#region License Information /* HeuristicLab * Copyright (C) 2002-2017 Heuristic and Evolutionary Algorithms Laboratory (HEAL) * * This file is part of HeuristicLab. * * HeuristicLab is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * HeuristicLab is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with HeuristicLab. If not, see . */ #endregion using System.Linq; using System.Threading; using HeuristicLab.Algorithms.DataAnalysis; using HeuristicLab.Algorithms.OffspringSelectionGeneticAlgorithm; using HeuristicLab.Common; using HeuristicLab.Core; using HeuristicLab.Core.Networks; using HeuristicLab.Data; using HeuristicLab.Encodings.BinaryVectorEncoding; using HeuristicLab.Optimization; using HeuristicLab.Persistence.Default.CompositeSerializers.Storable; namespace HeuristicLab.Networks.IntegratedOptimization.MachineLearning { [Item("Feature Selection Network 2", "")] [Creatable("Optimization Networks")] [StorableClass] public sealed class FeatureSelectionNetwork : Network { [StorableConstructor] private FeatureSelectionNetwork(bool deserializing) : base(deserializing) { } private FeatureSelectionNetwork(FeatureSelectionNetwork original, Cloner cloner) : base(original, cloner) { } public override IDeepCloneable Clone(Cloner cloner) { return new FeatureSelectionNetwork(this, cloner); } [Storable] private readonly OrchestratedAlgorithmNode FeatureSelectionAlgorithmNode; [Storable] private readonly OrchestratedAlgorithmNode RegressionAlgorithmNode; [Storable] private readonly FeatureSelectionOrchestrator Orchestrator; public FeatureSelectionNetwork() : base() { Orchestrator = new FeatureSelectionOrchestrator(this); Nodes.Add(Orchestrator); var featureSelectionAlgorithm = CreateFeatureSelectionAlgorithm(); //TODO configure FeatureSelectionProblem FeatureSelectionAlgorithmNode = new OrchestratedAlgorithmNode("Feature Selection"); FeatureSelectionAlgorithmNode.Algorithm = featureSelectionAlgorithm; FeatureSelectionAlgorithmNode.EvaluationPort.ConnectedPort = Orchestrator.FeatureSelectionEvaluationPort; FeatureSelectionAlgorithmNode.EvaluationPort.CloneParametersFromPort(Orchestrator.FeatureSelectionEvaluationPort); Nodes.Add(FeatureSelectionAlgorithmNode); var regressionAlgorithm = CreateRegressionAlgorithm(); //TODO set empty regression problem RegressionAlgorithmNode = new OrchestratedAlgorithmNode("Regression"); RegressionAlgorithmNode.Algorithm = regressionAlgorithm; Orchestrator.RegressionOrchestrationPort.ConnectedPort = RegressionAlgorithmNode.OrchestrationPort; RegressionAlgorithmNode.OrchestrationPort.CloneParametersFromPort(Orchestrator.RegressionOrchestrationPort); Nodes.Add(RegressionAlgorithmNode); } private IAlgorithm CreateFeatureSelectionAlgorithm() { var problem = new OrchestratedBinaryProblem(Orchestrator, 0, false); problem.Encoding.SolutionCreator = new RandomBinaryVectorCreator() { TrueProbability = new DoubleValue(0.2) }; var osga = new OffspringSelectionGeneticAlgorithm(); osga.Problem = problem; osga.PopulationSize.Value = 100; osga.ComparisonFactorLowerBound.Value = 1; osga.ComparisonFactorUpperBound.Value = 1; osga.SuccessRatio.Value = 1.0; osga.MutationProbability.Value = 0.15; osga.Mutator = osga.MutatorParameter.ValidValues.OfType().First(); return osga; } private static IAlgorithm CreateRegressionAlgorithm() { var linreg = new LinearRegression(); return linreg; } public void Prepare() { Prepare(false); } public void Prepare(bool clearRuns) { var msg = FeatureSelectionAlgorithmNode.OrchestrationPort.PrepareMessage(); if (clearRuns) msg["OrchestrationMessage"] = new EnumValue(OrchestrationMessage.Prepare | OrchestrationMessage.ClearRuns); else msg["OrchestrationMessage"] = new EnumValue(OrchestrationMessage.Prepare); FeatureSelectionAlgorithmNode.OrchestrationPort.ReceiveMessage(msg, new CancellationToken()); } public void Start() { var problem = (OrchestratedBinaryProblem)FeatureSelectionAlgorithmNode.Algorithm.Problem; problem.Encoding.Length = Orchestrator.RegressionProblemData.AllowedInputVariables.Count(); var msg = FeatureSelectionAlgorithmNode.OrchestrationPort.PrepareMessage(); msg["OrchestrationMessage"] = new EnumValue(OrchestrationMessage.Start); FeatureSelectionAlgorithmNode.OrchestrationPort.ReceiveMessage(msg, new CancellationToken()); } public void Pause() { var msg = FeatureSelectionAlgorithmNode.OrchestrationPort.PrepareMessage(); msg["OrchestrationMessage"] = new EnumValue(OrchestrationMessage.Pause); FeatureSelectionAlgorithmNode.OrchestrationPort.ReceiveMessage(msg, new CancellationToken()); } public void Stop() { var msg = FeatureSelectionAlgorithmNode.OrchestrationPort.PrepareMessage(); msg["OrchestrationMessage"] = new EnumValue(OrchestrationMessage.Stop); FeatureSelectionAlgorithmNode.OrchestrationPort.ReceiveMessage(msg, new CancellationToken()); ; } } }