/**
   * SVM trainer
   *
   * @param dataTrain
   * @param dataTest
   */
  public static void trainModelLibSVM(Instances dataTrain, Instances dataTest) {
    try {
      LibSVM classifier = new LibSVM();

      CVParameterSelection ps = new CVParameterSelection();
      ps.setClassifier(classifier);
      ps.setNumFolds(5); // using 5-fold CV
      // ps.addCVParameter("C 0.1 0.5 5");

      // build and output best options
      ps.buildClassifier(dataTrain);

      Evaluation eval = new Evaluation(dataTrain);
      eval.evaluateModel(ps, dataTest);
      System.out.println("Results of the set :::::::::::::::::::::: ");
      System.out.println(
          "Percentage of correctly classified instances : "
              + eval.pctCorrect()
              + "\n"
              + "Percentage of incorrectly classified instances : "
              + eval.pctIncorrect());
      System.out.println("No of correct predictions : " + eval.correct());
      System.out.println("TRUTHFUL");
      System.out.println(
          "Precision : "
              + eval.precision(0)
              + "\n"
              + "Recall : "
              + eval.recall(0)
              + "\n"
              + "F measure/score  : "
              + eval.fMeasure(0));
      System.out.println("DECEPTIVE");
      System.out.println(
          "Precision : "
              + eval.precision(0)
              + "\n"
              + "Recall : "
              + eval.recall(1)
              + "\n"
              + "F measure/score  : "
              + eval.fMeasure(1));

    } catch (Exception e) {
      // TODO Auto-generated catch block
      e.printStackTrace();
    }
  }
  public static void execSVM(String expName) {
    try {
      FileWriter outFile = null;
      PrintWriter out = null;
      outFile = new FileWriter(expName + "-SVM.results");
      out = new PrintWriter(outFile);
      DateFormat dateFormat = new SimpleDateFormat("yyyy/MM/dd HH:mm:ss");
      ProcessTweets tweetsProcessor = null;
      System.out.println("***************************************");
      System.out.println("***\tEXECUTING TEST\t" + expName + "***");
      System.out.println("+++++++++++++++++++++++++++++++++++++++");
      out.println("***************************************");
      out.println("***\tEXECUTING TEST\t" + expName + "***");
      out.println("+++++++++++++++++++++++++++++++++++++++");
      out.println("4-Generate classifier " + dateFormat.format(new Date()));

      Classifier cls = null;
      DataSource sourceTrain = new DataSource(expName + "-train.arff");
      Instances dataTrain = sourceTrain.getDataSet();
      if (dataTrain.classIndex() == -1) dataTrain.setClassIndex(dataTrain.numAttributes() - 1);
      // Entreno el clasificador
      // cls = new weka.classifiers.functions.LibSVM();
      int clase = dataTrain.numAttributes() - 1;
      cls = new weka.classifiers.bayes.ComplementNaiveBayes();
      dataTrain.setClassIndex(clase);
      cls.buildClassifier(dataTrain);
      ObjectOutputStream oos =
          new ObjectOutputStream(new FileOutputStream(expName + "-SVM.classifier"));
      oos.writeObject(cls);
      oos.flush();
      oos.close();
      DataSource sourceTest = new DataSource(expName + "-test.arff");
      Instances dataTest = sourceTest.getDataSet();
      dataTest.setClassIndex(clase);
      Evaluation eval = new Evaluation(dataTest);
      eval.evaluateModel(cls, dataTest);
      // Ahora calculo los valores precision, recall y fmeasure. Además saco las matrices de
      // confusion

