Merging mk branch revs. 587:589 (tool tips for statistics data fields)

src/eva2/gui/AbstractListSelectionEditor.java

@@ -67,6 +67,16 @@ public abstract class AbstractListSelectionEditor extends JPanel implements Prop
      */
     protected abstract String getElementName(int i);
     
+    /**
+     * Get the tool tip of an element or null if none is available.
+     * 
+     * @param i
+     * @return
+     */
+    protected String getElementToolTip(int i) {
+    	return null;
+    }
+    
     /**
      * Get the selection state of an element.
      * 
@@ -85,6 +95,7 @@ public abstract class AbstractListSelectionEditor extends JPanel implements Prop
             this.m_BlackCheck = new JCheckBox[getElementCount()];
             for (int i = 0; i < getElementCount(); i++) {
                  this.m_BlackCheck[i] = new JCheckBox(getElementName(i), isElementSelected(i));
+                 this.m_BlackCheck[i].setToolTipText(getElementToolTip(i));
                  this.m_BlackCheck[i].addActionListener(new ActionListener() {
                     public void actionPerformed(ActionEvent ev) {
                         if (actionOnSelect()) m_Support.firePropertyChange("AbstractListSelectionEditor", null, this);

src/eva2/gui/StringSelectionEditor.java

@@ -6,7 +6,7 @@ public class StringSelectionEditor extends AbstractListSelectionEditor {
 	StringSelection strs;
 	
 	public StringSelectionEditor() {
-		strs = new StringSelection(new String[]{});
+		strs = new StringSelection(new String[]{}, null);
 	}
 	
 	@Override
@@ -27,6 +27,11 @@ public class StringSelectionEditor extends AbstractListSelectionEditor {
 		return strs.getElement(i);
 	}
 	
+	@Override
+	protected String getElementToolTip(int i) {
+		return strs.getElementInfo(i);
+	}
+
 	@Override
 	public Object getValue() {
 		return strs;

src/eva2/server/go/problems/AbstractOptimizationProblem.java

@@ -32,7 +32,6 @@ import eva2.server.go.operators.terminators.EvaluationTerminator;
 import eva2.server.go.operators.terminators.PhenotypeConvergenceTerminator;
 import eva2.server.go.populations.Population;
 import eva2.server.go.strategies.InterfaceOptimizer;
-import eva2.tools.math.Mathematics;
 
 /**
  * Created by IntelliJ IDEA.
@@ -244,6 +243,18 @@ implements InterfaceOptimizationProblem /*, InterfaceParamControllable*/, Serial
     	else return new String[]{"Solution"};
     }
     
