/*
* 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/>.
*/
/*
* DecisionTable.java
* Copyright (C) 1999-2012 University of Waikato, Hamilton, New Zealand
*
*/
package weka.classifiers.rules;
import java.util.Arrays;
import java.util.BitSet;
import java.util.Enumeration;
import java.util.Hashtable;
import java.util.Random;
import java.util.Vector;
import weka.attributeSelection.ASEvaluation;
import weka.attributeSelection.ASSearch;
import weka.attributeSelection.BestFirst;
import weka.attributeSelection.SubsetEvaluator;
import weka.classifiers.AbstractClassifier;
import weka.classifiers.Evaluation;
import weka.classifiers.lazy.IBk;
import weka.core.AdditionalMeasureProducer;
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.SelectedTag;
import weka.core.Tag;
import weka.core.TechnicalInformation;
import weka.core.TechnicalInformation.Field;
import weka.core.TechnicalInformation.Type;
import weka.core.TechnicalInformationHandler;
import weka.core.Utils;
import weka.core.WeightedInstancesHandler;
import weka.filters.Filter;
import weka.filters.unsupervised.attribute.Remove;
/**
<!-- globalinfo-start -->
* Class for building and using a simple decision table majority classifier.<br/>
* <br/>
* For more information see: <br/>
* <br/>
* Ron Kohavi: The Power of Decision Tables. In: 8th European Conference on Machine Learning, 174-189, 1995.
* <p/>
<!-- globalinfo-end -->
*
<!-- technical-bibtex-start -->
* BibTeX:
* <pre>
* @inproceedings{Kohavi1995,
* author = {Ron Kohavi},
* booktitle = {8th European Conference on Machine Learning},
* pages = {174-189},
* publisher = {Springer},
* title = {The Power of Decision Tables},
* year = {1995}
* }
* </pre>
* <p/>
<!-- technical-bibtex-end -->
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -S <search method specification>
* Full class name of search method, followed
* by its options.
* eg: "weka.attributeSelection.BestFirst -D 1"
* (default weka.attributeSelection.BestFirst)</pre>
*
* <pre> -X <number of folds>
* Use cross validation to evaluate features.
* Use number of folds = 1 for leave one out CV.
* (Default = leave one out CV)</pre>
*
* <pre> -E <acc | rmse | mae | auc>
* Performance evaluation measure to use for selecting attributes.
* (Default = accuracy for discrete class and rmse for numeric class)</pre>
*
* <pre> -I
* Use nearest neighbour instead of global table majority.</pre>
*
* <pre> -R
* Display decision table rules.
* </pre>
*
* <pre>
* Options specific to search method weka.attributeSelection.BestFirst:
* </pre>
*
* <pre> -P <start set>
* Specify a starting set of attributes.
* Eg. 1,3,5-7.</pre>
*
* <pre> -D <0 = backward | 1 = forward | 2 = bi-directional>
* Direction of search. (default = 1).</pre>
*
* <pre> -N <num>
* Number of non-improving nodes to
* consider before terminating search.</pre>
*
* <pre> -S <num>
* Size of lookup cache for evaluated subsets.
* Expressed as a multiple of the number of
* attributes in the data set. (default = 1)</pre>
*
<!-- options-end -->
*
* @author Mark Hall (mhall@cs.waikato.ac.nz)
* @version $Revision: 8034 $
*/
public class DecisionTable
extends AbstractClassifier
implements OptionHandler, WeightedInstancesHandler,
AdditionalMeasureProducer, TechnicalInformationHandler {
/** for serialization */
static final long serialVersionUID = 2888557078165701326L;
/** The hashtable used to hold training instances */
protected Hashtable m_entries;
/** The class priors to use when there is no match in the table */
protected double [] m_classPriorCounts;
protected double [] m_classPriors;
/** Holds the final feature set */
protected int [] m_decisionFeatures;
/** Discretization filter */
protected Filter m_disTransform;
/** Filter used to remove columns discarded by feature selection */
protected Remove m_delTransform;
/** IB1 used to classify non matching instances rather than majority class */
protected IBk m_ibk;
/** Holds the original training instances */
protected Instances m_theInstances;
/** Holds the final feature selected set of instances */
protected Instances m_dtInstances;
/** The number of attributes in the dataset */
