[5500] | 1 | #region License Information
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| 2 | /* HeuristicLab
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[9835] | 3 | * Copyright (C) 2002-2013 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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[5500] | 4 | *
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| 5 | * This file is part of HeuristicLab.
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| 6 | *
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| 7 | * HeuristicLab is free software: you can redistribute it and/or modify
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| 8 | * it under the terms of the GNU General Public License as published by
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| 9 | * the Free Software Foundation, either version 3 of the License, or
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| 10 | * (at your option) any later version.
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| 11 | *
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| 12 | * HeuristicLab is distributed in the hope that it will be useful,
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| 13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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| 14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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| 15 | * GNU General Public License for more details.
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| 16 | *
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| 17 | * You should have received a copy of the GNU General Public License
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| 18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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| 19 | */
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| 20 | #endregion
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| 21 |
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| 22 | using System;
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| 23 | using System.Collections.Generic;
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| 24 | using System.Linq;
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| 25 | using HeuristicLab.Common;
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| 26 | using HeuristicLab.Core;
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| 27 | using HeuristicLab.Data;
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| 28 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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| 29 | using HeuristicLab.Operators;
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[9241] | 30 | using HeuristicLab.Optimization;
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[5500] | 31 | using HeuristicLab.Parameters;
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| 32 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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| 33 | using HeuristicLab.Random;
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| 34 |
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| 35 | namespace HeuristicLab.Problems.DataAnalysis.Symbolic {
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[5689] | 36 | [StorableClass]
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[5509] | 37 | public abstract class SymbolicDataAnalysisEvaluator<T> : SingleSuccessorOperator,
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[9241] | 38 | ISymbolicDataAnalysisEvaluator<T>, ISymbolicDataAnalysisInterpreterOperator, ISymbolicDataAnalysisBoundedOperator, IStochasticOperator
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[5509] | 39 | where T : class, IDataAnalysisProblemData {
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[5500] | 40 | private const string RandomParameterName = "Random";
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| 41 | private const string SymbolicExpressionTreeParameterName = "SymbolicExpressionTree";
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[5514] | 42 | private const string SymbolicDataAnalysisTreeInterpreterParameterName = "SymbolicExpressionTreeInterpreter";
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[5500] | 43 | private const string ProblemDataParameterName = "ProblemData";
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[5770] | 44 | private const string EstimationLimitsParameterName = "EstimationLimits";
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[5759] | 45 | private const string EvaluationPartitionParameterName = "EvaluationPartition";
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[5500] | 46 | private const string RelativeNumberOfEvaluatedSamplesParameterName = "RelativeNumberOfEvaluatedSamples";
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[9241] | 47 | private const string ApplyLinearScalingParameterName = "ApplyLinearScaling";
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| 48 | private const string ValidRowIndicatorParameterName = "ValidRowIndicator";
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[5500] | 49 |
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[5618] | 50 | public override bool CanChangeName { get { return false; } }
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| 51 |
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[5500] | 52 | #region parameter properties
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[9241] | 53 | ILookupParameter<IRandom> IStochasticOperator.RandomParameter {
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| 54 | get { return RandomParameter; }
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| 55 | }
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| 56 |
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[5514] | 57 | public IValueLookupParameter<IRandom> RandomParameter {
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| 58 | get { return (IValueLookupParameter<IRandom>)Parameters[RandomParameterName]; }
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[5500] | 59 | }
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| 60 | public ILookupParameter<ISymbolicExpressionTree> SymbolicExpressionTreeParameter {
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| 61 | get { return (ILookupParameter<ISymbolicExpressionTree>)Parameters[SymbolicExpressionTreeParameterName]; }
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| 62 | }
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[5649] | 63 | public ILookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter> SymbolicDataAnalysisTreeInterpreterParameter {
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| 64 | get { return (ILookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter>)Parameters[SymbolicDataAnalysisTreeInterpreterParameterName]; }
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[5500] | 65 | }
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[5514] | 66 | public IValueLookupParameter<T> ProblemDataParameter {
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| 67 | get { return (IValueLookupParameter<T>)Parameters[ProblemDataParameterName]; }
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[5500] | 68 | }
