#region License Information
/* HeuristicLab
* Copyright (C) 2002-2016 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 HeuristicLab.Analysis;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Optimization;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using System;
using System.Collections.Generic;
using System.Linq;
using HEAL.Attic;
namespace HeuristicLab.OptimizationExpertSystem.Common {
[Item("Overall Best Recommender", "")]
[StorableType("CCB14209-4C6F-4C72-A4D9-F803D2232CFB")]
public class OverallBestRecommender : ParameterizedNamedItem, IAlgorithmInstanceRecommender {
private IFixedValueParameter NeighborhoodFactorParameter {
get { return (IFixedValueParameter)Parameters["NeighborhoodFactor"]; }
}
[StorableConstructor]
private OverallBestRecommender(StorableConstructorFlag _) : base(_) { }
private OverallBestRecommender(OverallBestRecommender original, Cloner cloner) : base(original, cloner) { }
public OverallBestRecommender() { }
public override IDeepCloneable Clone(Cloner cloner) {
return new OverallBestRecommender(this, cloner);
}
public IRecommendationModel TrainModel(IRun[] problemInstances, KnowledgeCenter kc, string[] characteristics) {
var instances = new List>();
foreach (var relevantRuns in kc.GetKnowledgeBaseByAlgorithm()) {
var algorithm = relevantRuns.Key;
var pis = relevantRuns.Value.Select(x => ((StringValue)x.Parameters["Problem Name"]).Value).Distinct()
.Select(x => Tuple.Create(x, problemInstances.SingleOrDefault(y => ((StringValue)y.Parameters["Problem Name"]).Value == x)))
.Where(x => x.Item2 != null)
.Select(x => Tuple.Create(x.Item1, ((DoubleValue)x.Item2.Parameters["BestKnownQuality"]).Value))
.ToDictionary(x => x.Item1, x => x.Item2);
var avgERT = 0.0;
var count = 0;
foreach (var problemRuns in relevantRuns.Value.GroupBy(x => ((StringValue)x.Parameters["Problem Name"]).Value)) {
double bkq;
if (!pis.TryGetValue(problemRuns.Key, out bkq)) continue;
var ert = ExpectedRuntimeHelper.CalculateErt(problemRuns.ToList(), "QualityPerEvaluations", kc.GetTarget(bkq, kc.MinimumTarget.Value, kc.Maximization), kc.Maximization).ExpectedRuntime;
if (double.IsInfinity(ert)) ert = int.MaxValue;
avgERT += ert;
count++;
}
avgERT /= count;
instances.Add(new KeyValuePair(algorithm, avgERT));
}
return new FixedRankModel(instances.OrderBy(x => x.Value));
}
}
}