#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2014 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 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 {
  /// <summary>
  /// Represents a random forest model for regression and classification
  /// </summary>
  [StorableClass]
  [Item("RandomForestModel", "Represents a random forest for regression and classification.")]
  public sealed class RandomForestModel : NamedItem, IRandomForestModel {
    // not persisted
    private alglib.decisionforest randomForest;
    private alglib.decisionforest RandomForest {
      get {
        // recalculate lazily
        if (randomForest.innerobj.trees == null || randomForest.innerobj.trees.Length == 0) RecalculateModel();
        return randomForest;
      }
    }

    // instead of storing the data of the model itself
    // we instead only store data necessary to recalculate the same model lazily on demand
    [Storable]
    private int seed;
    [Storable]
    private IDataAnalysisProblemData originalTrainingData;
    [Storable]
    private double[] classValues;
    [Storable]
    private int nTrees;
    [Storable]
    private double r;
    [Storable]
    private double m;


    [StorableConstructor]
    private RandomForestModel(bool deserializing)
      : base(deserializing) {
      // for backwards compatibility (loading old solutions)
      randomForest = new alglib.decisionforest();
    }
    private RandomForestModel(RandomForestModel original, Cloner cloner)
      : base(original, cloner) {
      randomForest = new alglib.decisionforest();
      randomForest.innerobj.bufsize = original.randomForest.innerobj.bufsize;
      randomForest.innerobj.nclasses = original.randomForest.innerobj.nclasses;
      randomForest.innerobj.ntrees = original.randomForest.innerobj.ntrees;
      randomForest.innerobj.nvars = original.randomForest.innerobj.nvars;
      // we assume that the trees array (double[]) is immutable in alglib
      randomForest.innerobj.trees = original.randomForest.innerobj.trees;
      
      // allowedInputVariables is immutable so we don't need to clone
      allowedInputVariables = original.allowedInputVariables;

      // clone data which is necessary to rebuild the model
      this.seed = original.seed;
      this.originalTrainingData = cloner.Clone(original.originalTrainingData);
      // classvalues is immutable so we don't need to clone
      this.classValues = original.classValues;
      this.nTrees = original.nTrees;
      this.r = original.r;
      this.m = original.m;
    }

    // random forest models can only be created through the static factory methods CreateRegressionModel and CreateClassificationModel
    private RandomForestModel(alglib.decisionforest randomForest,
      int seed, IDataAnalysisProblemData originalTrainingData,
      int nTrees, double r, double m, double[] classValues = null)
      : base() {
      this.name = ItemName;
      this.description = ItemDescription;
      // the model itself
      this.randomForest = randomForest;
      // data which is necessary for recalculation of the model
      this.seed = seed;
      this.originalTrainingData = (IDataAnalysisProblemData)originalTrainingData.Clone();
      this.classValues = classValues;
      this.nTrees = nTrees;
      this.r = r;
      this.m = m;
    }

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

    private void RecalculateModel() {
      double rmsError, oobRmsError, relClassError, oobRelClassError;
      var regressionProblemData = originalTrainingData as IRegressionProblemData;
      var classificationProblemData = originalTrainingData as IClassificationProblemData;
      if (regressionProblemData != null) {
        var model = CreateRegressionModel(regressionProblemData,
                                              nTrees, r, m, seed, out rmsError, out oobRmsError,
                                              out relClassError, out oobRelClassError);
        randomForest = model.randomForest;
      } else if (classificationProblemData != null) {
        var model = CreateClassificationModel(classificationProblemData,
                                              nTrees, r, m, seed, out rmsError, out oobRmsError,
                                              out relClassError, out oobRelClassError);
        randomForest = model.randomForest;
      }
    }

    public IEnumerable<double> GetEstimatedValues(Dataset dataset, IEnumerable<int> rows) {
      double[,] inputData = AlglibUtil.PrepareInputMatrix(dataset, AllowedInputVariables, rows);
      AssertInputMatrix(inputData);

