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ffm-baseline
Commit 5ba17599 authored by 张彦钊's avatar 张彦钊
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parent a7a00784
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Showing with 29 additions and 26 deletions
make_data.py
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...@@ -129,7 +129,7 @@ def hospital(): ...@@ -129,7 +129,7 @@ def hospital():
"left join hippo_merchantrelevance b on api.id = b.doctor_id " \ "left join hippo_merchantrelevance b on api.id = b.doctor_id " \
"left join al_meigou_service_smart_rank_budan_payment budan on b.merchant_id = budan.merchant_id " \ "left join al_meigou_service_smart_rank_budan_payment budan on b.merchant_id = budan.merchant_id " \
"where api.doctor_type = 1 and h.date = '{}' " \ "where api.doctor_type = 1 and h.date = '{}' " \
"and budan.stat_date = '{}' limit 6;".format(date_str, date_tmp) "and budan.stat_date = '{}'; limit 6".format(date_str, date_tmp)
db = pymysql.connect(host='172.16.30.143', port=3306, user='work', passwd='BJQaT9VzDcuPBqkd', db='zhengxing') db = pymysql.connect(host='172.16.30.143', port=3306, user='work', passwd='BJQaT9VzDcuPBqkd', db='zhengxing')
cursor = db.cursor() cursor = db.cursor()
...@@ -144,45 +144,48 @@ def hospital(): ...@@ -144,45 +144,48 @@ def hospital():
print(df.head(6)) print(df.head(6))
# sql = "select merchant_id,doctor_ad_money_30_days,expand_rechange_amount_30," \ sql = "select merchant_id,doctor_ad_money_30_days," \
# "service_pv_30,expert_pv_30,organization_pv_30 from statistic_merchant_rank_factor " \ "service_pv_30,expert_pv_30,organization_pv_30,doctor_discount_30_days from statistic_merchant_rank_factor " \
# "where partition_date = '{}';".format(date_str) "where partition_date = '{}' limit 6;".format(date_str)
#
# cursor = db.cursor() cursor = db.cursor()
# cursor.execute(sql) cursor.execute(sql)
# result = cursor.fetchall() result = cursor.fetchall()
# db.close() db.close()
# tmp = pd.DataFrame(list(result)) tmp = pd.DataFrame(list(result))
# name = ["merchant_id", "doctor_ad_money_30_days", "expand_rechange_amount_30", "service_pv_30", name = ["merchant_id", "doctor_ad_money_30_days", "expand_rechange_amount_30", "service_pv_30",
# "mexpert_pv_30", "organization_pv_30"] "mexpert_pv_30", "organization_pv_30","doctor_discount_30_days"]
# tmp = tmp.rename(columns=dict(zip(list(range(len(name))), name))) tmp = tmp.rename(columns=dict(zip(list(range(len(name))), name)))
# print(tmp.head(6))
# df["merchant_id"] = df["merchant_id"].astype("str") # df["merchant_id"] = df["merchant_id"].astype("str")
# tmp["merchant_id"] = tmp["merchant_id"].astype("str") # tmp["merchant_id"] = tmp["merchant_id"].astype("str")
# df = pd.merge(df, tmp, on='merchant_id') # df = pd.merge(df, tmp, on='merchant_id')
# #
# for i in ["service_exposure_pv_30", "service_ctr_30", "expert_exposure_pv_30", "expert_pv_30", # for i in ["hospital_exposure_pv_30","service_exposure_pv_30","expert_exposure_pv_30",
# "doctor_ad_money_30_days", "expand_rechange_amount_30", "service_pv_30", # "service_ctr_30","hospital_ctr_30","expert_ctr_30",
# "mexpert_pv_30", "organization_pv_30", "budan_payment_30_days"]: # "doctor_ad_money_30_days", "service_pv_30",
# "mexpert_pv_30", "organization_pv_30", "budan_payment_30_days","doctor_discount_30_days"]:
# df[i] = df[i].astype("float") # df[i] = df[i].astype("float")
# #
# df["all_exposure"] = df["service_exposure_pv_30"] + df["expert_exposure_pv_30"] # df["all_exposure"] = df["hospital_exposure_pv_30"] + 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 = df[~df["all_exposure"].isin([0.0])]
# df["tmp"] = df["service_pv_30"] + df["mexpert_pv_30"] +df["organization_pv_30"] # df["tmp"] = df["service_pv_30"] + df["mexpert_pv_30"] +df["organization_pv_30"]
# df = df[~df["tmp"].isin([0.0])] # df = df[~df["tmp"].isin([0.0])]
# print("aaaaaaaa") # print("aaaaaaaa")
# df["ctr"] = df["service_exposure_pv_30"] / df["all_exposure"] * df["service_ctr_30"] + \ # 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["hospital_exposure_pv_30"]/ df["all_exposure"] * df["hospital_ctr_30"] + \
# df["expert_exposure_pv_30"]/df["all_exposure"] * df["expert_ctr_30"]
# df["commission"] = (df["doctor_ad_money_30_days"] + df["budan_payment_30_days"])/df["tmp"] # 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["cpt"] = df["doctor_discount_30_days"]/df["tmp"]
# df["score"] = df["ctr"]**0.5 * (df["commission"] + df["pv_ad"]) # df["score"] = df["ctr"]**0.5 * (df["commission"] + df["cpt"])
# columns = ["score","ctr","commission","pv_ad","service_exposure_pv_30","service_ctr_30","expert_exposure_pv_30","expert_pv_30", # columns = ["score","ctr","commission","cpt","hospital_exposure_pv_30","service_exposure_pv_30",
# "merchant_id","doctor_ad_money_30_days","expand_rechange_amount_30","service_pv_30", # "expert_exposure_pv_30",
# "mexpert_pv_30","organization_pv_30","budan_payment_30_days"] # "service_ctr_30","hospital_ctr_30","expert_ctr_30", "service_pv_30",
# "mexpert_pv_30", "organization_pv_30"]
# data = df.loc[:, columns] # data = df.loc[:, columns]
# print(data.head(6)) # print(data.head(6))
# data.to_csv('/home/gmuser/doctor.csv',index=False) # data.to_csv('/home/gmuser/hospital.csv',index=False)
if __name__ == "__main__": if __name__ == "__main__":
hospital() hospital()
......
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