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Ignore:
Timestamp:
01/11/11 15:03:46 (14 years ago)
Author:
gkronber
Message:

Merged changes from trunk to data analysis exploration branch and added fractional distance metric evaluator. #1142

Location:
branches/DataAnalysis/HeuristicLab.Problems.DataAnalysis.Regression/3.3/Symbolic/Analyzers
Files:
3 added
8 edited

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Added
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  • branches/DataAnalysis/HeuristicLab.Problems.DataAnalysis.Regression/3.3/Symbolic/Analyzers/BestSymbolicRegressionSolutionAnalyzer.cs

    r4475 r5275  
    2222using System.Linq;
    2323using HeuristicLab.Analysis;
     24using HeuristicLab.Common;
    2425using HeuristicLab.Core;
    2526using HeuristicLab.Data;
    … …  
    4041    private const string VariableImpactsResultName = "Integrated variable frequencies";
    4142    private const string BestSolutionParameterName = "BestSolution";
    42     private const string BestSolutionComplexity = "Best solution complexity";
    4343
    4444    #region parameter properties
    … …  
    6868    #endregion
    6969
     70    [StorableConstructor]
     71    private BestSymbolicRegressionSolutionAnalyzer(bool deserializing) : base(deserializing) { }
     72    private BestSymbolicRegressionSolutionAnalyzer(BestSymbolicRegressionSolutionAnalyzer original, Cloner cloner) : base(original, cloner) { }
    7073    public BestSymbolicRegressionSolutionAnalyzer()
    7174      : base() {
    … …  
    7679    }
    7780
     81    public override IDeepCloneable Clone(Cloner cloner) {
     82      return new BestSymbolicRegressionSolutionAnalyzer(this, cloner);
     83    }
     84
    7885    [StorableHook(HookType.AfterDeserialization)]
    79     private void Initialize() {
     86    private void AfterDeserialization() {
    8087      if (!Parameters.ContainsKey(VariableFrequenciesParameterName)) {
    8188        Parameters.Add(new LookupParameter<DataTable>(VariableFrequenciesParameterName, "The variable frequencies table to use for the calculation of variable impacts"));
    … …  
    9299        var model = new SymbolicRegressionModel((ISymbolicExpressionTreeInterpreter)SymbolicExpressionTreeInterpreter.Clone(),
    93100          SymbolicExpressionTree[i]);
    94         var solution = new SymbolicRegressionSolution((DataAnalysisProblemData)ProblemData.Clone(), model, lowerEstimationLimit, upperEstimationLimit);
     101        DataAnalysisProblemData problemDataClone = (DataAnalysisProblemData)ProblemData.Clone();
     102        var solution = new SymbolicRegressionSolution(problemDataClone, model, lowerEstimationLimit, upperEstimationLimit);
    95103        solution.Name = BestSolutionParameterName;
    96104        solution.Description = "Best solution on validation partition found over the whole run.";
    97105        BestSolutionParameter.ActualValue = solution;
    98106        BestSolutionQualityParameter.ActualValue = Quality[i];
    99         BestSymbolicRegressionSolutionAnalyzer.UpdateSymbolicRegressionBestSolutionResults(solution, ProblemData, Results, VariableFrequencies);
     107        BestSymbolicRegressionSolutionAnalyzer.UpdateSymbolicRegressionBestSolutionResults(solution, problemDataClone, Results, VariableFrequencies);
    100108      }
    101109      return BestSolutionParameter.ActualValue;
    102110    }
    103111
    104     public static void UpdateBestSolutionResults(SymbolicRegressionSolution bestSolution, DataAnalysisProblemData problemData, ResultCollection results, IntValue currentGeneration, DataTable variableFrequencies) {
    105       RegressionSolutionAnalyzer.UpdateBestSolutionResults(bestSolution, problemData, results, currentGeneration);
    106       UpdateSymbolicRegressionBestSolutionResults(bestSolution, problemData, results, variableFrequencies);
     112    public static void UpdateBestSolutionResults(SymbolicRegressionSolution solution, DataAnalysisProblemData problemData, ResultCollection results, IntValue currentGeneration, DataTable variableFrequencies) {
     113      RegressionSolutionAnalyzer.UpdateBestSolutionResults(solution, problemData, results, currentGeneration);
     114      UpdateSymbolicRegressionBestSolutionResults(solution, problemData, results, variableFrequencies);
    107115    }
    108116
    109     private static void UpdateSymbolicRegressionBestSolutionResults(SymbolicRegressionSolution bestSolution, DataAnalysisProblemData problemData, ResultCollection results, DataTable variableFrequencies) {
     117    private static void UpdateSymbolicRegressionBestSolutionResults(SymbolicRegressionSolution solution, DataAnalysisProblemData problemData, ResultCollection results, DataTable variableFrequencies) {
    110118      if (results.ContainsKey(BestSolutionInputvariableCountResultName)) {
    111         results[BestSolutionInputvariableCountResultName].Value = new IntValue(bestSolution.Model.InputVariables.Count());
     119        results[BestSolutionInputvariableCountResultName].Value = new IntValue(solution.Model.InputVariables.Count());
    112120        results[VariableImpactsResultName].Value = CalculateVariableImpacts(variableFrequencies);
    113         var sizeTable = (DataTable)results[BestSolutionComplexity].Value;
    114         sizeTable.Rows["Best solution size"].Values.Add(bestSolution.Model.SymbolicExpressionTree.Size);
    115         sizeTable.Rows["Best solution height"].Values.Add(bestSolution.Model.SymbolicExpressionTree.Height);
    116         sizeTable.Rows["Best solution variables"].Values.Add(bestSolution.Model.InputVariables.Count());
    117121      } else {
    118         results.Add(new Result(BestSolutionInputvariableCountResultName, new IntValue(bestSolution.Model.InputVariables.Count())));
     122        results.Add(new Result(BestSolutionInputvariableCountResultName, new IntValue(solution.Model.InputVariables.Count())));
    119123        results.Add(new Result(VariableImpactsResultName, CalculateVariableImpacts(variableFrequencies)));
    120         var sizeTable = new DataTable("Best solution complexity");
    121         sizeTable.Rows.Add(new DataRow("Best solution size"));
    122         sizeTable.Rows.Add(new DataRow("Best solution height"));
    123         sizeTable.Rows.Add(new DataRow("Best solution variables"));
    124         sizeTable.Rows["Best solution size"].Values.Add(bestSolution.Model.SymbolicExpressionTree.Size);
    125         sizeTable.Rows["Best solution height"].Values.Add(bestSolution.Model.SymbolicExpressionTree.Height);
    126         sizeTable.Rows["Best solution variables"].Values.Add(bestSolution.Model.InputVariables.Count());
    127         results.Add(new Result(BestSolutionComplexity, sizeTable));
    128124      }
    129125    }

