[15964] | 1 | #region License Information
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| 2 | /* HeuristicLab
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| 3 | * Copyright (C) 2002-2018 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.Diagnostics;
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[16653] | 25 | using System.Globalization;
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[15964] | 26 | using System.Linq;
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[15968] | 27 | using HeuristicLab.Analysis;
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| 28 | using HeuristicLab.Collections;
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[15964] | 29 | using HeuristicLab.Common;
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| 30 | using HeuristicLab.Core;
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[15968] | 31 | using HeuristicLab.Data;
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[15964] | 32 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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[15968] | 33 | using HeuristicLab.Optimization;
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[15964] | 34 | using HeuristicLab.Parameters;
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| 35 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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| 36 | using HeuristicLab.Problems.DataAnalysis;
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[16126] | 37 | using HeuristicLab.Problems.DataAnalysis.Symbolic;
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[15964] | 38 | using HeuristicLab.Problems.Instances;
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[16153] | 39 | using Variable = HeuristicLab.Problems.DataAnalysis.Symbolic.Variable;
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[16663] | 40 | using HEAL.Attic;
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[15964] | 41 |
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| 42 | namespace HeuristicLab.Problems.DynamicalSystemsModelling {
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| 43 | [Item("Dynamical Systems Modelling Problem", "TODO")]
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| 44 | [Creatable(CreatableAttribute.Categories.GeneticProgrammingProblems, Priority = 900)]
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[16663] | 45 | [StorableType("065C6A61-773A-42C9-9DE5-61A5D1D823EB")]
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[15968] | 46 | public sealed class Problem : SingleObjectiveBasicProblem<MultiEncoding>, IRegressionProblem, IProblemInstanceConsumer<IRegressionProblemData>, IProblemInstanceExporter<IRegressionProblemData> {
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[15964] | 47 | #region parameter names
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[15968] | 48 | private const string ProblemDataParameterName = "Data";
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| 49 | private const string TargetVariablesParameterName = "Target variables";
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| 50 | private const string FunctionSetParameterName = "Function set";
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| 51 | private const string MaximumLengthParameterName = "Size limit";
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| 52 | private const string MaximumParameterOptimizationIterationsParameterName = "Max. parameter optimization iterations";
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[15970] | 53 | private const string NumberOfLatentVariablesParameterName = "Number of latent variables";
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| 54 | private const string NumericIntegrationStepsParameterName = "Steps for numeric integration";
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[16153] | 55 | private const string TrainingEpisodesParameterName = "Training episodes";
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[16155] | 56 | private const string OptimizeParametersForEpisodesParameterName = "Optimize parameters for episodes";
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[16250] | 57 | private const string OdeSolverParameterName = "ODE Solver";
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[15964] | 58 | #endregion
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| 59 |
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| 60 | #region Parameter Properties
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| 61 | IParameter IDataAnalysisProblem.ProblemDataParameter { get { return ProblemDataParameter; } }
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| 62 |
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| 63 | public IValueParameter<IRegressionProblemData> ProblemDataParameter {
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| 64 | get { return (IValueParameter<IRegressionProblemData>)Parameters[ProblemDataParameterName]; }
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| 65 | }
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[16268] | 66 | public IValueParameter<ReadOnlyCheckedItemList<StringValue>> TargetVariablesParameter {
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| 67 | get { return (IValueParameter<ReadOnlyCheckedItemList<StringValue>>)Parameters[TargetVariablesParameterName]; }
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[15968] | 68 | }
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[16268] | 69 | public IValueParameter<ReadOnlyCheckedItemList<StringValue>> FunctionSetParameter {
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| 70 | get { return (IValueParameter<ReadOnlyCheckedItemList<StringValue>>)Parameters[FunctionSetParameterName]; }
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[15968] | 71 | }
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| 72 | public IFixedValueParameter<IntValue> MaximumLengthParameter {
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| 73 | get { return (IFixedValueParameter<IntValue>)Parameters[MaximumLengthParameterName]; }
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| 74 | }
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[16602] | 75 |
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[15968] | 76 | public IFixedValueParameter<IntValue> MaximumParameterOptimizationIterationsParameter {
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| 77 | get { return (IFixedValueParameter<IntValue>)Parameters[MaximumParameterOptimizationIterationsParameterName]; }