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| 69 |
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[5759] | 70 | public IValueLookupParameter<IntRange> EvaluationPartitionParameter {
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| 71 | get { return (IValueLookupParameter<IntRange>)Parameters[EvaluationPartitionParameterName]; }
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[5500] | 72 | }
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[5770] | 73 | public IValueLookupParameter<DoubleLimit> EstimationLimitsParameter {
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| 74 | get { return (IValueLookupParameter<DoubleLimit>)Parameters[EstimationLimitsParameterName]; }
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[5500] | 75 | }
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[5759] | 76 | public IValueLookupParameter<PercentValue> RelativeNumberOfEvaluatedSamplesParameter {
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| 77 | get { return (IValueLookupParameter<PercentValue>)Parameters[RelativeNumberOfEvaluatedSamplesParameterName]; }
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[5500] | 78 | }
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[9241] | 79 | public ILookupParameter<BoolValue> ApplyLinearScalingParameter {
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| 80 | get { return (ILookupParameter<BoolValue>)Parameters[ApplyLinearScalingParameterName]; }
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| 81 | }
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| 82 | public IValueLookupParameter<StringValue> ValidRowIndicatorParameter {
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| 83 | get { return (IValueLookupParameter<StringValue>)Parameters[ValidRowIndicatorParameterName]; }
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| 84 | }
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[5500] | 85 | #endregion
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| 86 |
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| 87 |
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| 88 | [StorableConstructor]
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| 89 | protected SymbolicDataAnalysisEvaluator(bool deserializing) : base(deserializing) { }
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[5509] | 90 | protected SymbolicDataAnalysisEvaluator(SymbolicDataAnalysisEvaluator<T> original, Cloner cloner)
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[5500] | 91 | : base(original, cloner) {
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| 92 | }
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| 93 | public SymbolicDataAnalysisEvaluator()
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| 94 | : base() {
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[5514] | 95 | Parameters.Add(new ValueLookupParameter<IRandom>(RandomParameterName, "The random generator to use."));
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[5649] | 96 | Parameters.Add(new LookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter>(SymbolicDataAnalysisTreeInterpreterParameterName, "The interpreter that should be used to calculate the output values of the symbolic data analysis tree."));
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[5618] | 97 | Parameters.Add(new LookupParameter<ISymbolicExpressionTree>(SymbolicExpressionTreeParameterName, "The symbolic data analysis solution encoded as a symbolic expression tree."));
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[5514] | 98 | Parameters.Add(new ValueLookupParameter<T>(ProblemDataParameterName, "The problem data on which the symbolic data analysis solution should be evaluated."));
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[5759] | 99 | Parameters.Add(new ValueLookupParameter<IntRange>(EvaluationPartitionParameterName, "The start index of the dataset partition on which the symbolic data analysis solution should be evaluated."));
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[5770] | 100 | Parameters.Add(new ValueLookupParameter<DoubleLimit>(EstimationLimitsParameterName, "The upper and lower limit that should be used as cut off value for the output values of symbolic data analysis trees."));
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[5759] | 101 | Parameters.Add(new ValueLookupParameter<PercentValue>(RelativeNumberOfEvaluatedSamplesParameterName, "The relative number of samples of the dataset partition, which should be randomly chosen for evaluation between the start and end index."));
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[9241] | 102 | Parameters.Add(new LookupParameter<BoolValue>(ApplyLinearScalingParameterName, "Flag that indicates if the individual should be linearly scaled before evaluating."));
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| 103 | Parameters.Add(new ValueLookupParameter<StringValue>(ValidRowIndicatorParameterName, "An indicator variable in the data set that specifies which rows should be evaluated (those for which the indicator <> 0) (optional)."));
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[5500] | 104 | }
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| 105 |
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[9241] | 106 | [StorableHook(HookType.AfterDeserialization)]
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| 107 | private void AfterDeserialization() {
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| 108 | if (Parameters.ContainsKey(ApplyLinearScalingParameterName) && !(Parameters[ApplyLinearScalingParameterName] is LookupParameter<BoolValue>))
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| 109 | Parameters.Remove(ApplyLinearScalingParameterName);
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| 110 | if (!Parameters.ContainsKey(ApplyLinearScalingParameterName))
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| 111 | Parameters.Add(new LookupParameter<BoolValue>(ApplyLinearScalingParameterName, "Flag that indicates if the individual should be linearly scaled before evaluating."));
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| 112 | if (!Parameters.ContainsKey(ValidRowIndicatorParameterName))
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| 113 | Parameters.Add(new ValueLookupParameter<StringValue>(ValidRowIndicatorParameterName, "An indicator variable in the data set that specifies which rows should be evaluated (those for which the indicator <> 0) (optional)."));
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| 114 | }
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| 115 |
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[5500] | 116 | protected IEnumerable<int> GenerateRowsToEvaluate() {
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[5914] | 117 | return GenerateRowsToEvaluate(RelativeNumberOfEvaluatedSamplesParameter.ActualValue.Value);
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| 118 | }
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| 119 |
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| 120 | protected IEnumerable<int> GenerateRowsToEvaluate(double percentageOfRows) {
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| 121 | IEnumerable<int> rows;
