 'third_data_source#baihang_nonRevolvingLoan_D360_applyTenantCount', 'third_data_source#baihang_nonRevolvingLoan_D360_averageLoanAmount', 'third_data_source#baihang_nonRevolvingLoan_D360_loanAmount', 'third_data_source#baihang_nonRevolvingLoan_D360_loanCount', 'third_data_source#baihang_nonRevolvingLoan_D360_loanTenantCount', 'third_data_source#baihang_nonRevolvingLoan_D360_maxLoanAmount', 'third_data_source#baihang_nonRevolvingLoan_D360_overdueLoanCount', 'third_data_source#baihang_nonRevolvingLoan_D90_applyTenantCount', 'third_data_source#baihang_nonRevolvingLoan_D90_averageLoanAmount', 'third_data_source#baihang_nonRevolvingLoan_D90_loanAmount', 'third_data_source#baihang_nonRevolvingLoan_D90_loanCount', 'third_data_source#baihang_nonRevolvingLoan_D90_loanTenantCount', 'third_data_source#baihang_nonRevolvingLoan_D90_maxLoanAmount', 'third_data_source#baihang_nonRevolvingLoan_D90_overdueLoanCount', 'third_data_source#baihang_nonRevolvingLoan_summary_loanCount', 'third_data_source#baihang_nonRevolvingLoan_summary_maxOverdueStatus', 'third_data_source#baihang_nonRevolvingLoan_summary_openLoanCount', 'third_data_source#baihang_nonRevolvingLoan_summary_overdueCount', 'third_data_source#baihang_nonRevolvingLoan_summary_remainingAmount', 'third_data_source#baihang_nonRevolvingLoan_summary_remainingMaxOverdueStatus', 'third_data_source#baihang_nonRevolvingLoan_summary_remainingOverdueAmount', 'third_data_source#baihang_nonRevolvingLoan_summary_remainingOverdueLoanCount', 'third_data_source#baihang_personalProfile_mobileCount', 'third_data_source#baihang_queryHistory', 'third_data_source#baihang_reportHeader_queryResult', 'third_data_source#baihang_revolvingLoan_D180_accountCount', 'third_data_source#baihang_revolvingLoan_D180_applyTenantCount', 'third_data_source#baihang_revolvingLoan_D180_creditLimitSum', 'third_data_source#baihang_revolvingLoan_D180_lendingAmount', 'third_data_source#baihang_revolvingLoan_D180_overdueAccountCount', 'third_data_source#baihang_revolvingLoan_D30_accountCount', 'third_data_source#baihang_revolvingLoan_D30_applyTenantCount', 'third_data_source#baihang_revolvingLoan_D30_creditLimitSum', 'third_data_source#baihang_revolvingLoan_D30_lendingAmount', 'third_data_source#baihang_revolvingLoan_D30_overdueAccountCount', 'third_data_source#baihang_revolvingLoan_D360_accountCount', 'third_data_source#baihang_revolvingLoan_D360_applyTenantCount', 'third_data_source#baihang_revolvingLoan_D360_creditLimitSum', 'third_data_source#baihang_revolvingLoan_D360_lendingAmount', 'third_data_source#baihang_revolvingLoan_D360_overdueAccountCount', 'third_data_source#baihang_revolvingLoan_D90_accountCount', 'third_data_source#baihang_revolvingLoan_D90_applyTenantCount', 'third_data_source#baihang_revolvingLoan_D90_creditLimitSum', 'third_data_source#baihang_revolvingLoan_D90_lendingAmount', 'third_data_source#baihang_revolvingLoan_D90_overdueAccountCount', 'third_data_source#baihang_revolvingLoan_summary_accountCount', 'third_data_source#baihang_revolvingLoan_summary_creditLimitSum', 'third_data_source#baihang_revolvingLoan_summary_maxCreditLimitPerTenant', 'third_data_source#baihang_revolvingLoan_summary_maxOverdueStatus', 'third_data_source#baihang_revolvingLoan_summary_overdueCount', 'third_data_source#baihang_revolvingLoan_summary_remainingAmount', 'third_data_source#baihang_revolvingLoan_summary_remainingMaxOverdueStatus', 'third_data_source#baihang_revolvingLoan_summary_remainingOverdueAccountCount', 'third_data_source#baihang_revolvingLoan_summary_remainingOverdueAmount', 'third_data_source#baihang_revolvingLoan_summary_validAccountCount', 'third_data_source#baihang_workInfo', 'third_data_source#baihang_revolvingLoan_D360_lendingAmount_add_nonRevo', 'third_data_source#baihang_revolvingLoan_D360_accountCount_div_add_nonRevo', 'third_data_source#baihang_revolvingLoan_D30_overdueAccountCount_add_nonRevo_div_90', 'third_data_source#baihang_revolvingLoan_D90_overdueAccountCount_add_nonRevo_div_360', 'third_data_source#baihang_revolvingLoan_D360_lendingAmount_sub_nonRevo', 'third_data_source#baihang_revolvingLoan_D90_accountCount_div_add_nonRevo', 'third_data_source#baihang_revolvingLoan_summary_overdueCount_div_summary_accountCount', 'third_data_source#baihang_nonRevolvingLoan_D30_overdueLoanCount_div_360', 'third_data_source#baihang_revolvingLoan_D90_accountCount_add_nonRevo_div_360', 'third_data_source#baihang_revolvingLoan_D90_accountCount_sub_nonRevo', 'third_data_source#baihang_revolvingLoan_D180_overdueAccountCount_add_nonRevo', 'third_data_source#baihang_revolvingLoan_D180_accountCount_div_add_nonRevo', 'third_data_source#baihang_revolvingLoan_D360_accountCount_add_nonRevo', 'third_data_source#baihang_revolvingLoan_D30_lendingAmount_add_nonRevo_div_360', 'third_data_source#baihang_revolvingLoan_D30_lendingAmount_div_360']
+base_url="https://finance-feature-calc.tjzimu.com"
+url="/calc/features"
+
+
+headers = {
+    'Content-Type': 'application/x-www-form-urlencoded',
+    'rc_auth_key': 'rc_offline',
+    'rc_auth_secret': '9d9}bc24!e1z1x3`(x~4r29d$+45n3)\'zb696b$85e>_]p2&4f{,a3~8b3e_ldt^'
+}
+
+body={
+    "user_uuid":"26484438-2655-4c89-a4d2-fbdabe9381ec",
+    "codes":"{code}",
+    "business_type":0
+}
+
+def httpre(code):
+    #code=','.join(codes[:])
+    body['codes']=code
+    #print(body)
+    t=requests.post(base_url+url,headers=headers,data=body)
+    return t.json()
+if __name__ == '__main__':
+    for i in codes:
+        print("跑的特征:",i)
+        print(httpre(i))
+
+