1

recommend/cid3brandname_group_recommend.py

 from recommend import *
+
+def backBatch(gid,isuuid=None):
+    temp=defaultdict(list)
+    if isuuid:
+        brandname_redis="user_recall_mapping_jg_Category:"+gid
+        cid_redis="user_recall_mapping_jg_brand:"+gid
+
+    else:
+        brandname_redis="device_recall_mapping_jg_Category:"+gid
+        cid_redis="device_recall_mapping_jg_brand:"+gid
+
+    brandname_recallBatchUuid=getRedisValue(brandname_redis).get(brandname_redis)
+    cid_recallBatchUuid=getRedisValue(cid_redis).get(cid_redis)
+
+    brandname_batch_redis= '1' + ":personal_recall_product_brand:" + brandname_recallBatchUuid
+
+    brandname_recallBatchUuid=getRedisValue(brandname_batch_redis).get(brandname_batch_redis)
+    print("brandname的批次数据：",brandname_recallBatchUuid)
+    temp['brandname_batch']=brandname_recallBatchUuid
+
+    cid_batch_redis= '2' + ":personal_recall_product_brand:" + cid_recallBatchUuid
+
+    cid_recallBatchUuid=getRedisValue(cid_batch_redis).get(cid_batch_redis)
+    temp['cid_batch']=cid_recallBatchUuid
+    print("cid3的批次数据：",cid_recallBatchUuid)
+    return temp
+
+def buzu():
+    '''
+    :return: 本次补足的数据和appstart触发的逻辑是一样的
+    '''
+    return appTopic()
+
 def appTopic():
     temp=defaultdict(list)
     cid3_cate_brand_heat_rank_change_sql=cate_brand_heat_rank_sql+' where show_type=1 order by rank asc limit 30'
+    print(cid3_cate_brand_heat_rank_change_sql)
     brandname_cate_brand_heat_rank_change_sql=cate_brand_heat_rank_sql+' where show_type=2 order by rank asc limit 30'
+    print(brandname_cate_brand_heat_rank_change_sql)
     cid3_df=execmysl(119,cid3_cate_brand_heat_rank_change_sql)
     brandname_df=execmysl(119,brandname_cate_brand_heat_rank_change_sql)
     temp['apptopic_cid3']=cid3_df['show_id'].to_list() if not cid3_df.empty else []
-    temp['apptopic_brandname']=cid3_df['show_id'].to_list() if not cid3_df.empty else []
+    temp['apptopic_brandname']=brandname_df['show_id'].to_list() if not cid3_df.empty else []
     return temp
 
