マルチクラス分類にApache Sparkデシジョンツリー分類子を使用するとエラーが発生する

Question

モバイルデバイスから取得したセンサーデータに基づいてユーザーアクティビティを分類しようとしています。データセットには、ユーザーID、センサーデータ、アクティビティが含まれます。アクティビティは整数で与えられ、アクティビティは12種類あります。私が私の活動認識分類問題に使用したコードを以下に示します。私はマルチクラスの分類問題のためにApache Sparkデシジョンツリーを使用しています。

import java.util.HashMap; 
import java.util.Map; 

import scala.Tuple2; 

import org.apache.spark.SparkConf; 
import org.apache.spark.api.java.JavaPairRDD; 
import org.apache.spark.api.java.JavaRDD; 
import org.apache.spark.api.java.JavaSparkContext; 
import org.apache.spark.api.java.function.Function; 
import org.apache.spark.api.java.function.PairFunction; 
import org.apache.spark.mllib.regression.LabeledPoint; 
import org.apache.spark.mllib.tree.DecisionTree; 
import org.apache.spark.mllib.tree.model.DecisionTreeModel; 
import org.apache.spark.mllib.util.MLUtils; 

public class DecisionTreeClass { 
    public static void main(String args[]){ 
     SparkConf sparkConf = new SparkConf().setAppName("DecisionTreeClass").setMaster("local[2]"); 
     JavaSparkContext jsc = new JavaSparkContext(sparkConf); 


     // Load and parse the data file. 
     String datapath = "/home/thamali/Desktop/Project/csv/libsvm/trainlib.txt"; 
     JavaRDD<LabeledPoint> data = MLUtils.loadLibSVMFile(jsc.sc(), datapath).toJavaRDD(); 
     // Split the data into training and test sets (30% held out for testing) 
     JavaRDD<LabeledPoint>[] splits = data.randomSplit(new double[]{0.7, 0.3}); 
     JavaRDD<LabeledPoint> trainingData = splits[0]; 
     JavaRDD<LabeledPoint> testData = splits[1]; 

     // Set parameters. 
     // Empty categoricalFeaturesInfo indicates all features are continuous. 
     Integer numClasses = 12; 
     Map<Integer, Integer> categoricalFeaturesInfo = new HashMap(); 
     String impurity = "gini"; 
     Integer maxDepth = 5; 
     Integer maxBins = 32; 

     // Train a DecisionTree model for classification. 
     final DecisionTreeModel model = DecisionTree.trainClassifier(trainingData, numClasses, 
       categoricalFeaturesInfo, impurity, maxDepth, maxBins); 

     // Evaluate model on test instances and compute test error 
     JavaPairRDD<Double, Double> predictionAndLabel = 
       testData.mapToPair(new PairFunction<LabeledPoint, Double, Double>() { 
        @Override 
        public Tuple2<Double, Double> call(LabeledPoint p) { 
         return new Tuple2(model.predict(p.features()), p.label()); 
        } 
       }); 
     Double testErr = 
       1.0 * predictionAndLabel.filter(new Function<Tuple2<Double, Double>, Boolean>() { 
        @Override 
        public Boolean call(Tuple2<Double, Double> pl) { 
         return !pl._1().equals(pl._2()); 
        } 
       }).count()/testData.count(); 

     System.out.println("Test Error: " + testErr); 
     System.out.println("Learned classification tree model:\n" + model.toDebugString()); 

     // Save and load model 
     model.save(jsc.sc(), "target/tmp/myDecisionTreeClassificationModel"); 
     DecisionTreeModel sameModel = DecisionTreeModel 
       .load(jsc.sc(), "target/tmp/myDecisionTreeClassificationModel"); 
// $example off$ 
    } 

} 

上記のコードを使用すると、次の例外が発生します。誰かが問題を解決するのを助けてくれますか？

Caused by: java.lang.IllegalArgumentException: GiniAggregator given label 17.0 but requires label < numClasses (= 12). 
    at org.apache.spark.mllib.tree.impurity.GiniAggregator.update(Gini.scala:92) 
    at org.apache.spark.ml.tree.impl.DTStatsAggregator.update(DTStatsAggregator.scala:109) 
    at org.apache.spark.ml.tree.impl.RandomForest$.orderedBinSeqOp(RandomForest.scala:326) 
    at org.apache.spark.ml.tree.impl.RandomForest$.org$apache$spark$ml$tree$impl$RandomForest$$nodeBinSeqOp$1(RandomForest.scala:416) 
    at org.apache.spark.ml.tree.impl.RandomForest$$anonfun$org$apache$spark$ml$tree$impl$RandomForest$$binSeqOp$1$1.apply(RandomForest.scala:441) 
    at org.apache.spark.ml.tree.impl.RandomForest$$anonfun$org$apache$spark$ml$tree$impl$RandomForest$$binSeqOp$1$1.apply(RandomForest.scala:439) 
    at scala.collection.immutable.Map$Map1.foreach(Map.scala:109) 
    at org.apache.spark.ml.tree.impl.RandomForest$.org$apache$spark$ml$tree$impl$RandomForest$$binSeqOp$1(RandomForest.scala:439) 
    at org.apache.spark.ml.tree.impl.RandomForest$$anonfun$9$$anonfun$apply$9.apply(RandomForest.scala:532) 
    at org.apache.spark.ml.tree.impl.RandomForest$$anonfun$9$$anonfun$apply$9.apply(RandomForest.scala:532) 
    at scala.collection.Iterator$class.foreach(Iterator.scala:727) 
    at org.apache.spark.InterruptibleIterator.foreach(InterruptibleIterator.scala:28) 
    at org.apache.spark.ml.tree.impl.RandomForest$$anonfun$9.apply(RandomForest.scala:532) 
    at org.apache.spark.ml.tree.impl.RandomForest$$anonfun$9.apply(RandomForest.scala:521) 
    at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:785) 
    at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:785) 
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) 
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319) 
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:283) 
    at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:79) 
    at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:47) 
    at org.apache.spark.scheduler.Task.run(Task.scala:86) 
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274) 
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142) 
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) 
    at java.lang.Thread.run(Thread.java:745) 

Answer 1

変更するには：

Integer numClasses = 17;