Free cookie consent management tool by TermsFeed Policy Generator

Changeset 11145


Ignore:
Timestamp:
07/08/14 19:29:00 (10 years ago)
Author:
mkommend
Message:

#2143: Merged r10368, r10375, r10378, r10414, r10417, r10418, r10428, r10469, r10470, r11013, r11025, r11026, r11027 into stable.

Location:
stable
Files:
12 edited
6 copied

Legend:

Unmodified
Added
Removed

  • stable

  • stable/HeuristicLab.Problems.DataAnalysis.Symbolic

  • stable/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification

  • stable/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification-3.4.csproj

    r9288 r11145  
    120120    <Compile Include="ModelCreators\NormalDistributedThresholdsModelCreator.cs" />
    121121    <Compile Include="MultiObjective\SymbolicClassificationMultiObjectiveValidationBestSolutionAnalyzer.cs" />
     122    <Compile Include="SymbolicClassificationPruningAnalyzer.cs" />
    122123    <Compile Include="SymbolicClassificationSolutionImpactValuesCalculator.cs" />
    123124    <Compile Include="SymbolicNearestNeighbourClassificationModel.cs" />
    … …  
    149150      <SubType>Code</SubType>
    150151    </Compile>
     152    <Compile Include="SymbolicClassificationPruningOperator.cs" />
    151153    <None Include="HeuristicLab.snk" />
    152154    <None Include="Plugin.cs.frame" />

  • stable/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SymbolicClassificationPruningAnalyzer.cs

    r10368 r11145  
    1 using System;
     1#region License Information
     2/* HeuristicLab
     3 * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     4 *
     5 * This file is part of HeuristicLab.
     6 *
     7 * HeuristicLab is free software: you can redistribute it and/or modify
     8 * it under the terms of the GNU General Public License as published by
     9 * the Free Software Foundation, either version 3 of the License, or
     10 * (at your option) any later version.
     11 *
     12 * HeuristicLab is distributed in the hope that it will be useful,
     13 * but WITHOUT ANY WARRANTY; without even the implied warranty of
     14 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
     15 * GNU General Public License for more details.
     16 *
     17 * You should have received a copy of the GNU General Public License
     18 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
     19 */
     20#endregion
     21
    222using HeuristicLab.Common;
    323using HeuristicLab.Core;
    4 using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
    524using HeuristicLab.Parameters;
    625using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
    … …  
    1029  [StorableClass]
    1130  public sealed class SymbolicClassificationPruningAnalyzer : SymbolicDataAnalysisSingleObjectivePruningAnalyzer {
    12     private const string ModelCreatorParameterName = "ModelCreator";
    13     #region parameter properties
    14     public ILookupParameter<ISymbolicClassificationModelCreator> ModelCreatorParameter {
    15       get { return (ILookupParameter<ISymbolicClassificationModelCreator>)Parameters[ModelCreatorParameterName]; }
    16     }
    17     #endregion
    18     #region properties
    19     private ISymbolicClassificationModelCreator ModelCreator {
    20       get { return ModelCreatorParameter.ActualValue; }
    21       set { ModelCreatorParameter.ActualValue = value; }
    22     }
    23     #endregion
    24 
    25     protected SymbolicClassificationPruningAnalyzer(SymbolicClassificationPruningAnalyzer original, Cloner cloner)
     31    private const string ImpactValuesCalculatorParameterName = "ImpactValuesCalculator";
     32    private const string PruningOperatorParameterName = "PruningOperator";
     33    private SymbolicClassificationPruningAnalyzer(SymbolicClassificationPruningAnalyzer original, Cloner cloner)
    2634      : base(original, cloner) {
    2735    }
    … …  
    3038    }
    3139
     40    [StorableConstructor]
     41    private SymbolicClassificationPruningAnalyzer(bool deserializing) : base(deserializing) { }
     42
    3243    public SymbolicClassificationPruningAnalyzer() {
    33       // pruning parameters
    34       Parameters.Add(new LookupParameter<ISymbolicClassificationModelCreator>(ModelCreatorParameterName));
    35       impactValuesCalculator = new SymbolicClassificationSolutionImpactValuesCalculator();
    36     }
    37 
    38     protected override ISymbolicDataAnalysisModel CreateModel(ISymbolicExpressionTree tree,
    39       ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, double lowerEstimationLimit = Double.MinValue,
    40       double upperEstimationLimit = Double.MaxValue) {
    41       var model = ModelCreator.CreateSymbolicClassificationModel(tree, Interpreter, lowerEstimationLimit, upperEstimationLimit);
    42       model.RecalculateModelParameters((IClassificationProblemData)ProblemData, ProblemData.TrainingIndices);
    43       return model;
     44      Parameters.Add(new ValueParameter<SymbolicDataAnalysisSolutionImpactValuesCalculator>(ImpactValuesCalculatorParameterName, "The impact values calculator", new SymbolicClassificationSolutionImpactValuesCalculator()));
     45      Parameters.Add(new ValueParameter<SymbolicDataAnalysisExpressionPruningOperator>(PruningOperatorParameterName, "The operator used to prune trees", new SymbolicClassificationPruningOperator()));
    4446    }
    4547  }

  • stable/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SymbolicClassificationPruningOperator.cs

    r10470 r11145  
    1 using System.Linq;
     1#region License Information
     2
     3/* HeuristicLab
     4 * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     5 *
     6 * This file is part of HeuristicLab.
     7 *
     8 * HeuristicLab is free software: you can redistribute it and/or modify
     9 * it under the terms of the GNU General Public License as published by
     10 * the Free Software Foundation, either version 3 of the License, or
     11 * (at your option) any later version.
     12 *
     13 * HeuristicLab is distributed in the hope that it will be useful,
     14 * but WITHOUT ANY WARRANTY; without even the implied warranty of
     15 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
     16 * GNU General Public License for more details.
     17 *
     18 * You should have received a copy of the GNU General Public License
     19 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
     20 */
     21
     22#endregion
     23
     24using System.Linq;
    225using HeuristicLab.Common;
    326using HeuristicLab.Core;
    4 using HeuristicLab.Data;
    527using HeuristicLab.Parameters;
    628using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
    … …  
    1234    private const string ImpactValuesCalculatorParameterName = "ImpactValuesCalculator";
    1335    private const string ModelCreatorParameterName = "ModelCreator";
    14     private const string ApplyLinearScalingParmameterName = "ApplyLinearScaling";
    1536
    1637    #region parameter properties
    … …  
    1839      get { return (ILookupParameter<ISymbolicClassificationModelCreator>)Parameters[ModelCreatorParameterName]; }
    1940    }
    20 
    21     public ILookupParameter<BoolValue> ApplyLinearScalingParameter {
    22       get { return (ILookupParameter<BoolValue>)Parameters[ApplyLinearScalingParmameterName]; }
    23     }
    24     #endregion
    25     #region properties
    26     private ISymbolicClassificationModelCreator ModelCreator { get { return ModelCreatorParameter.ActualValue; } }
    27     private BoolValue ApplyLinearScaling { get { return ApplyLinearScalingParameter.ActualValue; } }
    2841    #endregion
    2942
    … …  
    3144      : base(original, cloner) {
    3245    }
     46
    3347    public override IDeepCloneable Clone(Cloner cloner) {
    3448      return new SymbolicClassificationPruningOperator(this, cloner);
    … …  
    4458
    4559    protected override ISymbolicDataAnalysisModel CreateModel() {
    46       var model = ModelCreator.CreateSymbolicClassificationModel(SymbolicExpressionTree, Interpreter, EstimationLimits.Lower, EstimationLimits.Upper);
    47       var rows = Enumerable.Range(FitnessCalculationPartition.Start, FitnessCalculationPartition.Size);
     60      var model = ModelCreatorParameter.ActualValue.CreateSymbolicClassificationModel(SymbolicExpressionTree, Interpreter, EstimationLimits.Lower, EstimationLimits.Upper);
    4861      var problemData = (IClassificationProblemData)ProblemData;
     62      var rows = problemData.TrainingIndices;
    4963      model.RecalculateModelParameters(problemData, rows);
    5064      return model;
    … …  
    5468      var classificationModel = (IClassificationModel)model;
    5569      var classificationProblemData = (IClassificationProblemData)ProblemData;
    56       var trainingIndices = ProblemData.TrainingIndices.ToList();
     70      var trainingIndices = Enumerable.Range(FitnessCalculationPartition.Start, FitnessCalculationPartition.Size);
    5771      var estimatedValues = classificationModel.GetEstimatedClassValues(ProblemData.Dataset, trainingIndices);
    5872      var targetValues = ProblemData.Dataset.GetDoubleValues(classificationProblemData.TargetVariable, trainingIndices);
    5973      OnlineCalculatorError errorState;
    60       var quality = OnlinePearsonsRSquaredCalculator.Calculate(targetValues, estimatedValues, out errorState);
     74      var quality = OnlineAccuracyCalculator.Calculate(targetValues, estimatedValues, out errorState);
    6175      if (errorState != OnlineCalculatorError.None) return double.NaN;
    6276      return quality;