-def clickTopic(skuno,cid3=None,brandid=None):
+def clickTopic(skuno=None,cid3=None,brandid=None,cid2=None,cid1=None):
     temp=defaultdict()
     from recommend.publicFunc import skuinfo
+    if skuno:
         sku_infos=skuinfo(skuno)
-    if cid3 and brandid:
+    else:
+        sku_infos={}
+    if cid3 and brandid and cid1 and cid2:
         sku_infos['cid3']=cid3
-        sku_infos['brand_id']=brandid
-
+        sku_infos['brand_id']=int(brandid)
+        sku_infos['cid2']=cid2
+        sku_infos['cid1']=cid1
+    print(sku_infos)
     cid3_intention_change_sql=concatSql(cate_brand_intention_score_sql,
-                                        **{"show_id":[sku_infos.get('cid3'),sku_infos.get('cid2'),sku_infos.get('cid1')]})
+                                        **{"category_id":[sku_infos.get('cid3'),sku_infos.get('cid2'),sku_infos.get('cid1')]})
     cid3_intention_change_sql+=" or brand_id='{}'".format(sku_infos.get('brand_id'))
     print('cicktopic 转换后的sql:',cid3_intention_change_sql)
     cid3brandname_df=execmysl(119,cid3_intention_change_sql)#.sort_values(by=['rank'])['']
     print('cicktopic的结果:',cid3brandname_df.head(1))
     ##cid3+brandid
-    temp['clicktopic_cid3']=cid3brandname_df[(cid3brandname_df['show_id']==sku_infos.get('cid3'))
+    temp['clicktopic_cid3']=cid3brandname_df[(cid3brandname_df['category_id']==sku_infos.get('cid3'))
                                              & (cid3brandname_df['show_type']==1) &(cid3brandname_df['brand_id']==sku_infos.get('brand_id'))] \
-                    .sort_values(by=['rank'])['show_id'].to_list()[:30]  if not cid3brandname_df.empty else []
+                    .sort_values(by=['final_score'],ascending=False)['show_id'].to_list()[:30]  if not cid3brandname_df.empty else []
     ##cid2+brandid
-    temp['clicktopic_cid3']+=cid3brandname_df[(cid3brandname_df['show_id']==sku_infos.get('cid2'))
+    temp['clicktopic_cid3']+=cid3brandname_df[(cid3brandname_df['category_id']==sku_infos.get('cid2'))
                                               & (cid3brandname_df['show_type']==1) &(cid3brandname_df['brand_id']==sku_infos.get('brand_id'))] \
-                                 .sort_values(by=['rank'])['show_id'].to_list()[:30]  if not cid3brandname_df.empty else []
+                                 .sort_values(by=['final_score'],ascending=False)['show_id'].to_list()[:30]  if not cid3brandname_df.empty else []
     ##cid1+brandid
-    temp['clicktopic_cid3']+=cid3brandname_df[(cid3brandname_df['show_id']==sku_infos.get('cid1'))
+    temp['clicktopic_cid3']+=cid3brandname_df[(cid3brandname_df['category_id']==sku_infos.get('cid1'))
                                               & (cid3brandname_df['show_type']==1) &(cid3brandname_df['brand_id']==sku_infos.get('brand_id'))] \
-                                 .sort_values(by=['rank'])['show_id'].to_list()[:30]  if not cid3brandname_df.empty else []
+                                 .sort_values(by=['final_score'],ascending=False)['show_id'].to_list()[:30]  if not cid3brandname_df.empty else []
     ##cid3+无名品牌分
-    temp['clicktopic_cid3']+=cid3brandname_df[(cid3brandname_df['show_id']==sku_infos.get('cid3'))
+    temp['clicktopic_cid3']+=cid3brandname_df[(cid3brandname_df['category_id']==sku_infos.get('cid3'))
                                               & (cid3brandname_df['show_type']==1) &(cid3brandname_df['brand_id']==-1)] \
-                                 .sort_values(by=['rank'])['show_id'].to_list()[:30]  if not cid3brandname_df.empty else []
+                                 .sort_values(by=['final_score'],ascending=False)['show_id'].to_list()[:30]  if not cid3brandname_df.empty else []
     ##cid2+无名品牌分
-    temp['clicktopic_cid3']+=cid3brandname_df[(cid3brandname_df['show_id']==sku_infos.get('cid2'))
+    temp['clicktopic_cid3']+=cid3brandname_df[(cid3brandname_df['category_id']==sku_infos.get('cid2'))
                                               & (cid3brandname_df['show_type']==1) &(cid3brandname_df['brand_id']==-1)] \
-                                 .sort_values(by=['rank'])['show_id'].to_list()[:30]  if not cid3brandname_df.empty else []
+                                 .sort_values(by=['final_score'],ascending=False)['show_id'].to_list()[:30]  if not cid3brandname_df.empty else []
     ##cid1+无名品牌分
-    temp['clicktopic_cid3']+=cid3brandname_df[(cid3brandname_df['show_id']==sku_infos.get('cid1'))
+    temp['clicktopic_cid3']+=cid3brandname_df[(cid3brandname_df['category_id']==sku_infos.get('cid1'))
                                               & (cid3brandname_df['show_type']==1) &(cid3brandname_df['brand_id']==-1)] \
-                                 .sort_values(by=['rank'])['show_id'].to_list()[:30]  if not cid3brandname_df.empty else []
+                                 .sort_values(by=['final_score'],ascending=False)['show_id'].to_list()[:30]  if not cid3brandname_df.empty else []
     ##无品类分+品牌分
-    temp['clicktopic_cid3']+=cid3brandname_df[(cid3brandname_df['show_id']==-1)
+    temp['clicktopic_cid3']+=cid3brandname_df[(cid3brandname_df['category_id']==-1)
                                               & (cid3brandname_df['show_type']==1) &(cid3brandname_df['brand_id']==sku_infos.get('brand_id'))] \
-                                 .sort_values(by=['rank'])['show_id'].to_list()[:30]  if not cid3brandname_df.empty else []
+                                 .sort_values(by=['final_score'],ascending=False)['show_id'].to_list()[:30]  if not cid3brandname_df.empty else []
 
