[1044] | 1 | #region License Information
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| 2 | /* HeuristicLab
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| 3 | * Copyright (C) 2002-2008 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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| 4 | *
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| 5 | * This file is part of HeuristicLab.
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| 6 | *
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| 7 | * HeuristicLab is free software: you can redistribute it and/or modify
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| 8 | * it under the terms of the GNU General Public License as published by
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| 9 | * the Free Software Foundation, either version 3 of the License, or
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| 10 | * (at your option) any later version.
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| 11 | *
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| 12 | * HeuristicLab is distributed in the hope that it will be useful,
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| 13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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| 14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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| 15 | * GNU General Public License for more details.
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| 16 | *
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| 17 | * You should have received a copy of the GNU General Public License
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| 18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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| 19 | */
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| 20 | #endregion
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| 21 |
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| 22 | using System;
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| 23 | using System.Collections.Generic;
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| 24 | using System.Text;
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| 25 | using System.Windows.Forms;
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| 26 | using HeuristicLab.PluginInfrastructure;
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| 27 | using System.Net;
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| 28 | using System.ServiceModel;
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| 29 | using HeuristicLab.CEDMA.DB.Interfaces;
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| 30 | using HeuristicLab.CEDMA.DB;
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| 31 | using System.ServiceModel.Description;
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| 32 | using System.Linq;
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| 33 | using HeuristicLab.CEDMA.Core;
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[1053] | 34 | using HeuristicLab.GP.StructureIdentification;
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| 35 | using HeuristicLab.Data;
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[1060] | 36 | using HeuristicLab.Core;
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[1044] | 37 |
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| 38 | namespace HeuristicLab.CEDMA.Server {
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[1217] | 39 | public abstract class DispatcherBase :IDispatcher {
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| 40 | public enum ModelComplexity { Low, Medium, High };
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| 41 | public enum Algorithm { StandardGP };
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[1044] | 42 |
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| 43 | private IStore store;
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[1217] | 44 | private ModelComplexity[] possibleComplexities = new ModelComplexity[] { ModelComplexity.Low, ModelComplexity.Medium, ModelComplexity.High };
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| 45 | private Dictionary<LearningTask, Algorithm[]> possibleAlgorithms = new Dictionary<LearningTask, Algorithm[]>() {
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| 46 | {LearningTask.Classification, new Algorithm[] {}},
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| 47 | {LearningTask.Regression, new Algorithm[] { Algorithm.StandardGP }},
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| 48 | {LearningTask.TimeSeries, new Algorithm[] { }}
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| 49 | };
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[1044] | 50 |
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[1217] | 51 | public DispatcherBase(IStore store) {
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[1044] | 52 | this.store = store;
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| 53 | }
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| 54 |
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[1217] | 55 | public Execution GetNextJob() {
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[1130] | 56 | // find and select a dataset
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| 57 | var dataSetVar = new HeuristicLab.CEDMA.DB.Interfaces.Variable("DataSet");
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| 58 | var dataSetQuery = new Statement[] {
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| 59 | new Statement(dataSetVar, Ontology.PredicateInstanceOf, Ontology.TypeDataSet)
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| 60 | };
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| 61 |
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[1217] | 62 | Entity[] datasets = store.Query("?DataSet <" + Ontology.PredicateInstanceOf.Uri + "> <" + Ontology.TypeDataSet.Uri + "> .")
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| 63 | .Select(x => (Entity)x.Get("DataSet"))
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| 64 | .ToArray();
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[1130] | 65 |
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| 66 | // no datasets => do nothing
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[1217] | 67 | if (datasets.Length == 0) return null;
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[1130] | 68 |
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[1217] | 69 | Entity dataSetEntity = SelectDataSet(datasets);
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[1130] | 70 | DataSet dataSet = new DataSet(store, dataSetEntity);
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[1217] | 71 |
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| 72 | int targetVariable = SelectTargetVariable(dataSet, dataSet.Problem.AllowedInputVariables.ToArray());
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| 73 | Algorithm selectedAlgorithm = SelectAlgorithm(dataSet, targetVariable, possibleAlgorithms[dataSet.Problem.LearningTask]);
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[1216] | 74 | string targetVariableName = dataSet.Problem.GetVariableName(targetVariable);
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[1217] | 75 | ModelComplexity selectedComplexity = SelectComplexity(dataSet, targetVariable, selectedAlgorithm, possibleComplexities);
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| 76 |
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| 77 | Execution exec = CreateExecution(dataSet.Problem, targetVariable, selectedAlgorithm, selectedComplexity);
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[1216] | 78 | if (exec != null) {
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| 79 | exec.DataSetEntity = dataSetEntity;
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| 80 | exec.TargetVariable = targetVariableName;
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[1130] | 81 | }
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[1217] | 82 | return exec;
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[1044] | 83 | }
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| 84 |
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[1217] | 85 | public abstract Entity SelectDataSet(Entity[] datasets);
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| 86 | public abstract int SelectTargetVariable(DataSet dataSet, int[] targetVariables);
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| 87 | public abstract Algorithm SelectAlgorithm(DataSet dataSet, int targetVariable, Algorithm[] possibleAlgorithms);
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| 88 | public abstract ModelComplexity SelectComplexity(DataSet dataSet, int targetVariable, Algorithm algorithm, ModelComplexity[] possibleComplexities);
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[1060] | 89 |
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[1217] | 90 | private Execution CreateExecution(Problem problem, int targetVariable, Algorithm algorithm, ModelComplexity complexity) {
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| 91 | switch (algorithm) {
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| 92 | case Algorithm.StandardGP: {
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| 93 | return CreateStandardGpExecution(problem, targetVariable, complexity);
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[1060] | 94 | }
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[1217] | 95 | default: {
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| 96 | return null;
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| 97 | }
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[1060] | 98 | }
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| 99 | }
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| 100 |
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[1217] | 101 | private Execution CreateStandardGpExecution(Problem problem, int targetVariable, ModelComplexity complexity) {
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[1060] | 102 | ProblemInjector probInjector = new ProblemInjector(problem);
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[1053] | 103 | probInjector.TargetVariable = targetVariable;
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| 104 | StandardGP sgp = new StandardGP();
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| 105 | sgp.SetSeedRandomly = true;
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[1216] | 106 | sgp.MaxGenerations = 2;
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| 107 | sgp.PopulationSize = 100;
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[1053] | 108 | sgp.Elites = 1;
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| 109 | sgp.ProblemInjector = probInjector;
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[1217] | 110 |
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| 111 | int maxTreeHeight = 10;
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| 112 | int maxTreeSize = 100;
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| 113 | switch (complexity) {
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| 114 | case ModelComplexity.Low: {
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| 115 | maxTreeHeight = 5;
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| 116 | maxTreeSize = 20;
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| 117 | break;
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| 118 | }
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| 119 | case ModelComplexity.Medium: {
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| 120 | maxTreeHeight = 10;
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| 121 | maxTreeSize = 100;
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| 122 | break;
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| 123 | }
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| 124 | case ModelComplexity.High: {
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| 125 | maxTreeHeight = 12;
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| 126 | maxTreeSize = 200;
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| 127 | break;
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| 128 | }
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| 129 | }
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| 130 |
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[1216] | 131 | sgp.MaxTreeHeight = maxTreeHeight;
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| 132 | sgp.MaxTreeSize = maxTreeSize;
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| 133 | Execution exec = new Execution(sgp.Engine);
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[1217] | 134 | exec.Description = "StandardGP - Complexity: " + complexity;
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[1216] | 135 | return exec;
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[1053] | 136 | }
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[1044] | 137 | }
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| 138 | }
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