  • branches/DataAnalysis/HeuristicLab.Problems.DataAnalysis.Regression/3.3/Symbolic/Analyzers/FixedValidationBestScaledSymbolicRegressionSolutionAnalyzer.cs

    r4484 r5275  
    2323using System.Linq;
    2424using HeuristicLab.Analysis;
     25using HeuristicLab.Common;
    2526using HeuristicLab.Core;
    2627using HeuristicLab.Data;
    … …  
    3031using HeuristicLab.Parameters;
    3132using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
    32 using HeuristicLab.Problems.DataAnalysis.Evaluators;
    3333using HeuristicLab.Problems.DataAnalysis.Symbolic;
    34 using System;
    3534
    3635namespace HeuristicLab.Problems.DataAnalysis.Regression.Symbolic.Analyzers {
    … …  
    4039  [Item("FixedValidationBestScaledSymbolicRegressionSolutionAnalyzer", "An operator that analyzes the validation best scaled symbolic regression solution.")]
    4140  [StorableClass]
    42   public sealed class FixedValidationBestScaledSymbolicRegressionSolutionAnalyzer : SingleSuccessorOperator, ISymbolicRegressionAnalyzer {
    43     private const string RandomParameterName = "Random";
    44     private const string SymbolicExpressionTreeParameterName = "SymbolicExpressionTree";
    45     private const string SymbolicExpressionTreeInterpreterParameterName = "SymbolicExpressionTreeInterpreter";
    46     private const string ProblemDataParameterName = "ProblemData";
    47     private const string ValidationSamplesStartParameterName = "SamplesStart";
    48     private const string ValidationSamplesEndParameterName = "SamplesEnd";
    49     // private const string QualityParameterName = "Quality";
    50     private const string UpperEstimationLimitParameterName = "UpperEstimationLimit";
    51     private const string LowerEstimationLimitParameterName = "LowerEstimationLimit";
    52     private const string EvaluatorParameterName = "Evaluator";
     41  public sealed class FixedValidationBestScaledSymbolicRegressionSolutionAnalyzer : SymbolicRegressionValidationAnalyzer, ISymbolicRegressionAnalyzer {
    5342    private const string MaximizationParameterName = "Maximization";
     43    private const string CalculateSolutionComplexityParameterName = "CalculateSolutionComplexity";
    5444    private const string BestSolutionParameterName = "Best solution (validation)";
    5545    private const string BestSolutionQualityParameterName = "Best solution quality (validation)";
     46    private const string BestSolutionLengthParameterName = "Best solution length (validation)";
     47    private const string BestSolutionHeightParameterName = "Best solution height (validiation)";
    5648    private const string CurrentBestValidationQualityParameterName = "Current best validation quality";
    5749    private const string BestSolutionQualityValuesParameterName = "Validation Quality";
    … …  
    6052    private const string BestKnownQualityParameterName = "BestKnownQuality";
    6153    private const string GenerationsParameterName = "Generations";
    62     private const string RelativeNumberOfEvaluatedSamplesParameterName = "RelativeNumberOfEvaluatedSamples";
    63 
    64     private const string TrainingMeanSquaredErrorQualityParameterName = "Mean squared error (training)";
    65     private const string MinTrainingMeanSquaredErrorQualityParameterName = "Min mean squared error (training)";
    66     private const string MaxTrainingMeanSquaredErrorQualityParameterName = "Max mean squared error (training)";
    67     private const string AverageTrainingMeanSquaredErrorQualityParameterName = "Average mean squared error (training)";
    68     private const string BestTrainingMeanSquaredErrorQualityParameterName = "Best mean squared error (training)";
    69 
    70     private const string TrainingAverageRelativeErrorQualityParameterName = "Average relative error (training)";
    71     private const string MinTrainingAverageRelativeErrorQualityParameterName = "Min average relative error (training)";
    72     private const string MaxTrainingAverageRelativeErrorQualityParameterName = "Max average relative error (training)";
    73     private const string AverageTrainingAverageRelativeErrorQualityParameterName = "Average average relative error (training)";
    74     private const string BestTrainingAverageRelativeErrorQualityParameterName = "Best average relative error (training)";
    75 
    76     private const string TrainingRSquaredQualityParameterName = "R² (training)";
    77     private const string MinTrainingRSquaredQualityParameterName = "Min R² (training)";
    78     private const string MaxTrainingRSquaredQualityParameterName = "Max R² (training)";
    79     private const string AverageTrainingRSquaredQualityParameterName = "Average R² (training)";
    80     private const string BestTrainingRSquaredQualityParameterName = "Best R² (training)";
    81 
    82     private const string TestMeanSquaredErrorQualityParameterName = "Mean squared error (test)";
    83     private const string MinTestMeanSquaredErrorQualityParameterName = "Min mean squared error (test)";
    84     private const string MaxTestMeanSquaredErrorQualityParameterName = "Max mean squared error (test)";
    85     private const string AverageTestMeanSquaredErrorQualityParameterName = "Average mean squared error (test)";
    86     private const string BestTestMeanSquaredErrorQualityParameterName = "Best mean squared error (test)";
    87 
    88     private const string TestAverageRelativeErrorQualityParameterName = "Average relative error (test)";
    89     private const string MinTestAverageRelativeErrorQualityParameterName = "Min average relative error (test)";
    90     private const string MaxTestAverageRelativeErrorQualityParameterName = "Max average relative error (test)";
    91     private const string AverageTestAverageRelativeErrorQualityParameterName = "Average average relative error (test)";
    92     private const string BestTestAverageRelativeErrorQualityParameterName = "Best average relative error (test)";
    93 
    94     private const string TestRSquaredQualityParameterName = "R² (test)";
    95     private const string MinTestRSquaredQualityParameterName = "Min R² (test)";
    96     private const string MaxTestRSquaredQualityParameterName = "Max R² (test)";
    97     private const string AverageTestRSquaredQualityParameterName = "Average R² (test)";
    98     private const string BestTestRSquaredQualityParameterName = "Best R² (test)";
    99 
    100     private const string RSquaredValuesParameterName = "R²";
    101     private const string MeanSquaredErrorValuesParameterName = "Mean squared error";
    102     private const string RelativeErrorValuesParameterName = "Average relative error";
    10354
    10455    #region parameter properties
    105     public ILookupParameter<IRandom> RandomParameter {
    106       get { return (ILookupParameter<IRandom>)Parameters[RandomParameterName]; }
    107     }
    108     public ScopeTreeLookupParameter<SymbolicExpressionTree> SymbolicExpressionTreeParameter {
    109       get { return (ScopeTreeLookupParameter<SymbolicExpressionTree>)Parameters[SymbolicExpressionTreeParameterName]; }
    110     }
    111     public IValueLookupParameter<ISymbolicExpressionTreeInterpreter> SymbolicExpressionTreeInterpreterParameter {
    112       get { return (IValueLookupParameter<ISymbolicExpressionTreeInterpreter>)Parameters[SymbolicExpressionTreeInterpreterParameterName]; }
    113     }
    114     public ILookupParameter<ISymbolicRegressionEvaluator> EvaluatorParameter {