     temp['clicktopic_cid3']=removeRepeat(temp['clicktopic_cid3'])[:30]
     ##cid3+brandid
-    temp['clicktopic_brandname']=cid3brandname_df[(cid3brandname_df['show_id']==sku_infos.get('cid3'))
+    temp['clicktopic_brandname']=cid3brandname_df[(cid3brandname_df['category_id']==sku_infos.get('cid3'))
                                              & (cid3brandname_df['show_type']==2) &(cid3brandname_df['brand_id']==sku_infos.get('brand_id'))] \
-                                .sort_values(by=['rank'])['show_id'].to_list()[:30]  if not cid3brandname_df.empty else []
+                                .sort_values(by=['final_score'],ascending=False)['show_id'].to_list()[:30]  if not cid3brandname_df.empty else []
     ##cid2+brandid
-    temp['clicktopic_brandname']+=cid3brandname_df[(cid3brandname_df['show_id']==sku_infos.get('cid2'))
+    temp['clicktopic_brandname']+=cid3brandname_df[(cid3brandname_df['category_id']==sku_infos.get('cid2'))
                                               & (cid3brandname_df['show_type']==2) &(cid3brandname_df['brand_id']==sku_infos.get('brand_id'))] \
-                                 .sort_values(by=['rank'])['show_id'].to_list()[:30]  if not cid3brandname_df.empty else []
+                                 .sort_values(by=['final_score'],ascending=False)['show_id'].to_list()[:30]  if not cid3brandname_df.empty else []
     ##cid1+brandid
-    temp['clicktopic_brandname']+=cid3brandname_df[(cid3brandname_df['show_id']==sku_infos.get('cid1'))
+    temp['clicktopic_brandname']+=cid3brandname_df[(cid3brandname_df['category_id']==sku_infos.get('cid1'))
                                               & (cid3brandname_df['show_type']==2) &(cid3brandname_df['brand_id']==sku_infos.get('brand_id'))] \
-                                 .sort_values(by=['rank'])['show_id'].to_list()[:30]  if not cid3brandname_df.empty else []
+                                 .sort_values(by=['final_score'],ascending=False)['show_id'].to_list()[:30]  if not cid3brandname_df.empty else []
     ##cid3+无名品牌分
-    temp['clicktopic_brandname']+=cid3brandname_df[(cid3brandname_df['show_id']==sku_infos.get('cid3'))
+    temp['clicktopic_brandname']+=cid3brandname_df[(cid3brandname_df['category_id']==sku_infos.get('cid3'))
                                               & (cid3brandname_df['show_type']==2) &(cid3brandname_df['brand_id']==-1)] \
-                                 .sort_values(by=['rank'])['show_id'].to_list()[:30]  if not cid3brandname_df.empty else []
+                                 .sort_values(by=['final_score'],ascending=False)['show_id'].to_list()[:30]  if not cid3brandname_df.empty else []
     ##cid2+无名品牌分
-    temp['clicktopic_brandname']+=cid3brandname_df[(cid3brandname_df['show_id']==sku_infos.get('cid2'))
+    temp['clicktopic_brandname']+=cid3brandname_df[(cid3brandname_df['category_id']==sku_infos.get('cid2'))
                                               & (cid3brandname_df['show_type']==2) &(cid3brandname_df['brand_id']==-1)] \
-                                 .sort_values(by=['rank'])['show_id'].to_list()[:30]  if not cid3brandname_df.empty else []
+                                 .sort_values(by=['final_score'],ascending=False)['show_id'].to_list()[:30]  if not cid3brandname_df.empty else []
     ##cid1+无名品牌分
-    temp['clicktopic_brandname']+=cid3brandname_df[(cid3brandname_df['show_id']==sku_infos.get('cid1'))
+    temp['clicktopic_brandname']+=cid3brandname_df[(cid3brandname_df['category_id']==sku_infos.get('cid1'))
                                               & (cid3brandname_df['show_type']==2) &(cid3brandname_df['brand_id']==-1)] \
-                                 .sort_values(by=['rank'])['show_id'].to_list()[:30]  if not cid3brandname_df.empty else []
+                                 .sort_values(by=['final_score'],ascending=False)['show_id'].to_list()[:30]  if not cid3brandname_df.empty else []
     ##无品类分+品牌分
-    temp['clicktopic_brandname']+=cid3brandname_df[(cid3brandname_df['show_id']==-1)
+    temp['clicktopic_brandname']+=cid3brandname_df[(cid3brandname_df['category_id']==-1)
                                               & (cid3brandname_df['show_type']==2) &(cid3brandname_df['show_id']==sku_infos.get('brand_id'))] \
-                                 .sort_values(by=['rank'])['show_id'].to_list()[:30]  if not cid3brandname_df.empty else []
+                                 .sort_values(by=['final_score'],ascending=False)['show_id'].to_list()[:30]  if not cid3brandname_df.empty else []
 
