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+import pymysql
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+import pandas as pd
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+import numpy as np
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+from datetime import datetime, timedelta, timezone
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+
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+pd.set_option('display.max_columns', None)
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+pd.set_option('expand_frame_repr', False)
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+import warnings
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+from typing import Literal
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+import json
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+
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+warnings.filterwarnings('ignore')
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+
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+
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+class Automation_Bid_Budget:
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+ def __init__(self, campaign_id,
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+ time_period: Literal["1week", "2weeks", "4weeks", "6weeks", "8weeks", "12weeks"] = "8weeks"
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+ ):
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+ self.campaign_id = campaign_id
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+ self.time_period = time_period # 默认初始化历史周期8周
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+
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+ def database_conv_traf(self): # 连接数据库conversion、traffic
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+ conn = pymysql.connect(user="admin",
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+ password="pvmBNS8q3duiUvvp",
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+ host="amzn-retail.cluster-cnrgrbcygoap.us-east-1.rds.amazonaws.com",
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+ database="zosi_ad_marketing_stream",
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+ port=3306)
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+ return conn
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+
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+ def get_sp_conversion(self): # 获取转化
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+ conn = self.database_conv_traf()
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+ cursor = conn.cursor()
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+ sql = "select * from zosi_ad_marketing_stream.sp_conversion_raw"
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+ sql = sql + self.add_condition(isbudgetTable=False)
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+ cursor.execute(sql)
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+ columns_name = [i[0] for i in cursor.description]
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+ rel = cursor.fetchall()
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+ df = pd.DataFrame(rel, columns=columns_name)
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+ df = df.groupby('idempotency_id').head(1)
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+ return df
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+
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+ def get_sp_traffic(self): # 获取流量
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+ conn = self.database_conv_traf()
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+ cursor = conn.cursor()
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+ sql = "select * from zosi_ad_marketing_stream.sp_traffic_raw"
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+ sql = sql + self.add_condition(isbudgetTable=False)
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+ # print(sql)
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+ cursor.execute(sql)
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+ columns_name = [i[0] for i in cursor.description]
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+ rel = cursor.fetchall()
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+ df = pd.DataFrame(rel, columns=columns_name)
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+ df = df.groupby('idempotency_id').head(1)
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+ return df
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+
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+ def get_sp_budgetug(self): # 获取预算
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+ conn = self.database_conv_traf()
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+ cursor = conn.cursor()
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+ sql = "select * from zosi_ad_marketing_stream.sp_budget_usage"
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+ sql = sql + self.add_condition(isbudgetTable=True)
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+ cursor.execute(sql)
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+ columns_name = [i[0] for i in cursor.description]
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+ rel = cursor.fetchall()
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+ df = pd.DataFrame(rel, columns=columns_name)
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+ return df
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+
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+ def add_condition(self, isbudgetTable=False): # 添加筛选时间周期
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+ if self.time_period == '1week':
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+ time_ = datetime.today().date() + timedelta(days=-7)
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+ elif self.time_period == '2weeks':
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+ time_ = datetime.today().date() + timedelta(days=-14)
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+ elif self.time_period == '4weeks':
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+ time_ = datetime.today().date() + timedelta(days=-28)
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+ elif self.time_period == '6weeks':
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+ time_ = datetime.today().date() + timedelta(days=-42)
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+ elif self.time_period == '8weeks':
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+ time_ = datetime.today().date() + timedelta(days=-56)
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+ elif self.time_period == '12weeks':
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+ time_ = datetime.today().date() + timedelta(days=-84)
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+ if isbudgetTable:
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+ return f" where usage_updated_timestamp>='{time_}' and budget_scope_id='{self.campaign_id}'"
