Load Naïve Bayes model in java code using weka jar

问题:

I have used weka and made a Naive Bayes classifier, by using weka GUI. Then I have saved this model by following this tutorial. Now I want to load this model through Java code but I am unable to find any way to load a saved model using weka.

This is my requirement that I have to made model separately and then use it in a separate program.

If anyone can guide me in this regard I will be thankful to you.

回答1:

You can easily load a saved model in java using this command:

Classifier myCls = (Classifier) weka.core.SerializationHelper.read(pathToModel);

For a complete workflow in Java I wrote the following article in SO Documentation, now copied here:

Text Classification in Weka

Text Classification with LibLinear

• Create training instances from .arff file

  private static Instances getDataFromFile(String path) throws Exception{
  
      DataSource source = new DataSource(path);
      Instances data = source.getDataSet();
  
      if (data.classIndex() == -1){
          data.setClassIndex(data.numAttributes()-1);
          //last attribute as class index
      }
  
      return data;    
  }
  

---

Instances trainingData = getDataFromFile(pathToArffFile);

• Use StringToWordVector to transform your string attributes to number representation:

  • Important features of this filter:

    1. tf-idf representation
    2. stemming
    3. lowercase words
    4. stopwords
    5. n-gram representation*

   

  StringToWordVector() filter = new StringToWordVector();    
  filter.setWordsToKeep(1000000);
  if(useIdf){
      filter.setIDFTransform(true);
  }
  filter.setTFTransform(true);
  filter.setLowerCaseTokens(true);
  filter.setOutputWordCounts(true);
  filter.setMinTermFreq(minTermFreq);
  filter.setNormalizeDocLength(new SelectedTag(StringToWordVector.FILTER_NORMALIZE_ALL,StringToWordVector.TAGS_FILTER));
  NGramTokenizer t = new NGramTokenizer();
  t.setNGramMaxSize(maxGrams);
  t.setNGramMinSize(minGrams);    
  filter.setTokenizer(t);     
  WordsFromFile stopwords = new WordsFromFile();
  stopwords.setStopwords(new File("data/stopwords/stopwords.txt"));
  filter.setStopwordsHandler(stopwords);
  if (useStemmer){
      Stemmer s = new /*Iterated*/LovinsStemmer();
      filter.setStemmer(s);
  }
  filter.setInputFormat(trainingData);
  

  • Apply the filter to trainingData: trainingData = Filter.useFilter(trainingData, filter);

  • Create the LibLinear Classifier

    1. SVMType 0 below corresponds to the L2-regularized logistic regression

    2. Set setProbabilityEstimates(true) to print the output probabilities

       Classifier cls = null; LibLINEAR liblinear = new LibLINEAR(); liblinear.setSVMType(new SelectedTag(0, LibLINEAR.TAGS_SVMTYPE)); liblinear.setProbabilityEstimates(true); // liblinear.setBias(1); // default value cls = liblinear; cls.buildClassifier(trainingData);

  • Save model

    System.out.println("Saving the model..."); ObjectOutputStream oos; oos = new ObjectOutputStream(new FileOutputStream(path+"mymodel.model")); oos.writeObject(cls); oos.flush(); oos.close();

  • Create testing instances from .arff file

    Instances trainingData = getDataFromFile(pathToArffFile);

  • Load classifier

  Classifier myCls = (Classifier) weka.core.SerializationHelper.read(path+"mymodel.model");

  • Use the same StringToWordVector filter as above or create a new one for testingData, but remember to use the trainingData for this command:filter.setInputFormat(trainingData); This will make training and testing instances compatible. Alternatively you could use InputMappedClassifier

  • Apply the filter to testingData: testingData = Filter.useFilter(testingData, filter);

  • Classify!

  1.Get the class value for every instance in the testing set

  for (int j = 0; j < testingData.numInstances(); j++) { double res = myCls.classifyInstance(testingData.get(j)); } res is a double value that corresponds to the nominal class that is defined in .arff file. To get the nominal class use : testintData.classAttribute().value((int)res)

---

2.Get the probability distribution for every instance

 for (int j = 0; j < testingData.numInstances(); j++) {
    double[] dist = first.distributionForInstance(testInstances.get(j));
 }

dist is a double array that contains the probabilities for every class defined in .arff file

Note. Classifier should support probability distributions and enable them with: myClassifier.setProbabilityEstimates(true);