source: branches/DataAnalysis Refactoring/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression.Views/3.4/InteractiveSymbolicRegressionSolutionSimplifierView.cs @ 5736

Last change on this file since 5736 was 5736, checked in by gkronber, 11 years ago

#1418 implemented linear scaling for classification solutions, fixed bugs interactive simplifier view for classification solutions.

File size: 5.3 KB
Line 
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      Content.Model = new SymbolicRegressionModel(tree, Content.Model.Interpreter);
56      Content.ScaleModel();
57    }
58
59    protected override Dictionary<ISymbolicExpressionTreeNode, double> CalculateReplacementValues(ISymbolicExpressionTree tree) {
60      Dictionary<ISymbolicExpressionTreeNode, double> replacementValues = new Dictionary<ISymbolicExpressionTreeNode, double>();
61      foreach (ISymbolicExpressionTreeNode node in tree.IterateNodesPrefix()) {
62        if (!(node.Symbol is ProgramRootSymbol || node.Symbol is StartSymbol)) {
63          replacementValues[node] = CalculateReplacementValue(node);
64        }
65      }
66      return replacementValues;
67    }
68
69    protected override Dictionary<ISymbolicExpressionTreeNode, double> CalculateImpactValues(ISymbolicExpressionTree tree) {
70      var interpreter = Content.Model.Interpreter;
71      var dataset = Content.ProblemData.Dataset;
72      var rows = Content.ProblemData.TrainingIndizes;
73      string targetVariable = Content.ProblemData.TargetVariable;
74      Dictionary<ISymbolicExpressionTreeNode, double> impactValues = new Dictionary<ISymbolicExpressionTreeNode, double>();
75      List<ISymbolicExpressionTreeNode> nodes = tree.Root.GetSubtree(0).GetSubtree(0).IterateNodesPostfix().ToList();
76      var originalOutput = interpreter.GetSymbolicExpressionTreeValues(tree, dataset, rows)
77        .ToArray();
78      var targetValues = dataset.GetEnumeratedVariableValues(targetVariable, rows);
79
80      double originalR2 = OnlinePearsonsRSquaredEvaluator.Calculate(targetValues, originalOutput);
81
82      foreach (ISymbolicExpressionTreeNode node in nodes) {
83        var parent = node.Parent;
84        constantNode.Value = CalculateReplacementValue(node);
85        ISymbolicExpressionTreeNode replacementNode = constantNode;
86        SwitchNode(parent, node, replacementNode);
87        var newOutput = interpreter.GetSymbolicExpressionTreeValues(tree, dataset, rows);
88        double newR2 = OnlinePearsonsRSquaredEvaluator.Calculate(targetValues, newOutput);
89
90        // impact = 0 if no change
91        // impact < 0 if new solution is better
92        // impact > 0 if new solution is worse
93        impactValues[node] = originalR2 - newR2;
94        SwitchNode(parent, replacementNode, node);
95      }
96      return impactValues;
97    }
98
99    private double CalculateReplacementValue(ISymbolicExpressionTreeNode node) {
100      var start = tempTree.Root.GetSubtree(0);
101      while (start.SubtreesCount > 0) start.RemoveSubtree(0);
102      start.AddSubtree((ISymbolicExpressionTreeNode)node.Clone());
103      var interpreter = Content.Model.Interpreter;
104      var rows = Content.ProblemData.TrainingIndizes;
105      return interpreter.GetSymbolicExpressionTreeValues(tempTree, Content.ProblemData.Dataset, rows).Median();
106    }
107
108
109    private void SwitchNode(ISymbolicExpressionTreeNode root, ISymbolicExpressionTreeNode oldBranch, ISymbolicExpressionTreeNode newBranch) {
110      for (int i = 0; i < root.SubtreesCount; i++) {
111        if (root.GetSubtree(i) == oldBranch) {
112          root.RemoveSubtree(i);
113          root.InsertSubtree(i, newBranch);
114          return;
115        }
116      }
117    }
118  }
119}
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