* @phdthesis{Kohavi1995,
* address = {Department of Computer Science, Stanford University},
* author = {R. Kohavi},
* school = {Stanford University},
* title = {Wrappers for Performance Enhancement and Oblivious Decision Graphs},
* year = {1995}
* }
*
*
*
* Valid options are:
*
* -X <number of folds> * Number of folds used for cross validation (default 10).* *
-P <classifier parameter> * Classifier parameter options. * eg: "N 1 5 10" Sets an optimisation parameter for the * classifier with name -N, with lower bound 1, upper bound * 5, and 10 optimisation steps. The upper bound may be the * character 'A' or 'I' to substitute the number of * attributes or instances in the training data, * respectively. This parameter may be supplied more than * once to optimise over several classifier options * simultaneously.* *
-S <num> * Random number seed. * (default 1)* *
-D * If set, classifier is run in debug mode and * may output additional info to the console* *
-W * Full name of base classifier. * (default: weka.classifiers.rules.ZeroR)* *
* Options specific to classifier weka.classifiers.rules.ZeroR: ** *
-D * If set, classifier is run in debug mode and * may output additional info to the console* * * Options after -- are passed to the designated sub-classifier.
*
* @author Len Trigg (trigg@cs.waikato.ac.nz)
* @version $Revision: 5928 $
*/
public class CVParameterSelection
extends RandomizableSingleClassifierEnhancer
implements Drawable, Summarizable, TechnicalInformationHandler {
/** for serialization */
static final long serialVersionUID = -6529603380876641265L;
/**
* A data structure to hold values associated with a single
* cross-validation search parameter
*/
protected class CVParameter
implements Serializable, RevisionHandler {
/** for serialization */
static final long serialVersionUID = -4668812017709421953L;
/** Char used to identify the option of interest */
private char m_ParamChar;
/** Lower bound for the CV search */
private double m_Lower;
/** Upper bound for the CV search */
private double m_Upper;
/** Number of steps during the search */
private double m_Steps;
/** The parameter value with the best performance */
private double m_ParamValue;
/** True if the parameter should be added at the end of the argument list */
private boolean m_AddAtEnd;
/** True if the parameter should be rounded to an integer */
private boolean m_RoundParam;
/**
* Constructs a CVParameter.
*
* @param param the parameter definition
* @throws Exception if construction of CVParameter fails
*/
public CVParameter(String param) throws Exception {
// Tokenize the string into it's parts
StreamTokenizer st = new StreamTokenizer(new StringReader(param));
if (st.nextToken() != StreamTokenizer.TT_WORD) {
throw new Exception("CVParameter " + param
+ ": Character parameter identifier expected");
}
m_ParamChar = st.sval.charAt(0);
if (st.nextToken() != StreamTokenizer.TT_NUMBER) {
throw new Exception("CVParameter " + param
+ ": Numeric lower bound expected");
}
m_Lower = st.nval;
if (st.nextToken() == StreamTokenizer.TT_NUMBER) {
m_Upper = st.nval;
if (m_Upper < m_Lower) {
throw new Exception("CVParameter " + param
+ ": Upper bound is less than lower bound");
}
} else if (st.ttype == StreamTokenizer.TT_WORD) {
if (st.sval.toUpperCase().charAt(0) == 'A') {
m_Upper = m_Lower - 1;
} else if (st.sval.toUpperCase().charAt(0) == 'I') {
m_Upper = m_Lower - 2;
} else {
throw new Exception("CVParameter " + param
+ ": Upper bound must be numeric, or 'A' or 'N'");
}
} else {
throw new Exception("CVParameter " + param
+ ": Upper bound must be numeric, or 'A' or 'N'");
}
if (st.nextToken() != StreamTokenizer.TT_NUMBER) {
throw new Exception("CVParameter " + param
+ ": Numeric number of steps expected");
}
m_Steps = st.nval;
if (st.nextToken() == StreamTokenizer.TT_WORD) {
if (st.sval.toUpperCase().charAt(0) == 'R') {
m_RoundParam = true;
}
}
}
/**
* Returns a CVParameter as a string.
