| 1 | using HEAL.Attic;
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| 2 | using HeuristicLab.Common;
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| 3 | using HeuristicLab.Core;
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| 4 | using HeuristicLab.Data;
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| 5 | using HeuristicLab.Parameters;
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| 6 | using HeuristicLab.Problems.DataAnalysis.Symbolic.Regression;
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| 7 | using HeuristicLab.Random;
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| 8 | using System;
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| 9 | using System.Collections.Generic;
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| 10 | using System.Linq;
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| 11 | using System.Text;
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| 12 | using System.Threading.Tasks;
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| 13 |
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| 14 | namespace HeuristicLab.Algorithms.OESRALPS.Evaluators
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| 15 | {
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| 16 | [Item("Sliding Window Pearson R² Evaluator", "Calculates the square of the pearson correlation coefficient (also known as coefficient of determination) of a symbolic regression solution.")]
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| 17 | [StorableType("6FAEC6C2-C711-452A-A60D-29AE37898A90")]
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| 18 | public class SymbolicRegressionSingleObjectivePearsonRSquaredSlidingWindowEvaluator : SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator
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| 19 | {
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| 20 | private const string TestPartitionParameterName = "TestPartition";
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| 21 | private const string TrainingPartitionParameterName = "TrainingPartition";
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| 22 |
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| 23 | public ILookupParameter<IntRange> TrainingPartitionParameter {
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| 24 | get { return (ILookupParameter<IntRange>)Parameters[TrainingPartitionParameterName]; }
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| 25 | }
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| 26 | public ILookupParameter<IntRange> TestPartitionParameter {
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| 27 | get { return (ILookupParameter<IntRange>)Parameters[TestPartitionParameterName]; }
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| 28 | }
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| 29 |
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| 30 | [StorableConstructor]
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| 31 | protected SymbolicRegressionSingleObjectivePearsonRSquaredSlidingWindowEvaluator(StorableConstructorFlag _) : base(_) { }
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| 32 | protected SymbolicRegressionSingleObjectivePearsonRSquaredSlidingWindowEvaluator(SymbolicRegressionSingleObjectivePearsonRSquaredSlidingWindowEvaluator original, Cloner cloner)
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| 33 | : base(original, cloner)
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| 34 | {
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| 35 | }
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| 36 | public override IDeepCloneable Clone(Cloner cloner)
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| 37 | {
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| 38 | return new SymbolicRegressionSingleObjectivePearsonRSquaredSlidingWindowEvaluator(this, cloner);
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| 39 | }
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| 40 |
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| 41 | public SymbolicRegressionSingleObjectivePearsonRSquaredSlidingWindowEvaluator() : base()
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| 42 | {
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| 43 | Parameters.Add(new ValueLookupParameter<IntRange>(TrainingPartitionParameterName, "The current training sliding window position or range."));
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| 44 | Parameters.Add(new ValueLookupParameter<IntRange>(TestPartitionParameterName, "The current test sliding window position or range."));
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| 45 | }
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| 46 |
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| 47 | //protected override IEnumerable<int> GenerateRowsToEvaluate(double percentageOfRows)
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| 48 | //{
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| 49 | // if (TrainingPartitionParameter.ActualValue == null
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| 50 | // || TestPartitionParameter.ActualValue == null)
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| 51 | // return base.GenerateRowsToEvaluate(percentageOfRows);
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| 52 |
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| 53 | // IEnumerable<int> rows;
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| 54 | // int samplesStart = TrainingPartitionParameter.ActualValue.Start;
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| 55 | // int samplesEnd = TrainingPartitionParameter.ActualValue.End;
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| 56 | // int testPartitionStart = TestPartitionParameter.ActualValue.Start;
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| 57 | // int testPartitionEnd = TestPartitionParameter.ActualValue.End;
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| 58 | // if (samplesEnd < samplesStart) throw new ArgumentException("Start value is larger than end value.");
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| 59 |
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| 60 | // if (percentageOfRows.IsAlmost(1.0))
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| 61 | // rows = Enumerable.Range(samplesStart, samplesEnd - samplesStart);
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| 62 | // else
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| 63 | // {
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| 64 | // int seed = RandomParameter.ActualValue.Next();
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| 65 | // int count = (int)((samplesEnd - samplesStart) * percentageOfRows);
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| 66 | // if (count == 0) count = 1;
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| 67 | // rows = RandomEnumerable.SampleRandomNumbers(seed, samplesStart, samplesEnd, count);
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| 68 | // }
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| 69 |
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| 70 | // rows = rows.Where(i => i < testPartitionStart || testPartitionEnd <= i);
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| 71 | // if (ValidRowIndicatorParameter.ActualValue != null)
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| 72 | // {
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| 73 | // string indicatorVar = ValidRowIndicatorParameter.ActualValue.Value;
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| 74 | // var problemData = ProblemDataParameter.ActualValue;
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| 75 | // var indicatorRow = problemData.Dataset.GetReadOnlyDoubleValues(indicatorVar);
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| 76 | // rows = rows.Where(r => !indicatorRow[r].IsAlmost(0.0));
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| 77 | // }
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| 78 | // return rows;
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| 79 | //}
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| 80 | }
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| 81 | }
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