Index: trunk/CrossPare/src/de/ugoe/cs/cpdp/training/GPTraining.java
===================================================================
--- trunk/CrossPare/src/de/ugoe/cs/cpdp/training/GPTraining.java (revision 105)
+++ trunk/CrossPare/src/de/ugoe/cs/cpdp/training/GPTraining.java (revision 106)
@@ -44,9 +44,15 @@
/**
* Genetic Programming Trainer
+ *
*
+ * - GPRun is a Run of a complete Genetic Programm Evolution, we want several complete runs.
+ * - GPVClassifier is the Validation Classifier
+ * - GPVVClassifier is the Validation-Voting Classifier
+ *
+ * config:
*/
public class GPTraining implements ISetWiseTrainingStrategy, IWekaCompatibleTrainer {
- private GPVVClassifier classifier = new GPVVClassifier();
+ private GPVClassifier classifier = null;
private int populationSize = 1000;
@@ -54,10 +60,23 @@
private int initMaxDepth = 6;
private int tournamentSize = 7;
-
+ private int maxGenerations = 50;
+ private double errorType2Weight = 1;
+ private int numberRuns = 200; // 200 in the paper
+ private int maxDepth = 20; // max depth within one program
+ private int maxNodes = 100; // max nodes within one program
+
@Override
public void setParameter(String parameters) {
- // todo, which type of classifier? GPV, GPVV?
- // more config population size, etc.
- // todo: voting for gpvv only 3 votes necessary?
+ if(parameters.equals("GPVVClassifier")) {
+ this.classifier = new GPVVClassifier();
+ ((GPVVClassifier)this.classifier).configure(populationSize, initMinDepth, initMaxDepth, tournamentSize, maxGenerations, errorType2Weight, numberRuns, maxDepth, maxNodes);
+ }else if(parameters.equals("GPVClassifier")) {
+ this.classifier = new GPVClassifier();
+ ((GPVClassifier)this.classifier).configure(populationSize, initMinDepth, initMaxDepth, tournamentSize, maxGenerations, errorType2Weight, numberRuns, maxDepth, maxNodes);
+ }else {
+ // default
+ this.classifier = new GPVVClassifier();
+ ((GPVVClassifier)this.classifier).configure(populationSize, initMinDepth, initMaxDepth, tournamentSize, maxGenerations, errorType2Weight, numberRuns, maxDepth, maxNodes);
+ }
}
@@ -105,18 +124,24 @@
}
- // one gprun, we want several for voting
+ /**
+ * One Run of a GP Classifier
+ * we want several runs to mitigate problems with local maxima/minima
+ */
public class GPRun extends AbstractClassifier {
private static final long serialVersionUID = -4250422550107888789L;
- private int populationSize = 1000;
- private int initMinDepth = 2;
- private int initMaxDepth = 6;
- private int tournamentSize = 7;
- private int maxGenerations = 50;
+ private int populationSize;
+ private int initMinDepth;
+ private int initMaxDepth;
+ private int tournamentSize;
+ private int maxGenerations;
+ private double errorType2Weight;
+ private int maxDepth;
+ private int maxNodes;
private GPGenotype gp;
private GPProblem problem;
- public void configure(int populationSize, int initMinDepth, int initMaxDepth, int tournamentSize, int maxGenerations) {
+ public void configure(int populationSize, int initMinDepth, int initMaxDepth, int tournamentSize, int maxGenerations, double errorType2Weight, int maxDepth, int maxNodes) {
this.populationSize = populationSize;
this.initMinDepth = initMinDepth;
@@ -124,4 +149,7 @@
this.tournamentSize = tournamentSize;
this.maxGenerations = maxGenerations;
+ this.errorType2Weight = errorType2Weight;
+ this.maxDepth = maxDepth;
+ this.maxNodes = maxNodes;
}
@@ -133,7 +161,11 @@
return ((CrossPareGP)this.problem).getVariables();
}
-
- public void setEvaldata(Instances testdata) {
-
+
+ @Override
+ public void buildClassifier(Instances traindata) throws Exception {
+ InstanceData train = new InstanceData(traindata);
+ this.problem = new CrossPareGP(train.getX(), train.getY(), this.populationSize, this.initMinDepth, this.initMaxDepth, this.tournamentSize, this.errorType2Weight, this.maxDepth, this.maxNodes);
+ this.gp = problem.create();
+ this.gp.evolve(this.maxGenerations);
}
@@ -142,24 +174,24 @@
*/
class CrossPareGP extends GPProblem {
-
- //private static final long serialVersionUID = 7526472295622776147L;
-
private double[][] instances;
private boolean[] output;
+ private int maxDepth;
+ private int maxNodes;
+
private Variable[] x;
- public CrossPareGP(double[][] instances, boolean[] output, int populationSize, int minInitDept, int maxInitDepth, int tournamentSize) throws InvalidConfigurationException {
