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source: trunk/sources/HeuristicLab.Problems.DataAnalysis.Regression/3.3/Symbolic/Evaluators/SymbolicRegressionScaledMeanSquaredErrorEvaluator.cs @ 5538

Last change on this file since 5538 was 5445, checked in by swagner, 14 years ago

Updated year of copyrights (#1406)

File size: 7.8 KB
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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 HeuristicLab.Common;
25using HeuristicLab.Core;
26using HeuristicLab.Data;
27using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
28using HeuristicLab.Parameters;
29using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
30using HeuristicLab.Problems.DataAnalysis.Evaluators;
31using HeuristicLab.Problems.DataAnalysis.Symbolic;
32
33namespace HeuristicLab.Problems.DataAnalysis.Regression.Symbolic {
34  [Item("SymbolicRegressionScaledMeanSquaredErrorEvaluator", "Calculates the mean squared error of a linearly scaled symbolic regression solution.")]
35  [StorableClass]
36  public sealed class SymbolicRegressionScaledMeanSquaredErrorEvaluator : SymbolicRegressionMeanSquaredErrorEvaluator {
37
38    #region parameter properties
39    public ILookupParameter<DoubleValue> AlphaParameter {
40      get { return (ILookupParameter<DoubleValue>)Parameters["Alpha"]; }
41    }
42    public ILookupParameter<DoubleValue> BetaParameter {
43      get { return (ILookupParameter<DoubleValue>)Parameters["Beta"]; }
44    }
45    #endregion
46    #region properties
47    public DoubleValue Alpha {
48      get { return AlphaParameter.ActualValue; }
49      set { AlphaParameter.ActualValue = value; }
50    }
51    public DoubleValue Beta {
52      get { return BetaParameter.ActualValue; }
53      set { BetaParameter.ActualValue = value; }
54    }
55    #endregion
56    [StorableConstructor]
57    private SymbolicRegressionScaledMeanSquaredErrorEvaluator(bool deserializing) : base(deserializing) { }
58    private SymbolicRegressionScaledMeanSquaredErrorEvaluator(SymbolicRegressionScaledMeanSquaredErrorEvaluator original, Cloner cloner) : base(original, cloner) { }
59    public SymbolicRegressionScaledMeanSquaredErrorEvaluator()
60      : base() {
61      Parameters.Add(new LookupParameter<DoubleValue>("Alpha", "Alpha parameter for linear scaling of the estimated values."));
62      Parameters.Add(new LookupParameter<DoubleValue>("Beta", "Beta parameter for linear scaling of the estimated values."));
63    }
64
65    public override IDeepCloneable Clone(Cloner cloner) {
66      return new SymbolicRegressionScaledMeanSquaredErrorEvaluator(this, cloner);
67    }
68
69    public override double Evaluate(ISymbolicExpressionTreeInterpreter interpreter, SymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, Dataset dataset, string targetVariable, IEnumerable<int> rows) {
70      double alpha, beta;
71      double mse = Calculate(interpreter, solution, lowerEstimationLimit, upperEstimationLimit, dataset, targetVariable, rows, out beta, out alpha);
72      if (ExecutionContext != null) {
73        AlphaParameter.ActualValue = new DoubleValue(alpha);
74        BetaParameter.ActualValue = new DoubleValue(beta);
75      }
76      return mse;
77    }
78
79    public static double Calculate(ISymbolicExpressionTreeInterpreter interpreter, SymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, Dataset dataset, string targetVariable, IEnumerable<int> rows, out double beta, out double alpha) {
80      IEnumerable<double> originalValues = dataset.GetEnumeratedVariableValues(targetVariable, rows);
81      IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, dataset, rows);
82      CalculateScalingParameters(originalValues, estimatedValues, out beta, out alpha);
83
84      return CalculateWithScaling(interpreter, solution, lowerEstimationLimit, upperEstimationLimit, dataset, targetVariable, rows, beta, alpha);
85    }
86
87    public static double CalculateWithScaling(ISymbolicExpressionTreeInterpreter interpreter, SymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, Dataset dataset, string targetVariable, IEnumerable<int> rows, double beta, double alpha) {
88      IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, dataset, rows);
89      IEnumerable<double> originalValues = dataset.GetEnumeratedVariableValues(targetVariable, rows);
90      IEnumerator<double> originalEnumerator = originalValues.GetEnumerator();
91      IEnumerator<double> estimatedEnumerator = estimatedValues.GetEnumerator();
92      OnlineMeanSquaredErrorEvaluator mseEvaluator = new OnlineMeanSquaredErrorEvaluator();
93
94      while (originalEnumerator.MoveNext() & estimatedEnumerator.MoveNext()) {
95        double estimated = estimatedEnumerator.Current * beta + alpha;
96        double original = originalEnumerator.Current;
97        if (double.IsNaN(estimated))
98          estimated = upperEstimationLimit;
99        else
100          estimated = Math.Min(upperEstimationLimit, Math.Max(lowerEstimationLimit, estimated));
101        mseEvaluator.Add(original, estimated);
102      }
103
104      if (estimatedEnumerator.MoveNext() || originalEnumerator.MoveNext()) {
105        throw new ArgumentException("Number of elements in original and estimated enumeration doesn't match.");
106      } else {
107        return mseEvaluator.MeanSquaredError;
108      }
109    }
110
111    /// <summary>
112    /// Calculates linear scaling parameters in one pass.
113    /// The formulas to calculate the scaling parameters were taken from Scaled Symblic Regression by Maarten Keijzer.
114    /// http://www.springerlink.com/content/x035121165125175/
115    /// </summary>
116    public static void CalculateScalingParameters(IEnumerable<double> original, IEnumerable<double> estimated, out double beta, out double alpha) {
117      IEnumerator<double> originalEnumerator = original.GetEnumerator();
118      IEnumerator<double> estimatedEnumerator = estimated.GetEnumerator();
119      OnlineMeanAndVarianceCalculator yVarianceCalculator = new OnlineMeanAndVarianceCalculator();
120      OnlineMeanAndVarianceCalculator tMeanCalculator = new OnlineMeanAndVarianceCalculator();
121      OnlineCovarianceEvaluator ytCovarianceEvaluator = new OnlineCovarianceEvaluator();
122      int cnt = 0;
123
124      while (originalEnumerator.MoveNext() & estimatedEnumerator.MoveNext()) {
125        double y = estimatedEnumerator.Current;
126        double t = originalEnumerator.Current;
127        if (IsValidValue(t) && IsValidValue(y)) {
128          tMeanCalculator.Add(t);
129          yVarianceCalculator.Add(y);
130          ytCovarianceEvaluator.Add(y, t);
131
132          cnt++;
133        }
134      }
135
136      if (estimatedEnumerator.MoveNext() || originalEnumerator.MoveNext())
137        throw new ArgumentException("Number of elements in original and estimated enumeration doesn't match.");
138      if (cnt < 2) {
139        alpha = 0;
140        beta = 1;
141      } else {
142        if (yVarianceCalculator.PopulationVariance.IsAlmost(0.0))
143          beta = 1;
144        else
145          beta = ytCovarianceEvaluator.Covariance / yVarianceCalculator.PopulationVariance;
146
147        alpha = tMeanCalculator.Mean - beta * yVarianceCalculator.Mean;
148      }
149    }
150
151    private static bool IsValidValue(double d) {
152      return !double.IsInfinity(d) && !double.IsNaN(d) && d > -1.0E07 && d < 1.0E07;  // don't consider very large or very small values for scaling
153    }
154  }
155}
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