前提條件

一、準備工作

準備原始數據

一般對于推薦場景，特征生產通常需要以下三張原始表，以這三張原始表為基礎，特征生產可以生產出成百上千個特征，方便建立模型來擬合目標。

• 用戶表（rec_sln_demo_user_table_preprocess_v1）：包含一些基礎的用戶特征，例如性別、年齡、城市和關注數等。

• 物品表（rec_sln_demo_item_table_preprocess_v1）：包含一些基礎的物品特征，例如類別、作者、累計點擊數和累計點贊數等。

• 行為表（rec_sln_demo_behavior_table_preprocess_v1）：包含一些行為特征，例如某時用戶點擊某個物品等。

數據表存放在有公開讀取權限的pai_online_project中，其數據均為模擬數據生成。您需要在DataWorks中執行SQL命令，將上表數據從pai_online_project項目同步到您的MaxCompute項目中。具體操作步驟如下：

1. 登錄DataWorks控制臺。

2. 在左側導航欄單擊數據建模與開發 > 數據開發。

3. 選擇已創建的DataWorks工作空間后，單擊進入數據開發。

4. 鼠標懸停至新建，選擇新建節點 > MaxCompute > ODPS SQL，在彈出的頁面中配置節點參數。

   參數	取值建議
   引擎實例	選擇已創建的MaxCompute引擎。
   節點類型	ODPS SQL
   路徑	業務流程/Workflow/MaxCompute
   名稱	可自定義名稱。

5. 單擊確認。

6. 在新建節點區域運行以下SQL命令，將用戶表、物品表、行為表從pai_online_project項目同步到您自己的MaxCompute中。資源組選擇已創建的獨享資源組。

   • 同步用戶表：rec_sln_demo_user_table_preprocess_v1

     CREATE TABLE IF NOT EXISTS rec_sln_demo_user_table_preprocess_v1
     like pai_online_project.rec_sln_demo_user_table_preprocess_v1
     STORED AS ALIORC  
     LIFECYCLE 90;
     
     INSERT OVERWRITE TABLE rec_sln_demo_user_table_preprocess_v1 PARTITION(ds)
     SELECT *
     FROM pai_online_project.rec_sln_demo_user_table_preprocess_v1
     WHERE ds >= '20240530' and ds <='20240605';

   • 同步物品表：rec_sln_demo_item_table_preprocess_v1

     CREATE TABLE IF NOT EXISTS rec_sln_demo_item_table_preprocess_v1
     like pai_online_project.rec_sln_demo_item_table_preprocess_v1
     STORED AS ALIORC  
     LIFECYCLE 90;
     
     INSERT OVERWRITE TABLE rec_sln_demo_item_table_preprocess_v1 PARTITION(ds)
     SELECT *
     FROM pai_online_project.rec_sln_demo_item_table_preprocess_v1
     WHERE ds >= '20240530' and ds <='20240605';

   • 同步行為表：rec_sln_demo_behavior_table_preprocess_v1

     CREATE TABLE IF NOT EXISTS rec_sln_demo_behavior_table_preprocess_v1
     like pai_online_project.rec_sln_demo_behavior_table_preprocess_v1
     STORED AS ALIORC  
     LIFECYCLE 90;
     
     INSERT OVERWRITE TABLE rec_sln_demo_behavior_table_preprocess_v1 PARTITION(ds)
     SELECT *
     FROM pai_online_project.rec_sln_demo_behavior_table_preprocess_v1
     WHERE ds >= '20240530' and ds <='20240605';

二、原始表初步變換

準備流程完成后，您可以在自己的項目空間中查看已準備就緒的三張表：用戶表（rec_sln_demo_user_table_preprocess_v1）、物品表（rec_sln_demo_item_table_preprocess_v1）以及行為表（rec_sln_demo_behavior_table_preprocess_v1），您可以以這三張表為基礎進行特征生產。

在進行后續的特征生產加工之前，為了方便您后續做特征統計，您需要對數據表做以下預處理操作。您可以將以下SQL命令粘貼到已創建的ODPS SQL節點中執行，具體操作，請參見準備原始數據。

• 將用戶表、物品表的特征和行為表進行連接。

  CREATE TABLE IF NOT EXISTS rec_sln_demo_behavior_table_preprocess_wide_v1 
  (
      request_id bigint
      ,user_id string
      ,page string
      ,net_type string
      ,day_h bigint COMMENT '行為發生在當天的第幾小時'
      ,week_day bigint COMMENT '行為發生在當前周的第幾天'
      ,event_unix_time bigint
      ,item_id string
      ,event string
      ,playtime double
      ,gender string
      ,age bigint
      ,city string
      ,item_cnt bigint
      ,follow_cnt bigint
      ,follower_cnt bigint
      ,is_new_user bigint
      ,tags string
      ,duration double
      ,category string
      ,author bigint
      ,click_count bigint
      ,praise_count bigint
      ,is_new_item bigint
  )
  PARTITIONED BY 
  (
      ds string
  )
  LIFECYCLE 90
  ;
  INSERT OVERWRITE TABLE rec_sln_demo_behavior_table_preprocess_wide_v1 PARTITION(ds='${bdp.system.bizdate}')
  SELECT  sq0.request_id
          ,sq0.user_id
          ,sq0.page
          ,sq0.net_type
          ,sq0.day_h
          ,sq0.week_day
          ,sq0.event_unix_time
          ,sq0.item_id
          ,sq0.event
          ,sq0.playtime
          ,sq1.gender
          ,sq1.age
          ,sq1.city
          ,sq1.item_cnt
          ,sq1.follow_cnt
          ,sq1.follower_cnt
          ,sq1.is_new_user
          ,sq1.tags
          ,sq2.duration
          ,sq2.category
          ,sq2.author
          ,sq2.click_count
          ,sq2.praise_count
          ,sq2.is_new_item
  FROM    (
              SELECT  *
              FROM    rec_sln_demo_behavior_table_preprocess_v1
              WHERE   ds = '${bdp.system.bizdate}'
          ) sq0
  LEFT JOIN (
                SELECT  *
                FROM    rec_sln_demo_user_table_preprocess_v1
                WHERE   ds = '${bdp.system.bizdate}'
            ) sq1
  ON      sq0.user_id = sq1.user_id LEFT
  JOIN    (
              SELECT  *
              FROM    rec_sln_demo_item_table_preprocess_v1
              WHERE   ds = '${bdp.system.bizdate}'
          ) sq2
  ON      sq0.item_id = sq2.item_id
  ;

