add

make_data.py

@@ -15,7 +15,7 @@ def hospital():
           "left join hippo_merchantrelevance b on api.id = b.doctor_id " \
           "left join statistic_merchant_rank_factor m on b.merchant_id = m.merchant_id " \
           "where api.doctor_type = 1 and h.date = '{}' and m.partition_date = '{}';".format(date_str,date_str)
-    print(sql)
+
 
     db = pymysql.connect(host='172.16.30.141', port=3306, user='work', passwd='BJQaT9VzDcuPBqkd', db='zhengxing')
     cursor = db.cursor()
@@ -42,27 +42,25 @@ def hospital():
     print("filter 1")
     print(df.shape)
     df["tmp"] = df["service_pv_30"] + df["mexpert_pv_30"] +df["organization_pv_30"]
-    df = df[~df["tmp"].isin([0.0])]
-    print("filter 2")
-    print(df.shape)
-    print("ccc")
+
     df["ctr"] = df["service_exposure_pv_30"] / df["all_exposure"] * df["service_ctr_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.loc[df["doctor_discount_30_days"] < 0, ["doctor_discount_30_days"]] = 0
-    # df.loc[df["budan_payment_30_days"] < 0, ["budan_payment_30_days"]] = 0
     df.loc[df["expand_rechange_amount_30"] < 0, ["expand_rechange_amount_30"]] = 0
 
+    df.loc[df["tmp"] <= 1500, ["tmp"]] = 1500
+
     df["commission"] = df["doctor_discount_30_days"]/df["tmp"]
     df["cpt"] = df["expand_rechange_amount_30"]/df["tmp"]
 
     df.loc[df["all_exposure"] <= 1500, ["ctr"]] = 0.01
     df.loc[df["ctr"] < 0.01, ["ctr"]] = 0.01
     df.loc[df["ctr"] > 0.2, ["ctr"]] = 0.2
-    df.loc[df["cpt"] > 20, ["cpt"]] = 20
+    df.loc[df["cpt"] > 10, ["cpt"]] = 10
     df.loc[df["cpt"] < 0.01, ["cpt"]] = 0.01
-    df.loc[df["commission"] > 20, ["commission"]] = 20
+    df.loc[df["commission"] > 10, ["commission"]] = 10
     df.loc[df["commission"] < 0.01, ["commission"]] = 0.01
 
     df["score"] = df["ctr"] ** 0.5 * (df["commission"] + df["cpt"])
@@ -77,7 +75,7 @@ def hospital():
     data = data.drop_duplicates()
 
     print(data.shape)
-    data.to_csv('/tmp/21_hospital.csv',index=False)
+    data.to_csv('/tmp/25_hospital.csv',index=False)
 
 
 def new_doctor():
@@ -114,14 +112,12 @@ def new_doctor():
     print("all_exposure")
     print(df.shape)
     df["tmp"] = df["service_pv_30"] + df["mexpert_pv_30"] +df["organization_pv_30"]
-    df = df[~df["tmp"].isin([0.0])]
-    print("tmp")
-    print(df.shape)
+    df.loc[df["tmp"] <= 1500, ["tmp"]] = 1500
+
     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.loc[df["doctor_discount_30_days"] < 0, ["doctor_discount_30_days"]] = 0
-    # df.loc[df["budan_payment_30_days"] < 0, ["budan_payment_30_days"]] = 0
     df.loc[df["expand_rechange_amount_30"] < 0, ["expand_rechange_amount_30"]] = 0
 
     df["commission"] = df["doctor_discount_30_days"]/df["tmp"]
@@ -130,9 +126,9 @@ def new_doctor():
     df.loc[df["all_exposure"] <= 1500, ["ctr"]] = 0.01
     df.loc[df["ctr"] < 0.01, ["ctr"]] = 0.01
     df.loc[df["ctr"] > 0.2, ["ctr"]] = 0.2
-    df.loc[df["commission"] > 20, ["commission"]] = 20
+    df.loc[df["commission"] > 10, ["commission"]] = 10
     df.loc[df["commission"] < 0.01, ["commission"]] = 0.01
-    df.loc[df["pv_ad"] > 20, ["pv_ad"]] = 20
+    df.loc[df["pv_ad"] > 10, ["pv_ad"]] = 10
     df.loc[df["pv_ad"] < 0.01, ["pv_ad"]] = 0.01
 
     df["score"] = df["ctr"] ** 0.5 * (df["commission"] + df["pv_ad"])
@@ -146,7 +142,7 @@ def new_doctor():
 
     data = data.drop_duplicates()
     print(data.shape)
-    data.to_csv('/tmp/21_doctor.csv',index=False)
+    data.to_csv('/tmp/25_doctor.csv',index=False)
     print("doctor end")