source: branches/PerformanceComparison/HeuristicLab.OptimizationExpertSystem.Common/3.3/Recommenders/KNearestNeighborModel.cs @ 14691

#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 <http://www.gnu.org/licenses/>.
 */
#endregion

using HeuristicLab.Collections;
using HeuristicLab.Common;
using HeuristicLab.Optimization;
using System;
using System.Collections.Generic;
using System.Linq;

namespace HeuristicLab.OptimizationExpertSystem.Common {
  public class KNearestNeighborModel : IRecommendationModel {
    private readonly int K;
    private readonly string[] characteristics;
    private readonly Dictionary<IRun, Dictionary<IAlgorithm, double>> performance;
    private readonly BidirectionalDictionary<int, IRun> problemInstanceMap; 
    private readonly double[] medianValues;

    public KNearestNeighborModel(int k, Dictionary<IRun, Dictionary<IAlgorithm, double>> perfData, string[] characteristics) {
      this.K = k;
      this.performance = perfData;
      this.characteristics = characteristics;
      problemInstanceMap = new BidirectionalDictionary<int, IRun>();
      var i = 0;
      foreach (var pi in perfData.Keys) {
        problemInstanceMap.Add(i++, pi);
      }
      this.medianValues = KnowledgeCenter.GetMedianValues(perfData.Keys.OrderBy(problemInstanceMap.GetBySecond).ToArray(), characteristics);
    }

    public IEnumerable<KeyValuePair<IAlgorithm, double>> GetRanking(IRun problemInstance) {
      double[] means, sdevs;
      var features = KnowledgeCenter.GetFeaturesStandardized(performance.Keys.OrderBy(problemInstanceMap.GetBySecond).ToArray(), characteristics, out means, out sdevs, medianValues);
      var feature = KnowledgeCenter.GetFeatures(new [] { problemInstance }, characteristics, medianValues)[0];
      for (var f = 0; f < feature.Length; f++) {
        if (sdevs[f].IsAlmost(0)) feature[f] = 0;
        else feature[f] = (feature[f] - means[f]) / sdevs[f];
      }
      var nearestK = features.Select((f, i) => new { ProblemInstanceIndex = i, Feature = f })
                             .OrderBy(x => x.Feature.Select((f, i) => (f - feature[i]) * (f - feature[i])).Sum());
      
      var performances = new Dictionary<IAlgorithm, Performance>();

      var k = 0;
      foreach (var next in nearestK) {
        if (k >= K) break;
        var perfs = performance[problemInstanceMap.GetByFirst(next.ProblemInstanceIndex)];
        if (perfs.Count == 0) continue;
        
        foreach (var p in perfs) {
          var ert = Math.Pow(10, p.Value);
          Performance perf;
          if (!performances.TryGetValue(p.Key, out perf)) {
            perf = new Performance();
            performances[p.Key] = perf;
          }
          perf.Add(ert);
        }

        k++;
      }

      return performances.Select(x => new { Alg = x.Key, Perf = x.Value.ExpectedRuntime() })
                         .OrderBy(x => x.Perf)
                         .Select(x => new KeyValuePair<IAlgorithm, double>(x.Alg, x.Perf));
    }

    private class Performance {
      private readonly List<double> successful;
      private int runs;
      public int Fails { get { return runs - successful.Count; } }

      public Performance() {
        successful = new List<double>();
      }

      public void Add(double ert) {
        if (!double.IsInfinity(ert)) successful.Add(ert);
        runs++;
      }

      public double ExpectedRuntime() {
        if (successful.Count == 0) return int.MaxValue;
        return successful.Average() / (successful.Count / (double)runs);
      }
    }
  }
}