+    /** This method returns the header for the additional data that is to be written into a file
+     * @param pop   The population that is to be refined.
+     * @return String
+     */
+    public String[] getAdditionalFileStringInfo(PopulationInterface pop) {
+    	if (this instanceof InterfaceInterestingHistogram) 
+    		return new String[]{"Representation of the current best individual",
+    			"Fitness histogram of the current population",
+    			"Fitness threshold based score of the current population"};
+    	else return new String[]{"Representation of the current best individual"};
+    }
+    
     /** This method returns the additional data that is to be written into a file
      * @param pop   The population that is to be refined.
      * @return String
@@ -251,8 +262,16 @@ implements InterfaceOptimizationProblem /*, InterfaceParamControllable*/, Serial
     public Object[] getAdditionalFileStringValue(PopulationInterface pop) {
     	String solStr = AbstractEAIndividual.getDefaultDataString(pop.getBestIndividual()); 
     	if (this instanceof InterfaceInterestingHistogram) {
+    		int fitCrit=0;
     		SolutionHistogram hist = ((InterfaceInterestingHistogram)this).getHistogram();
-    		Population sols = PostProcess.clusterBestUpdateHistogram((Population)pop, this, hist, 0, getDefaultAccuracy());
+    		if (pop.getBestFitness()[fitCrit] < hist.getUpperBound()) {
+        		Population maybeFiltered = (Population)pop; 
+        		if (pop.size()>100) { // for efficiency reasons...
+        			maybeFiltered = maybeFiltered.filterByFitness(hist.getUpperBound(), fitCrit);
+//        			System.out.println("Reduced " + pop.size() + " to " + maybeFiltered.size());
+        		}
+    			Population sols = PostProcess.clusterBestUpdateHistogram((Population)maybeFiltered, this, hist, fitCrit, getDefaultAccuracy());
+    		}
     		return new Object[]{solStr, hist, hist.getScore()};
     	} else return new Object[]{solStr}; 
     }
@@ -341,13 +360,15 @@ implements InterfaceOptimizationProblem /*, InterfaceParamControllable*/, Serial
      * @param epsilonPhenoSpace maximal allowed improvement of an individual before considered premature (given as distance in the search space)
      * @param epsilonFitConv if positive: additional absolute convergence criterion (fitness space) as termination criterion of the local search  
      * @param clusterSigma minimum cluster distance
-     * @param maxEvalsPerIndy 
+     * @param maxEvalsPerIndy maximum number of evaluations or -1 to take the maximum
      * @see #isPotentialOptimumNMS(AbstractEAIndividual, double, double, int)
      * @return 
      */
-    public static Population extractPotentialOptima(AbstractOptimizationProblem prob, Population pop, double epsilonPhenoSpace, double epsilonFitConv, double clusterSigma, int maxEvalsPerIndy) {
+    public static Population extractPotentialOptima(AbstractOptimizationProblem prob, Population pop, 
+    		double epsilonPhenoSpace, double epsilonFitConv, double clusterSigma, int maxEvalsPerIndy) {
     	Population potOptima = new Population();
     	for (int i = 0; i < pop.size(); ++i){
+    		//System.out.println("Refining " + i + " of " + pop.size());
     		AbstractEAIndividual indy = pop.getEAIndividual(i);
 //    		System.out.println("bef: " + indy.toString());
     		boolean isConverged = AbstractOptimizationProblem.isPotentialOptimumNMS(prob, indy, epsilonPhenoSpace, epsilonFitConv, maxEvalsPerIndy);
@@ -429,7 +450,8 @@ implements InterfaceOptimizationProblem /*, InterfaceParamControllable*/, Serial
      * distance from the original individual in phenotype space. The maxEvaluations parameter gives the maximum evaluations
      * for the local search. Using the epsilonFitConv parameter one may define a convergence criterion as PhenotypeConvergenceTerminator 
      * which is combined (using OR) with the evaluation counter.
-     * If maxEvaluations is smaller than zero, 100*dim is used. Be aware that this may cost quite some runtime depending on the target
+     * If maxEvaluations is smaller than zero, a maximum of 500*dim evaluations is employed. 
+     * Be aware that this may cost quite some runtime depending on the target
      * function.
      * A double value for the distance by which it was moved is added to the individual using the key PostProcess.movedDistanceKey.
      * 
@@ -452,7 +474,7 @@ implements InterfaceOptimizationProblem /*, InterfaceParamControllable*/, Serial
 //    		initPerturb = epsilonPhenoSpace/(2*(Mathematics.getAvgRange(((InterfaceDataTypeDouble)orig).getDoubleRange())));
     		initRelPerturb = epsilonPhenoSpace*0.5;
     		dim=((InterfaceDataTypeDouble)orig).getDoubleRange().length;
-        	if (maxEvaluations<0) maxEvaluations = 100*AbstractEAIndividual.getDoublePositionShallow(prob.m_Template).length; // scales the effort with the number of problem dimensions
+        	if (maxEvaluations<0) maxEvaluations = 500*AbstractEAIndividual.getDoublePositionShallow(prob.m_Template).length; // scales the effort with the number of problem dimensions
     	} else {
     		System.err.println("Cannot initialize NMS on non-double valued individuals!");
     		return false;
@@ -461,9 +483,10 @@ implements InterfaceOptimizationProblem /*, InterfaceParamControllable*/, Serial
     	Population pop = new Population(1);
     	pop.add(orig);
     	InterfaceTerminator term = new EvaluationTerminator(maxEvaluations); 
-    	if (epsilonFitConv > 0) term = new CombinedTerminator(new PhenotypeConvergenceTerminator(epsilonFitConv, 10*dim, true, true), term, false);
+    	if (epsilonFitConv > 0) term = new CombinedTerminator(new PhenotypeConvergenceTerminator(epsilonFitConv, 100*dim, true, true), term, false);
     	int evalsPerf = PostProcess.processSingleCandidatesNMCMA(PostProcessMethod.nelderMead, pop, term, initRelPerturb, prob);
     	overallDist = metric.distance(indy, pop.getBestEAIndividual());
+    	//System.out.println(System.currentTimeMillis() + " in " + evalsPerf + " evals moved by "+ overallDist);
 //    	System.out.println("aft: " + pop.getBestEAIndividual().toString() + ", evals performed: " + evalsPerf + ", opt moved by " + overallDist);
 //    	System.out.println("terminated because: " + term.lastTerminationMessage());
     	orig.putData(PostProcess.movedDistanceKey, overallDist);
@@ -471,6 +494,14 @@ implements InterfaceOptimizationProblem /*, InterfaceParamControllable*/, Serial
 //    	if (overallDist==0) {
 //    		PostProcess.processSingleCandidatesNMCMA(PostProcessMethod.nelderMead, pop, term, initPerturb, this);
 //    	}
+
+//    		System.out.println("Checked "+ indy.getStringRepresentation());
+//    		String msg = 								"----discarding ";
+//    		if (overallDist <= epsilonPhenoSpace) msg=	"++++keeping    ";
+//    		System.out.println(msg + BeanInspector.toString(indy.getDoublePosition()));
+//    		System.out.println("which moved to " + BeanInspector.toString(pop.getBestEAIndividual().getDoublePosition()));
+//    		System.out.println(" by " + overallDist + " > " + epsilonPhenoSpace);
+
     	return (overallDist < epsilonPhenoSpace);
     }
     