      int n = inputData.GetLength(0);
      int columns = inputData.GetLength(1);
      double[] x = new double[columns];
      double[] y = new double[1];

      for (int row = 0; row < n; row++) {
        for (int column = 0; column < columns; column++) {
          x[column] = inputData[row, column];
        }
        alglib.dfprocess(RandomForest, x, ref y);
        yield return y[0];
      }
    }

    public IEnumerable<double> GetEstimatedClassValues(Dataset dataset, IEnumerable<int> rows) {
      double[,] inputData = AlglibUtil.PrepareInputMatrix(dataset, AllowedInputVariables, rows);
      AssertInputMatrix(inputData);

      int n = inputData.GetLength(0);
      int columns = inputData.GetLength(1);
      double[] x = new double[columns];
      double[] y = new double[RandomForest.innerobj.nclasses];

      for (int row = 0; row < n; row++) {
        for (int column = 0; column < columns; column++) {
          x[column] = inputData[row, column];
        }
        alglib.dfprocess(randomForest, x, ref y);
        // find class for with the largest probability value
        int maxProbClassIndex = 0;
        double maxProb = y[0];
        for (int i = 1; i < y.Length; i++) {
          if (maxProb < y[i]) {
            maxProb = y[i];
            maxProbClassIndex = i;
          }
        }
        yield return classValues[maxProbClassIndex];
      }
    }

    public IRandomForestRegressionSolution CreateRegressionSolution(IRegressionProblemData problemData) {
      return new RandomForestRegressionSolution(new RegressionProblemData(problemData), this);
    }
    IRegressionSolution IRegressionModel.CreateRegressionSolution(IRegressionProblemData problemData) {
      return CreateRegressionSolution(problemData);
    }
    public IRandomForestClassificationSolution CreateClassificationSolution(IClassificationProblemData problemData) {
      return new RandomForestClassificationSolution(new ClassificationProblemData(problemData), this);
    }
    IClassificationSolution IClassificationModel.CreateClassificationSolution(IClassificationProblemData problemData) {
      return CreateClassificationSolution(problemData);
    }

    public static RandomForestModel CreateRegressionModel(IRegressionProblemData problemData, int nTrees, double r, double m, int seed,
      out double rmsError, out double avgRelError, out double outOfBagAvgRelError, out double outOfBagRmsError) {

      var variables = problemData.AllowedInputVariables.Concat(new string[] { problemData.TargetVariable });
      double[,] inputMatrix = AlglibUtil.PrepareInputMatrix(problemData.Dataset, variables, problemData.TrainingIndices);

      alglib.dfreport rep;
      var dForest = CreateRandomForestModel(seed, inputMatrix, nTrees, r, m, 1, out rep);

      rmsError = rep.rmserror;
      avgRelError = rep.avgrelerror;
      outOfBagAvgRelError = rep.oobavgrelerror;
      outOfBagRmsError = rep.oobrmserror;

      return new RandomForestModel(dForest,
        seed, problemData,
        nTrees, r, m);
    }

    public static RandomForestModel CreateClassificationModel(IClassificationProblemData problemData, int nTrees, double r, double m, int seed,
      out double rmsError, out double outOfBagRmsError, out double relClassificationError, out double outOfBagRelClassificationError) {

      var variables = problemData.AllowedInputVariables.Concat(new string[] { problemData.TargetVariable });
      double[,] inputMatrix = AlglibUtil.PrepareInputMatrix(problemData.Dataset, variables, problemData.TrainingIndices);

      var classValues = problemData.ClassValues.ToArray();
      int nClasses = classValues.Length;

