/*
* 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/>.
*/
/*
* RemoveMisclassified.java
* Copyright (C) 2002-2012 University of Waikato, Hamilton, New Zealand
*
*/
package weka.filters.unsupervised.instance;
import java.util.Enumeration;
import java.util.Vector;
import weka.classifiers.AbstractClassifier;
import weka.classifiers.Classifier;
import weka.core.Capabilities;
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.UnsupervisedFilter;
/**
<!-- globalinfo-start -->
* A filter that removes instances which are incorrectly classified. Useful for removing outliers.
* <p/>
<!-- globalinfo-end -->
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -W <classifier specification>
* Full class name of classifier to use, followed
* by scheme options. eg:
* "weka.classifiers.bayes.NaiveBayes -D"
* (default: weka.classifiers.rules.ZeroR)</pre>
*
* <pre> -C <class index>
* Attribute on which misclassifications are based.
* If < 0 will use any current set class or default to the last attribute.</pre>
*
* <pre> -F <number of folds>
* The number of folds to use for cross-validation cleansing.
* (<2 = no cross-validation - default).</pre>
*
* <pre> -T <threshold>
* Threshold for the max error when predicting numeric class.
* (Value should be >= 0, default = 0.1).</pre>
*
* <pre> -I
* The maximum number of cleansing iterations to perform.
* (<1 = until fully cleansed - default)</pre>
*
* <pre> -V
* Invert the match so that correctly classified instances are discarded.
* </pre>
*
<!-- options-end -->
*
* @author Richard Kirkby (rkirkby@cs.waikato.ac.nz)
* @author Malcolm Ware (mfw4@cs.waikato.ac.nz)
* @version $Revision: 8034 $
*/
public class RemoveMisclassified
extends Filter
implements UnsupervisedFilter, OptionHandler {
/** for serialization */
static final long serialVersionUID = 5469157004717663171L;
/** The classifier used to do the cleansing */
protected Classifier m_cleansingClassifier = new weka.classifiers.rules.ZeroR();
/** The attribute to treat as the class for purposes of cleansing. */
protected int m_classIndex = -1;
/** The number of cross validation folds to perform (<2 = no cross validation) */
protected int m_numOfCrossValidationFolds = 0;
/** The maximum number of cleansing iterations to perform (<1 = until fully cleansed) */
protected int m_numOfCleansingIterations = 0;
/** The threshold for deciding when a numeric value is correctly classified */
protected double m_numericClassifyThreshold = 0.1;
/** Whether to invert the match so the correctly classified instances are discarded */
protected boolean m_invertMatching = false;
/** Have we processed the first batch (i.e. training data)? */
protected boolean m_firstBatchFinished = false;
/**
* Returns the Capabilities of this filter.
*
* @return the capabilities of this object
* @see Capabilities
*/
public Capabilities getCapabilities() {
Capabilities result;
if (getClassifier() == null) {
result = super.getCapabilities();
result.disableAll();
} else {
result = getClassifier().getCapabilities();
}
result.setMinimumNumberInstances(0);
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 if the outputFormat may be collected immediately
* @throws Exception if the inputFormat can't be set successfully
*/
public boolean setInputFormat(Instances instanceInfo) throws Exception {
super.setInputFormat(instanceInfo);
setOutputFormat(instanceInfo);
m_firstBatchFinished = false;
return true;
}
/**
* Cleanses the data based on misclassifications when used training data.
