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Timestamp:
04/16/19 11:43:52 (5 years ago)
Author:
mkommend
Message:

#2361: Merged all changes from the sensitivity branch to trunk.

Location:
trunk/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification
Files:
3 edited
2 copied

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

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

    r16658 r16788  
    126126    <Compile Include="ModelCreators\NormalDistributedThresholdsModelCreator.cs" />
    127127    <Compile Include="MultiObjective\SymbolicClassificationMultiObjectiveValidationBestSolutionAnalyzer.cs" />
     128    <Compile Include="SingleObjective\SymbolicClassificationSingleObjectiveWeightedPerformanceMeasuresEvaluator.cs" />
     129    <Compile Include="SingleObjective\SymbolicClassificationSingleObjectiveWeightedResidualsMeanSquaredErrorEvaluator.cs" />
    128130    <Compile Include="SymbolicClassificationPhenotypicDiversityAnalyzer.cs" />
    129131    <Compile Include="SymbolicClassificationPruningAnalyzer.cs" />

  • trunk/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SingleObjective/SymbolicClassificationSingleObjectiveWeightedPerformanceMeasuresEvaluator.cs

    r16784 r16788  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2019 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.
    … …  
    2323using System.Collections.Generic;
    2424using System.Linq;
     25using HEAL.Attic;
    2526using HeuristicLab.Common;
    2627using HeuristicLab.Core;
    … …  
    2829using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
    2930using HeuristicLab.Parameters;
    30 using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
     31using HeuristicLab.PluginInfrastructure;
    3132
    32 namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Classification.SingleObjective {
     33namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Classification {
     34  [NonDiscoverableType]
    3335  [Item("Weighted Performance Measures Evaluator", "Calculates the quality of a symbolic classification solution based on three weighted measures(normalized mean squared error, false negative rate(1-sensitivity) and false positve rate(1-specificity)).")]
    34   [StorableClass]
     36  [StorableType("0772F316-5E12-4153-857E-8625069B4677")]
    3537  public class SymbolicClassificationSingleObjectiveWeightedPerformanceMeasuresEvaluator : SymbolicClassificationSingleObjectiveEvaluator {
    3638    private const string NormalizedMeanSquaredErrorWeightingFactorParameterName = "NormalizedMeanSquaredErrorWeightingFactor";
    … …  
    5759
    5860    public double NormalizedMeanSquaredErrorWeightingFactor {
    59       get {
    60         return NormalizedMeanSquaredErrorWeightingFactorParameter.Value.Value;
    61       }
     61      get { return NormalizedMeanSquaredErrorWeightingFactorParameter.Value.Value; }
    6262    }
    6363    public double FalseNegativeRateWeightingFactor {
    64       get {
    65         return FalseNegativeRateWeightingFactorParameter.Value.Value;
    66       }
     64      get { return FalseNegativeRateWeightingFactorParameter.Value.Value; }
    6765    }
    6866    public double FalsePositiveRateWeightingFactor {
    69       get {
    70         return FalsePositiveRateWeightingFactorParameter.Value.Value;
    71       }
     67      get { return FalsePositiveRateWeightingFactorParameter.Value.Value; }
    7268    }
    7369
    7470    [StorableConstructor]
    75     protected SymbolicClassificationSingleObjectiveWeightedPerformanceMeasuresEvaluator(bool deserializing) : base(deserializing) { }
     71    protected SymbolicClassificationSingleObjectiveWeightedPerformanceMeasuresEvaluator(StorableConstructorFlag _) : base(_) { }
    7672    protected SymbolicClassificationSingleObjectiveWeightedPerformanceMeasuresEvaluator(SymbolicClassificationSingleObjectiveWeightedPerformanceMeasuresEvaluator original, Cloner cloner)
    7773      : base(original, cloner) {
    … …  
    7975    public override IDeepCloneable Clone(Cloner cloner) {