protected int m_numAttributes;
/** The number of instances in the dataset */
private int m_numInstances;
/** Class is nominal */
protected boolean m_classIsNominal;
/** Use the IBk classifier rather than majority class */
protected boolean m_useIBk;
/** Display Rules */
protected boolean m_displayRules;
/** Number of folds for cross validating feature sets */
private int m_CVFolds;
/** Random numbers for use in cross validation */
private Random m_rr;
/** Holds the majority class */
protected double m_majority;
/** The search method to use */
protected ASSearch m_search = new BestFirst();
/** Our own internal evaluator */
protected ASEvaluation m_evaluator;
/** The evaluation object used to evaluate subsets */
protected Evaluation m_evaluation;
/** default is accuracy for discrete class and RMSE for numeric class */
public static final int EVAL_DEFAULT = 1;
public static final int EVAL_ACCURACY = 2;
public static final int EVAL_RMSE = 3;
public static final int EVAL_MAE = 4;
public static final int EVAL_AUC = 5;
public static final Tag [] TAGS_EVALUATION = {
new Tag(EVAL_DEFAULT, "Default: accuracy (discrete class); RMSE (numeric class)"),
new Tag(EVAL_ACCURACY, "Accuracy (discrete class only"),
new Tag(EVAL_RMSE, "RMSE (of the class probabilities for discrete class)"),
new Tag(EVAL_MAE, "MAE (of the class probabilities for discrete class)"),
new Tag(EVAL_AUC, "AUC (area under the ROC curve - discrete class only)")
};
protected int m_evaluationMeasure = EVAL_DEFAULT;
/**
* Returns a string describing classifier
* @return a description suitable for
* displaying in the explorer/experimenter gui
*/
public String globalInfo() {
return
"Class for building and using a simple decision table majority "
+ "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.INPROCEEDINGS);
result.setValue(Field.AUTHOR, "Ron Kohavi");
result.setValue(Field.TITLE, "The Power of Decision Tables");
result.setValue(Field.BOOKTITLE, "8th European Conference on Machine Learning");
result.setValue(Field.YEAR, "1995");
result.setValue(Field.PAGES, "174-189");
result.setValue(Field.PUBLISHER, "Springer");
return result;
}
/**
* Inserts an instance into the hash table
*
* @param inst instance to be inserted
* @param instA to create the hash key from
* @throws Exception if the instance can't be inserted
*/
private void insertIntoTable(Instance inst, double [] instA)
throws Exception {
double [] tempClassDist2;
double [] newDist;
DecisionTableHashKey thekey;
if (instA != null) {
thekey = new DecisionTableHashKey(instA);
} else {
thekey = new DecisionTableHashKey(inst, inst.numAttributes(), false);
}
// see if this one is already in the table
tempClassDist2 = (double []) m_entries.get(thekey);
if (tempClassDist2 == null) {
if (m_classIsNominal) {
newDist = new double [m_theInstances.classAttribute().numValues()];
//Leplace estimation
for (int i = 0; i < m_theInstances.classAttribute().numValues(); i++) {
newDist[i] = 1.0;
}
newDist[(int)inst.classValue()] = inst.weight();
// add to the table
m_entries.put(thekey, newDist);
} else {
newDist = new double [2];
newDist[0] = inst.classValue() * inst.weight();
newDist[1] = inst.weight();
// add to the table
m_entries.put(thekey, newDist);
}
} else {
// update the distribution for this instance
if (m_classIsNominal) {
tempClassDist2[(int)inst.classValue()]+=inst.weight();
// update the table
m_entries.put(thekey, tempClassDist2);
} else {
tempClassDist2[0] += (inst.classValue() * inst.weight());
tempClassDist2[1] += inst.weight();
// update the table
m_entries.put(thekey, tempClassDist2);
}
}
}
/**
* Classifies an instance for internal leave one out cross validation
* of feature sets
*
* @param instance instance to be "left out" and classified
* @param instA feature values of the selected features for the instance
* @return the classification of the instance
* @throws Exception if something goes wrong
*/
double evaluateInstanceLeaveOneOut(Instance instance, double [] instA)
throws Exception {
DecisionTableHashKey thekey;
double [] tempDist;
double [] normDist;
thekey = new DecisionTableHashKey(instA);
if (m_classIsNominal) {
