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@@ -6,11 +6,11 @@ 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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-
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+import json
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warnings.filterwarnings('ignore')
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class AdjustB:
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- def __init__(self,campaign_id,time_period:Literal["1week","2weeks","1month","2months","45days"]):
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+ def __init__(self,campaign_id,time_period:Literal["1week","2weeks","4weeks","6weeks","8weeks","12weeks"]="8weeks"):
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self.campaign_id = campaign_id
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self.time_period = time_period
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@@ -58,7 +58,7 @@ class AdjustB:
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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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+ # 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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@@ -82,12 +82,14 @@ class AdjustB:
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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 =='month':
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- time_ = datetime.today().date() + timedelta(days=-30)
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- elif self.time_period =='45days':
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- time_ = datetime.today().date() + timedelta(days=-45)
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- elif self.time_period == '2months':
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- time_ = datetime.today().date() + timedelta(days=-60)
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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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# usage_updated_timestamp
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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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@@ -147,7 +149,7 @@ class AdjustB:
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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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- def func_rule(self,traffic_conversion):
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+ def pre_deal(self,traffic_conversion):
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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:
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@@ -166,6 +168,10 @@ class AdjustB:
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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):
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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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@@ -194,22 +200,30 @@ class AdjustB:
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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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- def merge_conv_traf(self): # 总结过去每天的数据,对单天预算分配
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+ def budget_allocate_singleDay(self): # 总结过去每天的数据,对单天预算分配
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traffic_conversion = self.merge_common_operation()
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- traffic_conversion = self.func_rule(traffic_conversion)
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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 traffic_conversion
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+ return json.dumps({"budget_allocate_singleDay":traffic_conversion.to_dict(orient='records')})
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- def merge_cvtf_budt_accdday(self): # 总结过去每个不同工作日的数据,对每周每天预算都进行不同分配
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+ def budget_allocate_week(self): # 总结过去每个不同工作日的数据,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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- # TODO 单独筛选周一至周日每天的traffic,再进行后续步骤
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- Monday_df = self.func_rule(traffic_conversion[traffic_conversion['day']==0])
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- Tuesday_df = self.func_rule(traffic_conversion[traffic_conversion['day']==1])
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- Wednesday_df = self.func_rule(traffic_conversion[traffic_conversion['day']==2])
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- Thursday_df = self.func_rule(traffic_conversion[traffic_conversion['day']==3])
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- Friday_df = self.func_rule(traffic_conversion[traffic_conversion['day']==4])
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- Saturday_df = self.func_rule(traffic_conversion[traffic_conversion['day']==5])
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- Sunday_df = self.func_rule(traffic_conversion[traffic_conversion['day']==6])
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+ # 单独筛选周一至周日每天的traffic,再进行后续步骤
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+ Monday_df = self.pre_deal(traffic_conversion[traffic_conversion['day'] == 0])
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+ Monday_df = self.func_rule_budget(Monday_df)
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+ Tuesday_df = self.pre_deal(traffic_conversion[traffic_conversion['day'] == 1])
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+ Tuesday_df = self.func_rule_budget(Tuesday_df)
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+ Wednesday_df = self.pre_deal(traffic_conversion[traffic_conversion['day'] == 2])
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+ Wednesday_df = self.func_rule_budget(Wednesday_df)
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+ Thursday_df = self.pre_deal(traffic_conversion[traffic_conversion['day'] == 3])
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+ Thursday_df = self.func_rule_budget(Thursday_df)
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+ Friday_df = self.pre_deal(traffic_conversion[traffic_conversion['day'] == 4])
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+ Friday_df = self.func_rule_budget(Friday_df)
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+ Saturday_df = self.pre_deal(traffic_conversion[traffic_conversion['day'] == 5])
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+ Saturday_df = self.func_rule_budget(Saturday_df)
