Changeset 15448 for trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective

Timestamp:

11/03/17 17:07:58 (7 years ago)

Author:

mkommend

Message:

#2852: Added counts of function and gradient evaluations by constants optimization as results.

File:

• trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionConstantOptimizationEvaluator.cs (modified) (16 diffs)

trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionConstantOptimizationEvaluator.cs

@@ -27 +27 @@
 using HeuristicLab.Data;
 using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
+using HeuristicLab.Optimization;
 using HeuristicLab.Parameters;
 using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
@@ -41 +42 @@
     private const string UpdateVariableWeightsParameterName = "Update Variable Weights";
 
+    private const string FunctionEvaluationsResultParameterName = "Constants Optimization Function Evaluations";
+    private const string GradientEvaluationsResultParameterName = "Constants Optimization Gradient Evaluations";
+
     public IFixedValueParameter<IntValue> ConstantOptimizationIterationsParameter {
       get { return (IFixedValueParameter<IntValue>)Parameters[ConstantOptimizationIterationsParameterName]; }
@@ -58 +62 @@
     public IFixedValueParameter<BoolValue> UpdateVariableWeightsParameter {
       get { return (IFixedValueParameter<BoolValue>)Parameters[UpdateVariableWeightsParameterName]; }
+    }
+
+    public IResultParameter<IntValue> FunctionEvaluationsResultParameter {
+      get { return (IResultParameter<IntValue>)Parameters[FunctionEvaluationsResultParameterName]; }
+    }
+    public IResultParameter<IntValue> GradientEvaluationsResultParameter {
+      get { return (IResultParameter<IntValue>)Parameters[GradientEvaluationsResultParameterName]; }
     }
 
@@ -100 +111 @@
       Parameters.Add(new FixedValueParameter<BoolValue>(UpdateConstantsInTreeParameterName, "Determines if the constants in the tree should be overwritten by the optimized constants.", new BoolValue(true)) { Hidden = true });
       Parameters.Add(new FixedValueParameter<BoolValue>(UpdateVariableWeightsParameterName, "Determines if the variable weights in the tree should be  optimized.", new BoolValue(true)) { Hidden = true });
+
+      Parameters.Add(new ResultParameter<IntValue>(FunctionEvaluationsResultParameterName, "The number of function evaluations performed by the constants optimization evaluator", "Results", new IntValue()));
+      Parameters.Add(new ResultParameter<IntValue>(GradientEvaluationsResultParameterName, "The number of gradient evaluations performed by the constants optimization evaluator", "Results", new IntValue()));
     }
 
@@ -112 +126 @@
       if (!Parameters.ContainsKey(UpdateVariableWeightsParameterName))
         Parameters.Add(new FixedValueParameter<BoolValue>(UpdateVariableWeightsParameterName, "Determines if the variable weights in the tree should be  optimized.", new BoolValue(true)));
-    }
-
+
+      if (!Parameters.ContainsKey(FunctionEvaluationsResultParameterName))
+        Parameters.Add(new ResultParameter<IntValue>(FunctionEvaluationsResultParameterName, "The number of function evaluations performed by the constants optimization evaluator", "Results", new IntValue()));
+      if (!Parameters.ContainsKey(GradientEvaluationsResultParameterName))
+        Parameters.Add(new ResultParameter<IntValue>(GradientEvaluationsResultParameterName, "The number of gradient evaluations performed by the constants optimization evaluator", "Results", new IntValue()));
+    }
+
+    private static readonly object locker = new object();
     public override IOperation InstrumentedApply() {
       var solution = SymbolicExpressionTreeParameter.ActualValue;
@@ -119 +139 @@
       if (RandomParameter.ActualValue.NextDouble() < ConstantOptimizationProbability.Value) {
         IEnumerable<int> constantOptimizationRows = GenerateRowsToEvaluate(ConstantOptimizationRowsPercentage.Value);
+        var counter = new EvaluationsCounter();
         quality = OptimizeConstants(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution, ProblemDataParameter.ActualValue,
-           constantOptimizationRows, ApplyLinearScalingParameter.ActualValue.Value, ConstantOptimizationIterations.Value, updateVariableWeights: UpdateVariableWeights, lowerEstimationLimit: EstimationLimitsParameter.ActualValue.Lower, upperEstimationLimit: EstimationLimitsParameter.ActualValue.Upper, updateConstantsInTree: UpdateConstantsInTree);
+           constantOptimizationRows, ApplyLinearScalingParameter.ActualValue.Value, ConstantOptimizationIterations.Value, updateVariableWeights: UpdateVariableWeights, lowerEstimationLimit: EstimationLimitsParameter.ActualValue.Lower, upperEstimationLimit: EstimationLimitsParameter.ActualValue.Upper, updateConstantsInTree: UpdateConstantsInTree, counter: counter);
 
