/*
* 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.
*/
/*
* SpreadSubsample.java
* Copyright (C) 2002 University of Waikato
*
*/
package weka.filters.supervised.instance;
import weka.filters.*;
import java.util.Enumeration;
import java.util.Hashtable;
import java.util.Random;
import java.util.Vector;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.Utils;
import weka.core.UnsupportedClassTypeException;
/**
* Produces a random subsample of a dataset. The original dataset must
* fit entirely in memory. This filter allows you to specify the maximum
* "spread" between the rarest and most common class. For example, you may
* specify that there be at most a 2:1 difference in class frequencies.
* 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>
*
* -M num <br>
* The maximum class distribution spread. <br>
* 0 = no maximum spread, 1 = uniform distribution, 10 = allow at most a
* 10:1 ratio between the classes (default 0)
* <p>
*
* -X num <br>
* The maximum count for any class value. <br>
* (default 0 = unlimited)
* <p>
*
* -W <br>
* Adjust weights so that total weight per class is maintained. Individual
* instance weighting is not preserved. (default no weights adjustment)
* <p>
*
* @author Stuart Inglis (stuart@reeltwo.com)
* @version $Revision: 1.1.1.1 $
**/
public class SpreadSubsample extends Filter implements SupervisedFilter,
OptionHandler {
/** The random number generator seed */
private int m_RandomSeed = 1;
/** The maximum count of any class */
private int m_MaxCount;
/** True if the first batch has been done */
private boolean m_FirstBatchDone = false;
/** True if the first batch has been done */
private double m_DistributionSpread = 0;
/**
* True if instance weights will be adjusted to maintain
* total weight per class.
*/
private boolean m_AdjustWeights = false;
/**
* Returns true if instance weights will be adjusted to maintain
* total weight per class.
*
* @return true if instance weights will be adjusted to maintain
* total weight per class.
*/
public boolean getAdjustWeights() {
return m_AdjustWeights;
}
/**
* Sets whether the instance weights will be adjusted to maintain
* total weight per class.
*
* @param newAdjustWeights
*/
public void setAdjustWeights(boolean newAdjustWeights) {
m_AdjustWeights = newAdjustWeights;
}
/**
* Returns an enumeration describing the available options.
*
* @return an enumeration of all the available options.
*/
public Enumeration listOptions() {
Vector newVector = new Vector(4);
newVector.addElement(new Option(
"\tSpecify the random number seed (default 1)",
"S", 1, "-S <num>"));
newVector.addElement(new Option(
"\tThe maximum class distribution spread.\n"
+"\t0 = no maximum spread, 1 = uniform distribution, 10 = allow at most\n"
+"\ta 10:1 ratio between the classes (default 0)",
"M", 1, "-M <num>"));
newVector.addElement(new Option(
"\tAdjust weights so that total weight per class is maintained.\n"
+"\tIndividual instance weighting is not preserved. (default no\n"
+"\tweights adjustment",
"W", 0, "-W"));
newVector.addElement(new Option(
"\tThe maximum count for any class value (default 0 = unlimited).\n",
"X", 0, "-X <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>
*
* -M num <br>
* The maximum class distribution spread. <br>
* 0 = no maximum spread, 1 = uniform distribution, 10 = allow at most a
* 10:1 ratio between the classes (default 0)
* <p>
*
* -X num <br>
* The maximum count for any class value. <br>
* (default 0 = unlimited)
* <p>
*
* -W <br>
* Adjust weights so that total weight per class is maintained. Individual
* instance weighting is not preserved. (default no weights adjustment)
* <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 maxString = Utils.getOption('M', options);
if (maxString.length() != 0) {
setDistributionSpread(Double.valueOf(maxString).doubleValue());
} else {
setDistributionSpread(0);
}
String maxCount = Utils.getOption('X', options);
if (maxCount.length() != 0) {
setMaxCount(Double.valueOf(maxCount).doubleValue());
} else {
setMaxCount(0);
}
setAdjustWeights(Utils.getFlag('W', options));
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 [7];
int current = 0;
options[current++] = "-M";
options[current++] = "" + getDistributionSpread();
options[current++] = "-X";
options[current++] = "" + getMaxCount();
options[current++] = "-S";
options[current++] = "" + getRandomSeed();
if (getAdjustWeights()) {
options[current++] = "-W";
}
while (current < options.length) {
options[current++] = "";
}
return options;
}
/**
* Sets the value for the distribution spread
*
* @param spread the new distribution spread
*/
public void setDistributionSpread(double spread) {
m_DistributionSpread = spread;
}
/**
* Gets the value for the distribution spread
*
* @return the distribution spread
*/
public double getDistributionSpread() {
return m_DistributionSpread;
}
/**
* Sets the value for the max count
*
* @param spread the new max count
*/
public void setMaxCount(double maxcount) {
m_MaxCount = (int)maxcount;
}
/**
* Gets the value for the max count
*
* @return the max count
*/
public double getMaxCount() {
return m_MaxCount;
}
/**
* 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;
}
/**
* 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 UnassignedClassException if no class attribute has been set.