// if this one is not in the table
if ((tempDist = (double [])m_entries.get(thekey)) == null) {
throw new Error("This should never happen!");
} else {
normDist = new double [tempDist.length];
System.arraycopy(tempDist,0,normDist,0,tempDist.length);
normDist[(int)instance.classValue()] -= instance.weight();
// update the table
// first check to see if the class counts are all zero now
boolean ok = false;
for (int i=0;i<normDist.length;i++) {
if (Utils.gr(normDist[i],1.0)) {
ok = true;
break;
}
}
// downdate the class prior counts
m_classPriorCounts[(int)instance.classValue()] -=
instance.weight();
double [] classPriors = m_classPriorCounts.clone();
Utils.normalize(classPriors);
if (!ok) { // majority class
normDist = classPriors;
}
m_classPriorCounts[(int)instance.classValue()] +=
instance.weight();
//if (ok) {
Utils.normalize(normDist);
if (m_evaluationMeasure == EVAL_AUC) {
m_evaluation.evaluateModelOnceAndRecordPrediction(normDist, instance);
} else {
m_evaluation.evaluateModelOnce(normDist, instance);
}
return Utils.maxIndex(normDist);
/*} else {
normDist = new double [normDist.length];
normDist[(int)m_majority] = 1.0;
if (m_evaluationMeasure == EVAL_AUC) {
m_evaluation.evaluateModelOnceAndRecordPrediction(normDist, instance);
} else {
m_evaluation.evaluateModelOnce(normDist, instance);
}
return m_majority;
} */
}
// return Utils.maxIndex(tempDist);
} else {
// see if this one is already in the table
if ((tempDist = (double[])m_entries.get(thekey)) != null) {
normDist = new double [tempDist.length];
System.arraycopy(tempDist,0,normDist,0,tempDist.length);
normDist[0] -= (instance.classValue() * instance.weight());
normDist[1] -= instance.weight();
if (Utils.eq(normDist[1],0.0)) {
double [] temp = new double[1];
temp[0] = m_majority;
m_evaluation.evaluateModelOnce(temp, instance);
return m_majority;
} else {
double [] temp = new double[1];
temp[0] = normDist[0] / normDist[1];
m_evaluation.evaluateModelOnce(temp, instance);
return temp[0];
}
} else {
throw new Error("This should never happen!");
}
}
// shouldn't get here
// return 0.0;
}
/**
* Calculates the accuracy on a test fold for internal cross validation
* of feature sets
*
* @param fold set of instances to be "left out" and classified
* @param fs currently selected feature set
* @return the accuracy for the fold
* @throws Exception if something goes wrong
*/
double evaluateFoldCV(Instances fold, int [] fs) throws Exception {
int i;
int ruleCount = 0;
int numFold = fold.numInstances();
int numCl = m_theInstances.classAttribute().numValues();
double [][] class_distribs = new double [numFold][numCl];
double [] instA = new double [fs.length];
double [] normDist;
DecisionTableHashKey thekey;
double acc = 0.0;
int classI = m_theInstances.classIndex();
Instance inst;
if (m_classIsNominal) {
normDist = new double [numCl];
} else {
normDist = new double [2];
}
// first *remove* instances
for (i=0;i<numFold;i++) {
inst = fold.instance(i);
for (int j=0;j<fs.length;j++) {
if (fs[j] == classI) {
instA[j] = Double.MAX_VALUE; // missing for the class
} else if (inst.isMissing(fs[j])) {
instA[j] = Double.MAX_VALUE;
} else{
instA[j] = inst.value(fs[j]);
}
}
thekey = new DecisionTableHashKey(instA);
if ((class_distribs[i] = (double [])m_entries.get(thekey)) == null) {
throw new Error("This should never happen!");
} else {
if (m_classIsNominal) {
class_distribs[i][(int)inst.classValue()] -= inst.weight();
} else {
class_distribs[i][0] -= (inst.classValue() * inst.weight());
class_distribs[i][1] -= inst.weight();
}
ruleCount++;
}
m_classPriorCounts[(int)inst.classValue()] -=
inst.weight();
}
double [] classPriors = m_classPriorCounts.clone();
Utils.normalize(classPriors);
// now classify instances
for (i=0;i<numFold;i++) {
inst = fold.instance(i);
System.arraycopy(class_distribs[i],0,normDist,0,normDist.length);
if (m_classIsNominal) {
boolean ok = false;
for (int j=0;j<normDist.length;j++) {
if (Utils.gr(normDist[j],1.0)) {
ok = true;
break;
}
}
if (!ok) { // majority class
normDist = classPriors.clone();
}
// if (ok) {
Utils.normalize(normDist);
if (m_evaluationMeasure == EVAL_AUC) {