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+ return f" where time_window_start>='{time_}' and campaign_id='{self.campaign_id}'"
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+
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+ def merge_common_operation(self): # 转化与流量连表
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+ conversion = self.get_sp_conversion()
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+ conversion_ = conversion.groupby(
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+ ['advertiser_id', 'marketplace_id', 'time_window_start', 'campaign_id', 'ad_group_id', 'ad_id',
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+ 'keyword_id', 'placement', 'currency']).agg({
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+ 'attributed_sales_1d': sum,
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+ 'attributed_sales_1d_same_sku': sum,
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+ 'attributed_sales_7d': sum,
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+ 'attributed_sales_7d_same_sku': sum,
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+ 'attributed_sales_14d': sum,
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+ 'attributed_sales_14d_same_sku': sum,
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+ 'attributed_sales_30d': sum,
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+ 'attributed_sales_30d_same_sku': sum,
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+ 'attributed_conversions_1d': sum,
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+ 'attributed_conversions_1d_same_sku': sum,
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+ 'attributed_conversions_7d': sum,
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+ 'attributed_conversions_14d_same_sku': sum,
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+ 'attributed_conversions_30d': sum,
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+ 'attributed_conversions_30d_same_sku': sum,
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+ 'attributed_units_ordered_1d': sum,
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+ 'attributed_units_ordered_1d_same_sku': sum,
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+ 'attributed_units_ordered_7d': sum,
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+ 'attributed_units_ordered_7d_same_sku': sum,
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+ 'attributed_units_ordered_14d': sum,
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+ 'attributed_units_ordered_14d_same_sku': sum,
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+ 'attributed_units_ordered_30d': sum,
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+ 'attributed_units_ordered_30d_same_sku': sum
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+ }).reset_index()
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+ traffic = self.get_sp_traffic()
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+ traffic[['impressions', 'clicks']] = traffic[['impressions', 'clicks']].astype('int64')
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+ traffic['cost'] = traffic['cost'].astype('float64')
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+
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+ traffic_ = traffic.groupby(
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+ ['advertiser_id', 'marketplace_id', 'time_window_start', 'campaign_id', 'ad_group_id', 'ad_id',
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+ 'keyword_id', 'keyword_text', 'placement', 'match_type', 'currency'
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+ ]).agg({'impressions': sum,
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+ 'clicks': sum,
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+ 'cost': sum
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+ }).reset_index()
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+ traffic_conversion = traffic_.merge(conversion_,
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+ on=['advertiser_id', 'marketplace_id', 'campaign_id', 'ad_group_id',
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+ 'ad_id', 'keyword_id', 'placement', 'time_window_start', 'currency'],
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+ how='inner')
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+ if len(traffic_conversion) < 1:
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+ return pd.DataFrame()
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+ traffic_conversion['hour'] = traffic_conversion['time_window_start'].dt.hour
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+ traffic_conversion['day'] = traffic_conversion['time_window_start'].dt.dayofweek
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+ traffic_conversion = traffic_conversion.groupby(
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+ ['campaign_id', 'ad_group_id', 'keyword_id', 'hour']).sum().reset_index()
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+ traffic_conversion['cpc'] = traffic_conversion['cost'] / traffic_conversion['clicks']
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+ # traffic_conversion['cpc'].fillna(0,inplace=True)
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+ # traffic_conversion['cpc'] = traffic_conversion['cpc'].replace([np.inf,np.nan,pd.NA],0)
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+ return traffic_conversion
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+
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+ def pre_deal(self, traffic_conversion): # 前处理，补全数据
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+ if len(traffic_conversion) < 1:
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+ return []
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+ pro_list = traffic_conversion.groupby(['campaign_id', 'ad_group_id', 'keyword_id']).head(1)[
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+ ['campaign_id', 'ad_group_id', 'keyword_id']].to_numpy().tolist()
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+ for i in pro_list: # 补全24小时的数据
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+ cam_, adg, kid = i[0], i[1], i[2]
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+ df0 = traffic_conversion.query("campaign_id==@cam_ and ad_group_id==@adg and keyword_id==@kid")
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+ for hour in range(24):
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+ if hour not in df0['hour'].tolist():
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+ traffic_conversion = traffic_conversion.append(
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+ {'campaign_id': cam_, 'ad_group_id': adg, 'keyword_id': kid, 'hour': hour},
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+ ignore_index=True)
