source: branches/SpectralKernelForGaussianProcesses/HeuristicLab.Problems.DataAnalysis.Symbolic.TimeSeriesPrognosis/3.4/SingleObjective/SymbolicTimeSeriesPrognosisSingleObjectiveMeanSquaredErrorEvaluator.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: 6.3 KB
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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 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.TimeSeriesPrognosis {
32  [Item("Mean squared error Evaluator", "Calculates the mean squared error of a symbolic time-series prognosis solution.")]
33  [StorableClass]
34  public class SymbolicTimeSeriesPrognosisSingleObjectiveMeanSquaredErrorEvaluator : SymbolicTimeSeriesPrognosisSingleObjectiveEvaluator {
35    [StorableConstructor]
36    protected SymbolicTimeSeriesPrognosisSingleObjectiveMeanSquaredErrorEvaluator(bool deserializing) : base(deserializing) { }
37    protected SymbolicTimeSeriesPrognosisSingleObjectiveMeanSquaredErrorEvaluator(SymbolicTimeSeriesPrognosisSingleObjectiveMeanSquaredErrorEvaluator original, Cloner cloner) : base(original, cloner) { }
38    public override IDeepCloneable Clone(Cloner cloner) {
39      return new SymbolicTimeSeriesPrognosisSingleObjectiveMeanSquaredErrorEvaluator(this, cloner);
40    }
41
42    public SymbolicTimeSeriesPrognosisSingleObjectiveMeanSquaredErrorEvaluator() : base() { }
43
44    public override bool Maximization { get { return false; } }
45
46    public override IOperation InstrumentedApply() {
47      var solution = SymbolicExpressionTreeParameter.ActualValue;
48      IEnumerable<int> rows = GenerateRowsToEvaluate();
49
50      var interpreter = (ISymbolicTimeSeriesPrognosisExpressionTreeInterpreter)SymbolicDataAnalysisTreeInterpreterParameter.ActualValue;
51
52      double quality = Calculate(interpreter, solution,
53        EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper,
54        ProblemDataParameter.ActualValue, rows, EvaluationPartitionParameter.ActualValue,
55        HorizonParameter.ActualValue.Value, ApplyLinearScalingParameter.ActualValue.Value);
56      QualityParameter.ActualValue = new DoubleValue(quality);
57
58      return base.InstrumentedApply();
59    }
60
61    public static double Calculate(ISymbolicTimeSeriesPrognosisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, ITimeSeriesPrognosisProblemData problemData, IEnumerable<int> rows, IntRange evaluationPartition, int horizon, bool applyLinearScaling) {
62      var horizions = rows.Select(r => Math.Min(horizon, evaluationPartition.End - r));
63      IEnumerable<double> targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows.Zip(horizions, Enumerable.Range).SelectMany(r => r));
64      IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, rows, horizions).SelectMany(x => x);
65      OnlineCalculatorError errorState;
66
67      double mse;
68      if (applyLinearScaling && horizon == 1) { //perform normal evaluation and afterwards scale the solution and calculate the fitness value       
69        var mseCalculator = new OnlineMeanSquaredErrorCalculator();
70        CalculateWithScaling(targetValues, estimatedValues, lowerEstimationLimit, upperEstimationLimit, mseCalculator, problemData.Dataset.Rows * horizon);
71        errorState = mseCalculator.ErrorState;
72        mse = mseCalculator.MeanSquaredError;
73      } else if (applyLinearScaling) { //first create model to perform linear scaling and afterwards calculate fitness for the scaled model
74        var model = new SymbolicTimeSeriesPrognosisModel((ISymbolicExpressionTree)solution.Clone(), interpreter, lowerEstimationLimit, upperEstimationLimit);
75        model.Scale(problemData);
76        var scaledSolution = model.SymbolicExpressionTree;
77        estimatedValues = interpreter.GetSymbolicExpressionTreeValues(scaledSolution, problemData.Dataset, rows, horizions).SelectMany(x => x);
78        var boundedEstimatedValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit);
79        mse = OnlineMeanSquaredErrorCalculator.Calculate(targetValues, boundedEstimatedValues, out errorState);
80      } else {
81        var boundedEstimatedValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit);
82        mse = OnlineMeanSquaredErrorCalculator.Calculate(targetValues, boundedEstimatedValues, out errorState);
83      }
84
85      if (errorState != OnlineCalculatorError.None) return Double.NaN;
86      else return mse;
87    }
88
89
90    public override double Evaluate(IExecutionContext context, ISymbolicExpressionTree tree, ITimeSeriesPrognosisProblemData problemData, IEnumerable<int> rows) {
91      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
92      EstimationLimitsParameter.ExecutionContext = context;
93      HorizonParameter.ExecutionContext = context;
94      EvaluationPartitionParameter.ExecutionContext = context;
95
96      double mse = Calculate((ISymbolicTimeSeriesPrognosisExpressionTreeInterpreter)SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows, EvaluationPartitionParameter.ActualValue, HorizonParameter.ActualValue.Value, ApplyLinearScalingParameter.ActualValue.Value);
97
98      HorizonParameter.ExecutionContext = null;
99      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
100      EstimationLimitsParameter.ExecutionContext = null;
101      EvaluationPartitionParameter.ExecutionContext = null;
102
103      return mse;
104    }
105  }
106}
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