consider node types during generation

1 files changed, 28 insertions, 17 deletions

diff --git a/Solvers/VIATRA-Solver/hu.bme.mit.inf.dslreasoner.viatrasolver.reasoner/src/hu/bme/mit/inf/dslreasoner/viatrasolver/reasoner/dse/HillClimbingOnRealisticMetricStrategyForModelGeneration.java b/Solvers/VIATRA-Solver/hu.bme.mit.inf.dslreasoner.viatrasolver.reasoner/src/hu/bme/mit/inf/dslreasoner/viatrasolver/reasoner/dse/HillClimbingOnRealisticMetricStrategyForModelGeneration.java
index d9f6f2aa..6c13e760 100644
--- a/Solvers/VIATRA-Solver/hu.bme.mit.inf.dslreasoner.viatrasolver.reasoner/src/hu/bme/mit/inf/dslreasoner/viatrasolver/reasoner/dse/HillClimbingOnRealisticMetricStrategyForModelGeneration.java
+++ b/Solvers/VIATRA-Solver/hu.bme.mit.inf.dslreasoner.viatrasolver.reasoner/src/hu/bme/mit/inf/dslreasoner/viatrasolver/reasoner/dse/HillClimbingOnRealisticMetricStrategyForModelGeneration.java
@@ -190,16 +190,17 @@ public class HillClimbingOnRealisticMetricStrategyForModelGeneration implements
                                 }
                                 
                                 //Output intermediate model
-                                if(model.getNewElements().size() >= 10) {
+                                if(model.getNewElements().size() > 0) {
                                         epsilon = 1.0/count;
                                         draw = Math.random();
                                         count++;
                                         // cut off the trajectory for bad graph
-                                        System.out.println("KS");
+                                        //System.out.println("KS");
                                         System.out.println("NA distance: " + heuristics.getNADistance());       
                                         System.out.println("MPC distance: " + heuristics.getMPCDistance());                             
                                         System.out.println("Out degree distance:" + heuristics.getOutDegreeDistance()); 
-                                        
+                                        System.out.println("Exit :" + heuristics.getNodeTypePercentage("Exit"));        
+
 //                                      MetricDistanceGroup lsHeuristic = metricDistance.calculateMetricDistance(model);
 //                                      System.out.println("LS");
 //                                      System.out.println("NA distance: " + lsHeuristic.getNADistance());      
@@ -208,7 +209,11 @@ public class HillClimbingOnRealisticMetricStrategyForModelGeneration implements
                                         
                                         //check for next value when doing greedy move
                                         
-                                        valueMap = sortWithWeight(activationIds, currentTrajectoryWithFittness.trajectory.length+1);
+                                        if(activationIds.size() > 50) {
+                                                activationIds = activationIds.subList(0, 50);
+                                        }
+                                        
+                                        valueMap = sortWithWeight(activationIds, model.getNewElements().size());
                                 }
                                 lastState = context.getCurrentStateId();
                                 while (!isInterrupted && !configuration.progressMonitor.isCancelled() && activationIds.size() > 0) {
@@ -224,7 +229,8 @@ public class HillClimbingOnRealisticMetricStrategyForModelGeneration implements
                                         
                                         boolean consistencyCheckResult = checkConsistency(currentTrajectoryWithFittness);
                                         if(consistencyCheckResult == true) { continue mainLoop; }
-
+                                        boolean shouldFinish = model.getNewElements().size() >= configuration.typeScopes.maxNewElements + 2;
+                                        
         /*                              if (context.isCurrentStateAlreadyTraversed()) {
 //                                              logger.info("The new state is already visited.");
 //                                              context.backtrack();
@@ -232,22 +238,22 @@ public class HillClimbingOnRealisticMetricStrategyForModelGeneration implements
                                                 logger.debug("Global contraint is not satisifed.");
                                                 context.backtrack();
                                         } else*/// {
-                                        /*if(getNumberOfViolations(mustMatchers) > 0) {
+                                        if(getNumberOfViolations(mustMatchers) > 0 && !shouldFinish) {
                                                 context.backtrack();
-                                        }else*/ if(model.getNewElements().size() > 90 && heuristics.getNADistance() + heuristics.getMPCDistance() + heuristics.getOutDegreeDistance() > 0.3) {
+                                        }else if(model.getNewElements().size() > 90 && heuristics.getNADistance() + heuristics.getMPCDistance() + heuristics.getOutDegreeDistance() > 5.3) {
                                                 context.backtrack();
-                                        }else if(model.getNewElements().size() > 70 && heuristics.getNADistance() + heuristics.getMPCDistance() + heuristics.getOutDegreeDistance() > 0.45) {
+                                        }else if(model.getNewElements().size() > 70 && heuristics.getNADistance() + heuristics.getMPCDistance() + heuristics.getOutDegreeDistance() > 5.45) {
                                                 context.backtrack();
-                                        } else if(model.getNewElements().size() > 50 && heuristics.getNADistance() + heuristics.getMPCDistance() + heuristics.getOutDegreeDistance() > 0.60) {
+                                        } else if(model.getNewElements().size() > 50 && heuristics.getNADistance() + heuristics.getMPCDistance() + heuristics.getOutDegreeDistance() > 5.60) {
                                                 context.backtrack();
-                                        } else if(model.getNewElements().size() > 30 && heuristics.getNADistance() + heuristics.getMPCDistance() + heuristics.getOutDegreeDistance() > 0.70) {
+                                        } else if(model.getNewElements().size() > 30 && heuristics.getNADistance() + heuristics.getMPCDistance() + heuristics.getOutDegreeDistance() > 5.70) {
                                                 context.backtrack();
                                         }else {
                                                 final Fitness nextFitness = context.calculateFitness();
                                                 