*
* @return the CVParameter as string
*/
public String toString() {
String result = m_ParamChar + " " + m_Lower + " ";
switch ((int)(m_Lower - m_Upper + 0.5)) {
case 1:
result += "A";
break;
case 2:
result += "I";
break;
default:
result += m_Upper;
break;
}
result += " " + m_Steps;
if (m_RoundParam) {
result += " R";
}
return result;
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 5928 $");
}
}
/**
* The base classifier options (not including those being set
* by cross-validation)
*/
protected String [] m_ClassifierOptions;
/** The set of all classifier options as determined by cross-validation */
protected String [] m_BestClassifierOptions;
/** The set of all options at initialization time. So that getOptions
can return this. */
protected String [] m_InitOptions;
/** The cross-validated performance of the best options */
protected double m_BestPerformance;
/** The set of parameters to cross-validate over */
protected FastVector m_CVParams = new FastVector();
/** The number of attributes in the data */
protected int m_NumAttributes;
/** The number of instances in a training fold */
protected int m_TrainFoldSize;
/** The number of folds used in cross-validation */
protected int m_NumFolds = 10;
/**
* Create the options array to pass to the classifier. The parameter
* values and positions are taken from m_ClassifierOptions and
* m_CVParams.
*
* @return the options array
*/
protected String [] createOptions() {
String [] options = new String [m_ClassifierOptions.length
+ 2 * m_CVParams.size()];
int start = 0, end = options.length;
// Add the cross-validation parameters and their values
for (int i = 0; i < m_CVParams.size(); i++) {
CVParameter cvParam = (CVParameter)m_CVParams.elementAt(i);
double paramValue = cvParam.m_ParamValue;
if (cvParam.m_RoundParam) {
// paramValue = (double)((int) (paramValue + 0.5));
paramValue = Math.rint(paramValue);
}
if (cvParam.m_AddAtEnd) {
options[--end] = "" +
Utils.doubleToString(paramValue,4);
options[--end] = "-" + cvParam.m_ParamChar;
} else {
options[start++] = "-" + cvParam.m_ParamChar;
options[start++] = ""
+ Utils.doubleToString(paramValue,4);
}
}
// Add the static parameters
System.arraycopy(m_ClassifierOptions, 0,
options, start,
m_ClassifierOptions.length);
return options;
}
/**
* Finds the best parameter combination. (recursive for each parameter
* being optimised).
*
* @param depth the index of the parameter to be optimised at this level
* @param trainData the data the search is based on
* @param random a random number generator
* @throws Exception if an error occurs
*/
protected void findParamsByCrossValidation(int depth, Instances trainData,
Random random)
throws Exception {
if (depth < m_CVParams.size()) {
CVParameter cvParam = (CVParameter)m_CVParams.elementAt(depth);
double upper;
switch ((int)(cvParam.m_Lower - cvParam.m_Upper + 0.5)) {
case 1:
upper = m_NumAttributes;
break;
case 2:
upper = m_TrainFoldSize;
break;
default:
upper = cvParam.m_Upper;
break;
}
double increment = (upper - cvParam.m_Lower) / (cvParam.m_Steps - 1);
for(cvParam.m_ParamValue = cvParam.m_Lower;
cvParam.m_ParamValue <= upper;
cvParam.m_ParamValue += increment) {
findParamsByCrossValidation(depth + 1, trainData, random);
}
} else {
Evaluation evaluation = new Evaluation(trainData);
// Set the classifier options
String [] options = createOptions();
if (m_Debug) {
System.err.print("Setting options for "
+ m_Classifier.getClass().getName() + ":");
for (int i = 0; i < options.length; i++) {
System.err.print(" " + options[i]);
}
System.err.println("");
}
((OptionHandler)m_Classifier).setOptions(options);
for (int j = 0; j < m_NumFolds; j++) {
// We want to randomize the data the same way for every
// learning scheme.
