[14072] | 1 | #region License Information
|
---|
| 2 | /* HeuristicLab
|
---|
| 3 | * Copyright (C) 2002-2016 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
|
---|
| 4 | *
|
---|
| 5 | * This file is part of HeuristicLab.
|
---|
| 6 | *
|
---|
| 7 | * HeuristicLab is free software: you can redistribute it and/or modify
|
---|
| 8 | * it under the terms of the GNU General Public License as published by
|
---|
| 9 | * the Free Software Foundation, either version 3 of the License, or
|
---|
| 10 | * (at your option) any later version.
|
---|
| 11 | *
|
---|
| 12 | * HeuristicLab is distributed in the hope that it will be useful,
|
---|
| 13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
|
---|
| 14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
---|
| 15 | * GNU General Public License for more details.
|
---|
| 16 | *
|
---|
| 17 | * You should have received a copy of the GNU General Public License
|
---|
| 18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
|
---|
| 19 | */
|
---|
| 20 | #endregion
|
---|
| 21 |
|
---|
| 22 | using System;
|
---|
| 23 | using System.Collections.Generic;
|
---|
| 24 | using System.Linq;
|
---|
| 25 | using HeuristicLab.Common;
|
---|
| 26 | using HeuristicLab.Core;
|
---|
| 27 | using HeuristicLab.Data;
|
---|
| 28 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
|
---|
| 29 | using HeuristicLab.Optimization;
|
---|
| 30 | using HeuristicLab.Parameters;
|
---|
| 31 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
|
---|
| 32 |
|
---|
| 33 | namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
|
---|
| 34 | [Item("SymbolicRegressionSingleObjectiveOSGAEvaluator", "An evaluator which tries to predict when a child will not be able to fullfil offspring selection criteria, to save evaluation time.")]
|
---|
| 35 | [StorableClass]
|
---|
| 36 | public class SymbolicRegressionSingleObjectiveOsgaEvaluator : SymbolicRegressionSingleObjectiveEvaluator {
|
---|
| 37 | private const string RelativeParentChildQualityThresholdParameterName = "RelativeParentChildQualityThreshold";
|
---|
| 38 | private const string RelativeFitnessEvaluationIntervalSizeParameterName = "RelativeFitnessEvaluationIntervalSize";
|
---|
| 39 | private const string ResultCollectionParameterName = "Results";
|
---|
[14231] | 40 | private const string AggregateStatisticsParameterName = "AggregateStatistics";
|
---|
[14279] | 41 | private const string ActualSelectionPressureParameterName = "SelectionPressure";
|
---|
| 42 | private const string UseAdaptiveQualityThresholdParameterName = "UseAdaptiveQualityThreshold";
|
---|
| 43 | private const string UseFixedEvaluationIntervalsParameterName = "UseFixedEvaluationIntervals";
|
---|
[14072] | 44 |
|
---|
| 45 | #region parameters
|
---|
[14279] | 46 | public IFixedValueParameter<BoolValue> UseFixedEvaluationIntervalsParameter {
|
---|
| 47 | get { return (IFixedValueParameter<BoolValue>)Parameters[UseFixedEvaluationIntervalsParameterName]; }
|
---|
| 48 | }
|
---|
| 49 | public IFixedValueParameter<BoolValue> UseAdaptiveQualityThresholdParameter {
|
---|
| 50 | get { return (IFixedValueParameter<BoolValue>)Parameters[UseAdaptiveQualityThresholdParameterName]; }
|
---|
| 51 | }
|
---|
| 52 | public ILookupParameter<DoubleValue> ActualSelectionPressureParameter {
|
---|
| 53 | get { return (ILookupParameter<DoubleValue>)Parameters[ActualSelectionPressureParameterName]; }
|
---|
| 54 | }
|
---|
[14072] | 55 | public ILookupParameter<ResultCollection> ResultCollectionParameter {
