source: branches/2893_BNLR/HeuristicLab.Algorithms.DataAnalysis/3.4/NonlinearRegression/BayesianNonlinearRegressionModel.cs @ 17717

#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2018 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.Encodings.SymbolicExpressionTreeEncoding;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.Problems.DataAnalysis;
using HeuristicLab.Problems.DataAnalysis.Symbolic;

namespace HeuristicLab.Algorithms.DataAnalysis {
  [StorableClass]
  [Item("BayesianNonlinearRegressionModel", "")]
  public sealed class BayesianNonlinearRegressionModel : RegressionModel, IConfidenceRegressionModel {
    private const int SAMPLE_SIZE = 100;


    [Storable]
    public ISymbolicExpressionTree Tree {
      get; private set;
    }

    private double[][] parameterEmpiricalDistribution;
    public IEnumerable<double[]> ParameterEmpiricalDistribution {
      get { return parameterEmpiricalDistribution; }
    }

    public ISymbolicDataAnalysisExpressionTreeInterpreter Interpreter {
      get; private set;
    }

    public override IEnumerable<string> VariablesUsedForPrediction {
      get { return allowedInputVariables; }
    }

    [Storable]
    private string[] allowedInputVariables;

    [StorableConstructor]
    private BayesianNonlinearRegressionModel(bool deserializing)
      : base(deserializing) {
    }
    private BayesianNonlinearRegressionModel(BayesianNonlinearRegressionModel original, Cloner cloner)
      : base(original, cloner) {
      allowedInputVariables = (string[])original.allowedInputVariables.Clone();
      this.Tree = cloner.Clone(original.Tree);
      this.parameterEmpiricalDistribution = original.parameterEmpiricalDistribution;
      this.Interpreter = cloner.Clone(original.Interpreter);
    }
    public BayesianNonlinearRegressionModel(
      ISymbolicExpressionTree tree,
      double[][] parameterEmpiricalDistribution,
      ISymbolicDataAnalysisExpressionTreeInterpreter interpreter,
      string targetVariable, 
      IEnumerable<string> allowedInputVariables
      )
      : base(targetVariable) {
      this.name = ItemName;
      this.description = ItemDescription;
      this.Tree = tree;
      this.parameterEmpiricalDistribution = (double[][])parameterEmpiricalDistribution.Clone();
      this.Interpreter = interpreter;
      this.allowedInputVariables = allowedInputVariables.ToArray();
    }

    [StorableHook(HookType.AfterDeserialization)]
    private void AfterDeserialization() {
    }

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


    public override IEnumerable<double> GetEstimatedValues(IDataset dataset, IEnumerable<int> rows) {
      var y = Sample(dataset, rows);
      return y.Select(yi => yi.Average());
    }

    public IEnumerable<double> GetEstimatedVariances(IDataset dataset, IEnumerable<int> rows) {
      var y = Sample(dataset, rows);
      return y.Select(yi => yi.VariancePop());
    }

    private IList<double>[] Sample(IDataset dataset, IEnumerable<int> rows) {
      List<double>[] y = rows.Select(_ => new List<double>(SAMPLE_SIZE)).ToArray();
      var rand = new System.Random(1234); // TODO;
      for (int s = 0; s < SAMPLE_SIZE; s++) {
        var paramIdx = rand.Next(parameterEmpiricalDistribution.Length);
        UpdateConstants(Tree, parameterEmpiricalDistribution[paramIdx]);
        int predRow = 0;
        foreach (var pred in Interpreter.GetSymbolicExpressionTreeValues(Tree, dataset, rows)) {
          y[predRow].Add(pred);
          predRow++;
        }
      }
      return y;
    }

    public override IRegressionSolution CreateRegressionSolution(IRegressionProblemData problemData) {
      return new ConfidenceRegressionSolution(this, new RegressionProblemData(problemData));
    }


    #region taken from ConstantOptEval TODO
    private static void UpdateConstants(ISymbolicExpressionTree tree, double[] constants) {
      int i = 0;
      foreach (var node in tree.Root.IterateNodesPrefix().OfType<SymbolicExpressionTreeTerminalNode>()) {
        ConstantTreeNode constantTreeNode = node as ConstantTreeNode;
        VariableTreeNodeBase variableTreeNodeBase = node as VariableTreeNodeBase;
        FactorVariableTreeNode factorVarTreeNode = node as FactorVariableTreeNode;
        if (constantTreeNode != null)
          constantTreeNode.Value = constants[i++];
        // else if (updateVariableWeights && variableTreeNodeBase != null)
        //   variableTreeNodeBase.Weight = constants[i++];
        else if (factorVarTreeNode != null) {
          for (int j = 0; j < factorVarTreeNode.Weights.Length; j++)
            factorVarTreeNode.Weights[j] = constants[i++];
        }
      }
    }
    #endregion

  }
}