#region License Information
/* HeuristicLab
* Copyright (C) 2002-2019 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 HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
using HEAL.Attic;
namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Classification {
///
/// Represents a symbolic classification model
///
[StorableType("8AEAF4A5-839D-4070-A348-440E79110C74")]
[Item(Name = "SymbolicClassificationModel", Description = "Represents a symbolic classification model.")]
public abstract class SymbolicClassificationModel : SymbolicDataAnalysisModel, ISymbolicClassificationModel {
[Storable]
private string targetVariable;
public string TargetVariable {
get { return targetVariable; }
set {
if (string.IsNullOrEmpty(value) || targetVariable == value) return;
targetVariable = value;
OnTargetVariableChanged(this, EventArgs.Empty);
}
}
[StorableConstructor]
protected SymbolicClassificationModel(StorableConstructorFlag _) : base(_) {
targetVariable = string.Empty;
}
protected SymbolicClassificationModel(SymbolicClassificationModel original, Cloner cloner)
: base(original, cloner) {
targetVariable = original.targetVariable;
}
protected SymbolicClassificationModel(string targetVariable, ISymbolicExpressionTree tree, ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, double lowerEstimationLimit = double.MinValue, double upperEstimationLimit = double.MaxValue)
: base(tree, interpreter, lowerEstimationLimit, upperEstimationLimit) {
this.targetVariable = targetVariable;
}
public abstract IEnumerable GetEstimatedClassValues(IDataset dataset, IEnumerable rows);
public abstract void RecalculateModelParameters(IClassificationProblemData problemData, IEnumerable rows);
public abstract ISymbolicClassificationSolution CreateClassificationSolution(IClassificationProblemData problemData);
IClassificationSolution IClassificationModel.CreateClassificationSolution(IClassificationProblemData problemData) {
return CreateClassificationSolution(problemData);
}
public void Scale(IClassificationProblemData problemData) {
Scale(problemData, problemData.TargetVariable);
}
public virtual bool IsProblemDataCompatible(IClassificationProblemData problemData, out string errorMessage) {
return ClassificationModel.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 classificationProblemData = problemData as IClassificationProblemData;
if (classificationProblemData == null)
throw new ArgumentException("The problem data is not a regression problem data. Instead a " + problemData.GetType().GetPrettyName() + " was provided.", "problemData");
return IsProblemDataCompatible(classificationProblemData, out errorMessage);
}
#region events
public event EventHandler TargetVariableChanged;
private void OnTargetVariableChanged(object sender, EventArgs args) {
var changed = TargetVariableChanged;
if (changed != null)
changed(sender, args);
}
#endregion
}
}