[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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[18181] | 43 | private const string ExtendedConstraintsParameterName = "ExtendedConstraints";
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[17958] | 44 |
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| 45 |
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| 46 | public IFixedValueParameter<BoolValue> OptimizerParametersParameter =>
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| 47 | (IFixedValueParameter<BoolValue>)Parameters[OptimizeParametersParameterName];
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| 48 |
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[18180] | 49 | public IFixedValueParameter<IntValue> ParameterOptimizationIterationsParameter =>
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[17958] | 50 | (IFixedValueParameter<IntValue>)Parameters[ParameterOptimizationIterationsParameterName];
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| 51 |
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| 52 | public IFixedValueParameter<BoolValue> UseSoftConstraintsParameter =>
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| 53 | (IFixedValueParameter<BoolValue>)Parameters[UseSoftConstraintsParameterName];
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| 54 |
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| 55 | public IValueParameter<IBoundsEstimator> BoundsEstimatorParameter =>
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| 56 | (IValueParameter<IBoundsEstimator>)Parameters[BoundsEstimatorParameterName];
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| 57 | public IFixedValueParameter<DoubleValue> PenaltyFactorParameter =>
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| 58 | (IFixedValueParameter<DoubleValue>)Parameters[PenaltyFactorParameterName];
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| 59 |
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[18181] | 60 | public IFixedValueParameter<IItemList<ExtendedConstraint>> ExtendedConstraintsParameter =>
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| 61 | (IFixedValueParameter<IItemList<ExtendedConstraint>>)Parameters[ExtendedConstraintsParameterName];
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| 62 |
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| 63 |
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[17958] | 64 | public bool OptimizeParameters {
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| 65 | get => OptimizerParametersParameter.Value.Value;
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| 66 | set => OptimizerParametersParameter.Value.Value = value;
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| 67 | }
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| 68 |
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[18180] | 69 | public int ParameterOptimizationIterations {
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| 70 | get => ParameterOptimizationIterationsParameter.Value.Value;
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| 71 | set => ParameterOptimizationIterationsParameter.Value.Value = value;
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[17958] | 72 | }
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| 73 |
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| 74 | public bool UseSoftConstraints {
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| 75 | get => UseSoftConstraintsParameter.Value.Value;
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| 76 | set => UseSoftConstraintsParameter.Value.Value = value;
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| 77 | }
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| 78 |
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| 79 | public IBoundsEstimator BoundsEstimator {
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| 80 | get => BoundsEstimatorParameter.Value;
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| 81 | set => BoundsEstimatorParameter.Value = value;
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| 82 | }
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| 83 |
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| 84 | public double PenalityFactor {
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| 85 | get => PenaltyFactorParameter.Value.Value;
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| 86 | set => PenaltyFactorParameter.Value.Value = value;
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| 87 | }
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| 88 |
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[18181] | 89 | public IEnumerable<ExtendedConstraint> ExtendedConstraints {
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| 90 | get => ExtendedConstraintsParameter.Value;
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| 91 | }
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| 92 |
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[17958] | 93 |
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[18181] | 94 |
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[17958] | 95 | public override bool Maximization => false; // NMSE is minimized
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| 96 |
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| 97 | #endregion
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| 98 |
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| 99 | #region Constructors/Cloning
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| 100 |
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| 101 | [StorableConstructor]
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| 102 | protected NMSESingleObjectiveConstraintsEvaluator(StorableConstructorFlag _) : base(_) { }
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| 103 |
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| 104 | protected NMSESingleObjectiveConstraintsEvaluator(
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| 105 | NMSESingleObjectiveConstraintsEvaluator original, Cloner cloner) : base(original, cloner) { }
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| 106 |
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| 107 | public NMSESingleObjectiveConstraintsEvaluator() {
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| 108 | Parameters.Add(new FixedValueParameter<BoolValue>(OptimizeParametersParameterName,
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[18180] | 109 | "Define whether optimization of parameters is active or not (default: false).", new BoolValue(false)));
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[17958] | 110 | Parameters.Add(new FixedValueParameter<IntValue>(ParameterOptimizationIterationsParameterName,
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| 111 | "Define how many parameter optimization steps should be performed (default: 10).", new IntValue(10)));
