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source: branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.DataAnalysis.Symbolic.TimeSeriesPrognosis.Views/3.4/InteractiveSymbolicTimeSeriesPrognosisSolutionSimplifierView.cs @ 7129

Last change on this file since 7129 was 7129, checked in by gkronber, 13 years ago

#1081 worked on multi-variate time series prognosis

File size: 6.4 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, Content.ProblemData.TargetVariables.ToArray());
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      Dictionary<ISymbolicExpressionTreeNode, double> impactValues =
70        new Dictionary<ISymbolicExpressionTreeNode, double>();
71      var originalOutput = interpreter.GetSymbolicExpressionTreeValues(tree, dataset, Content.ProblemData.TargetVariables.ToArray(), rows, 1)
72        .ToArray();
73      int i = 0;
74      foreach (var targetVariable in Content.ProblemData.TargetVariables) {
75        List<ISymbolicExpressionTreeNode> nodes = tree.Root.GetSubtree(0).GetSubtree(i).IterateNodesPostfix().ToList();
76        var targetValues = dataset.GetDoubleValues(targetVariable, rows);
77        OnlineCalculatorError errorState;
78        double originalR2 = OnlinePearsonsRSquaredCalculator.Calculate(targetValues, originalOutput.Select(v => v.ElementAt(i).First()), out errorState);
79        if (errorState != OnlineCalculatorError.None) originalR2 = 0.0;
80
81        foreach (ISymbolicExpressionTreeNode node in nodes) {
82          var parent = node.Parent;
83          constantNode.Value = CalculateReplacementValue(node, tree);
84          ISymbolicExpressionTreeNode replacementNode = constantNode;
85          SwitchNode(parent, node, replacementNode);
86          var newOutput = interpreter.GetSymbolicExpressionTreeValues(tree, dataset, Content.ProblemData.TargetVariables.ToArray(), rows, 1);
87          double newR2 = OnlinePearsonsRSquaredCalculator.Calculate(targetValues, newOutput.Select(v => v.ElementAt(i).First()), out errorState);
88          if (errorState != OnlineCalculatorError.None) newR2 = 0.0;
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        i++;
97      }
98      return impactValues;
99    }
100
101    private double CalculateReplacementValue(ISymbolicExpressionTreeNode node, ISymbolicExpressionTree sourceTree) {
102      // remove old ADFs
103      while (tempTree.Root.SubtreeCount > 1) tempTree.Root.RemoveSubtree(1);
104      // clone ADFs of source tree
105      for (int i = 1; i < sourceTree.Root.SubtreeCount; i++) {
106        tempTree.Root.AddSubtree((ISymbolicExpressionTreeNode)sourceTree.Root.GetSubtree(i).Clone());
107      }
108      var start = tempTree.Root.GetSubtree(0);
109      while (start.SubtreeCount > 0) start.RemoveSubtree(0);
110      start.AddSubtree((ISymbolicExpressionTreeNode)node.Clone());
111      var interpreter = Content.Model.Interpreter;
112      var rows = Content.ProblemData.TrainingIndizes;
113      var allPrognosedValues = interpreter.GetSymbolicExpressionTreeValues(tempTree, Content.ProblemData.Dataset, Content.ProblemData.TargetVariables.ToArray(), rows, 1);
114     
115      return allPrognosedValues.Select(x=>x.First().First()).Median();
116    }
117
118
119    private void SwitchNode(ISymbolicExpressionTreeNode root, ISymbolicExpressionTreeNode oldBranch, ISymbolicExpressionTreeNode newBranch) {
120      for (int i = 0; i < root.SubtreeCount; i++) {
121        if (root.GetSubtree(i) == oldBranch) {
122          root.RemoveSubtree(i);
123          root.InsertSubtree(i, newBranch);
124          return;
125        }
126      }
127    }
128
129    protected override void btnOptimizeConstants_Click(object sender, EventArgs e) {
130      throw new NotImplementedException();
131    }
132  }
133}
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