#region License Information
/* HeuristicLab
* Copyright (C) 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.Linq;
using System.Threading;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Parameters;
using HeuristicLab.Problems.DataAnalysis;
using HEAL.Attic;
namespace HeuristicLab.Algorithms.DataAnalysis {
[StorableType("F34A0ED9-2CF6-4DEE-850D-08790663B66D")]
[Item("ComplexLeaf", "A leaf type that uses an arbitrary RegressionAlgorithm to create leaf models")]
public sealed class ComplexLeaf : LeafBase {
public const string RegressionParameterName = "Regression";
public IValueParameter> RegressionParameter {
get { return (IValueParameter>)Parameters[RegressionParameterName]; }
}
public IDataAnalysisAlgorithm Regression {
get { return RegressionParameter.Value; }
set { RegressionParameter.Value = value; }
}
#region Constructors & Cloning
[StorableConstructor]
private ComplexLeaf(StorableConstructorFlag _) : base(_) { }
private ComplexLeaf(ComplexLeaf original, Cloner cloner) : base(original, cloner) { }
public ComplexLeaf() {
var regression = new KernelRidgeRegression();
Parameters.Add(new ValueParameter>(RegressionParameterName, "The algorithm creating RegressionModels", regression));
}
public override IDeepCloneable Clone(Cloner cloner) {
return new ComplexLeaf(this, cloner);
}
#endregion
#region IModelType
public override bool ProvidesConfidence {
get { return false; }
}
public override IRegressionModel Build(IRegressionProblemData pd, IRandom random, CancellationToken cancellationToken, out int noParameters) {
if (pd.Dataset.Rows < MinLeafSize(pd)) throw new ArgumentException("The number of training instances is too small to create a linear model");
noParameters = pd.Dataset.Rows + 1;
Regression.Problem = new RegressionProblem { ProblemData = pd };
var res = RegressionTreeUtilities.RunSubAlgorithm(Regression, random.Next(), cancellationToken);
var t = res.Select(x => x.Value).OfType().FirstOrDefault();
if (t == null) throw new ArgumentException("No RegressionSolution was provided by the algorithm");
return t.Model;
}
public override int MinLeafSize(IRegressionProblemData pd) {
return 3;
}
#endregion
}
}