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+ traffic_conversion['cpc_min'] = traffic_conversion.groupby(['campaign_id', 'ad_group_id', 'keyword_id'])[
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+ 'cpc'].transform('min')
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+
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+ traffic_conversion = traffic_conversion.sort_values(by=['campaign_id', 'ad_group_id', 'keyword_id', 'hour'])
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+
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+ # 给当前没有竞价信息的赋予竞价，为该关键词最小竞价的45%
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+ traffic_conversion['cpc'] = traffic_conversion.apply(
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+ lambda x: x['cpc_min'] * 0.45 if pd.isna(x['cpc']) or x['cpc'] is None else x['cpc'], axis=1)
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+ return traffic_conversion
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+
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+ def func_rule_budget(self, traffic_conversion): # 预算规则
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+ if len(traffic_conversion) < 1:
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+ return pd.DataFrame(columns=['hour', 'pre_percent_s3'])
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+ traffic_conversion = self.pre_deal(traffic_conversion)
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+ # total_spend = traffic_conversion['cpc'].sum()
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+ # 根据小时对竞价、转化、点击汇总
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+ tf_c = traffic_conversion.groupby(['hour']).agg(
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+ {'cpc': sum, 'attributed_conversions_1d': sum, 'clicks': sum}).reset_index()
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+ # 根据以下公式，突出转化高与竞价低的重要性
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+ tf_c['pre_percent'] = tf_c.apply(
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+ lambda x: (x['attributed_conversions_1d'] ** 3 - (x['clicks'] - x['attributed_conversions_1d']) ** 3) / x[
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+ 'cpc'] ** 3 + 1.001, axis=1)
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+ tf_c['pre_percent'] = tf_c['pre_percent'].map(lambda x: np.sqrt(x)) # 避免各时间之间差距太大，进行开根处理
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+ # 对无效数值/空值 赋值1.0001
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+ tf_c['pre_percent'] = tf_c['pre_percent'].map(lambda x: 1.0001 if pd.isna(x) or x is None else x)
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+ # 对23-5点的权重值降低至60%
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+ tf_c['pre_percent_s2'] = tf_c.apply(
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+ lambda x: x['pre_percent'] * 0.6 if x['hour'] < 6 or x['hour'] > 22 else x['pre_percent'], axis=1)
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+ total_val = tf_c['pre_percent_s2'].sum()
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+ # print(total_val)
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+ # 计算各小时权重(初次分配权重，后续修正)
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+ tf_c['pre_percent_s2'] = tf_c['pre_percent_s2'] / total_val
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+ # 对分配过度不均衡进行调整，对超过分配的25%部分只给予25%的权重百分比
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+ tf_c['pre_percent_s3'] = tf_c['pre_percent_s2'].map(lambda x: 0.25 if x > 0.25 else x)
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+ tf_c['temp'] = tf_c['pre_percent_s2'] - tf_c['pre_percent_s3']
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+ total_allocate = tf_c['temp'].sum()
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+ allocate_count = tf_c['temp'].tolist().count(0.25)
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+ allocate_val = total_allocate / allocate_count if allocate_count != 0 else 0
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+ # 将超过25%的权重分配到其余小时区间内
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+ tf_c['pre_percent_s3'] = tf_c['pre_percent_s3'].map(lambda x: x + allocate_val if x != 0.25 else 0.25)
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+ return tf_c[['hour', 'pre_percent_s3']]
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+
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+ def budget_allocate_singleDay(self): # 总结历史的数据，对单天预算分配
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+ traffic_conversion = self.merge_common_operation()
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+ traffic_conversion = self.pre_deal(traffic_conversion)
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+ traffic_conversion = self.func_rule_budget(traffic_conversion)
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+ traffic_conversion.columns = ['hour', 'SingleDay']
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+ return json.dumps({"budget_allocate_singleDay": traffic_conversion.to_dict(orient='records')})
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+
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+ def budget_allocate_week(self): # 总结过去每个不同日的数据，对每周每天预算都进行不同分配
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+ traffic_conversion = self.merge_common_operation()
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+ df = self.pre_deal(traffic_conversion[traffic_conversion['day'] == 0])
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+ df = self.func_rule_budget(df)
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+ for i in range(1, 7):
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+ df1 = self.pre_deal(traffic_conversion[traffic_conversion['day'] == i])
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+ df1 = self.func_rule_budget(df1)
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+ df = pd.merge(df, df1, how='left', on='hour')
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+
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+ df.columns = ["hour", 'Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']
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+ return json.dumps({"budget_allocate_week": df.round(4).to_dict(orient='records')})
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+
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+ def rule_set_bid(self, avg_weight, cr, avg_cr, ctr, avg_ctr, weight_value, hour): # 竞价规则设置
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+ if weight_value > avg_weight * 1.5: # 表现极好词
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+ return 2
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+ elif weight_value > avg_weight * 1.25: # 表现较好词
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+ if hour in [23, 0, 1, 2, 3, 4, 5]:
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+ return 1.5
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+ else:
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+ return 1.5 + np.random.randint(100, 300) / 1000
