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source: trunk/sources/HeuristicLab.GP.StructureIdentification/Evaluators/GPEvaluatorBase.cs @ 1109

Last change on this file since 1109 was 712, checked in by gkronber, 16 years ago

fixed a stupid mistake introduced with r702 #328 (GP evaluation doesn't work in a thread parallel engine).

File size: 4.8 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2008 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 System.Text;
26using HeuristicLab.Core;
27using HeuristicLab.Data;
28using HeuristicLab.Operators;
29using HeuristicLab.DataAnalysis;
30
31namespace HeuristicLab.GP.StructureIdentification {
32  public abstract class GPEvaluatorBase : OperatorBase {
33    public GPEvaluatorBase()
34      : base() {
35      AddVariableInfo(new VariableInfo("FunctionTree", "The function tree that should be evaluated", typeof(IFunctionTree), VariableKind.In));
36      AddVariableInfo(new VariableInfo("TreeSize", "Size (number of nodes) of the tree to evaluate", typeof(IntData), VariableKind.In));
37      AddVariableInfo(new VariableInfo("Dataset", "Dataset with all samples on which to apply the function", typeof(Dataset), VariableKind.In));
38      AddVariableInfo(new VariableInfo("TargetVariable", "Index of the column of the dataset that holds the target variable", typeof(IntData), VariableKind.In));
39      AddVariableInfo(new VariableInfo("PunishmentFactor", "Punishment factor for invalid estimations", typeof(DoubleData), VariableKind.In));
40      AddVariableInfo(new VariableInfo("TotalEvaluatedNodes", "Number of evaluated nodes", typeof(DoubleData), VariableKind.In | VariableKind.Out));
41      AddVariableInfo(new VariableInfo("SamplesStart", "Start index of samples in dataset to evaluate", typeof(IntData), VariableKind.In));
42      AddVariableInfo(new VariableInfo("SamplesEnd", "End index of samples in dataset to evaluate", typeof(IntData), VariableKind.In));
43      AddVariableInfo(new VariableInfo("UseEstimatedTargetValue", "Wether to use the original (measured) or the estimated (calculated) value for the targat variable when doing autoregressive modelling", typeof(BoolData), VariableKind.In));
44    }
45
46    public override IOperation Apply(IScope scope) {
47      // get all variable values
48      int targetVariable = GetVariableValue<IntData>("TargetVariable", scope, true).Data;
49      Dataset dataset = GetVariableValue<Dataset>("Dataset", scope, true);
50      BakedFunctionTree functionTree = GetVariableValue<BakedFunctionTree>("FunctionTree", scope, true);
51      double punishmentFactor = GetVariableValue<DoubleData>("PunishmentFactor", scope, true).Data;
52      int treeSize = scope.GetVariableValue<IntData>("TreeSize", false).Data;
53      double totalEvaluatedNodes = scope.GetVariableValue<DoubleData>("TotalEvaluatedNodes", true).Data;
54      int start = GetVariableValue<IntData>("SamplesStart", scope, true).Data;
55      int end = GetVariableValue<IntData>("SamplesEnd", scope, true).Data;
56      bool useEstimatedValues = GetVariableValue<BoolData>("UseEstimatedTargetValue", scope, true).Data;
57      double[] backupValues = null;
58      // prepare for autoregressive modelling by saving the original values of the target-variable to a backup array
59      if (useEstimatedValues &&
60        (backupValues == null || backupValues.Length != end - start)) {
61        backupValues = new double[end - start];
62        for (int i = start; i < end; i++) {
63          backupValues[i - start] = dataset.GetValue(i, targetVariable);
64        }
65      }
66
67      // initialize and reset the evaluator
68      BakedTreeEvaluator evaluator = new BakedTreeEvaluator();
69      evaluator.ResetEvaluator(functionTree, dataset, targetVariable, start, end, punishmentFactor);
70
71      Evaluate(scope, evaluator, dataset, targetVariable, start, end, useEstimatedValues);
72
73      // restore the values of the target variable from the backup array if necessary
74      if (useEstimatedValues) {
75        for (int i = start; i < end; i++) {
76          dataset.SetValue(i, targetVariable, backupValues[i - start]);
77        }
78      }
79
80      // update the value of total evaluated nodes
81      scope.GetVariableValue<DoubleData>("TotalEvaluatedNodes", true).Data = totalEvaluatedNodes + treeSize * (end - start);
82      return null;
83    }
84
85    public abstract void Evaluate(IScope scope, BakedTreeEvaluator evaluator, Dataset dataset, int targetVariable, int start, int end, bool updateTargetValues);
86  }
87}
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