• 因訓練模型需要有標簽，您需要對行為表進行預處理，將其轉換成Label表。本示例將點擊、播放時間、點贊作為標簽。

  CREATE TABLE IF NOT EXISTS rec_sln_demo_fs_label_table_v1
  (
      request_id bigint
      ,user_id string
      ,page string
      ,net_type string
      ,day_h bigint COMMENT '行為發生在當天的第幾小時'
      ,week_day bigint COMMENT '行為發生在當前周的第幾天'
      ,day_min string
      ,event_unix_time bigint
      ,item_id string
      ,playtime double
      ,is_click BIGINT
      ,ln_playtime DOUBLE
      ,is_praise BIGINT
  )
  PARTITIONED BY 
  (
      ds string
  )
  LIFECYCLE 90
  ;
  INSERT OVERWRITE TABLE rec_sln_demo_fs_label_table_v1 PARTITION(ds='${bdp.system.bizdate}')
  SELECT  request_id
          ,user_id
          ,MAX(page) page
          ,MAX(net_type) net_type
          ,MAX(day_h) day_h
          ,MAX(week_day) week_day
          ,TO_CHAR(FROM_UNIXTIME(MIN(event_unix_time)),'yyyymmddhhmi') day_min
          ,MAX(event_unix_time) event_unix_time
          ,item_id
          ,MAX(playtime) playtime
          ,max(if(event='click', 1, 0)) is_click
          ,ln(sum(playtime) + 1) ln_playtime
          ,max(if(event='praise', 1, 0)) is_praise
  FROM    rec_sln_demo_behavior_table_preprocess_v1
  WHERE   ds = '${bdp.system.bizdate}'
  GROUP BY request_id
           ,user_id
           ,item_id
  ;

經過上面的初步變換后，您將擁有以下兩種表：

• 新的行為表：rec_sln_demo_behavior_table_preprocess_wide_v1，后續的統計特征將以此表為基礎進行變換。

• Label表：rec_sln_demo_fs_label_table_v1，后續構建樣本表時需要用到此表。

三、特征生產加工

您可以調用特征生產中的自動擴展函數進行特征生產，僅需幾行代碼，就可以生產出上百種特征。

特征生產加工分別需要加工用戶側特征和物品側特征。具體操作步驟如下：

用戶側特征的特征生產

以下代碼均建議在Jupyter Notebook環境下運行。

1. 初始化Client。

   access_key_id = os.environ.get("ALIBABA_CLOUD_ACCESS_KEY_ID") # 填入您的Access Key ID
   access_key_secret = os.environ.get("ALIBABA_CLOUD_ACCESS_KEY_SECRET") # 填入您的Access Key Secret
   project = 'project_name' # 填入您的項目名
   region = 'cn-hangzhou' # 填入您的項目所在區域，例如華東1（杭州）為cn-hangzhou
   fs_client = FeatureStoreClient(access_key_id=access_key_id, access_key_secret=access_key_secret, region=region)

2. 指定要進行特征變換的數據源。

   input_bhv_table_name = "rec_sln_demo_behavior_table_preprocess_wide_v1"
   ds_bhv = MaxComputeDataSource(table=input_bhv_table_name, project=project)
   
   input_user_table_name = "rec_sln_demo_user_table_preprocess_v1"
   ds_user = MaxComputeDataSource(table=input_user_table_name, project=project)
   
   input_item_table_name = "rec_sln_demo_item_table_preprocess_v1"
   ds_item = MaxComputeDataSource(table=input_item_table_name, project=project)

3. 指定JoinTransform和AggregationTransform后輸出的用戶側特征表名稱。

   agg_user_table_v1 = 'rec_sln_demo_user_table_preprocess_agg_v1'

4. 利用自動擴展函數，對統計特征進行自動擴展。

   name_prefix = "user"
   input_list = ["playtime", "duration", "click_count", "praise_count"]
   event_name = 'event'
   event_type = 'expr'
   group_by_key = "user_id"
   window_size = [3,7,15]
   user_count_feature_list = auto_count_feature_transform(name_prefix, input_list, event_name, event_type, group_by_key, window_size)
   print("len_count_feature_list = ", len(user_count_feature_list))
   print("count_feature_list = ", user_count_feature_list)

5. 執行生產過程，并查看表運行結果。

   1. 創建Pipeline。

      agg_user_bhv_pipeline = fs_client.create_pipeline(ds_bhv).add_feature_transform(user_count_feature_list)
      agg_user_pipeline =fs_client.create_pipeline(ds_user, agg_user_table_v1).merge(agg_user_bhv_pipeline, keep_input_columns=False)

