source: trunk/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression.Views/3.4/SymbolicRegressionSolutionErrorCharacteristicsCurveView.cs @ 17976

Last change on this file since 17976 was 17976, checked in by mkommend, 14 months ago

#3125: Added error handling to ECC View and minor code improvements.

File size: 5.1 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 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;
24using System.Collections.Generic;
25using System.Linq;
26using HeuristicLab.Algorithms.DataAnalysis;
27using HeuristicLab.MainForm;
28using HeuristicLab.PluginInfrastructure;
29using HeuristicLab.Problems.DataAnalysis.Views;
30
31namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression.Views {
32  [View("Error Characteristics Curve")]
33  [Content(typeof(ISymbolicRegressionSolution))]
34  public partial class SymbolicRegressionSolutionErrorCharacteristicsCurveView : RegressionSolutionErrorCharacteristicsCurveView {
35    public SymbolicRegressionSolutionErrorCharacteristicsCurveView() {
36      InitializeComponent();
37    }
38
39    public new ISymbolicRegressionSolution Content {
40      get { return (ISymbolicRegressionSolution)base.Content; }
41      set { base.Content = value; }
42    }
43
44    private IRegressionSolution CreateLinearRegressionSolution() {
45      if (Content == null) throw new InvalidOperationException();
46      var problemData = (IRegressionProblemData)ProblemData.Clone();
47      if (!problemData.TrainingIndices.Any()) return null; // don't create an LR model if the problem does not have a training set (e.g. loaded into an existing model)
48
49      var usedVariables = Content.Model.VariablesUsedForPrediction;
50
51      var usedDoubleVariables = usedVariables
52        .Where(name => problemData.Dataset.VariableHasType<double>(name))
53      .Distinct();
54
55      var usedFactorVariables = usedVariables
56        .Where(name => problemData.Dataset.VariableHasType<string>(name))
57        .Distinct();
58
59      // gkronber: for binary factors we actually produce a binary variable in the new dataset
60      // but only if the variable is not used as a full factor anyway (LR creates binary columns anyway)
61      var usedBinaryFactors =
62        Content.Model.SymbolicExpressionTree.IterateNodesPostfix().OfType<BinaryFactorVariableTreeNode>()
63        .Where(node => !usedFactorVariables.Contains(node.VariableName))
64        .Select(node => Tuple.Create(node.VariableValue, node.VariableValue));
65
66      // create a new problem and dataset
67      var variableNames =
68        usedDoubleVariables
69        .Concat(usedFactorVariables)
70        .Concat(usedBinaryFactors.Select(t => t.Item1 + "=" + t.Item2))
71        .Concat(new string[] { problemData.TargetVariable })
72        .ToArray();
73      var variableValues =
74        usedDoubleVariables.Select(name => (IList)problemData.Dataset.GetDoubleValues(name).ToList())
75        .Concat(usedFactorVariables.Select(name => problemData.Dataset.GetStringValues(name).ToList()))
76        .Concat(
77          // create binary variable
78          usedBinaryFactors.Select(t => problemData.Dataset.GetReadOnlyStringValues(t.Item1).Select(val => val == t.Item2 ? 1.0 : 0.0).ToList())
79        )
80        .Concat(new[] { problemData.Dataset.GetDoubleValues(problemData.TargetVariable).ToList() });
81
82      var newDs = new Dataset(variableNames, variableValues);
83      var newProblemData = new RegressionProblemData(newDs, variableNames.Take(variableNames.Length - 1), variableNames.Last());
84      newProblemData.TrainingPartition.Start = problemData.TrainingPartition.Start;
85      newProblemData.TrainingPartition.End = problemData.TrainingPartition.End;
86      newProblemData.TestPartition.Start = problemData.TestPartition.Start;
87      newProblemData.TestPartition.End = problemData.TestPartition.End;
88
89      try {
90        var solution = LinearRegression.CreateSolution(newProblemData, out _, out _);
91        solution.Name = "Baseline (linear subset)";
92        return solution;
93      } catch (NotSupportedException e) {
94        ErrorHandling.ShowErrorDialog("Could not create a linear regression solution.", e);
95      } catch (ArgumentException e) {
96        ErrorHandling.ShowErrorDialog("Could not create a linear regression solution.", e);
97      }
98      return null;
99    }
100
101
102    protected override IEnumerable<IRegressionSolution> CreateBaselineSolutions() {
103      foreach (var sol in base.CreateBaselineSolutions()) yield return sol;
104
105      // does not support lagged variables
106      if (Content.Model.SymbolicExpressionTree.IterateNodesPrefix().OfType<LaggedVariableTreeNode>().Any()) yield break;
107
108      var linearRegressionSolution = CreateLinearRegressionSolution();
109      if (linearRegressionSolution != null) yield return linearRegressionSolution;
110    }
111  }
112}
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