[17958] | 1 | #region License Information
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| 2 | /* HeuristicLab
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| 3 | * Copyright (C) Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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| 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 HEAL.Attic;
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| 26 | using HeuristicLab.Common;
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| 27 | using HeuristicLab.Core;
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| 28 | using HeuristicLab.Data;
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| 29 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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| 30 | using HeuristicLab.Parameters;
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| 31 |
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| 32 | namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
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| 33 | [Item("NMSE Evaluator with shape-constraints (single-objective)", "Calculates NMSE of a symbolic regression solution and checks constraints. The fitness is a combination of NMSE and constraint violations.")]
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| 34 | [StorableType("27473973-DD8D-4375-997D-942E2280AE8E")]
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| 35 | public class NMSESingleObjectiveConstraintsEvaluator : SymbolicRegressionSingleObjectiveEvaluator {
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| 36 | #region Parameter/Properties
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| 37 |
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| 38 | private const string OptimizeParametersParameterName = "OptimizeParameters";
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| 39 | private const string ParameterOptimizationIterationsParameterName = "ParameterOptimizationIterations";
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| 40 | private const string UseSoftConstraintsParameterName = "UseSoftConstraintsEvaluation";
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| 41 | private const string BoundsEstimatorParameterName = "BoundsEstimator";
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| 42 | private const string PenaltyFactorParameterName = "PenaltyFactor";
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| 43 |
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| 44 |
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| 45 | public IFixedValueParameter<BoolValue> OptimizerParametersParameter =>
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| 46 | (IFixedValueParameter<BoolValue>)Parameters[OptimizeParametersParameterName];
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| 47 |
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| 48 | public IFixedValueParameter<IntValue> ConstantOptimizationIterationsParameter =>
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| 49 | (IFixedValueParameter<IntValue>)Parameters[ParameterOptimizationIterationsParameterName];
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| 50 |
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| 51 | public IFixedValueParameter<BoolValue> UseSoftConstraintsParameter =>
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| 52 | (IFixedValueParameter<BoolValue>)Parameters[UseSoftConstraintsParameterName];
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| 53 |
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| 54 | public IValueParameter<IBoundsEstimator> BoundsEstimatorParameter =>
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| 55 | (IValueParameter<IBoundsEstimator>)Parameters[BoundsEstimatorParameterName];
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| 56 | public IFixedValueParameter<DoubleValue> PenaltyFactorParameter =>
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| 57 | (IFixedValueParameter<DoubleValue>)Parameters[PenaltyFactorParameterName];
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| 58 |
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| 59 | public bool OptimizeParameters {
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| 60 | get => OptimizerParametersParameter.Value.Value;
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| 61 | set => OptimizerParametersParameter.Value.Value = value;
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| 62 | }
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| 63 |
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| 64 | public int ConstantOptimizationIterations {
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| 65 | get => ConstantOptimizationIterationsParameter.Value.Value;
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| 66 | set => ConstantOptimizationIterationsParameter.Value.Value = value;
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| 67 | }
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| 68 |
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| 69 | public bool UseSoftConstraints {
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| 70 | get => UseSoftConstraintsParameter.Value.Value;
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| 71 | set => UseSoftConstraintsParameter.Value.Value = value;
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| 72 | }
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| 73 |
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| 74 | public IBoundsEstimator BoundsEstimator {
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| 75 | get => BoundsEstimatorParameter.Value;
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| 76 | set => BoundsEstimatorParameter.Value = value;
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| 77 | }
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| 78 |
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| 79 | public double PenalityFactor {
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| 80 | get => PenaltyFactorParameter.Value.Value;
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| 81 | set => PenaltyFactorParameter.Value.Value = value;
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| 82 | }
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| 83 |
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| 84 |
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| 85 | public override bool Maximization => false; // NMSE is minimized
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| 86 |
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| 87 | #endregion
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| 88 |
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| 89 | #region Constructors/Cloning
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| 90 |
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| 91 | [StorableConstructor]
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| 92 | protected NMSESingleObjectiveConstraintsEvaluator(StorableConstructorFlag _) : base(_) { }
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| 93 |
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| 94 | protected NMSESingleObjectiveConstraintsEvaluator(
