我的spark程序从elasticsearch读取数据（以javapairdd>的形式）。这个javapairdd使用以下内部类转换为javardd

static class Transformer implements
        Function<Tuple2<String, Map<String, Object>>, LabeledPoint> {

    @Override
    public LabeledPoint call(Tuple2<String, Map<String, Object>> arg0)
            throws Exception {
        HashingTF tf = new HashingTF();
        Map<String, Object> map = (Map<String, Object>) arg0._2();

        // get values from Map
        Set<String> keys = map.keySet();
        List<Object> valuesList = new ArrayList<Object>();
        for (Iterator<String> i = keys.iterator(); i.hasNext();) {
            String key = (String) i.next();
            Object value = (Object) map.get(key);
            valuesList.add(value);
        }
        return new LabeledPoint(1d, tf.transform(valuesList));
    }
}

使用javardd数据生成随机森林模型并保存到同一台机器上的特定位置。模型保存成功。
但在加载模型时，我遇到了以下异常，
sun.reflect.nativemethodaccessorimpl.invoke0（本机方法）处的java.lang.reflect.invokeTargetException at sun.reflect.nativemethodaccessorimpl.invoke（本机方法）。java:62)在sun.reflect.delegatingmethodaccessorimpl.invoke（delegatingmethodaccessorimpl。java:43)在java.lang.reflect.method.invoke（方法。java:497)在org.codehaus.mojo.exec.execjavamojo$1.run（execjavamojo。java:293)在java.lang.thread.run（线程。java:745)原因：org.json4s.package$mappingexception:在org.json4s.reflect.package$.fail（package）中未找到可转换为java.lang.string的值。scala:96)在org.json4s.extraction$.convert（extraction。scala:554)在org.json4s.extraction$.extract（提取。scala:331)在org.json4s.extraction$.extract（extraction。scala:42)在org.json4s.extractablejsonastnode.extract（extractablejsonastnode。scala:21)在org.apache.spark.mllib.tree.model.decisiontreemodel$.load（decisiontreemodel。scala:326)在org.apache.spark.mllib.tree.model.decisiontreemodel.load（decisiontreemodel.scala）位于co.nttd.integration.sparkesintegration.main（sparkesintegration）。java:96)
为了保存模型，编写了以下代码

JavaPairRDD<String, Map<String, Object>> esRDD = JavaEsSpark
            .esRDD(javaSparkContext);
    // Transform data in JavaRDD<LabeledPoint>
    JavaRDD<LabeledPoint> data = esRDD.map(new Transformer());

    final RandomForestModel model = RandomForest.trainClassifier(
                inputTrainingData, numClasses, categoricalFeaturesInfo, numTrees,
                featureSubsetStrategy, impurity, maxDepth, maxBins, seed);

    model.save(JavaSparkContext.toSparkContext(jsc),
            "myPath");

要加载模型，decisiontreemodel model=decisiontreemodel.load（sc.sc（），“file:///mypath”）；