src/eva2/server/go/problems/AbstractProblemDouble.java

@@ -255,7 +255,8 @@ public abstract class AbstractProblemDouble extends AbstractOptimizationProblem
 //		rotation = new Matrix(dim, dim);
 //		rotation = Mathematics.getRotationMatrix(vec).transpose();
 		rotation = Mathematics.getRotationMatrix((rotAngle*Math.PI/180.), dim).transpose();
-		//	System.out.println(BeanInspector.toString(eval(vec.getColumnPackedCopy())));
+		//double[] t= new double[dim]; Arrays.fill(t, 1.);
+		//System.out.println(BeanInspector.toString(rotation.times(t)));
 		return rotation;
 	}
 	
@@ -354,7 +355,9 @@ public abstract class AbstractProblemDouble extends AbstractOptimizationProblem
     }
     
 	public double functionValue(double[] point) {
-		return eval(project2DPoint(point))[0];
+		double[] x=project2DPoint(point);
+		double v = eval(x)[0]; 
+		return v; 
 	}
 	
 	/**
@@ -498,6 +501,16 @@ public abstract class AbstractProblemDouble extends AbstractOptimizationProblem
 		else return superHeader;
 	}
 	
+	@Override
+	public String[] getAdditionalFileStringInfo(PopulationInterface pop) {
+		String[] superInfo = super.getAdditionalFileStringInfo(pop);
+		if (isWithConstraints()) 
+			return ToolBox.appendArrays(superInfo, new String[]{"Raw fitness (unpenalized) of the current best individual",
+				"The number of constraints violated by the current best individual",
+				"The sum of constraint violations of the current best individual"});
+		else return superInfo;
+	}
+	
 	@Override
 	public Object[] getAdditionalFileStringValue(PopulationInterface pop) {
 		Object[] superVal = super.getAdditionalFileStringValue(pop);

src/eva2/server/go/problems/InterfaceAdditionalPopulationInformer.java

@@ -19,6 +19,14 @@ public interface InterfaceAdditionalPopulationInformer {
 	 */
 	public String[] getAdditionalFileStringHeader(PopulationInterface pop);
 
