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source: branches/PersistenceSpeedUp/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression.Views/3.4/InteractiveSymbolicRegressionSolutionSimplifierView.cs @ 6305

Last change on this file since 6305 was 5993, checked in by gkronber, 14 years ago

#1418 fixed issues in interactive simplifier views for regression and classification solutions regarding automatic folding of nodes without impact on the solution quality and problems with ADFs.

File size: 5.9 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 System.Drawing;
25using System.Linq;
26using System.Windows.Forms;
27using HeuristicLab.Common;
28using HeuristicLab.MainForm.WindowsForms;
29using HeuristicLab.Problems.DataAnalysis.Symbolic.Views;
30using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
31
32namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression.Views {
33  public partial class InteractiveSymbolicRegressionSolutionSimplifierView : InteractiveSymbolicDataAnalysisSolutionSimplifierView {
34    private readonly ConstantTreeNode constantNode;
35    private readonly SymbolicExpressionTree tempTree;
36
37    public new SymbolicRegressionSolution Content {
38      get { return (SymbolicRegressionSolution)base.Content; }
39      set { base.Content = value; }
40    }
41
42    public InteractiveSymbolicRegressionSolutionSimplifierView()
43      : base() {
44      InitializeComponent();
45      this.Caption = "Interactive Regression Solution Simplifier";
46
47      constantNode = ((ConstantTreeNode)new Constant().CreateTreeNode());
48      ISymbolicExpressionTreeNode root = new ProgramRootSymbol().CreateTreeNode();
49      ISymbolicExpressionTreeNode start = new StartSymbol().CreateTreeNode();
50      root.AddSubtree(start);
51      tempTree = new SymbolicExpressionTree(root);
52    }
53
54    protected override void UpdateModel(ISymbolicExpressionTree tree) {
55      var model = new SymbolicRegressionModel(tree, Content.Model.Interpreter);
56      SymbolicRegressionModel.Scale(model, Content.ProblemData);
57      Content.Model = model;
58    }
59
60    protected override Dictionary<ISymbolicExpressionTreeNode, double> CalculateReplacementValues(ISymbolicExpressionTree tree) {
61      Dictionary<ISymbolicExpressionTreeNode, double> replacementValues = new Dictionary<ISymbolicExpressionTreeNode, double>();
62      foreach (ISymbolicExpressionTreeNode node in tree.Root.GetSubtree(0).GetSubtree(0).IterateNodesPrefix()) {
63        replacementValues[node] = CalculateReplacementValue(node, tree);
64      }
65      return replacementValues;
66    }
67
68    protected override Dictionary<ISymbolicExpressionTreeNode, double> CalculateImpactValues(ISymbolicExpressionTree tree) {
69      var interpreter = Content.Model.Interpreter;
70      var dataset = Content.ProblemData.Dataset;
71      var rows = Content.ProblemData.TrainingIndizes;
72      string targetVariable = Content.ProblemData.TargetVariable;
73      Dictionary<ISymbolicExpressionTreeNode, double> impactValues = new Dictionary<ISymbolicExpressionTreeNode, double>();
74      List<ISymbolicExpressionTreeNode> nodes = tree.Root.GetSubtree(0).GetSubtree(0).IterateNodesPostfix().ToList();
75      var originalOutput = interpreter.GetSymbolicExpressionTreeValues(tree, dataset, rows)
76        .ToArray();
77      var targetValues = dataset.GetEnumeratedVariableValues(targetVariable, rows);
78      OnlineCalculatorError errorState;
79      double originalR2 = OnlinePearsonsRSquaredCalculator.Calculate(targetValues, originalOutput, out errorState);
80      if (errorState != OnlineCalculatorError.None) originalR2 = 0.0;
81
82      foreach (ISymbolicExpressionTreeNode node in nodes) {
83        var parent = node.Parent;
84        constantNode.Value = CalculateReplacementValue(node, tree);
85        ISymbolicExpressionTreeNode replacementNode = constantNode;
86        SwitchNode(parent, node, replacementNode);
87        var newOutput = interpreter.GetSymbolicExpressionTreeValues(tree, dataset, rows);
88        double newR2 = OnlinePearsonsRSquaredCalculator.Calculate(targetValues, newOutput, out errorState);
89        if (errorState != OnlineCalculatorError.None) newR2 = 0.0;
90
91        // impact = 0 if no change
92        // impact < 0 if new solution is better
93        // impact > 0 if new solution is worse
94        impactValues[node] = originalR2 - newR2;
95        SwitchNode(parent, replacementNode, node);
96      }
97      return impactValues;
98    }
99
100    private double CalculateReplacementValue(ISymbolicExpressionTreeNode node, ISymbolicExpressionTree sourceTree) {
101      // remove old ADFs
102      while (tempTree.Root.SubtreesCount > 1) tempTree.Root.RemoveSubtree(1);
103      // clone ADFs of source tree
104      for (int i = 1; i < sourceTree.Root.SubtreesCount; i++) {
105        tempTree.Root.AddSubtree((ISymbolicExpressionTreeNode)sourceTree.Root.GetSubtree(i).Clone());
106      }
107      var start = tempTree.Root.GetSubtree(0);
108      while (start.SubtreesCount > 0) start.RemoveSubtree(0);
109      start.AddSubtree((ISymbolicExpressionTreeNode)node.Clone());
110      var interpreter = Content.Model.Interpreter;
111      var rows = Content.ProblemData.TrainingIndizes;
112      return interpreter.GetSymbolicExpressionTreeValues(tempTree, Content.ProblemData.Dataset, rows).Median();
113    }
114
115
116    private void SwitchNode(ISymbolicExpressionTreeNode root, ISymbolicExpressionTreeNode oldBranch, ISymbolicExpressionTreeNode newBranch) {
117      for (int i = 0; i < root.SubtreesCount; i++) {
118        if (root.GetSubtree(i) == oldBranch) {
119          root.RemoveSubtree(i);
120          root.InsertSubtree(i, newBranch);
121          return;
122        }
123      }
124    }
125  }
126}
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