     temp['clicktopic_brandname']=removeRepeat(temp['clicktopic_brandname'])[:30]
 
@@ -90,47 +130,68 @@ def clickTopic(skuno,cid3=None,brandid=None):
 def searchTopic():
     temp=defaultdict(list)
     from recommend.searchTopic import getseed,step1
-    top10_sku,_=step1()
+    #top10_sku,_=step1()
+    top10_sku=['108385546803201', '257610058310145', '255342753613825', '253183834981889', '261761672684545', '253184857873409', '310400617292289', '276970748256769', '100007743035', '7261582']
     sku_infos=getseed(top10_sku)
-    cid3_intention_change_sql=concatSql(cate_brand_intention_score_sql,
-                                        **{"show_id":sku_infos.get('cid3')})
-    cid3_intention_change_sql+=" or brand_id='{}'".format(sku_infos.get('brand_id'))
-    cid3brandname_df=execmysl(119,cid3_intention_change_sql)
-    temp['searchtopic_cid3']=cid3brandname_df[(cid3brandname_df['show_id']==sku_infos.get('cid3'))
-                                             & (cid3brandname_df['show_type']==1) &(cid3brandname_df['brand_id']==sku_infos.get('brand_id'))] \
-                                .sort_values(by=['rank'])['show_id'].to_list()[:30]  if not cid3brandname_df.empty else []
-
-    temp['searchtopic_cid3']+=cid3brandname_df[(cid3brandname_df['show_id']==sku_infos.get('cid3'))
-                                              & (cid3brandname_df['show_type']==1) &(cid3brandname_df['brand_id']==-1)] \
-                                 .sort_values(by=['rank'])['show_id'].to_list()[:30]  if not cid3brandname_df.empty else []
-
-    temp['searchtopic_cid3']+=cid3brandname_df[(cid3brandname_df['show_id']==-1)
-                                                   & (cid3brandname_df['show_type']==1) &(cid3brandname_df['brand_id']==sku_infos.get('brand_id'))] \
-                                      .sort_values(by=['rank'])['show_id'].to_list()[:30]  if not cid3brandname_df.empty else []
-
-    temp['searchtopic_brandname']=cid3brandname_df[(cid3brandname_df['show_id']==sku_infos.get('cid3'))
-                                              & (cid3brandname_df['show_type']==2) &(cid3brandname_df['brand_id']==sku_infos.get('brand_id'))] \
-                                 .sort_values(by=['rank'])['show_id'].to_list()[:30]  if not cid3brandname_df.empty else []
-
-    temp['searchtopic_brandname']+=cid3brandname_df[(cid3brandname_df['show_id']==sku_infos.get('cid3'))
-                                               & (cid3brandname_df['show_type']==2) &(cid3brandname_df['brand_id']==-1)] \
-                                  .sort_values(by=['rank'])['show_id'].to_list()[:30]  if not cid3brandname_df.empty else []
-
-    temp['searchtopic_brandname']+=cid3brandname_df[(cid3brandname_df['show_id']==-1)
-                                               & (cid3brandname_df['show_type']==2) &(cid3brandname_df['brand_id']==sku_infos.get('brand_id'))] \
-                                  .sort_values(by=['rank'])['show_id'].to_list()[:30]  if not cid3brandname_df.empty else []
-    temp['searchtopic_cid3']=removeRepeat(temp['searchtopic_cid3'])[:30]
-    temp['searchtopic_brandname']=removeRepeat(temp['searchtopic_brandname'])[:30]
+    print(sku_infos)
+    temp=clickTopic(cid3=sku_infos.get('cid3'),brandid=sku_infos.get('brand_id'),cid1=9987,cid2=653)