m_evaluation.evaluateModelOnceAndRecordPrediction(normDist, inst);
} else {
m_evaluation.evaluateModelOnce(normDist, inst);
}
/* } else {
normDist[(int)m_majority] = 1.0;
if (m_evaluationMeasure == EVAL_AUC) {
m_evaluation.evaluateModelOnceAndRecordPrediction(normDist, inst);
} else {
m_evaluation.evaluateModelOnce(normDist, inst);
}
} */
} else {
if (Utils.eq(normDist[1],0.0)) {
double [] temp = new double[1];
temp[0] = m_majority;
m_evaluation.evaluateModelOnce(temp, inst);
} else {
double [] temp = new double[1];
temp[0] = normDist[0] / normDist[1];
m_evaluation.evaluateModelOnce(temp, inst);
}
}
}
// now re-insert instances
for (i=0;i<numFold;i++) {
inst = fold.instance(i);
m_classPriorCounts[(int)inst.classValue()] +=
inst.weight();
if (m_classIsNominal) {
class_distribs[i][(int)inst.classValue()] += inst.weight();
} else {
class_distribs[i][0] += (inst.classValue() * inst.weight());
class_distribs[i][1] += inst.weight();
}
}
return acc;
}
/**
* Evaluates a feature subset by cross validation
*
* @param feature_set the subset to be evaluated
* @param num_atts the number of attributes in the subset
* @return the estimated accuracy
* @throws Exception if subset can't be evaluated
*/
protected double estimatePerformance(BitSet feature_set, int num_atts)
throws Exception {
m_evaluation = new Evaluation(m_theInstances);
int i;
int [] fs = new int [num_atts];
double [] instA = new double [num_atts];
int classI = m_theInstances.classIndex();
int index = 0;
for (i=0;i<m_numAttributes;i++) {
if (feature_set.get(i)) {
fs[index++] = i;
}
}
// create new hash table
m_entries = new Hashtable((int)(m_theInstances.numInstances() * 1.5));
// insert instances into the hash table
for (i=0;i<m_numInstances;i++) {
Instance inst = m_theInstances.instance(i);
for (int j=0;j<fs.length;j++) {
if (fs[j] == classI) {
instA[j] = Double.MAX_VALUE; // missing for the class
} else if (inst.isMissing(fs[j])) {
instA[j] = Double.MAX_VALUE;
} else {
instA[j] = inst.value(fs[j]);
}
}
insertIntoTable(inst, instA);
}
if (m_CVFolds == 1) {
// calculate leave one out error
for (i=0;i<m_numInstances;i++) {
Instance inst = m_theInstances.instance(i);
for (int j=0;j<fs.length;j++) {
if (fs[j] == classI) {
instA[j] = Double.MAX_VALUE; // missing for the class
} else if (inst.isMissing(fs[j])) {
instA[j] = Double.MAX_VALUE;
} else {
instA[j] = inst.value(fs[j]);
}
}
evaluateInstanceLeaveOneOut(inst, instA);
}
} else {
m_theInstances.randomize(m_rr);
m_theInstances.stratify(m_CVFolds);
// calculate 10 fold cross validation error
for (i=0;i<m_CVFolds;i++) {
Instances insts = m_theInstances.testCV(m_CVFolds,i);
evaluateFoldCV(insts, fs);
}
}
switch (m_evaluationMeasure) {
case EVAL_DEFAULT:
if (m_classIsNominal) {
return m_evaluation.pctCorrect();
}
return -m_evaluation.rootMeanSquaredError();
case EVAL_ACCURACY:
return m_evaluation.pctCorrect();
case EVAL_RMSE:
return -m_evaluation.rootMeanSquaredError();
case EVAL_MAE:
return -m_evaluation.meanAbsoluteError();
case EVAL_AUC:
double [] classPriors = m_evaluation.getClassPriors();
Utils.normalize(classPriors);
double weightedAUC = 0;
for (i = 0; i < m_theInstances.classAttribute().numValues(); i++) {
double tempAUC = m_evaluation.areaUnderROC(i);
if (!Utils.isMissingValue(tempAUC)) {
weightedAUC += (classPriors[i] * tempAUC);
} else {
System.err.println("Undefined AUC!!");
}
}
return weightedAUC;
}
// shouldn't get here
return 0.0;
}
/**
* Returns a String representation of a feature subset
*
* @param sub BitSet representation of a subset
* @return String containing subset
*/
private String printSub(BitSet sub) {
String s="";
for (int jj=0;jj<m_numAttributes;jj++) {
if (sub.get(jj)) {
s += " "+(jj+1);
}
}
return s;
}
/**
* Resets the options.
*/
protected void resetOptions() {
m_entries = null;
m_decisionFeatures = null;
m_useIBk = false;
m_CVFolds = 1;
m_displayRules = false;
m_evaluationMeasure = EVAL_DEFAULT;
}
/**
* Constructor for a DecisionTable
*/
public DecisionTable() {
resetOptions();
}
/**
* Returns an enumeration describing the available options.
*
* @return an enumeration of all the available options.