    115       get { return (ILookupParameter<ISymbolicRegressionEvaluator>)Parameters[EvaluatorParameterName]; }
    116     }
    11756    public ILookupParameter<BoolValue> MaximizationParameter {
    11857      get { return (ILookupParameter<BoolValue>)Parameters[MaximizationParameterName]; }
    11958    }
    120     public IValueLookupParameter<DataAnalysisProblemData> ProblemDataParameter {
    121       get { return (IValueLookupParameter<DataAnalysisProblemData>)Parameters[ProblemDataParameterName]; }
    122     }
    123     public IValueLookupParameter<IntValue> ValidationSamplesStartParameter {
    124       get { return (IValueLookupParameter<IntValue>)Parameters[ValidationSamplesStartParameterName]; }
    125     }
    126     public IValueLookupParameter<IntValue> ValidationSamplesEndParameter {
    127       get { return (IValueLookupParameter<IntValue>)Parameters[ValidationSamplesEndParameterName]; }
    128     }
    129     public IValueParameter<PercentValue> RelativeNumberOfEvaluatedSamplesParameter {
    130       get { return (IValueParameter<PercentValue>)Parameters[RelativeNumberOfEvaluatedSamplesParameterName]; }
    131     }
    132 
    133     public IValueLookupParameter<DoubleValue> UpperEstimationLimitParameter {
    134       get { return (IValueLookupParameter<DoubleValue>)Parameters[UpperEstimationLimitParameterName]; }
    135     }
    136     public IValueLookupParameter<DoubleValue> LowerEstimationLimitParameter {
    137       get { return (IValueLookupParameter<DoubleValue>)Parameters[LowerEstimationLimitParameterName]; }
     59    public IValueParameter<BoolValue> CalculateSolutionComplexityParameter {
     60      get { return (IValueParameter<BoolValue>)Parameters[CalculateSolutionComplexityParameterName]; }
    13861    }
    13962    public ILookupParameter<SymbolicRegressionSolution> BestSolutionParameter {
    14063      get { return (ILookupParameter<SymbolicRegressionSolution>)Parameters[BestSolutionParameterName]; }
    14164    }
    142     public ILookupParameter<SymbolicRegressionSolution> BestTrainingSolutionParameter {
    143       get { return (ILookupParameter<SymbolicRegressionSolution>)Parameters["BestTrainingSolution"]; }
    144     }
    145     public ScopeTreeLookupParameter<DoubleValue> QualityParameter {
    146       get { return (ScopeTreeLookupParameter<DoubleValue>)Parameters["Quality"]; }
    147     }
    148     public ScopeTreeLookupParameter<DoubleValue> ValidationQualityParameter {
    149       get { return (ScopeTreeLookupParameter<DoubleValue>)Parameters["ValidationQuality"]; }
    150     }
    151 
    15265    public ILookupParameter<IntValue> GenerationsParameter {
    15366      get { return (ILookupParameter<IntValue>)Parameters[GenerationsParameterName]; }
    … …  
    15669      get { return (ILookupParameter<DoubleValue>)Parameters[BestSolutionQualityParameterName]; }
    15770    }
    158     public ILookupParameter<DataTable> BestSolutionQualityValuesParameter {
    159       get { return (ILookupParameter<DataTable>)Parameters[BestSolutionQualityValuesParameterName]; }
     71    public ILookupParameter<IntValue> BestSolutionLengthParameter {
     72      get { return (ILookupParameter<IntValue>)Parameters[BestSolutionLengthParameterName]; }
     73    }
     74    public ILookupParameter<IntValue> BestSolutionHeightParameter {
     75      get { return (ILookupParameter<IntValue>)Parameters[BestSolutionHeightParameterName]; }
    16076    }
    16177    public ILookupParameter<ResultCollection> ResultsParameter {
    … …  
    16581      get { return (ILookupParameter<DoubleValue>)Parameters[BestKnownQualityParameterName]; }
    16682    }
    167     public ILookupParameter<DoubleValue> CurrentBestValidationQualityParameter {
    168       get { return (ILookupParameter<DoubleValue>)Parameters[CurrentBestValidationQualityParameterName]; }
    169     }
    170 
    17183    public ILookupParameter<DataTable> VariableFrequenciesParameter {
    17284      get { return (ILookupParameter<DataTable>)Parameters[VariableFrequenciesParameterName]; }
    … …  
    17587    #endregion
    17688    #region properties
    177     public IRandom Random {
    178       get { return RandomParameter.ActualValue; }
    179     }
    180     public ItemArray<SymbolicExpressionTree> SymbolicExpressionTree {
    181       get { return SymbolicExpressionTreeParameter.ActualValue; }
    182     }
    183     public ISymbolicExpressionTreeInterpreter SymbolicExpressionTreeInterpreter {
    184       get { return SymbolicExpressionTreeInterpreterParameter.ActualValue; }
    185     }
    186     public ISymbolicRegressionEvaluator Evaluator {
    187       get { return EvaluatorParameter.ActualValue; }
    188     }
    18989    public BoolValue Maximization {
    19090      get { return MaximizationParameter.ActualValue; }
    19191    }
    192     public DataAnalysisProblemData ProblemData {
    193       get { return ProblemDataParameter.ActualValue; }
    194     }
    195     public IntValue ValidationSamplesStart {
    196       get { return ValidationSamplesStartParameter.ActualValue; }
    197     }
    198     public IntValue ValidationSamplesEnd {
    199       get { return ValidationSamplesEndParameter.ActualValue; }
    200     }
    201     public PercentValue RelativeNumberOfEvaluatedSamples {
    202       get { return RelativeNumberOfEvaluatedSamplesParameter.Value; }
    203     }
    204 
    205     public DoubleValue UpperEstimationLimit {
    206       get { return UpperEstimationLimitParameter.ActualValue; }
    207     }
    208     public DoubleValue LowerEstimationLimit {
    209       get { return LowerEstimationLimitParameter.ActualValue; }
     92    public BoolValue CalculateSolutionComplexity {
     93      get { return CalculateSolutionComplexityParameter.Value; }
     94      set { CalculateSolutionComplexityParameter.Value = value; }
    21095    }
    21196    public ResultCollection Results {
    … …  
    221106      get { return BestSolutionQualityParameter.ActualValue; }
    222107    }
     108    public IntValue BestSolutionLength {
     109      get { return BestSolutionLengthParameter.ActualValue; }
     110      set { BestSolutionLengthParameter.ActualValue = value; }
     111    }
     112    public IntValue BestSolutionHeight {
     113      get { return BestSolutionHeightParameter.ActualValue; }
     114      set { BestSolutionHeightParameter.ActualValue = value; }
     115    }
    223116
    224117    #endregion
    225118
     119    [StorableConstructor]
     120    private FixedValidationBestScaledSymbolicRegressionSolutionAnalyzer(bool deserializing) : base(deserializing) { }
     121    private FixedValidationBestScaledSymbolicRegressionSolutionAnalyzer(FixedValidationBestScaledSymbolicRegressionSolutionAnalyzer original, Cloner cloner) : base(original, cloner) { }
    226122    public FixedValidationBestScaledSymbolicRegressionSolutionAnalyzer()
    227123      : base() {
    228       Parameters.Add(new LookupParameter<IRandom>(RandomParameterName, "The random generator to use."));
    229       Parameters.Add(new LookupParameter<ISymbolicRegressionEvaluator>(EvaluatorParameterName, "The evaluator which should be used to evaluate the solution on the validation set."));
    230       Parameters.Add(new ScopeTreeLookupParameter<SymbolicExpressionTree>(SymbolicExpressionTreeParameterName, "The symbolic expression trees to analyze."));
