• 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;    
}

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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 wrods
  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 probalities

        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)

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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);