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+ Sunday_df = self.pre_deal(traffic_conversion[traffic_conversion['day'] == 6])
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+ Sunday_df = self.func_rule_budget(Sunday_df)
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weeksummary_percent = pd.merge(Monday_df,Tuesday_df,how='inner',on='hour')
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weeksummary_percent = weeksummary_percent.merge(Wednesday_df,how='inner',on='hour')
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@@ -219,11 +233,98 @@ class AdjustB:
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weeksummary_percent = weeksummary_percent.merge(Sunday_df,how='inner',on='hour')
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weeksummary_percent.columns = ["hour",'Monday','Tuesday','Wednesday','Thursday','Friday','Saturday','Sunday']
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# weeksummary_percent.to_excel("S111.xlsx")
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- return weeksummary_percent
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+ return json.dumps({"budget_allocate_week":weeksummary_percent.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):
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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:
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+ return 1
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+ elif cr > avg_ctr:
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+ return 1.25
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+ elif cr > 0.75 * avg_cr:
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+ return 0.75
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+ else:
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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
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+ def func_rule_bid(self,traffic_conversion):
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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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+ tf_c['weight_allocate'] = tf_c.apply(lambda x:self.rule_set_bid(avg_weight,x['cr'],avg_cr,x['ctr'],avg_ctr,x['weight_value'],x['hour']),axis=1)
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+ # print(avg_bid,avg_cr,avg_ctr,avg_weight)
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+ return tf_c[['hour','weight_allocate']].round(2)
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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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+ # TODO 待完成
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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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+ # print(traffic_conversion)
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+ # traffic_conversion = self.pre_deal(traffic_conversion)
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+ # 单独筛选周一至周日每天的traffic,再进行后续步骤
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+ Monday_df = self.pre_deal(traffic_conversion[traffic_conversion['day'] == 0])
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+ Monday_df = self.func_rule_bid(Monday_df)
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+ Tuesday_df = self.pre_deal(traffic_conversion[traffic_conversion['day'] == 1])
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+ Tuesday_df = self.func_rule_bid(Tuesday_df)
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+ Wednesday_df = self.pre_deal(traffic_conversion[traffic_conversion['day'] == 2])
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+ Wednesday_df = self.func_rule_bid(Wednesday_df)
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+ Thursday_df = self.pre_deal(traffic_conversion[traffic_conversion['day'] == 3])
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+ Thursday_df = self.func_rule_bid(Thursday_df)
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+ Friday_df = self.pre_deal(traffic_conversion[traffic_conversion['day'] == 4])
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+ Friday_df = self.func_rule_bid(Friday_df)
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+ Saturday_df = self.pre_deal(traffic_conversion[traffic_conversion['day'] == 5])
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+ Saturday_df = self.func_rule_bid(Saturday_df)
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+ Sunday_df = self.pre_deal(traffic_conversion[traffic_conversion['day'] == 6])
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+ Sunday_df = self.func_rule_bid(Sunday_df)
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+
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+ weeksummary_percent = pd.merge(Monday_df, Tuesday_df, how='left', on='hour')
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+ weeksummary_percent = weeksummary_percent.merge(Wednesday_df, how='left', on='hour')
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+ weeksummary_percent = weeksummary_percent.merge(Thursday_df, how='left', on='hour')
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+ weeksummary_percent = weeksummary_percent.merge(Friday_df, how='left', on='hour')
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+ weeksummary_percent = weeksummary_percent.merge(Saturday_df, how='left', on='hour')
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+ weeksummary_percent = weeksummary_percent.merge(Sunday_df, how='left', on='hour')
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+ weeksummary_percent.columns = ["hour", 'Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday',
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+ 'Sunday']
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+ # weeksummary_percent.to_excel("S111.xlsx")
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+ return json.dumps({"bid_adjust_week":weeksummary_percent.to_dict(orient='records')})
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if __name__ == '__main__':
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- adjust_ = AdjustB(campaign_id='281441197839505',time_period='45days')
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- rel = adjust_.merge_conv_traf()
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- print(rel)
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+ adjust_ = AdjustB(campaign_id='325523075677132')
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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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+ #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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