   2. 執行Pipeline生產過程。

      execute_date = '20240605'
      output_agg_user_table = agg_user_pipeline.execute(execute_date, drop_table=True)

   3. 查看表運行結果。

      agg_user_ret = output_agg_user_table.to_pandas(execute_date, limit=20)
      agg_user_ret

6. 進行WindowTransform變換，指定WindowTransform后輸出的用戶側特征表名稱。

   win_user_table_v1 = 'rec_sln_demo_user_table_preprocess_win_v1'

7. 利用內置的自動擴展函數，自動生成WindowTransform特征定義。

   name_prefix = 'user'
   input_list = ['day_h', 'category']
   agg_field = ['duration', 'click_count']
   event_name = 'event'
   event_type = 'expr'
   group_by_key = 'user_id'
   window_size = [7, 15, 30, 45]
   user_win_feature_list = auto_window_feature_transform(name_prefix, input_list, agg_field, event_name, event_type, group_by_key, window_size)
   print("len_user_win_feature_list = ", len(user_win_feature_list))
   print("user_win_feature_list = ", user_win_feature_list)

8. 執行生產過程，并查看表運行結果。

   1. 創建pipeline。

      win_user_bhv_pipeline = fs_client.create_pipeline(ds_bhv).add_feature_transform(user_win_feature_list)
      win_user_pipeline = fs_client.create_pipeline(ds_user, win_user_table_v1).merge(win_user_bhv_pipeline, keep_input_columns=False)

   2. 執行pipeline生產過程。

      execute_date = '20240605'
      output_win_user_table = win_user_pipeline.execute(execute_date, drop_table=True)
      
      # 詳細實現見功能文檔。因為有中間表存在，第一次執行時需要補數據，可能需要較長時間，運行補數據可以執行下面的命令
      # output_win_user_table = win_user_pipeline.execute(execute_date, drop_table=True, backfill_partitions=True)

   3. 查看表運行結果。

      win_user_ret = output_win_user_table.to_pandas(execute_date, limit=20)
      win_user_ret

物品側特征的特征生產

在完成用戶側特征的提取和加工之后，您可以繼續對物品表進行特征生產加工，生成基于物品類型的特征。具體操作步驟如下：

1. 指定AggregationTransform后輸出的物品側特征表名稱。

   agg_item_table_v1 = 'rec_sln_demo_item_table_preprocess_agg_v1'

2. 利用自動擴展函數，對統計特征進行自動擴展。

   name_prefix = "item"
   input_list = ["item_cnt", "follow_cnt", "follower_cnt"]
   event_name = 'event'
   event_type = 'expr'
   group_by_key = "item_id"
   window_size = [3,7,15]
   item_count_feature_list = auto_count_feature_transform(name_prefix, input_list, event_name, event_type, group_by_key, window_size)
   print("len_count_feature_list = ", len(item_count_feature_list))
   print("count_feature_list = ", item_count_feature_list)

3. 執行生產過程，并查看表運行結果。

   1. 創建pipeline。

      agg_item_bhv_pipeline =fs_client.create_pipeline(ds_bhv).add_feature_transform(item_count_feature_list)
      agg_item_pipeline =fs_client.create_pipeline(ds_item, agg_item_table_v1).merge(agg_item_bhv_pipeline, keep_input_columns=False)

   2. 執行pipeline生產過程。

      execute_date = '20240605'
      output_agg_item_table = agg_item_pipeline.execute(execute_date, drop_table=True)

   3. 查看表運行結果。

      agg_item_ret = output_agg_item_table.to_pandas(execute_date, limit=20)
      agg_item_ret

4. 指定WindowTransform后輸出的用戶側特征表名稱。

   win_item_table_v1 = 'rec_sln_demo_item_table_preprocess_win_v1'

5. 利用內置的自動擴展函數，自動生成WindowTransform特征定義。

   name_prefix = 'item'
   input_list = ['day_h', 'category']
   agg_field = ['click_count', 'praise_count']
   event_name = 'event'
   event_type = 'expr'
   group_by_key = 'item_id'
   window_size = [7, 15, 30, 45]
   item_win_feature_list = auto_window_feature_transform(name_prefix, input_list, agg_field, event_name, event_type, group_by_key, window_size)
   print("len_item_win_feature_list = ", len(item_win_feature_list))
   print("item_win_feature_list = ", item_win_feature_list)

6. 執行生產過程，并查看表運行結果。

   1. 創建pipeline。

      win_item_bhv_pipeline = fs_client.create_pipeline(ds_bhv).add_feature_transform(item_win_feature_list)
      win_item_pipeline = fs_client.create_pipeline(ds_item, win_item_table_v1).merge(win_item_bhv_pipeline, keep_input_columns=False)

   2. 執行pipeline生產過程。

      execute_date = '20240605'
      output_win_item_table = win_item_pipeline.execute(execute_date, drop_table=True)
      
      # 詳細實現見功能文檔。因為有中間表存在，第一次執行時需要補數據，可能需要較長時間，運行補數據可以執行下面的命令
      # output_win_item_table = win_item_pipeline.execute(execute_date, drop_table=True, backfill_partitions=True)

   3. 查看表運行結果。

      win_item_ret = output_win_item_table.to_pandas(execute_date, limit=20)
      win_item_ret