    231124      Parameters.Add(new LookupParameter<BoolValue>(MaximizationParameterName, "The direction of optimization."));
    232       Parameters.Add(new ValueLookupParameter<ISymbolicExpressionTreeInterpreter>(SymbolicExpressionTreeInterpreterParameterName, "The interpreter that should be used for the analysis of symbolic expression trees."));
    233       Parameters.Add(new ValueLookupParameter<DataAnalysisProblemData>(ProblemDataParameterName, "The problem data for which the symbolic expression tree is a solution."));
    234       Parameters.Add(new ValueLookupParameter<IntValue>(ValidationSamplesStartParameterName, "The first index of the validation partition of the data set."));
    235       Parameters.Add(new ValueLookupParameter<IntValue>(ValidationSamplesEndParameterName, "The last index of the validation partition of the data set."));
    236       Parameters.Add(new ValueParameter<PercentValue>(RelativeNumberOfEvaluatedSamplesParameterName, "The relative number of samples of the dataset partition, which should be randomly chosen for evaluation between the start and end index.", new PercentValue(1)));
    237       Parameters.Add(new ValueLookupParameter<DoubleValue>(UpperEstimationLimitParameterName, "The upper estimation limit that was set for the evaluation of the symbolic expression trees."));
    238       Parameters.Add(new ValueLookupParameter<DoubleValue>(LowerEstimationLimitParameterName, "The lower estimation limit that was set for the evaluation of the symbolic expression trees."));
     125      Parameters.Add(new ValueParameter<BoolValue>(CalculateSolutionComplexityParameterName, "Determines if the length and height of the validation best solution should be calculated.", new BoolValue(false)));
    239126      Parameters.Add(new LookupParameter<SymbolicRegressionSolution>(BestSolutionParameterName, "The best symbolic regression solution."));
    240       Parameters.Add(new LookupParameter<SymbolicRegressionSolution>("BestTrainingSolution"));
    241       Parameters.Add(new ScopeTreeLookupParameter<DoubleValue>("Quality"));
    242       Parameters.Add(new ScopeTreeLookupParameter<DoubleValue>("ValidationQuality"));
    243127      Parameters.Add(new LookupParameter<IntValue>(GenerationsParameterName, "The number of generations calculated so far."));
    244128      Parameters.Add(new LookupParameter<DoubleValue>(BestSolutionQualityParameterName, "The quality of the best symbolic regression solution."));
     129      Parameters.Add(new LookupParameter<IntValue>(BestSolutionLengthParameterName, "The length of the best symbolic regression solution."));
     130      Parameters.Add(new LookupParameter<IntValue>(BestSolutionHeightParameterName, "The height of the best symbolic regression solution."));
    245131      Parameters.Add(new LookupParameter<ResultCollection>(ResultsParameterName, "The result collection where the best symbolic regression solution should be stored."));
    246132      Parameters.Add(new LookupParameter<DoubleValue>(BestKnownQualityParameterName, "The best known (validation) quality achieved on the data set."));
    247       Parameters.Add(new LookupParameter<DoubleValue>(CurrentBestValidationQualityParameterName, "The quality of the best solution (on the validation set) of the current generation."));
    248       Parameters.Add(new LookupParameter<DataTable>(BestSolutionQualityValuesParameterName));
    249133      Parameters.Add(new LookupParameter<DataTable>(VariableFrequenciesParameterName, "The variable frequencies table to use for the calculation of variable impacts"));
    250134    }
    251135
    252     [StorableConstructor]
    253     private FixedValidationBestScaledSymbolicRegressionSolutionAnalyzer(bool deserializing) : base(deserializing) { }
     136    public override IDeepCloneable Clone(Cloner cloner) {
     137      return new FixedValidationBestScaledSymbolicRegressionSolutionAnalyzer(this, cloner);
     138    }
    254139
    255140    [StorableHook(HookType.AfterDeserialization)]
    256141    private void AfterDeserialization() {
    257       #region compatibility remove before releasing 3.3.1
    258       if (!Parameters.ContainsKey(EvaluatorParameterName)) {
    259         Parameters.Add(new LookupParameter<ISymbolicRegressionEvaluator>(EvaluatorParameterName, "The evaluator which should be used to evaluate the solution on the validation set."));
     142      #region compatibility remove before releasing 3.4
     143      if (!Parameters.ContainsKey("Evaluator")) {
     144        Parameters.Add(new LookupParameter<ISymbolicRegressionEvaluator>("Evaluator", "The evaluator which should be used to evaluate the solution on the validation set."));
    260145      }
    261146      if (!Parameters.ContainsKey(MaximizationParameterName)) {
    262147        Parameters.Add(new LookupParameter<BoolValue>(MaximizationParameterName, "The direction of optimization."));
    263148      }
    264       if (!Parameters.ContainsKey(BestSolutionQualityValuesParameterName)) {
    265         Parameters.Add(new LookupParameter<DataTable>(BestSolutionQualityValuesParameterName));
    266       }
    267       if (!Parameters.ContainsKey("BestTrainingSolution")) {
    268         Parameters.Add(new LookupParameter<SymbolicRegressionSolution>("BestTrainingSolution"));
    269       }
    270       if (!Parameters.ContainsKey("Quality")) {
    271         Parameters.Add(new ScopeTreeLookupParameter<DoubleValue>("Quality"));
    272       }
    273       if (!Parameters.ContainsKey("ValidationQuality")) {
    274         Parameters.Add(new ScopeTreeLookupParameter<DoubleValue>("ValidationQuality"));
     149      if (!Parameters.ContainsKey(CalculateSolutionComplexityParameterName)) {
     150        Parameters.Add(new ValueParameter<BoolValue>(CalculateSolutionComplexityParameterName, "Determines if the length and height of the validation best solution should be calculated.", new BoolValue(false)));
     151      }
     152      if (!Parameters.ContainsKey(BestSolutionLengthParameterName)) {
     153        Parameters.Add(new LookupParameter<IntValue>(BestSolutionLengthParameterName, "The length of the best symbolic regression solution."));
     154      }
     155      if (!Parameters.ContainsKey(BestSolutionHeightParameterName)) {
     156        Parameters.Add(new LookupParameter<IntValue>(BestSolutionHeightParameterName, "The height of the best symbolic regression solution."));
    275157      }
    276158      #endregion
    277159    }
    278160
    279     public override IOperation Apply() {
    280       ItemArray<SymbolicExpressionTree> trees = SymbolicExpressionTree;
    281       ItemArray<DoubleValue> qualities = QualityParameter.ActualValue;
    282 
    283       string targetVariable = ProblemData.TargetVariable.Value;
    284 
    285       // select a random subset of rows in the validation set
    286       int validationStart = ValidationSamplesStart.Value;
    287       int validationEnd = ValidationSamplesEnd.Value;
    288       int seed = Random.Next();
    289       int count = (int)((validationEnd - validationStart) * RelativeNumberOfEvaluatedSamples.Value);
    290       if (count == 0) count = 1;
    291       IEnumerable<int> rows = RandomEnumerable.SampleRandomNumbers(seed, validationStart, validationEnd, count);
    292 
    293       double upperEstimationLimit = UpperEstimationLimit != null ? UpperEstimationLimit.Value : double.PositiveInfinity;
