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();
  }
예제 #2
0
 public static Instances numericToNominalLast(Instances inInstances) throws Exception {
   NumericToNominal classNominalFilter = new NumericToNominal();
   classNominalFilter.setInputFormat(inInstances);
   classNominalFilter.setAttributeIndices("last");
   return Filter.useFilter(inInstances, classNominalFilter);
 }