  • stable/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SymbolicClassificationSolutionImpactValuesCalculator.cs

    r10281 r11145  
    2020#endregion
    2121
     22using System;
    2223using System.Collections.Generic;
    2324using HeuristicLab.Common;
     25using HeuristicLab.Core;
    2426using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
     27using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
    2528
    2629namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Classification {
     30  [StorableClass]
     31  [Item("SymbolicClassificationSolutionImpactValuesCalculator", "Calculate symbolic expression tree node impact values for classification problems.")]
    2732  public class SymbolicClassificationSolutionImpactValuesCalculator : SymbolicDataAnalysisSolutionImpactValuesCalculator {
     33    public SymbolicClassificationSolutionImpactValuesCalculator() { }
     34    protected SymbolicClassificationSolutionImpactValuesCalculator(SymbolicClassificationSolutionImpactValuesCalculator original, Cloner cloner)
     35      : base(original, cloner) { }
     36    public override IDeepCloneable Clone(Cloner cloner) {
     37      return new SymbolicClassificationSolutionImpactValuesCalculator(this, cloner);
     38    }
     39    [StorableConstructor]
     40    protected SymbolicClassificationSolutionImpactValuesCalculator(bool deserializing) : base(deserializing) { }
     41
    2842    public override double CalculateReplacementValue(ISymbolicDataAnalysisModel model, ISymbolicExpressionTreeNode node, IDataAnalysisProblemData problemData, IEnumerable<int> rows) {
    2943      var classificationModel = (ISymbolicClassificationModel)model;
    … …  
    3448
    3549    public override double CalculateImpactValue(ISymbolicDataAnalysisModel model, ISymbolicExpressionTreeNode node, IDataAnalysisProblemData problemData, IEnumerable<int> rows, double originalQuality = double.NaN) {
     50      double impactValue, replacementValue;
     51      CalculateImpactAndReplacementValues(model, node, problemData, rows, out impactValue, out replacementValue, originalQuality);
     52      return impactValue;
     53    }
     54
     55    public override void CalculateImpactAndReplacementValues(ISymbolicDataAnalysisModel model, ISymbolicExpressionTreeNode node,
     56      IDataAnalysisProblemData problemData, IEnumerable<int> rows, out double impactValue, out double replacementValue,
     57      double originalQuality = Double.NaN) {
    3658      var classificationModel = (ISymbolicClassificationModel)model;
    3759      var classificationProblemData = (IClassificationProblemData)problemData;
    … …  
    4769      }
    4870
    49       var replacementValue = CalculateReplacementValue(classificationModel, node, classificationProblemData, rows);
     71      replacementValue = CalculateReplacementValue(classificationModel, node, classificationProblemData, rows);
    5072      var constantNode = new ConstantTreeNode(new Constant()) { Value = replacementValue };
    5173
    … …  
    6385      if (errorState != OnlineCalculatorError.None) newQuality = 0.0;
    6486
    65       return originalQuality - newQuality;
     87      impactValue = originalQuality - newQuality;
    6688    }
    67 
    6889  }
    6990}

  • stable/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression

  • stable/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression-3.4.csproj

    r10907 r11145  
    117117  </ItemGroup>
    118118  <ItemGroup>
    119     <Compile Include="MultiObjective\SymbolicRegressionMultiObjectiveValidationBestSolutionAnalyzer.cs" />
    120119    <Compile Include="Plugin.cs" />
    121120    <Compile Include="SingleObjective\ConstantOptimizationAnalyzer.cs" />
    122     <Compile Include="SingleObjective\Evaluators\SymbolicRegressionMeanRelativeErrorEvaluator.cs" />
    123     <Compile Include="SingleObjective\SymbolicRegressionSolutionsAnalyzer.cs" />
     121    <Compile Include="SingleObjective\SymbolicRegressionSolutionsAnalyzer.cs" />   
     122  <Compile Include="SymbolicRegressionPruningAnalyzer.cs" />   
     123    <Compile Include="SingleObjective\SymbolicRegressionSingleObjectiveProblem.cs" />
     124    <Compile Include="SingleObjective\SymbolicRegressionSingleObjectiveValidationBestSolutionAnalyzer.cs" />
     125  <Compile Include="SingleObjective\SymbolicRegressionSingleObjectiveValidationParetoBestSolutionAnalyzer.cs" />
     126    <Compile Include="SingleObjective\SymbolicRegressionSingleObjectiveTrainingBestSolutionAnalyzer.cs" />
     127  <Compile Include="SingleObjective\SymbolicRegressionSingleObjectiveTrainingParetoBestSolutionAnalyzer.cs" />
     128    <Compile Include="SingleObjective\SymbolicRegressionSingleObjectiveOverfittingAnalyzer.cs" />
    124129    <Compile Include="SingleObjective\Evaluators\SymbolicRegressionLogResidualEvaluator.cs" />
    125     <Compile Include="SingleObjective\SymbolicRegressionSingleObjectiveValidationParetoBestSolutionAnalyzer.cs" />
    126     <Compile Include="SingleObjective\SymbolicRegressionSingleObjectiveTrainingParetoBestSolutionAnalyzer.cs" />
     130    <Compile Include="SingleObjective\Evaluators\SymbolicRegressionMeanRelativeErrorEvaluator.cs" />   
    127131    <Compile Include="SingleObjective\Evaluators\SymbolicRegressionSingleObjectiveMaxAbsoluteErrorEvaluator.cs" />
    128132    <Compile Include="SingleObjective\Evaluators\SymbolicRegressionSingleObjectiveMeanAbsoluteErrorEvaluator.cs" />
    129133    <Compile Include="SingleObjective\Evaluators\SymbolicRegressionConstantOptimizationEvaluator.cs" />
    130     <Compile Include="SingleObjective\SymbolicRegressionSingleObjectiveOverfittingAnalyzer.cs" />
     134    <Compile Include="SingleObjective\Evaluators\SymbolicRegressionSingleObjectiveEvaluator.cs" />
     135    <Compile Include="SingleObjective\Evaluators\SymbolicRegressionSingleObjectiveMeanSquaredErrorEvaluator.cs" />
     136    <Compile Include="SingleObjective\Evaluators\SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.cs" />
    131137    <Compile Include="SymbolicRegressionModel.cs" />
    132138    <Compile Include="Interfaces\ISymbolicRegressionModel.cs" />
    … …  
    137143    <Compile Include="MultiObjective\SymbolicRegressionMultiObjectiveProblem.cs" />
    138144    <Compile Include="MultiObjective\SymbolicRegressionMultiObjectiveTrainingBestSolutionAnalyzer.cs" />
    139     <Compile Include="SingleObjective\SymbolicRegressionSingleObjectiveProblem.cs" />
    140     <Compile Include="SingleObjective\SymbolicRegressionSingleObjectiveValidationBestSolutionAnalyzer.cs" />
    141     <Compile Include="SingleObjective\SymbolicRegressionSingleObjectiveTrainingBestSolutionAnalyzer.cs" />
    142145    <Compile Include="MultiObjective\SymbolicRegressionMultiObjectiveEvaluator.cs" />
     146    <Compile Include="MultiObjective\SymbolicRegressionMultiObjectiveValidationBestSolutionAnalyzer.cs" />
    143147    <Compile Include="MultiObjective\SymbolicRegressionMultiObjectiveMeanSquaredErrorTreeSizeEvaluator.cs" />
    144148    <Compile Include="MultiObjective\SymbolicRegressionMultiObjectivePearsonRSquaredTreeSizeEvaluator.cs" />
    145     <Compile Include="SingleObjective\Evaluators\SymbolicRegressionSingleObjectiveEvaluator.cs" />
    146     <Compile Include="SingleObjective\Evaluators\SymbolicRegressionSingleObjectiveMeanSquaredErrorEvaluator.cs" />
    147     <Compile Include="SingleObjective\Evaluators\SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.cs" />
     149    <Compile Include="SymbolicRegressionPruningOperator.cs" />
    148150    <Compile Include="SymbolicRegressionSolution.cs" />
    149151    <Compile Include="SymbolicRegressionSolutionImpactValuesCalculator.cs" />