四、生成樣本表

經過上述流程，您已獲取了Label表，并在物品側和用戶側分別整理出三張特征表（包括一張原始表及兩張經過特征生產加工出的表）。隨后，您可以將以下SQL命令粘貼到已創建的ODPS SQL節點中執行，將這七張表進行合并，以構建完整的樣本表。具體操作，請參見準備原始數據。

CREATE TABLE IF NOT EXISTS fs_demo_fs_engineering_v1_training_set(
	request_id BIGINT,
	 user_id STRING,
	 page STRING,
	 net_type STRING,
	 day_h BIGINT,
	 week_day BIGINT,
	 day_min STRING,
	 event_unix_time BIGINT,
	 item_id STRING,
	 playtime DOUBLE,
	 is_click BIGINT,
	 ln_playtime DOUBLE,
	 is_praise BIGINT,
	 duration DOUBLE,
	 category STRING,
	 author BIGINT,
	 click_count BIGINT,
	 praise_count BIGINT,
	 is_new_item BIGINT,
	 item__sum_item_cnt_3d BIGINT,
	 item__sum_follow_cnt_3d BIGINT,
	 item__sum_follower_cnt_3d BIGINT,
	 item__max_item_cnt_3d BIGINT,
	 item__max_follow_cnt_3d BIGINT,
	 item__max_follower_cnt_3d BIGINT,
	 item__min_item_cnt_3d BIGINT,
	 item__min_follow_cnt_3d BIGINT,
	 item__min_follower_cnt_3d BIGINT,
	 item__avg_item_cnt_3d DOUBLE,
	 item__avg_follow_cnt_3d DOUBLE,
	 item__avg_follower_cnt_3d DOUBLE,
	 item__sum_item_cnt_7d BIGINT,
	 item__sum_follow_cnt_7d BIGINT,
	 item__sum_follower_cnt_7d BIGINT,
	 item__max_item_cnt_7d BIGINT,
	 item__max_follow_cnt_7d BIGINT,
	 item__max_follower_cnt_7d BIGINT,
	 item__min_item_cnt_7d BIGINT,
	 item__min_follow_cnt_7d BIGINT,
	 item__min_follower_cnt_7d BIGINT,
	 item__avg_item_cnt_7d DOUBLE,
	 item__avg_follow_cnt_7d DOUBLE,
	 item__avg_follower_cnt_7d DOUBLE,
	 item__sum_item_cnt_15d BIGINT,
	 item__sum_follow_cnt_15d BIGINT,
	 item__sum_follower_cnt_15d BIGINT,
	 item__max_item_cnt_15d BIGINT,
	 item__max_follow_cnt_15d BIGINT,
	 item__max_follower_cnt_15d BIGINT,
	 item__min_item_cnt_15d BIGINT,
	 item__min_follow_cnt_15d BIGINT,
	 item__min_follower_cnt_15d BIGINT,
	 item__avg_item_cnt_15d DOUBLE,
	 item__avg_follow_cnt_15d DOUBLE,
	 item__avg_follower_cnt_15d DOUBLE,
	 item__kv_day_h_click_count_sum_7d STRING,
	 item__kv_category_click_count_sum_7d STRING,
	 item__kv_day_h_praise_count_sum_7d STRING,
	 item__kv_category_praise_count_sum_7d STRING,
	 item__kv_day_h_click_count_max_7d STRING,
	 item__kv_category_click_count_max_7d STRING,
	 item__kv_day_h_praise_count_max_7d STRING,
	 item__kv_category_praise_count_max_7d STRING,
	 item__kv_day_h_click_count_min_7d STRING,
	 item__kv_category_click_count_min_7d STRING,
	 item__kv_day_h_praise_count_min_7d STRING,
	 item__kv_category_praise_count_min_7d STRING,
	 item__kv_day_h_click_count_avg_7d STRING,
	 item__kv_category_click_count_avg_7d STRING,
	 item__kv_day_h_praise_count_avg_7d STRING,
	 item__kv_category_praise_count_avg_7d STRING,
	 item__kv_day_h_click_count_sum_15d STRING,
	 item__kv_category_click_count_sum_15d STRING,
	 item__kv_day_h_praise_count_sum_15d STRING,
	 item__kv_category_praise_count_sum_15d STRING,
	 item__kv_day_h_click_count_max_15d STRING,
	 item__kv_category_click_count_max_15d STRING,
	 item__kv_day_h_praise_count_max_15d STRING,
	 item__kv_category_praise_count_max_15d STRING,
	 item__kv_day_h_click_count_min_15d STRING,
	 item__kv_category_click_count_min_15d STRING,
	 item__kv_day_h_praise_count_min_15d STRING,
	 item__kv_category_praise_count_min_15d STRING,
	 item__kv_day_h_click_count_avg_15d STRING,
	 item__kv_category_click_count_avg_15d STRING,
	 item__kv_day_h_praise_count_avg_15d STRING,
	 item__kv_category_praise_count_avg_15d STRING,
	 item__kv_day_h_click_count_sum_30d STRING,
	 item__kv_category_click_count_sum_30d STRING,
	 item__kv_day_h_praise_count_sum_30d STRING,
	 item__kv_category_praise_count_sum_30d STRING,
	 item__kv_day_h_click_count_max_30d STRING,
	 item__kv_category_click_count_max_30d STRING,
	 item__kv_day_h_praise_count_max_30d STRING,
	 item__kv_category_praise_count_max_30d STRING,
	 item__kv_day_h_click_count_min_30d STRING,
	 item__kv_category_click_count_min_30d STRING,
	 item__kv_day_h_praise_count_min_30d STRING,
	 item__kv_category_praise_count_min_30d STRING,
	 item__kv_day_h_click_count_avg_30d STRING,
	 item__kv_category_click_count_avg_30d STRING,
	 item__kv_day_h_praise_count_avg_30d STRING,
	 item__kv_category_praise_count_avg_30d STRING,
	 item__kv_day_h_click_count_sum_45d STRING,
	 item__kv_category_click_count_sum_45d STRING,
	 item__kv_day_h_praise_count_sum_45d STRING,
	 item__kv_category_praise_count_sum_45d STRING,
	 item__kv_day_h_click_count_max_45d STRING,
	 item__kv_category_click_count_max_45d STRING,
	 item__kv_day_h_praise_count_max_45d STRING,
	 item__kv_category_praise_count_max_45d STRING,
	 item__kv_day_h_click_count_min_45d STRING,
	 item__kv_category_click_count_min_45d STRING,
	 item__kv_day_h_praise_count_min_45d STRING,
	 item__kv_category_praise_count_min_45d STRING,
	 item__kv_day_h_click_count_avg_45d STRING,
	 item__kv_category_click_count_avg_45d STRING,
	 item__kv_day_h_praise_count_avg_45d STRING,
