source: trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.TimeSeriesPrognosis.Views/3.4/InteractiveSymbolicTimeSeriesPrognosisSolutionSimplifierView.cs @ 6807

Last change on this file since 6807 was 6807, checked in by gkronber, 10 years ago

#1081 added views for time series prognosis

File size: 6.0 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.Linq;
25using HeuristicLab.Common;
26using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
27using HeuristicLab.Problems.DataAnalysis.Symbolic.Views;
28
29namespace HeuristicLab.Problems.DataAnalysis.Symbolic.TimeSeriesPrognosis.Views {
30  public partial class InteractiveSymbolicTimeSeriesPrognosisSolutionSimplifierView : InteractiveSymbolicDataAnalysisSolutionSimplifierView {
31    private readonly ConstantTreeNode constantNode;
32    private readonly SymbolicExpressionTree tempTree;
33
34    public new SymbolicTimeSeriesPrognosisSolution Content {
35      get { return (SymbolicTimeSeriesPrognosisSolution)base.Content; }
36      set { base.Content = value; }
37    }
38
39    public InteractiveSymbolicTimeSeriesPrognosisSolutionSimplifierView()
40      : base() {
41      InitializeComponent();
42      this.Caption = "Interactive Time-Series Prognosis 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      var model = new SymbolicTimeSeriesPrognosisModel(tree, Content.Model.Interpreter);
53      SymbolicTimeSeriesPrognosisModel.Scale(model, Content.ProblemData);
54      Content.Model = model;
55    }
56
57    protected override Dictionary<ISymbolicExpressionTreeNode, double> CalculateReplacementValues(ISymbolicExpressionTree tree) {
58      Dictionary<ISymbolicExpressionTreeNode, double> replacementValues = new Dictionary<ISymbolicExpressionTreeNode, double>();
59      foreach (ISymbolicExpressionTreeNode node in tree.Root.GetSubtree(0).GetSubtree(0).IterateNodesPrefix()) {
60        replacementValues[node] = CalculateReplacementValue(node, tree);
61      }
62      return replacementValues;
63    }
64
65    protected override Dictionary<ISymbolicExpressionTreeNode, double> CalculateImpactValues(ISymbolicExpressionTree tree) {
66      var interpreter = Content.Model.Interpreter;
67      var dataset = Content.ProblemData.Dataset;
68      var rows = Content.ProblemData.TrainingIndizes;
69      string targetVariable = Content.ProblemData.TargetVariable;
70      Dictionary<ISymbolicExpressionTreeNode, double> impactValues = new Dictionary<ISymbolicExpressionTreeNode, double>();
71      List<ISymbolicExpressionTreeNode> nodes = tree.Root.GetSubtree(0).GetSubtree(0).IterateNodesPostfix().ToList();
72      var originalOutput = interpreter.GetSymbolicExpressionTreeValues(tree, dataset, rows)
73        .ToArray();
74      var targetValues = dataset.GetDoubleValues(targetVariable, rows);
75      OnlineCalculatorError errorState;
76      double originalR2 = OnlinePearsonsRSquaredCalculator.Calculate(targetValues, originalOutput, out errorState);
77      if (errorState != OnlineCalculatorError.None) originalR2 = 0.0;
78
79      foreach (ISymbolicExpressionTreeNode node in nodes) {
80        var parent = node.Parent;
81        constantNode.Value = CalculateReplacementValue(node, tree);
82        ISymbolicExpressionTreeNode replacementNode = constantNode;
83        SwitchNode(parent, node, replacementNode);
84        var newOutput = interpreter.GetSymbolicExpressionTreeValues(tree, dataset, rows);
85        double newR2 = OnlinePearsonsRSquaredCalculator.Calculate(targetValues, newOutput, out errorState);
86        if (errorState != OnlineCalculatorError.None) newR2 = 0.0;
87
88        // impact = 0 if no change
89        // impact < 0 if new solution is better
90        // impact > 0 if new solution is worse
91        impactValues[node] = originalR2 - newR2;
92        SwitchNode(parent, replacementNode, node);
93      }
94      return impactValues;
95    }
96
97    private double CalculateReplacementValue(ISymbolicExpressionTreeNode node, ISymbolicExpressionTree sourceTree) {
98      // remove old ADFs
99      while (tempTree.Root.SubtreesCount > 1) tempTree.Root.RemoveSubtree(1);
100      // clone ADFs of source tree
101      for (int i = 1; i < sourceTree.Root.SubtreesCount; i++) {
102        tempTree.Root.AddSubtree((ISymbolicExpressionTreeNode)sourceTree.Root.GetSubtree(i).Clone());
103      }
104      var start = tempTree.Root.GetSubtree(0);
105      while (start.SubtreesCount > 0) start.RemoveSubtree(0);
106      start.AddSubtree((ISymbolicExpressionTreeNode)node.Clone());
107      var interpreter = Content.Model.Interpreter;
108      var rows = Content.ProblemData.TrainingIndizes;
109      return interpreter.GetSymbolicExpressionTreeValues(tempTree, Content.ProblemData.Dataset, rows).Median();
110    }
111
112
113    private void SwitchNode(ISymbolicExpressionTreeNode root, ISymbolicExpressionTreeNode oldBranch, ISymbolicExpressionTreeNode newBranch) {
114      for (int i = 0; i < root.SubtreesCount; i++) {
115        if (root.GetSubtree(i) == oldBranch) {
116          root.RemoveSubtree(i);
117          root.InsertSubtree(i, newBranch);
118          return;
119        }
120      }
121    }
122
123    protected override void btnOptimizeConstants_Click(object sender, EventArgs e) {
124      throw new NotImplementedException();
125    }
126  }
127}
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