  • stable/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SymbolicRegressionPruningAnalyzer.cs

    r10368 r11145  
    1 using System;
     1#region License Information
     2
     3/* HeuristicLab
     4 * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     5 *
     6 * This file is part of HeuristicLab.
     7 *
     8 * HeuristicLab is free software: you can redistribute it and/or modify
     9 * it under the terms of the GNU General Public License as published by
     10 * the Free Software Foundation, either version 3 of the License, or
     11 * (at your option) any later version.
     12 *
     13 * HeuristicLab is distributed in the hope that it will be useful,
     14 * but WITHOUT ANY WARRANTY; without even the implied warranty of
     15 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
     16 * GNU General Public License for more details.
     17 *
     18 * You should have received a copy of the GNU General Public License
     19 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
     20 */
     21
     22#endregion
     23
    224using HeuristicLab.Common;
    325using HeuristicLab.Core;
    4 using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
     26using HeuristicLab.Parameters;
    527using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
    628
    … …  
    931  [StorableClass]
    1032  public sealed class SymbolicRegressionPruningAnalyzer : SymbolicDataAnalysisSingleObjectivePruningAnalyzer {
    11     protected SymbolicRegressionPruningAnalyzer(SymbolicRegressionPruningAnalyzer original, Cloner cloner)
     33    private const string ImpactValuesCalculatorParameterName = "ImpactValuesCalculator";
     34    private const string PruningOperatorParameterName = "PruningOperator";
     35    private SymbolicRegressionPruningAnalyzer(SymbolicRegressionPruningAnalyzer original, Cloner cloner)
    1236      : base(original, cloner) {
    1337    }
    … …  
    1640    }
    1741
     42    [StorableConstructor]
     43    private SymbolicRegressionPruningAnalyzer(bool deserializing) : base(deserializing) { }
     44
    1845    public SymbolicRegressionPruningAnalyzer() {
    19       impactValuesCalculator = new SymbolicRegressionSolutionImpactValuesCalculator();
    20     }
    21 
    22     protected override ISymbolicDataAnalysisModel CreateModel(ISymbolicExpressionTree tree,
    23       ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, double lowerEstimationLimit = Double.MinValue,
    24       double upperEstimationLimit = Double.MaxValue) {
    25       return new SymbolicRegressionModel(tree, interpreter, lowerEstimationLimit, upperEstimationLimit);
     46      Parameters.Add(new ValueParameter<SymbolicDataAnalysisSolutionImpactValuesCalculator>(ImpactValuesCalculatorParameterName, "The impact values calculator", new SymbolicRegressionSolutionImpactValuesCalculator()));
     47      Parameters.Add(new ValueParameter<SymbolicDataAnalysisExpressionPruningOperator>(PruningOperatorParameterName, "The operator used to prune trees", new SymbolicRegressionPruningOperator()));
    2648    }
    2749  }

  • stable/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SymbolicRegressionPruningOperator.cs

    r10470 r11145  
    1 using System.Linq;
     1#region License Information
     2
     3/* HeuristicLab
     4 * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     5 *
     6 * This file is part of HeuristicLab.
     7 *
     8 * HeuristicLab is free software: you can redistribute it and/or modify
     9 * it under the terms of the GNU General Public License as published by
     10 * the Free Software Foundation, either version 3 of the License, or
     11 * (at your option) any later version.
     12 *
     13 * HeuristicLab is distributed in the hope that it will be useful,
     14 * but WITHOUT ANY WARRANTY; without even the implied warranty of
     15 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
     16 * GNU General Public License for more details.
     17 *
     18 * You should have received a copy of the GNU General Public License
     19 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
     20 */
     21
     22#endregion
     23
     24using System.Linq;
    225using HeuristicLab.Common;
    326using HeuristicLab.Core;
    … …  
    1033  public class SymbolicRegressionPruningOperator : SymbolicDataAnalysisExpressionPruningOperator {
    1134    private const string ImpactValuesCalculatorParameterName = "ImpactValuesCalculator";
    12     private const string ImpactValuesCalculatorParameterDescription = "The impact values calculator to be used for figuring out the node impacts.";
    13 
    14     private const string EvaluatorParameterName = "Evaluator";
    15 
    16     public ILookupParameter<ISymbolicRegressionSingleObjectiveEvaluator> EvaluatorParameter {
    17       get { return (ILookupParameter<ISymbolicRegressionSingleObjectiveEvaluator>)Parameters[EvaluatorParameterName]; }
    18     }
    1935
    2036    protected SymbolicRegressionPruningOperator(SymbolicRegressionPruningOperator original, Cloner cloner)
    … …  
    3046    public SymbolicRegressionPruningOperator() {
    3147      var impactValuesCalculator = new SymbolicRegressionSolutionImpactValuesCalculator();
    32       Parameters.Add(new ValueParameter<ISymbolicDataAnalysisSolutionImpactValuesCalculator>(ImpactValuesCalculatorParameterName, ImpactValuesCalculatorParameterDescription, impactValuesCalculator));
    33       Parameters.Add(new LookupParameter<ISymbolicRegressionSingleObjectiveEvaluator>(EvaluatorParameterName));
     48      Parameters.Add(new ValueParameter<ISymbolicDataAnalysisSolutionImpactValuesCalculator>(ImpactValuesCalculatorParameterName, "The impact values calculator to be used for figuring out the node impacts.", impactValuesCalculator));
    3449    }
    3550
    … …  
    4156      var regressionModel = (IRegressionModel)model;
    4257      var regressionProblemData = (IRegressionProblemData)ProblemData;
    43       var trainingIndices = ProblemData.TrainingIndices.ToList();
     58      var trainingIndices = Enumerable.Range(FitnessCalculationPartition.Start, FitnessCalculationPartition.Size);
    4459      var estimatedValues = regressionModel.GetEstimatedValues(ProblemData.Dataset, trainingIndices); // also bounds the values
    4560      var targetValues = ProblemData.Dataset.GetDoubleValues(regressionProblemData.TargetVariable, trainingIndices);

  • stable/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SymbolicRegressionSolutionImpactValuesCalculator.cs

    r9976 r11145  
    2020#endregion
    2121
     22using System;
    2223using System.Collections.Generic;
    2324using HeuristicLab.Common;
     25using HeuristicLab.Core;
    2426using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
     27using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
    2528
    2629namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
     30  [StorableClass]
     31  [Item("SymbolicRegressionSolutionImpactValuesCalculator", "Calculate symbolic expression tree node impact values for regression problems.")]
    2732  public class SymbolicRegressionSolutionImpactValuesCalculator : SymbolicDataAnalysisSolutionImpactValuesCalculator {
     33    public SymbolicRegressionSolutionImpactValuesCalculator() { }
     34
     35    protected SymbolicRegressionSolutionImpactValuesCalculator(SymbolicRegressionSolutionImpactValuesCalculator original, Cloner cloner)
     36      : base(original, cloner) { }
     37    public override IDeepCloneable Clone(Cloner cloner) {
     38      return new SymbolicRegressionSolutionImpactValuesCalculator(this, cloner);
     39    }
     40
     41    [StorableConstructor]
     42    protected SymbolicRegressionSolutionImpactValuesCalculator(bool deserializing) : base(deserializing) { }
    2843    public override double CalculateReplacementValue(ISymbolicDataAnalysisModel model, ISymbolicExpressionTreeNode node, IDataAnalysisProblemData problemData, IEnumerable<int> rows) {
    2944      var regressionModel = (ISymbolicRegressionModel)model;
    … …  
    3449
    3550    public override double CalculateImpactValue(ISymbolicDataAnalysisModel model, ISymbolicExpressionTreeNode node, IDataAnalysisProblemData problemData, IEnumerable<int> rows, double originalQuality = double.NaN) {
     51      double impactValue, replacementValue;
     52      CalculateImpactAndReplacementValues(model, node, problemData, rows, out impactValue, out replacementValue, originalQuality);
     53      return impactValue;
     54    }
     55
     56    public override void CalculateImpactAndReplacementValues(ISymbolicDataAnalysisModel model, ISymbolicExpressionTreeNode node,
     57      IDataAnalysisProblemData problemData, IEnumerable<int> rows, out double impactValue, out double replacementValue,
     58      double originalQuality = Double.NaN) {
    3659      var regressionModel = (ISymbolicRegressionModel)model;
    3760      var regressionProblemData = (IRegressionProblemData)problemData;
    … …  
    4770      }
    4871
    49       var replacementValue = CalculateReplacementValue(regressionModel, node, regressionProblemData, rows);
     72      replacementValue = CalculateReplacementValue(regressionModel, node, regressionProblemData, rows);
    5073      var constantNode = new ConstantTreeNode(new Constant()) { Value = replacementValue };
    5174
    … …  
    6386      if (errorState != OnlineCalculatorError.None) newQuality = 0.0;
    6487
    65       return originalQuality - newQuality;
     88      impactValue = originalQuality - newQuality;
    6689    }
    67 
    6890  }
    6991}