+ public CrossPareGP(double[][] instances, boolean[] output, int populationSize, int minInitDept, int maxInitDepth, int tournamentSize, double errorType2Weight, int maxDepth, int maxNodes) throws InvalidConfigurationException {
super(new GPConfiguration());
this.instances = instances;
this.output = output;
+ this.maxDepth = maxDepth;
+ this.maxNodes = maxNodes;
Configuration.reset();
GPConfiguration config = this.getGPConfiguration();
- //config.reset();
this.x = new Variable[this.instances[0].length];
-
for(int j=0; j < this.x.length; j++) {
@@ -170,11 +202,7 @@
//config.setGPFitnessEvaluator(new DefaultGPFitnessEvaluator()); // bigger fitness is better
- // from paper: 2-6
config.setMinInitDepth(minInitDept);
config.setMaxInitDepth(maxInitDepth);
-
- // missing from paper
- // config.setMaxDepth(20);
-
+
config.setCrossoverProb((float)0.60);
config.setReproductionProb((float)0.10);
@@ -183,10 +211,8 @@
config.setSelectionMethod(new TournamentSelector(tournamentSize));
- // from paper 1000
config.setPopulationSize(populationSize);
- // BranchTypingCross
config.setMaxCrossoverDepth(4);
- config.setFitnessFunction(new CrossPareFitness(this.x, this.instances, this.output));
+ config.setFitnessFunction(new CrossPareFitness(this.x, this.instances, this.output, errorType2Weight));
config.setStrictProgramCreation(true);
}
@@ -230,6 +256,9 @@
comb,
};
-
- GPGenotype result = GPGenotype.randomInitialGenotype(config, types, argTypes, nodeSets, 20, true); // 20 = maxNodes, true = verbose output
+
+ // we only have one chromosome so this suffices
+ int minDepths[] = {config.getMinInitDepth()};
+ int maxDepths[] = {this.maxDepth};
+ GPGenotype result = GPGenotype.randomInitialGenotype(config, types, argTypes, nodeSets, minDepths, maxDepths, this.maxNodes, false); // 40 = maxNodes, true = verbose output
return result;
@@ -250,25 +279,26 @@
private boolean[] output;
- private double error_type2_weight = 1.0;
+ private double errorType2Weight = 1.0;
// needed in evaluate
- private Object[] NO_ARGS = new Object[0];
+ //private Object[] NO_ARGS = new Object[0];
private double sfitness = 0.0f;
- private int error_type1 = 0;
- private int error_type2 = 0;
-
- public CrossPareFitness(Variable[] x, double[][] instances, boolean[] output) {
+ private int errorType1 = 0;
+ private int errorType2 = 0;
+
+ public CrossPareFitness(Variable[] x, double[][] instances, boolean[] output, double errorType2Weight) {
this.x = x;
this.instances = instances;
this.output = output;
+ this.errorType2Weight = errorType2Weight;
}
public int getErrorType1() {
- return this.error_type1;
+ return this.errorType1;
}
public int getErrorType2() {
- return this.error_type2;
+ return this.errorType2;
}
@@ -288,6 +318,6 @@
// count classification errors
- this.error_type1 = 0;
- this.error_type2 = 0;
+ this.errorType1 = 0;
+ this.errorType2 = 0;
for(int i=0; i < this.instances.length; i++) {
@@ -299,13 +329,13 @@
// value gives us a double, if < 0.5 we set this instance as faulty
- value = program.execute_double(0, NO_ARGS); // todo: test with this.x
+ value = program.execute_double(0, this.x); // todo: test with this.x
if(value < 0.5) {
if(this.output[i] != true) {
- this.error_type1 += 1;
+ this.errorType1 += 1;
}
}else {
if(this.output[i] == true) {
- this.error_type2 += 1;
+ this.errorType2 += 1;
}
}
@@ -313,5 +343,5 @@
// now calc pfitness
- pfitness = (this.error_type1 + this.error_type2_weight * this.error_type2) / this.instances.length;
+ pfitness = (this.errorType1 + this.errorType2Weight * this.errorType2) / this.instances.length;
//System.out.println("pfitness: " + pfitness);
@@ -321,22 +351,61 @@
if(program.getChromosome(0).getSize(0) < 10) {
program.setApplicationData(10.0f);
- this.sfitness = 10.0f;
- //System.out.println("wenige nodes: "+program.getChromosome(0).getSize(0));
- //System.out.println(program.toStringNorm(0));
- }
-
- // sfitness counts the number of nodes in the tree, if it is lower than 10 fitness is increased by 10
+ }
return pfitness;
}
}
-
- @Override
- public void buildClassifier(Instances traindata) throws Exception {
- InstanceData train = new InstanceData(traindata);
- this.problem = new CrossPareGP(train.getX(), train.getY(), this.populationSize, this.initMinDepth, this.initMaxDepth, this.tournamentSize);
- this.gp = problem.create();
- this.gp.evolve(this.maxGenerations);
- }
+
+ /**
+ * Custom GT implementation used in the GP Algorithm.