     return temp
-
-
-
+    # cid3_intention_change_sql=concatSql(cate_brand_intention_score_sql,
+    #                                     **{"category_id":sku_infos.get('cid3')})
+    # cid3_intention_change_sql+=" or brand_id='{}'".format(sku_infos.get('brand_id'))
+    # print("sql==",cid3_intention_change_sql)
+    # cid3brandname_df=execmysl(119,cid3_intention_change_sql)
+    # print("sql结果:",cid3brandname_df)
+    # cid3brandname_df['category_id']=cid3brandname_df['category_id'].astype('string')
+    # cid3brandname_df['brand_id']=cid3brandname_df['brand_id'].astype('string')
+    #
+    # temp['searchtopic_cid3']=cid3brandname_df[(cid3brandname_df['category_id']==sku_infos.get('cid3'))
+    #                                          & (cid3brandname_df['show_type']==1) &(cid3brandname_df['brand_id']==sku_infos.get('brand_id'))] \
+    #                             .sort_values(by=['final_score'],ascending=False)['show_id'].to_list()[:30]  if not cid3brandname_df.empty else []
+    #
+    # temp['searchtopic_cid3']+=cid3brandname_df[(cid3brandname_df['category_id']==sku_infos.get('cid3'))
+    #                                           & (cid3brandname_df['show_type']==1) &(cid3brandname_df['brand_id']=='-1')] \
+    #                              .sort_values(by=['final_score'],ascending=False)['show_id'].to_list()[:30]  if not cid3brandname_df.empty else []
+    #
+    # temp['searchtopic_cid3']+=cid3brandname_df[(cid3brandname_df['category_id']=='-1')
+    #                                                & (cid3brandname_df['show_type']==1) &(cid3brandname_df['brand_id']==sku_infos.get('brand_id'))] \
+    #                                   .sort_values(by=['final_score'],ascending=False)['show_id'].to_list()[:30]  if not cid3brandname_df.empty else []
+    # print(temp)
+    # temp['searchtopic_brandname']=cid3brandname_df[(cid3brandname_df['category_id']==sku_infos.get('cid3'))
+    #                                           & (cid3brandname_df['show_type']==2) &(cid3brandname_df['brand_id']==sku_infos.get('brand_id'))] \
+    #                              .sort_values(by=['final_score'],ascending=False)['show_id'].to_list()[:30]  if not cid3brandname_df.empty else []
+    #
+    # temp['searchtopic_brandname']+=cid3brandname_df[(cid3brandname_df['category_id']==sku_infos.get('cid3'))
+    #                                            & (cid3brandname_df['show_type']==2) &(cid3brandname_df['brand_id']=='-1')] \
+    #                               .sort_values(by=['final_score'],ascending=False)['show_id'].to_list()[:30]  if not cid3brandname_df.empty else []
+    #
+    # temp['searchtopic_brandname']+=cid3brandname_df[(cid3brandname_df['category_id']=='-1')
+    #                                            & (cid3brandname_df['show_type']==2) &(cid3brandname_df['brand_id']==sku_infos.get('brand_id'))] \
+    #                               .sort_values(by=['final_score'],ascending=False)['show_id'].to_list()[:30]  if not cid3brandname_df.empty else []
+    # temp['searchtopic_cid3']=removeRepeat(temp['searchtopic_cid3'])[:30]
+    # temp['searchtopic_brandname']=removeRepeat(temp['searchtopic_brandname'])[:30]
+    # return temp
+
+
+def allrun(skuno):
+    t=appTopic()
+    tt=clickTopic()
+    print(t)
 
