old.CFS.java Source code

Introduction

Here is the source code for old.CFS.java

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package old;

import weka.attributeSelection.*;
import weka.core.*;
import weka.core.converters.ConverterUtils.*;
import weka.classifiers.*;
import weka.classifiers.meta.*;
import weka.classifiers.trees.*;
import weka.filters.*;

import java.util.*;

/**
 * performs attribute selection using CfsSubsetEval and GreedyStepwise
 * (backwards) and trains J48 with that. Needs 3.5.5 or higher to compile.
 *
 * @author FracPete (fracpete at waikato dot ac dot nz)
 */
public class CFS {

    /**
     * uses the meta-classifier
     */
    protected static void useClassifier(Instances data) throws Exception {
        System.out.println("\n1. Meta-classfier");
        AttributeSelectedClassifier classifier = new AttributeSelectedClassifier();
        ChiSquaredAttributeEval eval = new ChiSquaredAttributeEval();
        Ranker search = new Ranker();
        search.setThreshold(-1.7976931348623157E308);
        search.setNumToSelect(1000);
        J48 base = new J48();
        classifier.setClassifier(base);
        classifier.setEvaluator(eval);
        classifier.setSearch(search);
        Evaluation evaluation = new Evaluation(data);
        evaluation.crossValidateModel(classifier, data, 10, new Random(1));
        System.out.println(evaluation.toSummaryString());
    }

    /**
     * uses the filter
     */
    protected static void useFilter(Instances data) throws Exception {
        System.out.println("\n2. Filter");
        weka.filters.supervised.attribute.AttributeSelection filter = new weka.filters.supervised.attribute.AttributeSelection();
        ChiSquaredAttributeEval eval = new ChiSquaredAttributeEval();

        Ranker search = new Ranker();
        search.setThreshold(-1.7976931348623157E308);
        search.setNumToSelect(1000);
        filter.setEvaluator(eval);

        filter.setSearch(search);
        filter.setInputFormat(data);
        Instances newData = Filter.useFilter(data, filter);
        System.out.println(newData);
    }

    /**
     * uses the low level approach
       * @param data
     */
    protected static void useLowLevel(Instances data) throws Exception {
        System.out.println("\n3. Low-level");
        AttributeSelection attsel = new AttributeSelection();
        ChiSquaredAttributeEval eval = new ChiSquaredAttributeEval();
        Ranker search = new Ranker();
        search.setThreshold(-1.7976931348623157E308);
        search.setNumToSelect(1000);
        attsel.setEvaluator(eval);
        attsel.setSearch(search);
        attsel.setFolds(10);
        attsel.setXval(true);
        attsel.SelectAttributes(data);
        //    System.out.println(data.toSummaryString());
        //    attsel.selectAttributesCVSplit(data);
        //    attsel.SelectAttributes(data);

        System.out.println(attsel.CrossValidateAttributes());
        //    attsel.SelectAttributes(data);
        //    attsel.selectAttributesCVSplit(data);
        Instances newData = attsel.reduceDimensionality(data);

        int[] indices = attsel.selectedAttributes();
        System.out.println(newData);
        System.out.println("selected attribute indices (starting with 0):\n" + Utils.arrayToString(indices));
    }

    /**
     * takes a dataset as first argument
     *
     * @param args        the commandline arguments
     * @throws Exception  if something goes wrong
     */
    public static void main(String[] args) throws Exception {
        // load data
        System.out.println("\n0. Loading data");
        DataSource source = new DataSource("D:\\ALL\\imdb_grid_size=1000_MIN=50_genres=5.arff");
        Instances data = source.getDataSet();
        data.setClass(data.attribute("Horror"));

        if (data.classIndex() == -1)
            data.setClassIndex(data.numAttributes() - 1);

        //    // 1. meta-classifier
        //    useClassifier(data);
        //
        //    // 2. filter
        //    useFilter(data);

        // 3. low-level
        useLowLevel(data);
    }
}