java – 向Spark ML LabeldPoint添加自定义字段

2023年3月31日 306次阅读

如何在预测结果中添加一些自定义字段(即用户ID)？

        List<org.apache.spark.mllib.regression.LabeledPoint> localTesting = ... ;//
        // I want to add some identifier to each LabeledPoint

        DataFrame localTestDF = jsql.createDataFrame(jsc.parallelize(studyData.localTesting), LabeledPoint.class);
        DataFrame predictions = model.transform(localTestDF);
        Row[] collect = predictions.select("label", "probability", "prediction").collect();
        for (Row r : collect) {
            // and want to return identifier here.
            // so do I save I to database.
            int userNo = Integer.parseInt(r.get(0).toString());
            double prob = Double.parseDouble(r.get(1).toString());
            int prediction = Integer.parseInt(r.get(2).toString());
            log.debug(userNo + "," + prob + ", " + prediction);
        }

但是当我使用这个类进行localTesting而不是LabeledPoint时,

class NoLabeledPoint extends LabeledPoint implements Serializable {
    private static final long serialVersionUID = -2488661810406135403L;
    int userNo;
    public NoLabeledPoint(double label, Vector features) {
        super(label, features);
    }

    public int getUserNo() {
        return userNo;
    }

    public void setUserNo(int userNo) {
        this.userNo = userNo;
    }
}

        List<NoLabeledPoint> localTesting = ... ;// set every user'no to the field userNo
        // I want to add some identifier to each LabeledPoint

        DataFrame localTestDF = jsql.createDataFrame(jsc.parallelize(studyData.localTesting), LabeledPoint.class);
        DataFrame predictions = model.transform(localTestDF);
        Row[] collect = predictions.select("userNo", "probability", "prediction").collect();
        for (Row r : collect) {
            // and want to return identifier here.
            // so do I save I to database.
            int userNo = Integer.parseInt(r.get(0).toString());
            double prob = Double.parseDouble(r.get(1).toString());
            int prediction = Integer.parseInt(r.get(2).toString());
            log.debug(userNo + "," + prob + ", " + prediction);
        }

异常抛出

org.apache.spark.sql.AnalysisException: cannot resolve 'userNo' given input columns rawPrediction, probability, features, label, prediction;
        at org.apache.spark.sql.catalyst.analysis.package$AnalysisErrorAt.failAnalysis(package.scala:42)
        at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$2.applyOrElse(CheckAnalysis.scala:63)
        at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$2.applyOrElse(CheckAnalysis.scala:52)
        at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:286)
        at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:286)
        at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:51)

我的意思是我不仅要获得预测数据(功能,标签,概率……),还要获得我想要的一些自定义字段.例如userNo,user_id等
从结果：predictions.select(“……”)

更新

解决了.一行应该是固定的

从

            DataFrame localTestDF = jsql.createDataFrame(jsc.parallelize(studyData.localTesting), LabeledPoint.class);

至

            DataFrame localTestDF = jsql.createDataFrame(jsc.parallelize(studyData.localTesting), NoLabeledPoint.class);

最佳答案 由于您不使用低级别的MLlib API,因此根本不需要使用LabeledPoint.在创建DataFrame之后,您只需使用具有特定值的Row,并且所有重要的*是与管道中的参数匹配的类型和列名称.

在Scala中,您可以使用任何案例类

org.apache.spark.mllib.linalg.Vector; case class 

case class LabeledPointWithMeta(userNo: String, label: Double, features: Vector)

val rdd: RDD[LabeledPointWithMeta] = ???
val df = rdd.toDF

为了能够使用它,你应该添加@BeanInfo注释：

import scala.beans.BeanInfo

@BeanInfo
case class LabeledPointWithMeta(...)

基于Spark SQL and DataFrame Guide它看起来像普通的Java你可以做这样的事情**：

import org.apache.spark.mllib.linalg.Vector;

public static class LabeledPointWithMeta implements Serializable {
  private int userNo;
  private double label;
  private Vector vector;

  public int getUserNo() {
    return userNo;
  }

  public void setUserNo(int userNo) {
    this.userNo = userNo;
  }

  public double getLabel() {
    return label;
  }

  public void setLabel(double label) {
    this.label = label;
  }

  public Vector getVector() {
    return vector;
  }

  public void seVector(Vector vector) {
    this.vector = vector;
  }

}

在那之后：

JavaRDD<LabeledPointWithMeta> myPoints = ...;

DataFrame df = sqlContext.createDataFrame(myPoints LabeledPointWithMeta.class);

我认为代码中的一个简单更改也应该起作用：

DataFrame localTestDF = jsql.createDataFrame(
    jsc.parallelize(studyData.localTesting),
    NoLabeledPoint.class
);

如果你想使用MLlib,它对你没有帮助,但是这个部分可以通过简单的RDD转换(如zip)轻松处理.

*一些元数据,但你不会从LabeledPoint获得

**我没有测试上面的代码,所以它可能包含一些错误.

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