*
* @param data the data to train with and cleanse
* @return the cleansed data
* @throws Exception if something goes wrong
*/
private Instances cleanseTrain(Instances data) throws Exception {
Instance inst;
Instances buildSet = new Instances(data);
Instances temp = new Instances(data, data.numInstances());
Instances inverseSet = new Instances(data, data.numInstances());
int count = 0;
double ans;
int iterations = 0;
int classIndex = m_classIndex;
if (classIndex < 0) classIndex = data.classIndex();
if (classIndex < 0) classIndex = data.numAttributes()-1;
// loop until perfect
while(count != buildSet.numInstances()) {
// check if hit maximum number of iterations
iterations++;
if (m_numOfCleansingIterations > 0 && iterations > m_numOfCleansingIterations) break;
// build classifier
count = buildSet.numInstances();
buildSet.setClassIndex(classIndex);
m_cleansingClassifier.buildClassifier(buildSet);
temp = new Instances(buildSet, buildSet.numInstances());
// test on training data
for (int i = 0; i < buildSet.numInstances(); i++) {
inst = buildSet.instance(i);
ans = m_cleansingClassifier.classifyInstance(inst);
if (buildSet.classAttribute().isNumeric()) {
if (ans >= inst.classValue() - m_numericClassifyThreshold &&
ans <= inst.classValue() + m_numericClassifyThreshold) {
temp.add(inst);
} else if (m_invertMatching) {
inverseSet.add(inst);
}
}
else { //class is nominal
if (ans == inst.classValue()) {
temp.add(inst);
} else if (m_invertMatching) {
inverseSet.add(inst);
}
}
}
buildSet = temp;
}
if (m_invertMatching) {
inverseSet.setClassIndex(data.classIndex());
return inverseSet;
}
else {
buildSet.setClassIndex(data.classIndex());
return buildSet;
}
}
/**
* Cleanses the data based on misclassifications when performing cross-validation.
*
* @param data the data to train with and cleanse
* @return the cleansed data
* @throws Exception if something goes wrong
*/
private Instances cleanseCross(Instances data) throws Exception {
Instance inst;
Instances crossSet = new Instances(data);
Instances temp = new Instances(data, data.numInstances());
Instances inverseSet = new Instances(data, data.numInstances());
int count = 0;
double ans;
int iterations = 0;
int classIndex = m_classIndex;
if (classIndex < 0) classIndex = data.classIndex();
if (classIndex < 0) classIndex = data.numAttributes()-1;
// loop until perfect
while (count != crossSet.numInstances() &&
crossSet.numInstances() >= m_numOfCrossValidationFolds) {
count = crossSet.numInstances();
// check if hit maximum number of iterations
iterations++;
if (m_numOfCleansingIterations > 0 && iterations > m_numOfCleansingIterations) break;
crossSet.setClassIndex(classIndex);
if (crossSet.classAttribute().isNominal()) {
crossSet.stratify(m_numOfCrossValidationFolds);
}
// do the folds
temp = new Instances(crossSet, crossSet.numInstances());
for (int fold = 0; fold < m_numOfCrossValidationFolds; fold++) {
Instances train = crossSet.trainCV(m_numOfCrossValidationFolds, fold);
m_cleansingClassifier.buildClassifier(train);
Instances test = crossSet.testCV(m_numOfCrossValidationFolds, fold);
//now test
for (int i = 0; i < test.numInstances(); i++) {
inst = test.instance(i);
ans = m_cleansingClassifier.classifyInstance(inst);
if (crossSet.classAttribute().isNumeric()) {
if (ans >= inst.classValue() - m_numericClassifyThreshold &&
ans <= inst.classValue() + m_numericClassifyThreshold) {
temp.add(inst);
} else if (m_invertMatching) {
inverseSet.add(inst);
}
}
else { //class is nominal
if (ans == inst.classValue()) {
temp.add(inst);
} else if (m_invertMatching) {
inverseSet.add(inst);
}
}
}
}
crossSet = temp;
}
if (m_invertMatching) {
inverseSet.setClassIndex(data.classIndex());
return inverseSet;
}
else {
crossSet.setClassIndex(data.classIndex());
return crossSet;
}
}
/**
* Input an instance for filtering.
*
* @param instance the input instance
* @return true if the filtered instance may now be
* collected with output().
* @throws NullPointerException if the input format has not been
* defined.
* @throws Exception if the input instance was not of the correct
* format or if there was a problem with the filtering.
*/
public boolean input(Instance instance) throws Exception {
if (inputFormatPeek() == null) {
throw new NullPointerException("No input instance format defined");
}
if (m_NewBatch) {
resetQueue();
m_NewBatch = false;
}
if (m_firstBatchFinished) {
push(instance);
return true;
} else {
bufferInput(instance);
return false;
}
}
/**
* Signify that this batch of input to the filter is finished.
*
* @return true if there are instances pending output
* @throws IllegalStateException if no input structure has been defined
*/
public boolean batchFinished() throws Exception {
if (getInputFormat() == null) {
throw new IllegalStateException("No input instance format defined");
}
if (!m_firstBatchFinished) {
Instances filtered;
if (m_numOfCrossValidationFolds < 2) {
filtered = cleanseTrain(getInputFormat());
} else {
filtered = cleanseCross(getInputFormat());
}
for (int i=0; i<filtered.numInstances(); i++) {
push(filtered.instance(i));
}
m_firstBatchFinished = true;
flushInput();
}
m_NewBatch = true;
return (numPendingOutput() != 0);
}
/**
* Returns an enumeration describing the available options.