*/
public Enumeration listOptions() {
Vector newVector = new Vector(7);
newVector.addElement(new Option(
"\tFull class name of search method, followed\n"
+ "\tby its options.\n"
+ "\teg: \"weka.attributeSelection.BestFirst -D 1\"\n"
+ "\t(default weka.attributeSelection.BestFirst)",
"S", 1, "-S <search method specification>"));
newVector.addElement(new Option(
"\tUse cross validation to evaluate features.\n" +
"\tUse number of folds = 1 for leave one out CV.\n" +
"\t(Default = leave one out CV)",
"X", 1, "-X <number of folds>"));
newVector.addElement(new Option(
"\tPerformance evaluation measure to use for selecting attributes.\n" +
"\t(Default = accuracy for discrete class and rmse for numeric class)",
"E", 1, "-E <acc | rmse | mae | auc>"));
newVector.addElement(new Option(
"\tUse nearest neighbour instead of global table majority.",
"I", 0, "-I"));
newVector.addElement(new Option(
"\tDisplay decision table rules.\n",
"R", 0, "-R"));
newVector.addElement(new Option(
"",
"", 0, "\nOptions specific to search method "
+ m_search.getClass().getName() + ":"));
Enumeration enu = ((OptionHandler)m_search).listOptions();
while (enu.hasMoreElements()) {
newVector.addElement(enu.nextElement());
}
return newVector.elements();
}
/**
* Returns the tip text for this property
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String crossValTipText() {
return "Sets the number of folds for cross validation (1 = leave one out).";
}
/**
* Sets the number of folds for cross validation (1 = leave one out)
*
* @param folds the number of folds
*/
public void setCrossVal(int folds) {
m_CVFolds = folds;
}
/**
* Gets the number of folds for cross validation
*
* @return the number of cross validation folds
*/
public int getCrossVal() {
return m_CVFolds;
}
/**
* Returns the tip text for this property
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String useIBkTipText() {
return "Sets whether IBk should be used instead of the majority class.";
}
/**
* Sets whether IBk should be used instead of the majority class
*
* @param ibk true if IBk is to be used
*/
public void setUseIBk(boolean ibk) {
m_useIBk = ibk;
}
/**
* Gets whether IBk is being used instead of the majority class
*
* @return true if IBk is being used
*/
public boolean getUseIBk() {
return m_useIBk;
}
/**
* Returns the tip text for this property
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String displayRulesTipText() {
return "Sets whether rules are to be printed.";
}
/**
* Sets whether rules are to be printed
*
* @param rules true if rules are to be printed
*/
public void setDisplayRules(boolean rules) {
m_displayRules = rules;
}
/**
* Gets whether rules are being printed
*
* @return true if rules are being printed
*/
public boolean getDisplayRules() {
return m_displayRules;
}
/**
* Returns the tip text for this property
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String searchTipText() {
return "The search method used to find good attribute combinations for the "
+ "decision table.";
}
/**
* Sets the search method to use
*
* @param search
*/
public void setSearch(ASSearch search) {
m_search = search;
}
/**
* Gets the current search method
*
* @return the search method used
*/
public ASSearch getSearch() {
return m_search;
}
/**
* Returns the tip text for this property
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String evaluationMeasureTipText() {
return "The measure used to evaluate the performance of attribute combinations "
+ "used in the decision table.";
}
/**
* Gets the currently set performance evaluation measure used for selecting
* attributes for the decision table
*
* @return the performance evaluation measure
*/
public SelectedTag getEvaluationMeasure() {
return new SelectedTag(m_evaluationMeasure, TAGS_EVALUATION);
}
/**
* Sets the performance evaluation measure to use for selecting attributes
* for the decision table
*
* @param newMethod the new performance evaluation metric to use
*/
public void setEvaluationMeasure(SelectedTag newMethod) {
if (newMethod.getTags() == TAGS_EVALUATION) {
m_evaluationMeasure = newMethod.getSelectedTag().getID();
}
}
/**
* Parses the options for this object. <p/>
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -S <search method specification>
* Full class name of search method, followed
* by its options.
* eg: "weka.attributeSelection.BestFirst -D 1"
* (default weka.attributeSelection.BestFirst)</pre>
*
* <pre> -X <number of folds>
* Use cross validation to evaluate features.
* Use number of folds = 1 for leave one out CV.
* (Default = leave one out CV)</pre>
*
* <pre> -E <acc | rmse | mae | auc>
* Performance evaluation measure to use for selecting attributes.
* (Default = accuracy for discrete class and rmse for numeric class)</pre>
*
* <pre> -I
* Use nearest neighbour instead of global table majority.</pre>
*
* <pre> -R
* Display decision table rules.
* </pre>
*
* <pre>
* Options specific to search method weka.attributeSelection.BestFirst:
* </pre>
*
* <pre> -P <start set>
* Specify a starting set of attributes.
