模型上线

spark/featureEngSk.py

@@ -764,7 +764,7 @@ def getPredictItemStatisticSql(start, end):
             SELECT TT.card_id, TT.label, count(1) as label_count
             FROM 
             (
-                SELECT T.partition_date, T.card_id, T.label 
+                SELECT T.card_id, T.label 
                 FROM 
                 (
                 SELECT t1.card_id, 1 as label
@@ -1080,13 +1080,13 @@ if __name__ == '__main__':
     spark = get_spark("SERVICE_FEATURE_CSV_EXPORT_SK")
     spark.sparkContext.setLogLevel("ERROR")
     #获取点击曝光数据
-    clickDF, expDF, ratingDF, startDay, endDay = get_click_exp_rating_df(trainDays, spark)
+    # clickDF, expDF, ratingDF, startDay, endDay = get_click_exp_rating_df(trainDays, spark)
 
     #item Es Feature
-    itemEsFeatureDF = get_item_es_feature_df()
+    # itemEsFeatureDF = get_item_es_feature_df()
 
     #计算 item 统计特征
-    clickStaticFeatures, expStaticFeatures = getItemStaticFeatures(itemStatisticStartDays + trainDays + 1, startDay, endDay)
+    # clickStaticFeatures, expStaticFeatures = getItemStaticFeatures(itemStatisticStartDays + trainDays + 1, startDay, endDay)
 
     #计算线上推理 item 统计特征
     predictClickStaticFeatures, predictExpStaticFeatures = getPredictItemStaticFeatures(itemStatisticStartDays)