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ffm-baseline
Commit ac22ea27 authored by 张彦钊's avatar 张彦钊
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parent 0661416b
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Showing with 12 additions and 16 deletions
make_data.py
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...@@ -15,7 +15,7 @@ def hospital(): ...@@ -15,7 +15,7 @@ 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 statistic_merchant_rank_factor m on b.merchant_id = m.merchant_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) "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') db = pymysql.connect(host='172.16.30.141', port=3306, user='work', passwd='BJQaT9VzDcuPBqkd', db='zhengxing')
cursor = db.cursor() cursor = db.cursor()
...@@ -42,27 +42,25 @@ def hospital(): ...@@ -42,27 +42,25 @@ def hospital():
print("filter 1") print("filter 1")
print(df.shape) print(df.shape)
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])]
print("filter 2")
print(df.shape)
print("ccc")
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["hospital_exposure_pv_30"]/ df["all_exposure"] * df["hospital_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["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["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["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["commission"] = df["doctor_discount_30_days"]/df["tmp"]
df["cpt"] = df["expand_rechange_amount_30"]/df["tmp"] df["cpt"] = df["expand_rechange_amount_30"]/df["tmp"]
df.loc[df["all_exposure"] <= 1500, ["ctr"]] = 0.01 df.loc[df["all_exposure"] <= 1500, ["ctr"]] = 0.01
df.loc[df["ctr"] < 0.01, ["ctr"]] = 0.01 df.loc[df["ctr"] < 0.01, ["ctr"]] = 0.01
df.loc[df["ctr"] > 0.2, ["ctr"]] = 0.2 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["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.loc[df["commission"] < 0.01, ["commission"]] = 0.01
df["score"] = df["ctr"] ** 0.5 * (df["commission"] + df["cpt"]) df["score"] = df["ctr"] ** 0.5 * (df["commission"] + df["cpt"])
...@@ -77,7 +75,7 @@ def hospital(): ...@@ -77,7 +75,7 @@ def hospital():
data = data.drop_duplicates() data = data.drop_duplicates()
print(data.shape) print(data.shape)
data.to_csv('/tmp/21_hospital.csv',index=False) data.to_csv('/tmp/25_hospital.csv',index=False)
def new_doctor(): def new_doctor():
...@@ -114,14 +112,12 @@ def new_doctor(): ...@@ -114,14 +112,12 @@ def new_doctor():
print("all_exposure") print("all_exposure")
print(df.shape) print(df.shape)
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.loc[df["tmp"] <= 1500, ["tmp"]] = 1500
print("tmp")
print(df.shape)
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["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["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["expand_rechange_amount_30"] < 0, ["expand_rechange_amount_30"]] = 0
df["commission"] = df["doctor_discount_30_days"]/df["tmp"] df["commission"] = df["doctor_discount_30_days"]/df["tmp"]
...@@ -130,9 +126,9 @@ def new_doctor(): ...@@ -130,9 +126,9 @@ def new_doctor():
df.loc[df["all_exposure"] <= 1500, ["ctr"]] = 0.01 df.loc[df["all_exposure"] <= 1500, ["ctr"]] = 0.01
df.loc[df["ctr"] < 0.01, ["ctr"]] = 0.01 df.loc[df["ctr"] < 0.01, ["ctr"]] = 0.01
df.loc[df["ctr"] > 0.2, ["ctr"]] = 0.2 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["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.loc[df["pv_ad"] < 0.01, ["pv_ad"]] = 0.01
df["score"] = df["ctr"] ** 0.5 * (df["commission"] + df["pv_ad"]) df["score"] = df["ctr"] ** 0.5 * (df["commission"] + df["pv_ad"])
...@@ -146,7 +142,7 @@ def new_doctor(): ...@@ -146,7 +142,7 @@ def new_doctor():
data = data.drop_duplicates() data = data.drop_duplicates()
print(data.shape) print(data.shape)
data.to_csv('/tmp/21_doctor.csv',index=False) data.to_csv('/tmp/25_doctor.csv',index=False)
print("doctor end") print("doctor end")
......
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