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| 112 | Parameters.Add(new FixedValueParameter<BoolValue>(UseSoftConstraintsParameterName,
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| 113 | "Define whether the constraints are penalized by soft or hard constraints (default: false).", new BoolValue(false)));
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| 114 | Parameters.Add(new ValueParameter<IBoundsEstimator>(BoundsEstimatorParameterName,
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| 115 | "The estimator which is used to estimate output ranges of models (default: interval arithmetic).", new IntervalArithBoundsEstimator()));
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| 116 | Parameters.Add(new FixedValueParameter<DoubleValue>(PenaltyFactorParameterName,
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| 117 | "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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[18181] | 118 | Parameters.Add(new FixedValueParameter<ItemList<ExtendedConstraint>>(ExtendedConstraintsParameterName, "", new ItemList<ExtendedConstraint>()));
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[17958] | 119 | }
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| 120 |
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| 121 | [StorableHook(HookType.AfterDeserialization)]
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| 122 | private void AfterDeserialization() { }
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| 123 |
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| 124 | public override IDeepCloneable Clone(Cloner cloner) {
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| 125 | return new NMSESingleObjectiveConstraintsEvaluator(this, cloner);
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| 126 | }
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| 127 |
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| 128 | #endregion
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| 129 |
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| 130 | public override IOperation InstrumentedApply() {
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| 131 | var rows = GenerateRowsToEvaluate();
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| 132 | var tree = SymbolicExpressionTreeParameter.ActualValue;
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| 133 | var problemData = ProblemDataParameter.ActualValue;
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| 134 | var interpreter = SymbolicDataAnalysisTreeInterpreterParameter.ActualValue;
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| 135 | var estimationLimits = EstimationLimitsParameter.ActualValue;
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| 136 | var applyLinearScaling = ApplyLinearScalingParameter.ActualValue.Value;
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| 137 |
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| 138 | if (OptimizeParameters) {
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[18180] | 139 | SymbolicRegressionParameterOptimizationEvaluator.OptimizeParameters(interpreter, tree, problemData, rows,
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| 140 | false, ParameterOptimizationIterations, true,
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[17958] | 141 | estimationLimits.Lower, estimationLimits.Upper);
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| 142 | } else {
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| 143 | if (applyLinearScaling) {
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| 144 | var rootNode = new ProgramRootSymbol().CreateTreeNode();
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| 145 | var startNode = new StartSymbol().CreateTreeNode();
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| 146 | var offset = tree.Root.GetSubtree(0) //Start
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| 147 | .GetSubtree(0); //Offset
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| 148 | var scaling = offset.GetSubtree(0);
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| 149 |
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| 150 | //Check if tree contains offset and scaling nodes
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| 151 | if (!(offset.Symbol is Addition) || !(scaling.Symbol is Multiplication))
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| 152 | throw new ArgumentException($"{ItemName} can only be used with LinearScalingGrammar.");
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| 153 |
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| 154 | var t = (ISymbolicExpressionTreeNode)scaling.GetSubtree(0).Clone();
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| 155 | rootNode.AddSubtree(startNode);
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| 156 | startNode.AddSubtree(t);
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| 157 | var newTree = new SymbolicExpressionTree(rootNode);
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| 158 |
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| 159 | //calculate alpha and beta for scaling
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| 160 | var estimatedValues = interpreter.GetSymbolicExpressionTreeValues(newTree, problemData.Dataset, rows);
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| 161 |
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| 162 | var targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
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| 163 | OnlineLinearScalingParameterCalculator.Calculate(estimatedValues, targetValues, out var alpha, out var beta,
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| 164 | out var errorState);
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| 165 |
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| 166 | if (errorState == OnlineCalculatorError.None) {
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| 167 | //Set alpha and beta to the scaling nodes from ia grammar
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[18180] | 168 | var offsetParameter = offset.GetSubtree(1) as NumberTreeNode;
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[17958] | 169 | offsetParameter.Value = alpha;
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[18180] | 170 | var scalingParameter = scaling.GetSubtree(1) as NumberTreeNode;
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[17958] | 171 | scalingParameter.Value = beta;
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| 172 | }
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| 173 | } // else: alpha and beta are evolved
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| 174 | }
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| 175 |
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| 176 | var quality = Calculate(interpreter, tree, estimationLimits.Lower, estimationLimits.Upper, problemData, rows,