Instances train = trainData.trainCV(m_NumFolds, j, new Random(1));
Instances test = trainData.testCV(m_NumFolds, j);
m_Classifier.buildClassifier(train);
evaluation.setPriors(train);
evaluation.evaluateModel(m_Classifier, test);
}
double error = evaluation.errorRate();
if (m_Debug) {
System.err.println("Cross-validated error rate: "
+ Utils.doubleToString(error, 6, 4));
}
if ((m_BestPerformance == -99) || (error < m_BestPerformance)) {
m_BestPerformance = error;
m_BestClassifierOptions = createOptions();
}
}
}
/**
* Returns a string describing this classifier
* @return a description of the classifier suitable for
* displaying in the explorer/experimenter gui
*/
public String globalInfo() {
return "Class for performing parameter selection by cross-validation "
+ "for any classifier.\n\n"
+ "For more information, see:\n\n"
+ getTechnicalInformation().toString();
}
/**
* Returns an instance of a TechnicalInformation object, containing
* detailed information about the technical background of this class,
* e.g., paper reference or book this class is based on.
*
* @return the technical information about this class
*/
public TechnicalInformation getTechnicalInformation() {
TechnicalInformation result;
result = new TechnicalInformation(Type.PHDTHESIS);
result.setValue(Field.AUTHOR, "R. Kohavi");
result.setValue(Field.YEAR, "1995");
result.setValue(Field.TITLE, "Wrappers for Performance Enhancement and Oblivious Decision Graphs");
result.setValue(Field.SCHOOL, "Stanford University");
result.setValue(Field.ADDRESS, "Department of Computer Science, Stanford University");
return result;
}
/**
* Returns an enumeration describing the available options.
*
* @return an enumeration of all the available options.
*/
public Enumeration listOptions() {
Vector newVector = new Vector(2);
newVector.addElement(new Option(
"\tNumber of folds used for cross validation (default 10).",
"X", 1, "-X
*
* @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 foldsString = Utils.getOption('X', options);
if (foldsString.length() != 0) {
setNumFolds(Integer.parseInt(foldsString));
} else {
setNumFolds(10);
}
String cvParam;
m_CVParams = new FastVector();
do {
cvParam = Utils.getOption('P', options);
if (cvParam.length() != 0) {
addCVParameter(cvParam);
}
} while (cvParam.length() != 0);
super.setOptions(options);
}
/**
* Gets the current settings of the Classifier.
*
* @return an array of strings suitable for passing to setOptions
*/
public String [] getOptions() {
String[] superOptions;
if (m_InitOptions != null) {
try {
((OptionHandler)m_Classifier).setOptions((String[])m_InitOptions.clone());
superOptions = super.getOptions();
((OptionHandler)m_Classifier).setOptions((String[])m_BestClassifierOptions.clone());
} catch (Exception e) {
throw new RuntimeException("CVParameterSelection: could not set options " +
"in getOptions().");
}
} else {
superOptions = super.getOptions();
}
String [] options = new String [superOptions.length + m_CVParams.size() * 2 + 2];
int current = 0;
for (int i = 0; i < m_CVParams.size(); i++) {
options[current++] = "-P"; options[current++] = "" + getCVParameter(i);
}
options[current++] = "-X"; options[current++] = "" + getNumFolds();
System.arraycopy(superOptions, 0, options, current,
superOptions.length);
return options;
}
/**
* Returns (a copy of) the best options found for the classifier.
*
* @return the best options
*/
public String[] getBestClassifierOptions() {
return (String[]) m_BestClassifierOptions.clone();
}
/**
* Returns default capabilities of the classifier.
*
* @return the capabilities of this classifier
*/
public Capabilities getCapabilities() {
Capabilities result = super.getCapabilities();
result.setMinimumNumberInstances(m_NumFolds);
return result;
}
/**
* Generates the classifier.