+	/**
+	 * Optionally return informative descriptions of the data fields.
+	 * 
+	 * @param pop
+	 * @return
+	 */
+	public String[] getAdditionalFileStringInfo(PopulationInterface pop);
+	
 	/** 
 	 * This method returns additional statistical data.
 	 * @param pop   The population that is to be refined.

src/eva2/server/go/strategies/ClusterBasedNichingEA.java

@@ -1143,7 +1143,23 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
 	}
 
 	public String[] getAdditionalFileStringHeader(PopulationInterface pop) {
-		return new String[]{"Undiff.","#Act.spec.","Avg.Spec.Meas.","#Archived."}; 
+		return new String[]{"numUndiff","numActSpec","avgSpecMeas","numArchived", 
+				"archivedMedCorr", "archivedMeanDist"
+//				, "numCollisions", "clustSig"
+				}; 
+	}
+	
+	public String[] getAdditionalFileStringInfo(PopulationInterface pop) { // TODO use these as Tool Tip Texts???
+		return new String[] {
+				"The number of exploring individuals in the main population",
+				"The number of active species (sub-populations)",
+				"The average of the mean distance of individuals within a species",
+				"The number of stored potential local optima",
+				"The median correlation of archived solutions",
+				"The mean distance of archived solutions",
+//				"The number of collisions events that happened so far",
+//				"The clustering distance"
+		};
 	}
 	
 	public Object[] getAdditionalFileStringValue(PopulationInterface pop) {
@@ -1152,7 +1168,12 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
 				m_Undifferentiated.size(),
 				m_Species.size(),
 				getAvgSpeciesMeasures()[0],
-				m_Archive.size()};
+				m_Archive.size(),
+				m_Archive.getCorrelations()[3],
+				m_Archive.getPopulationMeasures()[0],
+//				collisions,
+//				getClusterDiffDist()
+				};
 //		return m_Undifferentiated.size() + " \t " + m_Species.size() + " \t " + BeanInspector.toString(getAvgSpeciesMeasures()[0]) + " \t " + (m_Archive.size());
 	}
 	

src/eva2/server/go/strategies/ClusteringHillClimbing.java

@@ -419,7 +419,11 @@ public class ClusteringHillClimbing implements InterfacePopulationChangedEventLi
 	}
 
 	public String[] getAdditionalFileStringHeader(PopulationInterface pop) {
-		return new String[]{"#Indies"};
+		return new String[]{"numIndies"};
+	}
+	
+	public String[] getAdditionalFileStringInfo(PopulationInterface pop) {
+		return new String[]{"The current population size"};
 	}
 	
 	public Object[] getAdditionalFileStringValue(PopulationInterface pop) {

src/eva2/server/go/strategies/ParticleSwarmOptimization.java

@@ -2060,10 +2060,14 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
 //	public void setDoLocalSearch(boolean doLocalSearch) {
 //		this.doLocalSearch = doLocalSearch;
 //	}
-
 	public String[] getAdditionalFileStringHeader(PopulationInterface pop) {
-		if (emaPeriods > 0) return new String[]{"MeanCurSpeed","MeanEMASpeed"};
+		if (emaPeriods > 0) return new String[]{"meanEMASpeed", "meanCurSpeed"};
-		else return new String[]{"MeanCurSpeed"};
+		else return new String[]{"meanCurSpeed"};
+	}	
+	
+	public String[] getAdditionalFileStringInfo(PopulationInterface pop) {
+		if (emaPeriods > 0) return new String[]{"Exponential moving average of the (range-relative) speed of all particles", "The mean (range-relative) current speed of all particles"};
+		else return new String[]{"The mean (range-relative) current speed of all particles"};
 	}
 