	 item__kv_category_praise_count_avg_45d STRING,
	 gender STRING,
	 age BIGINT,
	 city STRING,
	 item_cnt BIGINT,
	 follow_cnt BIGINT,
	 follower_cnt BIGINT,
	 is_new_user BIGINT,
	 tags STRING,
	 user__sum_playtime_3d DOUBLE,
	 user__sum_duration_3d DOUBLE,
	 user__sum_click_count_3d BIGINT,
	 user__sum_praise_count_3d BIGINT,
	 user__max_playtime_3d DOUBLE,
	 user__max_duration_3d DOUBLE,
	 user__max_click_count_3d BIGINT,
	 user__max_praise_count_3d BIGINT,
	 user__min_playtime_3d DOUBLE,
	 user__min_duration_3d DOUBLE,
	 user__min_click_count_3d BIGINT,
	 user__min_praise_count_3d BIGINT,
	 user__avg_playtime_3d DOUBLE,
	 user__avg_duration_3d DOUBLE,
	 user__avg_click_count_3d DOUBLE,
	 user__avg_praise_count_3d DOUBLE,
	 user__sum_playtime_7d DOUBLE,
	 user__sum_duration_7d DOUBLE,
	 user__sum_click_count_7d BIGINT,
	 user__sum_praise_count_7d BIGINT,
	 user__max_playtime_7d DOUBLE,
	 user__max_duration_7d DOUBLE,
	 user__max_click_count_7d BIGINT,
	 user__max_praise_count_7d BIGINT,
	 user__min_playtime_7d DOUBLE,
	 user__min_duration_7d DOUBLE,
	 user__min_click_count_7d BIGINT,
	 user__min_praise_count_7d BIGINT,
	 user__avg_playtime_7d DOUBLE,
	 user__avg_duration_7d DOUBLE,
	 user__avg_click_count_7d DOUBLE,
	 user__avg_praise_count_7d DOUBLE,
	 user__sum_playtime_15d DOUBLE,
	 user__sum_duration_15d DOUBLE,
	 user__sum_click_count_15d BIGINT,
	 user__sum_praise_count_15d BIGINT,
	 user__max_playtime_15d DOUBLE,
	 user__max_duration_15d DOUBLE,
	 user__max_click_count_15d BIGINT,
	 user__max_praise_count_15d BIGINT,
	 user__min_playtime_15d DOUBLE,
	 user__min_duration_15d DOUBLE,
	 user__min_click_count_15d BIGINT,
	 user__min_praise_count_15d BIGINT,
	 user__avg_playtime_15d DOUBLE,
	 user__avg_duration_15d DOUBLE,
	 user__avg_click_count_15d DOUBLE,
	 user__avg_praise_count_15d DOUBLE,
	 user__kv_day_h_duration_sum_7d STRING,
	 user__kv_category_duration_sum_7d STRING,
	 user__kv_day_h_click_count_sum_7d STRING,
	 user__kv_category_click_count_sum_7d STRING,
	 user__kv_day_h_duration_max_7d STRING,
	 user__kv_category_duration_max_7d STRING,
	 user__kv_day_h_click_count_max_7d STRING,
	 user__kv_category_click_count_max_7d STRING,
	 user__kv_day_h_duration_min_7d STRING,
	 user__kv_category_duration_min_7d STRING,
	 user__kv_day_h_click_count_min_7d STRING,
	 user__kv_category_click_count_min_7d STRING,
	 user__kv_day_h_duration_avg_7d STRING,
	 user__kv_category_duration_avg_7d STRING,
	 user__kv_day_h_click_count_avg_7d STRING,
	 user__kv_category_click_count_avg_7d STRING,
	 user__kv_day_h_duration_sum_15d STRING,
	 user__kv_category_duration_sum_15d STRING,
	 user__kv_day_h_click_count_sum_15d STRING,
	 user__kv_category_click_count_sum_15d STRING,
	 user__kv_day_h_duration_max_15d STRING,
	 user__kv_category_duration_max_15d STRING,
	 user__kv_day_h_click_count_max_15d STRING,
	 user__kv_category_click_count_max_15d STRING,
	 user__kv_day_h_duration_min_15d STRING,
	 user__kv_category_duration_min_15d STRING,
	 user__kv_day_h_click_count_min_15d STRING,
	 user__kv_category_click_count_min_15d STRING,
	 user__kv_day_h_duration_avg_15d STRING,
	 user__kv_category_duration_avg_15d STRING,
	 user__kv_day_h_click_count_avg_15d STRING,
	 user__kv_category_click_count_avg_15d STRING,
	 user__kv_day_h_duration_sum_30d STRING,
	 user__kv_category_duration_sum_30d STRING,
	 user__kv_day_h_click_count_sum_30d STRING,
	 user__kv_category_click_count_sum_30d STRING,
	 user__kv_day_h_duration_max_30d STRING,
	 user__kv_category_duration_max_30d STRING,
	 user__kv_day_h_click_count_max_30d STRING,
	 user__kv_category_click_count_max_30d STRING,
	 user__kv_day_h_duration_min_30d STRING,
	 user__kv_category_duration_min_30d STRING,
	 user__kv_day_h_click_count_min_30d STRING,
	 user__kv_category_click_count_min_30d STRING,
	 user__kv_day_h_duration_avg_30d STRING,
	 user__kv_category_duration_avg_30d STRING,
	 user__kv_day_h_click_count_avg_30d STRING,
	 user__kv_category_click_count_avg_30d STRING,
	 user__kv_day_h_duration_sum_45d STRING,
	 user__kv_category_duration_sum_45d STRING,
	 user__kv_day_h_click_count_sum_45d STRING,
	 user__kv_category_click_count_sum_45d STRING,
	 user__kv_day_h_duration_max_45d STRING,
	 user__kv_category_duration_max_45d STRING,
	 user__kv_day_h_click_count_max_45d STRING,
	 user__kv_category_click_count_max_45d STRING,
	 user__kv_day_h_duration_min_45d STRING,
	 user__kv_category_duration_min_45d STRING,
	 user__kv_day_h_click_count_min_45d STRING,
	 user__kv_category_click_count_min_45d STRING,
	 user__kv_day_h_duration_avg_45d STRING,
	 user__kv_category_duration_avg_45d STRING,
	 user__kv_day_h_click_count_avg_45d STRING,
	 user__kv_category_click_count_avg_45d STRING
) 
PARTITIONED BY (ds STRING)
LIFECYCLE 90;

