RunBestFirstSearch.java Source code

Introduction

Here is the source code for RunBestFirstSearch.java

Source

//
// RunBestFirstSearch.java - A program that run command-line version of Weka to
// perform feature selection using Best-First Search (BFS) as the algorithm and
// the Correlation-based Feature Selection (CFS) as the cost function.
//
//    This file is part of the featsel program
//    Copyright (C) 2017  Marcelo S. Reis
//
//    This program is free software: you can redistribute it and/or modify
//    it under the terms of the GNU General Public License as published by
//    the Free Software Foundation, either version 3 of the License, or
//    (at your option) any later version.
//
//    This program is distributed in the hope that it will be useful,
//    but WITHOUT ANY WARRANTY; without even the implied warranty of
//    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
//    GNU General Public License for more details.
//
//    You should have received a copy of the GNU General Public License
//    along with this program.  If not, see <http://www.gnu.org/licenses/>.

// Requires "weka" program, which can be downloaded through apt tool.

// To compile, call javac with the Weka classpath in your system. Example:
//
// javac -Xlint -classpath ".:/usr/share/java/weka.jar" RunBestFirstSearch.java
//
// To execute, call java with your Weka classpath and also the arguments:
//
// java -classpath "./:/usr/share/java/weka.jar" RunBestFirstSearch file.arff n
//
// where "file.arff" is a sample file in Weka's format and n is the
// number of features contained in that file.

// Package weka.attributeSelection.
//
import weka.attributeSelection.*;
import weka.core.*;
import weka.core.converters.ConverterUtils.*;

import java.lang.String;
import java.util.*;

public class RunBestFirstSearch {
    protected static void runAttributeSelection(Instances data, int n) throws Exception {
        AttributeSelection attsel = new AttributeSelection();
        CfsSubsetEval cost_function = new CfsSubsetEval(); // CFS cost function.
        BestFirst algorithm = new BestFirst(); // BFS algorithm.

        cost_function.buildEvaluator(data);

        algorithm.setLookupCacheSize(n);

        // BFS with forward direction and terminating search after five
        // non-improving nodes.
        //
        String[] parameters = { "-D 1", "-N 5" };

        algorithm.setOptions(parameters);

        cost_function.setLocallyPredictive(false);

        attsel.setEvaluator(cost_function);
        attsel.setSearch(algorithm);

        attsel.SelectAttributes(data);

        int[] indices = attsel.selectedAttributes();

        System.out.println("Selected features:\n" + Utils.arrayToString(indices));
    }

    public static void main(String[] arg) throws Exception {
        // Load data.
        //
        System.out.println("\nLoading sample file...");
        DataSource source = new DataSource(arg[0]);
        Instances data = source.getDataSet();

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

        int n = Integer.parseInt(arg[1]);

        System.out.println("Instance with " + n + " features!");
        System.out.println("\nRunning BFS algorithm with CFS cost function...");

        // Run feature selection algorithm BFS using CFS cost function.
        //
        runAttributeSelection(data, n);
    }

}

// End of program.