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source: trunk/sources/HeuristicLab.GP.StructureIdentification/3.3/Evaluators/GPEvaluatorBase.cs @ 2568

Last change on this file since 2568 was 2440, checked in by gkronber, 15 years ago

Fixed #784 (ProblemInjector should be changed to read variable names instead of indexes for input and target variables)

File size: 4.6 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 HeuristicLab.Core;
23using HeuristicLab.Data;
24using HeuristicLab.DataAnalysis;
25using HeuristicLab.GP.Interfaces;
26
27namespace HeuristicLab.GP.StructureIdentification {
28  public abstract class GPEvaluatorBase : OperatorBase {
29    public GPEvaluatorBase()
30      : base() {
31      AddVariableInfo(new VariableInfo("TreeEvaluator", "The evaluator that should be used to evaluate the expression tree", typeof(ITreeEvaluator), VariableKind.In));
32      AddVariableInfo(new VariableInfo("FunctionTree", "The function tree that should be evaluated", typeof(IGeneticProgrammingModel), VariableKind.In));
33      AddVariableInfo(new VariableInfo("Dataset", "Dataset with all samples on which to apply the function", typeof(Dataset), VariableKind.In));
34      AddVariableInfo(new VariableInfo("TargetVariable", "Name of the target variable", typeof(StringData), VariableKind.In));
35      AddVariableInfo(new VariableInfo("TotalEvaluatedNodes", "Number of evaluated nodes", typeof(DoubleData), VariableKind.In | VariableKind.Out));
36      AddVariableInfo(new VariableInfo("SamplesStart", "Start index of samples in dataset to evaluate", typeof(IntData), VariableKind.In));
37      AddVariableInfo(new VariableInfo("SamplesEnd", "End index of samples in dataset to evaluate", typeof(IntData), VariableKind.In));
38      AddVariableInfo(new VariableInfo("UseEstimatedTargetValue", "(optional) Wether to use the original (measured) or the estimated (calculated) value for the target variable for autoregressive modelling", typeof(BoolData), VariableKind.In));
39    }
40
41    public override IOperation Apply(IScope scope) {
42      // get all variable values
43      Dataset dataset = GetVariableValue<Dataset>("Dataset", scope, true);
44      int targetVariable = dataset.GetVariableIndex(GetVariableValue<StringData>("TargetVariable", scope, true).Data);
45      IGeneticProgrammingModel gpModel = GetVariableValue<IGeneticProgrammingModel>("FunctionTree", scope, true);
46      double totalEvaluatedNodes = scope.GetVariableValue<DoubleData>("TotalEvaluatedNodes", true).Data;
47      int start = GetVariableValue<IntData>("SamplesStart", scope, true).Data;
48      int end = GetVariableValue<IntData>("SamplesEnd", scope, true).Data;
49      BoolData useEstimatedValuesData = GetVariableValue<BoolData>("UseEstimatedTargetValue", scope, true, false);
50      bool useEstimatedValues = useEstimatedValuesData == null ? false : useEstimatedValuesData.Data;
51      ITreeEvaluator evaluator = GetVariableValue<ITreeEvaluator>("TreeEvaluator", scope, true);
52      evaluator.PrepareForEvaluation(dataset, gpModel.FunctionTree);
53
54      double[] backupValues = null;
55      // prepare for autoregressive modelling by saving the original values of the target-variable to a backup array
56      if (useEstimatedValues &&
57        (backupValues == null || backupValues.Length != end - start)) {
58        backupValues = new double[end - start];
59        for (int i = start; i < end; i++) {
60          backupValues[i - start] = dataset.GetValue(i, targetVariable);
61        }
62      }
63      dataset.FireChangeEvents = false;
64
65      Evaluate(scope, evaluator, dataset, targetVariable, start, end, useEstimatedValues);
66
67      // restore the values of the target variable from the backup array if necessary
68      if (useEstimatedValues) {
69        for (int i = start; i < end; i++) {
70          dataset.SetValue(i, targetVariable, backupValues[i - start]);
71        }
72      }
73      dataset.FireChangeEvents = true;
74      dataset.FireChanged();
75
76      // update the value of total evaluated nodes
77      scope.GetVariableValue<DoubleData>("TotalEvaluatedNodes", true).Data = totalEvaluatedNodes + gpModel.Size * (end - start);
78      return null;
79    }
80
81    public abstract void Evaluate(IScope scope, ITreeEvaluator evaluator, Dataset dataset, int targetVariable, int start, int end, bool updateTargetValues);
82  }
83}
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