* @exception UnsupportedClassTypeException if the class attribute
* is not nominal.
*/
public boolean setInputFormat(Instances instanceInfo)
throws Exception {
super.setInputFormat(instanceInfo);
if (instanceInfo.classAttribute().isNominal() == false) {
throw new UnsupportedClassTypeException("The class attribute must be nominal.");
}
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 classI = getInputFormat().classIndex();
// Sort according to class attribute.
getInputFormat().sort(classI);
// Determine where each class starts in the sorted dataset
int [] classIndices = getClassIndices();
// Get the existing class distribution
int [] counts = new int [getInputFormat().numClasses()];
double [] weights = new double [getInputFormat().numClasses()];
int min = -1;
for (int i = 0; i < getInputFormat().numInstances(); i++) {
Instance current = getInputFormat().instance(i);
if (current.classIsMissing() == false) {
counts[(int)current.classValue()]++;
weights[(int)current.classValue()]+= current.weight();
}
}
// Convert from total weight to average weight
for (int i = 0; i < counts.length; i++) {
if (counts[i] > 0) {
weights[i] = weights[i] / counts[i];
}
/*
System.err.println("Class:" + i + " " + getInputFormat().classAttribute().value(i)
+ " Count:" + counts[i]
+ " Total:" + weights[i] * counts[i]
+ " Avg:" + weights[i]);
*/
}
// find the class with the minimum number of instances
for (int i = 0; i < counts.length; i++) {
if ( (min < 0) && (counts[i] > 0) ) {
min = counts[i];
} else if ((counts[i] < min) && (counts[i] > 0)) {
min = counts[i];
}
}
if (min < 0) {
System.err.println("SpreadSubsample: *warning* none of the classes have any values in them.");
return;
}
// determine the new distribution
int [] new_counts = new int [getInputFormat().numClasses()];
for (int i = 0; i < counts.length; i++) {
new_counts[i] = (int)Math.abs(Math.min(counts[i],
min * m_DistributionSpread));
if (m_DistributionSpread == 0) {
new_counts[i] = counts[i];
}
if (m_MaxCount > 0) {
new_counts[i] = Math.min(new_counts[i], m_MaxCount);
}
}
// Sample without replacement
Random random = new Random(m_RandomSeed);
Hashtable t = new Hashtable();
for (int j = 0; j < new_counts.length; j++) {
double newWeight = 1.0;
if (m_AdjustWeights && (new_counts[j] > 0)) {
newWeight = weights[j] * counts[j] / new_counts[j];
/*
System.err.println("Class:" + j + " " + getInputFormat().classAttribute().value(j)
+ " Count:" + counts[j]
+ " Total:" + weights[j] * counts[j]
+ " Avg:" + weights[j]
+ " NewCount:" + new_counts[j]
+ " NewAvg:" + newWeight);
*/
}
for (int k = 0; k < new_counts[j]; k++) {
boolean ok = false;
do {
int index = classIndices[j] + (Math.abs(random.nextInt())
% (classIndices[j + 1] - classIndices[j])) ;
// Have we used this instance before?
if (t.get("" + index) == null) {
// if not, add it to the hashtable and use it
t.put("" + index, "");
ok = true;
if(index >= 0) {
Instance newInst = (Instance)getInputFormat().instance(index).copy();
if (m_AdjustWeights) {
newInst.setWeight(newWeight);
}
push(newInst);
}
}
} while (!ok);
}
}
}
/**
* Creates an index containing the position where each class starts in
* the getInputFormat(). m_InputFormat must be sorted on the class attribute.
*/
private int []getClassIndices() {
// 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();
}
}
return classIndices;
}
/**
* 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 SpreadSubsample(), argv);
} else {
Filter.filterFile(new SpreadSubsample(), argv);
}
} catch (Exception ex) {
ex.printStackTrace();
System.out.println(ex.getMessage());
}
}
}