* Eg. 1,3,5-7.</pre>
*
* <pre> -D <0 = backward | 1 = forward | 2 = bi-directional>
* Direction of search. (default = 1).</pre>
*
* <pre> -N <num>
* Number of non-improving nodes to
* consider before terminating search.</pre>
*
* <pre> -S <num>
* Size of lookup cache for evaluated subsets.
* Expressed as a multiple of the number of
* attributes in the data set. (default = 1)</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 optionString;
resetOptions();
optionString = Utils.getOption('X',options);
if (optionString.length() != 0) {
m_CVFolds = Integer.parseInt(optionString);
}
m_useIBk = Utils.getFlag('I',options);
m_displayRules = Utils.getFlag('R',options);
optionString = Utils.getOption('E', options);
if (optionString.length() != 0) {
if (optionString.equals("acc")) {
setEvaluationMeasure(new SelectedTag(EVAL_ACCURACY, TAGS_EVALUATION));
} else if (optionString.equals("rmse")) {
setEvaluationMeasure(new SelectedTag(EVAL_RMSE, TAGS_EVALUATION));
} else if (optionString.equals("mae")) {
setEvaluationMeasure(new SelectedTag(EVAL_MAE, TAGS_EVALUATION));
} else if (optionString.equals("auc")) {
setEvaluationMeasure(new SelectedTag(EVAL_AUC, TAGS_EVALUATION));
} else {
throw new IllegalArgumentException("Invalid evaluation measure");
}
}
String searchString = Utils.getOption('S', options);
if (searchString.length() == 0)
searchString = weka.attributeSelection.BestFirst.class.getName();
String [] searchSpec = Utils.splitOptions(searchString);
if (searchSpec.length == 0) {
throw new IllegalArgumentException("Invalid search specification string");
}
String searchName = searchSpec[0];
searchSpec[0] = "";
setSearch(ASSearch.forName(searchName, searchSpec));
}
/**
* Gets the current settings of the classifier.
*
* @return an array of strings suitable for passing to setOptions
*/
public String [] getOptions() {
String [] options = new String [9];
int current = 0;
options[current++] = "-X"; options[current++] = "" + m_CVFolds;
if (m_evaluationMeasure != EVAL_DEFAULT) {
options[current++] = "-E";
switch (m_evaluationMeasure) {
case EVAL_ACCURACY:
options[current++] = "acc";
break;
case EVAL_RMSE:
options[current++] = "rmse";
break;
case EVAL_MAE:
options[current++] = "mae";
break;
case EVAL_AUC:
options[current++] = "auc";
break;
}
}
if (m_useIBk) {
options[current++] = "-I";
}
if (m_displayRules) {
options[current++] = "-R";
}
options[current++] = "-S";
options[current++] = "" + getSearchSpec();
while (current < options.length) {
options[current++] = "";
}
return options;
}
/**
* Gets the search specification string, which contains the class name of
* the search method and any options to it
*
* @return the search string.
*/
protected String getSearchSpec() {
ASSearch s = getSearch();
if (s instanceof OptionHandler) {
return s.getClass().getName() + " "
+ Utils.joinOptions(((OptionHandler)s).getOptions());
}
return s.getClass().getName();
}
/**
* Returns default capabilities of the classifier.
*
* @return the capabilities of this classifier
*/
public Capabilities getCapabilities() {
Capabilities result = super.getCapabilities();
result.disableAll();
// attributes
result.enable(Capability.NOMINAL_ATTRIBUTES);
result.enable(Capability.NUMERIC_ATTRIBUTES);
result.enable(Capability.DATE_ATTRIBUTES);
result.enable(Capability.MISSING_VALUES);
// class
result.enable(Capability.NOMINAL_CLASS);
if (m_evaluationMeasure != EVAL_ACCURACY && m_evaluationMeasure != EVAL_AUC) {
result.enable(Capability.NUMERIC_CLASS);
result.enable(Capability.DATE_CLASS);
}
result.enable(Capability.MISSING_CLASS_VALUES);
return result;
}
private class DummySubsetEvaluator extends ASEvaluation implements SubsetEvaluator {
/** for serialization */
private static final long serialVersionUID = 3927442457704974150L;
public void buildEvaluator(Instances data) throws Exception {
}
public double evaluateSubset(BitSet subset) throws Exception {
int fc = 0;
for (int jj = 0;jj < m_numAttributes; jj++) {
if (subset.get(jj)) {
fc++;
}
}
return estimatePerformance(subset, fc);
}
}
/**
* Sets up a dummy subset evaluator that basically just delegates
* evaluation to the estimatePerformance method in DecisionTable
*/
protected void setUpEvaluator() throws Exception {
m_evaluator = new DummySubsetEvaluator();
}
protected boolean m_saveMemory = true;
/**
* Generates the classifier.
