特征生产最佳实践

前提条件

一、准备工作

准备原始数据

一般对于推荐场景，特征生产通常需要以下三张原始表，以这三张原始表为基础，特征生产可以生产出成百上千个特征，方便建立模型来拟合目标。

• 用户表（rec_sln_demo_user_table_preprocess_v1）：包含一些基础的用户特征，例如性别、年龄、城市和关注数等。

• 物品表（rec_sln_demo_item_table_preprocess_v1）：包含一些基础的物品特征，例如类别、作者、累计点击数和累计点赞数等。

• 行为表（rec_sln_demo_behavior_table_preprocess_v1）：包含一些行为特征，例如某时用户点击某个物品等。

数据表存放在有公开读取权限的pai_online_project中，其数据均为模拟数据生成。您需要在DataWorks中执行SQL命令，将上表数据从pai_online_project项目同步到您的MaxCompute项目中。具体操作步骤如下：

1. 登录DataWorks控制台。

2. 在左侧导航栏单击数据开发与治理 > 数据开发。

3. 选择已创建的DataWorks工作空间后，单击进入数据开发。

4. 鼠标悬停至新建，选择新建节点 > MaxCompute > ODPS SQL，在弹出的页面中配置节点参数。

   参数	取值建议
   引擎实例	选择已创建的MaxCompute引擎。
   节点类型	ODPS SQL
   路径	业务流程/Workflow/MaxCompute
   名称	可自定义名称。

5. 单击确认。

6. 在新建节点区域运行以下SQL命令，将用户表、物品表、行为表从pai_online_project项目同步到您自己的MaxCompute中。资源组选择已创建的独享资源组。

   • 同步用户表：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';

   • 同步行为表：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、原始表初步变换

准备流程完成后，您可以在自己的项目空间中查看已准备就绪的三张表：用户表（rec_sln_demo_user_table_preprocess_v1）、物品表（rec_sln_demo_item_table_preprocess_v1）以及行为表（rec_sln_demo_behavior_table_preprocess_v1），您可以以这三张表为基础进行特征生产。

在进行后续的特征生产加工之前，为了方便您后续做特征统计，您需要对数据表做以下预处理操作。您可以将以下SQL命令粘贴到已创建的ODPS SQL节点中执行，具体操作，请参见准备原始数据。

• 将用户表、物品表的特征和行为表进行连接。

  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 '行为发生在当天的第几小时'
      ,week_day bigint COMMENT '行为发生在当前周的第几天'
      ,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
  ;

• 因训练模型需要有标签，您需要对行为表进行预处理，将其转换成Label表。本示例将点击、播放时间、点赞作为标签。

  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 '行为发生在当天的第几小时'
      ,week_day bigint COMMENT '行为发生在当前周的第几天'
      ,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
  ;

经过上面的初步变换后，您将拥有以下两种表：

• 新的行为表：rec_sln_demo_behavior_table_preprocess_wide_v1，后续的统计特征将以此表为基础进行变换。

• Label表：rec_sln_demo_fs_label_table_v1，后续构建样本表时需要用到此表。

三、特征生产加工

您可以调用特征生产中的自动扩展函数进行特征生产，仅需几行代码，就可以生产出上百种特征。

特征生产加工分别需要加工用户侧特征和物品侧特征。具体操作步骤如下：

用户侧特征的特征生产

以下代码均建议在Jupyter Notebook环境下运行。

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' # 填入您的项目名
   region = 'cn-hangzhou' # 填入您的项目所在区域，例如华东1（杭州）为cn-hangzhou
   fs_client = FeatureStoreClient(access_key_id=access_key_id, access_key_secret=access_key_secret, region=region)

2. 指定要进行特征变换的数据源。

   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后输出的用户侧特征表名称。

   agg_user_table_v1 = 'rec_sln_demo_user_table_preprocess_agg_v1'

4. 利用自动扩展函数，对统计特征进行自动扩展。

   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. 执行生产过程，并查看表运行结果。

   1. 创建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. 执行Pipeline生产过程。

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

   3. 查看表运行结果。

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

6. 进行WindowTransform变换，指定WindowTransform后输出的用户侧特征表名称。

   win_user_table_v1 = 'rec_sln_demo_user_table_preprocess_win_v1'

7. 利用内置的自动扩展函数，自动生成WindowTransform特征定义。

   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. 执行生产过程，并查看表运行结果。

   1. 创建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. 执行pipeline生产过程。

      execute_date = '20240605'
      output_win_user_table = win_user_pipeline.execute(execute_date, drop_table=True)
      
      # 详细实现见功能文档。因为有中间表存在，第一次执行时需要补数据，可能需要较长时间，运行补数据可以执行下面的命令
      # output_win_user_table = win_user_pipeline.execute(execute_date, drop_table=True, backfill_partitions=True)

   3. 查看表运行结果。

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

物品侧特征的特征生产

在完成用户侧特征的提取和加工之后，您可以继续对物品表进行特征生产加工，生成基于物品类型的特征。具体操作步骤如下：

1. 指定AggregationTransform后输出的物品侧特征表名称。

   agg_item_table_v1 = 'rec_sln_demo_item_table_preprocess_agg_v1'

2. 利用自动扩展函数，对统计特征进行自动扩展。

   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. 执行生产过程，并查看表运行结果。

   1. 创建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. 执行pipeline生产过程。

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

   3. 查看表运行结果。

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

4. 指定WindowTransform后输出的用户侧特征表名称。

   win_item_table_v1 = 'rec_sln_demo_item_table_preprocess_win_v1'

5. 利用内置的自动扩展函数，自动生成WindowTransform特征定义。

   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. 执行生产过程，并查看表运行结果。

   1. 创建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. 执行pipeline生产过程。

      execute_date = '20240605'
      output_win_item_table = win_item_pipeline.execute(execute_date, drop_table=True)
      
      # 详细实现见功能文档。因为有中间表存在，第一次执行时需要补数据，可能需要较长时间，运行补数据可以执行下面的命令
      # output_win_item_table = win_item_pipeline.execute(execute_date, drop_table=True, backfill_partitions=True)

   3. 查看表运行结果。

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

四、生成样本表

经过上述流程，您已获取了Label表，并在物品侧和用户侧分别整理出三张特征表（包括一张原始表及两张经过特征生产加工出的表）。随后，您可以将以下SQL命令粘贴到已创建的ODPS SQL节点中执行，将这七张表进行合并，以构建完整的样本表。具体操作，请参见准备原始数据。

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>需要替换为您的项目名称。

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