/*
* 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.
*/
/*
* Resample.java
* Copyright (C) 2002 University of Waikato
*
*/
package weka.filters.supervised.instance;
import weka.filters.*;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.OptionHandler;
import weka.core.Option;
import weka.core.Utils;
import java.util.Random;
import java.util.Enumeration;
import java.util.Vector;
/**
* Produces a random subsample of a dataset. The original dataset must
* fit entirely in memory. The number of instances in the generated
* dataset may be specified. The dataset must have a nominal class
* attribute. If not, use the unsupervised version. The filter can be
* made to maintain the class distribution in the subsample, or to bias
* the class distribution toward a uniform distribution. When used in batch
* mode, subsequent batches are <b>not</b> resampled.
*
* Valid options are:<p>
*
* -S num <br>
* Specify the random number seed (default 1).<p>
*
* -B num <br>
* Specify a bias towards uniform class distribution. 0 = distribution
* in input data, 1 = uniform class distribution (default 0). <p>
*
* -Z percent <br>
* Specify the size of the output dataset, as a percentage of the input
* dataset (default 100). <p>
*
* @author Len Trigg (len@reeltwo.com)
* @version $Revision: 1.1.1.1 $
**/
public class Resample extends Filter implements SupervisedFilter,
OptionHandler {
/** The subsample size, percent of original set, default 100% */
private double m_SampleSizePercent = 100;
/** The random number generator seed */
private int m_RandomSeed = 1;
/** The degree of bias towards uniform (nominal) class distribution */
private double m_BiasToUniformClass = 0;
/** True if the first batch has been done */
private boolean m_FirstBatchDone = false;
/**
* Returns an enumeration describing the available options.
*
* @return an enumeration of all the available options.
*/
public Enumeration listOptions() {
Vector newVector = new Vector(1);
newVector.addElement(new Option(
"\tSpecify the random number seed (default 1)",
"S", 1, "-S <num>"));
newVector.addElement(new Option(
"\tThe size of the output dataset, as a percentage of\n"
+"\tthe input dataset (default 100)",
"Z", 1, "-Z <num>"));
newVector.addElement(new Option(
"\tBias factor towards uniform class distribution.\n"
+"\t0 = distribution in input data -- 1 = uniform distribution.\n"
+"\t(default 0)",
"B", 1, "-B <num>"));
return newVector.elements();
}
/**
* Parses a list of options for this object. Valid options are:<p>
*
* -S num <br>
* Specify the random number seed (default 1).<p>
*
* -B num <br>
* Specify a bias towards uniform class distribution. 0 = distribution
* in input data, 1 = uniform class distribution (default 0). <p>
*
* -Z percent <br>
* Specify the size of the output dataset, as a percentage of the input
* dataset (default 100). <p>
*
* @param options the list of options as an array of strings
* @exception Exception if an option is not supported
*/
public void setOptions(String[] options) throws Exception {
String seedString = Utils.getOption('S', options);
if (seedString.length() != 0) {
setRandomSeed(Integer.parseInt(seedString));
} else {
setRandomSeed(1);
}
String biasString = Utils.getOption('B', options);
if (biasString.length() != 0) {
setBiasToUniformClass(Double.valueOf(biasString).doubleValue());
} else {
setBiasToUniformClass(0);
}
String sizeString = Utils.getOption('Z', options);
if (sizeString.length() != 0) {
setSampleSizePercent(Double.valueOf(sizeString).doubleValue());
} else {
setSampleSizePercent(100);
}
if (getInputFormat() != null) {
setInputFormat(getInputFormat());
}
}
/**
* Gets the current settings of the filter.
*
* @return an array of strings suitable for passing to setOptions
*/
public String [] getOptions() {
String [] options = new String [6];
int current = 0;
options[current++] = "-B";
options[current++] = "" + getBiasToUniformClass();
options[current++] = "-S"; options[current++] = "" + getRandomSeed();
options[current++] = "-Z"; options[current++] = "" + getSampleSizePercent();
while (current < options.length) {
options[current++] = "";
}
return options;
}
/**
* Gets the bias towards a uniform class. A value of 0 leaves the class
* distribution as-is, a value of 1 ensures the class distributions are
* uniform in the output data.
*
* @return the current bias
*/
public double getBiasToUniformClass() {
return m_BiasToUniformClass;
}
/**
* Sets the bias towards a uniform class. A value of 0 leaves the class
* distribution as-is, a value of 1 ensures the class distributions are
* uniform in the output data.
*
* @param newBiasToUniformClass the new bias value, between 0 and 1.
*/
public void setBiasToUniformClass(double newBiasToUniformClass) {
m_BiasToUniformClass = newBiasToUniformClass;
}
/**
* Gets the random number seed.
*
* @return the random number seed.
*/
public int getRandomSeed() {
return m_RandomSeed;
}
/**
* Sets the random number seed.
*
* @param newSeed the new random number seed.