      float precision = 0;
      float recall = 0;
      float fmeasure = 0;
      int topeTopics = 8;
      for (int ind = 0; ind < topeTopics; ind++) {
        precision += eval.precision(ind);
        recall += eval.recall(ind);
        fmeasure += eval.fMeasure(ind);
      }
      precision = precision / topeTopics;
      recall = recall / topeTopics;
      fmeasure = fmeasure / topeTopics;
      System.out.println("++++++++++++++ CNB ++++++++++++++++++++");
      System.out.println(eval.toMatrixString());
      System.out.println("+++++++++++++++++++++++++++++++++++++++");
      System.out.printf("Precision: %.3f\n", precision);
      System.out.printf("Recall: %.3f\n", recall);
      System.out.printf("F-measure: %.3f\n", fmeasure);
      System.out.println("***************************************");
      out.println("++++++++++++++ CNB ++++++++++++++++++++");
      out.println(eval.toMatrixString());
      out.println("+++++++++++++++++++++++++++++++++++++++");
      out.printf("Precision: %.3f\n", precision);
      out.printf("Recall: %.3f\n", recall);
      out.printf("F-measure: %.3f\n", fmeasure);
      out.println("***************************************");
      // OTRO CLASIFICADOR ZeroR
      cls = new weka.classifiers.rules.ZeroR();
      dataTrain.setClassIndex(clase);
      cls.buildClassifier(dataTrain);
      eval = new Evaluation(dataTest);
      eval.evaluateModel(cls, dataTest);
      precision = 0;
      recall = 0;
      fmeasure = 0;
      for (int ind = 0; ind < topeTopics; ind++) {
        precision += eval.precision(ind);
        recall += eval.recall(ind);
        fmeasure += eval.fMeasure(ind);
      }
      precision = precision / topeTopics;
      recall = recall / topeTopics;
      fmeasure = fmeasure / topeTopics;
      System.out.println("++++++++++++++ ZEROR ++++++++++++++++++++");
      System.out.println(eval.toMatrixString());
      System.out.println("+++++++++++++++++++++++++++++++++++++++");
      System.out.printf("Precision: %.3f\n", precision);
      System.out.printf("Recall: %.3f\n", recall);
      System.out.printf("F-measure: %.3f\n", fmeasure);
      System.out.println("***************************************");
      out.println("++++++++++++++ ZEROR ++++++++++++++++++++");
      out.println(eval.toMatrixString());
      out.println("+++++++++++++++++++++++++++++++++++++++");
      out.printf("Precision: %.3f\n", precision);
      out.printf("Recall: %.3f\n", recall);
      out.printf("F-measure: %.3f\n", fmeasure);
      out.println("***************************************");
      // OTRO CLASIFICADOR J48
      /*
      			cls = new weka.classifiers.trees.J48();
      			dataTrain.setClassIndex(clase);
      			cls.buildClassifier(dataTrain);
      			eval = new Evaluation(dataTest);
      			eval.evaluateModel(cls, dataTest);
      			precision=0;
      			recall=0;
      			fmeasure=0;
      			for(int ind=0; ind<topeTopics; ind++)
      			{
      				precision += eval.precision(ind);
      				recall += eval.recall(ind);
      				fmeasure += eval.fMeasure(ind);
      			}
      			precision = precision / topeTopics;
      			recall = recall / topeTopics;
      			fmeasure = fmeasure / topeTopics;
      			System.out.println("++++++++++++++ J48 ++++++++++++++++++++");
      			System.out.println(eval.toMatrixString());
      			System.out.println("+++++++++++++++++++++++++++++++++++++++");
      			System.out.printf("Precision: %.3f\n", precision);
      			System.out.printf("Recall: %.3f\n", recall);
      			System.out.printf("F-measure: %.3f\n", fmeasure);
      			System.out.println("***************************************");
      			out.println("++++++++++++++ J48 ++++++++++++++++++++");
      			out.println(eval.toMatrixString());
      			out.println("+++++++++++++++++++++++++++++++++++++++");
      			out.printf("Precision: %.3f\n", precision);
      			out.printf("Recall: %.3f\n", recall);
      			out.printf("F-measure: %.3f\n", fmeasure);
      			out.println("***************************************");

