weka java加載模型和使用測試數據集

我嘗試通過執行序列化和反序列化來構建weka模型，如weka wikii中的說明所述。使用培訓中的bayesnet構建並希望加載該模型進行測試。培訓和測試具有相同的屬性過濾器的設置是這樣的：weka java加載模型和使用測試數據集

Remove rm = generateFilter(filterOption); 

    FilteredClassifier fc = new FilteredClassifier(); 
    fc.setFilter(rm); 
    filterClassifier.setClassifier(randomTree); 
    filterClassifier.buildClassifier(data); 
    exportClassifier("randomTree", file, filterClassifier);

導出的代碼是這個樣子：

private void exportClassifier(String method, String file, 
     FilteredClassifier filterClassifier) throws IOException, 
     FileNotFoundException { 
    System.out.println(file + "." + method + ".model"); 

    ObjectOutputStream oos = new ObjectOutputStream(new FileOutputStream(
      file + "." + method + ".model")); 
    oos.writeObject(filterClassifier); 
    oos.flush(); 
    oos.close(); 
}

但是當我嘗試另一個測試加載它們設置這樣的：

public String EvaluateModel(String file, File modelFile) throws Exception { 
    Instances data = populateInstance(file); 

    if (data.classIndex() == -1) { 
     System.out.println("reset index..."); 
     data.setClassIndex(data.numAttributes() - 1); 
    } 

    FilteredClassifier classifier = (FilteredClassifier) weka.core.SerializationHelper 
      .read(new FileInputStream(modelFile)); 

    //classifier.buildClassifier(data); 
    Evaluation eval = new Evaluation(data); 
    //eval.crossValidateModel(classifier, data, 10, new Random(1)); 
    eval.evaluateModel(classifier, data); 

    String summaryString = eval 
      .toSummaryString("\nResults\n======\n", false); 

    System.out.println(summaryString); 
    System.out.println(eval.fMeasure(1) + " " + eval.precision(1) + " " 
      + eval.recall(1)); 
    return formatOutput(eval); 
}

我喜歡例外：

Exception in thread "main" java.lang.ArrayIndexOutOfBoundsException: 1200 
at weka.classifiers.bayes.net.estimate.DiscreteEstimatorBayes.getProbability(DiscreteEstimatorBayes.java:106) 
at weka.classifiers.bayes.net.estimate.SimpleEstimator.distributionForInstance(SimpleEstimator.java:183) 
at weka.classifiers.bayes.BayesNet.distributionForInstance(BayesNet.java:386) 
at weka.classifiers.meta.FilteredClassifier.distributionForInstance(FilteredClassifier.java:437) 
at weka.classifiers.Evaluation.evaluateModelOnceAndRecordPrediction(Evaluation.java:1439) 
at weka.classifiers.Evaluation.evaluateModel(Evaluation.java:1407) 
at com.besmart.raynor.dataprocessing.dataprocessor.weka.WekaRunner.EvaluateModel(WekaRunner.java:138) 
at com.besmart.raynor.dataprocessing.dataprocessor.weka.WekaBatchRunner.batchReEvaluation(WekaBatchRunner.java:80) 
at com.besmart.raynor.dataprocessing.dataprocessor.weka.WekaBatchRunner.main(WekaBatchRunner.java:103)

來源

2013-05-29 Tim Raynor

可以使用weka.core.SerializationHelper.write方法代替使用ObjectOutputStream編寫對象。

來源

2014-01-30 18:18:30 girip11

weka java加載模型和使用測試數據集

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