|
---|
| 56 | get { return (ILookupParameter<ResultCollection>)Parameters[ResultCollectionParameterName]; }
|
---|
| 57 | }
|
---|
[14231] | 58 | public IValueParameter<BoolValue> AggregateStatisticsParameter {
|
---|
| 59 | get { return (IValueParameter<BoolValue>)Parameters[AggregateStatisticsParameterName]; }
|
---|
| 60 | }
|
---|
[14104] | 61 | public IValueParameter<IntMatrix> RejectedStatsParameter {
|
---|
| 62 | get { return (IValueParameter<IntMatrix>)Parameters["RejectedStats"]; }
|
---|
[14072] | 63 | }
|
---|
[14104] | 64 | public IValueParameter<IntMatrix> NotRejectedStatsParameter {
|
---|
| 65 | get { return (IValueParameter<IntMatrix>)Parameters["TotalStats"]; }
|
---|
[14072] | 66 | }
|
---|
| 67 | public IValueLookupParameter<DoubleValue> ComparisonFactorParameter {
|
---|
| 68 | get { return (ValueLookupParameter<DoubleValue>)Parameters["ComparisonFactor"]; }
|
---|
| 69 | }
|
---|
| 70 | public IFixedValueParameter<PercentValue> RelativeParentChildQualityThresholdParameter {
|
---|
| 71 | get { return (IFixedValueParameter<PercentValue>)Parameters[RelativeParentChildQualityThresholdParameterName]; }
|
---|
| 72 | }
|
---|
| 73 | public IFixedValueParameter<PercentValue> RelativeFitnessEvaluationIntervalSizeParameter {
|
---|
| 74 | get { return (IFixedValueParameter<PercentValue>)Parameters[RelativeFitnessEvaluationIntervalSizeParameterName]; }
|
---|
| 75 | }
|
---|
| 76 | public IScopeTreeLookupParameter<DoubleValue> ParentQualitiesParameter { get { return (IScopeTreeLookupParameter<DoubleValue>)Parameters["ParentQualities"]; } }
|
---|
| 77 | #endregion
|
---|
| 78 |
|
---|
| 79 | #region parameter properties
|
---|
[14279] | 80 | public bool UseFixedEvaluationIntervals {
|
---|
| 81 | get { return UseFixedEvaluationIntervalsParameter.Value.Value; }
|
---|
| 82 | set { UseFixedEvaluationIntervalsParameter.Value.Value = value; }
|
---|
| 83 | }
|
---|
| 84 | public bool UseAdaptiveQualityThreshold {
|
---|
| 85 | get { return UseAdaptiveQualityThresholdParameter.Value.Value; }
|
---|
| 86 | set { UseAdaptiveQualityThresholdParameter.Value.Value = value; }
|
---|
| 87 | }
|
---|
[14072] | 88 | public double RelativeParentChildQualityThreshold {
|
---|
| 89 | get { return RelativeParentChildQualityThresholdParameter.Value.Value; }
|
---|
| 90 | set { RelativeParentChildQualityThresholdParameter.Value.Value = value; }
|
---|
| 91 | }
|
---|
| 92 |
|
---|
| 93 | public double RelativeFitnessEvaluationIntervalSize {
|
---|
| 94 | get { return RelativeFitnessEvaluationIntervalSizeParameter.Value.Value; }
|
---|
| 95 | set { RelativeFitnessEvaluationIntervalSizeParameter.Value.Value = value; }
|
---|
| 96 | }
|
---|
| 97 |
|
---|
[14104] | 98 | public IntMatrix RejectedStats {
|
---|
| 99 | get { return RejectedStatsParameter.Value; }
|
---|
| 100 | set { RejectedStatsParameter.Value = value; }
|
---|
[14072] | 101 | }
|
---|
| 102 |
|
---|
[14104] | 103 | public IntMatrix TotalStats {
|
---|
| 104 | get { return NotRejectedStatsParameter.Value; }
|
---|
| 105 | set { NotRejectedStatsParameter.Value = value; }
|
---|
[14072] | 106 | }
|
---|
| 107 | #endregion
|
---|
| 108 |
|
---|
| 109 | public override bool Maximization {
|
---|
| 110 | get { return true; }
|
---|
| 111 | }
|
---|
| 112 |
|
---|
| 113 | public SymbolicRegressionSingleObjectiveOsgaEvaluator() {
|
---|
| 114 | Parameters.Add(new ValueLookupParameter<DoubleValue>("ComparisonFactor", "Determines if the quality should be compared to the better parent (1.0), to the worse (0.0) or to any linearly interpolated value between them."));
