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source: stable/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SingleObjective/SymbolicClassificationSingleObjectiveBoundedMeanSquaredErrorEvaluator.cs @ 13730

Last change on this file since 13730 was 12009, checked in by ascheibe, 10 years ago

#2212 updated copyright year

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1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2015 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 System.Linq;
25using HeuristicLab.Common;
26using HeuristicLab.Core;
27using HeuristicLab.Data;
28using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
29using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
30
31namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Classification.SingleObjective {
32  [Item("Bounded Mean squared error Evaluator", "Calculates the bounded mean squared error of a symbolic classification solution (estimations above or below the class values are only penaltilized linearly.")]
33  [StorableClass]
34  public class SymbolicClassificationSingleObjectiveBoundedMeanSquaredErrorEvaluator : SymbolicClassificationSingleObjectiveEvaluator {
35    [StorableConstructor]
36    protected SymbolicClassificationSingleObjectiveBoundedMeanSquaredErrorEvaluator(bool deserializing) : base(deserializing) { }
37    protected SymbolicClassificationSingleObjectiveBoundedMeanSquaredErrorEvaluator(SymbolicClassificationSingleObjectiveBoundedMeanSquaredErrorEvaluator original, Cloner cloner) : base(original, cloner) { }
38    public override IDeepCloneable Clone(Cloner cloner) {
39      return new SymbolicClassificationSingleObjectiveBoundedMeanSquaredErrorEvaluator(this, cloner);
40    }
41
42    public SymbolicClassificationSingleObjectiveBoundedMeanSquaredErrorEvaluator() : base() { }
43
44    public override bool Maximization { get { return false; } }
45
46    public override IOperation InstrumentedApply() {
47      IEnumerable<int> rows = GenerateRowsToEvaluate();
48      var solution = SymbolicExpressionTreeParameter.ActualValue;
49      double quality = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, ProblemDataParameter.ActualValue, rows, ApplyLinearScalingParameter.ActualValue.Value);
50      QualityParameter.ActualValue = new DoubleValue(quality);
51      return base.InstrumentedApply();
52    }
53
54    public static double Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IClassificationProblemData 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 lowestClassValue = problemData.ClassValues.OrderBy(x => x).First();
60      double upmostClassValue = problemData.ClassValues.OrderByDescending(x => x).First();
61
62      double boundedMse;
63      if (applyLinearScaling) {
64        var boundedMseCalculator = new OnlineBoundedMeanSquaredErrorCalculator(lowestClassValue, upmostClassValue);
65        CalculateWithScaling(targetValues, estimatedValues, lowerEstimationLimit, upperEstimationLimit, boundedMseCalculator, problemData.Dataset.Rows);
66        errorState = boundedMseCalculator.ErrorState;
67        boundedMse = boundedMseCalculator.BoundedMeanSquaredError;
68      } else {
69        IEnumerable<double> boundedEstimatedValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit);
70        boundedMse = OnlineBoundedMeanSquaredErrorCalculator.Calculate(targetValues, boundedEstimatedValues, lowestClassValue, upmostClassValue, out errorState);
71      }
72      if (errorState != OnlineCalculatorError.None) return Double.NaN;
73      return boundedMse;
74    }
75
76    public override double Evaluate(IExecutionContext context, ISymbolicExpressionTree tree, IClassificationProblemData problemData, IEnumerable<int> rows) {
77      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
78      EstimationLimitsParameter.ExecutionContext = context;
79      ApplyLinearScalingParameter.ExecutionContext = context;
80
81      double mse = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows, ApplyLinearScalingParameter.ActualValue.Value);
82
83      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
84      EstimationLimitsParameter.ExecutionContext = null;
85      ApplyLinearScalingParameter.ExecutionContext = null;
86
87      return mse;
88    }
89  }
90}
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