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[5759] | 122 | int samplesStart = EvaluationPartitionParameter.ActualValue.Start;
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| 123 | int samplesEnd = EvaluationPartitionParameter.ActualValue.End;
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[5823] | 124 | int testPartitionStart = ProblemDataParameter.ActualValue.TestPartition.Start;
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| 125 | int testPartitionEnd = ProblemDataParameter.ActualValue.TestPartition.End;
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[5759] | 126 | if (samplesEnd < samplesStart) throw new ArgumentException("Start value is larger than end value.");
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[5914] | 127 |
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| 128 | if (percentageOfRows.IsAlmost(1.0))
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| 129 | rows = Enumerable.Range(samplesStart, samplesEnd - samplesStart);
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| 130 | else {
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| 131 | int seed = RandomParameter.ActualValue.Next();
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| 132 | int count = (int)((samplesEnd - samplesStart) * percentageOfRows);
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| 133 | if (count == 0) count = 1;
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| 134 | rows = RandomEnumerable.SampleRandomNumbers(seed, samplesStart, samplesEnd, count);
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| 135 | }
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| 136 |
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[9241] | 137 | rows = rows.Where(i => i < testPartitionStart || testPartitionEnd <= i);
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| 138 | if (ValidRowIndicatorParameter.ActualValue != null) {
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| 139 | string indicatorVar = ValidRowIndicatorParameter.ActualValue.Value;
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| 140 | var problemData = ProblemDataParameter.ActualValue;
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| 141 | var indicatorRow = problemData.Dataset.GetReadOnlyDoubleValues(indicatorVar);
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| 142 | rows = rows.Where(r => !indicatorRow[r].IsAlmost(0.0));
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| 143 | }
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| 144 | return rows;
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[5500] | 145 | }
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[9241] | 146 |
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| 147 | [ThreadStatic]
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| 148 | private static double[] cache;
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| 149 | protected static void CalculateWithScaling(IEnumerable<double> targetValues, IEnumerable<double> estimatedValues,
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| 150 | double lowerEstimationLimit, double upperEstimationLimit,
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| 151 | IOnlineCalculator calculator, int maxRows) {
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| 152 | if (cache == null || cache.Length < maxRows) {
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| 153 | cache = new double[maxRows];
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| 154 | }
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| 155 |
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| 156 | // calculate linear scaling
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| 157 | int i = 0;
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| 158 | var linearScalingCalculator = new OnlineLinearScalingParameterCalculator();
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| 159 | var targetValuesEnumerator = targetValues.GetEnumerator();
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| 160 | var estimatedValuesEnumerator = estimatedValues.GetEnumerator();
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| 161 | while (targetValuesEnumerator.MoveNext() & estimatedValuesEnumerator.MoveNext()) {
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| 162 | double target = targetValuesEnumerator.Current;
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| 163 | double estimated = estimatedValuesEnumerator.Current;
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| 164 | cache[i] = estimated;
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| 165 | if (!double.IsNaN(estimated) && !double.IsInfinity(estimated))
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| 166 | linearScalingCalculator.Add(estimated, target);
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| 167 | i++;
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| 168 | }
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| 169 | if (linearScalingCalculator.ErrorState == OnlineCalculatorError.None && (targetValuesEnumerator.MoveNext() || estimatedValuesEnumerator.MoveNext()))
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| 170 | throw new ArgumentException("Number of elements in target and estimated values enumeration do not match.");
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| 171 |
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| 172 | double alpha = linearScalingCalculator.Alpha;
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| 173 | double beta = linearScalingCalculator.Beta;
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| 174 | if (linearScalingCalculator.ErrorState != OnlineCalculatorError.None) {
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| 175 | alpha = 0.0;
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| 176 | beta = 1.0;
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| 177 | }
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| 178 |
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| 179 | //calculate the quality by using the passed online calculator
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| 180 | targetValuesEnumerator = targetValues.GetEnumerator();
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| 181 | var scaledBoundedEstimatedValuesEnumerator = Enumerable.Range(0, i).Select(x => cache[x] * beta + alpha)
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| 182 | .LimitToRange(lowerEstimationLimit, upperEstimationLimit).GetEnumerator();
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| 183 |
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| 184 | while (targetValuesEnumerator.MoveNext() & scaledBoundedEstimatedValuesEnumerator.MoveNext()) {
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| 185 | calculator.Add(targetValuesEnumerator.Current, scaledBoundedEstimatedValuesEnumerator.Current);
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| 186 | }
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| 187 | }
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[5500] | 188 | }
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| 189 | }
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