source: branches/HeuristicLab.TreeSimplifier/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SymbolicRegressionSolutionImpactValuesCalculator.cs @ 8409

#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
 *
 * This file is part of HeuristicLab.
 *
 * HeuristicLab is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * HeuristicLab is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
 */
#endregion

using System.Collections.Generic;
using System.Linq;
using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;

namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
  public class SymbolicRegressionSolutionImpactValuesCalculator : SymbolicDataAnalysisSolutionImpactValuesCalculator {
    public override Dictionary<ISymbolicExpressionTreeNode, double> CalculateReplacementValues(ISymbolicExpressionTree tree,
                                                                                               ISymbolicDataAnalysisExpressionTreeInterpreter interpreter,
                                                                                               IDataAnalysisProblemData problemData) {
      var replacementValues = new Dictionary<ISymbolicExpressionTreeNode, double>();
      foreach (ISymbolicExpressionTreeNode node in tree.Root.GetSubtree(0).GetSubtree(0).IterateNodesPrefix()) {
        replacementValues[node] = CalculateReplacementValue(node, tree, interpreter, problemData);
      }
      return replacementValues;
    }

    public override Dictionary<ISymbolicExpressionTreeNode, double> CalculateImpactValues(ISymbolicExpressionTree tree, ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, IDataAnalysisProblemData regressionProblemData, double lowerEstimationLimit, double upperEstimationLimit) {
      var problemData = (IRegressionProblemData)regressionProblemData;
      var dataset = problemData.Dataset;
      var rows = problemData.TrainingIndices.ToList();
      string targetVariable = problemData.TargetVariable;
      var impactValues = new Dictionary<ISymbolicExpressionTreeNode, double>();
      List<ISymbolicExpressionTreeNode> nodes = tree.Root.GetSubtree(0).GetSubtree(0).IterateNodesPostfix().ToList();
      var originalOutput = interpreter.GetSymbolicExpressionTreeValues(tree, dataset, rows).ToArray();
      var targetValues = dataset.GetDoubleValues(targetVariable, rows).ToList();
      OnlineCalculatorError errorState;
      double originalR2 = OnlinePearsonsRSquaredCalculator.Calculate(targetValues, originalOutput, out errorState);
      if (errorState != OnlineCalculatorError.None) originalR2 = 0.0;

      foreach (ISymbolicExpressionTreeNode node in nodes) {
        var parent = node.Parent;
        var constantNode = ((ConstantTreeNode)new Constant().CreateTreeNode());
        constantNode.Value = CalculateReplacementValue(node, tree, interpreter, problemData);
        ISymbolicExpressionTreeNode replacementNode = constantNode;
        SwitchNode(parent, node, replacementNode);
        var newOutput = interpreter.GetSymbolicExpressionTreeValues(tree, dataset, rows);
        double newR2 = OnlinePearsonsRSquaredCalculator.Calculate(targetValues, newOutput, out errorState);
        if (errorState != OnlineCalculatorError.None) newR2 = 0.0;

        // impact = 0 if no change
        // impact < 0 if new solution is better
        // impact > 0 if new solution is worse
        impactValues[node] = originalR2 - newR2;
        SwitchNode(parent, replacementNode, node);
      }
      return impactValues;
    }
  }
}