ass

ai_test.py

@@ -6,16 +6,12 @@ url="http://doris.paas.test/v1/once"
 # 如果在生产环境doris 01上执行，把url改成"http://127.0.0.1:4050/v1/once"
 header_dict={'Content-Type': 'application/x-www-form-urlencoded'}
 param_dict={}
-# param_dict["method"]="doris/search/top_doctor"
-#
-# param_detail = {"size":10, "offset":0, "is_officer":True,"device_id":"hello",
-#                 "user_city_tag_id":328}
 
-# param_dict["method"]="doris/search/doctor_officer"
-# param_detail = {"device_id": 'xx',"size": 10,"offset":0, "query": "双眼皮","user_city_tag_id": 328,"is_officer":False}
+param_dict["method"]="doris/search/doctor_officer"
+param_detail = {"device_id": 'xx',"size": 10,"offset":0, "query": "双眼皮","user_city_tag_id": 328,"is_officer":False}
 
-param_dict["method"]="doris/search/bangdan"
-param_detail = {"device_id": '2',"size": 10,"offset":0, "user_city_tag_id": 328,"is_officer":False,"unit_ids":[]}
+# param_dict["method"]="doris/search/bangdan"
+# param_detail = {"device_id": '2',"size": 10,"offset":0, "user_city_tag_id": 328,"is_officer":False,"unit_ids":[]}
 
 
 # a = [105, 2941, 780, 102, 873, 2060, 187, 2935, 2108, 912, 928, 2059, 4437, 822, 2866, 2778, 824, 821, 2938]

make_data.py

@@ -6,58 +6,6 @@ import pandas as pd
 def doctor():
     date_str = (datetime.datetime.now() - datetime.timedelta(days=1)).strftime("%Y%m%d")
     date_tmp = (datetime.datetime.now() - datetime.timedelta(days=1)).strftime("%Y-%m-%d")
-    # sql = "select distinct doctor_id from statistic_doctor_rank_factor where partition_date = '{}'".format(date_str)
-    # doctor_list = pd.DataFrame(list(result))[0].values.tolist()
-
-    sql = "select d.doctor_id,d.service_exposure_pv_30,d.service_ctr_30,d.expert_exposure_pv_30,d.expert_pv_30," \
-    "b.merchant_id," \
-    "m.doctor_ad_money_30_days,m.expand_rechange_amount_30,m.service_pv_30,m.expert_pv_30,m.organization_pv_30," \
-    "budan.budan_payment_30_days " \
-    "from statistic_doctor_rank_factor d " \
-    "left join hippo_merchantrelevance b on d.doctor_id = b.doctor_id " \
-    "left join statistic_merchant_rank_factor m on b.merchant_id = m.merchant_id " \
-    "left join al_meigou_service_smart_rank_budan_payment budan on b.merchant_id = budan.merchant_id " \
-    "where d.partition_date = '{}' and m.partition_date = '{}' " \
-    "and budan.stat_date = '{}';".format(date_str,date_str,date_tmp)
-
-    db = pymysql.connect(host='172.16.30.143', port=3306, user='work', passwd='BJQaT9VzDcuPBqkd', db='zhengxing')
-    cursor = db.cursor()
-    cursor.execute(sql)
-    result = cursor.fetchall()
-    df = pd.DataFrame(list(result))
-
-    name = ["doctor_id","service_exposure_pv_30","service_ctr_30","expert_exposure_pv_30","expert_pv_30",
-            "merchant_id","doctor_ad_money_30_days","expand_rechange_amount_30","service_pv_30",
-            "mexpert_pv_30","organization_pv_30","budan_payment_30_days"]
-
-    df = df.rename(columns=dict(zip(list(range(len(name))),name)))
-    for i in ["service_exposure_pv_30", "service_ctr_30", "expert_exposure_pv_30", "expert_pv_30",
-      "doctor_ad_money_30_days", "expand_rechange_amount_30", "service_pv_30",
-     "mexpert_pv_30", "organization_pv_30", "budan_payment_30_days"]:
-        df[i] = df[i].astype("float")
-
-    df["all_exposure"] = df["service_exposure_pv_30"] + df["expert_exposure_pv_30"]
-    df = df[~df["expert_exposure_pv_30"].isin([0.0])]
-    df = df[~df["all_exposure"].isin([0.0])]
-    df["tmp"] = df["service_pv_30"] + df["mexpert_pv_30"] +df["organization_pv_30"]
-    df = df[~df["tmp"].isin([0.0])]
-
-    df["ctr"] = df["service_exposure_pv_30"] / df["all_exposure"] * df["service_ctr_30"] + \
-                df["expert_exposure_pv_30"]/df["all_exposure"] * (df["expert_pv_30"] / df["expert_exposure_pv_30"])
-    df["commission"] = (df["doctor_ad_money_30_days"] + df["budan_payment_30_days"])/df["tmp"]
-    df["pv_ad"] = df["expand_rechange_amount_30"]/df["tmp"]
-    df["score"] = df["ctr"]**0.5 * (df["commission"] + df["pv_ad"])
-    columns = ["score","ctr","commission","pv_ad","service_exposure_pv_30","service_ctr_30","expert_exposure_pv_30","expert_pv_30",
-            "merchant_id","doctor_ad_money_30_days","expand_rechange_amount_30","service_pv_30",
-            "mexpert_pv_30","organization_pv_30","budan_payment_30_days"]
-    data = df.loc[:, columns]
-    # print(data)
-    data.to_csv('/home/gmuser/doctor.csv',index=False)
-
-
-def v1_doctor():
-    date_str = (datetime.datetime.now() - datetime.timedelta(days=1)).strftime("%Y%m%d")
-    date_tmp = (datetime.datetime.now() - datetime.timedelta(days=1)).strftime("%Y-%m-%d")
 
