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Timestamp:
09/14/18 11:36:59 (6 years ago)
Author:
abeham
Message:

#2817: updated to trunk r15680

Location:
branches/2817-BinPackingSpeedup
Files:
19 edited

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  • branches/2817-BinPackingSpeedup

  • branches/2817-BinPackingSpeedup/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression

  • branches/2817-BinPackingSpeedup/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4

  • branches/2817-BinPackingSpeedup/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/ConstantOptimizationAnalyzer.cs

    r14185 r16140  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2016 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2018 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.

  • branches/2817-BinPackingSpeedup/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionConstantOptimizationEvaluator.cs

    r14951 r16140  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2016 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2018 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.
    … …  
    2727using HeuristicLab.Data;
    2828using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
     29using HeuristicLab.Optimization;
    2930using HeuristicLab.Parameters;
    3031using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
    … …  
    4142    private const string UpdateVariableWeightsParameterName = "Update Variable Weights";
    4243
     44    private const string FunctionEvaluationsResultParameterName = "Constants Optimization Function Evaluations";
     45    private const string GradientEvaluationsResultParameterName = "Constants Optimization Gradient Evaluations";
     46    private const string CountEvaluationsParameterName = "Count Function and Gradient Evaluations";
     47
    4348    public IFixedValueParameter<IntValue> ConstantOptimizationIterationsParameter {
    4449      get { return (IFixedValueParameter<IntValue>)Parameters[ConstantOptimizationIterationsParameterName]; }
    … …  
    5863    public IFixedValueParameter<BoolValue> UpdateVariableWeightsParameter {
    5964      get { return (IFixedValueParameter<BoolValue>)Parameters[UpdateVariableWeightsParameterName]; }
     65    }
     66
     67    public IResultParameter<IntValue> FunctionEvaluationsResultParameter {
     68      get { return (IResultParameter<IntValue>)Parameters[FunctionEvaluationsResultParameterName]; }
     69    }
     70    public IResultParameter<IntValue> GradientEvaluationsResultParameter {
     71      get { return (IResultParameter<IntValue>)Parameters[GradientEvaluationsResultParameterName]; }
     72    }
     73    public IFixedValueParameter<BoolValue> CountEvaluationsParameter {
     74      get { return (IFixedValueParameter<BoolValue>)Parameters[CountEvaluationsParameterName]; }
    6075    }
    6176
    … …  
    8196      get { return UpdateVariableWeightsParameter.Value.Value; }
    8297      set { UpdateVariableWeightsParameter.Value.Value = value; }
     98    }
     99
     100    public bool CountEvaluations {
     101      get { return CountEvaluationsParameter.Value.Value; }
     102      set { CountEvaluationsParameter.Value.Value = value; }
    83103    }
    84104
    … …  
    100120      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 });
    101121      Parameters.Add(new FixedValueParameter<BoolValue>(UpdateVariableWeightsParameterName, "Determines if the variable weights in the tree should be  optimized.", new BoolValue(true)) { Hidden = true });
     122
     123      Parameters.Add(new FixedValueParameter<BoolValue>(CountEvaluationsParameterName, "Determines if function and gradient evaluation should be counted.", new BoolValue(false)));
     124      Parameters.Add(new ResultParameter<IntValue>(FunctionEvaluationsResultParameterName, "The number of function evaluations performed by the constants optimization evaluator", "Results", new IntValue()));
     125      Parameters.Add(new ResultParameter<IntValue>(GradientEvaluationsResultParameterName, "The number of gradient evaluations performed by the constants optimization evaluator", "Results", new IntValue()));
    102126    }
    103127
    … …  
    112136      if (!Parameters.ContainsKey(UpdateVariableWeightsParameterName))
    113137        Parameters.Add(new FixedValueParameter<BoolValue>(UpdateVariableWeightsParameterName, "Determines if the variable weights in the tree should be  optimized.", new BoolValue(true)));
    114     }
    115 
     138
     139      if (!Parameters.ContainsKey(CountEvaluationsParameterName))
     140        Parameters.Add(new FixedValueParameter<BoolValue>(CountEvaluationsParameterName, "Determines if function and gradient evaluation should be counted.", new BoolValue(false)));
     141