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[18181] | 177 | BoundsEstimator, UseSoftConstraints, PenalityFactor, ExtendedConstraints);
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[17958] | 178 | QualityParameter.ActualValue = new DoubleValue(quality);
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| 179 |
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| 180 | return base.InstrumentedApply();
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| 181 | }
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| 182 |
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| 183 | public static double Calculate(
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| 184 | ISymbolicDataAnalysisExpressionTreeInterpreter interpreter,
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| 185 | ISymbolicExpressionTree tree,
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| 186 | double lowerEstimationLimit, double upperEstimationLimit,
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| 187 | IRegressionProblemData problemData, IEnumerable<int> rows,
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| 188 | IBoundsEstimator estimator,
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[18181] | 189 | bool useSoftConstraints = false, double penaltyFactor = 1.0,
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| 190 | IEnumerable<ExtendedConstraint> extendedConstraints = null) {
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[17958] | 191 |
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| 192 | var estimatedValues = interpreter.GetSymbolicExpressionTreeValues(tree, problemData.Dataset, rows);
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| 193 | var targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
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| 194 | var constraints = Enumerable.Empty<ShapeConstraint>();
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| 195 | if (problemData is ShapeConstrainedRegressionProblemData scProbData) {
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| 196 | constraints = scProbData.ShapeConstraints.EnabledConstraints;
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| 197 | }
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| 198 | var intervalCollection = problemData.VariableRanges;
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| 199 |
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| 200 | var boundedEstimatedValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit);
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| 201 | var nmse = OnlineNormalizedMeanSquaredErrorCalculator.Calculate(targetValues, boundedEstimatedValues,
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| 202 | out var errorState);
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| 203 |
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| 204 | if (errorState != OnlineCalculatorError.None) {
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| 205 | return 1.0;
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| 206 | }
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| 207 |
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| 208 | var constraintViolations = IntervalUtil.GetConstraintViolations(constraints, estimator, intervalCollection, tree);
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| 209 |
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| 210 | if (constraintViolations.Any(x => double.IsNaN(x) || double.IsInfinity(x))) {
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| 211 | return 1.0;
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| 212 | }
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| 213 |
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| 214 | if (useSoftConstraints) {
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| 215 | if (penaltyFactor < 0.0)
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| 216 | throw new ArgumentException("The parameter has to be >= 0.0.", nameof(penaltyFactor));
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| 217 |
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| 218 | var weightedViolationsAvg = constraints
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| 219 | .Zip(constraintViolations, (c, v) => c.Weight * v)
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| 220 | .Average();
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| 221 |
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| 222 | return Math.Min(nmse, 1.0) + penaltyFactor * weightedViolationsAvg;
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| 223 | } else if (constraintViolations.Any(x => x > 0.0)) {
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| 224 | return 1.0;
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| 225 | }
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| 226 |
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| 227 | return nmse;
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| 228 | }
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| 229 |
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| 230 | public override double Evaluate(
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| 231 | IExecutionContext context, ISymbolicExpressionTree tree, IRegressionProblemData problemData,
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| 232 | IEnumerable<int> rows) {
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| 233 | SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
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| 234 | EstimationLimitsParameter.ExecutionContext = context;
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| 235 | ApplyLinearScalingParameter.ExecutionContext = context;
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| 236 |
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| 237 | var nmse = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree,
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| 238 | EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper,
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[18181] | 239 | problemData, rows, BoundsEstimator, UseSoftConstraints, PenalityFactor, ExtendedConstraints);
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[17958] | 240 |
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| 241 | SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
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| 242 | EstimationLimitsParameter.ExecutionContext = null;
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| 243 | ApplyLinearScalingParameter.ExecutionContext = null;
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| 244 |
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| 245 | return nmse;
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| 246 | }
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| 247 | }
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| 248 | } |
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