insert overwrite table fs_demo_fs_engineering_v1_training_set partition (ds = '${bdp.system.bizdate}')
select 
sq0.request_id,
sq0.user_id,
sq0.page,
sq0.net_type,
sq0.day_h,
sq0.week_day,
sq0.day_min,
sq0.event_unix_time,
sq0.item_id,
sq0.playtime,
sq0.is_click,
sq0.ln_playtime,
sq0.is_praise,
sq2.duration,
sq2.category,
sq2.author,
sq2.click_count,
sq2.praise_count,
sq2.is_new_item,
sq5.item__sum_item_cnt_3d,
sq5.item__sum_follow_cnt_3d,
sq5.item__sum_follower_cnt_3d,
sq5.item__max_item_cnt_3d,
sq5.item__max_follow_cnt_3d,
sq5.item__max_follower_cnt_3d,
sq5.item__min_item_cnt_3d,
sq5.item__min_follow_cnt_3d,
sq5.item__min_follower_cnt_3d,
sq5.item__avg_item_cnt_3d,
sq5.item__avg_follow_cnt_3d,
sq5.item__avg_follower_cnt_3d,
sq5.item__sum_item_cnt_7d,
sq5.item__sum_follow_cnt_7d,
sq5.item__sum_follower_cnt_7d,
sq5.item__max_item_cnt_7d,
sq5.item__max_follow_cnt_7d,
sq5.item__max_follower_cnt_7d,
sq5.item__min_item_cnt_7d,
sq5.item__min_follow_cnt_7d,
sq5.item__min_follower_cnt_7d,
sq5.item__avg_item_cnt_7d,
sq5.item__avg_follow_cnt_7d,
sq5.item__avg_follower_cnt_7d,
sq5.item__sum_item_cnt_15d,
sq5.item__sum_follow_cnt_15d,
sq5.item__sum_follower_cnt_15d,
sq5.item__max_item_cnt_15d,
sq5.item__max_follow_cnt_15d,
sq5.item__max_follower_cnt_15d,
sq5.item__min_item_cnt_15d,
sq5.item__min_follow_cnt_15d,
sq5.item__min_follower_cnt_15d,
sq5.item__avg_item_cnt_15d,
sq5.item__avg_follow_cnt_15d,
sq5.item__avg_follower_cnt_15d,
sq6.item__kv_day_h_click_count_sum_7d,
sq6.item__kv_category_click_count_sum_7d,
sq6.item__kv_day_h_praise_count_sum_7d,
sq6.item__kv_category_praise_count_sum_7d,
sq6.item__kv_day_h_click_count_max_7d,
sq6.item__kv_category_click_count_max_7d,
sq6.item__kv_day_h_praise_count_max_7d,
sq6.item__kv_category_praise_count_max_7d,
sq6.item__kv_day_h_click_count_min_7d,
sq6.item__kv_category_click_count_min_7d,
sq6.item__kv_day_h_praise_count_min_7d,
sq6.item__kv_category_praise_count_min_7d,
sq6.item__kv_day_h_click_count_avg_7d,
sq6.item__kv_category_click_count_avg_7d,
sq6.item__kv_day_h_praise_count_avg_7d,
sq6.item__kv_category_praise_count_avg_7d,
sq6.item__kv_day_h_click_count_sum_15d,
sq6.item__kv_category_click_count_sum_15d,
sq6.item__kv_day_h_praise_count_sum_15d,
sq6.item__kv_category_praise_count_sum_15d,
sq6.item__kv_day_h_click_count_max_15d,
sq6.item__kv_category_click_count_max_15d,
sq6.item__kv_day_h_praise_count_max_15d,
sq6.item__kv_category_praise_count_max_15d,
sq6.item__kv_day_h_click_count_min_15d,
sq6.item__kv_category_click_count_min_15d,
sq6.item__kv_day_h_praise_count_min_15d,
sq6.item__kv_category_praise_count_min_15d,
sq6.item__kv_day_h_click_count_avg_15d,
sq6.item__kv_category_click_count_avg_15d,
sq6.item__kv_day_h_praise_count_avg_15d,
sq6.item__kv_category_praise_count_avg_15d,
sq6.item__kv_day_h_click_count_sum_30d,
sq6.item__kv_category_click_count_sum_30d,
sq6.item__kv_day_h_praise_count_sum_30d,
sq6.item__kv_category_praise_count_sum_30d,
sq6.item__kv_day_h_click_count_max_30d,
sq6.item__kv_category_click_count_max_30d,
sq6.item__kv_day_h_praise_count_max_30d,
sq6.item__kv_category_praise_count_max_30d,
sq6.item__kv_day_h_click_count_min_30d,
sq6.item__kv_category_click_count_min_30d,
sq6.item__kv_day_h_praise_count_min_30d,
sq6.item__kv_category_praise_count_min_30d,