      //OTRO SMO
      			cls = new weka.classifiers.functions.SMO();
      			dataTrain.setClassIndex(clase);
      			cls.buildClassifier(dataTrain);
      			eval = new Evaluation(dataTest);
      			eval.evaluateModel(cls, dataTest);
      			precision=0;
      			recall=0;
      			fmeasure=0;
      			for(int ind=0; ind<topeTopics; ind++)
      			{
      				precision += eval.precision(ind);
      				recall += eval.recall(ind);
      				fmeasure += eval.fMeasure(ind);
      			}
      			precision = precision / topeTopics;
      			recall = recall / topeTopics;
      			fmeasure = fmeasure / topeTopics;
      			System.out.println("++++++++++++++ SMO ++++++++++++++++++++");
      			System.out.println(eval.toMatrixString());
      			System.out.println("+++++++++++++++++++++++++++++++++++++++");
      			System.out.printf("Precision: %.3f\n", precision);
      			System.out.printf("Recall: %.3f\n", recall);
      			System.out.printf("F-measure: %.3f\n", fmeasure);
      			System.out.println("***************************************");
      			out.println("++++++++++++++ SMO ++++++++++++++++++++");
      			out.println(eval.toMatrixString());
      			out.println("+++++++++++++++++++++++++++++++++++++++");
      			out.printf("Precision: %.3f\n", precision);
      			out.printf("Recall: %.3f\n", recall);
      			out.printf("F-measure: %.3f\n", fmeasure);
      			out.println("***************************************");
      */
      out.flush();
      out.close();
      dataTest.delete();
      dataTrain.delete();
    } catch (FileNotFoundException e) {
      // TODO Auto-generated catch block
      e.printStackTrace();
    } catch (IOException e) {
      // TODO Auto-generated catch block
      e.printStackTrace();
    } catch (Exception e) {
      // TODO Auto-generated catch block
      e.printStackTrace();
    }
  }
  public void exec(PrintWriter printer) {
    try {
      FileWriter outFile = null;
      PrintWriter out = null;
      if (printer == null) {
        outFile = new FileWriter(id + ".results");
        out = new PrintWriter(outFile);
      } else out = printer;

      DateFormat dateFormat = new SimpleDateFormat("yyyy/MM/dd HH:mm:ss");
      ProcessTweets tweetsProcessor = null;
      System.out.println("***************************************");
      System.out.println("***\tEXECUTING TEST\t" + id + "***");
      System.out.println("+++++++++++++++++++++++++++++++++++++++");
      System.out.println("Train size:" + traincorpus.size());
      System.out.println("Test size:" + testcorpus.size());
      out.println("***************************************");
      out.println("***\tEXECUTING TEST\t***");
      out.println("+++++++++++++++++++++++++++++++++++++++");
      out.println("Train size:" + traincorpus.size());
      out.println("Test size:" + testcorpus.size());
      String cloneID = "";
      boolean clonar = false;
      if (baseline) {
        System.out.println("***************************************");
        System.out.println("***\tEXECUTING TEST BASELINE\t***");
        System.out.println("+++++++++++++++++++++++++++++++++++++++");
        System.out.println("Train size:" + traincorpus.size());
        System.out.println("Test size:" + testcorpus.size());
        out.println("***************************************");
        out.println("***\tEXECUTING TEST\t***");
        out.println("+++++++++++++++++++++++++++++++++++++++");
        out.println("Train size:" + traincorpus.size());
        out.println("Test size:" + testcorpus.size());