      // map original class values to values [0..nClasses-1]
      var classIndices = new Dictionary<double, double>();
      for (int i = 0; i < nClasses; i++) {
        classIndices[classValues[i]] = i;
      }

      int nRows = inputMatrix.GetLength(0);
      int nColumns = inputMatrix.GetLength(1);
      for (int row = 0; row < nRows; row++) {
        inputMatrix[row, nColumns - 1] = classIndices[inputMatrix[row, nColumns - 1]];
      }

      alglib.dfreport rep;
      var dForest = CreateRandomForestModel(seed, inputMatrix, nTrees, r, m, nClasses, out rep);

      rmsError = rep.rmserror;
      outOfBagRmsError = rep.oobrmserror;
      relClassificationError = rep.relclserror;
      outOfBagRelClassificationError = rep.oobrelclserror;

      return new RandomForestModel(dForest,
        seed, problemData,
        nTrees, r, m, classValues);
    }

    private static alglib.decisionforest CreateRandomForestModel(int seed, double[,] inputMatrix, int nTrees, double r, double m, int nClasses, out alglib.dfreport rep) {
      AssertParameters(r, m);
      AssertInputMatrix(inputMatrix);

      int info = 0;
      alglib.math.rndobject = new System.Random(seed);
      var dForest = new alglib.decisionforest();
      rep = new alglib.dfreport();
      int nRows = inputMatrix.GetLength(0);
      int nColumns = inputMatrix.GetLength(1);
      int sampleSize = Math.Max((int)Math.Round(r * nRows), 1);
      int nFeatures = Math.Max((int)Math.Round(m * (nColumns - 1)), 1);

      alglib.dforest.dfbuildinternal(inputMatrix, nRows, nColumns - 1, nClasses, nTrees, sampleSize, nFeatures, alglib.dforest.dfusestrongsplits + alglib.dforest.dfuseevs, ref info, dForest.innerobj, rep.innerobj);
      if (info != 1) throw new ArgumentException("Error in calculation of random forest model");
      return dForest;
    }

    private static void AssertParameters(double r, double m) {
      if (r <= 0 || r > 1) throw new ArgumentException("The R parameter for random forest modeling must be between 0 and 1.");
      if (m <= 0 || m > 1) throw new ArgumentException("The M parameter for random forest modeling must be between 0 and 1.");
    }

    private static void AssertInputMatrix(double[,] inputMatrix) {
      if (inputMatrix.Cast<double>().Any(x => double.IsNaN(x) || double.IsInfinity(x)))
        throw new NotSupportedException("Random forest modeling does not support NaN or infinity values in the input dataset.");
    }

    #region persistence for backwards compatibility
    // when the originalTrainingData is null this means the model was loaded from an old file 
    // therefore, we cannot use the new persistence mechanism because the original data is not available anymore
    // in such cases we still store the compete model
    private bool IsCompatibilityLoaded { get { return originalTrainingData == null; } }

    private string[] allowedInputVariables;
    [Storable(Name = "allowedInputVariables")]
    private string[] AllowedInputVariables {
      get {
        if (IsCompatibilityLoaded) return allowedInputVariables;
        else return originalTrainingData.AllowedInputVariables.ToArray();
      }
      set { allowedInputVariables = value; }
    }
    [Storable]
    private int RandomForestBufSize {
      get {
        if (IsCompatibilityLoaded) return randomForest.innerobj.bufsize;
        else return 0;
      }
      set {
        randomForest.innerobj.bufsize = value;
      }
    }
    [Storable]
    private int RandomForestNClasses {
      get {
        if (IsCompatibilityLoaded) return randomForest.innerobj.nclasses;
        else return 0;
      }
      set {
        randomForest.innerobj.nclasses = value;
      }
    }
    [Storable]
    private int RandomForestNTrees {
      get {
        if (IsCompatibilityLoaded) return randomForest.innerobj.ntrees;
        else return 0;
      }
      set {
        randomForest.innerobj.ntrees = value;
      }
    }
    [Storable]
    private int RandomForestNVars {
      get {
        if (IsCompatibilityLoaded) return randomForest.innerobj.nvars;
        else return 0;
      }
      set {
        randomForest.innerobj.nvars = value;
      }
    }
    [Storable]
    private double[] RandomForestTrees {
      get {
        if (IsCompatibilityLoaded) return randomForest.innerobj.trees;
        else return new double[] { };
      }
      set {
        randomForest.innerobj.trees = value;
      }
    }
    #endregion
  }
}