*
* @return an enumeration of all the available options.
*/
public Enumeration listOptions() {
Vector newVector = new Vector(6);
newVector.addElement(new Option(
"\tFull class name of classifier to use, followed\n"
+ "\tby scheme options. eg:\n"
+ "\t\t\"weka.classifiers.bayes.NaiveBayes -D\"\n"
+ "\t(default: weka.classifiers.rules.ZeroR)",
"W", 1, "-W <classifier specification>"));
newVector.addElement(new Option(
"\tAttribute on which misclassifications are based.\n"
+ "\tIf < 0 will use any current set class or default to the last attribute.",
"C", 1, "-C <class index>"));
newVector.addElement(new Option(
"\tThe number of folds to use for cross-validation cleansing.\n"
+"\t(<2 = no cross-validation - default).",
"F", 1, "-F <number of folds>"));
newVector.addElement(new Option(
"\tThreshold for the max error when predicting numeric class.\n"
+"\t(Value should be >= 0, default = 0.1).",
"T", 1, "-T <threshold>"));
newVector.addElement(new Option(
"\tThe maximum number of cleansing iterations to perform.\n"
+"\t(<1 = until fully cleansed - default)",
"I", 1,"-I"));
newVector.addElement(new Option(
"\tInvert the match so that correctly classified instances are discarded.\n",
"V", 0,"-V"));
return newVector.elements();
}
/**
* Parses a given list of options. <p/>
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -W <classifier specification>
* Full class name of classifier to use, followed
* by scheme options. eg:
* "weka.classifiers.bayes.NaiveBayes -D"
* (default: weka.classifiers.rules.ZeroR)</pre>
*
* <pre> -C <class index>
* Attribute on which misclassifications are based.
* If < 0 will use any current set class or default to the last attribute.</pre>
*
* <pre> -F <number of folds>
* The number of folds to use for cross-validation cleansing.
* (<2 = no cross-validation - default).</pre>
*
* <pre> -T <threshold>
* Threshold for the max error when predicting numeric class.
* (Value should be >= 0, default = 0.1).</pre>
*
* <pre> -I
* The maximum number of cleansing iterations to perform.
* (<1 = until fully cleansed - default)</pre>
*
* <pre> -V
* Invert the match so that correctly classified instances are discarded.
* </pre>
*
<!-- options-end -->
*
* @param options the list of options as an array of strings
* @throws Exception if an option is not supported
*/
public void setOptions(String[] options) throws Exception {
String classifierString = Utils.getOption('W', options);
if (classifierString.length() == 0)
classifierString = weka.classifiers.rules.ZeroR.class.getName();
String[] classifierSpec = Utils.splitOptions(classifierString);
if (classifierSpec.length == 0) {
throw new Exception("Invalid classifier specification string");
}
String classifierName = classifierSpec[0];
classifierSpec[0] = "";
setClassifier(AbstractClassifier.forName(classifierName, classifierSpec));
String cString = Utils.getOption('C', options);
if (cString.length() != 0) {
setClassIndex((new Double(cString)).intValue());
} else {
setClassIndex(-1);
}
String fString = Utils.getOption('F', options);
if (fString.length() != 0) {
setNumFolds((new Double(fString)).intValue());
} else {
setNumFolds(0);
}
String tString = Utils.getOption('T', options);
if (tString.length() != 0) {
setThreshold((new Double(tString)).doubleValue());
} else {
setThreshold(0.1);
}
String iString = Utils.getOption('I', options);
if (iString.length() != 0) {
setMaxIterations((new Double(iString)).intValue());
} else {
setMaxIterations(0);
}
if (Utils.getFlag('V', options)) {
setInvert(true);
} else {
setInvert(false);
}
Utils.checkForRemainingOptions(options);
}
/**
* Gets the current settings of the filter.