    294       double lowerEstimationLimit = LowerEstimationLimit != null ? LowerEstimationLimit.Value : double.NegativeInfinity;
    295 
     161    protected override void Analyze(SymbolicExpressionTree[] trees, double[] validationQuality) {
    296162      double bestQuality = Maximization.Value ? double.NegativeInfinity : double.PositiveInfinity;
    297163      SymbolicExpressionTree bestTree = null;
    298       SymbolicExpressionTree bestTrainingTree = trees[0];
    299       double bestTrainingQuality = qualities[0].Value;
    300       ItemArray<DoubleValue> validationQualites = new ItemArray<DoubleValue>(qualities.Length);
     164
    301165      for (int i = 0; i < trees.Length; i++) {
    302         SymbolicExpressionTree tree = trees[i];
    303         double quality = Evaluator.Evaluate(SymbolicExpressionTreeInterpreter, tree,
    304           lowerEstimationLimit, upperEstimationLimit,
    305           ProblemData.Dataset, targetVariable,
    306          rows);
    307         validationQualites[i] = new DoubleValue(quality);
     166        double quality = validationQuality[i];
    308167        if ((Maximization.Value && quality > bestQuality) ||
    309168            (!Maximization.Value && quality < bestQuality)) {
    310169          bestQuality = quality;
    311           bestTree = tree;
     170          bestTree = trees[i];
    312171        }
    313         if ((Maximization.Value && qualities[i].Value > bestTrainingQuality) ||
    314             (!Maximization.Value && qualities[i].Value < bestTrainingQuality)) {
    315           bestTrainingQuality = qualities[i].Value;
    316           bestTrainingTree = tree;
    317         }
    318       }
    319       ValidationQualityParameter.ActualValue = validationQualites;
    320 
    321       var scaledBestTrainingTree = GetScaledTree(bestTrainingTree);
    322 
    323       SymbolicRegressionSolution bestTrainingSolution = new SymbolicRegressionSolution((DataAnalysisProblemData)ProblemData.Clone(),
    324         new SymbolicRegressionModel((ISymbolicExpressionTreeInterpreter)SymbolicExpressionTreeInterpreter.Clone(), scaledBestTrainingTree),
    325         lowerEstimationLimit, upperEstimationLimit);
    326       bestTrainingSolution.Name = "Best solution (training)";
    327       bestTrainingSolution.Description = "The solution of the population with the highest fitness";
     172      }
    328173
    329174      // if the best validation tree is better than the current best solution => update
    … …  
    333178        (!Maximization.Value && bestQuality < BestSolutionQuality.Value);
    334179      if (newBest) {
    335         var scaledTree = GetScaledTree(bestTree);
     180        double lowerEstimationLimit = LowerEstimationLimit.Value;
     181        double upperEstimationLimit = UpperEstimationLimit.Value;
     182        string targetVariable = ProblemData.TargetVariable.Value;
     183
     184        // calculate scaling parameters and only for the best tree using the full training set
     185        double alpha, beta;
     186        SymbolicRegressionScaledMeanSquaredErrorEvaluator.Calculate(SymbolicExpressionTreeInterpreter, bestTree,
     187          lowerEstimationLimit, upperEstimationLimit,
     188          ProblemData.Dataset, targetVariable,
     189          ProblemData.TrainingIndizes, out beta, out alpha);
     190
     191        // scale tree for solution
     192        var scaledTree = SymbolicRegressionSolutionLinearScaler.Scale(bestTree, alpha, beta);
    336193        var model = new SymbolicRegressionModel((ISymbolicExpressionTreeInterpreter)SymbolicExpressionTreeInterpreter.Clone(),
    337194          scaledTree);
    … …  
    343200        BestSolutionQualityParameter.ActualValue = new DoubleValue(bestQuality);
    344201
     202        if (CalculateSolutionComplexity.Value) {
     203          BestSolutionLength = new IntValue(solution.Model.SymbolicExpressionTree.Size);
     204          BestSolutionHeight = new IntValue(solution.Model.SymbolicExpressionTree.Height);
     205          if (!Results.ContainsKey(BestSolutionLengthParameterName)) {
     206            Results.Add(new Result(BestSolutionLengthParameterName, "Length of the best solution on the validation set", new IntValue()));
     207            Results.Add(new Result(BestSolutionHeightParameterName, "Height of the best solution on the validation set", new IntValue()));
     208          }
     209          Results[BestSolutionLengthParameterName].Value = BestSolutionLength;
     210          Results[BestSolutionHeightParameterName].Value = BestSolutionHeight;
     211        }
     212
    345213        BestSymbolicRegressionSolutionAnalyzer.UpdateBestSolutionResults(solution, ProblemData, Results, Generations, VariableFrequencies);
    346214      }
    347 
    348       CurrentBestValidationQualityParameter.ActualValue = new DoubleValue(bestQuality);
    349215
    350216      if (!Results.ContainsKey(BestSolutionQualityValuesParameterName)) {
    … …  
    352218        Results.Add(new Result(BestSolutionQualityParameterName, new DoubleValue()));
    353219        Results.Add(new Result(CurrentBestValidationQualityParameterName, new DoubleValue()));
    354         Results.Add(new Result("Best solution (training)", bestTrainingSolution));
    355220      }
    356221      Results[BestSolutionQualityParameterName].Value = new DoubleValue(BestSolutionQualityParameter.ActualValue.Value);
    357222      Results[CurrentBestValidationQualityParameterName].Value = new DoubleValue(bestQuality);
    358       Results["Best solution (training)"].Value = bestTrainingSolution;
    359223
    360224      DataTable validationValues = (DataTable)Results[BestSolutionQualityValuesParameterName].Value;
    361225      AddValue(validationValues, BestSolutionQualityParameter.ActualValue.Value, BestSolutionQualityParameterName, BestSolutionQualityParameterName);
    362226      AddValue(validationValues, bestQuality, CurrentBestValidationQualityParameterName, CurrentBestValidationQualityParameterName);
    363 
    364       BestSolutionQualityValuesParameter.ActualValue = validationValues;
    365 
    366       return base.Apply();
    367     }
    368 
    369     private SymbolicExpressionTree GetScaledTree(SymbolicExpressionTree tree) {
    370       // calculate scaling parameters and only for the best tree using the full training set
    371       double alpha, beta;
    372       int trainingStart = ProblemData.TrainingSamplesStart.Value;
    373       int trainingEnd = ProblemData.TrainingSamplesEnd.Value;
    374       IEnumerable<int> trainingRows = Enumerable.Range(trainingStart, trainingEnd - trainingStart);
    375       IEnumerable<double> originalValues = ProblemData.Dataset.GetEnumeratedVariableValues(ProblemData.TargetVariable.Value, trainingRows);
    376       IEnumerable<double> estimatedValues = SymbolicExpressionTreeInterpreter.GetSymbolicExpressionTreeValues(tree, ProblemData.Dataset, trainingRows);
    377 
    378       SymbolicRegressionScaledMeanSquaredErrorEvaluator.CalculateScalingParameters(originalValues, estimatedValues, out beta, out alpha);
    379 
    380       // scale tree for solution
    381       return SymbolicRegressionSolutionLinearScaler.Scale(tree, alpha, beta);
    382     }
    383 
    384     [StorableHook(HookType.AfterDeserialization)]
    385     private void Initialize() { }
     227    }
    386228
    387229    private static void AddValue(DataTable table, double data, string name, string description) {

