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 |
|
---|
22 | using System;
|
---|
23 | using System.Collections.Generic;
|
---|
24 | using System.Linq;
|
---|
25 | using System.Text;
|
---|
26 | using HeuristicLab.Core;
|
---|
27 | using HeuristicLab.Data;
|
---|
28 | using HeuristicLab.GP.Interfaces;
|
---|
29 | using HeuristicLab.Modeling;
|
---|
30 | using HeuristicLab.DataAnalysis;
|
---|
31 |
|
---|
32 | namespace HeuristicLab.GP.StructureIdentification {
|
---|
33 | public class PredictorBuilder : OperatorBase {
|
---|
34 | public PredictorBuilder()
|
---|
35 | : base() {
|
---|
36 | AddVariableInfo(new VariableInfo("FunctionTree", "The function tree", typeof(IGeneticProgrammingModel), VariableKind.In));
|
---|
37 | AddVariableInfo(new VariableInfo("TreeEvaluator", "The tree evaluator used to evaluate the model", typeof(ITreeEvaluator), VariableKind.In));
|
---|
38 | AddVariableInfo(new VariableInfo("PunishmentFactor", "The punishment factor limits the estimated values to a certain range", typeof(DoubleData), VariableKind.In));
|
---|
39 | AddVariableInfo(new VariableInfo("Dataset", "The dataset", typeof(Dataset), VariableKind.In));
|
---|
40 | AddVariableInfo(new VariableInfo("TrainingSamplesStart", "Start index of training set", typeof(DoubleData), VariableKind.In));
|
---|
41 | AddVariableInfo(new VariableInfo("TrainingSamplesEnd", "End index of training set", typeof(DoubleData), VariableKind.In));
|
---|
42 | AddVariableInfo(new VariableInfo("TargetVariable", "Index of the target variable", typeof(IntData), VariableKind.In));
|
---|
43 | AddVariableInfo(new VariableInfo("Predictor", "The predictor combines the function tree and the evaluator and can be used to generate estimated values", typeof(IPredictor), VariableKind.New));
|
---|
44 | }
|
---|
45 |
|
---|
46 | public override string Description {
|
---|
47 | get { return "Extracts the function tree and the tree evaluator and combines them to a predictor for the model analyzer."; }
|
---|
48 | }
|
---|
49 |
|
---|
50 | public override IOperation Apply(IScope scope) {
|
---|
51 | IGeneticProgrammingModel model = GetVariableValue<IGeneticProgrammingModel>("FunctionTree", scope, true);
|
---|
52 | ITreeEvaluator evaluator = (ITreeEvaluator)GetVariableValue<ITreeEvaluator>("TreeEvaluator", scope, true).Clone();
|
---|
53 | double punishmentFactor = GetVariableValue<DoubleData>("PunishmentFactor", scope, true).Data;
|
---|
54 | Dataset dataset = GetVariableValue<Dataset>("Dataset", scope, true);
|
---|
55 | int start = GetVariableValue<IntData>("TrainingSamplesStart", scope, true).Data;
|
---|
56 | int end = GetVariableValue<IntData>("TrainingSamplesEnd", scope, true).Data;
|
---|
57 | int targetVariable = GetVariableValue<IntData>("TargetVariable", scope, true).Data;
|
---|
58 | IPredictor predictor = CreatePredictor(model, evaluator, punishmentFactor, dataset, targetVariable, start, end);
|
---|
59 | scope.AddVariable(new HeuristicLab.Core.Variable(scope.TranslateName("Predictor"), predictor));
|
---|
60 | return null;
|
---|
61 | }
|
---|
62 |
|
---|
63 | public static IPredictor CreatePredictor(IGeneticProgrammingModel model, ITreeEvaluator evaluator, double punishmentFactor,
|
---|
64 | Dataset dataset, int targetVariable, int start, int end) {
|
---|
65 | double mean = dataset.GetMean(targetVariable, start, end);
|
---|
66 | double range = dataset.GetRange(targetVariable, start, end);
|
---|
67 | double minEstimatedValue = mean - punishmentFactor * range;
|
---|
68 | double maxEstimatedValue = mean + punishmentFactor * range;
|
---|
69 | return new Predictor(evaluator, model, minEstimatedValue, maxEstimatedValue);
|
---|
70 | }
|
---|
71 |
|
---|
72 | public static IPredictor CreatePredictor(IGeneticProgrammingModel model, ITreeEvaluator evaluator, double punishmentFactor,
|
---|
73 | Dataset dataset, string targetVariable, int start, int end) {
|
---|
74 | return CreatePredictor(model, evaluator, punishmentFactor, dataset, dataset.GetVariableIndex(targetVariable), start, end);
|
---|
75 | }
|
---|
76 | }
|
---|
77 | }
|
---|