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| 95 | NMSESingleObjectiveConstraintsEvaluator original, Cloner cloner) : base(original, cloner) { }
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| 96 |
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| 97 | public NMSESingleObjectiveConstraintsEvaluator() {
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| 98 | Parameters.Add(new FixedValueParameter<BoolValue>(OptimizeParametersParameterName,
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| 99 | "Define whether optimization of numeric parameters is active or not (default: false).", new BoolValue(false)));
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| 100 | Parameters.Add(new FixedValueParameter<IntValue>(ParameterOptimizationIterationsParameterName,
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| 101 | "Define how many parameter optimization steps should be performed (default: 10).", new IntValue(10)));
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| 102 | Parameters.Add(new FixedValueParameter<BoolValue>(UseSoftConstraintsParameterName,
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| 103 | "Define whether the constraints are penalized by soft or hard constraints (default: false).", new BoolValue(false)));
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| 104 | Parameters.Add(new ValueParameter<IBoundsEstimator>(BoundsEstimatorParameterName,
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| 105 | "The estimator which is used to estimate output ranges of models (default: interval arithmetic).", new IntervalArithBoundsEstimator()));
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| 106 | Parameters.Add(new FixedValueParameter<DoubleValue>(PenaltyFactorParameterName,
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| 107 | "Punishment factor for constraint violations for soft constraint handling (fitness = NMSE + penaltyFactor * avg(violations)) (default: 1.0)", new DoubleValue(1.0)));
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| 108 | }
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| 109 |
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| 110 | [StorableHook(HookType.AfterDeserialization)]
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| 111 | private void AfterDeserialization() { }
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| 112 |
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| 113 | public override IDeepCloneable Clone(Cloner cloner) {
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| 114 | return new NMSESingleObjectiveConstraintsEvaluator(this, cloner);
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| 115 | }
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| 116 |
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| 117 | #endregion
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| 118 |
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| 119 | public override IOperation InstrumentedApply() {
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| 120 | var rows = GenerateRowsToEvaluate();
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| 121 | var tree = SymbolicExpressionTreeParameter.ActualValue;
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| 122 | var problemData = ProblemDataParameter.ActualValue;
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| 123 | var interpreter = SymbolicDataAnalysisTreeInterpreterParameter.ActualValue;
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| 124 | var estimationLimits = EstimationLimitsParameter.ActualValue;
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| 125 | var applyLinearScaling = ApplyLinearScalingParameter.ActualValue.Value;
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| 126 |
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| 127 | if (OptimizeParameters) {
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| 128 | SymbolicRegressionConstantOptimizationEvaluator.OptimizeConstants(interpreter, tree, problemData, rows,
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| 129 | false, ConstantOptimizationIterations, true,
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| 130 | estimationLimits.Lower, estimationLimits.Upper);
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| 131 | } else {
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| 132 | if (applyLinearScaling) {
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| 133 | var rootNode = new ProgramRootSymbol().CreateTreeNode();
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| 134 | var startNode = new StartSymbol().CreateTreeNode();
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| 135 | var offset = tree.Root.GetSubtree(0) //Start
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| 136 | .GetSubtree(0); //Offset
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| 137 | var scaling = offset.GetSubtree(0);
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| 138 |
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| 139 | //Check if tree contains offset and scaling nodes
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| 140 | if (!(offset.Symbol is Addition) || !(scaling.Symbol is Multiplication))
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| 141 | throw new ArgumentException($"{ItemName} can only be used with LinearScalingGrammar.");
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| 142 |
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| 143 | var t = (ISymbolicExpressionTreeNode)scaling.GetSubtree(0).Clone();
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| 144 | rootNode.AddSubtree(startNode);
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| 145 | startNode.AddSubtree(t);
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| 146 | var newTree = new SymbolicExpressionTree(rootNode);
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| 147 |
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| 148 | //calculate alpha and beta for scaling
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| 149 | var estimatedValues = interpreter.GetSymbolicExpressionTreeValues(newTree, problemData.Dataset, rows);
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| 150 |
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| 151 | var targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
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| 152 | OnlineLinearScalingParameterCalculator.Calculate(estimatedValues, targetValues, out var alpha, out var beta,
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| 153 | out var errorState);
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| 154 |
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| 155 | if (errorState == OnlineCalculatorError.None) {
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| 156 | //Set alpha and beta to the scaling nodes from ia grammar
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| 157 | var offsetParameter = offset.GetSubtree(1) as ConstantTreeNode;
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| 158 | offsetParameter.Value = alpha;
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| 159 | var scalingParameter = scaling.GetSubtree(1) as ConstantTreeNode;
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| 160 | scalingParameter.Value = beta;