        BaselineClassifier base = new BaselineClassifier(testcorpus, 8);
        precision = base.getPrecision();
        recall = base.getRecall();
        fmeasure = base.getFmeasure();
        System.out.println("+++++++++++++++++++++++++++++++++++++++");
        System.out.printf("Precision: %.3f\n", precision);
        System.out.printf("Recall: %.3f\n", recall);
        System.out.printf("F-measure: %.3f\n", fmeasure);
        System.out.println("***************************************");
        out.println("+++++++++++++++++++++++++++++++++++++++");
        out.printf("Precision: %.3f\n", precision);
        out.printf("Recall: %.3f\n", recall);
        out.printf("F-measure: %.3f\n", fmeasure);
        out.println("***************************************");
        out.flush();
        out.close();
        return;
      } else {
        System.out.println("Stemming: " + stemming);
        System.out.println("Lematization:" + lematization);
        System.out.println("URLs:" + urls);
        System.out.println("Hashtags:" + hashtags);
        System.out.println("Mentions:" + mentions);
        System.out.println("Unigrams:" + unigrams);
        System.out.println("Bigrams:" + bigrams);
        System.out.println("TF:" + tf);
        System.out.println("TF-IDF:" + tfidf);
        out.println("Stemming: " + stemming);
        out.println("Lematization:" + lematization);
        out.println("URLs:" + urls);
        out.println("Hashtags:" + hashtags);
        out.println("Mentions:" + mentions);
        out.println("Unigrams:" + unigrams);
        out.println("Bigrams:" + bigrams);
        out.println("TF:" + tf);
        out.println("TF-IDF:" + tfidf);
      }
      // Si tengo los tweets procesados, me evito un nuevo proceso
      System.out.println("1-Process tweets " + dateFormat.format(new Date()));
      out.println("1-Process tweets " + dateFormat.format(new Date()));

      List<ProcessedTweet> train = null;
      String[] ids = id.split("-");
      cloneID = ids[0] + "-" + (Integer.valueOf(ids[1]) + 6);
      if (((Integer.valueOf(ids[1]) / 6) % 2) == 0) clonar = true;

      if (new File(id + "-train.ptweets").exists()) {
        train = ProcessedTweetSerialization.fromFile(id + "-train.ptweets");
        tweetsProcessor =
            new ProcessTweets(stemming, lematization, urls, hashtags, mentions, unigrams, bigrams);
        if (lematization) {
          tweetsProcessor.doLematization(train);
        }
        if (stemming) {
          tweetsProcessor.doStemming(train);
        }
      } else {
        tweetsProcessor =
            new ProcessTweets(stemming, lematization, urls, hashtags, mentions, unigrams, bigrams);
        // Esto del set training es un añadido para poder diferenciar los idiomas de las url en el
        // corpus paralelo
        //				tweetsProcessor.setTraining(true);
        train = tweetsProcessor.processTweets(traincorpus);
        //				tweetsProcessor.setTraining(false);
        ProcessedTweetSerialization.toFile(id + "-train.ptweets", train);
        /*
        				if (clonar)
        				{
        					File f = new File (id+"-train.ptweets");
        					Path p = f.toPath();
        					CopyOption[] options = new CopyOption[]{
        						      StandardCopyOption.REPLACE_EXISTING,
        						      StandardCopyOption.COPY_ATTRIBUTES
        						     };
        					Files.copy(p, new File (cloneID+"-train.ptweets").toPath(), options);
        					Files.copy(p, new File (ids[0]+"-"+(Integer.valueOf(ids[1])+12)+"-train.ptweets").toPath(), options);
        					Files.copy(p, new File (ids[0]+"-"+(Integer.valueOf(ids[1])+18)+"-train.ptweets").toPath(), options);
        					Files.copy(p, new File (ids[0]+"-"+(Integer.valueOf(ids[1])+24)+"-train.ptweets").toPath(), options);
        					Files.copy(p, new File (ids[0]+"-"+(Integer.valueOf(ids[1])+30)+"-train.ptweets").toPath(), options);
        				}
        */
      }

      // Generamos las BOW. Igual que antes, si existen no las creo.
      System.out.println("2-Fill topics " + dateFormat.format(new Date()));
      out.println("2-Fill topics " + dateFormat.format(new Date()));
      TopicsList topics = null;
      if (new File(id + ".topics").exists()) {
        topics = TopicsSerialization.fromFile(id + ".topics");
        if (tf) topics.setSelectionFeature(TopicDesc.TERM_TF);
        else topics.setSelectionFeature(TopicDesc.TERM_TF_IDF);
        topics.prepareTopics();
      } else {