  • stable/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Analyzers/SymbolicDataAnalysisSingleObjectivePruningAnalyzer.cs

    r10368 r11145  
    1 using System;
     1#region License Information
     2
     3/* HeuristicLab
     4 * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     5 *
     6 * This file is part of HeuristicLab.
     7 *
     8 * HeuristicLab is free software: you can redistribute it and/or modify
     9 * it under the terms of the GNU General Public License as published by
     10 * the Free Software Foundation, either version 3 of the License, or
     11 * (at your option) any later version.
     12 *
     13 * HeuristicLab is distributed in the hope that it will be useful,
     14 * but WITHOUT ANY WARRANTY; without even the implied warranty of
     15 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
     16 * GNU General Public License for more details.
     17 *
     18 * You should have received a copy of the GNU General Public License
     19 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
     20 */
     21
     22#endregion
     23
     24using System;
    225using System.Linq;
    326using HeuristicLab.Analysis;
    … …  
    528using HeuristicLab.Core;
    629using HeuristicLab.Data;
    7 using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
    8 using HeuristicLab.Optimization;
     30using HeuristicLab.Operators;
     31using HeuristicLab.Optimization.Operators;
    932using HeuristicLab.Parameters;
    1033using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
    … …  
    1437  [Item("SymbolicDataAnalysisSingleObjectivePruningAnalyzer", "An analyzer that prunes introns from trees in single objective symbolic data analysis problems.")]
    1538  public abstract class SymbolicDataAnalysisSingleObjectivePruningAnalyzer : SymbolicDataAnalysisSingleObjectiveAnalyzer {
     39    #region parameter names
    1640    private const string ProblemDataParameterName = "ProblemData";
    17     private const string InterpreterParameterName = "SymbolicExpressionTreeInterpreter";
    18 
    1941    private const string UpdateIntervalParameterName = "UpdateInverval";
    2042    private const string UpdateCounterParameterName = "UpdateCounter";
    21 
    2243    private const string PopulationSliceParameterName = "PopulationSlice";
    2344    private const string PruningProbabilityParameterName = "PruningProbability";
    24 
    25     private const string NumberOfPrunedSubtreesParameterName = "PrunedSubtrees";
    26     private const string NumberOfPrunedTreesParameterName = "PrunedTrees";
    27 
     45    private const string TotalNumberOfPrunedSubtreesParameterName = "Number of pruned subtrees";
     46    private const string TotalNumberOfPrunedTreesParameterName = "Number of pruned trees";
    2847    private const string RandomParameterName = "Random";
    29     private const string EstimationLimitsParameterName = "EstimationLimits";
    30 
    3148    private const string PruneOnlyZeroImpactNodesParameterName = "PruneOnlyZeroImpactNodes";
    3249    private const string NodeImpactThresholdParameterName = "ImpactThreshold";
    33 
    34     private bool reentry;
    35     [Storable]
    36     protected ISymbolicDataAnalysisSolutionImpactValuesCalculator impactValuesCalculator;
     50    private const string PruningOperatorParameterName = "PruningOperator";
     51    private const string ResultsParameterName = "Results";
     52    private const string PopulationSizeParameterName = "PopulationSize";
     53    #endregion
     54
     55    #region private members
     56    private DataReducer prunedSubtreesReducer;
     57    private DataReducer prunedTreesReducer;
     58    private DataTableValuesCollector valuesCollector;
     59    private ResultsCollector resultsCollector;
     60    #endregion
    3761
    3862    #region parameter properties
     63    public IValueParameter<SymbolicDataAnalysisExpressionPruningOperator> PruningOperatorParameter {
     64      get { return (IValueParameter<SymbolicDataAnalysisExpressionPruningOperator>)Parameters[PruningOperatorParameterName]; }
     65    }
    3966    public IFixedValueParameter<BoolValue> PruneOnlyZeroImpactNodesParameter {
    4067      get { return (IFixedValueParameter<BoolValue>)Parameters[PruneOnlyZeroImpactNodesParameterName]; }
    … …  
    4370      get { return (IFixedValueParameter<DoubleValue>)Parameters[NodeImpactThresholdParameterName]; }
    4471    }
    45     public ILookupParameter<DoubleLimit> EstimationLimitsParameter {
    46       get { return (ILookupParameter<DoubleLimit>)Parameters[EstimationLimitsParameterName]; }
    47     }
    4872    public ILookupParameter<IRandom> RandomParameter {
    4973      get { return (ILookupParameter<IRandom>)Parameters[RandomParameterName]; }
    5074    }
    51     private ILookupParameter<IDataAnalysisProblemData> ProblemDataParameter {
    52       get { return (ILookupParameter<IDataAnalysisProblemData>)Parameters[ProblemDataParameterName]; }
    53     }
    54     private ILookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter> InterpreterParameter {
    55       get { return (ILookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter>)Parameters[InterpreterParameterName]; }
    56     }
    57     public IValueParameter<IntValue> UpdateIntervalParameter {
    58       get { return (IValueParameter<IntValue>)Parameters[UpdateIntervalParameterName]; }
    59     }
    60     public IValueParameter<IntValue> UpdateCounterParameter {
    61       get { return (IValueParameter<IntValue>)Parameters[UpdateCounterParameterName]; }
    62     }
    63     public IValueParameter<DoubleRange> PopulationSliceParameter {
    64       get { return (IValueParameter<DoubleRange>)Parameters[PopulationSliceParameterName]; }
    65     }
    66     public IValueParameter<DoubleValue> PruningProbabilityParameter {
    67       get { return (IValueParameter<DoubleValue>)Parameters[PruningProbabilityParameterName]; }
    68     }
    69     public IFixedValueParameter<DoubleValue> NumberOfPrunedSubtreesParameter {
    70       get { return (IFixedValueParameter<DoubleValue>)Parameters[NumberOfPrunedSubtreesParameterName]; }
    71     }
    72     public IFixedValueParameter<DoubleValue> NumberOfPrunedTreesParameter {
    73       get { return (IFixedValueParameter<DoubleValue>)Parameters[NumberOfPrunedTreesParameterName]; }
    74     }
    75     #endregion
     75    public IFixedValueParameter<IntValue> UpdateIntervalParameter {
     76      get { return (IFixedValueParameter<IntValue>)Parameters[UpdateIntervalParameterName]; }
     77    }
     78    public IFixedValueParameter<IntValue> UpdateCounterParameter {
     79      get { return (IFixedValueParameter<IntValue>)Parameters[UpdateCounterParameterName]; }
     80    }
     81    public IFixedValueParameter<DoubleRange> PopulationSliceParameter {
     82      get { return (IFixedValueParameter<DoubleRange>)Parameters[PopulationSliceParameterName]; }
     83    }
     84    public IFixedValueParameter<DoubleValue> PruningProbabilityParameter {
     85      get { return (IFixedValueParameter<DoubleValue>)Parameters[PruningProbabilityParameterName]; }
     86    }
     87    public ILookupParameter<IntValue> PopulationSizeParameter {
     88      get { return (ILookupParameter<IntValue>)Parameters[PopulationSizeParameterName]; }
     89    }
     90    #endregion
     91
    7692    #region properties
    77     protected IDataAnalysisProblemData ProblemData { get { return ProblemDataParameter.ActualValue; } }
    78     protected ISymbolicDataAnalysisExpressionTreeInterpreter Interpreter { get { return InterpreterParameter.ActualValue; } }
    79     protected IntValue UpdateInterval { get { return UpdateIntervalParameter.Value; } }
    80     protected IntValue UpdateCounter { get { return UpdateCounterParameter.Value; } }
    81     protected DoubleRange PopulationSlice { get { return PopulationSliceParameter.Value; } }
    82     protected DoubleValue PruningProbability { get { return PruningProbabilityParameter.Value; } }
    83     protected DoubleValue PrunedSubtrees { get { return NumberOfPrunedSubtreesParameter.Value; } }
    84     protected DoubleValue PrunedTrees { get { return NumberOfPrunedTreesParameter.Value; } }
    85     protected DoubleLimit EstimationLimits { get { return EstimationLimitsParameter.ActualValue; } }
    86     protected IRandom Random { get { return RandomParameter.ActualValue; } }
    87     protected DoubleValue NodeImpactThreshold { get { return NodeImpactThresholdParameter.Value; } }