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| 78 | }
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[15970] | 79 | public IFixedValueParameter<IntValue> NumberOfLatentVariablesParameter {
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| 80 | get { return (IFixedValueParameter<IntValue>)Parameters[NumberOfLatentVariablesParameterName]; }
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| 81 | }
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| 82 | public IFixedValueParameter<IntValue> NumericIntegrationStepsParameter {
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| 83 | get { return (IFixedValueParameter<IntValue>)Parameters[NumericIntegrationStepsParameterName]; }
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| 84 | }
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[16153] | 85 | public IValueParameter<ItemList<IntRange>> TrainingEpisodesParameter {
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| 86 | get { return (IValueParameter<ItemList<IntRange>>)Parameters[TrainingEpisodesParameterName]; }
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| 87 | }
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[16155] | 88 | public IFixedValueParameter<BoolValue> OptimizeParametersForEpisodesParameter {
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| 89 | get { return (IFixedValueParameter<BoolValue>)Parameters[OptimizeParametersForEpisodesParameterName]; }
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| 90 | }
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[16250] | 91 | public IConstrainedValueParameter<StringValue> OdeSolverParameter {
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| 92 | get { return (IConstrainedValueParameter<StringValue>)Parameters[OdeSolverParameterName]; }
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| 93 | }
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[15964] | 94 | #endregion
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| 95 |
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| 96 | #region Properties
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| 97 | public IRegressionProblemData ProblemData {
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| 98 | get { return ProblemDataParameter.Value; }
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| 99 | set { ProblemDataParameter.Value = value; }
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| 100 | }
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| 101 | IDataAnalysisProblemData IDataAnalysisProblem.ProblemData { get { return ProblemData; } }
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| 102 |
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[16268] | 103 | public ReadOnlyCheckedItemList<StringValue> TargetVariables {
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[15968] | 104 | get { return TargetVariablesParameter.Value; }
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| 105 | }
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| 106 |
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[16268] | 107 | public ReadOnlyCheckedItemList<StringValue> FunctionSet {
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[15968] | 108 | get { return FunctionSetParameter.Value; }
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| 109 | }
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| 110 |
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| 111 | public int MaximumLength {
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| 112 | get { return MaximumLengthParameter.Value.Value; }
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| 113 | }
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| 114 | public int MaximumParameterOptimizationIterations {
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| 115 | get { return MaximumParameterOptimizationIterationsParameter.Value.Value; }
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| 116 | }
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[15970] | 117 | public int NumberOfLatentVariables {
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| 118 | get { return NumberOfLatentVariablesParameter.Value.Value; }
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| 119 | }
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| 120 | public int NumericIntegrationSteps {
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| 121 | get { return NumericIntegrationStepsParameter.Value.Value; }
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| 122 | }
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[16153] | 123 | public IEnumerable<IntRange> TrainingEpisodes {
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| 124 | get { return TrainingEpisodesParameter.Value; }
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| 125 | }
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[16155] | 126 | public bool OptimizeParametersForEpisodes {
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| 127 | get { return OptimizeParametersForEpisodesParameter.Value.Value; }
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| 128 | }
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[15970] | 129 |
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[16250] | 130 | public string OdeSolver {
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| 131 | get { return OdeSolverParameter.Value.Value; }
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| 132 | set {
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| 133 | var matchingValue = OdeSolverParameter.ValidValues.FirstOrDefault(v => v.Value == value);
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| 134 | if (matchingValue == null) throw new ArgumentOutOfRangeException();
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| 135 | else OdeSolverParameter.Value = matchingValue;
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| 136 | }
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| 137 | }
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| 138 |
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[16153] | 139 | #endregion
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[15968] | 140 |
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[15964] | 141 | public event EventHandler ProblemDataChanged;
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| 142 |
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| 143 | public override bool Maximization {