*
* @param data set of instances serving as training data
* @throws Exception if the classifier has not been generated successfully
*/
public void buildClassifier(Instances data) throws Exception {
// can classifier handle the data?
getCapabilities().testWithFail(data);
// remove instances with missing class
m_theInstances = new Instances(data);
m_theInstances.deleteWithMissingClass();
m_rr = new Random(1);
if (m_theInstances.classAttribute().isNominal()) {// Set up class priors
m_classPriorCounts =
new double [data.classAttribute().numValues()];
Arrays.fill(m_classPriorCounts, 1.0);
for (int i = 0; i <data.numInstances(); i++) {
Instance curr = data.instance(i);
m_classPriorCounts[(int)curr.classValue()] +=
curr.weight();
}
m_classPriors = m_classPriorCounts.clone();
Utils.normalize(m_classPriors);
}
setUpEvaluator();
if (m_theInstances.classAttribute().isNumeric()) {
m_disTransform = new weka.filters.unsupervised.attribute.Discretize();
m_classIsNominal = false;
// use binned discretisation if the class is numeric
((weka.filters.unsupervised.attribute.Discretize)m_disTransform).
setBins(10);
((weka.filters.unsupervised.attribute.Discretize)m_disTransform).
setInvertSelection(true);
// Discretize all attributes EXCEPT the class
String rangeList = "";
rangeList+=(m_theInstances.classIndex()+1);
//System.out.println("The class col: "+m_theInstances.classIndex());
((weka.filters.unsupervised.attribute.Discretize)m_disTransform).
setAttributeIndices(rangeList);
} else {
m_disTransform = new weka.filters.supervised.attribute.Discretize();
((weka.filters.supervised.attribute.Discretize)m_disTransform).setUseBetterEncoding(true);
m_classIsNominal = true;
}
m_disTransform.setInputFormat(m_theInstances);
m_theInstances = Filter.useFilter(m_theInstances, m_disTransform);
m_numAttributes = m_theInstances.numAttributes();
m_numInstances = m_theInstances.numInstances();
m_majority = m_theInstances.meanOrMode(m_theInstances.classAttribute());
// Perform the search
int [] selected = m_search.search(m_evaluator, m_theInstances);
m_decisionFeatures = new int [selected.length+1];
System.arraycopy(selected, 0, m_decisionFeatures, 0, selected.length);
m_decisionFeatures[m_decisionFeatures.length-1] = m_theInstances.classIndex();
// reduce instances to selected features
m_delTransform = new Remove();
m_delTransform.setInvertSelection(true);
// set features to keep
m_delTransform.setAttributeIndicesArray(m_decisionFeatures);
m_delTransform.setInputFormat(m_theInstances);
m_dtInstances = Filter.useFilter(m_theInstances, m_delTransform);
// reset the number of attributes
m_numAttributes = m_dtInstances.numAttributes();
// create hash table
m_entries = new Hashtable((int)(m_dtInstances.numInstances() * 1.5));
// insert instances into the hash table
for (int i = 0; i < m_numInstances; i++) {
Instance inst = m_dtInstances.instance(i);
insertIntoTable(inst, null);
}
// Replace the global table majority with nearest neighbour?
if (m_useIBk) {
m_ibk = new IBk();
m_ibk.buildClassifier(m_theInstances);
}
// Save memory
if (m_saveMemory) {
m_theInstances = new Instances(m_theInstances, 0);
m_dtInstances = new Instances(m_dtInstances, 0);
}
m_evaluation = null;
}
/**
* Calculates the class membership probabilities for the given
* test instance.
