source: branches/SpectralKernelForGaussianProcesses/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SingleObjective/SymbolicClassificationSingleObjectiveMeanSquaredErrorEvaluator.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: 4.7 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;
23using System.Collections.Generic;
24using HeuristicLab.Common;
25using HeuristicLab.Core;
26using HeuristicLab.Data;
27using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
28using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
29
30namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Classification {
31  [Item("Mean squared error Evaluator", "Calculates the mean squared error of a symbolic classification solution.")]
32  [StorableClass]
33  public class SymbolicClassificationSingleObjectiveMeanSquaredErrorEvaluator : SymbolicClassificationSingleObjectiveEvaluator {
34    [StorableConstructor]
35    protected SymbolicClassificationSingleObjectiveMeanSquaredErrorEvaluator(bool deserializing) : base(deserializing) { }
36    protected SymbolicClassificationSingleObjectiveMeanSquaredErrorEvaluator(SymbolicClassificationSingleObjectiveMeanSquaredErrorEvaluator original, Cloner cloner)
37      : base(original, cloner) {
38    }
39    public override IDeepCloneable Clone(Cloner cloner) {
40      return new SymbolicClassificationSingleObjectiveMeanSquaredErrorEvaluator(this, cloner);
41    }
42
43    public SymbolicClassificationSingleObjectiveMeanSquaredErrorEvaluator() : base() { }
44
45    public override bool Maximization { get { return false; } }
46
47    public override IOperation InstrumentedApply() {
48      IEnumerable<int> rows = GenerateRowsToEvaluate();
49      var solution = SymbolicExpressionTreeParameter.ActualValue;
50      double quality = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, ProblemDataParameter.ActualValue, rows, ApplyLinearScalingParameter.ActualValue.Value);
51      QualityParameter.ActualValue = new DoubleValue(quality);
52      return base.InstrumentedApply();
53    }
54
55    public static double Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IClassificationProblemData problemData, IEnumerable<int> rows, bool applyLinearScaling) {
56      IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, rows);
57      IEnumerable<double> targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
58      OnlineCalculatorError errorState;
59
60      double mse;
61      if (applyLinearScaling) {
62        var mseCalculator = new OnlineMeanSquaredErrorCalculator();
63        CalculateWithScaling(targetValues, estimatedValues, lowerEstimationLimit, upperEstimationLimit, mseCalculator, problemData.Dataset.Rows);
64        errorState = mseCalculator.ErrorState;
65        mse = mseCalculator.MeanSquaredError;
66      } else {
67        IEnumerable<double> boundedEstimatedValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit);
68        mse = OnlineMeanSquaredErrorCalculator.Calculate(targetValues, boundedEstimatedValues, out errorState);
69      }
70      if (errorState != OnlineCalculatorError.None) return Double.NaN;
71      return mse;
72    }
73
74    public override double Evaluate(IExecutionContext context, ISymbolicExpressionTree tree, IClassificationProblemData problemData, IEnumerable<int> rows) {
75      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
76      EstimationLimitsParameter.ExecutionContext = context;
77      ApplyLinearScalingParameter.ExecutionContext = context;
78
79      double mse = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows, ApplyLinearScalingParameter.ActualValue.Value);
80
81      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
82      EstimationLimitsParameter.ExecutionContext = null;
83      ApplyLinearScalingParameter.ExecutionContext = null;
84
85      return mse;
86    }
87  }
88}
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