*
* @param instances set of instances serving as training data
* @throws Exception if the classifier has not been generated successfully
*/
public void buildClassifier(Instances instances) throws Exception {
// can classifier handle the data?
getCapabilities().testWithFail(instances);
// remove instances with missing class
Instances trainData = new Instances(instances);
trainData.deleteWithMissingClass();
if (!(m_Classifier instanceof OptionHandler)) {
throw new IllegalArgumentException("Base classifier should be OptionHandler.");
}
m_InitOptions = ((OptionHandler)m_Classifier).getOptions();
m_BestPerformance = -99;
m_NumAttributes = trainData.numAttributes();
Random random = new Random(m_Seed);
trainData.randomize(random);
m_TrainFoldSize = trainData.trainCV(m_NumFolds, 0).numInstances();
// Check whether there are any parameters to optimize
if (m_CVParams.size() == 0) {
m_Classifier.buildClassifier(trainData);
m_BestClassifierOptions = m_InitOptions;
return;
}
if (trainData.classAttribute().isNominal()) {
trainData.stratify(m_NumFolds);
}
m_BestClassifierOptions = null;
// Set up m_ClassifierOptions -- take getOptions() and remove
// those being optimised.
m_ClassifierOptions = ((OptionHandler)m_Classifier).getOptions();
for (int i = 0; i < m_CVParams.size(); i++) {
Utils.getOption(((CVParameter)m_CVParams.elementAt(i)).m_ParamChar,
m_ClassifierOptions);
}
findParamsByCrossValidation(0, trainData, random);
String [] options = (String [])m_BestClassifierOptions.clone();
((OptionHandler)m_Classifier).setOptions(options);
m_Classifier.buildClassifier(trainData);
}
/**
* Predicts the class distribution for the given test instance.
*
* @param instance the instance to be classified
* @return the predicted class value
* @throws Exception if an error occurred during the prediction
*/
public double[] distributionForInstance(Instance instance) throws Exception {
return m_Classifier.distributionForInstance(instance);
}
/**
* Adds a scheme parameter to the list of parameters to be set
* by cross-validation
*
* @param cvParam the string representation of a scheme parameter. The
* format is: -X <number of folds>
* Number of folds used for cross validation (default 10).
*
* -P <classifier parameter>
* Classifier parameter options.
* eg: "N 1 5 10" Sets an optimisation parameter for the
* classifier with name -N, with lower bound 1, upper bound
* 5, and 10 optimisation steps. The upper bound may be the
* character 'A' or 'I' to substitute the number of
* attributes or instances in the training data,
* respectively. This parameter may be supplied more than
* once to optimise over several classifier options
* simultaneously.
*
* -S <num>
* Random number seed.
* (default 1)
*
* -D
* If set, classifier is run in debug mode and
* may output additional info to the console
*
* -W
* Full name of base classifier.
* (default: weka.classifiers.rules.ZeroR)
*
*
* Options specific to classifier weka.classifiers.rules.ZeroR:
*
*
* -D
* If set, classifier is run in debug mode and
* may output additional info to the console
*
*
* Options after -- are passed to the designated sub-classifier.
* param_char lower_bound upper_bound number_of_steps
* eg to search a parameter -P from 1 to 10 by increments of 1:
* P 1 10 11
* @throws Exception if the parameter specifier is of the wrong format
*/
public void addCVParameter(String cvParam) throws Exception {
CVParameter newCV = new CVParameter(cvParam);
m_CVParams.addElement(newCV);
}
/**
* Gets the scheme paramter with the given index.
*
* @param index the index for the parameter
* @return the scheme parameter
*/
public String getCVParameter(int index) {
if (m_CVParams.size() <= index) {
return "";
}
return ((CVParameter)m_CVParams.elementAt(index)).toString();
}
/**
* Returns the tip text for this property
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String CVParametersTipText() {
return "Sets the scheme parameters which are to be set "+
"by cross-validation.\n"+
"The format for each string should be:\n"+
"param_char lower_bound upper_bound number_of_steps\n"+
"eg to search a parameter -P from 1 to 10 by increments of 1:\n"+
" \"P 1 10 10\" ";
}
/**
* Get method for CVParameters.
*
* @return the CVParameters
*/
public Object[] getCVParameters() {
Object[] CVParams = m_CVParams.toArray();
String params[] = new String[CVParams.length];
for(int i=0; i