feat:添加ros特征文件

recommend-model-produce/src/main/scala/com/tzld/piaoquan/recommend/model/recsys_01_ros_multi_class_xgb_train.scala

@@ -146,28 +146,28 @@ object recsys_01_ros_multi_class_xgb_train {
     }
 
 
-    val evaluator = new BinaryClassificationEvaluator()
-      .setLabelCol("label")
-      .setRawPredictionCol("probability")
-      .setMetricName("areaUnderROC")
-    val auc = evaluator.evaluate(predictions.select("label", "probability"))
-    println("zhangbo:auc:" + auc)
+//    val evaluator = new BinaryClassificationEvaluator()
+//      .setLabelCol("label")
+//      .setRawPredictionCol("probability")
+//      .setMetricName("areaUnderROC")
+//    val auc = evaluator.evaluate(predictions.select("label", "probability"))
+//    println("zhangbo:auc:" + auc)
 
     // 统计分cid的分数
-    sc.textFile(hdfsPath).map(r => {
-      val rList = r.split("\t")
-      val vid = JSON.parseObject(rList(3)).getString("vid")
-      val label = rList(0).toDouble
-      val score = RosUtil.multiClassModelScore(rList(2), predictLabelList_br.value)
-
-      ((vid, label), (1, score))
-    }).reduceByKey {
-      case ((c1, s1), (c2, s2)) =>
-        (c1 + c2, (s1 + s2))
-    }.map {
-      case ((vid, label), (count, sumScore)) =>
-        (vid, label, count, sumScore, sumScore / count)
-    }.collect().sortBy(_._1).map(_.productIterator.mkString("\t")).foreach(println)
+//    sc.textFile(hdfsPath).map(r => {
+//      val rList = r.split("\t")
+//      val vid = JSON.parseObject(rList(3)).getString("vid")
+//      val label = rList(0).toDouble
+//      val score = RosUtil.multiClassModelScore(rList(2), predictLabelList_br.value)
+//
+//      ((vid, label), (1, score))
+//    }).reduceByKey {
+//      case ((c1, s1), (c2, s2)) =>
+//        (c1 + c2, (s1 + s2))
+//    }.map {
+//      case ((vid, label), (count, sumScore)) =>
+//        (vid, label, count, sumScore, sumScore / count)
+//    }.collect().sortBy(_._1).map(_.productIterator.mkString("\t")).foreach(println)
 
   }