source: trunk/sources/HeuristicLab.SupportVectorMachines/3.2/PredictorBuilder.cs @ 2620

#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2008 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 System.Text;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Modeling;
using HeuristicLab.DataAnalysis;

namespace HeuristicLab.SupportVectorMachines {
  public class PredictorBuilder : OperatorBase {
    public PredictorBuilder()
      : base() {
      AddVariableInfo(new VariableInfo("Dataset", "The input dataset", typeof(Dataset), VariableKind.In));
      AddVariableInfo(new VariableInfo("SVMModel", "The SVM model", typeof(SVMModel), VariableKind.In));
      AddVariableInfo(new VariableInfo("TargetVariable", "The target variable", typeof(StringData), VariableKind.In));
      AddVariableInfo(new VariableInfo("InputVariables", "The input variable names", typeof(ItemList), VariableKind.In));
      AddVariableInfo(new VariableInfo("TrainingSamplesStart", "Start index of the training set", typeof(IntData), VariableKind.In));
      AddVariableInfo(new VariableInfo("TrainingSamplesEnd", "End index of the training set", typeof(IntData), VariableKind.In));
      AddVariableInfo(new VariableInfo("MaxTimeOffset", "(optional) highest allowed time offset value", typeof(IntData), VariableKind.In));
      AddVariableInfo(new VariableInfo("MinTimeOffset", "(optional) lowest allowed time offset value", typeof(IntData), VariableKind.In));
      AddVariableInfo(new VariableInfo("PunishmentFactor", "The punishment factor limits the range of predicted values", typeof(DoubleData), VariableKind.In));
      AddVariableInfo(new VariableInfo("Predictor", "The predictor can be used to generate estimated values", typeof(IPredictor), VariableKind.New));
    }

    public override string Description {
      get { return "Extracts the SVM Model and generates a predictor for the model analyzer."; }
    }

    public override IOperation Apply(IScope scope) {
      Dataset ds = GetVariableValue<Dataset>("Dataset", scope, true);
      SVMModel model = GetVariableValue<SVMModel>("SVMModel", scope, true);
      string targetVariable = GetVariableValue<StringData>("TargetVariable", scope, true).Data;
      int start = GetVariableValue<IntData>("TrainingSamplesStart", scope, true).Data;
      int end = GetVariableValue<IntData>("TrainingSamplesEnd", scope, true).Data;
      IntData maxTimeOffsetData = GetVariableValue<IntData>("MaxTimeOffset", scope, true, false);
      int maxTimeOffset = maxTimeOffsetData == null ? 0 : maxTimeOffsetData.Data;
      IntData minTimeOffsetData = GetVariableValue<IntData>("MinTimeOffset", scope, true, false);
      int minTimeOffset = minTimeOffsetData == null ? 0 : minTimeOffsetData.Data;
      double punishmentFactor = GetVariableValue<DoubleData>("PunishmentFactor", scope, true).Data;


      ItemList inputVariables = GetVariableValue<ItemList>("InputVariables", scope, true);
      var inputVariableNames = from x in inputVariables
                               select ((StringData)x).Data;

      var predictor = CreatePredictor(model, ds, targetVariable, inputVariableNames, punishmentFactor, start, end, minTimeOffset, maxTimeOffset);
      scope.AddVariable(new HeuristicLab.Core.Variable(scope.TranslateName("Predictor"), predictor));
      return null;
    }

    public static Predictor CreatePredictor(SVMModel model, Dataset ds, string targetVariable, IEnumerable<string> inputVariables, double punishmentFactor,
      int start, int end, int minTimeOffset, int maxTimeOffset) {
      Predictor predictor = new Predictor(model, targetVariable, inputVariables, minTimeOffset, maxTimeOffset);
      double mean = ds.GetMean(targetVariable, start, end);
      double range = ds.GetRange(targetVariable, start, end);
      predictor.LowerPredictionLimit = mean - punishmentFactor * range;
      predictor.UpperPredictionLimit = mean + punishmentFactor * range;
      return predictor;
    }
  }
}