  • branches/DataAnalysis/HeuristicLab.Problems.DataAnalysis.Regression/3.3/Symbolic/Analyzers/SymbolicRegressionModelQualityAnalyzer.cs

    r4068 r5275  
    2424using System.Linq;
    2525using HeuristicLab.Analysis;
     26using HeuristicLab.Common;
    2627using HeuristicLab.Core;
    2728using HeuristicLab.Data;
    … …  
    118119    #endregion
    119120
     121    [StorableConstructor]
     122    private SymbolicRegressionModelQualityAnalyzer(bool deserializing) : base(deserializing) { }
     123    private SymbolicRegressionModelQualityAnalyzer(SymbolicRegressionModelQualityAnalyzer original, Cloner cloner) : base(original, cloner) { }
    120124    public SymbolicRegressionModelQualityAnalyzer()
    121125      : base() {
    … …  
    131135    }
    132136
    133     [StorableConstructor]
    134     private SymbolicRegressionModelQualityAnalyzer(bool deserializing) : base() { }
     137    public override IDeepCloneable Clone(Cloner cloner) {
     138      return new SymbolicRegressionModelQualityAnalyzer(this, cloner);
     139    }
    135140
    136141    public override IOperation Apply() {
    137142      Analyze(SymbolicExpressionTreeParameter.ActualValue, SymbolicExpressionTreeInterpreterParameter.ActualValue,
    138143        UpperEstimationLimit.Value, LowerEstimationLimit.Value, ProblemDataParameter.ActualValue,
    139         ProblemDataParameter.ActualValue.TrainingSamplesStart.Value, ProblemDataParameter.ActualValue.TrainingSamplesEnd.Value,
    140         ProblemDataParameter.ActualValue.TestSamplesStart.Value, ProblemDataParameter.ActualValue.TestSamplesEnd.Value,
    141144        ResultsParameter.ActualValue);
    142145      return base.Apply();
    … …  
    145148    public static void Analyze(IEnumerable<SymbolicExpressionTree> trees, ISymbolicExpressionTreeInterpreter interpreter,
    146149      double upperEstimationLimit, double lowerEstimationLimit,
    147       DataAnalysisProblemData problemData, int trainingStart, int trainingEnd, int testStart, int testEnd, ResultCollection results) {
     150      DataAnalysisProblemData problemData, ResultCollection results) {
    148151      int targetVariableIndex = problemData.Dataset.GetVariableIndex(problemData.TargetVariable.Value);
    149       IEnumerable<double> originalTrainingValues = problemData.Dataset.GetEnumeratedVariableValues(targetVariableIndex, trainingStart, trainingEnd);
    150       IEnumerable<double> originalTestValues = problemData.Dataset.GetEnumeratedVariableValues(targetVariableIndex, testStart, testEnd);
     152      IEnumerable<double> originalTrainingValues = problemData.Dataset.GetEnumeratedVariableValues(targetVariableIndex, problemData.TrainingIndizes);
     153      IEnumerable<double> originalTestValues = problemData.Dataset.GetEnumeratedVariableValues(targetVariableIndex, problemData.TestIndizes);
    151154      List<double> trainingMse = new List<double>();
    152155      List<double> trainingR2 = new List<double>();
    … …  
    162165      foreach (var tree in trees) {
    163166        #region training
    164         var estimatedTrainingValues = interpreter.GetSymbolicExpressionTreeValues(tree, problemData.Dataset, Enumerable.Range(trainingStart, trainingEnd - trainingStart));
     167        var estimatedTrainingValues = interpreter.GetSymbolicExpressionTreeValues(tree, problemData.Dataset, problemData.TrainingIndizes);
    165168        mseEvaluator.Reset();
    166169        r2Evaluator.Reset();
    … …  
    184187        #endregion
    185188        #region test
    186         var estimatedTestValues = interpreter.GetSymbolicExpressionTreeValues(tree, problemData.Dataset, Enumerable.Range(testStart, testEnd - testStart));
     189        var estimatedTestValues = interpreter.GetSymbolicExpressionTreeValues(tree, problemData.Dataset, problemData.TestIndizes);
    187190
    188191        mseEvaluator.Reset();

