[5074] | 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 |
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| 23 | using System;
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| 24 | using System.Collections.Generic;
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| 25 | using System.Linq;
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| 26 | using HeuristicLab.Common;
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| 27 | using HeuristicLab.Core;
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| 28 | using HeuristicLab.Data;
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| 29 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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| 30 | using HeuristicLab.Parameters;
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| 31 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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| 32 | using HeuristicLab.Problems.DataAnalysis.Evaluators;
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| 33 | using HeuristicLab.Problems.DataAnalysis.Symbolic;
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| 34 | using HeuristicLab.Problems.DataAnalysis.Symbolic.Symbols;
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| 35 |
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| 36 | namespace HeuristicLab.Problems.DataAnalysis.Regression.Symbolic.Evaluators {
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| 37 | [Item("SymbolicRegressionConditionalPearsonsRSquaredEvaluator", "Evaluates a symbolic regression solution.")]
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| 38 | [StorableClass]
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| 39 | public class SymbolicRegressionConditionalPearsonsRSquaredEvaluator : SingleObjectiveSymbolicRegressionEvaluator {
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| 40 | private const string ConditionVariableParameterName = "ConditionVariable";
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| 41 |
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| 42 | public IValueParameter<StringValue> ConditionVariableParameter {
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| 43 | get { return (IValueParameter<StringValue>)Parameters[ConditionVariableParameterName]; }
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| 44 | }
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| 45 | public StringValue ConditionVariable {
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| 46 | get { return ConditionVariableParameter.Value; }
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| 47 | }
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| 48 |
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| 49 | public SymbolicRegressionConditionalPearsonsRSquaredEvaluator()
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| 50 | : base() {
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| 51 | Parameters.Add(new ValueLookupParameter<StringValue>(ConditionVariableParameterName, "The variable name that states which samples should be skipped for evaluation."));
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| 52 | }
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| 53 | [StorableConstructor]
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| 54 | protected SymbolicRegressionConditionalPearsonsRSquaredEvaluator(bool deserializing) : base(deserializing) { }
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| 55 | protected SymbolicRegressionConditionalPearsonsRSquaredEvaluator(SymbolicRegressionConditionalPearsonsRSquaredEvaluator original, Cloner cloner)
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| 56 | : base(original, cloner) {
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| 57 | }
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| 58 | public override IDeepCloneable Clone(Cloner cloner) {
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| 59 | return new SymbolicRegressionConditionalPearsonsRSquaredEvaluator(this, cloner);
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| 60 | }
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| 61 |
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| 62 | public override double Evaluate(ISymbolicExpressionTreeInterpreter interpreter, SymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, Dataset dataset, string targetVariable, IEnumerable<int> rows) {
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| 63 | double mse = Calculate(interpreter, solution, lowerEstimationLimit, upperEstimationLimit, dataset, targetVariable, rows, ConditionVariable.Value);
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| 64 | return mse;
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| 65 | }
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| 66 |
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| 67 | public static double Calculate(ISymbolicExpressionTreeInterpreter interpreter, SymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, Dataset dataset, string targetVariable, IEnumerable<int> rows, string conditionVariable) {
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| 68 | IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, dataset, rows);
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| 69 | IEnumerable<double> originalValues = dataset.GetEnumeratedVariableValues(targetVariable, rows);
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| 70 | IEnumerator<double> originalEnumerator = originalValues.GetEnumerator();
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| 71 | IEnumerator<double> estimatedEnumerator = estimatedValues.GetEnumerator();
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| 72 | IEnumerator<int> rowsEnumerator = rows.GetEnumerator();
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| 73 | OnlinePearsonsRSquaredEvaluator r2Evaluator = new OnlinePearsonsRSquaredEvaluator();
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| 74 |
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| 75 | int minLag = 0;
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| 76 | var laggedTreeNodes = solution.IterateNodesPrefix().OfType<LaggedVariableTreeNode>();
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| 77 | if (laggedTreeNodes.Any())
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| 78 | minLag = laggedTreeNodes.Min(laggedTreeNode => laggedTreeNode.Symbol.MinLag);
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| 79 |
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| 80 | while (originalEnumerator.MoveNext() && estimatedEnumerator.MoveNext() && rowsEnumerator.MoveNext()) {
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| 81 | double estimated = estimatedEnumerator.Current;
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| 82 | double original = originalEnumerator.Current;
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| 83 | int row = rowsEnumerator.Current;
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| 84 |
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| 85 | bool evaluate = true;
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| 86 | for (int i = minLag; i <= 0 && evaluate; i++) {
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| 87 | evaluate = evaluate && dataset[conditionVariable, row - i].IsAlmost(0.0);
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| 88 | }
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| 89 |
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| 90 | if (evaluate)
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| 91 | if (dataset[conditionVariable, row].IsAlmost(0.0) && dataset[conditionVariable, row - 1].IsAlmost(0.0)) {
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| 92 | if (double.IsNaN(estimated))
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| 93 | estimated = upperEstimationLimit;
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| 94 | else
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| 95 | estimated = Math.Min(upperEstimationLimit, Math.Max(lowerEstimationLimit, estimated));
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| 96 | r2Evaluator.Add(original, estimated);
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| 97 | }
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| 98 | }
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| 99 |
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| 100 | if (estimatedEnumerator.MoveNext() || originalEnumerator.MoveNext() || rowsEnumerator.MoveNext()) {
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| 101 | throw new ArgumentException("Number of elements in original and estimated enumeration doesn't match.");
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| 102 | } else {
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| 103 | return r2Evaluator.RSquared;
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| 104 | }
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| 105 | }
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| 106 |
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| 107 |
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| 108 | }
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| 109 | }
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