     142      if (!Parameters.ContainsKey(FunctionEvaluationsResultParameterName))
     143        Parameters.Add(new ResultParameter<IntValue>(FunctionEvaluationsResultParameterName, "The number of function evaluations performed by the constants optimization evaluator", "Results", new IntValue()));
     144      if (!Parameters.ContainsKey(GradientEvaluationsResultParameterName))
     145        Parameters.Add(new ResultParameter<IntValue>(GradientEvaluationsResultParameterName, "The number of gradient evaluations performed by the constants optimization evaluator", "Results", new IntValue()));
     146    }
     147
     148    private static readonly object locker = new object();
    116149    public override IOperation InstrumentedApply() {
    117150      var solution = SymbolicExpressionTreeParameter.ActualValue;
    … …  
    119152      if (RandomParameter.ActualValue.NextDouble() < ConstantOptimizationProbability.Value) {
    120153        IEnumerable<int> constantOptimizationRows = GenerateRowsToEvaluate(ConstantOptimizationRowsPercentage.Value);
     154        var counter = new EvaluationsCounter();
    121155        quality = OptimizeConstants(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution, ProblemDataParameter.ActualValue,
    122            constantOptimizationRows, ApplyLinearScalingParameter.ActualValue.Value, ConstantOptimizationIterations.Value, updateVariableWeights: UpdateVariableWeights, lowerEstimationLimit: EstimationLimitsParameter.ActualValue.Lower, upperEstimationLimit: EstimationLimitsParameter.ActualValue.Upper, updateConstantsInTree: UpdateConstantsInTree);
     156           constantOptimizationRows, ApplyLinearScalingParameter.ActualValue.Value, ConstantOptimizationIterations.Value, updateVariableWeights: UpdateVariableWeights, lowerEstimationLimit: EstimationLimitsParameter.ActualValue.Lower, upperEstimationLimit: EstimationLimitsParameter.ActualValue.Upper, updateConstantsInTree: UpdateConstantsInTree, counter: counter);
    123157
    124158        if (ConstantOptimizationRowsPercentage.Value != RelativeNumberOfEvaluatedSamplesParameter.ActualValue.Value) {
    … …  
    126160          quality = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, ProblemDataParameter.ActualValue, evaluationRows, ApplyLinearScalingParameter.ActualValue.Value);
    127161        }
     162
     163        if (CountEvaluations) {
     164          lock (locker) {
     165            FunctionEvaluationsResultParameter.ActualValue.Value += counter.FunctionEvaluations;
     166            GradientEvaluationsResultParameter.ActualValue.Value += counter.GradientEvaluations;
     167          }
     168        }
     169
    128170      } else {
    129171        var evaluationRows = GenerateRowsToEvaluate();
    … …  
    139181      EstimationLimitsParameter.ExecutionContext = context;
    140182      ApplyLinearScalingParameter.ExecutionContext = context;
     183      FunctionEvaluationsResultParameter.ExecutionContext = context;
     184      GradientEvaluationsResultParameter.ExecutionContext = context;
    141185
    142186      // Pearson R² evaluator is used on purpose instead of the const-opt evaluator,
    … …  
    148192      EstimationLimitsParameter.ExecutionContext = null;
    149193      ApplyLinearScalingParameter.ExecutionContext = null;
     194      FunctionEvaluationsResultParameter.ExecutionContext = null;
     195      GradientEvaluationsResultParameter.ExecutionContext = null;
    150196
    151197      return r2;
     198    }
     199
     200    public class EvaluationsCounter {
     201      public int FunctionEvaluations = 0;
     202      public int GradientEvaluations = 0;
    152203    }
    153204
    … …  
    156207      int maxIterations, bool updateVariableWeights = true,
    157208      double lowerEstimationLimit = double.MinValue, double upperEstimationLimit = double.MaxValue,
    158       bool updateConstantsInTree = true) {
     209      bool updateConstantsInTree = true, Action<double[], double, object> iterationCallback = null, EvaluationsCounter counter = null) {
    159210
    160211      // numeric constants in the tree become variables for constant opt
    … …  
    168219      TreeToAutoDiffTermConverter.ParametricFunction func;
    169220      TreeToAutoDiffTermConverter.ParametricFunctionGradient func_grad;
    170       if (!TreeToAutoDiffTermConverter.TryConvertToAutoDiff(tree, updateVariableWeights, out parameters, out initialConstants, out func, out func_grad))
     221      if (!TreeToAutoDiffTermConverter.TryConvertToAutoDiff(tree, updateVariableWeights, applyLinearScaling, out parameters, out initialConstants, out func, out func_grad))
    171222        throw new NotSupportedException("Could not optimize constants of symbolic expression tree due to not supported symbols used in the tree.");
