#region License Information
/* HeuristicLab
 * Copyright (C) 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

extern alias alglib_3_7;

using System;
using System.Collections.Generic;
using System.Linq;
using HEAL.Attic;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
using HeuristicLab.Problems.DataAnalysis;
using HeuristicLab.Problems.DataAnalysis.Symbolic;

namespace HeuristicLab.Algorithms.DataAnalysis {
  /// <summary>
  /// Represents a random forest model for regression and classification
  /// </summary>
  [Obsolete("This class only exists for backwards compatibility reasons for stored models with the XML Persistence. Use RFModelSurrogate or RFModelFull instead.")]
  [StorableType("9AA4CCC2-CD75-4471-8DF6-949E5B783642")]
  [Item("RandomForestModel", "Represents a random forest for regression and classification.")]
  public sealed class RandomForestModel : ClassificationModel, IRandomForestModel {
    // not persisted
    private alglib_3_7.alglib.decisionforest randomForest;
    private alglib_3_7.alglib.decisionforest RandomForest {
      get {
        // recalculate lazily
        if (randomForest.innerobj.trees == null || randomForest.innerobj.trees.Length == 0) RecalculateModel();
        return randomForest;
      }
    }

    public override IEnumerable<string> VariablesUsedForPrediction {
      get { return originalTrainingData.AllowedInputVariables; }
    }

    public int NumberOfTrees {
      get { return nTrees; }
    }

    // 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(StorableConstructorFlag _) : base(_) {
      // for backwards compatibility (loading old solutions)
      randomForest = new alglib_3_7.alglib.decisionforest();
    }
    private RandomForestModel(RandomForestModel original, Cloner cloner)
      : base(original, cloner) {
      randomForest = new alglib_3_7.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(string targetVariable, alglib_3_7.alglib.decisionforest randomForest,
      int seed, IDataAnalysisProblemData originalTrainingData,
      int nTrees, double r, double m, double[] classValues = null)
      : base(targetVariable) {
      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(IDataset dataset, IEnumerable<int> rows) {
      double[,] inputData = dataset.ToArray(AllowedInputVariables, rows);
      RandomForestUtil.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_3_7.alglib.dfprocess(RandomForest, x, ref y);
        yield return y[0];
      }
    }

    public IEnumerable<double> GetEstimatedVariances(IDataset dataset, IEnumerable<int> rows) {
      double[,] inputData = dataset.ToArray(AllowedInputVariables, rows);
      RandomForestUtil.AssertInputMatrix(inputData);

      int n = inputData.GetLength(0);
      int columns = inputData.GetLength(1);
      double[] x = new double[columns];
      double[] ys = new double[this.RandomForest.innerobj.ntrees];

      for (int row = 0; row < n; row++) {
        for (int column = 0; column < columns; column++) {
          x[column] = inputData[row, column];
        }
        alglib_3_7.alglib.dforest.dfprocessraw(RandomForest.innerobj, x, ref ys);
        yield return ys.VariancePop();
      }
    }

    public override IEnumerable<double> GetEstimatedClassValues(IDataset dataset, IEnumerable<int> rows) {
      double[,] inputData = dataset.ToArray(AllowedInputVariables, rows);
      RandomForestUtil.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_3_7.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 ISymbolicExpressionTree ExtractTree(int treeIdx) {
      var rf = RandomForest;
      // hoping that the internal representation of alglib is stable

      // TREE FORMAT
      // W[Offs]      -   size of sub-array (for the tree)
      //     node info:
      // W[K+0]       -   variable number        (-1 for leaf mode)
      // W[K+1]       -   threshold              (class/value for leaf node)
      // W[K+2]       -   ">=" branch index      (absent for leaf node)

      // skip irrelevant trees
      int offset = 0;
      for (int i = 0; i < treeIdx - 1; i++) {
        offset = offset + (int)Math.Round(rf.innerobj.trees[offset]);
      }

      var numSy = new Number();
      var varCondSy = new VariableCondition() { IgnoreSlope = true };

      var node = CreateRegressionTreeRec(rf.innerobj.trees, offset, offset + 1, numSy, varCondSy);

      var startNode = new StartSymbol().CreateTreeNode();
      startNode.AddSubtree(node);
      var root = new ProgramRootSymbol().CreateTreeNode();
      root.AddSubtree(startNode);
      return new SymbolicExpressionTree(root);
    }

    private ISymbolicExpressionTreeNode CreateRegressionTreeRec(double[] trees, int offset, int k, Number numSy, VariableCondition varCondSy) {