        topics = new TopicsList();
        if (tf) topics.setSelectionFeature(TopicDesc.TERM_TF);
        else topics.setSelectionFeature(TopicDesc.TERM_TF_IDF);
        System.out.println("Filling topics " + dateFormat.format(new Date()));
        topics.fillTopics(train);
        System.out.println("Preparing topics topics " + dateFormat.format(new Date()));
        // Aquí tengo que serializar antes de preparar, porque si no no puedo calcular los tf y
        // tfidf
        System.out.println("Serializing topics topics " + dateFormat.format(new Date()));
        /*
        				if (clonar)
        				{
        					TopicsSerialization.toFile(cloneID+".topics", topics);
        				}
        */
        topics.prepareTopics();
        TopicsSerialization.toFile(id + ".topics", topics);
      }
      System.out.println("3-Generate arff train file " + dateFormat.format(new Date()));
      out.println("3-Generate arff train file " + dateFormat.format(new Date()));

      // Si el fichero arff no existe, lo creo. en caso contrario vengo haciendo lo que hasta ahora,
      // aprovechar trabajo previo
      if (!new File(id + "-train.arff").exists()) {

        BufferedWriter bw = topics.generateArffHeader(id + "-train.arff");
        int tope = traincorpus.size();
        if (tweetsProcessor == null)
          tweetsProcessor =
              new ProcessTweets(
                  stemming, lematization, urls, hashtags, mentions, unigrams, bigrams);
        for (int indTweet = 0; indTweet < tope; indTweet++) {
          topics.generateArffVector(bw, train.get(indTweet));
        }
        bw.flush();
        bw.close();
      }

      // Ahora proceso los datos de test
      System.out.println("5-build test dataset " + dateFormat.format(new Date()));
      out.println("5-build test dataset " + dateFormat.format(new Date()));

      List<ProcessedTweet> test = null;
      if (new File(id + "-test.ptweets").exists())
        test = ProcessedTweetSerialization.fromFile(id + "-test.ptweets");
      else {
        if (tweetsProcessor == null)
          tweetsProcessor =
              new ProcessTweets(
                  stemming, lematization, urls, hashtags, mentions, unigrams, bigrams);
        test = tweetsProcessor.processTweets(testcorpus);
        ProcessedTweetSerialization.toFile(id + "-test.ptweets", test);
        /*
        				if (clonar)
        				{
        					File f = new File (id+"-test.ptweets");
        					Path p = f.toPath();
        					CopyOption[] options = new CopyOption[]{
        						      StandardCopyOption.REPLACE_EXISTING,
        						      StandardCopyOption.COPY_ATTRIBUTES
        						     };
        					Files.copy(p, new File (cloneID+"-test.ptweets").toPath(), options);
        				}
        */

      }

      // Si el fichero arff no existe, lo creo. en caso contrario vengo haciendo lo que hasta ahora,
      // aprovechar trabajo previo
      if (!new File(id + "-test.arff").exists()) {
        BufferedWriter bw = topics.generateArffHeader(id + "-test.arff");
        int tope = testcorpus.size();
        if (tweetsProcessor == null)
          tweetsProcessor =
              new ProcessTweets(
                  stemming, lematization, urls, hashtags, mentions, unigrams, bigrams);
        for (int indTweet = 0; indTweet < tope; indTweet++) {
          topics.generateArffVector(bw, test.get(indTweet));
        }
        bw.flush();
        bw.close();
      }
      int topeTopics = topics.getTopicsList().size();
      topics.getTopicsList().clear();
      // Genero el clasificador
      // FJRM 25-08-2013 Lo cambio de orden para intentar liberar la memoria de los topics y tener
      // más libre
      System.out.println("4-Generate classifier " + dateFormat.format(new Date()));
      out.println("4-Generate classifier " + dateFormat.format(new Date()));