sq6.item__kv_day_h_click_count_avg_30d,
sq6.item__kv_category_click_count_avg_30d,
sq6.item__kv_day_h_praise_count_avg_30d,
sq6.item__kv_category_praise_count_avg_30d,
sq6.item__kv_day_h_click_count_sum_45d,
sq6.item__kv_category_click_count_sum_45d,
sq6.item__kv_day_h_praise_count_sum_45d,
sq6.item__kv_category_praise_count_sum_45d,
sq6.item__kv_day_h_click_count_max_45d,
sq6.item__kv_category_click_count_max_45d,
sq6.item__kv_day_h_praise_count_max_45d,
sq6.item__kv_category_praise_count_max_45d,
sq6.item__kv_day_h_click_count_min_45d,
sq6.item__kv_category_click_count_min_45d,
sq6.item__kv_day_h_praise_count_min_45d,
sq6.item__kv_category_praise_count_min_45d,
sq6.item__kv_day_h_click_count_avg_45d,
sq6.item__kv_category_click_count_avg_45d,
sq6.item__kv_day_h_praise_count_avg_45d,
sq6.item__kv_category_praise_count_avg_45d,
sq1.gender,
sq1.age,
sq1.city,
sq1.item_cnt,
sq1.follow_cnt,
sq1.follower_cnt,
sq1.is_new_user,
sq1.tags,
sq3.user__sum_playtime_3d,
sq3.user__sum_duration_3d,
sq3.user__sum_click_count_3d,
sq3.user__sum_praise_count_3d,
sq3.user__max_playtime_3d,
sq3.user__max_duration_3d,
sq3.user__max_click_count_3d,
sq3.user__max_praise_count_3d,
sq3.user__min_playtime_3d,
sq3.user__min_duration_3d,
sq3.user__min_click_count_3d,
sq3.user__min_praise_count_3d,
sq3.user__avg_playtime_3d,
sq3.user__avg_duration_3d,
sq3.user__avg_click_count_3d,
sq3.user__avg_praise_count_3d,
sq3.user__sum_playtime_7d,
sq3.user__sum_duration_7d,
sq3.user__sum_click_count_7d,
sq3.user__sum_praise_count_7d,
sq3.user__max_playtime_7d,
sq3.user__max_duration_7d,
sq3.user__max_click_count_7d,
sq3.user__max_praise_count_7d,
sq3.user__min_playtime_7d,
sq3.user__min_duration_7d,
sq3.user__min_click_count_7d,
sq3.user__min_praise_count_7d,
sq3.user__avg_playtime_7d,
sq3.user__avg_duration_7d,
sq3.user__avg_click_count_7d,
sq3.user__avg_praise_count_7d,
sq3.user__sum_playtime_15d,
sq3.user__sum_duration_15d,
sq3.user__sum_click_count_15d,
sq3.user__sum_praise_count_15d,
sq3.user__max_playtime_15d,
sq3.user__max_duration_15d,
sq3.user__max_click_count_15d,
sq3.user__max_praise_count_15d,
sq3.user__min_playtime_15d,
sq3.user__min_duration_15d,
sq3.user__min_click_count_15d,
sq3.user__min_praise_count_15d,
sq3.user__avg_playtime_15d,
sq3.user__avg_duration_15d,
sq3.user__avg_click_count_15d,
sq3.user__avg_praise_count_15d,
sq4.user__kv_day_h_duration_sum_7d,
sq4.user__kv_category_duration_sum_7d,
sq4.user__kv_day_h_click_count_sum_7d,
sq4.user__kv_category_click_count_sum_7d,
sq4.user__kv_day_h_duration_max_7d,
sq4.user__kv_category_duration_max_7d,
sq4.user__kv_day_h_click_count_max_7d,
sq4.user__kv_category_click_count_max_7d,
sq4.user__kv_day_h_duration_min_7d,
sq4.user__kv_category_duration_min_7d,
sq4.user__kv_day_h_click_count_min_7d,
sq4.user__kv_category_click_count_min_7d,
sq4.user__kv_day_h_duration_avg_7d,
sq4.user__kv_category_duration_avg_7d,
sq4.user__kv_day_h_click_count_avg_7d,
sq4.user__kv_category_click_count_avg_7d,
sq4.user__kv_day_h_duration_sum_15d,
sq4.user__kv_category_duration_sum_15d,
sq4.user__kv_day_h_click_count_sum_15d,
sq4.user__kv_category_click_count_sum_15d,
sq4.user__kv_day_h_duration_max_15d,
sq4.user__kv_category_duration_max_15d,
sq4.user__kv_day_h_click_count_max_15d,