    8076      return new SymbolicClassificationSingleObjectiveWeightedPerformanceMeasuresEvaluator(this, cloner);
    81     }
    82 
    83     [StorableHook(HookType.AfterDeserialization)]
    84     private void AfterDeserialization() {
    85       // BackwardsCompatibility
    86       #region Backwards compatible code
    87       if (Parameters.ContainsKey(ModelCreatorParameterName)) {
    88         Parameters.Remove(ModelCreatorParameterName);
    89         Parameters.Add(new ValueLookupParameter<ISymbolicClassificationModelCreator>(ModelCreatorParameterName, "The model creator which is used during the evaluations."));
    90       }
    91       #endregion
    9277    }
    9378
    … …  
    10287    public override IOperation InstrumentedApply() {
    10388      IEnumerable<int> rows = GenerateRowsToEvaluate();
    104       var solution = SymbolicExpressionTreeParameter.ActualValue;
     89      var tree = SymbolicExpressionTreeParameter.ActualValue;
    10590      var creator = ModelCreatorParameter.ActualValue;
    106       double quality = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper,
    107               ProblemDataParameter.ActualValue, rows, ApplyLinearScalingParameter.ActualValue.Value, creator, NormalizedMeanSquaredErrorWeightingFactor, FalseNegativeRateWeightingFactor, FalsePositiveRateWeightingFactor);
     91      var interpreter = SymbolicDataAnalysisTreeInterpreterParameter.ActualValue;
     92      var estimationLimits = EstimationLimitsParameter.ActualValue;
     93      var applyLinearScaling = ApplyLinearScalingParameter.ActualValue.Value;
     94
     95
     96      double quality = Calculate(interpreter, tree, estimationLimits.Lower, estimationLimits.Upper,
     97              ProblemDataParameter.ActualValue, rows, applyLinearScaling, creator, NormalizedMeanSquaredErrorWeightingFactor, FalseNegativeRateWeightingFactor, FalsePositiveRateWeightingFactor);
    10898      QualityParameter.ActualValue = new DoubleValue(quality);
    10999      return base.InstrumentedApply();
    110100    }
    111101
    112     public static double Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IClassificationProblemData problemData,
     102    public static double Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree tree, double lowerEstimationLimit, double upperEstimationLimit, IClassificationProblemData problemData,
    113103                IEnumerable<int> rows, bool applyLinearScaling, ISymbolicClassificationModelCreator modelCreator, double normalizedMeanSquaredErrorWeightingFactor, double falseNegativeRateWeightingFactor, double falsePositiveRateWeightingFactor) {
    114       IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, rows);
     104      var estimatedValues = interpreter.GetSymbolicExpressionTreeValues(tree, problemData.Dataset, rows);
    115105      var targetClassValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
    116106      var boundedEstimatedValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit).ToArray();
    … …  
    124114      ISymbolicDiscriminantFunctionClassificationModel m;
    125115
    126       var model = modelCreator.CreateSymbolicClassificationModel(solution, interpreter, lowerEstimationLimit, upperEstimationLimit);
     116      var model = modelCreator.CreateSymbolicClassificationModel(problemData.TargetVariable, tree, interpreter, lowerEstimationLimit, upperEstimationLimit);
    127117      if ((m = model as ISymbolicDiscriminantFunctionClassificationModel) != null) {
    128118        m.ThresholdCalculator.Calculate(problemData, boundedEstimatedValues, targetClassValues, out classValues, out thresholds);
    … …  
    133123        estimatedClassValues = model.GetEstimatedClassValues(problemData.Dataset, rows);
    134124      }
     125
    135126      var performanceCalculator = new ClassificationPerformanceMeasuresCalculator(positiveClassName, problemData.GetClassValue(positiveClassName));
    136127      performanceCalculator.Calculate(targetClassValues, estimatedClassValues);