      Classifier cls = null;
      DataSource sourceTrain = null;
      Instances dataTrain = null;
      if (new File(id + "-MNB.classifier").exists()) {
        ObjectInputStream ois = new ObjectInputStream(new FileInputStream(id + "-MNB.classifier"));
        cls = (Classifier) ois.readObject();
        ois.close();
      } else {
        sourceTrain = new DataSource(id + "-train.arff");
        dataTrain = sourceTrain.getDataSet();
        if (dataTrain.classIndex() == -1) dataTrain.setClassIndex(dataTrain.numAttributes() - 1);
        // Entreno el clasificador
        cls = new weka.classifiers.bayes.NaiveBayesMultinomial();
        int clase = dataTrain.numAttributes() - 1;
        dataTrain.setClassIndex(clase);
        cls.buildClassifier(dataTrain);
        ObjectOutputStream oos =
            new ObjectOutputStream(new FileOutputStream(id + "-MNB.classifier"));
        oos.writeObject(cls);
        oos.flush();
        oos.close();
        // data.delete();//no borro para el svm
      }
      // Ahora evaluo el clasificador con los datos de test
      System.out.println("6-Evaluate classifier MNB " + dateFormat.format(new Date()));
      out.println("6-Evaluate classifier MNB" + dateFormat.format(new Date()));
      DataSource sourceTest = new DataSource(id + "-test.arff");
      Instances dataTest = sourceTest.getDataSet();
      int clase = dataTest.numAttributes() - 1;
      dataTest.setClassIndex(clase);
      Evaluation eval = new Evaluation(dataTest);
      eval.evaluateModel(cls, dataTest);
      // Ahora calculo los valores precision, recall y fmeasure. Además saco las matrices de
      // confusion

      precision = 0;
      recall = 0;
      fmeasure = 0;
      for (int ind = 0; ind < topeTopics; ind++) {
        precision += eval.precision(ind);
        recall += eval.recall(ind);
        fmeasure += eval.fMeasure(ind);
      }
      precision = precision / topeTopics;
      recall = recall / topeTopics;
      fmeasure = fmeasure / topeTopics;
      System.out.println("+++++++++++++++++++++++++++++++++++++++");
      System.out.println(eval.toMatrixString());
      System.out.println("+++++++++++++++++++++++++++++++++++++++");
      System.out.printf("Precision: %.3f\n", precision);
      System.out.printf("Recall: %.3f\n", recall);
      System.out.printf("F-measure: %.3f\n", fmeasure);
      System.out.println("***************************************");
      out.println("+++++++++++++++++++++++++++++++++++++++");
      out.println(eval.toMatrixString());
      out.println("+++++++++++++++++++++++++++++++++++++++");
      out.printf("Precision: %.3f\n", precision);
      out.printf("Recall: %.3f\n", recall);
      out.printf("F-measure: %.3f\n", fmeasure);
      out.println("***************************************");
      /*			NO BORRAR
      			System.out.println("7-Evaluate classifier SVM"+dateFormat.format(new Date()));
      			out.println("7-Evaluate classifier SVM"+dateFormat.format(new Date()));
      			if (new File(id+"-SVM.classifier").exists())
      			{
      				ObjectInputStream ois = new ObjectInputStream(new FileInputStream(id+"-SVM.classifier"));
      				cls = (Classifier) ois.readObject();
      				ois.close();
      			}
      			else
      			{
      				if (dataTrain==null)
      				{
      					sourceTrain = new DataSource(id+"-train.arff");
      					dataTrain = sourceTrain.getDataSet();
      					if (dataTrain.classIndex() == -1)
      						dataTrain.setClassIndex(dataTrain.numAttributes() - 1);
      				}
      	//Entreno el clasificador
      				cls = new weka.classifiers.functions.LibSVM();
      				clase = dataTrain.numAttributes()-1;
      				dataTrain.setClassIndex(clase);
      				cls.buildClassifier(dataTrain);
      				ObjectOutputStream oos = new ObjectOutputStream(new FileOutputStream(id+"-SVM.classifier"));
      				oos.writeObject(cls);
      				oos.flush();
      				oos.close();
      				dataTrain.delete();
      			}
      			eval.evaluateModel(cls, dataTest);
      			precision=0;
      			recall=0;
      			fmeasure=0;
      			for(int ind=0; ind<topeTopics; ind++)
      			{
      				precision += eval.precision(ind);
      				recall += eval.recall(ind);
      				fmeasure += eval.fMeasure(ind);
      			}
      			precision = precision / topeTopics;
      			recall = recall / topeTopics;
      			fmeasure = fmeasure / topeTopics;
      			System.out.println("+++++++++++++++++++++++++++++++++++++++");
      			System.out.println(eval.toMatrixString());
      			System.out.println("+++++++++++++++++++++++++++++++++++++++");
      			System.out.printf("Precision: %.3f\n", precision);
      			System.out.printf("Recall: %.3f\n", recall);
      			System.out.printf("F-measure: %.3f\n", fmeasure);
      			System.out.println("***************************************");
      			out.println("+++++++++++++++++++++++++++++++++++++++");
      			out.println(eval.toMatrixString());
      			out.println("+++++++++++++++++++++++++++++++++++++++");
      			out.printf("Precision: %.3f\n", precision);
      			out.printf("Recall: %.3f\n", recall);
      			out.printf("F-measure: %.3f\n", fmeasure);
      			out.println("***************************************");
      */
      System.out.println("Done " + dateFormat.format(new Date()));
      out.println("Done " + dateFormat.format(new Date()));
      if (printer == null) {
        out.flush();
        out.close();
      }
      // Intento de liberar memoria
      if (dataTrain != null) dataTrain.delete();
      if (dataTest != null) dataTest.delete();
      if (train != null) train.clear();
      if (test != null) test.clear();
      if (topics != null) {
        topics.getTopicsList().clear();
        topics = null;
      }
      if (dataTest != null) dataTest.delete();
      if (cls != null) cls = null;
      if (tweetsProcessor != null) tweetsProcessor = null;
      System.gc();
    } catch (Exception e) {
      e.printStackTrace();
    }
  }
  public void runFilter() throws Exception {
    System.out.println("filtering attributes...");
    System.out.println("running weka filters and weka-libsvm");
    File svmfile = new File(sentiAnalysis.DIR.concat(sentiAnalysis.outout.concat(".libsvm")));
    LibSVMLoader libl = new LibSVMLoader();
    libl.setFile(svmfile);
    Instances data = libl.getDataSet();