  • branches/DataAnalysis/HeuristicLab.Problems.DataAnalysis.Regression/3.3/Symbolic/Analyzers/SymbolicRegressionModelQualityCalculator.cs

    r4068 r5275  
    2020#endregion
    2121
     22using System;
     23using HeuristicLab.Common;
    2224using HeuristicLab.Core;
    2325using HeuristicLab.Data;
    … …  
    3537  [Item("SymbolicRegressionModelQualityCalculator", "An operator to calculate the quality values of a symbolic regression solution symbolic expression tree encoding.")]
    3638  [StorableClass]
     39  [Obsolete("This class should not be used anymore because of performance reasons and will therefore not be updated.")]
    3740  public sealed class SymbolicRegressionModelQualityCalculator : AlgorithmOperator {
    3841    private const string SymbolicExpressionTreeInterpreterParameterName = "SymbolicExpressionTreeInterpreter";
    … …  
    8184    #endregion
    8285
     86    [StorableConstructor]
     87    private SymbolicRegressionModelQualityCalculator(bool deserializing) : base(deserializing) { }
     88    private SymbolicRegressionModelQualityCalculator(SymbolicRegressionModelQualityCalculator original, Cloner cloner) : base(original, cloner) { }
    8389    public SymbolicRegressionModelQualityCalculator()
    8490      : base() {
    … …  
    136142
    137143    }
     144    public override IDeepCloneable Clone(Cloner cloner) {
     145      return new SymbolicRegressionModelQualityCalculator(this, cloner);
     146    }
    138147  }
    139148}

  • branches/DataAnalysis/HeuristicLab.Problems.DataAnalysis.Regression/3.3/Symbolic/Analyzers/SymbolicRegressionSolutionLinearScaler.cs

    r4068 r5275  
    2020#endregion
    2121
     22using HeuristicLab.Common;
    2223using HeuristicLab.Core;
    2324using HeuristicLab.Data;
    … …  
    5354    }
    5455
     56    [StorableConstructor]
     57    private SymbolicRegressionSolutionLinearScaler(bool deserializing) : base(deserializing) { }
     58    private SymbolicRegressionSolutionLinearScaler(SymbolicRegressionSolutionLinearScaler original, Cloner cloner) : base(original, cloner) { }
    5559    public SymbolicRegressionSolutionLinearScaler()
    5660      : base() {
    … …  
    5963      Parameters.Add(new LookupParameter<DoubleValue>(AlphaParameterName, "Alpha parameter for linear transformation."));
    6064      Parameters.Add(new LookupParameter<DoubleValue>(BetaParameterName, "Beta parameter for linear transformation."));
     65    }
     66
     67    public override IDeepCloneable Clone(Cloner cloner) {
     68      return new SymbolicRegressionSolutionLinearScaler(this, cloner);
    6169    }
    6270

