source: trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/NeuralNetwork/NeuralNetworkRegression.cs @ 6577

#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2011 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.Data;
using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
using HeuristicLab.Optimization;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.Problems.DataAnalysis;
using HeuristicLab.Problems.DataAnalysis.Symbolic;
using HeuristicLab.Problems.DataAnalysis.Symbolic.Regression;
using HeuristicLab.Parameters;

namespace HeuristicLab.Algorithms.DataAnalysis {
  /// <summary>
  /// Neural network regression data analysis algorithm.
  /// </summary>
  [Item("Neural Network Regression", "Neural network regression data analysis algorithm (wrapper for ALGLIB).")]
  [Creatable("Data Analysis")]
  [StorableClass]
  public sealed class NeuralNetworkRegression : FixedDataAnalysisAlgorithm<IRegressionProblem> {
    private const string NeuralNetworkRegressionModelResultName = "Neural network regression solution";
    [StorableConstructor]
    private NeuralNetworkRegression(bool deserializing) : base(deserializing) { }
    private NeuralNetworkRegression(NeuralNetworkRegression original, Cloner cloner)
      : base(original, cloner) {
    }
    public NeuralNetworkRegression()
      : base() {
      Problem = new RegressionProblem();
    }
    [StorableHook(HookType.AfterDeserialization)]
    private void AfterDeserialization() { }

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

    #region neural network
    protected override void Run() {
      double decay = 0.01;
      int nLayers = 2;
      int nHidden1 = 10;
      int nHidden2 = 10;
      int nRestarts = 5;
      double rmsError, avgRelError;
      var solution = CreateNeuralNetworkRegressionSolution(Problem.ProblemData, nLayers, nHidden1, nHidden2, decay, nRestarts, out rmsError, out avgRelError);
      Results.Add(new Result(NeuralNetworkRegressionModelResultName, "The neural network regression solution.", solution));
      Results.Add(new Result("Root mean square error", "The root of the mean of squared errors of the neural network regression solution on the training set.", new DoubleValue(rmsError)));
      Results.Add(new Result("Average relative error", "The average of relative errors of the neural network regression solution on the training set.", new PercentValue(avgRelError)));
    }

    public static IRegressionSolution CreateNeuralNetworkRegressionSolution(IRegressionProblemData problemData, int nLayers, int nHiddenNodes1, int nHiddenNodes2, double decay, int restarts,
      out double rmsError, out double avgRelError) {
      Dataset dataset = problemData.Dataset;
      string targetVariable = problemData.TargetVariable;
      IEnumerable<string> allowedInputVariables = problemData.AllowedInputVariables;
      IEnumerable<int> rows = problemData.TrainingIndizes;
      double[,] inputMatrix = AlglibUtil.PrepareInputMatrix(dataset, allowedInputVariables.Concat(new string[] { targetVariable }), rows);
      if (inputMatrix.Cast<double>().Any(x => double.IsNaN(x) || double.IsInfinity(x)))
        throw new NotSupportedException("Neural network regression does not support NaN or infinity values in the input dataset.");

      double targetMin = problemData.Dataset.GetEnumeratedVariableValues(targetVariable).Min();
      targetMin = targetMin - targetMin * 0.1; // -10%
      double targetMax = problemData.Dataset.GetEnumeratedVariableValues(targetVariable).Max();
      targetMax = targetMax + targetMax * 0.1; // + 10%

      alglib.multilayerperceptron multiLayerPerceptron = null;
      if (nLayers == 0) {
        alglib.mlpcreater0(allowedInputVariables.Count(), 1, targetMin, targetMax, out multiLayerPerceptron);
      } else if (nLayers == 1) {
        alglib.mlpcreater1(allowedInputVariables.Count(), nHiddenNodes1, 1, targetMin, targetMax, out multiLayerPerceptron);
      } else if (nLayers == 2) {
        alglib.mlpcreater2(allowedInputVariables.Count(), nHiddenNodes1, nHiddenNodes2, 1, targetMin, targetMax, out multiLayerPerceptron);
      } else throw new ArgumentException("Number of layers must be zero, one, or two.", "nLayers");
      alglib.mlpreport rep;
      int nRows = inputMatrix.GetLength(0);

      int info;
      // using mlptrainlm instead of mlptraines or mlptrainbfgs because only one parameter is necessary
      alglib.mlptrainlm(multiLayerPerceptron, inputMatrix, nRows, decay, restarts, out info, out rep);
      if (info != 2) throw new ArgumentException("Error in calculation of neural network regression solution");

      rmsError = alglib.mlprmserror(multiLayerPerceptron, inputMatrix, nRows);
      avgRelError = alglib.mlpavgerror(multiLayerPerceptron, inputMatrix, nRows);

      return new NeuralNetworkRegressionSolution(problemData, new NeuralNetworkModel(multiLayerPerceptron, targetVariable, allowedInputVariables));
    }
    #endregion
  }
}