     sql = "select d.doctor_id,d.service_exposure_pv_30,d.service_ctr_30,d.expert_exposure_pv_30,d.expert_pv_30," \
     "b.merchant_id,budan.budan_payment_30_days " \
@@ -109,12 +57,13 @@ def v1_doctor():
     df["commission"] = (df["doctor_ad_money_30_days"] + df["budan_payment_30_days"])/df["tmp"]
     df["pv_ad"] = df["expand_rechange_amount_30"]/df["tmp"]
     df["score"] = df["ctr"]**0.5 * (df["commission"] + df["pv_ad"])
-    columns = ["score","ctr","commission","pv_ad","service_exposure_pv_30","service_ctr_30","expert_exposure_pv_30","expert_pv_30",
+    columns = ["doctor_id","score","ctr","commission","pv_ad","service_exposure_pv_30",
+               "service_ctr_30","expert_exposure_pv_30","expert_pv_30",
             "merchant_id","doctor_ad_money_30_days","expand_rechange_amount_30","service_pv_30",
             "mexpert_pv_30","organization_pv_30","budan_payment_30_days"]
     data = df.loc[:, columns]
-    print(data.head(6))
     data.to_csv('/home/gmuser/doctor.csv',index=False)
+    print("doctor end")
 
 
 def hospital():
@@ -179,14 +128,17 @@ def hospital():
     df["commission"] = (df["doctor_ad_money_30_days"] + df["budan_payment_30_days"])/df["tmp"]
     df["cpt"] = df["doctor_discount_30_days"]/df["tmp"]
     df["score"] = df["ctr"]**0.5 * (df["commission"] + df["cpt"])
-    columns = ["score","ctr","commission","cpt","hospital_exposure_pv_30","service_exposure_pv_30",
-               "expert_exposure_pv_30",
-              "service_ctr_30","hospital_ctr_30","expert_ctr_30", "service_pv_30",
-              "mexpert_pv_30", "organization_pv_30"]
+    columns = ["doctor_id","score","ctr","commission","cpt","hospital_id","hospital_exposure_pv_30",
+               "service_exposure_pv_30","expert_exposure_pv_30",
+              "service_ctr_30","hospital_ctr_30","expert_ctr_30","merchant_id","budan_payment_30_days",
+              "doctor_ad_money_30_days","service_pv_30","mexpert_pv_30","organization_pv_30",
+              "doctor_discount_30_days"]
     data = df.loc[:, columns]
     print(data.head(6))
     data.to_csv('/home/gmuser/hospital.csv',index=False)
 
+
 if __name__ == "__main__":
+    doctor()
     hospital()