 	public Object[] getAdditionalFileStringValue(PopulationInterface pop) {

src/eva2/server/stat/GraphSelectionEnum.java

@@ -11,7 +11,26 @@ import eva2.tools.StringSelection;
  */
 public enum GraphSelectionEnum {
 	// DONT change this order, or the relation to AbstractStatistics will be lost
-	currentBest, meanFit, currentWorst, runBest, currentBestFeasible, runBestFeasible, avgPopDistance, maxPopDistance;
+	currentBest, meanFit, currentWorst, runBest, currentBestFeasible, runBestFeasible, 
+	avgPopDistance, maxPopDistance;
+	
+	private static String[] toolTips = { 
+		"The current best fitness within the population",
+		"The mean fitness within the population",
+		"The current worst fitness within the population",
+		"The best fitness up to the current generation",
+		"The best feasible fitness within the population",
+		"The best feasible fitness up to the current generation",
+		"The average phenotypic distance of individuals in the population",
+		"The maximum phenotypic distance of individuals in the population"
+	};
+	
+	public static String[] getInfoStrings() {
+		if (GraphSelectionEnum.values().length != toolTips.length) {
+			System.err.println("Error, mismatching length of info strings in GraphSelectionEnum");
+			return null;
+		} else return toolTips;
+	}
 	
 //	public static boolean doPlotCurrentBest(StringSelection sel) {
 //		return sel.isSelected(GraphSelectionEnum.currentBest.ordinal());

src/eva2/server/stat/StatsParameter.java

@@ -52,7 +52,7 @@ public class StatsParameter implements InterfaceStatisticsParameter, InterfaceNo
 	private boolean showAdditionalProblemInfo = false;
 	private double m_ConvergenceRateThreshold=0.001;
 
-	private StringSelection graphSel = new StringSelection(GraphSelectionEnum.currentBest);
+	private StringSelection graphSel = new StringSelection(GraphSelectionEnum.currentBest, GraphSelectionEnum.getInfoStrings());
 
 	/**
 	 *
@@ -381,14 +381,21 @@ public class StatsParameter implements InterfaceStatisticsParameter, InterfaceNo
 	public void setInformers(
 			List<InterfaceAdditionalPopulationInformer> informers) {
 		ArrayList<String> headerFields = new ArrayList<String>();
+		ArrayList<String> infoFields = new ArrayList<String>();
 		// parse list of header elements, show additional Strings according to names.
 		for (InterfaceAdditionalPopulationInformer inf : informers) {
 			headerFields.addAll(Arrays.asList(inf.getAdditionalFileStringHeader(null)));
+			if (infoFields.size()<headerFields.size()) { // add info strings for tool tips - fill up with null if none have been returned.
+				String[] infos = inf.getAdditionalFileStringInfo(null);
+				if (infos!=null) infoFields.addAll(Arrays.asList(infos));
+				while (infoFields.size()<headerFields.size()) infoFields.add(null);
+			}
 //			header += inf.getAdditionalFileStringHeader(null); // lets hope this works with a null 
 		}
 		// create additional fields to be selectable by the user, defined by the informer headers
 //		StringSelection ss = new StringSelection(GraphSelectionEnum.getAndAppendArray(headerFields.toArray(new String[headerFields.size()])));
-		StringSelection ss = new StringSelection(GraphSelectionEnum.currentBest, headerFields);
+		StringSelection ss = new StringSelection(GraphSelectionEnum.currentBest, GraphSelectionEnum.getInfoStrings(), 
+				headerFields, infoFields.toArray(new String[infoFields.size()]));
 		ss.takeOverSelection(graphSel);
 //		System.out.println("In " + this + ": setting new informers: " + BeanInspector.toString(ss.getStrings()));
 		// This works!