    88     protected BoolValue PruneOnlyZeroImpactNodes { get { return PruneOnlyZeroImpactNodesParameter.Value; } }
    89     #endregion
     93    protected SymbolicDataAnalysisExpressionPruningOperator PruningOperator { get { return PruningOperatorParameter.Value; } }
     94    protected int UpdateInterval { get { return UpdateIntervalParameter.Value.Value; } }
     95
     96    protected int UpdateCounter {
     97      get { return UpdateCounterParameter.Value.Value; }
     98      set { UpdateCounterParameter.Value.Value = value; }
     99    }
     100
     101    protected double PopulationSliceStart {
     102      get { return PopulationSliceParameter.Value.Start; }
     103      set { PopulationSliceParameter.Value.Start = value; }
     104    }
     105
     106    protected double PopulationSliceEnd {
     107      get { return PopulationSliceParameter.Value.End; }
     108      set { PopulationSliceParameter.Value.End = value; }
     109    }
     110
     111    protected double PruningProbability {
     112      get { return PruningProbabilityParameter.Value.Value; }
     113      set { PruningProbabilityParameter.Value.Value = value; }
     114    }
     115
     116    protected bool PruneOnlyZeroImpactNodes {
     117      get { return PruneOnlyZeroImpactNodesParameter.Value.Value; }
     118      set { PruneOnlyZeroImpactNodesParameter.Value.Value = value; }
     119    }
     120    protected double NodeImpactThreshold {
     121      get { return NodeImpactThresholdParameter.Value.Value; }
     122      set { NodeImpactThresholdParameter.Value.Value = value; }
     123    }
     124    #endregion
     125
     126    #region IStatefulItem members
     127    public override void InitializeState() {
     128      base.InitializeState();
     129      UpdateCounter = 0;
     130    }
     131    public override void ClearState() {
     132      base.ClearState();
     133      UpdateCounter = 0;
     134    }
     135    #endregion
     136
     137    [StorableConstructor]
     138    protected SymbolicDataAnalysisSingleObjectivePruningAnalyzer(bool deserializing) : base(deserializing) { }
     139
    90140    protected SymbolicDataAnalysisSingleObjectivePruningAnalyzer(SymbolicDataAnalysisSingleObjectivePruningAnalyzer original, Cloner cloner)
    91141      : base(original, cloner) {
    92       impactValuesCalculator = original.impactValuesCalculator;
    93     }
     142      if (original.prunedSubtreesReducer != null)
     143        this.prunedSubtreesReducer = (DataReducer)original.prunedSubtreesReducer.Clone();
     144      if (original.prunedTreesReducer != null)
     145        this.prunedTreesReducer = (DataReducer)original.prunedTreesReducer.Clone();
     146      if (original.valuesCollector != null)
     147        this.valuesCollector = (DataTableValuesCollector)original.valuesCollector.Clone();
     148      if (original.resultsCollector != null)
     149        this.resultsCollector = (ResultsCollector)original.resultsCollector.Clone();
     150    }
     151
     152    [StorableHook(HookType.AfterDeserialization)]
     153    private void AfterDeserialization() {
     154      if (!Parameters.ContainsKey(PopulationSizeParameterName)) {
     155        Parameters.Add(new LookupParameter<IntValue>(PopulationSizeParameterName, "The population of individuals."));
     156      }
     157      if (Parameters.ContainsKey(UpdateCounterParameterName)) {
     158        var fixedValueParameter = Parameters[UpdateCounterParameterName] as FixedValueParameter<IntValue>;
     159        if (fixedValueParameter == null) {
     160          var valueParameter = (ValueParameter<IntValue>)Parameters[UpdateCounterParameterName];
     161          Parameters.Remove(UpdateCounterParameterName);
     162          Parameters.Add(new FixedValueParameter<IntValue>(UpdateCounterParameterName, valueParameter.Value));
     163        }
     164      }
     165      if (Parameters.ContainsKey(UpdateIntervalParameterName)) {
     166        var fixedValueParameter = Parameters[UpdateIntervalParameterName] as FixedValueParameter<IntValue>;
     167        if (fixedValueParameter == null) {
     168          var valueParameter = (ValueParameter<IntValue>)Parameters[UpdateIntervalParameterName];
     169          Parameters.Remove(UpdateIntervalParameterName);
     170          Parameters.Add(new FixedValueParameter<IntValue>(UpdateIntervalParameterName, valueParameter.Value));
     171        }
     172      }
     173      if (Parameters.ContainsKey(PopulationSliceParameterName)) {
     174        var fixedValueParameter = Parameters[PopulationSliceParameterName] as FixedValueParameter<DoubleRange>;
     175        if (fixedValueParameter == null) {
     176          var valueParameter = (ValueParameter<DoubleRange>)Parameters[PopulationSliceParameterName];
     177          Parameters.Remove(PopulationSliceParameterName);
     178          Parameters.Add(new FixedValueParameter<DoubleRange>(PopulationSliceParameterName, valueParameter.Value));
     179        }
     180      }
     181      if (Parameters.ContainsKey(PruningProbabilityParameterName)) {
     182        var fixedValueParameter = Parameters[PruningProbabilityParameterName] as FixedValueParameter<DoubleValue>;
     183        if (fixedValueParameter == null) {
     184          var valueParameter = (ValueParameter<DoubleValue>)Parameters[PruningProbabilityParameterName];
     185          Parameters.Remove(PruningProbabilityParameterName);
     186          Parameters.Add(new FixedValueParameter<DoubleValue>(PruningProbabilityParameterName, valueParameter.Value));
     187        }
     188      }
     189    }
     190
    94191    protected SymbolicDataAnalysisSingleObjectivePruningAnalyzer() {
    95       Parameters.Add(new ValueParameter<DoubleRange>(PopulationSliceParameterName, new DoubleRange(0.75, 1)));
    96       Parameters.Add(new ValueParameter<DoubleValue>(PruningProbabilityParameterName, new DoubleValue(0.5)));
    97       // analyzer parameters
    98       Parameters.Add(new ValueParameter<IntValue>(UpdateIntervalParameterName, "The interval in which the tree length analysis should be applied.", new IntValue(1)));
    99       Parameters.Add(new ValueParameter<IntValue>(UpdateCounterParameterName, "The value which counts how many times the operator was called", new IntValue(0)));
    100       Parameters.Add(new LookupParameter<IRandom>(RandomParameterName));
    101       Parameters.Add(new LookupParameter<IDataAnalysisProblemData>(ProblemDataParameterName));
    102       Parameters.Add(new LookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter>(InterpreterParameterName));
    103 
    104       Parameters.Add(new FixedValueParameter<DoubleValue>(NumberOfPrunedSubtreesParameterName, new DoubleValue(0)));
    105       Parameters.Add(new FixedValueParameter<DoubleValue>(NumberOfPrunedTreesParameterName, new DoubleValue(0)));
    106       Parameters.Add(new LookupParameter<DoubleLimit>(EstimationLimitsParameterName));
    107       Parameters.Add(new FixedValueParameter<DoubleValue>(NodeImpactThresholdParameterName, new DoubleValue(0.0)));
    108       Parameters.Add(new FixedValueParameter<BoolValue>(PruneOnlyZeroImpactNodesParameterName, new BoolValue(false)));
     192      #region add parameters
     193      Parameters.Add(new FixedValueParameter<DoubleRange>(PopulationSliceParameterName, "The slice of the population where pruning should be applied.", new DoubleRange(0.75, 1)));
     194      Parameters.Add(new FixedValueParameter<DoubleValue>(PruningProbabilityParameterName, "The probability for pruning an individual.", new DoubleValue(0.5)));
     195      Parameters.Add(new FixedValueParameter<IntValue>(UpdateIntervalParameterName, "The interval in which the tree length analysis should be applied.", new IntValue(1)));
     196      Parameters.Add(new FixedValueParameter<IntValue>(UpdateCounterParameterName, "The value which counts how many times the operator was called", new IntValue(0)));
     197      Parameters.Add(new LookupParameter<IRandom>(RandomParameterName, "The random number generator."));
     198      Parameters.Add(new LookupParameter<IDataAnalysisProblemData>(ProblemDataParameterName, "The problem data."));
     199      Parameters.Add(new FixedValueParameter<DoubleValue>(NodeImpactThresholdParameterName, "The impact threshold  below which an individual should be pruned.", new DoubleValue(0.0)));