*/
public void setRandomSeed(int newSeed) {
m_RandomSeed = newSeed;
}
/**
* Gets the subsample size as a percentage of the original set.
*
* @return the subsample size
*/
public double getSampleSizePercent() {
return m_SampleSizePercent;
}
/**
* Sets the size of the subsample, as a percentage of the original set.
*
* @param newSampleSizePercent the subsample set size, between 0 and 100.
*/
public void setSampleSizePercent(double newSampleSizePercent) {
m_SampleSizePercent = newSampleSizePercent;
}
/**
* Sets the format of the input instances.
*
* @param instanceInfo an Instances object containing the input
* instance structure (any instances contained in the object are
* ignored - only the structure is required).
* @return true if the outputFormat may be collected immediately
* @exception Exception if the input format can't be set
* successfully
*/
public boolean setInputFormat(Instances instanceInfo)
throws Exception {
if (instanceInfo.classIndex() < 0 || !instanceInfo.classAttribute().isNominal()) {
throw new IllegalArgumentException("Supervised resample requires nominal class");
}
super.setInputFormat(instanceInfo);
setOutputFormat(instanceInfo);
m_FirstBatchDone = false;
return true;
}
/**
* Input an instance for filtering. Filter requires all
* training instances be read before producing output.
*
* @param instance the input instance
* @return true if the filtered instance may now be
* collected with output().
* @exception IllegalStateException if no input structure has been defined
*/
public boolean input(Instance instance) {
if (getInputFormat() == null) {
throw new IllegalStateException("No input instance format defined");
}
if (m_NewBatch) {
resetQueue();
m_NewBatch = false;
}
if (m_FirstBatchDone) {
push(instance);
return true;
} else {
bufferInput(instance);
return false;
}
}
/**
* Signify that this batch of input to the filter is finished.
* If the filter requires all instances prior to filtering,
* output() may now be called to retrieve the filtered instances.
*
* @return true if there are instances pending output
* @exception IllegalStateException if no input structure has been defined
*/
public boolean batchFinished() {
if (getInputFormat() == null) {
throw new IllegalStateException("No input instance format defined");
}
if (!m_FirstBatchDone) {
// Do the subsample, and clear the input instances.
createSubsample();
}
flushInput();
m_NewBatch = true;
m_FirstBatchDone = true;
return (numPendingOutput() != 0);
}
/**
* Creates a subsample of the current set of input instances. The output
* instances are pushed onto the output queue for collection.
*/
private void createSubsample() {
int origSize = getInputFormat().numInstances();
int sampleSize = (int) (origSize * m_SampleSizePercent / 100);
// Subsample that takes class distribution into consideration
// Sort according to class attribute.
getInputFormat().sort(getInputFormat().classIndex());
// Create an index of where each class value starts
int [] classIndices = new int [getInputFormat().numClasses() + 1];
int currentClass = 0;
classIndices[currentClass] = 0;
for (int i = 0; i < getInputFormat().numInstances(); i++) {
Instance current = getInputFormat().instance(i);
if (current.classIsMissing()) {
for (int j = currentClass + 1; j < classIndices.length; j++) {
classIndices[j] = i;
}
break;
} else if (current.classValue() != currentClass) {
for (int j = currentClass + 1; j <= current.classValue(); j++) {
classIndices[j] = i;
}
currentClass = (int) current.classValue();
}
}
if (currentClass <= getInputFormat().numClasses()) {
for (int j = currentClass + 1; j < classIndices.length; j++) {
classIndices[j] = getInputFormat().numInstances();
}
}
int actualClasses = 0;
for (int i = 0; i < classIndices.length - 1; i++) {
if (classIndices[i] != classIndices[i + 1]) {
actualClasses++;
}
}
// Create the new sample
Random random = new Random(m_RandomSeed);
// Convert pending input instances
for(int i = 0; i < sampleSize; i++) {
int index = 0;
if (random.nextDouble() < m_BiasToUniformClass) {
// Pick a random class (of those classes that actually appear)
int cIndex = Math.abs(random.nextInt()) % actualClasses;
for (int j = 0, k = 0; j < classIndices.length - 1; j++) {
if ((classIndices[j] != classIndices[j + 1])
&& (k++ >= cIndex)) {
// Pick a random instance of the designated class
index = classIndices[j]
+ (Math.abs(random.nextInt()) % (classIndices[j + 1]
- classIndices[j]));
break;
}
}
} else {
index = (int) (random.nextDouble() * origSize);
}
push((Instance)getInputFormat().instance(index).copy());
}
}
/**
* Main method for testing this class.
*
* @param argv should contain arguments to the filter:
* use -h for help
*/
public static void main(String [] argv) {
try {
if (Utils.getFlag('b', argv)) {
Filter.batchFilterFile(new Resample(), argv);
} else {
Filter.filterFile(new Resample(), argv);
}
} catch (Exception ex) {
System.out.println(ex.getMessage());
}
}
}