All Downloads are FREE. Search and download functionalities are using the official Maven repository.
Please wait. This can take some minutes ...
Many resources are needed to download a project. Please understand that we have to compensate our server costs. Thank you in advance.
Project price only 1 $
You can buy this project and download/modify it how often you want.
weka.filters.supervised.instance.StratifiedRemoveFolds Maven / Gradle / Ivy
Go to download
The Waikato Environment for Knowledge Analysis (WEKA), a machine
learning workbench. This is the stable version. Apart from bugfixes, this version
does not receive any other updates.
/*
* 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 .
*/
/*
* StratifiedRemoveFolds.java
* Copyright (C) 1999-2012 University of Waikato, Hamilton, New Zealand
*
*/
package weka.filters.supervised.instance;
import java.util.Enumeration;
import java.util.Random;
import java.util.Vector;
import weka.core.Capabilities;
import weka.core.Capabilities.Capability;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.RevisionUtils;
import weka.core.Utils;
import weka.filters.Filter;
import weka.filters.SupervisedFilter;
/**
* This filter takes a dataset and outputs a specified
* fold for cross validation. If you do not want the folds to be stratified use
* the unsupervised version.
*
*
*
* Valid options are:
*
*
*
* -V
* Specifies if inverse of selection is to be output.
*
*
*
* -N <number of folds>
* Specifies number of folds dataset is split into.
* (default 10)
*
*
*
* -F <fold>
* Specifies which fold is selected. (default 1)
*
*
*
* -S <seed>
* Specifies random number seed. (default 0, no randomizing)
*
*
*
*
* @author Eibe Frank ([email protected] )
* @version $Revision: 12037 $
*/
public class StratifiedRemoveFolds extends Filter implements SupervisedFilter,
OptionHandler {
/** for serialization */
static final long serialVersionUID = -7069148179905814324L;
/** Indicates if inverse of selection is to be output. */
private boolean m_Inverse = false;
/** Number of folds to split dataset into */
private int m_NumFolds = 10;
/** Fold to output */
private int m_Fold = 1;
/** Random number seed. */
private long m_Seed = 0;
/**
* Gets an enumeration describing the available options..
*
* @return an enumeration of all the available options.
*/
@Override
public Enumeration listOptions() {
Vector newVector = new Vector (4);
newVector.addElement(new Option(
"\tSpecifies if inverse of selection is to be output.\n", "V", 0, "-V"));
newVector.addElement(new Option(
"\tSpecifies number of folds dataset is split into. \n"
+ "\t(default 10)\n", "N", 1, "-N "));
newVector
.addElement(new Option(
"\tSpecifies which fold is selected. (default 1)\n", "F", 1,
"-F "));
newVector.addElement(new Option(
"\tSpecifies random number seed. (default 0, no randomizing)\n", "S", 1,
"-S "));
return newVector.elements();
}
/**
* Parses a given list of options.
*
*
* Valid options are:
*
*
*
* -V
* Specifies if inverse of selection is to be output.
*
*
*
* -N <number of folds>
* Specifies number of folds dataset is split into.
* (default 10)
*
*
*
* -F <fold>
* Specifies which fold is selected. (default 1)
*
*
*
* -S <seed>
* Specifies random number seed. (default 0, no randomizing)
*
*
*
*
* @param options the list of options as an array of strings
* @throws Exception if an option is not supported
*/
@Override
public void setOptions(String[] options) throws Exception {
setInvertSelection(Utils.getFlag('V', options));
String numFolds = Utils.getOption('N', options);
if (numFolds.length() != 0) {
setNumFolds(Integer.parseInt(numFolds));
} else {
setNumFolds(10);
}
String fold = Utils.getOption('F', options);
if (fold.length() != 0) {
setFold(Integer.parseInt(fold));
} else {
setFold(1);
}
String seed = Utils.getOption('S', options);
if (seed.length() != 0) {
setSeed(Integer.parseInt(seed));
} else {
setSeed(0);
}
if (getInputFormat() != null) {
setInputFormat(getInputFormat());
}
Utils.checkForRemainingOptions(options);
}
/**
* Gets the current settings of the filter.
*
* @return an array of strings suitable for passing to setOptions
*/
@Override
public String[] getOptions() {
Vector options = new Vector();
options.add("-S");
options.add("" + getSeed());
if (getInvertSelection()) {
options.add("-V");
}
options.add("-N");
options.add("" + getNumFolds());
options.add("-F");
options.add("" + getFold());
return options.toArray(new String[0]);
}
/**
* Returns a string describing this filter
*
* @return a description of the filter suitable for displaying in the
* explorer/experimenter gui
*/
public String globalInfo() {
return "This filter takes a dataset and outputs a specified fold for "
+ "cross validation. If you do not want the folds to be stratified "
+ "use the unsupervised version.";
}
/**
* Returns the tip text for this property
*
* @return tip text for this property suitable for displaying in the
* explorer/experimenter gui
*/
public String invertSelectionTipText() {
return "Whether to invert the selection.";
}
/**
* Gets if selection is to be inverted.
