#region License Information /* HeuristicLab * Copyright (C) 2002-2019 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; using System.Collections.Generic; using System.Linq; using HeuristicLab.Problems.DataAnalysis; using HeuristicLab.Random; namespace HeuristicLab.Problems.Instances.DataAnalysis { public class FeatureSelectionInstanceProvider : ArtificialRegressionInstanceProvider { public override string Name { get { return "Feature Selection Problems"; } } public override string Description { get { return "A set of artificial feature selection benchmark problems"; } } public override Uri WebLink { get { return new Uri("http://dev.heuristiclab.com"); } } public override string ReferencePublication { get { return ""; } } public int Seed { get; private set; } public FeatureSelectionInstanceProvider() : base() { Seed = (int)DateTime.Now.Ticks; } public FeatureSelectionInstanceProvider(int seed) : base() { Seed = seed; } public override IEnumerable GetDataDescriptors() { var sizes = new int[] { 50, 100, 200 }; var pp = new double[] { 0.1, 0.25, 0.5 }; var noiseRatios = new double[] { 0.01, 0.05, 0.1, 0.2 }; var rand = new MersenneTwister((uint)Seed); // use fixed seed for deterministic problem generation return (from size in sizes from p in pp from noiseRatio in noiseRatios let instanceSeed = rand.Next() let mt = new MersenneTwister((uint)instanceSeed) let xGenerator = new NormalDistributedRandom(mt, 0, 1) let weightGenerator = new UniformDistributedRandom(mt, 0, 10) select new FeatureSelection(size, p, noiseRatio, xGenerator, weightGenerator)) .Cast() .ToList(); } public override IRegressionProblemData LoadData(IDataDescriptor descriptor) { var featureSelectionDescriptor = descriptor as FeatureSelection; if (featureSelectionDescriptor == null) throw new ArgumentException("FeatureSelectionInstanceProvider expects an FeatureSelection data descriptor."); // base call generates a regression problem data var regProblemData = base.LoadData(featureSelectionDescriptor); var problemData = new FeatureSelectionRegressionProblemData( regProblemData.Dataset, regProblemData.AllowedInputVariables, regProblemData.TargetVariable, featureSelectionDescriptor.SelectedFeatures, featureSelectionDescriptor.Weights, featureSelectionDescriptor.OptimalRSquared); // copy values from regProblemData to feature selection problem data problemData.Name = regProblemData.Name; problemData.Description = regProblemData.Description; problemData.TrainingPartition.Start = regProblemData.TrainingPartition.Start; problemData.TrainingPartition.End = regProblemData.TrainingPartition.End; problemData.TestPartition.Start = regProblemData.TestPartition.Start; problemData.TestPartition.End = regProblemData.TestPartition.End; return problemData; } } }