|
---|
[14104] | 115 | Parameters.Add(new FixedValueParameter<PercentValue>(RelativeParentChildQualityThresholdParameterName, new PercentValue(0.9)));
|
---|
[14072] | 116 | Parameters.Add(new FixedValueParameter<PercentValue>(RelativeFitnessEvaluationIntervalSizeParameterName, new PercentValue(0.1)));
|
---|
| 117 | Parameters.Add(new LookupParameter<ResultCollection>(ResultCollectionParameterName));
|
---|
| 118 | Parameters.Add(new ScopeTreeLookupParameter<DoubleValue>("ParentQualities") { ActualName = "Quality" });
|
---|
[14104] | 119 | Parameters.Add(new ValueParameter<IntMatrix>("RejectedStats", new IntMatrix()));
|
---|
| 120 | Parameters.Add(new ValueParameter<IntMatrix>("TotalStats", new IntMatrix()));
|
---|
[14231] | 121 | Parameters.Add(new ValueParameter<BoolValue>(AggregateStatisticsParameterName, new BoolValue(false)));
|
---|
[14279] | 122 | Parameters.Add(new LookupParameter<DoubleValue>(ActualSelectionPressureParameterName));
|
---|
| 123 | Parameters.Add(new FixedValueParameter<BoolValue>(UseAdaptiveQualityThresholdParameterName, new BoolValue(false)));
|
---|
| 124 | Parameters.Add(new FixedValueParameter<BoolValue>(UseFixedEvaluationIntervalsParameterName, new BoolValue(false)));
|
---|
[14072] | 125 | }
|
---|
| 126 |
|
---|
| 127 | [StorableHook(HookType.AfterDeserialization)]
|
---|
| 128 | private void AfterDeserialization() {
|
---|
[14279] | 129 | if (!Parameters.ContainsKey(ActualSelectionPressureParameterName))
|
---|
| 130 | Parameters.Add(new LookupParameter<DoubleValue>(ActualSelectionPressureParameterName));
|
---|
[14072] | 131 |
|
---|
[14279] | 132 | if (!Parameters.ContainsKey(UseAdaptiveQualityThresholdParameterName))
|
---|
| 133 | Parameters.Add(new FixedValueParameter<BoolValue>(UseAdaptiveQualityThresholdParameterName, new BoolValue(false)));
|
---|
[14104] | 134 |
|
---|
[14279] | 135 | if (!Parameters.ContainsKey(UseFixedEvaluationIntervalsParameterName))
|
---|
| 136 | Parameters.Add(new FixedValueParameter<BoolValue>(UseFixedEvaluationIntervalsParameterName, new BoolValue(false)));
|
---|
[14072] | 137 | }
|
---|
| 138 |
|
---|
| 139 | [StorableConstructor]
|
---|
| 140 | protected SymbolicRegressionSingleObjectiveOsgaEvaluator(bool deserializing) : base(deserializing) { }
|
---|
| 141 |
|
---|
| 142 | protected SymbolicRegressionSingleObjectiveOsgaEvaluator(SymbolicRegressionSingleObjectiveOsgaEvaluator original, Cloner cloner) : base(original, cloner) { }
|
---|
| 143 |
|
---|
| 144 | public override IDeepCloneable Clone(Cloner cloner) {
|
---|
| 145 | return new SymbolicRegressionSingleObjectiveOsgaEvaluator(this, cloner);
|
---|
| 146 | }
|
---|
| 147 |
|
---|
| 148 | public override void ClearState() {
|
---|
| 149 | base.ClearState();
|
---|
[14104] | 150 | RejectedStats = new IntMatrix();
|
---|
| 151 | TotalStats = new IntMatrix();
|
---|
[14072] | 152 | }
|
---|
| 153 |
|
---|
| 154 | public override IOperation InstrumentedApply() {
|
---|
| 155 | var solution = SymbolicExpressionTreeParameter.ActualValue;
|
---|
| 156 | IEnumerable<int> rows = GenerateRowsToEvaluate();
|
---|
| 157 |
|
---|
| 158 | var interpreter = SymbolicDataAnalysisTreeInterpreterParameter.ActualValue;
|
---|
| 159 | var estimationLimits = EstimationLimitsParameter.ActualValue;
|
---|
| 160 | var problemData = ProblemDataParameter.ActualValue;
|
---|
| 161 | var applyLinearScaling = ApplyLinearScalingParameter.ActualValue.Value;
