Changeset 10142 for branches/DataAnalysis.IslandAlgorithms/HeuristicLab.Algorithms.DataAnalysis.Symbolic/3.3/SymbolicDataAnalysisIslandGAEvaluator.cs

Timestamp:

11/20/13 10:52:38 (10 years ago)

Author:

mkommend

Message:

#1997: Worked on symbolic data analysis island algorithms:

• Refactored and removed unnecessary code in symbolic island GA and symbolic island GA evaluator.
• Added EnumerableItem to ease the passing of rows to the actual evaluator.
• Added prototype of island offspring selection GA.
• Corrected build configuration (plugin classes, project settings, ...)

File:

• branches/DataAnalysis.IslandAlgorithms/HeuristicLab.Algorithms.DataAnalysis.Symbolic/3.3/SymbolicDataAnalysisIslandGAEvaluator.cs (modified) (3 diffs)

branches/DataAnalysis.IslandAlgorithms/HeuristicLab.Algorithms.DataAnalysis.Symbolic/3.3/SymbolicDataAnalysisIslandGAEvaluator.cs

@@ -71 +71 @@
       get { return (ILookupParameter<IntValue>)Parameters[RandomSamplesParameterName]; }
     }
+
+    public IValueParameter<EnumerableItem<int>> RowsParameter {
+      get { return (IValueParameter<EnumerableItem<int>>)Parameters["Rows"]; }
+    }
     #endregion
 
@@ -93 +97 @@
       Parameters.Add(new LookupParameter<IntRange>(FixedSamplesPartitionParameterName, "The data partition which is used to calculate the fitness on the fixed samples."));
       Parameters.Add(new LookupParameter<IntValue>(RandomSamplesParameterName, "The number of random samples used for fitness calculation in each island."));
+      Parameters.Add(new OptionalValueParameter<EnumerableItem<int>>("Rows", "TODO"));
 
       EvaluatorParameter.Hidden = true;
@@ -99 +104 @@
     public override IOperation Apply() {
       var evaluator = EvaluatorParameter.ActualValue;
-      var tree = SymbolicExpressionTreeParameter.ActualValue;
       var problemData = ProblemDataParameter.ActualValue;
 
-      var samplesStart = FitnessCalculationPartitionParameter.ActualValue.Start;
-      var samplesEnd = FitnessCalculationPartitionParameter.ActualValue.End;
-      var fixedSamplesStart = FixedSamplesPartitionParameter.ActualValue.Start;
-      var fixedSamplesEnd = FixedSamplesPartitionParameter.ActualValue.End;
+      var samples = FitnessCalculationPartitionParameter.ActualValue;
+      var fixedSamples = FixedSamplesPartitionParameter.ActualValue;
       var randomSamples = RandomSamplesParameter.ActualValue.Value;
-      var maxRandomSamples = samplesEnd - samplesStart - fixedSamplesEnd + fixedSamplesStart;
 
-      //create rows for evaluation
-      var fixedRows = Enumerable.Range(fixedSamplesStart, fixedSamplesEnd - fixedSamplesStart);
-      var randomRows = Enumerable.Range(samplesStart, samplesEnd - samplesStart).Where(r => r < fixedSamplesStart || r >= fixedSamplesEnd);
-      randomRows = randomRows.SampleRandomWithoutRepetition(RandomParameter.ActualValue, randomSamples, maxRandomSamples);
-      var rows = fixedRows.Concat(randomRows);
+      //create fixed rows enumerable
+      var rows = Enumerable.Range(fixedSamples.Start, fixedSamples.Size);
+      //create randomly chosen rows enumerable
+      if (randomSamples > 0) {
+        if (randomSamples > samples.Size - fixedSamples.Size) {
+          var error = string.Format("Could not select {0} random samples, because there are {1} total samples present from which {2} where used in the fixed partition. Please lower the number of random samples in the algorithm configuration.", randomSamples, samples.Size, fixedSamples.Size);
+          throw new OperatorExecutionException(this, error);
+        }
+        var randomRows = Enumerable.Range(samples.Start, samples.Size).Where(r => r < fixedSamples.Start || r >= fixedSamples.End);
+        randomRows = randomRows.SampleRandomWithoutRepetition(RandomParameter.ActualValue, randomSamples, samples.Size - fixedSamples.Size);
 
+        rows = rows.Concat(randomRows);
+      }
+      //filter out test rows
+      rows = rows.Where(r => r < problemData.TestPartition.Start && r > problemData.TestPartition.End);
+
+      //execution context is created manually to be able to clear the rows parameter easily
+      RowsParameter.Value = new EnumerableItem<int>(rows);
       var executionContext = new ExecutionContext(ExecutionContext, evaluator, ExecutionContext.Scope);
-      var fitness = evaluator.Evaluate(executionContext, tree, problemData, rows);
-      QualityParameter.ActualValue = new DoubleValue(fitness);
-      return base.Apply();
+      var successor = evaluator.Execute(executionContext, this.CancellationToken);
+      RowsParameter.Value = null;
+      return new OperationCollection(successor, base.Apply());
     }
   }