     200      Parameters.Add(new FixedValueParameter<BoolValue>(PruneOnlyZeroImpactNodesParameterName, "Switch to determine of only zero impact individuals should be pruned.", new BoolValue(false)));
     201      Parameters.Add(new LookupParameter<IntValue>(PopulationSizeParameterName, "The population of individuals."));
     202      #endregion
     203    }
     204
     205    //
     206    /// <summary>
     207    /// Computes the closed interval bounding the portion of the population that is to be pruned.
     208    /// </summary>
     209    /// <returns>Returns an int range [start, end]</returns>
     210    private IntRange GetSliceBounds() {
     211      if (PopulationSliceStart < 0 || PopulationSliceEnd < 0) throw new ArgumentOutOfRangeException("The slice bounds cannot be negative.");
     212      if (PopulationSliceStart > 1 || PopulationSliceEnd > 1) throw new ArgumentOutOfRangeException("The slice bounds should be expressed as unit percentages.");
     213      var count = PopulationSizeParameter.ActualValue.Value;
     214      var start = (int)Math.Round(PopulationSliceStart * count);
     215      var end = (int)Math.Round(PopulationSliceEnd * count);
     216      if (end > count) end = count;
     217
     218      if (start >= end) throw new ArgumentOutOfRangeException("Invalid PopulationSlice bounds.");
     219      return new IntRange(start, end);
     220    }
     221
     222    private IOperation CreatePruningOperation() {
     223      var operations = new OperationCollection { Parallel = true };
     224      var range = GetSliceBounds();
     225      var qualities = Quality.Select(x => x.Value).ToArray();
     226      var indices = Enumerable.Range(0, qualities.Length).ToArray();
     227      Array.Sort(qualities, indices);
     228      if (!Maximization.Value) Array.Reverse(indices);
     229
     230      var subscopes = ExecutionContext.Scope.SubScopes;
     231      var random = RandomParameter.ActualValue;
     232
     233      var empty = new EmptyOperator();
     234
     235      for (int i = 0; i < subscopes.Count; ++i) {
     236        IOperator @operator;
     237        if (range.Start <= i && i < range.End && random.NextDouble() <= PruningProbability)
     238          @operator = PruningOperator;
     239        else @operator = empty;
     240        var index = indices[i];
     241        var subscope = subscopes[index];
     242        operations.Add(ExecutionContext.CreateChildOperation(@operator, subscope));
     243      }
     244      return operations;
    109245    }
    110246
    111247    public override IOperation Apply() {
    112       if (reentry) {
    113         UpdateCounter.Value++;
    114 
    115         if (UpdateCounter.Value != UpdateInterval.Value) return base.Apply();
    116         UpdateCounter.Value = 0;
    117 
    118         var trees = SymbolicExpressionTreeParameter.ActualValue.ToList();
    119         var qualities = QualityParameter.ActualValue.ToList();
    120 
    121         var population = trees.Zip(qualities, (tree, quality) => new { Tree = tree, Quality = quality }).ToList();
    122         Func<double, double, int> compare = (a, b) => Maximization.Value ? a.CompareTo(b) : b.CompareTo(a);
    123         population.Sort((a, b) => compare(a.Quality.Value, b.Quality.Value));
    124 
    125         var start = (int)Math.Round(PopulationSlice.Start * trees.Count);
    126         var end = (int)Math.Round(PopulationSlice.End * trees.Count);
    127 
    128         if (end == population.Count) end--;
    129 
    130         if (start >= end || end >= population.Count) throw new Exception("Invalid PopulationSlice bounds.");
    131 
    132         PrunedSubtrees.Value = 0;
    133         PrunedTrees.Value = 0;
    134 
    135         reentry = false;
    136 
    137         var operations = new OperationCollection { Parallel = true };
    138         foreach (var p in population.Skip(start).Take(end)) {
    139           if (Random.NextDouble() > PruningProbability.Value) continue;
    140           var op = new SymbolicDataAnalysisExpressionPruningOperator {
    141             Model = CreateModel(p.Tree, Interpreter, EstimationLimits.Lower, EstimationLimits.Upper),
    142             ImpactsCalculator = impactValuesCalculator,
    143             ProblemData = ProblemData,
    144             Random = Random,
    145             PruneOnlyZeroImpactNodes = PruneOnlyZeroImpactNodes.Value,
    146             NodeImpactThreshold = NodeImpactThreshold.Value
    147           };
    148           operations.Add(ExecutionContext.CreateChildOperation(op, ExecutionContext.Scope));
    149         }
    150         return new OperationCollection { operations, ExecutionContext.CreateOperation(this) };
    151       }
    152 
    153       DataTable table;
    154 
    155       if (ResultCollection.ContainsKey("Population Pruning")) {
    156         table = (DataTable)ResultCollection["Population Pruning"].Value;
    157       } else {
    158         table = new DataTable("Population Pruning");
    159         table.Rows.Add(new DataRow("Pruned Trees") { VisualProperties = { StartIndexZero = true } });
    160         table.Rows.Add(new DataRow("Pruned Subtrees") { VisualProperties = { StartIndexZero = true } });
    161         ResultCollection.Add(new Result("Population Pruning", table));
    162       }
    163 
    164       table.Rows["Pruned Trees"].Values.Add(PrunedTrees.Value);
    165       table.Rows["Pruned Subtrees"].Values.Add(PrunedSubtrees.Value);
    166 
    167       reentry = true;
    168 
    169       return base.Apply();
    170     }
    171 
    172     protected abstract ISymbolicDataAnalysisModel CreateModel(ISymbolicExpressionTree tree, ISymbolicDataAnalysisExpressionTreeInterpreter interpreter,
    173       double lowerEstimationLimit = double.MinValue, double upperEstimationLimit = double.MaxValue);
     248      UpdateCounter++;
     249      if (UpdateCounter != UpdateInterval) return base.Apply();
     250      UpdateCounter = 0;
     251
     252      if (prunedSubtreesReducer == null || prunedTreesReducer == null || valuesCollector == null || resultsCollector == null) { InitializeOperators(); }
     253
     254      var prune = CreatePruningOperation();
     255      var reducePrunedSubtrees = ExecutionContext.CreateChildOperation(prunedSubtreesReducer);
     256      var reducePrunedTrees = ExecutionContext.CreateChildOperation(prunedTreesReducer);
     257      var collectValues = ExecutionContext.CreateChildOperation(valuesCollector);
     258      var collectResults = ExecutionContext.CreateChildOperation(resultsCollector);
     259
     260      return new OperationCollection { prune, reducePrunedSubtrees, reducePrunedTrees, collectValues, collectResults, base.Apply() };
     261    }
     262
     263    private void InitializeOperators() {
     264      prunedSubtreesReducer = new DataReducer();
     265      prunedSubtreesReducer.ParameterToReduce.ActualName = PruningOperator.PrunedSubtreesParameter.ActualName;
     266      prunedSubtreesReducer.ReductionOperation.Value = new ReductionOperation(ReductionOperations.Sum); // sum all the pruned subtrees parameter values
     267      prunedSubtreesReducer.TargetOperation.Value = new ReductionOperation(ReductionOperations.Assign); // asign the sum to the target parameter
     268      prunedSubtreesReducer.TargetParameter.ActualName = TotalNumberOfPrunedSubtreesParameterName;
     269
     270      prunedTreesReducer = new DataReducer();
     271      prunedTreesReducer.ParameterToReduce.ActualName = PruningOperator.PrunedTreesParameter.ActualName;
     272      prunedTreesReducer.ReductionOperation.Value = new ReductionOperation(ReductionOperations.Sum);
     273      prunedTreesReducer.TargetOperation.Value = new ReductionOperation(ReductionOperations.Assign);
     274      prunedTreesReducer.TargetParameter.ActualName = TotalNumberOfPrunedTreesParameterName;
     275
     276      valuesCollector = new DataTableValuesCollector();
     277      valuesCollector.CollectedValues.Add(new LookupParameter<IntValue>(TotalNumberOfPrunedSubtreesParameterName));
     278      valuesCollector.CollectedValues.Add(new LookupParameter<IntValue>(TotalNumberOfPrunedTreesParameterName));
     279      valuesCollector.DataTableParameter.ActualName = "Population pruning";
     280
     281      resultsCollector = new ResultsCollector();
     282      resultsCollector.CollectedValues.Add(new LookupParameter<DataTable>("Population pruning"));
     283      resultsCollector.ResultsParameter.ActualName = ResultsParameterName;
     284    }
    174285  }
    175286}

