source: trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.TimeSeriesPrognosis.Views/3.4/InteractiveSymbolicTimeSeriesPrognosisSolutionSimplifierView.cs @ 10492

Last change on this file since 10492 was 10492, checked in by bburlacu, 6 years ago

#2156: Modified the InteractiveSymbolicDataAnalysisSolutionSimplifierView to use the unified method of calculating impact and replacement values.

File size: 6.4 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2013 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 Dictionary<ISymbolicExpressionTreeNode, Tuple<double, double>> CalculateImpactAndReplacementValues(ISymbolicExpressionTree tree) {
52      var interpreter = Content.Model.Interpreter;
53      var rows = Content.ProblemData.TrainingIndices;
54      var dataset = Content.ProblemData.Dataset;
55      var targetVariable = Content.ProblemData.TargetVariable;
56      var targetValues = dataset.GetDoubleValues(targetVariable, rows);
57      var originalOutput = interpreter.GetSymbolicExpressionTreeValues(tree, dataset, rows).ToArray();
58
59      var impactAndReplacementValues = new Dictionary<ISymbolicExpressionTreeNode, Tuple<double, double>>();
60      List<ISymbolicExpressionTreeNode> nodes = tree.Root.GetSubtree(0).GetSubtree(0).IterateNodesPostfix().ToList();
61      OnlineCalculatorError errorState;
62      double originalR2 = OnlinePearsonsRSquaredCalculator.Calculate(targetValues, originalOutput, out errorState);
63      if (errorState != OnlineCalculatorError.None) originalR2 = 0.0;
64
65      foreach (ISymbolicExpressionTreeNode node in nodes) {
66        var parent = node.Parent;
67        constantNode.Value = CalculateReplacementValue(node, tree);
68        ISymbolicExpressionTreeNode replacementNode = constantNode;
69        SwitchNode(parent, node, replacementNode);
70        var newOutput = interpreter.GetSymbolicExpressionTreeValues(tree, dataset, rows);
71        double newR2 = OnlinePearsonsRSquaredCalculator.Calculate(targetValues, newOutput, out errorState);
72        if (errorState != OnlineCalculatorError.None) newR2 = 0.0;
73
74        // impact = 0 if no change
75        // impact < 0 if new solution is better
76        // impact > 0 if new solution is worse
77        double impact = originalR2 - newR2;
78        impactAndReplacementValues[node] = new Tuple<double, double>(impact, constantNode.Value);
79        SwitchNode(parent, replacementNode, node);
80      }
81      return impactAndReplacementValues;
82    }
83
84    protected override void UpdateModel(ISymbolicExpressionTree tree) {
85      var model = new SymbolicTimeSeriesPrognosisModel(tree, Content.Model.Interpreter);
86      model.Scale(Content.ProblemData);
87      Content.Model = model;
88    }
89
90    protected override Dictionary<ISymbolicExpressionTreeNode, double> CalculateReplacementValues(ISymbolicExpressionTree tree) {
91      var replacementValues = new Dictionary<ISymbolicExpressionTreeNode, double>();
92      foreach (var componentBranch in tree.Root.GetSubtree(0).Subtrees)
93        foreach (ISymbolicExpressionTreeNode node in componentBranch.IterateNodesPrefix()) {
94          replacementValues[node] = CalculateReplacementValue(node, tree);
95        }
96      return replacementValues;
97    }
98
99    protected override Dictionary<ISymbolicExpressionTreeNode, double> CalculateImpactValues(ISymbolicExpressionTree tree) {
100      var impactAndReplacementValues = CalculateImpactAndReplacementValues(tree);
101      return impactAndReplacementValues.ToDictionary(x => x.Key, x => x.Value.Item1); // item1 of the tuple is the impact value
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      var allPrognosedValues = interpreter.GetSymbolicExpressionTreeValues(tempTree, Content.ProblemData.Dataset, rows);
117
118      return allPrognosedValues.Median();
119    }
120
121
122    private void SwitchNode(ISymbolicExpressionTreeNode root, ISymbolicExpressionTreeNode oldBranch, ISymbolicExpressionTreeNode newBranch) {
123      for (int i = 0; i < root.SubtreeCount; i++) {
124        if (root.GetSubtree(i) == oldBranch) {
125          root.RemoveSubtree(i);
126          root.InsertSubtree(i, newBranch);
127          return;
128        }
129      }
130    }
131
132    protected override void btnOptimizeConstants_Click(object sender, EventArgs e) {
133      throw new NotImplementedException();
134    }
135  }
136}
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