                                                 // the only hard objectives are the size
                                                 
-                                                if(model.getNewElements().size() >= configuration.typeScopes.maxNewElements + 2) {
+                                                if(shouldFinish) {
                                                         System.out.println("Solution Found!!");
                                                         System.out.println("# violations: " + (getNumberOfViolations(mustMatchers) + getNumberOfViolations(mayMatchers)));
                                                         nextFitness.setSatisifiesHardObjectives(true);
@@ -269,8 +275,9 @@ public class HillClimbingOnRealisticMetricStrategyForModelGeneration implements
                                                 int step = nextTrajectoryWithFittness.trajectory.length;
                                                 int violation = getNumberOfViolations(mustMatchers) + getNumberOfViolations(mayMatchers);
                                                 metricDistance.getLinearModel().feedData(context.getCurrentStateId(), metricDistance.calculateFeature(step, violation), calculateCurrentStateValue(step, violation), lastState);
+                                                double value = calculateCurrentStateValue(step, violation);
                                                 
-                                                trajectoryFit.put(nextTrajectoryWithFittness, calculateCurrentStateValue(step, violation));
+                                                trajectoryFit.put(nextTrajectoryWithFittness, value);
                                                 trajectoiresToExplore.add(nextTrajectoryWithFittness);
                                                 
                                                 int compare = objectiveComparatorHelper.compare(currentTrajectoryWithFittness.fitness,
@@ -311,19 +318,21 @@ public class HillClimbingOnRealisticMetricStrategyForModelGeneration implements
                         // do hill climbing
                         for(Object id : activationIds) {
                                 context.executeAcitvationId(id);
-                                int violation = getNumberOfViolations(mayMatchers) + getNumberOfViolations(mustMatchers);
+                                int violation = getNumberOfViolations(mayMatchers);
                                 valueMap.put(id, calculateFutureStateValue(factor, violation));
                                 context.backtrack();
                         }
                         
+                        activationIds.removeIf(li -> valueMap.get(li) >= 10000);
                         Collections.sort(activationIds, Comparator.comparing(li -> valueMap.get(li)));
                         return valueMap;
                 }
                 
                 private double calculateFutureStateValue(int step, int violation) {
                         double currentValue = calculateCurrentStateValue(step, violation);
-                        if(step > 40) {
-                                double[] toPredict = metricDistance.calculateFeature(200, violation);
+                        
+                        if(step > 10 && currentValue < 10000) {
+                                double[] toPredict = metricDistance.calculateFeature(100, violation);
                                 try {
                                         return metricDistance.getLinearModel().getPredictionForNextDataSample(metricDistance.calculateFeature(step, violation), currentValue, toPredict);
                                 }catch(IllegalArgumentException e) {
@@ -337,10 +346,12 @@ public class HillClimbingOnRealisticMetricStrategyForModelGeneration implements
                 private double calculateCurrentStateValue(int factor, int violation) {
                         PartialInterpretation model = (PartialInterpretation) context.getModel();
                         MetricDistanceGroup g = metricDistance.calculateMetricDistanceKS(model);
-                        
+                        if(g.getNodeTypePercentage("Exit") > 0.0) {
+                                return 10000;
+                        }
                         double consistenceWeights = 1- 1.0/(1+violation);
                                                 
-                        return( /*/ Math.log(factor)*/(g.getNADistance() + g.getMPCDistance() + g.getOutDegreeDistance()) + consistenceWeights);
+                        return( 5.0 *(g.getNADistance() + g.getMPCDistance() + g.getOutDegreeDistance()) + consistenceWeights);
                 }
                 
                 private int getNumberOfViolations(Collection<ViatraQueryMatcher<? extends IPatternMatch>> matchers) {