  • stable/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/HeuristicLab.Problems.DataAnalysis.Symbolic-3.4.csproj

    r10910 r11145  
    112112  </ItemGroup>
    113113  <ItemGroup>
     114    <Compile Include="Analyzers\SymbolicDataAnalysisSingleObjectivePruningAnalyzer.cs" />
    114115    <Compile Include="Analyzers\SymbolicDataAnalysisSingleObjectiveValidationParetoBestSolutionAnalyzer.cs" />
    115116    <Compile Include="Analyzers\SymbolicDataAnalysisSingleObjectiveTrainingParetoBestSolutionAnalyzer.cs" />
    … …  
    125126      <SubType>Code</SubType>
    126127    </Compile>
     128    <Compile Include="SymbolicDataAnalysisExpressionPruningOperator.cs" />
    127129    <Compile Include="Analyzers\SymbolicDataAnalysisVariableFrequencyAnalyzer.cs" />
    128130    <Compile Include="Analyzers\SymbolicDataAnalysisAlleleFrequencyAnalyzer.cs" />

  • stable/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Interfaces/ISymbolicDataAnalysisImpactValuesCalculator.cs

    r8946 r11145  
    11using System.Collections.Generic;
     2using HeuristicLab.Core;
    23using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
    34
    45namespace HeuristicLab.Problems.DataAnalysis.Symbolic {
    5   public interface ISymbolicDataAnalysisSolutionImpactValuesCalculator {
     6  public interface ISymbolicDataAnalysisSolutionImpactValuesCalculator : IItem {
    67    double CalculateReplacementValue(ISymbolicDataAnalysisModel model, ISymbolicExpressionTreeNode node, IDataAnalysisProblemData problemData, IEnumerable<int> rows);
    78    double CalculateImpactValue(ISymbolicDataAnalysisModel model, ISymbolicExpressionTreeNode node, IDataAnalysisProblemData problemData, IEnumerable<int> rows, double originalQuality = double.NaN);
     9    void CalculateImpactAndReplacementValues(ISymbolicDataAnalysisModel model, ISymbolicExpressionTreeNode node, IDataAnalysisProblemData problemData,
     10      IEnumerable<int> rows, out double impactValue, out double replacementValue, double originalQuality = double.NaN);
    811  }
    912}

  • stable/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Plugin.cs.frame

    r10032 r11145  
    3838  [PluginDependency("HeuristicLab.Operators", "3.3")]
    3939  [PluginDependency("HeuristicLab.Optimization", "3.3")]
     40  [PluginDependency("HeuristicLab.Optimization.Operators", "3.3")]
    4041  [PluginDependency("HeuristicLab.Parameters", "3.3")]
    4142  [PluginDependency("HeuristicLab.Persistence", "3.3")]

  • stable/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/SymbolicDataAnalysisExpressionPruningOperator.cs