|
---|
| 162 |
|
---|
| 163 | double quality;
|
---|
| 164 | var parentQualities = ParentQualitiesParameter.ActualValue;
|
---|
| 165 |
|
---|
| 166 | // parent subscopes are not present during evaluation of the initial population
|
---|
| 167 | if (parentQualities.Length > 0) {
|
---|
[14279] | 168 | quality = Calculate(interpreter, solution, estimationLimits, problemData, rows);
|
---|
[14072] | 169 | } else {
|
---|
| 170 | quality = Calculate(interpreter, solution, estimationLimits.Lower, estimationLimits.Upper, problemData, rows, applyLinearScaling);
|
---|
| 171 | }
|
---|
| 172 | QualityParameter.ActualValue = new DoubleValue(quality);
|
---|
| 173 |
|
---|
| 174 | return base.InstrumentedApply();
|
---|
| 175 | }
|
---|
| 176 |
|
---|
| 177 | public static double Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IRegressionProblemData problemData, IEnumerable<int> rows, bool applyLinearScaling) {
|
---|
| 178 | IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, rows);
|
---|
| 179 | IEnumerable<double> targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
|
---|
| 180 | OnlineCalculatorError errorState;
|
---|
| 181 |
|
---|
| 182 | double r;
|
---|
| 183 | if (applyLinearScaling) {
|
---|
| 184 | var rCalculator = new OnlinePearsonsRCalculator();
|
---|
| 185 | CalculateWithScaling(targetValues, estimatedValues, lowerEstimationLimit, upperEstimationLimit, rCalculator, problemData.Dataset.Rows);
|
---|
| 186 | errorState = rCalculator.ErrorState;
|
---|
| 187 | r = rCalculator.R;
|
---|
| 188 | } else {
|
---|
| 189 | IEnumerable<double> boundedEstimatedValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit);
|
---|
| 190 | r = OnlinePearsonsRCalculator.Calculate(targetValues, boundedEstimatedValues, out errorState);
|
---|
| 191 | }
|
---|
| 192 | if (errorState != OnlineCalculatorError.None) return double.NaN;
|
---|
| 193 | return r * r;
|
---|
| 194 | }
|
---|
| 195 |
|
---|
[14279] | 196 | private double Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, DoubleLimit estimationLimits, IRegressionProblemData problemData, IEnumerable<int> rows) {
|
---|
[14184] | 197 | var estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, rows).LimitToRange(estimationLimits.Lower, estimationLimits.Upper);
|
---|
[14279] | 198 | var targetValues = problemData.Dataset.GetReadOnlyDoubleValues(problemData.TargetVariable);
|
---|
[14072] | 199 |
|
---|
| 200 | var parentQualities = ParentQualitiesParameter.ActualValue.Select(x => x.Value);
|
---|
| 201 | var minQuality = parentQualities.Min();
|
---|
| 202 | var maxQuality = parentQualities.Max();
|
---|
| 203 | var comparisonFactor = ComparisonFactorParameter.ActualValue.Value;
|
---|
| 204 | var parentQuality = minQuality + (maxQuality - minQuality) * comparisonFactor;
|
---|
| 205 |
|
---|
[14279] | 206 | var e = estimatedValues.GetEnumerator();
|
---|
[14072] | 207 |
|
---|
[14280] | 208 | #region fixed intervals
|
---|
[14279] | 209 | if (UseFixedEvaluationIntervals) {
|
---|
| 210 | double threshold = parentQuality * RelativeParentChildQualityThreshold;
|
---|
| 211 | if (UseAdaptiveQualityThreshold) {
|
---|
| 212 | var actualSelectionPressure = ActualSelectionPressureParameter.ActualValue;
|
---|
| 213 | if (actualSelectionPressure != null)
|
---|
| 214 | threshold = parentQuality * (1 - actualSelectionPressure.Value / 100.0);
|
---|
[14072] | 215 | }
|
---|
[14231] | 216 |
|
---|
[14279] | 217 | var pearsonRCalculator = new OnlinePearsonsRCalculator();
