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    Platform For AI：特徵生產最佳實務

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    Platform For AI：特徵生產最佳實務

    更新時間：Aug 02, 2025

    特徵平台當前提供的特徵生產功能旨在簡化特徵建立過程，通過固化常用的和普遍的生產步驟，您僅需進行簡單配置就能輕鬆產生特徵，從而有效降低了特徵生產的複雜性。特徵生產在多個領域（包括推薦、廣告、風控以及機器學習等）都有廣泛應用，本文將以推薦情境為例，為您介紹從原始表到特徵生產加工產生樣本表的完整過程。

    前提條件

    在開始執行操作前，請確認您已完成以下準備工作：

    依賴產品

    具體操作

    人工智慧平台PAI

    雲原生MaxCompute

    巨量資料開發治理平台DataWorks

    一、準備工作

    準備未經處理資料

    一般對於推薦情境，特徵生產通常需要以下三張原始表，以這三張原始表為基礎，特徵生產可以生產出成百上千個特徵，方便建立模型來擬合目標。

    • 使用者表（rec_sln_demo_user_table_preprocess_v1）：包含一些基礎的使用者特徵，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徵，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）：包含一些行為特徵，例如某時使用者點擊某個物品等。

    資料表存放在有公開讀取許可權的pai_online_project中，其資料均為類比資料產生。您需要在DataWorks中執行SQL命令，將上表資料從pai_online_project專案同步到您的MaxCompute專案中。具體操作步驟如下：

    1. 登入DataWorks控制台。

    2. 在左側導覽列單擊資料開發與營運 > 資料開發。

    3. 選擇已建立的DataWorks工作空間後，單擊進入資料開發。

    4. 滑鼠移至上方至建立，選擇建立節點 > MaxCompute > ODPS SQL，在彈出的頁面中配置節點參數。

      參數

      取值建議

      引擎執行個體

      選擇已建立的MaxCompute引擎。

      節點類型

      ODPS SQL

      路徑

      商務程序/Workflow/MaxCompute

      名稱

      可自訂名稱。

    5. 單擊確認。

    6. 在建立節點地區運行以下SQL命令，將使用者表、物品表、行為表從pai_online_project專案同步到您自己的MaxCompute中。資源群組選擇已建立的獨享資源群組。

      • 同步處理的使用者表：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';

    安裝FeatureStore Python SDK

    以下代碼均建議在Jupyter Notebook環境下運行。

    • 安裝特徵平台Python SDK，要求在Python3環境下運行。

      %pip install https://feature-store-py.oss-cn-beijing.aliyuncs.com/package/feature_store_py-2.0.2-py3-none-any.whl

    • 匯入需要的功能模組：

      import os
from feature_store_py import FeatureStoreClient
from feature_store_py.fs_datasource import MaxComputeDataSource
from feature_store_py.feature_engineering import TableTransform, Condition, DayOf, ComboTransform, Feature, AggregationTransform, auto_count_feature_transform, WindowTransform, auto_window_feature_transform

    二、原始表初步變換

    準備流程完成後，您可以在自己的專案空間中查看已準備就緒的三張表：使用者表（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徵生產。

    在進行後續的特徵生產加工之前，為了方便您後續做特徵統計，您需要對資料表做以下預先處理操作。您可以將以下SQL命令粘貼到已建立的ODPS SQL節點中執行，具體操作，請參見準備未經處理資料。

    • 將使用者表、物品表的特徵和行為表進行串連。

      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籤，您需要對行為表進行預先處理，將其轉換成Label表。本樣本將點擊、播放時間、點贊作為標籤。

      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後，您將擁有以下兩種表：

    • 新的行為表：rec_sln_demo_behavior_table_preprocess_wide_v1，後續的統計特徵將以此表為基礎進行變換。

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

    三、特徵生產加工

    您可以調用特徵生產中的自動擴充函數進行特徵生產，僅需幾行代碼，就可以生產出上百種特徵。

    特徵生產加工分別需要加工使用者側特徵和物品側特徵。具體操作步驟如下：

    使用者側特徵的特徵生產

    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' # 填入您的專案名
region = 'cn-hangzhou' # 填入您的專案所在地區，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. 指定要進行特徵變換的資料來源。

      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後輸出的使用者側特徵表名稱。

      agg_user_table_v1 = 'rec_sln_demo_user_table_preprocess_agg_v1'

    4. 利用自動擴充函數，對統計特徵進行自動擴充。

      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，並查看錶運行結果。

      1. 建立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. 查看錶運行結果。

        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後輸出的使用者側特徵表名稱。

      win_user_table_v1 = 'rec_sln_demo_user_table_preprocess_win_v1'

    7. 利用內建的自動擴充函數，自動產生WindowTransform特徵定義。

      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，並查看錶運行結果。

      1. 建立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資料，可能需要較長時間，運行補資料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. 查看錶運行結果。

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

    物品側特徵的特徵生產

    在完成使用者側特徵的提取和加工之後，您可以繼續對物品表進行特徵生產加工，產生基於物品類型的特徵。具體操作步驟如下：

    1. 指定AggregationTransform後輸出的物品側特徵表名稱。

      agg_item_table_v1 = 'rec_sln_demo_item_table_preprocess_agg_v1'

    2. 利用自動擴充函數，對統計特徵進行自動擴充。

      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，並查看錶運行結果。

      1. 建立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. 查看錶運行結果。

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

    4. 指定WindowTransform後輸出的使用者側特徵表名稱。

      win_item_table_v1 = 'rec_sln_demo_item_table_preprocess_win_v1'

    5. 利用內建的自動擴充函數，自動產生WindowTransform特徵定義。

      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，並查看錶運行結果。

      1. 建立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資料，可能需要較長時間，運行補資料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. 查看錶運行結果。

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

    四、產生樣本表

    經過上述流程，您已擷取了Label表，並在物品側和使用者側分別整理出三張特徵表（包括一張原始表及兩張經過特徵生產加工出的表）。隨後，您可以將以下SQL命令粘貼到已建立的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，請參見準備未經處理資料。

    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>需要替換為您的專案名稱。

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    • 特徵生產的詳細過程，請參見特徵平台與特徵生產。

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