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source: trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SingleObjective/SymbolicClassificationSingleObjectiveBoundedMeanSquaredErrorEvaluator.cs @ 5851

Last change on this file since 5851 was 5851, checked in by gkronber, 13 years ago

#1411 added evaluated nodes parameter to symbolic data analysis evaluators.

File size: 5.5 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2011 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 Apply() {
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);
50      QualityParameter.ActualValue = new DoubleValue(quality);
51      AddEvaluatedNodes(solution.Length * rows.Count());
52      return base.Apply();
53    }
54
55    public static double Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IClassificationProblemData problemData, IEnumerable<int> rows) {
56      IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, rows);
57      IEnumerable<double> originalValues = problemData.Dataset.GetEnumeratedVariableValues(problemData.TargetVariable, rows);
58      IEnumerable<double> boundedEstimationValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit);
59
60      double minClassValue = problemData.ClassValues.OrderBy(x => x).First();
61      double maxClassValue = problemData.ClassValues.OrderBy(x => x).Last();
62
63      IEnumerator<double> originalEnumerator = originalValues.GetEnumerator();
64      IEnumerator<double> estimatedEnumerator = estimatedValues.GetEnumerator();
65      OnlineMeanSquaredErrorEvaluator mseEvaluator = new OnlineMeanSquaredErrorEvaluator();
66      double errorSum = 0.0;
67      int n = 0;
68
69      // always move forward both enumerators (do not use short-circuit evaluation!)
70      while (originalEnumerator.MoveNext() & estimatedEnumerator.MoveNext()) {
71        double estimated = estimatedEnumerator.Current;
72        double original = originalEnumerator.Current;
73        double error = estimated - original;
74
75        if (estimated < minClassValue || estimated > maxClassValue)
76          errorSum += Math.Abs(error);
77        else
78          errorSum += Math.Pow(error, 2);
79        n++;
80      }
81
82      // check if both enumerators are at the end to make sure both enumerations have the same length
83      if (estimatedEnumerator.MoveNext() || originalEnumerator.MoveNext()) {
84        throw new ArgumentException("Number of elements in first and second enumeration doesn't match.");
85      } else {
86        return errorSum / n;
87      }
88    }
89
90    public override double Evaluate(IExecutionContext context, ISymbolicExpressionTree tree, IClassificationProblemData problemData, IEnumerable<int> rows) {
91      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
92      EstimationLimitsParameter.ExecutionContext = context;
93      EvaluatedNodesParameter.ExecutionContext = context;
94
95      double mse = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows);
96      AddEvaluatedNodes(tree.Length * rows.Count());
97
98      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
99      EstimationLimitsParameter.ExecutionContext = null;
100      EvaluatedNodesParameter.ExecutionContext = null;
101
102      return mse;
103    }
104  }
105}
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