adjust_budget_bid modify

sync_amz_data/public/adjust_budget_bid.py

@@ -6,11 +6,11 @@ pd.set_option('display.max_columns', None)
 pd.set_option('expand_frame_repr', False)
 import warnings
 from typing import Literal
-
+import json
 warnings.filterwarnings('ignore')
 
 class AdjustB:
-    def __init__(self,campaign_id,time_period:Literal["1week","2weeks","1month","2months","45days"]):
+    def __init__(self,campaign_id,time_period:Literal["1week","2weeks","4weeks","6weeks","8weeks","12weeks"]="8weeks"):
         self.campaign_id = campaign_id
         self.time_period = time_period
 
@@ -58,7 +58,7 @@ class AdjustB:
         cursor = conn.cursor()
         sql = "select * from zosi_ad_marketing_stream.sp_traffic_raw"
         sql = sql + self.add_condition(isbudgetTable=False)
-        print(sql)
+        # print(sql)
         cursor.execute(sql)
         columns_name = [i[0] for i in cursor.description]
         rel = cursor.fetchall()
@@ -82,12 +82,14 @@ class AdjustB:
             time_ = datetime.today().date() + timedelta(days=-7)
         elif self.time_period =='2weeks':
             time_ = datetime.today().date() + timedelta(days=-14)
-        elif self.time_period =='month':
-            time_ = datetime.today().date() + timedelta(days=-30)
-        elif self.time_period =='45days':
-            time_ = datetime.today().date() + timedelta(days=-45)
-        elif self.time_period == '2months':
-            time_ = datetime.today().date() + timedelta(days=-60)
+        elif self.time_period =='4weeks':
+            time_ = datetime.today().date() + timedelta(days=-28)
+        elif self.time_period =='6weeks':
+            time_ = datetime.today().date() + timedelta(days=-42)
+        elif self.time_period == '8weeks':
+            time_ = datetime.today().date() + timedelta(days=-56)
+        elif self.time_period == '12weeks':
+            time_ = datetime.today().date() + timedelta(days=-84)
         # usage_updated_timestamp
         if isbudgetTable:
             return f" where usage_updated_timestamp>='{time_}' and budget_scope_id='{self.campaign_id}'"
@@ -147,7 +149,7 @@ class AdjustB:
         # traffic_conversion['cpc'] = traffic_conversion['cpc'].replace([np.inf,np.nan,pd.NA],0)
         return traffic_conversion
 
-    def func_rule(self,traffic_conversion):
+    def pre_deal(self,traffic_conversion):
         pro_list = traffic_conversion.groupby(['campaign_id', 'ad_group_id', 'keyword_id']).head(1)[
             ['campaign_id', 'ad_group_id', 'keyword_id']].to_numpy().tolist()
         for i in pro_list:
@@ -166,6 +168,10 @@ class AdjustB:
         # 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%
         traffic_conversion['cpc'] = traffic_conversion.apply(
             lambda x: x['cpc_min'] * 0.45 if pd.isna(x['cpc']) or x['cpc'] is None else x['cpc'], axis=1)
+        return traffic_conversion
+
+    def func_rule_budget(self,traffic_conversion):
+        traffic_conversion = self.pre_deal(traffic_conversion)
         # total_spend = traffic_conversion['cpc'].sum()
         # 根据小时对竞价、转化、点击汇总
         tf_c = traffic_conversion.groupby(['hour']).agg(
@@ -194,22 +200,30 @@ class AdjustB:
         tf_c['pre_percent_s3'] = tf_c['pre_percent_s3'].map(lambda x: x + allocate_val if x != 0.25 else 0.25)
         return tf_c[['hour','pre_percent_s3']]
 
-    def merge_conv_traf(self): # 总结过去每天的数据，对单天预算分配
+    def budget_allocate_singleDay(self): # 总结过去每天的数据，对单天预算分配
         traffic_conversion = self.merge_common_operation()
-        traffic_conversion = self.func_rule(traffic_conversion)
+        traffic_conversion = self.pre_deal(traffic_conversion)
+        traffic_conversion = self.func_rule_budget(traffic_conversion)
         traffic_conversion.columns = ['hour','SingleDay']
-        return traffic_conversion
+        return json.dumps({"budget_allocate_singleDay":traffic_conversion.to_dict(orient='records')})
 
