[651] | 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.Operators;
|
---|
| 29 | using HeuristicLab.DataAnalysis;
|
---|
| 30 |
|
---|
| 31 | namespace 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
|
---|
[702] | 48 | int targetVariable = GetVariableValue<IntData>("TargetVariable", scope, true).Data;
|
---|
| 49 | Dataset dataset = GetVariableValue<Dataset>("Dataset", scope, true);
|
---|
[651] | 50 | BakedFunctionTree functionTree = GetVariableValue<BakedFunctionTree>("FunctionTree", scope, true);
|
---|
[702] | 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;
|
---|
[651] | 54 | int start = GetVariableValue<IntData>("SamplesStart", scope, true).Data;
|
---|
| 55 | int end = GetVariableValue<IntData>("SamplesEnd", scope, true).Data;
|
---|
[702] | 56 | bool useEstimatedValues = GetVariableValue<BoolData>("UseEstimatedTargetValue", scope, true).Data;
|
---|
| 57 | double[] backupValues = null;
|
---|
[651] | 58 | // prepare for autoregressive modelling by saving the original values of the target-variable to a backup array
|
---|
[712] | 59 | if (useEstimatedValues &&
|
---|
| 60 | (backupValues == null || backupValues.Length != end - start)) {
|
---|
[651] | 61 | backupValues = new double[end - start];
|
---|
[712] | 62 | for (int i = start; i < end; i++) {
|
---|
[651] | 63 | backupValues[i - start] = dataset.GetValue(i, targetVariable);
|
---|
| 64 | }
|
---|
| 65 | }
|
---|
| 66 |
|
---|
| 67 | // initialize and reset the evaluator
|
---|
[702] | 68 | BakedTreeEvaluator evaluator = new BakedTreeEvaluator();
|
---|
| 69 | evaluator.ResetEvaluator(functionTree, dataset, targetVariable, start, end, punishmentFactor);
|
---|
[651] | 70 |
|
---|
[702] | 71 | Evaluate(scope, evaluator, dataset, targetVariable, start, end, useEstimatedValues);
|
---|
[651] | 72 |
|
---|
| 73 | // restore the values of the target variable from the backup array if necessary
|
---|
[712] | 74 | if (useEstimatedValues) {
|
---|
| 75 | for (int i = start; i < end; i++) {
|
---|
[702] | 76 | dataset.SetValue(i, targetVariable, backupValues[i - start]);
|
---|
| 77 | }
|
---|
[651] | 78 | }
|
---|
| 79 |
|
---|
[702] | 80 | // update the value of total evaluated nodes
|
---|
[712] | 81 | scope.GetVariableValue<DoubleData>("TotalEvaluatedNodes", true).Data = totalEvaluatedNodes + treeSize * (end - start);
|
---|
[702] | 82 | return null;
|
---|
[651] | 83 | }
|
---|
| 84 |
|
---|
[702] | 85 | public abstract void Evaluate(IScope scope, BakedTreeEvaluator evaluator, Dataset dataset, int targetVariable, int start, int end, bool updateTargetValues);
|
---|
[651] | 86 | }
|
---|
| 87 | }
|
---|