+ */
+ public class GT extends MathCommand implements ICloneable {
+
+ private static final long serialVersionUID = 113454184817L;
+
+ public GT(final GPConfiguration a_conf, java.lang.Class a_returnType) throws InvalidConfigurationException {
+ super(a_conf, 2, a_returnType);
+ }
+
+ public String toString() {
+ return "GT(&1, &2)";
+ }
+
+ public String getName() {
+ return "GT";
+ }
+
+ public float execute_float(ProgramChromosome c, int n, Object[] args) {
+ float f1 = c.execute_float(n, 0, args);
+ float f2 = c.execute_float(n, 1, args);
+
+ float ret = 1.0f;
+ if(f1 > f2) {
+ ret = 0.0f;
+ }
+
+ return ret;
+ }
+
+ public double execute_double(ProgramChromosome c, int n, Object[] args) {
+ double f1 = c.execute_double(n, 0, args);
+ double f2 = c.execute_double(n, 1, args);
+
+ double ret = 1;
+ if(f1 > f2) {
+ ret = 0;
+ }
+ return ret;
+ }
+
+ public Object clone() {
+ try {
+ GT result = new GT(getGPConfiguration(), getReturnType());
+ return result;
+ }catch(Exception ex) {
+ throw new CloneException(ex);
+ }
+ }
+ }
}
@@ -349,5 +418,6 @@
*/
public class GPVVClassifier extends GPVClassifier {
-
+
+ private static final long serialVersionUID = -654710583852839901L;
private List classifiers = null;
@@ -362,4 +432,5 @@
// each classifier is trained with one project from the set
// then is evaluated on the rest
+ classifiers = new LinkedList<>();
for(int i=0; i < traindataSet.size(); i++) {
@@ -367,8 +438,8 @@
LinkedList candidates = new LinkedList<>();
- // 200 runs
-
- for(int k=0; k < 200; k++) {
+ // number of runs
+ for(int k=0; k < this.numberRuns; k++) {
Classifier classifier = new GPRun();
+ ((GPRun)classifier).configure(this.populationSize, this.initMinDepth, this.initMaxDepth, this.tournamentSize, this.maxGenerations, this.errorType2Weight, this.maxDepth, this.maxNodes);
// one project is training data
@@ -381,6 +452,6 @@
// if type1 and type2 errors are < 0.5 we allow the model in the final voting
errors = this.evaluate((GPRun)classifier, traindataSet.get(j));
- if((errors[0] / traindataSet.get(j).numInstances()) < 0.5 && (errors[0] / traindataSet.get(j).numInstances()) < 0.5) {
- candidates.add(classifier);
+ if((errors[0] < 0.5) && (errors[0] < 0.5)) {
+ candidates.add(classifier);
}
}
@@ -406,5 +477,4 @@
// now we have the best classifier for this training data
classifiers.add(best);
-
}
}
@@ -417,5 +487,4 @@
int vote_positive = 0;
- int vote_negative = 0;
for (int i = 0; i < classifiers.size(); i++) {
@@ -432,6 +501,4 @@
if(fitest.execute_double(0, vars) < 0.5) {
vote_positive += 1;
- }else {
- vote_negative += 1;
}
}
@@ -450,6 +517,6 @@
*
* for one test data set:
- * for one in 6 possible training data sets:
- * For 200 GP Runs:
+ * for one in X possible training data sets:
+ * For Y GP Runs:
* train one Classifier with this training data
* then evaluate the classifier with the remaining project
@@ -465,5 +532,36 @@
private static final long serialVersionUID = 3708714057579101522L;
-
+ protected int populationSize;
+ protected int initMinDepth;
+ protected int initMaxDepth;
+ protected int tournamentSize;
+ protected int maxGenerations;
+ protected double errorType2Weight;
+ protected int numberRuns;
+ protected int maxDepth;
+ protected int maxNodes;
+
+ /**
+ * Configure the GP Params and number of Runs
+ *
+ * @param populationSize
+ * @param initMinDepth
+ * @param initMaxDepth
+ * @param tournamentSize
+ * @param maxGenerations
+ * @param errorType2Weight
+ */
+ public void configure(int populationSize, int initMinDepth, int initMaxDepth, int tournamentSize, int maxGenerations, double errorType2Weight, int numberRuns, int maxDepth, int maxNodes) {