  • trunk/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SingleObjective/SymbolicClassificationSingleObjectiveWeightedResidualsMeanSquaredErrorEvaluator.cs

    r16784 r16788  
    11#region License Information
    22/* HeuristicLab
    3  * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
     3 * Copyright (C) 2002-2019 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
    44 *
    55 * This file is part of HeuristicLab.
    … …  
    2323using System.Collections.Generic;
    2424using System.Linq;
     25using HEAL.Attic;
    2526using HeuristicLab.Common;
    2627using HeuristicLab.Core;
    … …  
    2829using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
    2930using HeuristicLab.Parameters;
    30 using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
     31using HeuristicLab.PluginInfrastructure;
    3132
    3233namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Classification {
     34  [NonDiscoverableType]
    3335  [Item("Weighted Residuals Mean Squared Error Evaluator", @"A modified mean squared error evaluator that enables the possibility to weight residuals differently.
    3436The first residual category belongs to estimated values which definitely belong to a specific class because the estimated value is located above the maximum or below the minimum of all the class values (DefiniteResidualsWeight).
    … …  
    3638All other cases are represented by the third category (NegativeClassesResidualsWeight).
    3739The weight gets multiplied to the squared error. Note that the Evaluator acts like a normal MSE-Evaluator if all the weights are set to 1.")]
    38   [StorableClass]
    39   public class SymbolicClassificationSingleObjectiveWeightedResidualsMeanSquaredErrorEvaluator : SymbolicClassificationSingleObjectiveEvaluator {
     40  [StorableType("A3193296-1A0F-46E2-8F43-22E2ED9CFFC5")]
     41  public sealed class SymbolicClassificationSingleObjectiveWeightedResidualsMeanSquaredErrorEvaluator : SymbolicClassificationSingleObjectiveEvaluator {
    4042    private const string DefiniteResidualsWeightParameterName = "DefiniteResidualsWeight";
    4143    private const string PositiveClassResidualsWeightParameterName = "PositiveClassResidualsWeight";
    4244    private const string NegativeClassesResidualsWeightParameterName = "NegativeClassesResidualsWeight";
    4345    [StorableConstructor]
    44     protected SymbolicClassificationSingleObjectiveWeightedResidualsMeanSquaredErrorEvaluator(bool deserializing) : base(deserializing) { }
    45     protected SymbolicClassificationSingleObjectiveWeightedResidualsMeanSquaredErrorEvaluator(SymbolicClassificationSingleObjectiveWeightedResidualsMeanSquaredErrorEvaluator original, Cloner cloner)
     46    private SymbolicClassificationSingleObjectiveWeightedResidualsMeanSquaredErrorEvaluator(StorableConstructorFlag _) : base(_) { }
     47    private SymbolicClassificationSingleObjectiveWeightedResidualsMeanSquaredErrorEvaluator(SymbolicClassificationSingleObjectiveWeightedResidualsMeanSquaredErrorEvaluator original, Cloner cloner)
    4648      : base(original, cloner) {
    4749    }
    … …  
    7375
    7476    public double DefiniteResidualsWeight {
    75       get {
    76         return DefiniteResidualsWeightParameter.Value.Value;
    77       }
     77      get { return DefiniteResidualsWeightParameter.Value.Value; }
    7878    }
    7979    public double PositiveClassResidualsWeight {
    80       get {
    81         return PositiveClassResidualsWeightParameter.Value.Value;
    82       }
     80      get { return PositiveClassResidualsWeightParameter.Value.Value; }
    8381    }
    8482    public double NegativeClassesResidualsWeight {
    85       get {
    86         return NegativeClassesResidualsWeightParameter.Value.Value;
    87       }
     83      get { return NegativeClassesResidualsWeightParameter.Value.Value; }
    8884    }
    8985    #endregion
    … …  
    9894    }
    9995
    100     public static double Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IClassificationProblemData problemData, IEnumerable<int> rows, bool applyLinearScaling,
     96    public static double Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree tree, double lowerEstimationLimit, double upperEstimationLimit, IClassificationProblemData problemData, IEnumerable<int> rows, bool applyLinearScaling,
    10197      double definiteResidualsWeight, double positiveClassResidualsWeight, double negativeClassesResidualsWeight) {
    102       IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, rows);
     98      IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(tree, problemData.Dataset, rows);
    10399      IEnumerable<double> targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
    104100      OnlineCalculatorError errorState;
    … …  
    115111      double quality;
    116112      if (applyLinearScaling) {
    117         var calculator = new OnlineWeightedResidualsMeanSquaredErrorCalculator(positiveClassValue, classValuesMax, classValuesMin,
     113        var calculator = new OnlineWeightedClassificationMeanSquaredErrorCalculator(positiveClassValue, classValuesMax, classValuesMin,
    118114          definiteResidualsWeight, positiveClassResidualsWeight, negativeClassesResidualsWeight);
    119115        CalculateWithScaling(targetValues, estimatedValues, lowerEstimationLimit, upperEstimationLimit, calculator, problemData.Dataset.Rows);
    … …  
    122118      } else {
    123119        IEnumerable<double> boundedEstimatedValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit);
    124         quality = OnlineWeightedResidualsMeanSquaredErrorCalculator.Calculate(targetValues, boundedEstimatedValues, positiveClassValue, classValuesMax,
     120        quality = OnlineWeightedClassificationMeanSquaredErrorCalculator.Calculate(targetValues, boundedEstimatedValues, positiveClassValue, classValuesMax,
    125121          classValuesMin, definiteResidualsWeight, positiveClassResidualsWeight, negativeClassesResidualsWeight, out errorState);
    126122      }

  • trunk/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SymbolicDiscriminantFunctionClassificationModel.cs

    r16565 r16788  
    112112
    113113    public override IEnumerable<double> GetEstimatedClassValues(IDataset dataset, IEnumerable<int> rows) {
     114      var estimatedValues = GetEstimatedValues(dataset, rows);
     115      return GetEstimatedClassValues(estimatedValues);
     116    }
     117    public IEnumerable<double> GetEstimatedClassValues(IEnumerable<double> estimatedValues) {
    114118      if (!Thresholds.Any() && !ClassValues.Any()) throw new ArgumentException("No thresholds and class values were set for the current symbolic classification model.");
    115       foreach (var x in GetEstimatedValues(dataset, rows)) {
     119      foreach (var x in estimatedValues) {
    116120        int classIndex = 0;
    117121        // find first threshold value which is larger than x => class index = threshold index + 1
    … …  
    123127      }
    124128    }
    125 
    126129
    127130    public override ISymbolicClassificationSolution CreateClassificationSolution(IClassificationProblemData problemData) {
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