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[DL-Learner SVN] SF.net SVN: dl-learner: [122] trunk/src/dl-learner/org/dllearner/algorithms /gp
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From: <jen...@us...> - 2007-08-31 15:46:03
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Revision: 122
http://dl-learner.svn.sourceforge.net/dl-learner/?rev=122&view=rev
Author: jenslehmann
Date: 2007-08-31 07:21:57 -0700 (Fri, 31 Aug 2007)
Log Message:
-----------
fixed performance issue in genetic programming algorithm (2nd child was overwritten when performing crossover)
Modified Paths:
--------------
trunk/src/dl-learner/org/dllearner/algorithms/gp/GP.java
trunk/src/dl-learner/org/dllearner/algorithms/gp/Program.java
Modified: trunk/src/dl-learner/org/dllearner/algorithms/gp/GP.java
===================================================================
--- trunk/src/dl-learner/org/dllearner/algorithms/gp/GP.java 2007-08-31 10:23:51 UTC (rev 121)
+++ trunk/src/dl-learner/org/dllearner/algorithms/gp/GP.java 2007-08-31 14:21:57 UTC (rev 122)
@@ -1,6 +1,25 @@
+/**
+ * Copyright (C) 2007, Jens Lehmann
+ *
+ * This file is part of DL-Learner.
+ *
+ * DL-Learner 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.
+ *
+ * DL-Learner 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/>.
+ *
+ */
+
package org.dllearner.algorithms.gp;
-
import static org.dllearner.Config.GP.algorithmType;
import static org.dllearner.Config.GP.crossoverProbability;
import static org.dllearner.Config.GP.elitism;
@@ -26,7 +45,6 @@
import org.dllearner.Config;
import org.dllearner.LearningProblem;
-import org.dllearner.Main;
import org.dllearner.Score;
import org.dllearner.algorithms.LearningAlgorithm;
import org.dllearner.algorithms.hybridgp.Psi;
@@ -34,7 +52,6 @@
import org.dllearner.dl.Top;
import org.dllearner.utilities.Helper;
-
/**
* This class implements the genetic programming (GP) algorithm and provides
* methods to configure and run it.
@@ -85,6 +102,8 @@
private Comparator<Program> fitnessComparator;
private static Random rand = new Random();
+
+ private long startTime;
private Score bestScore;
private Concept bestConcept;
@@ -105,7 +124,7 @@
this.learningProblem = learningProblem;
}
- public void start() {
+ public void start() {
// falls refinement-Wahrscheinlichkeit größer 0, dann erzeuge psi
psi = new Psi(learningProblem);
@@ -180,6 +199,8 @@
}
};
+ startTime = System.nanoTime();
+
// System.out.println("If Java does not allocate enough memory on your system\n" +
// "use \"java -Xmx128m Start\" to start the program. This allocates\n" +
// "a maximum of 128 MB of memory. 64 MB are sufficient so it is usually\n" +
@@ -201,8 +222,9 @@
Program[] newIndividuals = new Program[numberOfNewIndividuals];
Program[] tmp = new Program[2];
- long startTime = System.currentTimeMillis();
+ // long startTime = System.currentTimeMillis();
+
int generation = 0;
// MAIN LOOP
@@ -259,6 +281,12 @@
newIndividuals[i] = tmp[0];
newIndividuals[i + 1] = tmp[1];
+
+ // Incrementing i here is a significant code change! (2007/08/31)
+ // This is done, because crossover uses two individuals as input and
+ // outputs two. Without the increment that second produced child is
+ // overwritten, which caused a significant loss in performance.