+ this.populationSize = populationSize;
+ this.initMinDepth = initMinDepth;
+ this.initMaxDepth = initMaxDepth;
+ this.tournamentSize = tournamentSize;
+ this.maxGenerations = maxGenerations;
+ this.errorType2Weight = errorType2Weight;
+ this.numberRuns = numberRuns;
+ this.maxDepth = maxDepth;
+ this.maxNodes = maxNodes;
+ }
+
/** Build the GP Multiple Data Sets Validation Classifier
*
@@ -485,6 +583,7 @@
// 200 runs
- for(int k=0; k < 200; k++) {
+ for(int k=0; k < this.numberRuns; k++) {
Classifier classifier = new GPRun();
+ ((GPRun)classifier).configure(this.populationSize, this.initMinDepth, this.initMaxDepth, this.tournamentSize, this.maxGenerations, this.errorType2Weight, this.maxDepth, this.maxNodes);
// one project is training data
@@ -498,5 +597,5 @@
// if type1 and type2 errors are < 0.5 we allow the model in the final voting
errors = this.evaluate((GPRun)classifier, traindataSet.get(j));
- if((errors[0] / traindataSet.get(j).numInstances()) < 0.5 && (errors[0] / traindataSet.get(j).numInstances()) < 0.5) {
+ if((errors[0] < 0.5) && (errors[0] < 0.5)) {
candidates.add(classifier);
}
@@ -549,4 +648,5 @@
public void buildClassifier(Instances traindata) throws Exception {
final Classifier classifier = new GPRun();
+ ((GPRun)classifier).configure(populationSize, initMinDepth, initMaxDepth, tournamentSize, maxGenerations, errorType2Weight, this.maxDepth, this.maxNodes);
classifier.buildClassifier(traindata);
classifiers.add(classifier);
@@ -562,5 +662,6 @@
int error_type1 = 0;
int error_type2 = 0;
- int number_instances = evalData.numInstances();
+ int positive = 0;
+ int negative = 0;
for(Instance instance: evalData) {
@@ -571,4 +672,11 @@
classification = fitest.execute_double(0, vars);
+
+ // we need to count the absolutes of positives for percentage
+ if(instance.classValue() == 1.0) {
+ positive +=1;
+ }else {
+ negative +=1;
+ }
// classification < 0.5 we say defective
@@ -584,10 +692,8 @@
}
- double et1_per = error_type1 / number_instances;
- double et2_per = error_type2 / number_instances;
-
- // return some kind of fehlerquote?
- //return (error_type1 + error_type2) / number_instances;
- return new double[]{error_type1, error_type2};
+ // return error types percentages for the types
+ double et1_per = error_type1 / negative;
+ double et2_per = error_type2 / positive;
+ return new double[]{et1_per, et2_per};
}
@@ -614,56 +720,3 @@
}
}
-
-
- /**
- * Custom GT implementation from the paper
- */
- public class GT extends MathCommand implements ICloneable {
-
- private static final long serialVersionUID = 113454184817L;
-
- public GT(final GPConfiguration a_conf, java.lang.Class a_returnType) throws InvalidConfigurationException {
- super(a_conf, 2, a_returnType);
- }
-
- public String toString() {
- return "GT(&1, &2)";
- }
-
- public String getName() {
- return "GT";
- }
-
- public float execute_float(ProgramChromosome c, int n, Object[] args) {
- float f1 = c.execute_float(n, 0, args);
- float f2 = c.execute_float(n, 1, args);
-
- float ret = 1.0f;
- if(f1 > f2) {
- ret = 0.0f;
- }
-
- return ret;
- }
-
- public double execute_double(ProgramChromosome c, int n, Object[] args) {
- double f1 = c.execute_double(n, 0, args);
- double f2 = c.execute_double(n, 1, args);
-
- double ret = 1;
- if(f1 > f2) {
- ret = 0;
- }
- return ret;
- }
-
- public Object clone() {
- try {
- GT result = new GT(getGPConfiguration(), getReturnType());
- return result;
- }catch(Exception ex) {
- throw new CloneException(ex);
- }
- }
- }
}