  • branches/DataAnalysis/HeuristicLab.Problems.DataAnalysis.Regression/3.3/Symbolic/Analyzers/SymbolicRegressionTournamentPruning.cs

    r4350 r5275  
    3333using System;
    3434using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding.Symbols;
     35using HeuristicLab.Common;
    3536
    3637namespace HeuristicLab.Problems.DataAnalysis.Regression.Symbolic.Analyzers {
    … …  
    192193    [StorableConstructor]
    193194    protected SymbolicRegressionTournamentPruning(bool deserializing) : base(deserializing) { }
     195    protected SymbolicRegressionTournamentPruning(SymbolicRegressionTournamentPruning original, Cloner cloner)
     196      : base(original, cloner) {
     197    }
    194198    public SymbolicRegressionTournamentPruning()
    195199      : base() {
    … …  
    218222      Parameters.Add(new ValueLookupParameter<PercentValue>("RelativeNumberOfEvaluatedRows", new PercentValue(1.0)));
    219223      Parameters.Add(new ValueLookupParameter<IntValue>("MinimalTreeSize", new IntValue(15)));
     224    }
     225
     226    public override IDeepCloneable Clone(Cloner cloner) {
     227      return new SymbolicRegressionTournamentPruning(this, cloner);
    220228    }
    221229

  • branches/DataAnalysis/HeuristicLab.Problems.DataAnalysis.Regression/3.3/Symbolic/Analyzers/SymbolicRegressionVariableFrequencyAnalyzer.cs

    r4068 r5275  
    2222using System.Linq;
    2323using HeuristicLab.Analysis;
     24using HeuristicLab.Common;
    2425using HeuristicLab.Core;
    2526using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
    … …  
    6061    #endregion
    6162
     63    [StorableConstructor]
     64    private SymbolicRegressionVariableFrequencyAnalyzer(bool deserializing) : base(deserializing) { }
     65    private SymbolicRegressionVariableFrequencyAnalyzer(SymbolicRegressionVariableFrequencyAnalyzer original, Cloner cloner) : base(original, cloner) { }
    6266    public SymbolicRegressionVariableFrequencyAnalyzer()
    6367      : base() {
    … …  
    6872    }
    6973
     74    public override IDeepCloneable Clone(Cloner cloner) {
     75      return new SymbolicRegressionVariableFrequencyAnalyzer(this, cloner);
     76    }
     77
    7078    public override IOperation Apply() {
    7179      ItemArray<SymbolicExpressionTree> expressions = SymbolicExpressionTreeParameter.ActualValue;
    7280      DataAnalysisProblemData problemData = ProblemDataParameter.ActualValue;
    73       var inputVariables = problemData.InputVariables.Select(x => x.Value);
     81      var inputVariables = problemData.InputVariables.CheckedItems.Select(x => x.Value.Value);
    7482      ResultCollection results = ResultsParameter.ActualValue;
    7583
    7684      if (VariableFrequencies == null) {
    7785        VariableFrequencies = new DataTable("Variable frequencies", "Relative frequency of variable references aggregated over the whole population.");
     86        VariableFrequencies.VisualProperties.XAxisTitle = "Generation";
     87        VariableFrequencies.VisualProperties.YAxisTitle = "Relative Variable Frequency";
    7888        // add a data row for each input variable
    79         foreach (var inputVariable in inputVariables)
    80           VariableFrequencies.Rows.Add(new DataRow(inputVariable));
     89        foreach (var inputVariable in inputVariables) {
     90          DataRow row = new DataRow(inputVariable);
     91          row.VisualProperties.StartIndexZero = true;
     92          VariableFrequencies.Rows.Add(row);
     93        }
    8194        results.Add(new Result("Variable frequencies", VariableFrequencies));
    8295      }

  • branches/DataAnalysis/HeuristicLab.Problems.DataAnalysis.Regression/3.3/Symbolic/Analyzers/ValidationBestScaledSymbolicRegressionSolutionAnalyzer.cs

    r4068 r5275  
    3939  [Item("ValidationBestScaledSymbolicRegressionSolutionAnalyzer", "An operator that analyzes the validation best scaled symbolic regression solution.")]
    4040  [StorableClass]
     41  [Obsolete("This class should not be used anymore because of performance reasons and will therefore not be updated.")]
    4142  public sealed class ValidationBestScaledSymbolicRegressionSolutionAnalyzer : AlgorithmOperator, ISymbolicRegressionAnalyzer {
    4243    private const string SymbolicExpressionTreeParameterName = "SymbolicExpressionTree";
    … …  
    134135    private ResultsCollector resultsCollector;
    135136
     137    [StorableConstructor]
     138    private ValidationBestScaledSymbolicRegressionSolutionAnalyzer(bool deserializing) : base(deserializing) { }
     139    private ValidationBestScaledSymbolicRegressionSolutionAnalyzer(ValidationBestScaledSymbolicRegressionSolutionAnalyzer original, Cloner cloner)
     140      : base(original, cloner) {
     141      Initialize();
     142    }
    136143    public ValidationBestScaledSymbolicRegressionSolutionAnalyzer()
    137144      : base() {
    … …  
    248255    }
    249256
    250     [StorableConstructor]
    251     private ValidationBestScaledSymbolicRegressionSolutionAnalyzer(bool deserializing) : base() { }
     257    public override IDeepCloneable Clone(Cloner cloner) {
     258      return new ValidationBestScaledSymbolicRegressionSolutionAnalyzer(this, cloner);
     259    }
    252260
    253261    [StorableHook(HookType.AfterDeserialization)]
     262    private void AfterDeserialization() {
     263      Initialize();
     264    }
    254265    private void Initialize() {
    255266      SymbolicExpressionTreeParameter.DepthChanged += new EventHandler(SymbolicExpressionTreeParameter_DepthChanged);
    256267    }
    257268
    258     public override IDeepCloneable Clone(Cloner cloner) {
    259       ValidationBestScaledSymbolicRegressionSolutionAnalyzer clone = (ValidationBestScaledSymbolicRegressionSolutionAnalyzer)base.Clone(cloner);
    260       clone.Initialize();
    261       return clone;
    262     }
    263269
    264270    private void SymbolicExpressionTreeParameter_DepthChanged(object sender, EventArgs e) {
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