         if (ConstantOptimizationRowsPercentage.Value != RelativeNumberOfEvaluatedSamplesParameter.ActualValue.Value) {
@@ -126 +147 @@
           quality = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, ProblemDataParameter.ActualValue, evaluationRows, ApplyLinearScalingParameter.ActualValue.Value);
         }
+
+        lock (locker) {
+          FunctionEvaluationsResultParameter.ActualValue.Value += counter.FunctionEvaluations;
+          GradientEvaluationsResultParameter.ActualValue.Value += counter.GradientEvaluations;
+        }
+
       } else {
         var evaluationRows = GenerateRowsToEvaluate();
@@ -139 +166 @@
       EstimationLimitsParameter.ExecutionContext = context;
       ApplyLinearScalingParameter.ExecutionContext = context;
+      FunctionEvaluationsResultParameter.ExecutionContext = context;
+      GradientEvaluationsResultParameter.ExecutionContext = context;
 
       // Pearson R² evaluator is used on purpose instead of the const-opt evaluator, 
@@ -148 +177 @@
       EstimationLimitsParameter.ExecutionContext = null;
       ApplyLinearScalingParameter.ExecutionContext = null;
+      FunctionEvaluationsResultParameter.ExecutionContext = null;
+      GradientEvaluationsResultParameter.ExecutionContext = null;
 
       return r2;
+    }
+
+    public class EvaluationsCounter {
+      public int FunctionEvaluations = 0;
+      public int GradientEvaluations = 0;
     }
 
@@ -156 +192 @@
       int maxIterations, bool updateVariableWeights = true,
       double lowerEstimationLimit = double.MinValue, double upperEstimationLimit = double.MaxValue,
-      bool updateConstantsInTree = true, Action<double[], double, object> iterationCallback = null) {
+      bool updateConstantsInTree = true, Action<double[], double, object> iterationCallback = null, EvaluationsCounter counter = null) {
 
       // numeric constants in the tree become variables for constant opt
@@ -171 +207 @@
         throw new NotSupportedException("Could not optimize constants of symbolic expression tree due to not supported symbols used in the tree.");
       if (parameters.Count == 0) return 0.0; // gkronber: constant expressions always have a R² of 0.0 
-
       var parameterEntries = parameters.ToArray(); // order of entries must be the same for x
 
@@ -188 +223 @@
 
       double originalQuality = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(interpreter, tree, lowerEstimationLimit, upperEstimationLimit, problemData, rows, applyLinearScaling);
+
+      if (counter == null) counter = new EvaluationsCounter();
+      var rowEvaluationsCounter = new EvaluationsCounter();
 
       alglib.lsfitstate state;
@@ -222 +260 @@
         alglib.lsfitsetxrep(state, iterationCallback != null);
         //alglib.lsfitsetgradientcheck(state, 0.001);
-        alglib.lsfitfit(state, function_cx_1_func, function_cx_1_grad, xrep, null);
+        alglib.lsfitfit(state, function_cx_1_func, function_cx_1_grad, xrep, rowEvaluationsCounter);
         alglib.lsfitresults(state, out retVal, out c, out rep);
       } catch (ArithmeticException) {
@@ -229 +267 @@
         return originalQuality;
       }
+
+      counter.FunctionEvaluations += rowEvaluationsCounter.FunctionEvaluations / n;
+      counter.GradientEvaluations += rowEvaluationsCounter.GradientEvaluations / n;
 
       //retVal == -7  => constant optimization failed due to wrong gradient
@@ -266 +307 @@
       return (double[] c, double[] x, ref double fx, object o) => {
         fx = func(c, x);
+        var counter = (EvaluationsCounter)o;
+        counter.FunctionEvaluations++;
       };
     }
@@ -274 +317 @@
         fx = tupel.Item2;
         Array.Copy(tupel.Item1, grad, grad.Length);
+        var counter = (EvaluationsCounter)o;
+        counter.GradientEvaluations++;
       };
     }