    NumericToNominal nm = new NumericToNominal(); // Converting last index
    // attribute to type
    // nominal from numeric
    nm.setAttributeIndices("last"); // as the last index would be class
    // label for the data
    nm.setInputFormat(data);

    filteredData = Filter.useFilter(data, nm); // filtered data stored in
    // new Instances object

    AttrNo = filteredData.numAttributes(); // number of attributes in given
    // file
    RecordNo = filteredData.numInstances(); // Number of records in given
    // file
    lowerBound = 0;
    upperBound = AttrNo - 1;
    AttributeSelection atsl = new AttributeSelection();
    Ranker search = new Ranker();
    InfoGainAttributeEval infog = new InfoGainAttributeEval(); // Applying
    // Attribute
    // Selection
    // using
    // InfoGain
    // evaluator
    // with
    // Ranker
    // search
    atsl.setEvaluator(infog);
    atsl.setSearch(search);
    atsl.SelectAttributes(filteredData);
    InfoGain = atsl.rankedAttributes();
    SelectedAttributes = atsl.selectedAttributes();

    // count non zero infoGain
    int count = 0;
    for (int i = 0; i < InfoGain.length; i++) {
      count = (InfoGain[i][1] > 0) ? count + 1 : count;
    }

    System.out.println("writing attributes with non-zero InfoGain...");
    FileWriter svmout =
        new FileWriter(sentiAnalysis.DIR.concat(sentiAnalysis.outout.concat("_new.libsvm")));

    for (int i = 0; i < RecordNo; i++) {
      int index = 1;
      svmout.write((int) filteredData.instance(i).value(filteredData.classIndex()) + " ");
      for (int j = 0; j < count; j++) {
        svmout.write(
            index + ":" + (int) filteredData.instance(i).value((int) InfoGain[j][0]) + " ");
        index++;
      }
      svmout.write("\n");
    }
    svmout.close();

