#region License Information
/* HeuristicLab
* Copyright (C) 2002-2008 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 HeuristicLab.Core;
using HeuristicLab.Data;
using System;
using HeuristicLab.GP.Interfaces;
using HeuristicLab.DataAnalysis;
using System.Collections;
using System.Collections.Generic;
using System.Linq;
namespace HeuristicLab.GP.StructureIdentification {
public class EarlyStoppingMeanSquaredErrorEvaluator : MeanSquaredErrorEvaluator {
public override string Description {
get {
return @"Evaluates 'FunctionTree' for all samples of the dataset and calculates the mean-squared-error
for the estimated values vs. the real values of 'TargetVariable'.
This operator stops the computation as soon as an upper limit for the mean-squared-error is reached.";
}
}
public EarlyStoppingMeanSquaredErrorEvaluator()
: base() {
AddVariableInfo(new VariableInfo("QualityLimit", "The upper limit of the MSE which is used as early stopping criterion.", typeof(DoubleData), VariableKind.In));
}
// evaluates the function-tree for the given target-variable and the whole dataset and returns the MSE
public override void Evaluate(IScope scope, IFunctionTree tree, ITreeEvaluator evaluator, Dataset dataset, int targetVariable, int start, int end) {
double qualityLimit = GetVariableValue("QualityLimit", scope, true).Data;
DoubleData mse = GetVariableValue("MSE", scope, false, false);
if (mse == null) {
mse = new DoubleData();
scope.AddVariable(new HeuristicLab.Core.Variable(scope.TranslateName("MSE"), mse));
}
double errorsSquaredSum = 0;
int rows = end - start;
int n = 0;
int sample = start;
foreach (var estimatedValue in evaluator.Evaluate(dataset, tree, Enumerable.Range(start, end - start))) {
double original = dataset.GetValue(sample, targetVariable);
if (!double.IsNaN(original) && !double.IsInfinity(original)) {
double error = estimatedValue - original;
errorsSquaredSum += error * error;
n++;
}
// check the limit every 30 samples and stop as soon as we hit the limit
if (n % 30 == 29 && errorsSquaredSum / rows >= qualityLimit) {
mse.Data = errorsSquaredSum / (n + 1); // return estimated MSE (when the remaining errors are on average the same)
return;
}
sample++;
}
errorsSquaredSum /= n;
if (double.IsNaN(errorsSquaredSum) || double.IsInfinity(errorsSquaredSum)) {
errorsSquaredSum = double.MaxValue;
}
mse.Data = errorsSquaredSum;
}
}
}