-    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
+    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
         traffic_conversion = self.merge_common_operation()
-        # TODO 单独筛选周一至周日每天的traffic，再进行后续步骤
-        Monday_df = self.func_rule(traffic_conversion[traffic_conversion['day']==0])
-        Tuesday_df = self.func_rule(traffic_conversion[traffic_conversion['day']==1])
-        Wednesday_df = self.func_rule(traffic_conversion[traffic_conversion['day']==2])
-        Thursday_df = self.func_rule(traffic_conversion[traffic_conversion['day']==3])
-        Friday_df = self.func_rule(traffic_conversion[traffic_conversion['day']==4])
-        Saturday_df = self.func_rule(traffic_conversion[traffic_conversion['day']==5])
-        Sunday_df = self.func_rule(traffic_conversion[traffic_conversion['day']==6])
+        # 单独筛选周一至周日每天的traffic，再进行后续步骤
+        Monday_df = self.pre_deal(traffic_conversion[traffic_conversion['day'] == 0])
+        Monday_df = self.func_rule_budget(Monday_df)
+        Tuesday_df = self.pre_deal(traffic_conversion[traffic_conversion['day'] == 1])
+        Tuesday_df = self.func_rule_budget(Tuesday_df)
+        Wednesday_df = self.pre_deal(traffic_conversion[traffic_conversion['day'] == 2])
+        Wednesday_df = self.func_rule_budget(Wednesday_df)
+        Thursday_df = self.pre_deal(traffic_conversion[traffic_conversion['day'] == 3])
+        Thursday_df = self.func_rule_budget(Thursday_df)
+        Friday_df = self.pre_deal(traffic_conversion[traffic_conversion['day'] == 4])
+        Friday_df = self.func_rule_budget(Friday_df)
+        Saturday_df = self.pre_deal(traffic_conversion[traffic_conversion['day'] == 5])
+        Saturday_df = self.func_rule_budget(Saturday_df)
+        Sunday_df = self.pre_deal(traffic_conversion[traffic_conversion['day'] == 6])
+        Sunday_df = self.func_rule_budget(Sunday_df)
 
         weeksummary_percent = pd.merge(Monday_df,Tuesday_df,how='inner',on='hour')
         weeksummary_percent = weeksummary_percent.merge(Wednesday_df,how='inner',on='hour')
@@ -219,11 +233,98 @@ class AdjustB:
         weeksummary_percent = weeksummary_percent.merge(Sunday_df,how='inner',on='hour')
         weeksummary_percent.columns = ["hour",'Monday','Tuesday','Wednesday','Thursday','Friday','Saturday','Sunday']
         # weeksummary_percent.to_excel("S111.xlsx")
-        return weeksummary_percent
+        return json.dumps({"budget_allocate_week":weeksummary_percent.round(4).to_dict(orient='records')})
+
+    def rule_set_bid(self,avg_weight, cr, avg_cr, ctr, avg_ctr, weight_value, hour):
+        if weight_value > avg_weight * 1.5:  # 表现极好词
+            return 2
+        elif weight_value > avg_weight * 1.25:  # 表现较好词
+            if hour in [23, 0, 1, 2, 3, 4, 5]:
+                return 1.5
+            else:
+                return 1.5 + np.random.randint(100, 300) / 1000
+        elif weight_value > avg_weight * 1.15:  # 表现稍好词
+            if hour in [23, 0, 1, 2, 3, 4, 5]:
+                return 1.25
+            else:
+                return 1.5 + np.random.randint(100, 200) / 1000
+        elif weight_value > avg_weight:  # 标准权重词
+            return 1
+        else:
+            if ctr >= avg_ctr and cr >= 0.75 * avg_ctr:
+                return 1
+            elif cr > avg_ctr:
+                return 1.25
+            elif cr > 0.75 * avg_cr:
+                return 0.75
+            else:
+                if ((pd.isna(cr) and pd.isna(ctr)) or None in [cr, ctr]) and hour not in [23, 0, 1, 2, 3, 4, 5]:
+                    return [0.5, 0.7, 0.8, 0.9, 1, 1.1][np.random.randint(0, 5)]
+            return 0.5
+    def func_rule_bid(self,traffic_conversion):
+        tf_c = traffic_conversion.groupby(['hour']).agg(
+            {'cost': sum, 'attributed_conversions_1d': sum, 'clicks': sum, 'impressions': sum}).reset_index()
+        tf_c['cpc'] = tf_c['cost'] / tf_c['clicks']
+        tf_c['cr'] = tf_c['attributed_conversions_1d'] / tf_c['clicks']
+        tf_c['ctr'] = tf_c['clicks'] / tf_c['impressions']
+        avg_bid = tf_c['cpc'].mean()
+        avg_cr = tf_c['attributed_conversions_1d'].sum()/tf_c['clicks'].sum()
+        avg_ctr = tf_c['clicks'].sum()/tf_c['impressions'].sum()
+        tf_c['weight_value'] = tf_c['cr']/tf_c['cpc']
+        avg_weight = avg_cr/avg_bid
+        # avg_weight = tf_c['weight_value'].mean()
 
