remove useless feature

dssm/get_tractate_data.py

@@ -351,12 +351,6 @@ def get_card_feature_df(spark, card_type, yesterday):
                sixty_reply_pure_rate,
                ninety_reply_pure_rate,
                history_reply_pure_rate,
-               CAST(IFNULL(content2.goodclick_7, 0.0) AS DOUBLE) AS goodclick_7,
-               CAST(IFNULL(content2.goodclick_14, 0.0) AS DOUBLE) AS goodclick_14,
-               CAST(IFNULL(content2.goodclick_30, 0.0) AS DOUBLE) AS goodclick_30,
-               CAST(IFNULL(content2.goodclick_60, 0.0) AS DOUBLE) AS goodclick_60,
-               CAST(IFNULL(content2.goodclick_90, 0.0) AS DOUBLE) AS goodclick_90,
-               content_tag3.smart_rank_score smart_rank_score,
                IFNULL(content_tag3.first_demands, "") AS card_first_demands,
                IFNULL(content_tag3.second_demands, "") AS card_second_demands,
                IFNULL(content_tag3.first_solutions, "") AS card_first_solutions,
@@ -368,15 +362,10 @@ def get_card_feature_df(spark, card_type, yesterday):
           online.al_community_forecast_character_day_v3 card_feature
           JOIN content_tag3
           ON card_feature.card_id = content_tag3.id
-          LEFT JOIN
-          (select * from online.al_community_detail_page_goodclick_ctr
-          where page_name = '{}'
-          ) AS content2
-          ON card_feature.card_id = content2.business_id
           where partition_date = '{}'
            and card_content_type = '{}'
            and card_id rlike '{}'
-       """.format(card_type, yesterday, card_type, reg)
+       """.format(yesterday, card_type, reg)
     return spark.sql(sql)