Spark 朴素贝叶斯模型 - 没有这样的方法错误

Question

我是 Spark 的新手，正在尝试运行 Eclipse 中的朴素贝叶斯 Java 示例。尝试保存模型时出现 No such method 错误。

SparkConf sparkConf = new SparkConf().setAppName("JavaNaiveBayesExample").setMaster("local");
JavaSparkContext jsc = new JavaSparkContext(sparkConf);

String path = "SPARK_HOME\data\mllib\sample_libsvm_data.txt";
JavaRDD<LabeledPoint> inputData = MLUtils.loadLibSVMFile(jsc.sc(), path).toJavaRDD();
JavaRDD<LabeledPoint>[] tmp = inputData.randomSplit(new double[]{0.6, 0.4});
JavaRDD<LabeledPoint> training = tmp[0]; // training set
JavaRDD<LabeledPoint> test = tmp[1]; // test set
final NaiveBayesModel model = NaiveBayes.train(training.rdd(), 1.0);
JavaPairRDD<Double, Double> predictionAndLabel =
  test.mapToPair(new PairFunction<LabeledPoint, Double, Double>() {
      public Tuple2<Double, Double> call(LabeledPoint p) {
      return new Tuple2(model.predict(p.features()), p.label());
    }
  });
double accuracy = predictionAndLabel.filter(new Function<Tuple2<Double, Double>, Boolean>() {
  public Boolean call(Tuple2<Double, Double> pl) {
    return pl._1().equals(pl._2());
  }
}).count() / (double) test.count();

// Save and load model    
model.save(jsc.sc(), "PATH_TO_FOLDER\myNaiveBayesModel");
NaiveBayesModel sameModel = NaiveBayesModel.load(jsc.sc(), "PATH_TO_FOLDER\myNaiveBayesModel");

jsc.close();

错误发生在model.save()行。

堆栈跟踪如下：

Exception in thread "main" java.lang.NoSuchMethodError: scala.reflect.api.JavaUniverse.runtimeMirror(Ljava/lang/ClassLoader;)Lscala/reflect/api/JavaMirrors$JavaMirror;
at org.apache.spark.mllib.classification.NaiveBayesModel$SaveLoadV2_0$.save(NaiveBayes.scala:205)
at org.apache.spark.mllib.classification.NaiveBayesModel.save(NaiveBayes.scala:170)
at AppMain.main(AppMain.java:42)

我该如何解决这个问题？感谢任何帮助。

运行 Spark 2.0.1 和 Scala 2.11.8。

为 spark-core_2.11 和 spark-mllib_2.10.

添加了依赖项

Answer 1

Dependencies added for spark-core_2.11 and spark-mllib_2.10.

请使用Scala 2.11编译的spark mllib解决问题。

Spark 朴素贝叶斯模型 - 没有这样的方法错误

Spark Naive Bayes Model - No such method error

apache-spark

naivebayes