source: branches/GBT-trunkintegration/HeuristicLab.Algorithms.DataAnalysis/3.4/GradientBoostedTrees/GradientBoostedTreesAlgorithmStatic.cs @ 12632

#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2015 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.Diagnostics.Contracts;
using System.Linq;
using HeuristicLab.Problems.DataAnalysis;
using HeuristicLab.Random;

namespace HeuristicLab.Algorithms.DataAnalysis {
  public static class GradientBoostedTreesAlgorithmStatic {
    #region static API

    public interface IGbmState {
      IRegressionModel GetModel();
      double GetTrainLoss();
      double GetTestLoss();
      IEnumerable<KeyValuePair<string, double>> GetVariableRelevance();
    }

    // created through factory method
    // GbmState details are private API users can only use methods from IGbmState
    private class GbmState : IGbmState {
      internal IRegressionProblemData problemData { get; set; }
      internal MersenneTwister random { get; private set; }
      internal ILossFunction lossFunction { get; set; }
      internal int maxSize { get; set; }
      internal double nu { get; set; }
      internal double r { get; set; }
      internal double m { get; set; }
      internal readonly RegressionTreeBuilder treeBuilder;


      // array members (allocate only once)
      internal double[] pred;
      internal double[] predTest;
      internal double[] w;
      internal double[] y;
      internal int[] activeIdx;
      internal double[] pseudoRes;

      internal IList<IRegressionModel> models;
      internal IList<double> weights;

      public GbmState(IRegressionProblemData problemData, ILossFunction lossFunction, uint randSeed, int maxSize, double r, double m, double nu) {
        // default settings for MaxSize, Nu and R
        this.maxSize = maxSize;
        this.nu = nu;
        this.r = r;
        this.m = m;

        random = new MersenneTwister(randSeed);
        this.problemData = problemData;
        this.lossFunction = lossFunction;

        int nRows = problemData.TrainingIndices.Count();

        y = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, problemData.TrainingIndices).ToArray();
        // weights are all 1 for now (HL doesn't support weights yet)
        w = Enumerable.Repeat(1.0, nRows).ToArray();

        treeBuilder = new RegressionTreeBuilder(problemData, random);

        activeIdx = Enumerable.Range(0, nRows).ToArray();

        var zeros = Enumerable.Repeat(0.0, nRows);
        var ones = Enumerable.Repeat(1.0, nRows);
        double f0 = lossFunction.GetLineSearchFunc(y, zeros, ones)(activeIdx, 0, nRows - 1); // initial constant value (mean for squared errors)
        pred = Enumerable.Repeat(f0, nRows).ToArray();
        predTest = Enumerable.Repeat(f0, problemData.TestIndices.Count()).ToArray();
        pseudoRes = new double[nRows];

        models = new List<IRegressionModel>();
        weights = new List<double>();
        // add constant model
        models.Add(new ConstantRegressionModel(f0));
        weights.Add(1.0);
      }

      public IRegressionModel GetModel() {
        return new GradientBoostedTreesModel(models, weights);
      }
      public IEnumerable<KeyValuePair<string, double>> GetVariableRelevance() {
        return treeBuilder.GetVariableRelevance();
      }

      public double GetTrainLoss() {
        int nRows = y.Length;
        return lossFunction.GetLoss(y, pred, w) / nRows;
      }
      public double GetTestLoss() {
        var yTest = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, problemData.TestIndices);
        var wTest = problemData.TestIndices.Select(_ => 1.0); // ones
        var nRows = problemData.TestIndices.Count();
        return lossFunction.GetLoss(yTest, predTest, wTest) / nRows;
      }
    }

    // simple interface
    public static IRegressionSolution TrainGbm(IRegressionProblemData problemData, int maxSize, double nu, double r, int maxIterations) {
      return TrainGbm(problemData, new SquaredErrorLoss(), maxSize, nu, r, maxIterations);
    }

    // simple interface
    public static IRegressionSolution TrainGbm(IRegressionProblemData problemData, ILossFunction lossFunction,
      int maxSize, double nu, double r, int maxIterations, uint randSeed = 31415) {
      Contract.Assert(r > 0);
      Contract.Assert(r <= 1.0);
      Contract.Assert(nu > 0);
      Contract.Assert(nu <= 1.0);

      var state = (GbmState)CreateGbmState(problemData, lossFunction, randSeed);
      state.maxSize = maxSize;
      state.r = r;
      state.nu = nu;

      for (int iter = 0; iter < maxIterations; iter++) {
        MakeStep(state);
      }

      var model = new GradientBoostedTreesModel(state.models, state.weights);
      return new RegressionSolution(model, (IRegressionProblemData)problemData.Clone());
    }

    // for custom stepping & termination
    public static IGbmState CreateGbmState(IRegressionProblemData problemData, ILossFunction lossFunction, uint randSeed, int maxSize = 3, double r = 0.66, double m = 0.5, double nu = 0.01) {
      return new GbmState(problemData, lossFunction, randSeed, maxSize, r, m, nu);
    }

    // use default settings for maxDepth, nu, r from state
    public static void MakeStep(IGbmState state) {
      var gbmState = state as GbmState;
      if (gbmState == null) throw new ArgumentException("state");

      MakeStep(gbmState, gbmState.maxSize, gbmState.nu, gbmState.r, gbmState.m);
    }

    // allow dynamic adaptation of maxDepth, nu and r (even though this is not used)
    public static void MakeStep(IGbmState state, int maxSize, double nu, double r, double m) {
      var gbmState = state as GbmState;
      if (gbmState == null) throw new ArgumentException("state");

      var problemData = gbmState.problemData;
      var lossFunction = gbmState.lossFunction;
      var yPred = gbmState.pred;
      var yPredTest = gbmState.predTest;
      var w = gbmState.w;
      var treeBuilder = gbmState.treeBuilder;
      var y = gbmState.y;
      var activeIdx = gbmState.activeIdx;
      var pseudoRes = gbmState.pseudoRes;

      // copy output of gradient function to pre-allocated rim array (pseudo-residuals)
      int rimIdx = 0;
      foreach (var g in lossFunction.GetLossGradient(y, yPred, w)) {
        pseudoRes[rimIdx++] = g;
      }

      var tree = treeBuilder.CreateRegressionTreeForGradientBoosting(pseudoRes, maxSize, activeIdx, lossFunction.GetLineSearchFunc(y, yPred, w), r, m);

      int i = 0;
      foreach (var pred in tree.GetEstimatedValues(problemData.Dataset, problemData.TrainingIndices)) {
        yPred[i] = yPred[i] + nu * pred;
        i++;
      }
      // update predictions for validation set
      i = 0;
      foreach (var pred in tree.GetEstimatedValues(problemData.Dataset, problemData.TestIndices)) {
        yPredTest[i] = yPredTest[i] + nu * pred;
        i++;
      }

      gbmState.weights.Add(nu);
      gbmState.models.Add(tree);
    }
    #endregion
  }
}