+ i++;
// mutation
} else if(rand >= crossoverBoundary && rand < mutationBoundary) {
newIndividuals[i] = GPUtilities.mutation(learningProblem, individuals[selectedIndividuals[i]]);
@@ -346,7 +374,7 @@
// fittestIndividual.optimize();
- long endTime = System.currentTimeMillis();
+ long endTime = System.nanoTime(); // .currentTimeMillis();
// R�ckgabewert des Algorithmus speichern
bestScore = fittestIndividual.getScore();
bestConcept = fittestIndividual.getTree();
@@ -381,7 +409,7 @@
System.out.println("generations: " + generation);
System.out.println("fittest individual found after "
+ fittestIndividualGeneration + " generations");
- System.out.println("runtime in ms: " + (endTime - startTime));
+ System.out.println("runtime in ms: " + Helper.prettyPrintNanoSeconds(endTime - startTime));
System.out.println("fitness evaluations: "
+ GPUtilities.fitnessEvaluations);
if(Config.algorithm == Config.Algorithm.HYBRID_GP) {
@@ -660,9 +688,14 @@
conceptLengthSum += p.getTree().getLength();
double conceptLengthAverage = conceptLengthSum/(double)individuals.length;
System.out.println("average concept length: " + df.format(conceptLengthAverage));
- long algorithmTime = System.nanoTime() - Main.getAlgorithmStartTime();
+ // long algorithmTime = System.nanoTime() - Main.getAlgorithmStartTime();
+ long algorithmTime = System.nanoTime() - startTime;
System.out.println("overall algorithm runtime: " + Helper.prettyPrintNanoSeconds(algorithmTime));
+ // System.out.println("instance checks: " + learningProblem.getReasoningService().getNrOfInstanceChecks());
+ // System.out.println("fitness evals: " + Program.fitnessEvaluations);
+ // System.out.println("nr. of individuals: " + individuals.length + " (" + numberOfSelectedIndividuals + " selected)");
+
// für temporäre Performance-Werte
// double some = 100*(psi.someTime/(double)algorithmTime);
// System.out.println("some: " + df.format(some) + "%");
Modified: trunk/src/dl-learner/org/dllearner/algorithms/gp/Program.java
===================================================================
--- trunk/src/dl-learner/org/dllearner/algorithms/gp/Program.java 2007-08-31 10:23:51 UTC (rev 121)
+++ trunk/src/dl-learner/org/dllearner/algorithms/gp/Program.java 2007-08-31 14:21:57 UTC (rev 122)
@@ -1,3 +1,23 @@
+/**
+ * Copyright (C) 2007, Jens Lehmann
+ *
+ * This file is part of DL-Learner.
+ *
+ * DL-Learner 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.
+ *
+ * DL-Learner 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/>.
+ *
+ */
+
package org.dllearner.algorithms.gp;
import org.dllearner.Score;
@@ -6,25 +26,12 @@
/**
* This class represents a program, i.e. an individual.
*
- * Um spaeter KAON2-Queries zu unterstuetzen, muesste man aus Program ein Interface
- * oder eine abstrakte Klasse machen. Diese Klasse wird zu OwnProgram o.ä. und
- * es wird eine zusaetzliche Klasse KAON2Program erstellt, die dann Queries an
- * KAON2 stellt und im Konstruktor eine KAON2-DL entgegennimmt. Programmiert
- * wird im GP-Algorithmus dann natuerlich nur gegen das Interface. Schwierig wird
- * lediglich, dass KAON2 eine andere Struktur hat, also ev. die Utility-Methoden
- * angepasst werden muessen. :-/
- *
- * Neu: Das Ganze wurde jetzt ueber einen abstrahierten Reasoner und die Klasse
- * LearningProblem erledigt. TODO: Eventuell ist etwas wie "ProblemSolution" ein
- * besserer Name. Bei Program scheint unklar, warum das LearningProblem hier eine
- * Rolle spielt.
- *
* @author Jens Lehmann
*
*/
public class Program {
- // private static int fitnessEvaluations = 0;
+ // public static int fitnessEvaluations = 0;
private Concept hypothesis;
@@ -39,7 +46,7 @@
// private LearningProblem learningProblem;
private double fitness;
-
+
/**
* Create a new program.
*
@@ -60,7 +67,10 @@
// fitness = score.getScore() - hypothesis.getLength() * Config.percentPerLengthUnit;
// => in getScore() ist jetzt schon der length penalty integriert
fitness = score.getScore();
-
+ // fitnessEvaluations++;
+
+ // System.out.println("new program: " + hypothesis);
+
/*
// falls R�ckgabetyp spezifiziert ist, dann muss hier der Baum
// entsprechend ver�ndert werden
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