    172223      if (parameters.Count == 0) return 0.0; // gkronber: constant expressions always have a R² of 0.0
    173 
    174224      var parameterEntries = parameters.ToArray(); // order of entries must be the same for x
    175225
    176226      //extract inital constants
    177       double[] c = new double[initialConstants.Length + 2];
    178       {
     227      double[] c;
     228      if (applyLinearScaling) {
     229        c = new double[initialConstants.Length + 2];
    179230        c[0] = 0.0;
    180231        c[1] = 1.0;
    181232        Array.Copy(initialConstants, 0, c, 2, initialConstants.Length);
    182       }
    183       double[] originalConstants = (double[])c.Clone();
     233      } else {
     234        c = (double[])initialConstants.Clone();
     235      }
     236
    184237      double originalQuality = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(interpreter, tree, lowerEstimationLimit, upperEstimationLimit, problemData, rows, applyLinearScaling);
     238
     239      if (counter == null) counter = new EvaluationsCounter();
     240      var rowEvaluationsCounter = new EvaluationsCounter();
    185241
    186242      alglib.lsfitstate state;
    … …  
    210266      alglib.ndimensional_pfunc function_cx_1_func = CreatePFunc(func);
    211267      alglib.ndimensional_pgrad function_cx_1_grad = CreatePGrad(func_grad);
     268      alglib.ndimensional_rep xrep = (p, f, obj) => iterationCallback(p, f, obj);
    212269
    213270      try {
    214271        alglib.lsfitcreatefg(x, y, c, n, m, k, false, out state);
    215272        alglib.lsfitsetcond(state, 0.0, 0.0, maxIterations);
     273        alglib.lsfitsetxrep(state, iterationCallback != null);
    216274        //alglib.lsfitsetgradientcheck(state, 0.001);
    217         alglib.lsfitfit(state, function_cx_1_func, function_cx_1_grad, null, null);
     275        alglib.lsfitfit(state, function_cx_1_func, function_cx_1_grad, xrep, rowEvaluationsCounter);
    218276        alglib.lsfitresults(state, out retVal, out c, out rep);
    219       }
    220       catch (ArithmeticException) {
     277      } catch (ArithmeticException) {
    221278        return originalQuality;
    222       }
    223       catch (alglib.alglibexception) {
     279      } catch (alglib.alglibexception) {
    224280        return originalQuality;
    225281      }
    226282
     283      counter.FunctionEvaluations += rowEvaluationsCounter.FunctionEvaluations / n;
     284      counter.GradientEvaluations += rowEvaluationsCounter.GradientEvaluations / n;
     285
    227286      //retVal == -7  => constant optimization failed due to wrong gradient
    228       if (retVal != -7) UpdateConstants(tree, c.Skip(2).ToArray(), updateVariableWeights);
     287      if (retVal != -7) {
     288        if (applyLinearScaling) {
     289          var tmp = new double[c.Length - 2];
     290          Array.Copy(c, 2, tmp, 0, tmp.Length);
     291          UpdateConstants(tree, tmp, updateVariableWeights);
     292        } else UpdateConstants(tree, c, updateVariableWeights);
     293      }
    229294      var quality = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(interpreter, tree, lowerEstimationLimit, upperEstimationLimit, problemData, rows, applyLinearScaling);
    230295
    231       if (!updateConstantsInTree) UpdateConstants(tree, originalConstants.Skip(2).ToArray(), updateVariableWeights);
     296      if (!updateConstantsInTree) UpdateConstants(tree, initialConstants, updateVariableWeights);
     297
    232298      if (originalQuality - quality > 0.001 || double.IsNaN(quality)) {
    233         UpdateConstants(tree, originalConstants.Skip(2).ToArray(), updateVariableWeights);
     299        UpdateConstants(tree, initialConstants, updateVariableWeights);
    234300        return originalQuality;
    235301      }
    … …  
    257323      return (double[] c, double[] x, ref double fx, object o) => {
    258324        fx = func(c, x);
     325        var counter = (EvaluationsCounter)o;
     326        counter.FunctionEvaluations++;
    259327      };
    260328    }
    … …  
    262330    private static alglib.ndimensional_pgrad CreatePGrad(TreeToAutoDiffTermConverter.ParametricFunctionGradient func_grad) {
    263331      return (double[] c, double[] x, ref double fx, double[] grad, object o) => {
    264         var tupel = func_grad(c, x);
    265         fx = tupel.Item2;
    266         Array.Copy(tupel.Item1, grad, grad.Length);
     332        var tuple = func_grad(c, x);
     333        fx = tuple.Item2;
     334        Array.Copy(tuple.Item1, grad, grad.Length);
     335        var counter = (EvaluationsCounter)o;
     336        counter.GradientEvaluations++;
    267337      };
    268338    }