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| 161 | }
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| 162 | } // else: alpha and beta are evolved
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| 163 | }
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| 164 |
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| 165 | var quality = Calculate(interpreter, tree, estimationLimits.Lower, estimationLimits.Upper, problemData, rows,
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| 166 | BoundsEstimator, UseSoftConstraints, PenalityFactor);
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| 167 | QualityParameter.ActualValue = new DoubleValue(quality);
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| 168 |
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| 169 | return base.InstrumentedApply();
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| 170 | }
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| 171 |
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| 172 | public static double Calculate(
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| 173 | ISymbolicDataAnalysisExpressionTreeInterpreter interpreter,
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| 174 | ISymbolicExpressionTree tree,
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| 175 | double lowerEstimationLimit, double upperEstimationLimit,
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| 176 | IRegressionProblemData problemData, IEnumerable<int> rows,
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| 177 | IBoundsEstimator estimator,
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| 178 | bool useSoftConstraints = false, double penaltyFactor = 1.0) {
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| 179 |
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| 180 | var estimatedValues = interpreter.GetSymbolicExpressionTreeValues(tree, problemData.Dataset, rows);
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| 181 | var targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
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| 182 | var constraints = Enumerable.Empty<ShapeConstraint>();
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| 183 | if (problemData is ShapeConstrainedRegressionProblemData scProbData) {
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| 184 | constraints = scProbData.ShapeConstraints.EnabledConstraints;
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| 185 | }
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| 186 | var intervalCollection = problemData.VariableRanges;
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| 187 |
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| 188 | var boundedEstimatedValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit);
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| 189 | var nmse = OnlineNormalizedMeanSquaredErrorCalculator.Calculate(targetValues, boundedEstimatedValues,
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| 190 | out var errorState);
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| 191 |
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| 192 | if (errorState != OnlineCalculatorError.None) {
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| 193 | return 1.0;
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| 194 | }
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| 195 |
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| 196 | var constraintViolations = IntervalUtil.GetConstraintViolations(constraints, estimator, intervalCollection, tree);
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| 197 |
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| 198 | if (constraintViolations.Any(x => double.IsNaN(x) || double.IsInfinity(x))) {
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| 199 | return 1.0;
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| 200 | }
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| 201 |
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| 202 | if (useSoftConstraints) {
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| 203 | if (penaltyFactor < 0.0)
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| 204 | throw new ArgumentException("The parameter has to be >= 0.0.", nameof(penaltyFactor));
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| 205 |
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| 206 | var weightedViolationsAvg = constraints
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| 207 | .Zip(constraintViolations, (c, v) => c.Weight * v)
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| 208 | .Average();
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| 209 |
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| 210 | return Math.Min(nmse, 1.0) + penaltyFactor * weightedViolationsAvg;
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| 211 | } else if (constraintViolations.Any(x => x > 0.0)) {
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| 212 | return 1.0;
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| 213 | }
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| 214 |
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| 215 | return nmse;
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| 216 | }
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| 217 |
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| 218 | public override double Evaluate(
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| 219 | IExecutionContext context, ISymbolicExpressionTree tree, IRegressionProblemData problemData,
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| 220 | IEnumerable<int> rows) {
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| 221 | SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
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| 222 | EstimationLimitsParameter.ExecutionContext = context;
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| 223 | ApplyLinearScalingParameter.ExecutionContext = context;
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| 224 |
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| 225 | var nmse = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree,
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| 226 | EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper,
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| 227 | problemData, rows, BoundsEstimator, UseSoftConstraints, PenalityFactor);
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| 228 |
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| 229 | SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
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| 230 | EstimationLimitsParameter.ExecutionContext = null;
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| 231 | ApplyLinearScalingParameter.ExecutionContext = null;
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| 232 |
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| 233 | return nmse;
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| 234 | }
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| 235 | }
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| 236 | } |
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