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+ elif weight_value > avg_weight * 1.15: # 表现稍好词
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+ if hour in [23, 0, 1, 2, 3, 4, 5]:
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+ return 1.25
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+ else:
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+ return 1.5 + np.random.randint(100, 200) / 1000
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+ elif weight_value > avg_weight: # 标准权重词
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+ return 1
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+ else:
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+ if ctr >= avg_ctr and cr >= 0.75 * avg_ctr: # 点击较高，转化稍差词
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+ return 1
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+ elif cr > avg_ctr: # 转化高词，点击不好的词
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+ return 1.25
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+ elif cr > 0.75 * avg_cr: # 转化较差词
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+ return 0.75
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+ else: # 该小时无cr、ctr记录的，并且时间不在23-5点的词
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+ if ((pd.isna(cr) and pd.isna(ctr)) or None in [cr, ctr]) and hour not in [23, 0, 1, 2, 3, 4, 5]:
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+ return [0.5, 0.7, 0.8, 0.9, 1, 1.1][np.random.randint(0, 5)]
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+ return 0.5 # 其余条件的词
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+
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+ def func_rule_bid(self, traffic_conversion): # 竞价规则应用
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+ if len(traffic_conversion) < 1:
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+ return pd.DataFrame(columns=['hour', 'weight_allocate'])
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+ tf_c = traffic_conversion.groupby(['hour']).agg(
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+ {'cost': sum, 'attributed_conversions_1d': sum, 'clicks': sum, 'impressions': sum}).reset_index()
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+ tf_c['cpc'] = tf_c['cost'] / tf_c['clicks']
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+ tf_c['cr'] = tf_c['attributed_conversions_1d'] / tf_c['clicks']
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+ tf_c['ctr'] = tf_c['clicks'] / tf_c['impressions']
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+ avg_bid = tf_c['cpc'].mean()
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+ avg_cr = tf_c['attributed_conversions_1d'].sum() / tf_c['clicks'].sum()
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+ avg_ctr = tf_c['clicks'].sum() / tf_c['impressions'].sum()
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+ tf_c['weight_value'] = tf_c['cr'] / tf_c['cpc']
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+ avg_weight = avg_cr / avg_bid
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+ # avg_weight = tf_c['weight_value'].mean()
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+
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+ tf_c['weight_allocate'] = tf_c.apply(
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+ lambda x: self.rule_set_bid(avg_weight, x['cr'], avg_cr, x['ctr'], avg_ctr, x['weight_value'], x['hour']),
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+ axis=1)
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+ return tf_c[['hour', 'weight_allocate']].round(2)
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+
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+ def bid_adjust_singleDay(self):
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+ traffic_conversion = self.merge_common_operation()
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+ # traffic_conversion = self.pre_deal(traffic_conversion)
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+ tf_c = self.pre_deal(traffic_conversion)
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+ tf_c = self.func_rule_bid(tf_c)
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+ tf_c.columns = ['hour', 'SingleDay']
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+ # 完成
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+ return json.dumps({"bid_adjust_singleDay": tf_c.to_dict(orient='records')})
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+
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+ def bid_adjust_week(self):
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+ traffic_conversion = self.merge_common_operation()
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+ # 单独筛选周一至周日每天的traffic，再进行聚合
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+ df = self.pre_deal(traffic_conversion[traffic_conversion['day'] == 0])
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+ df = self.func_rule_bid(df)
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+ for i in range(1, 7):
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+ df1 = self.pre_deal(traffic_conversion[traffic_conversion['day'] == i])
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+ df1 = self.func_rule_bid(df1)
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+ df = pd.merge(df, df1, how='left', on='hour')
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+
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+ df.columns = ["hour", 'Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']
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+
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+ return json.dumps({"bid_adjust_week": df.to_dict(orient='records')})
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+
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+
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+if __name__ == '__main__':
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+ adjust_ = Automation_Bid_Budget(campaign_id='325523075677132')
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+
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+ # 竞价分配
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+ bid_adjust = adjust_.bid_adjust_week()
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+ print(bid_adjust)
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+
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+ print()
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+
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+ # 预算分配
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+ budget_adjust = adjust_.budget_allocate_week()
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+ print(budget_adjust)
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+
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+
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guojing_wu