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source: trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification.Views/3.4/InteractiveSymbolicDiscriminantFunctionClassificationSolutionSimplifierView.cs @ 8139

Last change on this file since 8139 was 8139, checked in by mkommend, 10 years ago

#1722: Renamed indizes to indices in the whole trunk solution.

File size: 6.6 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2012 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.Linq;
25using HeuristicLab.Common;
26using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
27using HeuristicLab.Problems.DataAnalysis.Symbolic.Views;
28
29namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Classification.Views {
30  public partial class InteractiveSymbolicDiscriminantFunctionClassificationSolutionSimplifierView : InteractiveSymbolicDataAnalysisSolutionSimplifierView {
31    private readonly ConstantTreeNode constantNode;
32    private readonly SymbolicExpressionTree tempTree;
33
34    public new SymbolicDiscriminantFunctionClassificationSolution Content {
35      get { return (SymbolicDiscriminantFunctionClassificationSolution)base.Content; }
36      set { base.Content = value; }
37    }
38
39    public InteractiveSymbolicDiscriminantFunctionClassificationSolutionSimplifierView()
40      : base() {
41      InitializeComponent();
42      this.Caption = "Interactive Classification Solution Simplifier";
43
44      constantNode = ((ConstantTreeNode)new Constant().CreateTreeNode());
45      ISymbolicExpressionTreeNode root = new ProgramRootSymbol().CreateTreeNode();
46      ISymbolicExpressionTreeNode start = new StartSymbol().CreateTreeNode();
47      root.AddSubtree(start);
48      tempTree = new SymbolicExpressionTree(root);
49    }
50
51    protected override void UpdateModel(ISymbolicExpressionTree tree) {
52      Content.Model = new SymbolicDiscriminantFunctionClassificationModel(tree, Content.Model.Interpreter);
53      // the default policy for setting thresholds in classification models is the accuarcy maximizing policy.
54      // This is rather slow to calculate and can lead to a very laggy UI in the interactive solution simplifier.
55      // However, since we automatically prune sub-trees based on the threshold reaching the maximum accuracy we must
56      // also use maximum accuracy threshold calculation here in order to prevent incoherent behavior of the simplifier.
57      Content.SetAccuracyMaximizingThresholds();
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.TrainingIndices;
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
76      var targetClassValues = dataset.GetDoubleValues(targetVariable, rows);
77      var originalOutput = interpreter.GetSymbolicExpressionTreeValues(tree, dataset, rows)
78        .LimitToRange(Content.Model.LowerEstimationLimit, Content.Model.UpperEstimationLimit)
79        .ToArray();
80      OnlineCalculatorError errorState;
81      double originalGini = NormalizedGiniCalculator.Calculate(targetClassValues, originalOutput, out errorState);
82      if (errorState != OnlineCalculatorError.None) originalGini = 0.0;
83
84      foreach (ISymbolicExpressionTreeNode node in nodes) {
85        var parent = node.Parent;
86        constantNode.Value = CalculateReplacementValue(node, tree);
87        ISymbolicExpressionTreeNode replacementNode = constantNode;
88        SwitchNode(parent, node, replacementNode);
89        var newOutput = interpreter.GetSymbolicExpressionTreeValues(tree, dataset, rows)
90          .LimitToRange(Content.Model.LowerEstimationLimit, Content.Model.UpperEstimationLimit)
91          .ToArray();
92        double newGini = NormalizedGiniCalculator.Calculate(targetClassValues, newOutput, out errorState);
93        if (errorState != OnlineCalculatorError.None) newGini = 0.0;
94
95        // impact = 0 if no change
96        // impact < 0 if new solution is better
97        // impact > 0 if new solution is worse
98        impactValues[node] = originalGini - newGini;
99        SwitchNode(parent, replacementNode, node);
100      }
101      return impactValues;
102    }
103
104    private double CalculateReplacementValue(ISymbolicExpressionTreeNode node, ISymbolicExpressionTree sourceTree) {
105      // remove old ADFs
106      while (tempTree.Root.SubtreeCount > 1) tempTree.Root.RemoveSubtree(1);
107      // clone ADFs of source tree
108      for (int i = 1; i < sourceTree.Root.SubtreeCount; i++) {
109        tempTree.Root.AddSubtree((ISymbolicExpressionTreeNode)sourceTree.Root.GetSubtree(i).Clone());
110      }
111      var start = tempTree.Root.GetSubtree(0);
112      while (start.SubtreeCount > 0) start.RemoveSubtree(0);
113      start.AddSubtree((ISymbolicExpressionTreeNode)node.Clone());
114      var interpreter = Content.Model.Interpreter;
115      var rows = Content.ProblemData.TrainingIndices;
116      return interpreter.GetSymbolicExpressionTreeValues(tempTree, Content.ProblemData.Dataset, rows).Median();
117    }
118
119
120    private void SwitchNode(ISymbolicExpressionTreeNode root, ISymbolicExpressionTreeNode oldBranch, ISymbolicExpressionTreeNode newBranch) {
121      for (int i = 0; i < root.SubtreeCount; i++) {
122        if (root.GetSubtree(i) == oldBranch) {
123          root.RemoveSubtree(i);
124          root.InsertSubtree(i, newBranch);
125          return;
126        }
127      }
128    }
129
130    protected override void btnOptimizeConstants_Click(object sender, EventArgs e) {
131
132    }
133  }
134}
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