[7784] | 1 | #region License Information
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| 2 | /* HeuristicLab
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| 3 | * Copyright (C) 2002-2012 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.Collections.Generic;
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| 23 | using System.Linq;
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| 24 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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| 25 |
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| 26 | namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
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| 27 | public class SymbolicRegressionSolutionValuesCalculator : SymbolicDataAnalysisSolutionValuesCalculator {
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| 28 | public override Dictionary<ISymbolicExpressionTreeNode, double> CalculateReplacementValues(ISymbolicExpressionTree tree,
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| 29 | ISymbolicDataAnalysisExpressionTreeInterpreter interpreter,
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| 30 | IDataAnalysisProblemData problemData) {
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| 31 | var replacementValues = new Dictionary<ISymbolicExpressionTreeNode, double>();
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| 32 | foreach (ISymbolicExpressionTreeNode node in tree.Root.GetSubtree(0).GetSubtree(0).IterateNodesPrefix()) {
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| 33 | replacementValues[node] = CalculateReplacementValue(node, tree, interpreter, problemData);
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| 34 | }
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| 35 | return replacementValues;
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| 36 | }
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| 37 |
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| 38 | public override Dictionary<ISymbolicExpressionTreeNode, double> CalculateImpactValues(ISymbolicExpressionTree tree, ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, IDataAnalysisProblemData regressionProblemData,
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| 39 | double lowerEstimationLimit, double upperEstimationLimit) {
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| 40 | var problemData = (IRegressionProblemData)regressionProblemData;
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| 41 | var dataset = problemData.Dataset;
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| 42 | var rows = problemData.TrainingIndizes.ToList();
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| 43 | string targetVariable = problemData.TargetVariable;
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| 44 | var impactValues = new Dictionary<ISymbolicExpressionTreeNode, double>();
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| 45 | List<ISymbolicExpressionTreeNode> nodes = tree.Root.GetSubtree(0).GetSubtree(0).IterateNodesPostfix().ToList();
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| 46 | var originalOutput = interpreter.GetSymbolicExpressionTreeValues(tree, dataset, rows).ToArray();
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| 47 | var targetValues = dataset.GetDoubleValues(targetVariable, rows).ToList();
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| 48 | OnlineCalculatorError errorState;
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| 49 | double originalR2 = OnlinePearsonsRSquaredCalculator.Calculate(targetValues, originalOutput, out errorState);
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| 50 | if (errorState != OnlineCalculatorError.None) originalR2 = 0.0;
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| 51 |
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| 52 | foreach (ISymbolicExpressionTreeNode node in nodes) {
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| 53 | var parent = node.Parent;
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| 54 | constantNode.Value = CalculateReplacementValue(node, tree, interpreter, problemData);
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| 55 | ISymbolicExpressionTreeNode replacementNode = constantNode;
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| 56 | SwitchNode(parent, node, replacementNode);
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| 57 | var newOutput = interpreter.GetSymbolicExpressionTreeValues(tree, dataset, rows);
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| 58 | double newR2 = OnlinePearsonsRSquaredCalculator.Calculate(targetValues, newOutput, out errorState);
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| 59 | if (errorState != OnlineCalculatorError.None) newR2 = 0.0;
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| 60 |
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| 61 | // impact = 0 if no change
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| 62 | // impact < 0 if new solution is better
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| 63 | // impact > 0 if new solution is worse
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| 64 | impactValues[node] = originalR2 - newR2;
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| 65 | SwitchNode(parent, replacementNode, node);
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| 66 | }
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| 67 | return impactValues;
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| 68 | }
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| 69 | }
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| 70 | }
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