 
 
 
 if __name__ == '__main__':
-    print(clickTopic('179240378044417'))
+    #print(clickTopic('179240378044417'))
+    #print(allrun())
+    gid='479610b6-f346-474f-8d3a-e4de04db8f6e-002'
+    gid1='479610b6-f346-474f-8d3a-e4de04db8f6e-001'
+    print('批次:',backBatch(gid,isuuid=0))
+    print("***"*50)
+    print('批次:',backBatch(gid1,isuuid=1))
+    #skuno='201782177569280'
+    #print(clickTopic(skuno))
+    #print(searchTopic())
 
 
 

recommend/payTopic.py

@@ -53,7 +53,7 @@ def payRecall(skuno,num=100):
             chaji=subtraction(subtraction(final_skus,temp.get('70011')),temp.get('70012'))
             temp['70013']=chaji
             print("加购图关联表召回stop")
-            return temp
+            #return temp
 
     print("ALS相关表召回start",'**'*50)
     als_correlation_change_redis=als_correlation_redis.format(sku_no=skuno).strip()

recommend/publicFunc.py

@@ -39,6 +39,24 @@ def minPriceFill(skus,num=100):
     return temp[:num]
 
 
+
+def execCondition(df,condition,needcolums,orderby=None):
+    '''
+    :param df: dataframe格式
+    :param condition: 字符串，筛选条件
+    :param needcolums: 字符串，需要从df拿到的字段
+    :param orderby: 默认dict格式，如果为空，则不排序。
+    {'ordercolums':XX,'type':XX}.ordercolums表示按照某个字段排序，type表示正排还是倒排
+    :return: 条件筛选后并返回数据
+    '''
+    if orderby:
+        col=orderby.get('ordercolums')
+        isasc=orderby.get('type') or 1
+        return df[eval(condition)].sort_values(by=[col],ascending=isasc)[needcolums].to_list()
+    else:
+        return df[eval(condition)][needcolums].to_list()
+
+
 def skuinfo(sku):
     change_sql=concatSql(skuinfo_sql,**{"sku_no":sku})
     sku_df=execmysl(119,change_sql)

recommend/publicSql.py

@@ -3,6 +3,12 @@ dapan_sql='''
 select id,sku_no,price,cid1,cid2,cid3,brand_name,brand_id from
     offline_recommend.recommend_same_product 
 '''
+
+##根据cid3获取cid2，cid1等数据
+cid1_3_sql="""
+select distinct c_id1,c_id2,c_id3 from kdsp.t_sku_info
+"""
+
 ##商品表
 skuinfo_sql='''
 select id,sku_no,sku_name,price,cid1,cid2,cid3,brand_name,brand_id,source_type from

recommend/searchTopic.py

@@ -21,9 +21,12 @@ def getseed(top10_sku):
     print(sql)
     df=execmysl(119,sql)
     df['cid3']=df['cid3'].astype('string')
+    df['brand_id']=df['brand_id'].astype('string')
     cid3=df.groupby(by=['cid3']).groups.__repr__()#['cid3']#.max()
     brandname=df.groupby(by=['brand_name']).groups.__repr__()
     brandid=df.groupby(by=['brand_id']).groups.__repr__()
+    # print('--',brandid,type(brandid))
+    # print('--',brandname,type(brandid))
     result['cid3']=maxdict(**json.loads(cid3.replace("'",'"')))
     result['brand_name']=maxdict(**json.loads(brandname.replace("'",'"')))
     result['brand_id']=maxdict(**json.loads(brandid.replace("'",'"')))

tools/changeLXQpassword.py

@@ -14,7 +14,7 @@ def modifyPasseord(name,namespace):
     salt_df=pd.read_sql(get_salt,con=conn_db_11)
     temp=salt_df.to_dict(orient='records')
     if not temp:
-        print('[name] is not exist'.format(name=name))
+        print('[{name}] is not exist'.format(name=name))
         return 0
     salt=temp[0]['salt'].encode()
     #salt=b'UwKESe3cvf703Z30' #t_sys_user.salt
@@ -28,4 +28,4 @@ def modifyPasseord(name,namespace):
         print("this is update")
 
 if __name__ == '__main__':
-    modifyPasseord('haiyuan.wen','qa')
\ No newline at end of file
+    modifyPasseord('dong.chao','qa')
\ No newline at end of file