source: branches/2883_GBTModelStorage/HeuristicLab.Algorithms.DataAnalysis/3.4/GradientBoostedTrees/GradientBoostedTreesModel.cs @ 15678

#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2018 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
 * and the BEACON Center for the Study of Evolution in Action.
 * 
 * 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 System;
using System.Collections.Generic;
using System.Linq;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.Problems.DataAnalysis;

namespace HeuristicLab.Algorithms.DataAnalysis {
  [StorableClass]
  [Item("Gradient boosted trees model", "")]
  // this is essentially a collection of weighted regression models
  public sealed class GradientBoostedTreesModel : RegressionModel, IGradientBoostedTreesModel {
    // BackwardsCompatibility3.4 for allowing deserialization & serialization of old models
    #region Backwards compatible code, remove with 3.5
    private bool isCompatibilityLoaded = false; // only set to true if the model is deserialized from the old format, needed to make sure that information is serialized again if it was loaded from the old format
    internal bool IsCompatibilityLoaded { get { return this.isCompatibilityLoaded; } set { this.isCompatibilityLoaded = value; } }

    [Storable(Name = "models")]
    private IList<IRegressionModel> __persistedModels {
      set {
        this.isCompatibilityLoaded = true;
        this.models.Clear();
        foreach (var m in value) this.models.Add(m);
      }
      get { if (this.isCompatibilityLoaded) return models; else return null; }
    }
    [Storable(Name = "weights")]
    private IList<double> __persistedWeights {
      set {
        this.isCompatibilityLoaded = true;
        this.weights.Clear();
        foreach (var w in value) this.weights.Add(w);
      }
      get { if (this.isCompatibilityLoaded) return weights; else return null; }
    }
    #endregion

    public override IEnumerable<string> VariablesUsedForPrediction {
      get { return models.SelectMany(x => x.VariablesUsedForPrediction).Distinct().OrderBy(x => x); }
    }

    private readonly IList<IRegressionModel> models;
    public IEnumerable<IRegressionModel> Models { get { return models; } }

    private readonly IList<double> weights;
    public IEnumerable<double> Weights { get { return weights; } }

    [StorableConstructor]
    private GradientBoostedTreesModel(bool deserializing)
      : base(deserializing) {
      models = new List<IRegressionModel>();
      weights = new List<double>();
    }
    private GradientBoostedTreesModel(GradientBoostedTreesModel original, Cloner cloner)
      : base(original, cloner) {
      this.weights = new List<double>(original.weights);
      this.models = new List<IRegressionModel>(original.models.Select(m => cloner.Clone(m)));
      this.isCompatibilityLoaded = original.isCompatibilityLoaded;
    }
    [Obsolete("The constructor of GBTModel should not be used directly anymore (use GBTModelSurrogate instead)")]
    internal GradientBoostedTreesModel(IEnumerable<IRegressionModel> models, IEnumerable<double> weights)
      : base(string.Empty, "Gradient boosted tree model", string.Empty) {
      this.models = new List<IRegressionModel>(models);
      this.weights = new List<double>(weights);

      if (this.models.Count != this.weights.Count) throw new ArgumentException();
    }

    public override IDeepCloneable Clone(Cloner cloner) {
      return new GradientBoostedTreesModel(this, cloner);
    }

    public override IEnumerable<double> GetEstimatedValues(IDataset dataset, IEnumerable<int> rows) {
      // allocate target array go over all models and add up weighted estimation for each row
      if (!rows.Any()) return Enumerable.Empty<double>(); // return immediately if rows is empty. This prevents multiple iteration over lazy rows enumerable.
      // (which essentially looks up indexes in a dictionary)
      var res = new double[rows.Count()];
      for (int i = 0; i < models.Count; i++) {
        var w = weights[i];
        var m = models[i];
        int r = 0;
        foreach (var est in m.GetEstimatedValues(dataset, rows)) {
          res[r++] += w * est;
        }
      }
      return res;
    }

    public override IRegressionSolution CreateRegressionSolution(IRegressionProblemData problemData) {
      return new RegressionSolution(this, (IRegressionProblemData)problemData.Clone());
    }

  }
}