|
---|
| 218 | var targetValuesEnumerator = targetValues.GetEnumerator();
|
---|
| 219 | var trainingPartitionSize = problemData.TrainingPartition.Size;
|
---|
| 220 | var interval = (int)Math.Floor(trainingPartitionSize * RelativeFitnessEvaluationIntervalSize);
|
---|
[14231] | 221 |
|
---|
[14279] | 222 | var aggregateStatistics = AggregateStatisticsParameter.Value.Value;
|
---|
| 223 | var i = 0;
|
---|
| 224 | if (aggregateStatistics) {
|
---|
| 225 | var trainingEnd = problemData.TrainingPartition.End;
|
---|
| 226 | var qualityPerInterval = new List<double>();
|
---|
| 227 | while (targetValuesEnumerator.MoveNext() && e.MoveNext()) {
|
---|
| 228 | pearsonRCalculator.Add(targetValuesEnumerator.Current, e.Current);
|
---|
| 229 | ++i;
|
---|
| 230 | if (i % interval == 0 || i == trainingPartitionSize) {
|
---|
| 231 | var q = pearsonRCalculator.ErrorState != OnlineCalculatorError.None ? double.NaN : pearsonRCalculator.R;
|
---|
| 232 | qualityPerInterval.Add(q * q);
|
---|
| 233 | }
|
---|
[14231] | 234 | }
|
---|
[14279] | 235 | var r = pearsonRCalculator.ErrorState != OnlineCalculatorError.None ? double.NaN : pearsonRCalculator.R;
|
---|
| 236 | var actualQuality = r * r;
|
---|
[14231] | 237 |
|
---|
[14279] | 238 | bool predictedRejected = false;
|
---|
[14231] | 239 |
|
---|
[14279] | 240 | i = 0;
|
---|
| 241 | double quality = actualQuality;
|
---|
| 242 | foreach (var q in qualityPerInterval) {
|
---|
| 243 | if (double.IsNaN(q) || !(q > threshold)) {
|
---|
| 244 | predictedRejected = true;
|
---|
| 245 | quality = q;
|
---|
| 246 | break;
|
---|
| 247 | }
|
---|
| 248 | ++i;
|
---|
| 249 | }
|
---|
| 250 |
|
---|
| 251 | var actuallyRejected = !(actualQuality > parentQuality);
|
---|
| 252 |
|
---|
| 253 | if (RejectedStats.Rows == 0 || TotalStats.Rows == 0) {
|
---|
| 254 | RejectedStats = new IntMatrix(2, qualityPerInterval.Count);
|
---|
| 255 | RejectedStats.RowNames = new[] { "Predicted", "Actual" };
|
---|
| 256 | RejectedStats.ColumnNames = Enumerable.Range(1, RejectedStats.Columns).Select(x => string.Format("0-{0}", Math.Min(trainingEnd, x * interval)));
|
---|
| 257 | TotalStats = new IntMatrix(2, 2);
|
---|
| 258 | TotalStats.RowNames = new[] { "Predicted", "Actual" };
|
---|
| 259 | TotalStats.ColumnNames = new[] { "Rejected", "Not Rejected" };
|
---|
| 260 | }
|
---|
| 261 | // gather some statistics
|
---|
| 262 | if (predictedRejected) {
|
---|
| 263 | RejectedStats[0, i]++;
|
---|
| 264 | TotalStats[0, 0]++;
|
---|
| 265 | } else {
|
---|
| 266 | TotalStats[0, 1]++;
|
---|
| 267 | }
|
---|
| 268 | if (actuallyRejected) {
|
---|
| 269 | TotalStats[1, 0]++;
|
---|
| 270 | } else {
|
---|
| 271 | TotalStats[1, 1]++;
|
---|
| 272 | }
|
---|
| 273 | if (predictedRejected && actuallyRejected) {
|
---|
| 274 | RejectedStats[1, i]++;
|
---|
| 275 | }
|
---|
| 276 | return quality;
|
---|
[14231] | 277 | } else {
|
---|
[14279] | 278 | while (targetValuesEnumerator.MoveNext() && e.MoveNext()) {
|
---|
| 279 | pearsonRCalculator.Add(targetValuesEnumerator.Current, e.Current);
|
---|
| 280 | ++i;
|
---|
| 281 | if (i % interval == 0 || i == trainingPartitionSize) {
|
---|
| 282 | var q = pearsonRCalculator.ErrorState != OnlineCalculatorError.None ? double.NaN : pearsonRCalculator.R;
|
---|
| 283 | var quality = q * q;
|
---|
| 284 | if (!(quality > threshold))
|
---|
| 285 | return quality;
|
---|
| 286 | }
|
---|
| 287 | }
|
---|
| 288 | var r = pearsonRCalculator.ErrorState != OnlineCalculatorError.None ? double.NaN : pearsonRCalculator.R;