+        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)
+        # print(avg_bid,avg_cr,avg_ctr,avg_weight)
+        return tf_c[['hour','weight_allocate']].round(2)
 
+    def bid_adjust_singleDay(self):
+        traffic_conversion = self.merge_common_operation()
+        # traffic_conversion = self.pre_deal(traffic_conversion)
+        tf_c = self.pre_deal(traffic_conversion)
+        tf_c = self.func_rule_bid(tf_c)
+        tf_c.columns = ['hour','SingleDay']
+        # TODO 待完成
+        return json.dumps({"bid_adjust_singleDay":tf_c.to_dict(orient='records')})
+
+    def bid_adjust_week(self):
+        traffic_conversion = self.merge_common_operation()
+        # print(traffic_conversion)
+        # traffic_conversion = self.pre_deal(traffic_conversion)
+        # 单独筛选周一至周日每天的traffic，再进行后续步骤
+        Monday_df = self.pre_deal(traffic_conversion[traffic_conversion['day'] == 0])
+        Monday_df = self.func_rule_bid(Monday_df)
+        Tuesday_df = self.pre_deal(traffic_conversion[traffic_conversion['day'] == 1])
+        Tuesday_df = self.func_rule_bid(Tuesday_df)
+        Wednesday_df = self.pre_deal(traffic_conversion[traffic_conversion['day'] == 2])
+        Wednesday_df = self.func_rule_bid(Wednesday_df)
+        Thursday_df = self.pre_deal(traffic_conversion[traffic_conversion['day'] == 3])
+        Thursday_df = self.func_rule_bid(Thursday_df)
+        Friday_df = self.pre_deal(traffic_conversion[traffic_conversion['day'] == 4])
+        Friday_df = self.func_rule_bid(Friday_df)
+        Saturday_df = self.pre_deal(traffic_conversion[traffic_conversion['day'] == 5])
+        Saturday_df = self.func_rule_bid(Saturday_df)
+        Sunday_df = self.pre_deal(traffic_conversion[traffic_conversion['day'] == 6])
+        Sunday_df = self.func_rule_bid(Sunday_df)
+
+        weeksummary_percent = pd.merge(Monday_df, Tuesday_df, how='left', on='hour')
+        weeksummary_percent = weeksummary_percent.merge(Wednesday_df, how='left', on='hour')
+        weeksummary_percent = weeksummary_percent.merge(Thursday_df, how='left', on='hour')
+        weeksummary_percent = weeksummary_percent.merge(Friday_df, how='left', on='hour')
+        weeksummary_percent = weeksummary_percent.merge(Saturday_df, how='left', on='hour')
+        weeksummary_percent = weeksummary_percent.merge(Sunday_df, how='left', on='hour')
+        weeksummary_percent.columns = ["hour", 'Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday',
+                                       'Sunday']
+        # weeksummary_percent.to_excel("S111.xlsx")
+        return json.dumps({"bid_adjust_week":weeksummary_percent.to_dict(orient='records')})
 
 if __name__ == '__main__':
-    adjust_ = AdjustB(campaign_id='281441197839505',time_period='45days')
-    rel = adjust_.merge_conv_traf()
-    print(rel)
+    adjust_ = AdjustB(campaign_id='325523075677132')
+    # 竞价分配
+    bid_adjust = adjust_.bid_adjust_week()
+    print(bid_adjust)
+
+    print()
+    #预算分配
+    budget_adjust = adjust_.budget_allocate_week()
+    print(budget_adjust)