*
* @return an array of strings suitable for passing to setOptions
*/
public String [] getOptions() {
String [] options = new String [15];
int current = 0;
options[current++] = "-W"; options[current++] = "" + getClassifierSpec();
options[current++] = "-C"; options[current++] = "" + getClassIndex();
options[current++] = "-F"; options[current++] = "" + getNumFolds();
options[current++] = "-T"; options[current++] = "" + getThreshold();
options[current++] = "-I"; options[current++] = "" + getMaxIterations();
if (getInvert()) {
options[current++] = "-V";
}
while (current < options.length) {
options[current++] = "";
}
return options;
}
/**
* Returns a string describing this filter
*
* @return a description of the filter suitable for
* displaying in the explorer/experimenter gui
*/
public String globalInfo() {
return
"A filter that removes instances which are incorrectly classified. "
+ "Useful for removing outliers.";
}
/**
* Returns the tip text for this property
*
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String classifierTipText() {
return "The classifier upon which to base the misclassifications.";
}
/**
* Sets the classifier to classify instances with.
*
* @param classifier The classifier to be used (with its options set).
*/
public void setClassifier(Classifier classifier) {
m_cleansingClassifier = classifier;
}
/**
* Gets the classifier used by the filter.
*
* @return The classifier to be used.
*/
public Classifier getClassifier() {
return m_cleansingClassifier;
}
/**
* Gets the classifier specification string, which contains the class name of
* the classifier and any options to the classifier.
*
* @return the classifier string.
*/
protected String getClassifierSpec() {
Classifier c = getClassifier();
if (c instanceof OptionHandler) {
return c.getClass().getName() + " "
+ Utils.joinOptions(((OptionHandler)c).getOptions());
}
return c.getClass().getName();
}
/**
* Returns the tip text for this property
*
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String classIndexTipText() {
return "Index of the class upon which to base the misclassifications. "
+ "If < 0 will use any current set class or default to the last attribute.";
}
/**
* Sets the attribute on which misclassifications are based.
* If < 0 will use any current set class or default to the last attribute.
*
* @param classIndex the class index.
*/
public void setClassIndex(int classIndex) {
m_classIndex = classIndex;
}
/**
* Gets the attribute on which misclassifications are based.
*
* @return the class index.
*/
public int getClassIndex() {
return m_classIndex;
}
/**
* 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 cross-validation folds to use. If < 2 then no cross-validation will be performed.";
}
/**
* Sets the number of cross-validation folds to use
* - < 2 means no cross-validation.
*
* @param numOfFolds the number of folds.
*/
public void setNumFolds(int numOfFolds) {
m_numOfCrossValidationFolds = numOfFolds;
}
/**
* Gets the number of cross-validation folds used by the filter.
*
* @return the number of folds.
*/
public int getNumFolds() {
return m_numOfCrossValidationFolds;
}
/**
* Returns the tip text for this property
*
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String thresholdTipText() {
return "Threshold for the max allowable error when predicting a numeric class. Should be >= 0.";
}
/**
* Sets the threshold for the max error when predicting a numeric class.
* The value should be >= 0.
*
* @param threshold the numeric theshold.
*/
public void setThreshold(double threshold) {
m_numericClassifyThreshold = threshold;
}
/**
* Gets the threshold for the max error when predicting a numeric class.
*
* @return the numeric threshold.
*/
public double getThreshold() {
return m_numericClassifyThreshold;
}
/**
* Returns the tip text for this property
*
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String maxIterationsTipText() {
return "The maximum number of iterations to perform. < 1 means filter will go until fully cleansed.";
}
/**
* Sets the maximum number of cleansing iterations to perform
* - < 1 means go until fully cleansed
*
* @param iterations the maximum number of iterations.
*/
public void setMaxIterations(int iterations) {
m_numOfCleansingIterations = iterations;
}
/**
* Gets the maximum number of cleansing iterations performed
*
* @return the maximum number of iterations.
*/
public int getMaxIterations() {
return m_numOfCleansingIterations;
}
/**
* Returns the tip text for this property
*
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String invertTipText() {
return "Whether or not to invert the selection. If true, correctly classified instances will be discarded.";
}
/**
* Set whether selection is inverted.
*
* @param invert whether or not to invert selection.
*/
public void setInvert(boolean invert) {
m_invertMatching = invert;
}
/**
* Get whether selection is inverted.
*
* @return whether or not selection is inverted.
*/
public boolean getInvert() {
return m_invertMatching;
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 8034 $");
}
/**
* 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 RemoveMisclassified(), argv);
}
}