source: branches/SpectralKernelForGaussianProcesses/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/MultiObjective/SymbolicRegressionMultiObjectiveMeanSquaredErrorTreeSizeEvaluator.cs @ 10479

Last change on this file since 10479 was 10479, checked in by gkronber, 6 years ago

#2124 merged all changes from trunk to prepare for trunk-reintegration

File size: 4.9 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2013 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
22using System.Collections.Generic;
23using HeuristicLab.Common;
24using HeuristicLab.Core;
25using HeuristicLab.Data;
26using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
27using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
28
29namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
30  [Item("Mean squared error & Tree size Evaluator", "Calculates the mean squared error and the tree size of a symbolic regression solution.")]
31  [StorableClass]
32  public class SymbolicRegressionMultiObjectiveMeanSquaredErrorSolutionSizeEvaluator : SymbolicRegressionMultiObjectiveEvaluator {
33    [StorableConstructor]
34    protected SymbolicRegressionMultiObjectiveMeanSquaredErrorSolutionSizeEvaluator(bool deserializing) : base(deserializing) { }
35    protected SymbolicRegressionMultiObjectiveMeanSquaredErrorSolutionSizeEvaluator(SymbolicRegressionMultiObjectiveMeanSquaredErrorSolutionSizeEvaluator original, Cloner cloner)
36      : base(original, cloner) {
37    }
38    public override IDeepCloneable Clone(Cloner cloner) {
39      return new SymbolicRegressionMultiObjectiveMeanSquaredErrorSolutionSizeEvaluator(this, cloner);
40    }
41
42    public SymbolicRegressionMultiObjectiveMeanSquaredErrorSolutionSizeEvaluator() : base() { }
43
44    public override IEnumerable<bool> Maximization { get { return new bool[2] { false, false }; } }
45
46    public override IOperation InstrumentedApply() {
47      IEnumerable<int> rows = GenerateRowsToEvaluate();
48      var solution = SymbolicExpressionTreeParameter.ActualValue;
49      double[] qualities = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, ProblemDataParameter.ActualValue, rows, ApplyLinearScalingParameter.ActualValue.Value);
50      QualitiesParameter.ActualValue = new DoubleArray(qualities);
51      return base.InstrumentedApply();
52    }
53
54    public static double[] Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IRegressionProblemData problemData, IEnumerable<int> rows, bool applyLinearScaling) {
55      IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, rows);
56      IEnumerable<double> targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
57      OnlineCalculatorError errorState;
58
59      double mse;
60      if (applyLinearScaling) {
61        var mseCalculator = new OnlineMeanSquaredErrorCalculator();
62        CalculateWithScaling(targetValues, estimatedValues, lowerEstimationLimit, upperEstimationLimit, mseCalculator, problemData.Dataset.Rows);
63        errorState = mseCalculator.ErrorState;
64        mse = mseCalculator.MeanSquaredError;
65      } else {
66        IEnumerable<double> boundedEstimatedValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit);
67        mse = OnlineMeanSquaredErrorCalculator.Calculate(targetValues, boundedEstimatedValues, out errorState);
68      }
69      if (errorState != OnlineCalculatorError.None) mse = double.NaN;
70      return new double[2] { mse, solution.Length };
71    }
72
73    public override double[] Evaluate(IExecutionContext context, ISymbolicExpressionTree tree, IRegressionProblemData problemData, IEnumerable<int> rows) {
74      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
75      EstimationLimitsParameter.ExecutionContext = context;
76      ApplyLinearScalingParameter.ExecutionContext = context;
77
78      double[] quality = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows, ApplyLinearScalingParameter.ActualValue.Value);
79
80      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
81      EstimationLimitsParameter.ExecutionContext = null;
82      ApplyLinearScalingParameter.ExecutionContext = null;
83
84      return quality;
85    }
86  }
87}
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