sq4.user__kv_category_click_count_max_15d,
sq4.user__kv_day_h_duration_min_15d,
sq4.user__kv_category_duration_min_15d,
sq4.user__kv_day_h_click_count_min_15d,
sq4.user__kv_category_click_count_min_15d,
sq4.user__kv_day_h_duration_avg_15d,
sq4.user__kv_category_duration_avg_15d,
sq4.user__kv_day_h_click_count_avg_15d,
sq4.user__kv_category_click_count_avg_15d,
sq4.user__kv_day_h_duration_sum_30d,
sq4.user__kv_category_duration_sum_30d,
sq4.user__kv_day_h_click_count_sum_30d,
sq4.user__kv_category_click_count_sum_30d,
sq4.user__kv_day_h_duration_max_30d,
sq4.user__kv_category_duration_max_30d,
sq4.user__kv_day_h_click_count_max_30d,
sq4.user__kv_category_click_count_max_30d,
sq4.user__kv_day_h_duration_min_30d,
sq4.user__kv_category_duration_min_30d,
sq4.user__kv_day_h_click_count_min_30d,
sq4.user__kv_category_click_count_min_30d,
sq4.user__kv_day_h_duration_avg_30d,
sq4.user__kv_category_duration_avg_30d,
sq4.user__kv_day_h_click_count_avg_30d,
sq4.user__kv_category_click_count_avg_30d,
sq4.user__kv_day_h_duration_sum_45d,
sq4.user__kv_category_duration_sum_45d,
sq4.user__kv_day_h_click_count_sum_45d,
sq4.user__kv_category_click_count_sum_45d,
sq4.user__kv_day_h_duration_max_45d,
sq4.user__kv_category_duration_max_45d,
sq4.user__kv_day_h_click_count_max_45d,
sq4.user__kv_category_click_count_max_45d,
sq4.user__kv_day_h_duration_min_45d,
sq4.user__kv_category_duration_min_45d,
sq4.user__kv_day_h_click_count_min_45d,
sq4.user__kv_category_click_count_min_45d,
sq4.user__kv_day_h_duration_avg_45d,
sq4.user__kv_category_duration_avg_45d,
sq4.user__kv_day_h_click_count_avg_45d,
sq4.user__kv_category_click_count_avg_45d
from 
(
	select *
	from rec_sln_demo_fs_label_table_v1
	where ds = '${bdp.system.bizdate}'
) sq0
 left join (
	select 
	*
	from <project_name>.rec_sln_demo_user_table_preprocess_v1
	where ds = TO_CHAR(DATEADD(TO_DATE('${bdp.system.bizdate}','yyyymmdd'), - 1,'dd'),'yyyymmdd')
) sq1 on sq0.user_id = sq1.user_id
 left join (
	select 
	*
	from <project_name>.rec_sln_demo_item_table_preprocess_v1
	where ds = TO_CHAR(DATEADD(TO_DATE('${bdp.system.bizdate}','yyyymmdd'), - 1,'dd'),'yyyymmdd')
) sq2 on sq0.item_id = sq2.item_id
 left join (
	select 
	*
	from <project_name>.rec_sln_demo_user_table_preprocess_win_v1
	where ds = TO_CHAR(DATEADD(TO_DATE('${bdp.system.bizdate}','yyyymmdd'), - 1,'dd'),'yyyymmdd')
) sq4 on sq0.user_id = sq4.user_id
 left join (
	select 
	*
	from <project_name>.rec_sln_demo_item_table_preprocess_agg_v1
	where ds = TO_CHAR(DATEADD(TO_DATE('${bdp.system.bizdate}','yyyymmdd'), - 1,'dd'),'yyyymmdd')
) sq5 on sq0.item_id = sq5.item_id
 left join (
	select 
	*
	from <project_name>.rec_sln_demo_item_table_preprocess_win_v1
	where ds = TO_CHAR(DATEADD(TO_DATE('${bdp.system.bizdate}','yyyymmdd'), - 1,'dd'),'yyyymmdd')
) sq6 on sq0.item_id = sq6.item_id
 left join (
	select 
	*
	from <project_name>.rec_sln_demo_user_table_preprocess_agg_v1
	where ds = TO_CHAR(DATEADD(TO_DATE('${bdp.system.bizdate}','yyyymmdd'), - 1,'dd'),'yyyymmdd')
) sq3 on sq0.user_id = sq3.user_id
;

其中<project_name>需要替換為您的項目名稱。

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• 特征生產的詳細過程，請參見特征平臺與特征生產。

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• 如果您在配置或使用過程中有任何問題，可以搜索釘釘群號：34415007523，進入答疑群聯系技術人員進行咨詢。