    // filtered
    File newsvm = new File(sentiAnalysis.DIR.concat(sentiAnalysis.outout.concat("_new.libsvm")));
    LibSVMLoader liblnew = new LibSVMLoader();
    liblnew.setFile(newsvm);
    Instances newdata = liblnew.getDataSet();
    nm = new NumericToNominal(); // Converting last index attribute to type
    // nominal from numeric
    nm.setAttributeIndices("last"); // as the last index would be class
    // label for the data
    nm.setInputFormat(newdata);
    Instances filteredDataNew = Filter.useFilter(newdata, nm); // filtered
    // data
    // stored in
    // new
    // Instances
    // object

    // test file
    File newsvmtest =
        new File(sentiAnalysis.DIR.concat(sentiAnalysis.outout.concat("_test.libsvm")));
    LibSVMLoader libltest = new LibSVMLoader();
    libltest.setFile(newsvmtest);
    Instances newdatatest = libltest.getDataSet();
    nm = new NumericToNominal(); // Converting last index attribute to type
    // nominal from numeric
    nm.setAttributeIndices("last"); // as the last index would be class
    // label for the data
    nm.setInputFormat(newdatatest);
    Instances filteredDataTest = Filter.useFilter(newdatatest, nm); // filtered
    // data
    // stored
    // in
    // new
    // Instances
    // object

    // weka.classifiers.functions.LibSVM -S 0 -K 2 -D 3 -G 0.0 -R 0.0 -N 0.5
    // -M 40.0 -C 1.0 -E 0.001 -P 0.1 -seed 1
    String[] options = new String[1];
    options[0] = "-S 0 -K 2 -D 3 -G 0.1 -R 0.0 -N 0.5 -M 40.0 -C 1.0 -E 0.001 -P 0.1 -seed 1 -h 0";
    System.out.println("building classifier...");
    LibSVM svm_model = new LibSVM();
    svm_model.setOptions(options); // set the options
    svm_model.buildClassifier(filteredData); // build classifier

    DecimalFormat df = new DecimalFormat("0.00");

    System.out.println("running cross validation...");
    Evaluation eval = new Evaluation(filteredData);
    // eval.crossValidateModel(svm_model, filteredDataNew, 10, new
    // Random(1));
    eval.evaluateModel(svm_model, filteredDataTest);

    FileWriter results =
        new FileWriter(sentiAnalysis.DIR.concat(sentiAnalysis.outout.concat("_results.txt")));

    results.write("Classifier 1: Support Vector Machines\n");
    results.write("Positive class precision: " + df.format(eval.precision(0)) + "\n");
    results.write("Positive class recall: " + df.format(eval.recall(0)) + "\n");
    results.write("Positive class f-score: " + df.format(eval.fMeasure(0)) + "\n");
    results.write("Negative class precision: " + df.format(eval.precision(0)) + "\n");
    results.write("Negative class recall: " + df.format(eval.precision(0)) + "\n");
    results.write("Negative class f-score: " + df.format(eval.fMeasure(0)) + "\n");

    System.out.println("generating results...");
    System.out.println("*" + sentiAnalysis.outout + "*\t" + "\tPositive\tNegative\tNeutral");
    System.out.println(
        "Precision\t"
            + df.format(eval.precision(0))
            + "\t"
            + df.format(eval.precision(2))
            + "\t"
            + df.format(eval.precision(1)));
    System.out.println(
        "Recall\t"
            + df.format(eval.recall(0))
            + "\t"
            + df.format(eval.recall(2))
            + "\t"
            + df.format(eval.recall(1)));
    System.out.println(
        "F-score\t"
            + df.format(eval.fMeasure(0))
            + "\t"
            + df.format(eval.fMeasure(2))
            + "\t"
            + df.format(eval.fMeasure(1)));

    results.close();
  }