/*
* 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 2 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, write to the Free Software
* Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
*/
/*
* WekaDemo.java
* Copyright (C) 2009 University of Waikato, Hamilton, New Zealand
*
*/
package wekaexamples.classifiers;
import weka.classifiers.AbstractClassifier;
import weka.classifiers.Classifier;
import weka.classifiers.Evaluation;
import weka.core.Instances;
import weka.core.OptionHandler;
import weka.core.Utils;
import weka.filters.Filter;
import java.io.BufferedReader;
import java.io.FileReader;
import java.util.Vector;
/**
* A little demo java program for using WEKA.<br/>
* Check out the Evaluation class for more details.
*
* @author FracPete (fracpete at waikato dot ac dot nz)
* @version $Revision$
* @see Evaluation
*/
public class WekaDemo {
/** the classifier used internally */
protected Classifier m_Classifier = null;
/** the filter to use */
protected Filter m_Filter = null;
/** the training file */
protected String m_TrainingFile = null;
/** the training instances */
protected Instances m_Training = null;
/** for evaluating the classifier */
protected Evaluation m_Evaluation = null;
/**
* initializes the demo
*/
public WekaDemo() {
super();
}
/**
* sets the classifier to use
* @param name the classname of the classifier
* @param options the options for the classifier
*/
public void setClassifier(String name, String[] options) throws Exception {
m_Classifier = AbstractClassifier.forName(name, options);
}
/**
* sets the filter to use
* @param name the classname of the filter
* @param options the options for the filter
*/
public void setFilter(String name, String[] options) throws Exception {
m_Filter = (Filter) Class.forName(name).newInstance();
if (m_Filter instanceof OptionHandler)
((OptionHandler) m_Filter).setOptions(options);
}
/**
* sets the file to use for training
*/
public void setTraining(String name) throws Exception {
m_TrainingFile = name;
m_Training = new Instances(
new BufferedReader(new FileReader(m_TrainingFile)));
m_Training.setClassIndex(m_Training.numAttributes() - 1);
}
/**
* runs 10fold CV over the training file
*/
public void execute() throws Exception {
// run filter
m_Filter.setInputFormat(m_Training);
Instances filtered = Filter.useFilter(m_Training, m_Filter);
// train classifier on complete file for tree
m_Classifier.buildClassifier(filtered);
// 10fold CV with seed=1
m_Evaluation = new Evaluation(filtered);
m_Evaluation.crossValidateModel(
m_Classifier, filtered, 10, m_Training.getRandomNumberGenerator(1));
}
/**
* outputs some data about the classifier
*/
public String toString() {
StringBuffer result;
result = new StringBuffer();
result.append("Weka - Demo\n===========\n\n");
result.append("Classifier...: "
+ Utils.toCommandLine(m_Classifier) + "\n");
if (m_Filter instanceof OptionHandler)
result.append("Filter.......: "
+ m_Filter.getClass().getName() + " "
+ Utils.joinOptions(((OptionHandler) m_Filter).getOptions()) + "\n");
else
result.append("Filter.......: "
+ m_Filter.getClass().getName() + "\n");
result.append("Training file: "
+ m_TrainingFile + "\n");
result.append("\n");
result.append(m_Classifier.toString() + "\n");
result.append(m_Evaluation.toSummaryString() + "\n");
try {
result.append(m_Evaluation.toMatrixString() + "\n");
}
catch (Exception e) {
e.printStackTrace();
}
try {
result.append(m_Evaluation.toClassDetailsString() + "\n");
}
catch (Exception e) {
e.printStackTrace();
}
return result.toString();
}
/**
* returns the usage of the class
*/
public static String usage() {
return
"\nusage:\n " + WekaDemo.class.getName()
+ " CLASSIFIER <classname> [options] \n"
+ " FILTER <classname> [options]\n"
+ " DATASET <trainingfile>\n\n"
+ "e.g., \n"
+ " java -classpath \".:weka.jar\" WekaDemo \n"
+ " CLASSIFIER weka.classifiers.trees.J48 -U \n"
+ " FILTER weka.filters.unsupervised.instance.Randomize \n"
+ " DATASET iris.arff\n";
}
/**
* runs the program, the command line looks like this:<br/>
* WekaDemo CLASSIFIER classname [options]
* FILTER classname [options]
* DATASET filename
* <br/>
* e.g., <br/>
* java -classpath ".:weka.jar" WekaDemo \<br/>
* CLASSIFIER weka.classifiers.trees.J48 -U \<br/>
* FILTER weka.filters.unsupervised.instance.Randomize \<br/>
* DATASET iris.arff<br/>
*/
public static void main(String[] args) throws Exception {
WekaDemo demo;
if (args.length < 6) {
System.out.println(WekaDemo.usage());
System.exit(1);
}
// parse command line
String classifier = "";
String filter = "";
String dataset = "";
Vector classifierOptions = new Vector();
Vector filterOptions = new Vector();
int i = 0;
String current = "";
boolean newPart = false;
do {
// determine part of command line
if (args[i].equals("CLASSIFIER")) {
current = args[i];
i++;
newPart = true;
}
else if (args[i].equals("FILTER")) {
current = args[i];
i++;
newPart = true;
}
else if (args[i].equals("DATASET")) {
current = args[i];
i++;
newPart = true;
}
if (current.equals("CLASSIFIER")) {
if (newPart)
classifier = args[i];
else
classifierOptions.add(args[i]);
}
else if (current.equals("FILTER")) {
if (newPart)
filter = args[i];
else
filterOptions.add(args[i]);
}
else if (current.equals("DATASET")) {
if (newPart)
dataset = args[i];
}
// next parameter
i++;
newPart = false;
}
while (i < args.length);
// everything provided?
if ( classifier.equals("") || filter.equals("") || dataset.equals("") ) {
System.out.println("Not all parameters provided!");
System.out.println(WekaDemo.usage());
System.exit(2);
}
// run
demo = new WekaDemo();
demo.setClassifier(
classifier,
(String[]) classifierOptions.toArray(new String[classifierOptions.size()]));
demo.setFilter(
filter,
(String[]) filterOptions.toArray(new String[filterOptions.size()]));
demo.setTraining(dataset);
demo.execute();
System.out.println(demo.toString());
}
}