#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 } }