#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 .
*/
#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", scope, true);
SVMModel model = GetVariableValue("SVMModel", scope, true);
string targetVariable = GetVariableValue("TargetVariable", scope, true).Data;
int start = GetVariableValue("TrainingSamplesStart", scope, true).Data;
int end = GetVariableValue("TrainingSamplesEnd", scope, true).Data;
IntData maxTimeOffsetData = GetVariableValue("MaxTimeOffset", scope, true, false);
int maxTimeOffset = maxTimeOffsetData == null ? 0 : maxTimeOffsetData.Data;
IntData minTimeOffsetData = GetVariableValue("MinTimeOffset", scope, true, false);
int minTimeOffset = minTimeOffsetData == null ? 0 : minTimeOffsetData.Data;
double punishmentFactor = GetVariableValue("PunishmentFactor", scope, true).Data;
ItemList inputVariables = GetVariableValue("InputVariables", scope, true);
var inputVariableNames = from x in inputVariables
select ((StringData)x).Data;
double mean = ds.GetMean(targetVariable, start, end);
double range = ds.GetRange(targetVariable, start, end);
Predictor predictor = new Predictor(model, targetVariable, inputVariableNames, minTimeOffset, maxTimeOffset);
predictor.LowerPredictionLimit = mean - punishmentFactor * range;
predictor.UpperPredictionLimit = mean + punishmentFactor * range;
scope.AddVariable(new HeuristicLab.Core.Variable(scope.TranslateName("Predictor"), predictor));
return null;
}
}
}