[4127] | 1 | #region License Information
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| 2 | /* HeuristicLab
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| 3 | * Copyright (C) 2002-2010 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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| 4 | *
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| 5 | * This file is part of HeuristicLab.
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| 6 | *
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| 7 | * HeuristicLab is free software: you can redistribute it and/or modify
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| 8 | * it under the terms of the GNU General Public License as published by
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| 9 | * the Free Software Foundation, either version 3 of the License, or
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| 10 | * (at your option) any later version.
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| 11 | *
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| 12 | * HeuristicLab is distributed in the hope that it will be useful,
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| 13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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| 14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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| 15 | * GNU General Public License for more details.
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| 16 | *
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| 17 | * You should have received a copy of the GNU General Public License
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| 18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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| 19 | */
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| 20 | #endregion
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| 21 |
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| 22 | using System;
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| 23 | using System.Collections.Generic;
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| 24 | using HeuristicLab.Core;
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| 25 | using HeuristicLab.Data;
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| 26 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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| 27 | using HeuristicLab.Parameters;
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| 28 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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| 29 | using HeuristicLab.Problems.DataAnalysis.Evaluators;
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| 30 | using HeuristicLab.Problems.DataAnalysis.Symbolic;
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| 31 |
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| 32 | namespace HeuristicLab.Problems.DataAnalysis.Regression.Symbolic {
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| 33 | [Item("SymbolicRegressionPearsonsRSquaredEvaluator", "Calculates the pearson r² correlation coefficient of a symbolic regression solution.")]
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| 34 | [StorableClass]
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[4128] | 35 | public class SymbolicRegressionPearsonsRSquaredEvaluator : SingleObjectiveSymbolicRegressionEvaluator {
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[4127] | 36 | private const string UpperEstimationLimitParameterName = "UpperEstimationLimit";
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| 37 | private const string LowerEstimationLimitParameterName = "LowerEstimationLimit";
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| 38 |
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| 39 | #region parameter properties
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| 40 | public IValueLookupParameter<DoubleValue> UpperEstimationLimitParameter {
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| 41 | get { return (IValueLookupParameter<DoubleValue>)Parameters[UpperEstimationLimitParameterName]; }
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| 42 | }
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| 43 | public IValueLookupParameter<DoubleValue> LowerEstimationLimitParameter {
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| 44 | get { return (IValueLookupParameter<DoubleValue>)Parameters[LowerEstimationLimitParameterName]; }
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| 45 | }
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| 46 | #endregion
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| 47 | #region properties
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| 48 | public DoubleValue UpperEstimationLimit {
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| 49 | get { return UpperEstimationLimitParameter.ActualValue; }
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| 50 | }
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| 51 | public DoubleValue LowerEstimationLimit {
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| 52 | get { return LowerEstimationLimitParameter.ActualValue; }
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| 53 | }
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| 54 | #endregion
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| 55 | public SymbolicRegressionPearsonsRSquaredEvaluator()
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| 56 | : base() {
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| 57 | Parameters.Add(new ValueLookupParameter<DoubleValue>(UpperEstimationLimitParameterName, "The upper limit that should be used as cut off value for the output values of symbolic expression trees."));
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| 58 | Parameters.Add(new ValueLookupParameter<DoubleValue>(LowerEstimationLimitParameterName, "The lower limit that should be used as cut off value for the output values of symbolic expression trees."));
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| 59 | }
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| 60 |
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| 61 | protected override double Evaluate(ISymbolicExpressionTreeInterpreter interpreter, SymbolicExpressionTree solution, Dataset dataset, StringValue targetVariable, IEnumerable<int> rows) {
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| 62 | double mse = Calculate(interpreter, solution, LowerEstimationLimit.Value, UpperEstimationLimit.Value, dataset, targetVariable.Value, rows);
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| 63 | return mse;
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| 64 | }
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| 65 |
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| 66 | public static double Calculate(ISymbolicExpressionTreeInterpreter interpreter, SymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, Dataset dataset, string targetVariable, IEnumerable<int> rows) {
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| 67 | IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, dataset, rows);
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| 68 | IEnumerable<double> originalValues = dataset.GetEnumeratedVariableValues(targetVariable, rows);
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| 69 | IEnumerator<double> originalEnumerator = originalValues.GetEnumerator();
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| 70 | IEnumerator<double> estimatedEnumerator = estimatedValues.GetEnumerator();
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| 71 | OnlinePearsonsRSquaredEvaluator r2Evaluator = new OnlinePearsonsRSquaredEvaluator();
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| 72 |
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| 73 | while (originalEnumerator.MoveNext() & estimatedEnumerator.MoveNext()) {
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| 74 | double estimated = estimatedEnumerator.Current;
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| 75 | double original = originalEnumerator.Current;
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| 76 | if (double.IsNaN(estimated))
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| 77 | estimated = upperEstimationLimit;
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| 78 | else
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| 79 | estimated = Math.Min(upperEstimationLimit, Math.Max(lowerEstimationLimit, estimated));
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| 80 | r2Evaluator.Add(original, estimated);
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| 81 | }
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| 82 |
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| 83 | if (estimatedEnumerator.MoveNext() || originalEnumerator.MoveNext()) {
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| 84 | throw new ArgumentException("Number of elements in original and estimated enumeration doesn't match.");
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| 85 | } else {
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| 86 | return r2Evaluator.RSquared;
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| 87 | }
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| 88 | }
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| 89 | }
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| 90 | }
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