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source: trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionSingleObjectiveMeanSquaredErrorEvaluator.cs @ 7677

Last change on this file since 7677 was 7677, checked in by mkommend, 12 years ago

#1788: Implemente new symbolic regression evaluators.

File size: 4.5 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2012 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.Regression {
31  [Item("Mean squared error Evaluator", "Calculates the mean squared error of a symbolic regression solution.")]
32  [StorableClass]
33  public class SymbolicRegressionSingleObjectiveMeanSquaredErrorEvaluator : SymbolicRegressionSingleObjectiveEvaluator {
34    public override bool Maximization { get { return false; } }
35    [StorableConstructor]
36    protected SymbolicRegressionSingleObjectiveMeanSquaredErrorEvaluator(bool deserializing) : base(deserializing) { }
37    protected SymbolicRegressionSingleObjectiveMeanSquaredErrorEvaluator(SymbolicRegressionSingleObjectiveMeanSquaredErrorEvaluator original, Cloner cloner)
38      : base(original, cloner) {
39    }
40    public override IDeepCloneable Clone(Cloner cloner) {
41      return new SymbolicRegressionSingleObjectiveMeanSquaredErrorEvaluator(this, cloner);
42    }
43    public SymbolicRegressionSingleObjectiveMeanSquaredErrorEvaluator() : base() { }
44
45    public override IOperation Apply() {
46      var solution = SymbolicExpressionTreeParameter.ActualValue;
47      IEnumerable<int> rows = GenerateRowsToEvaluate();
48
49      double quality = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, ProblemDataParameter.ActualValue, rows, ApplyLinearScaling);
50      QualityParameter.ActualValue = new DoubleValue(quality);
51
52      return base.Apply();
53    }
54
55    public static double Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IRegressionProblemData 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      IEnumerable<double> boundedEstimatedValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit);
59      OnlineCalculatorError errorState;
60
61      double mse;
62      if (applyLinearScaling) {
63        var mseCalculator = new OnlineMeanSquaredErrorCalculator();
64        CalculateWithScaling(targetValues, boundedEstimatedValues, mseCalculator, problemData.Dataset.Rows);
65        errorState = mseCalculator.ErrorState;
66        mse = mseCalculator.MeanSquaredError;
67      } else
68        mse = OnlineMeanSquaredErrorCalculator.Calculate(targetValues, boundedEstimatedValues, out errorState);
69
70      if (errorState != OnlineCalculatorError.None) return Double.NaN;
71      else return mse;
72    }
73
74    public override double Evaluate(IExecutionContext context, ISymbolicExpressionTree tree, IRegressionProblemData problemData, IEnumerable<int> rows) {
75      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
76      EstimationLimitsParameter.ExecutionContext = context;
77
78      double mse = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows, ApplyLinearScaling);
79
80      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
81      EstimationLimitsParameter.ExecutionContext = null;
82
83      return mse;
84    }
85  }
86}
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