#region License Information /* HeuristicLab * Copyright (C) 2002-2015 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.Linq; using HeuristicLab.Analysis; using HeuristicLab.Common; using HeuristicLab.Core; using HeuristicLab.Operators; using HeuristicLab.Optimization; using HeuristicLab.Parameters; using HeuristicLab.Persistence.Default.CompositeSerializers.Storable; namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression { [StorableClass] public class SymbolicRegressionSolutionsAnalyzer : SingleSuccessorOperator, IAnalyzer { private const string ResultCollectionParameterName = "Results"; private const string RegressionSolutionQualitiesResultName = "Regression Solution Qualities"; public ILookupParameter ResultCollectionParameter { get { return (ILookupParameter)Parameters[ResultCollectionParameterName]; } } public virtual bool EnabledByDefault { get { return false; } } [StorableConstructor] protected SymbolicRegressionSolutionsAnalyzer(bool deserializing) : base(deserializing) { } protected SymbolicRegressionSolutionsAnalyzer(SymbolicRegressionSolutionsAnalyzer original, Cloner cloner) : base(original, cloner) { } public override IDeepCloneable Clone(Cloner cloner) { return new SymbolicRegressionSolutionsAnalyzer(this, cloner); } public SymbolicRegressionSolutionsAnalyzer() { Parameters.Add(new LookupParameter(ResultCollectionParameterName, "The result collection to store the analysis results.")); } public override IOperation Apply() { var results = ResultCollectionParameter.ActualValue; if (!results.ContainsKey(RegressionSolutionQualitiesResultName)) { var newDataTable = new DataTable(RegressionSolutionQualitiesResultName); results.Add(new Result(RegressionSolutionQualitiesResultName, "Chart displaying the training and test qualities of the regression solutions.", newDataTable)); } var dataTable = (DataTable)results[RegressionSolutionQualitiesResultName].Value; foreach (var result in results.Where(r => r.Value is IRegressionSolution)) { var solution = (IRegressionSolution)result.Value; var trainingR2 = result.Name + Environment.NewLine + "Training R²"; if (!dataTable.Rows.ContainsKey(trainingR2)) dataTable.Rows.Add(new DataRow(trainingR2)); var testR2 = result.Name + Environment.NewLine + " Test R²"; if (!dataTable.Rows.ContainsKey(testR2)) dataTable.Rows.Add(new DataRow(testR2)); dataTable.Rows[trainingR2].Values.Add(solution.TrainingRSquared); dataTable.Rows[testR2].Values.Add(solution.TestRSquared); } return base.Apply(); } } }