*
* @return true if the selection is to be inverted
*/
public boolean getInvertSelection() {
return m_Inverse;
}
/**
* Sets if selection is to be inverted.
*
* @param inverse true if inversion is to be performed
*/
public void setInvertSelection(boolean inverse) {
m_Inverse = inverse;
}
/**
* Returns the tip text for this property
*
* @return tip text for this property suitable for displaying in the
* explorer/experimenter gui
*/
public String numFoldsTipText() {
return "The number of folds to split the dataset into.";
}
/**
* Gets the number of folds in which dataset is to be split into.
*
* @return the number of folds the dataset is to be split into.
*/
public int getNumFolds() {
return m_NumFolds;
}
/**
* Sets the number of folds the dataset is split into. If the number of folds
* is zero, it won't split it into folds.
*
* @param numFolds number of folds dataset is to be split into
* @throws IllegalArgumentException if number of folds is negative
*/
public void setNumFolds(int numFolds) {
if (numFolds < 0) {
throw new IllegalArgumentException(
"Number of folds has to be positive or zero.");
}
m_NumFolds = numFolds;
}
/**
* Returns the tip text for this property
*
* @return tip text for this property suitable for displaying in the
* explorer/experimenter gui
*/
public String foldTipText() {
return "The fold which is selected.";
}
/**
* Gets the fold which is selected.
*
* @return the fold which is selected
*/
public int getFold() {
return m_Fold;
}
/**
* Selects a fold.
*
* @param fold the fold to be selected.
* @throws IllegalArgumentException if fold's index is smaller than 1
*/
public void setFold(int fold) {
if (fold < 1) {
throw new IllegalArgumentException(
"Fold's index has to be greater than 0.");
}
m_Fold = fold;
}
/**
* Returns the tip text for this property
*
* @return tip text for this property suitable for displaying in the
* explorer/experimenter gui
*/
public String seedTipText() {
return "the random number seed for shuffling the dataset. If seed is negative, shuffling will not be performed.";
}
/**
* Gets the random number seed used for shuffling the dataset.
*
* @return the random number seed
*/
public long getSeed() {
return m_Seed;
}
/**
* Sets the random number seed for shuffling the dataset. If seed is negative,
* shuffling won't be performed.
*
* @param seed the random number seed
*/
public void setSeed(long seed) {
m_Seed = seed;
}
/**
* Returns the Capabilities of this filter.
*
* @return the capabilities of this object
* @see Capabilities
*/
@Override
public Capabilities getCapabilities() {
Capabilities result = super.getCapabilities();
result.disableAll();
// attributes
result.enableAllAttributes();
result.enable(Capability.MISSING_VALUES);
// class
result.enableAllClasses();
result.enable(Capability.MISSING_CLASS_VALUES);
return result;
}
/**
* 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 because outputFormat can be collected immediately
* @throws Exception if the input format can't be set successfully
*/
@Override
public boolean setInputFormat(Instances instanceInfo) throws Exception {
if ((m_NumFolds > 0) && (m_NumFolds < m_Fold)) {
throw new IllegalArgumentException("Fold has to be smaller or equal to "
+ "number of folds.");
}
super.setInputFormat(instanceInfo);
setOutputFormat(instanceInfo);
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().
* @throws IllegalStateException if no input structure has been defined
*/
@Override
public boolean input(Instance instance) {
if (getInputFormat() == null) {
throw new IllegalStateException("No input instance format defined");
}
if (m_NewBatch) {
resetQueue();
m_NewBatch = false;
}
if (isFirstBatchDone()) {
push(instance);
return true;
} else {
bufferInput(instance);
return false;
}
}
/**
* Signify that this batch of input to the filter is finished. Output() may
* now be called to retrieve the filtered instances.
*
* @return true if there are instances pending output
* @throws IllegalStateException if no input structure has been defined
*/
@Override
public boolean batchFinished() {
if (getInputFormat() == null) {
throw new IllegalStateException("No input instance format defined");
}
Instances instances;
if (!isFirstBatchDone()) {
if (m_Seed > 0) {
// User has provided a random number seed.
getInputFormat().randomize(new Random(m_Seed));
}
// Select out a fold
getInputFormat().stratify(m_NumFolds);
if (!m_Inverse) {
instances = getInputFormat().testCV(m_NumFolds, m_Fold - 1);
} else {
instances = getInputFormat().trainCV(m_NumFolds, m_Fold - 1);
}
} else {
instances = getInputFormat();
}
flushInput();
for (int i = 0; i < instances.numInstances(); i++) {
push(instances.instance(i), false); // No need to copy instance
}
m_NewBatch = true;
m_FirstBatchDone = true;
return (numPendingOutput() != 0);
}
/**
* Returns the revision string.
*
* @return the revision
*/
@Override
public String getRevision() {
return RevisionUtils.extract("$Revision: 12037 $");
}
/**
* Main method for testing this class.
*
* @param argv should contain arguments to the filter: use -h for help
*/
public static void main(String[] argv) {
runFilter(new StratifiedRemoveFolds(), argv);
}
}