      // alglib source for evaluation of one tree (dfprocessinternal)
      // offs = 0
      //
      // Set pointer to the root
      //
      // k = offs + 1;
      // 
      // //
      // // Navigate through the tree
      // //
      // while (true) {
      //   if ((double)(df.trees[k]) == (double)(-1)) {
      //     if (df.nclasses == 1) {
      //       y[0] = y[0] + df.trees[k + 1];
      //     } else {
      //       idx = (int)Math.Round(df.trees[k + 1]);
      //       y[idx] = y[idx] + 1;
      //     }
      //     break;
      //   }
      //   if ((double)(x[(int)Math.Round(df.trees[k])]) < (double)(df.trees[k + 1])) {
      //     k = k + innernodewidth;
      //   } else {
      //     k = offs + (int)Math.Round(df.trees[k + 2]);
      //   }
      // }

      if ((double)(trees[k]) == (double)(-1)) {
        var numNode = (NumberTreeNode)numSy.CreateTreeNode();
        numNode.Value = trees[k + 1];
        return numNode;
      } else {
        var condNode = (VariableConditionTreeNode)varCondSy.CreateTreeNode();
        condNode.VariableName = AllowedInputVariables[(int)Math.Round(trees[k])];
        condNode.Threshold = trees[k + 1];
        condNode.Slope = double.PositiveInfinity;

        var left = CreateRegressionTreeRec(trees, offset, k + 3, numSy, varCondSy);
        var right = CreateRegressionTreeRec(trees, offset, offset + (int)Math.Round(trees[k + 2]), numSy, varCondSy);

        condNode.AddSubtree(left); // not 100% correct because interpreter uses: if(x <= thres) left() else right() and RF uses if(x < thres) left() else right() (see above)
        condNode.AddSubtree(right);
        return condNode;
      }
    }


    public IRegressionSolution CreateRegressionSolution(IRegressionProblemData problemData) {
      return new RandomForestRegressionSolution(this, new RegressionProblemData(problemData));
    }
    public override IClassificationSolution CreateClassificationSolution(IClassificationProblemData problemData) {
      return new RandomForestClassificationSolution(this, new ClassificationProblemData(problemData));
    }

    public bool IsProblemDataCompatible(IRegressionProblemData problemData, out string errorMessage) {
      return RegressionModel.IsProblemDataCompatible(this, problemData, out errorMessage);
    }

    public override bool IsProblemDataCompatible(IDataAnalysisProblemData problemData, out string errorMessage) {
      if (problemData == null) throw new ArgumentNullException("problemData", "The provided problemData is null.");

      var regressionProblemData = problemData as IRegressionProblemData;
      if (regressionProblemData != null)
        return IsProblemDataCompatible(regressionProblemData, out errorMessage);

      var classificationProblemData = problemData as IClassificationProblemData;
      if (classificationProblemData != null)
        return IsProblemDataCompatible(classificationProblemData, out errorMessage);

      throw new ArgumentException("The problem data is not compatible with this random forest. Instead a " + problemData.GetType().GetPrettyName() + " was provided.", "problemData");
    }

    public static RandomForestModel CreateRegressionModel(IRegressionProblemData problemData, int nTrees, double r, double m, int seed,
      out double rmsError, out double outOfBagRmsError, out double avgRelError, out double outOfBagAvgRelError) {
      return CreateRegressionModel(problemData, problemData.TrainingIndices, nTrees, r, m, seed,
       rmsError: out rmsError, outOfBagRmsError: out outOfBagRmsError, avgRelError: out avgRelError, outOfBagAvgRelError: out outOfBagAvgRelError);
    }

    public static RandomForestModel CreateRegressionModel(IRegressionProblemData problemData, IEnumerable<int> trainingIndices, int nTrees, double r, double m, int seed,
      out double rmsError, out double outOfBagRmsError, out double avgRelError, out double outOfBagAvgRelError) {
      var variables = problemData.AllowedInputVariables.Concat(new string[] { problemData.TargetVariable });
      double[,] inputMatrix = problemData.Dataset.ToArray(variables, trainingIndices);

      var dForest = RandomForestUtil.CreateRandomForestModelAlglib_3_7(seed, inputMatrix, nTrees, r, m, 1, out var rep);

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

      return new RandomForestModel(problemData.TargetVariable, 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) {
      return CreateClassificationModel(problemData, problemData.TrainingIndices, nTrees, r, m, seed,
        out rmsError, out outOfBagRmsError, out relClassificationError, out outOfBagRelClassificationError);
    }

    public static RandomForestModel CreateClassificationModel(IClassificationProblemData problemData, IEnumerable<int> trainingIndices, 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 = problemData.Dataset.ToArray(variables, 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]];
      }

      var dForest = RandomForestUtil.CreateRandomForestModelAlglib_3_7(seed, inputMatrix, nTrees, r, m, nClasses, out var rep);

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

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

    #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
  }
}