*
* @param instance the instance to be classified
* @return predicted class probability distribution
* @throws Exception if distribution can't be computed
*/
public double [] distributionForInstance(Instance instance)
throws Exception {
DecisionTableHashKey thekey;
double [] tempDist;
double [] normDist;
m_disTransform.input(instance);
m_disTransform.batchFinished();
instance = m_disTransform.output();
m_delTransform.input(instance);
m_delTransform.batchFinished();
instance = m_delTransform.output();
thekey = new DecisionTableHashKey(instance, instance.numAttributes(), false);
// if this one is not in the table
if ((tempDist = (double [])m_entries.get(thekey)) == null) {
if (m_useIBk) {
tempDist = m_ibk.distributionForInstance(instance);
} else {
if (!m_classIsNominal) {
tempDist = new double[1];
tempDist[0] = m_majority;
} else {
tempDist = m_classPriors.clone();
/*tempDist = new double [m_theInstances.classAttribute().numValues()];
tempDist[(int)m_majority] = 1.0; */
}
}
} else {
if (!m_classIsNominal) {
normDist = new double[1];
normDist[0] = (tempDist[0] / tempDist[1]);
tempDist = normDist;
} else {
// normalise distribution
normDist = new double [tempDist.length];
System.arraycopy(tempDist,0,normDist,0,tempDist.length);
Utils.normalize(normDist);
tempDist = normDist;
}
}
return tempDist;
}
/**
* Returns a string description of the features selected
*
* @return a string of features
*/
public String printFeatures() {
int i;
String s = "";
for (i=0;i<m_decisionFeatures.length;i++) {
if (i==0) {
s = ""+(m_decisionFeatures[i]+1);
} else {
s += ","+(m_decisionFeatures[i]+1);
}
}
return s;
}
/**
* Returns the number of rules
* @return the number of rules
*/
public double measureNumRules() {
return m_entries.size();
}
/**
* Returns an enumeration of the additional measure names
* @return an enumeration of the measure names
*/
public Enumeration enumerateMeasures() {
Vector newVector = new Vector(1);
newVector.addElement("measureNumRules");
return newVector.elements();
}
/**
* Returns the value of the named measure
* @param additionalMeasureName the name of the measure to query for its value
* @return the value of the named measure
* @throws IllegalArgumentException if the named measure is not supported
*/
public double getMeasure(String additionalMeasureName) {
if (additionalMeasureName.compareToIgnoreCase("measureNumRules") == 0) {
return measureNumRules();
} else {
throw new IllegalArgumentException(additionalMeasureName
+ " not supported (DecisionTable)");
}
}
/**
* Returns a description of the classifier.
*
* @return a description of the classifier as a string.
*/
public String toString() {
if (m_entries == null) {
return "Decision Table: No model built yet.";
} else {
StringBuffer text = new StringBuffer();
text.append("Decision Table:"+
"\n\nNumber of training instances: "+m_numInstances+
"\nNumber of Rules : "+m_entries.size()+"\n");
if (m_useIBk) {
text.append("Non matches covered by IB1.\n");
} else {
text.append("Non matches covered by Majority class.\n");
}
text.append(m_search.toString());
/*text.append("Best first search for feature set,\nterminated after "+
m_maxStale+" non improving subsets.\n"); */
text.append("Evaluation (for feature selection): CV ");
if (m_CVFolds > 1) {
text.append("("+m_CVFolds+" fold) ");
} else {
text.append("(leave one out) ");
}
text.append("\nFeature set: "+printFeatures());
if (m_displayRules) {
// find out the max column width
int maxColWidth = 0;
for (int i=0;i<m_dtInstances.numAttributes();i++) {
if (m_dtInstances.attribute(i).name().length() > maxColWidth) {
maxColWidth = m_dtInstances.attribute(i).name().length();
}
if (m_classIsNominal || (i != m_dtInstances.classIndex())) {
Enumeration e = m_dtInstances.attribute(i).enumerateValues();
while (e.hasMoreElements()) {
String ss = (String)e.nextElement();
if (ss.length() > maxColWidth) {
maxColWidth = ss.length();
}
}
}
}
text.append("\n\nRules:\n");
StringBuffer tm = new StringBuffer();
for (int i=0;i<m_dtInstances.numAttributes();i++) {
if (m_dtInstances.classIndex() != i) {
int d = maxColWidth - m_dtInstances.attribute(i).name().length();
tm.append(m_dtInstances.attribute(i).name());
for (int j=0;j<d+1;j++) {
tm.append(" ");
}
}
}
tm.append(m_dtInstances.attribute(m_dtInstances.classIndex()).name()+" ");
for (int i=0;i<tm.length()+10;i++) {
text.append("=");
}
text.append("\n");
text.append(tm);
text.append("\n");
for (int i=0;i<tm.length()+10;i++) {
text.append("=");
}
text.append("\n");
Enumeration e = m_entries.keys();
while (e.hasMoreElements()) {
DecisionTableHashKey tt = (DecisionTableHashKey)e.nextElement();
text.append(tt.toString(m_dtInstances,maxColWidth));
double [] ClassDist = (double []) m_entries.get(tt);
if (m_classIsNominal) {
int m = Utils.maxIndex(ClassDist);
try {
text.append(m_dtInstances.classAttribute().value(m)+"\n");
} catch (Exception ee) {
System.out.println(ee.getMessage());
}
} else {
text.append((ClassDist[0] / ClassDist[1])+"\n");
}
}
for (int i=0;i<tm.length()+10;i++) {
text.append("=");
}
text.append("\n");
text.append("\n");
}
return text.toString();
}
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 8034 $");
}
/**
* Main method for testing this class.
*
* @param argv the command-line options
*/
public static void main(String [] argv) {
runClassifier(new DecisionTable(), argv);
}
}