|
---|
| 289 | var actualQuality = r * r;
|
---|
| 290 | return actualQuality;
|
---|
[14231] | 291 | }
|
---|
[14280] | 292 | #endregion
|
---|
[14231] | 293 | } else {
|
---|
[14279] | 294 | var calculator = new OnlinePearsonsRCalculator();
|
---|
| 295 | var trainingPartitionSize = problemData.TrainingPartition.Size;
|
---|
| 296 | var interval = (int)Math.Floor(trainingPartitionSize * RelativeFitnessEvaluationIntervalSize);
|
---|
| 297 | double quality = double.NaN;
|
---|
| 298 | var estimated = new List<double>(); // save estimated values in a list so we don't re-evaluate
|
---|
| 299 | // use the actual estimated values for the first i * interval rows of the training partition and and assume the remaining rows are perfectly correlated
|
---|
| 300 | // if the quality of the individual still falls below the parent quality, then we can reject it sooner, otherwise as i increases the whole estimated series will be used
|
---|
| 301 | for (int i = 0; i < trainingPartitionSize; i += interval) {
|
---|
[14280] | 302 | calculator.Reset();
|
---|
| 303 | // save estimated values into the list (for caching)
|
---|
[14279] | 304 | int j = i;
|
---|
| 305 | int end = Math.Min(trainingPartitionSize, i + interval);
|
---|
| 306 | while (j < end && e.MoveNext()) {
|
---|
| 307 | estimated.Add(e.Current);
|
---|
| 308 | j++;
|
---|
[14231] | 309 | }
|
---|
[14279] | 310 | var start = problemData.TrainingPartition.Start;
|
---|
| 311 | // add (estimated, target) pairs to the calculator
|
---|
[14280] | 312 | for (j = 0; j < end; ++j) {
|
---|
| 313 | var index = j + start;
|
---|
| 314 | calculator.Add(targetValues[index], estimated[j]);
|
---|
| 315 | }
|
---|
[14279] | 316 | // add (target, target) pairs to the calculator (simulate perfect correlation on the remaining rows)
|
---|
| 317 | for (; j < trainingPartitionSize; ++j) {
|
---|
| 318 | var index = j + start;
|
---|
[14280] | 319 | var v = targetValues[index];
|
---|
| 320 | calculator.Add(v, v);
|
---|
[14279] | 321 | }
|
---|
| 322 | var r = calculator.ErrorState == OnlineCalculatorError.None ? calculator.R : double.NaN;
|
---|
| 323 | quality = r * r;
|
---|
| 324 | if (!(quality > parentQuality))
|
---|
| 325 | break;
|
---|
[14231] | 326 | }
|
---|
[14279] | 327 | return quality;
|
---|
[14072] | 328 | }
|
---|
| 329 | }
|
---|
| 330 |
|
---|
| 331 | public override double Evaluate(IExecutionContext context, ISymbolicExpressionTree tree, IRegressionProblemData problemData, IEnumerable<int> rows) {
|
---|
| 332 | SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
|
---|
| 333 | EstimationLimitsParameter.ExecutionContext = context;
|
---|
| 334 | ApplyLinearScalingParameter.ExecutionContext = context;
|
---|
| 335 |
|
---|
| 336 | var interpreter = SymbolicDataAnalysisTreeInterpreterParameter.ActualValue;
|
---|
| 337 | var estimationLimits = EstimationLimitsParameter.ActualValue;
|
---|
| 338 | var applyLinearScaling = ApplyLinearScalingParameter.ActualValue.Value;
|
---|
| 339 |
|
---|
| 340 | double r2 = Calculate(interpreter, tree, estimationLimits.Lower, estimationLimits.Upper, problemData, rows, applyLinearScaling);
|
---|
| 341 |
|
---|
| 342 | SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
|
---|
| 343 | EstimationLimitsParameter.ExecutionContext = null;
|
---|
| 344 | ApplyLinearScalingParameter.ExecutionContext = null;
|
---|
| 345 |
|
---|
| 346 | return r2;
|
---|
| 347 | }
|
---|
| 348 | }
|
---|
| 349 | }
|
---|