#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.Data;
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";
private const string TrainingQualityParameterName = "TrainingRSquared";
private const string TestQualityParameterName = "TestRSquared";
public ILookupParameter ResultCollectionParameter {
get { return (ILookupParameter)Parameters[ResultCollectionParameterName]; }
}
public ILookupParameter TrainingQualityParameter {
get { return (ILookupParameter)Parameters[TrainingQualityParameterName]; }
}
public ILookupParameter TestQualityParameter {
get { return (ILookupParameter)Parameters[TestQualityParameterName]; }
}
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."));
Parameters.Add(new LookupParameter(TrainingQualityParameterName));
Parameters.Add(new LookupParameter(TestQualityParameterName));
}
[StorableHook(HookType.AfterDeserialization)]
private void AfterDeserialization() {
// BackwardsCompatibility3.3
#region Backwards compatible code, remove with 3.4
if (!Parameters.ContainsKey(TrainingQualityParameterName))
Parameters.Add(new LookupParameter(TrainingQualityParameterName));
if (!Parameters.ContainsKey(TestQualityParameterName))
Parameters.Add(new LookupParameter(TestQualityParameterName));
#endregion
}
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;
// only if the parameters are available (not available in old persisted code)
ILookupParameter trainingQualityParam = null;
ILookupParameter testQualityParam = null;
// store actual names of parameter because it is changed below
string prevTrainingQualityParamName = TrainingQualityParameterName;
string prevTestQualityParamName = TestQualityParameterName;
if (Parameters.ContainsKey(TrainingQualityParameterName)) {
trainingQualityParam = TrainingQualityParameter;
prevTrainingQualityParamName = trainingQualityParam.ActualName;
}
if (Parameters.ContainsKey(TestQualityParameterName)) {
testQualityParam = TestQualityParameter;
prevTestQualityParamName = testQualityParam.ActualName;
}
foreach (var result in results.Where(r => r.Value is IRegressionSolution)) {
var solution = (IRegressionSolution)result.Value;
var trainingR2Name = result.Name + " Training R²";
if (!dataTable.Rows.ContainsKey(trainingR2Name))
dataTable.Rows.Add(new DataRow(trainingR2Name));
var testR2Name = result.Name + " Test R²";
if (!dataTable.Rows.ContainsKey(testR2Name))
dataTable.Rows.Add(new DataRow(testR2Name));
dataTable.Rows[trainingR2Name].Values.Add(solution.TrainingRSquared);
dataTable.Rows[testR2Name].Values.Add(solution.TestRSquared);
// also add training and test R² to the scope using the parameters
// HACK: we change the ActualName of the parameter to write two variables for each solution in the results collection
if (trainingQualityParam != null) {
trainingQualityParam.ActualName = trainingR2Name;
trainingQualityParam.ActualValue = new DoubleValue(solution.TrainingRSquared);
}
if (testQualityParam != null) {
testQualityParam.ActualName = testR2Name;
testQualityParam.ActualValue = new DoubleValue(solution.TestRSquared);
}
}
if (trainingQualityParam != null) trainingQualityParam.ActualName = prevTrainingQualityParamName;
if (testQualityParam != null) testQualityParam.ActualName = prevTestQualityParamName;
return base.Apply();
}
}
}