#region License Information
/* HeuristicLab
* Copyright (C) 2002-2013 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.Data;
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 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 mt = new MersenneTwister();
var xGenerator = new NormalDistributedRandom(mt, 0, 1);
var weightGenerator = new UniformDistributedRandom(mt, 0, 10);
return (from size in sizes
from p in pp
from noiseRatio in noiseRatios
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;
}
}
}