    r10368 r11145  
    3434  [StorableClass]
    3535  [Item("SymbolicExpressionTreePruningOperator", "An operator that replaces introns with constant values in a symbolic expression tree.")]
    36   public class SymbolicDataAnalysisExpressionPruningOperator : SingleSuccessorOperator {
    37     private const string NumberOfPrunedSubtreesParameterName = "PrunedSubtrees";
    38     private const string NumberOfPrunedTreesParameterName = "PrunedTrees";
     36  public abstract class SymbolicDataAnalysisExpressionPruningOperator : SingleSuccessorOperator {
     37    #region parameter names
     38    private const string ProblemDataParameterName = "ProblemData";
     39    private const string SymbolicDataAnalysisModelParameterName = "SymbolicDataAnalysisModel";
     40    private const string ImpactValuesCalculatorParameterName = "ImpactValuesCalculator";
     41    private const string PrunedSubtreesParameterName = "PrunedSubtrees";
     42    private const string PrunedTreesParameterName = "PrunedTrees";
     43    private const string FitnessCalculationPartitionParameterName = "FitnessCalculationPartition";
     44    private const string NodeImpactThresholdParameterName = "ImpactThreshold";
     45    private const string PruneOnlyZeroImpactNodesParameterName = "PruneOnlyZeroImpactNodes";
     46    private const string SymbolicExpressionTreeParameterName = "SymbolicExpressionTree"; // the tree to be pruned
     47    private const string QualityParameterName = "Quality"; // the quality
     48    private const string EstimationLimitsParameterName = "EstimationLimits";
     49    private const string InterpreterParameterName = "SymbolicExpressionTreeInterpreter";
     50    #endregion
     51
    3952    #region parameter properties
    40     public ILookupParameter<DoubleValue> NumberOfPrunedSubtreesParameter {
    41       get { return (ILookupParameter<DoubleValue>)Parameters[NumberOfPrunedSubtreesParameterName]; }
     53    public ILookupParameter<ISymbolicExpressionTree> SymbolicExpressionTreeParameter {
     54      get { return (ILookupParameter<ISymbolicExpressionTree>)Parameters[SymbolicExpressionTreeParameterName]; }
    4255    }
    43     public ILookupParameter<DoubleValue> NumberOfPrunedTreesParameter {
    44       get { return (ILookupParameter<DoubleValue>)Parameters[NumberOfPrunedTreesParameterName]; }
     56    public ILookupParameter<DoubleValue> QualityParameter {
     57      get { return (ILookupParameter<DoubleValue>)Parameters[QualityParameterName]; }
     58    }
     59    public ILookupParameter<IDataAnalysisProblemData> ProblemDataParameter {
     60      get { return (ILookupParameter<IDataAnalysisProblemData>)Parameters[ProblemDataParameterName]; }
     61    }
     62    public IValueParameter<ISymbolicDataAnalysisSolutionImpactValuesCalculator> ImpactValuesCalculatorParameter {
     63      get { return (IValueParameter<ISymbolicDataAnalysisSolutionImpactValuesCalculator>)Parameters[ImpactValuesCalculatorParameterName]; }
     64    }
     65    public ILookupParameter<IntRange> FitnessCalculationPartitionParameter {
     66      get { return (ILookupParameter<IntRange>)Parameters[FitnessCalculationPartitionParameterName]; }
     67    }
     68    public ILookupParameter<IntValue> PrunedSubtreesParameter {
     69      get { return (ILookupParameter<IntValue>)Parameters[PrunedSubtreesParameterName]; }
     70    }
     71    public ILookupParameter<IntValue> PrunedTreesParameter {
     72      get { return (ILookupParameter<IntValue>)Parameters[PrunedTreesParameterName]; }
     73    }
     74    public IFixedValueParameter<DoubleValue> NodeImpactThresholdParameter {
     75      get { return (IFixedValueParameter<DoubleValue>)Parameters[NodeImpactThresholdParameterName]; }
     76    }
     77    public IFixedValueParameter<BoolValue> PruneOnlyZeroImpactNodesParameter {
     78      get { return (IFixedValueParameter<BoolValue>)Parameters[PruneOnlyZeroImpactNodesParameterName]; }
     79    }
     80    public ILookupParameter<DoubleLimit> EstimationLimitsParameter {
     81      get { return (ILookupParameter<DoubleLimit>)Parameters[EstimationLimitsParameterName]; }
     82    }
     83    public ILookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter> InterpreterParameter {
     84      get { return (ILookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter>)Parameters[InterpreterParameterName]; }
    4585    }
    4686    #endregion
     87
    4788    #region properties
    48     private DoubleValue PrunedSubtrees { get { return NumberOfPrunedSubtreesParameter.ActualValue; } }
    49     private DoubleValue PrunedTrees { get { return NumberOfPrunedTreesParameter.ActualValue; } }
     89    protected IDataAnalysisProblemData ProblemData { get { return ProblemDataParameter.ActualValue; } }
     90    protected ISymbolicDataAnalysisSolutionImpactValuesCalculator ImpactValuesCalculator { get { return ImpactValuesCalculatorParameter.Value; } }
     91    protected IntRange FitnessCalculationPartition { get { return FitnessCalculationPartitionParameter.ActualValue; } }
     92    protected bool PruneOnlyZeroImpactNodes {
     93      get { return PruneOnlyZeroImpactNodesParameter.Value.Value; }
     94      set { PruneOnlyZeroImpactNodesParameter.Value.Value = value; }
     95    }
     96    protected double NodeImpactThreshold {
     97      get { return NodeImpactThresholdParameter.Value.Value; }
     98      set { NodeImpactThresholdParameter.Value.Value = value; }
     99    }
     100    protected ISymbolicExpressionTree SymbolicExpressionTree { get { return SymbolicExpressionTreeParameter.ActualValue; } }
     101    protected DoubleValue Quality { get { return QualityParameter.ActualValue; } }
     102    protected DoubleLimit EstimationLimits { get { return EstimationLimitsParameter.ActualValue; } }
     103    protected ISymbolicDataAnalysisExpressionTreeInterpreter Interpreter { get { return InterpreterParameter.ActualValue; } }
    50104    #endregion
    51     public override IDeepCloneable Clone(Cloner cloner) {
    52       return new SymbolicDataAnalysisExpressionPruningOperator(this, cloner);
    53     }
    54     private SymbolicDataAnalysisExpressionPruningOperator(SymbolicDataAnalysisExpressionPruningOperator original, Cloner cloner)
    55       : base(original, cloner) {
     105
     106    [StorableConstructor]
     107    protected SymbolicDataAnalysisExpressionPruningOperator(bool deserializing) : base(deserializing) { }
     108    protected SymbolicDataAnalysisExpressionPruningOperator(SymbolicDataAnalysisExpressionPruningOperator original, Cloner cloner)
     109      : base(original, cloner) { }
     110
     111    protected SymbolicDataAnalysisExpressionPruningOperator() {
     112      #region add parameters
     113      Parameters.Add(new LookupParameter<IDataAnalysisProblemData>(ProblemDataParameterName));
     114      Parameters.Add(new LookupParameter<ISymbolicDataAnalysisModel>(SymbolicDataAnalysisModelParameterName));
     115      Parameters.Add(new LookupParameter<IntRange>(FitnessCalculationPartitionParameterName));
     116      Parameters.Add(new LookupParameter<IntValue>(PrunedSubtreesParameterName, "A counter of how many subtrees were replaced."));
     117      Parameters.Add(new LookupParameter<IntValue>(PrunedTreesParameterName, "A counter of how many trees were pruned."));
     118      Parameters.Add(new FixedValueParameter<BoolValue>(PruneOnlyZeroImpactNodesParameterName, "Specify whether or not only zero impact nodes should be pruned."));
     119      Parameters.Add(new FixedValueParameter<DoubleValue>(NodeImpactThresholdParameterName, "Specifies an impact value threshold below which nodes should be pruned."));
     120      Parameters.Add(new LookupParameter<DoubleLimit>(EstimationLimitsParameterName));
     121      Parameters.Add(new LookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter>(InterpreterParameterName));
     122      Parameters.Add(new LookupParameter<ISymbolicExpressionTree>(SymbolicExpressionTreeParameterName));
     123      Parameters.Add(new LookupParameter<DoubleValue>(QualityParameterName));
     124      #endregion
    56125    }
    57126
    58     public SymbolicDataAnalysisExpressionPruningOperator() {
    59       Parameters.Add(new LookupParameter<DoubleValue>(NumberOfPrunedSubtreesParameterName));
    60       Parameters.Add(new LookupParameter<DoubleValue>(NumberOfPrunedTreesParameterName));
    61     }
     127    protected abstract ISymbolicDataAnalysisModel CreateModel();
    62128
    63     public ISymbolicDataAnalysisModel Model { get; set; }
    64     public IDataAnalysisProblemData ProblemData { get; set; }
    65     public ISymbolicDataAnalysisSolutionImpactValuesCalculator ImpactsCalculator { get; set; }
    66     public IRandom Random { get; set; }
    67 
    68     public bool PruneOnlyZeroImpactNodes { get; set; }
    69     public double NodeImpactThreshold { get; set; }
     129    protected abstract double Evaluate(IDataAnalysisModel model);
    70130
    71131    public override IOperation Apply() {
    72       int prunedSubtrees = 0;
     132      var model = CreateModel();
     133      var nodes = SymbolicExpressionTree.Root.GetSubtree(0).GetSubtree(0).IterateNodesPrefix().ToList();
     134      var rows = Enumerable.Range(FitnessCalculationPartition.Start, FitnessCalculationPartition.Size);
     135      var prunedSubtrees = 0;
     136      var prunedTrees = 0;
    73137
    74       var nodes = Model.SymbolicExpressionTree.Root.GetSubtree(0).GetSubtree(0).IterateNodesPrefix().ToList();
     138      double quality = Evaluate(model);
    75139
    76       for (int j = 0; j < nodes.Count; ++j) {
    77         var node = nodes[j];
     140      for (int i = 0; i < nodes.Count; ++i) {
     141        var node = nodes[i];
    78142        if (node is ConstantTreeNode) continue;
    79143
    80         var impact = ImpactsCalculator.CalculateImpactValue(Model, node, ProblemData, ProblemData.TrainingIndices);
     144        double impactValue, replacementValue;
     145        ImpactValuesCalculator.CalculateImpactAndReplacementValues(model, node, ProblemData, rows, out impactValue, out replacementValue, quality);
    81146
    82147        if (PruneOnlyZeroImpactNodes) {
    83           if (!impact.IsAlmost(0.0)) continue;
    84         } else {
    85           if (NodeImpactThreshold < impact) continue;
     148          if (!impactValue.IsAlmost(0.0)) continue;
     149        } else if (NodeImpactThreshold < impactValue) {
     150          continue;
    86151        }
    87152
    88         var replacementValue = ImpactsCalculator.CalculateReplacementValue(Model, node, ProblemData, ProblemData.TrainingIndices);
    89         var constantNode = new ConstantTreeNode(new Constant()) { Value = replacementValue };
     153        var constantNode = (ConstantTreeNode)node.Grammar.GetSymbol("Constant").CreateTreeNode();
     154        constantNode.Value = replacementValue;
     155
    90156        ReplaceWithConstant(node, constantNode);
    91         j += node.GetLength() - 1; // skip subtrees under the node that was folded
     157        i += node.GetLength() - 1; // skip subtrees under the node that was folded
     158
     159        quality -= impactValue;
    92160
    93161        prunedSubtrees++;
    94162      }
    95163
    96       if (prunedSubtrees > 0) {
    97         lock (PrunedSubtrees) { PrunedSubtrees.Value += prunedSubtrees; }
    98         lock (PrunedTrees) { PrunedTrees.Value += 1; }
    99       }
     164      if (prunedSubtrees > 0) prunedTrees = 1;
     165      PrunedSubtreesParameter.ActualValue = new IntValue(prunedSubtrees);
     166      PrunedTreesParameter.ActualValue = new IntValue(prunedTrees);
     167
    100168      return base.Apply();
    101169    }
     170
    102171    private static void ReplaceWithConstant(ISymbolicExpressionTreeNode original, ISymbolicExpressionTreeNode replacement) {
    103172      var parent = original.Parent;

  • stable/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/SymbolicDataAnalysisSolutionImpactValuesCalculator.cs

    r9456 r11145  
    2222using System.Collections.Generic;
    2323using HeuristicLab.Common;
     24using HeuristicLab.Core;
    2425using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
     26using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
    2527
    2628namespace HeuristicLab.Problems.DataAnalysis.Symbolic {
    27   public abstract class SymbolicDataAnalysisSolutionImpactValuesCalculator : ISymbolicDataAnalysisSolutionImpactValuesCalculator {
     29  [StorableClass]
     30  [Item("SymbolicDataAnalysisSolutionImpactValuesCalculator", "Calculates the impact values and replacements values for symbolic expression tree nodes.")]
     31  public abstract class SymbolicDataAnalysisSolutionImpactValuesCalculator : Item, ISymbolicDataAnalysisSolutionImpactValuesCalculator {
     32    protected SymbolicDataAnalysisSolutionImpactValuesCalculator() { }
     33
     34    protected SymbolicDataAnalysisSolutionImpactValuesCalculator(SymbolicDataAnalysisSolutionImpactValuesCalculator original, Cloner cloner)
     35      : base(original, cloner) { }
     36    [StorableConstructor]
     37    protected SymbolicDataAnalysisSolutionImpactValuesCalculator(bool deserializing) : base(deserializing) { }
    2838    public abstract double CalculateReplacementValue(ISymbolicDataAnalysisModel model, ISymbolicExpressionTreeNode node, IDataAnalysisProblemData problemData, IEnumerable<int> rows);
    2939    public abstract double CalculateImpactValue(ISymbolicDataAnalysisModel model, ISymbolicExpressionTreeNode node, IDataAnalysisProblemData problemData, IEnumerable<int> rows, double originalQuality = double.NaN);
     40    public abstract void CalculateImpactAndReplacementValues(ISymbolicDataAnalysisModel model, ISymbolicExpressionTreeNode node, IDataAnalysisProblemData problemData, IEnumerable<int> rows, out double impactValue, out double replacementValue, double originalQuality = double.NaN);
    3041
    3142    protected static double CalculateReplacementValue(ISymbolicExpressionTreeNode node, ISymbolicExpressionTree sourceTree, ISymbolicDataAnalysisExpressionTreeInterpreter interpreter,
Note: See TracChangeset for help on using the changeset viewer.