  • branches/2817-BinPackingSpeedup/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionLogResidualEvaluator.cs

    r14185 r16140  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2016 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2018 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.

  • branches/2817-BinPackingSpeedup/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionMeanRelativeErrorEvaluator.cs

    r14185 r16140  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2016 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2018 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.

  • branches/2817-BinPackingSpeedup/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionSingleObjectiveEvaluator.cs

    r14185 r16140  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2016 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2018 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.

  • branches/2817-BinPackingSpeedup/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionSingleObjectiveMaxAbsoluteErrorEvaluator.cs

    r14185 r16140  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2016 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2018 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.

  • branches/2817-BinPackingSpeedup/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionSingleObjectiveMeanAbsoluteErrorEvaluator.cs

    r14185 r16140  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2016 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2018 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.

  • branches/2817-BinPackingSpeedup/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionSingleObjectiveMeanSquaredErrorEvaluator.cs

    r14185 r16140  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2016 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2018 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.

  • branches/2817-BinPackingSpeedup/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.cs

    r14354 r16140  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2016 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2018 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.

  • branches/2817-BinPackingSpeedup/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/SymbolicRegressionSingleObjectiveOverfittingAnalyzer.cs

    r14185 r16140  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2016 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2018 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.

  • branches/2817-BinPackingSpeedup/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/SymbolicRegressionSingleObjectiveProblem.cs

    r14185 r16140  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2016 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2018 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.

  • branches/2817-BinPackingSpeedup/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/SymbolicRegressionSingleObjectiveTrainingBestSolutionAnalyzer.cs

    r14185 r16140  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2016 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2018 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.

  • branches/2817-BinPackingSpeedup/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/SymbolicRegressionSingleObjectiveTrainingParetoBestSolutionAnalyzer.cs

    r14185 r16140  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2016 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2018 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.

  • branches/2817-BinPackingSpeedup/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/SymbolicRegressionSingleObjectiveValidationBestSolutionAnalyzer.cs

    r14185 r16140  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2016 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2018 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.

  • branches/2817-BinPackingSpeedup/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/SymbolicRegressionSingleObjectiveValidationParetoBestSolutionAnalyzer.cs

    r14185 r16140  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2016 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2018 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.

  • branches/2817-BinPackingSpeedup/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/SymbolicRegressionSolutionsAnalyzer.cs

    r14185 r16140  
    22
    33/* HeuristicLab
    4  * Copyright (C) 2002-2016 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     4 * Copyright (C) 2002-2018 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    55 *
    66 * This file is part of HeuristicLab.
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