#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.Collections.Generic;
using System.Linq;
using HeuristicLab.Analysis;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Optimization;
using HeuristicLab.Parameters;
using HEAL.Attic;
namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Classification {
[Item("SymbolicClassificationSingleObjectiveOverfittingAnalyzer", "Calculates and tracks correlation of training and validation fitness of symbolic classification models.")]
[StorableType("551DB3E3-56F8-4E92-B538-D453DBA23D77")]
public sealed class SymbolicClassificationSingleObjectiveOverfittingAnalyzer : SymbolicDataAnalysisSingleObjectiveValidationAnalyzer {
private const string TrainingValidationCorrelationParameterName = "Training and validation fitness correlation";
private const string TrainingValidationCorrelationTableParameterName = "Training and validation fitness correlation table";
private const string LowerCorrelationThresholdParameterName = "LowerCorrelationThreshold";
private const string UpperCorrelationThresholdParameterName = "UpperCorrelationThreshold";
private const string OverfittingParameterName = "IsOverfitting";
#region parameter properties
public ILookupParameter TrainingValidationQualityCorrelationParameter {
get { return (ILookupParameter)Parameters[TrainingValidationCorrelationParameterName]; }
}
public ILookupParameter TrainingValidationQualityCorrelationTableParameter {
get { return (ILookupParameter)Parameters[TrainingValidationCorrelationTableParameterName]; }
}
public IValueLookupParameter LowerCorrelationThresholdParameter {
get { return (IValueLookupParameter)Parameters[LowerCorrelationThresholdParameterName]; }
}
public IValueLookupParameter UpperCorrelationThresholdParameter {
get { return (IValueLookupParameter)Parameters[UpperCorrelationThresholdParameterName]; }
}
public ILookupParameter OverfittingParameter {
get { return (ILookupParameter)Parameters[OverfittingParameterName]; }
}
#endregion
[StorableConstructor]
private SymbolicClassificationSingleObjectiveOverfittingAnalyzer(StorableConstructorFlag _) : base(_) { }
private SymbolicClassificationSingleObjectiveOverfittingAnalyzer(SymbolicClassificationSingleObjectiveOverfittingAnalyzer original, Cloner cloner) : base(original, cloner) { }
public SymbolicClassificationSingleObjectiveOverfittingAnalyzer()
: base() {
Parameters.Add(new LookupParameter(TrainingValidationCorrelationParameterName, "Correlation of training and validation fitnesses"));
Parameters.Add(new LookupParameter(TrainingValidationCorrelationTableParameterName, "Data table of training and validation fitness correlation values over the whole run."));
Parameters.Add(new ValueLookupParameter(LowerCorrelationThresholdParameterName, "Lower threshold for correlation value that marks the boundary from non-overfitting to overfitting.", new DoubleValue(0.65)));
Parameters.Add(new ValueLookupParameter(UpperCorrelationThresholdParameterName, "Upper threshold for correlation value that marks the boundary from overfitting to non-overfitting.", new DoubleValue(0.75)));
Parameters.Add(new LookupParameter(OverfittingParameterName, "Boolean indicator for overfitting."));
}
public override IDeepCloneable Clone(Cloner cloner) {
return new SymbolicClassificationSingleObjectiveOverfittingAnalyzer(this, cloner);
}
public override IOperation Apply() {
double[] trainingQuality = QualityParameter.ActualValue.Select(x => x.Value).ToArray();
var problemData = ProblemDataParameter.ActualValue;
var evaluator = EvaluatorParameter.ActualValue;
// evaluate on validation partition
IEnumerable rows = GenerateRowsToEvaluate();
if (!rows.Any()) return base.Apply();
IExecutionContext childContext = (IExecutionContext)ExecutionContext.CreateChildOperation(evaluator);
double[] validationQuality = SymbolicExpressionTree
.Select(t => evaluator.Evaluate(childContext, t, problemData, rows))
.ToArray();
double r = 0.0;
try {
r = alglib.spearmancorr2(trainingQuality, validationQuality);
}
catch (alglib.alglibexception) {
r = 0.0;
}
TrainingValidationQualityCorrelationParameter.ActualValue = new DoubleValue(r);
if (TrainingValidationQualityCorrelationTableParameter.ActualValue == null) {
var dataTable = new DataTable(TrainingValidationQualityCorrelationTableParameter.Name, TrainingValidationQualityCorrelationTableParameter.Description);
dataTable.Rows.Add(new DataRow(TrainingValidationQualityCorrelationParameter.Name, TrainingValidationQualityCorrelationParameter.Description));
dataTable.Rows[TrainingValidationQualityCorrelationParameter.Name].VisualProperties.StartIndexZero = true;
TrainingValidationQualityCorrelationTableParameter.ActualValue = dataTable;
ResultCollectionParameter.ActualValue.Add(new Result(TrainingValidationQualityCorrelationTableParameter.Name, dataTable));
}
TrainingValidationQualityCorrelationTableParameter.ActualValue.Rows[TrainingValidationQualityCorrelationParameter.Name].Values.Add(r);
if (OverfittingParameter.ActualValue != null && OverfittingParameter.ActualValue.Value) {
// overfitting == true
// => r must reach the upper threshold to switch back to non-overfitting state
OverfittingParameter.ActualValue = new BoolValue(r < UpperCorrelationThresholdParameter.ActualValue.Value);
} else {
// overfitting == false
// => r must drop below lower threshold to switch to overfitting state
OverfittingParameter.ActualValue = new BoolValue(r < LowerCorrelationThresholdParameter.ActualValue.Value);
}
return base.Apply();
}
}
}