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| 144 | get { return false; } // we minimize NMSE
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| 145 | }
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| 146 |
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| 147 | #region item cloning and persistence
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| 148 | // persistence
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| 149 | [StorableConstructor]
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[16663] | 150 | private Problem(StorableConstructorFlag _) : base(_) { }
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[15964] | 151 | [StorableHook(HookType.AfterDeserialization)]
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| 152 | private void AfterDeserialization() {
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[16215] | 153 | if (!Parameters.ContainsKey(OptimizeParametersForEpisodesParameterName)) {
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[16155] | 154 | Parameters.Add(new FixedValueParameter<BoolValue>(OptimizeParametersForEpisodesParameterName, "Flag to select if parameters should be optimized globally or for each episode individually.", new BoolValue(false)));
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| 155 | }
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[15964] | 156 | RegisterEventHandlers();
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| 157 | }
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| 158 |
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| 159 | // cloning
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| 160 | private Problem(Problem original, Cloner cloner)
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| 161 | : base(original, cloner) {
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| 162 | RegisterEventHandlers();
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| 163 | }
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| 164 | public override IDeepCloneable Clone(Cloner cloner) { return new Problem(this, cloner); }
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| 165 | #endregion
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| 166 |
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| 167 | public Problem()
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| 168 | : base() {
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[16268] | 169 | var targetVariables = new CheckedItemList<StringValue>().AsReadOnly(); // HACK: it would be better to provide a new class derived from IDataAnalysisProblem
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[15968] | 170 | var functions = CreateFunctionSet();
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[15970] | 171 | Parameters.Add(new ValueParameter<IRegressionProblemData>(ProblemDataParameterName, "The data captured from the dynamical system. Use CSV import functionality to import data.", new RegressionProblemData()));
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[16268] | 172 | Parameters.Add(new ValueParameter<ReadOnlyCheckedItemList<StringValue>>(TargetVariablesParameterName, "Target variables (overrides setting in ProblemData)", targetVariables));
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| 173 | Parameters.Add(new ValueParameter<ReadOnlyCheckedItemList<StringValue>>(FunctionSetParameterName, "The list of allowed functions", functions));
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[15970] | 174 | Parameters.Add(new FixedValueParameter<IntValue>(MaximumLengthParameterName, "The maximally allowed length of each expression. Set to a small value (5 - 25). Default = 10", new IntValue(10)));
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| 175 | Parameters.Add(new FixedValueParameter<IntValue>(MaximumParameterOptimizationIterationsParameterName, "The maximum number of iterations for optimization of parameters (using L-BFGS). More iterations makes the algorithm slower, fewer iterations might prevent convergence in the optimization scheme. Default = 100", new IntValue(100)));
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| 176 | Parameters.Add(new FixedValueParameter<IntValue>(NumberOfLatentVariablesParameterName, "Latent variables (unobserved variables) allow us to produce expressions which are integrated up and can be used in other expressions. They are handled similarly to target variables in forward simulation / integration. The difference to target variables is that there are no data to which the calculated values of latent variables are compared. Set to a small value (0 .. 5) as necessary (default = 0)", new IntValue(0)));
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| 177 | Parameters.Add(new FixedValueParameter<IntValue>(NumericIntegrationStepsParameterName, "Number of steps in the numeric integration that are taken from one row to the next (set to 1 to 100). More steps makes the algorithm slower, less steps worsens the accuracy of the numeric integration scheme.", new IntValue(10)));
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[16153] | 178 | Parameters.Add(new ValueParameter<ItemList<IntRange>>(TrainingEpisodesParameterName, "A list of ranges that should be used for training, each range represents an independent episode. This overrides the TrainingSet parameter in ProblemData.", new ItemList<IntRange>()));
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[16155] | 179 | Parameters.Add(new FixedValueParameter<BoolValue>(OptimizeParametersForEpisodesParameterName, "Flag to select if parameters should be optimized globally or for each episode individually.", new BoolValue(false)));
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[16250] | 180 |
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[16601] | 181 | var solversStr = new string[] { "HeuristicLab" /* , "CVODES" */};
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[16250] | 182 | var solvers = new ItemSet<StringValue>(
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| 183 | solversStr.Select(s => new StringValue(s).AsReadOnly())
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| 184 | );
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[16251] | 185 | Parameters.Add(new ConstrainedValueParameter<StringValue>(OdeSolverParameterName, "The solver to use for solving the initial value ODE problems", solvers, solvers.First()));
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[16250] | 186 |
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[15964] | 187 | RegisterEventHandlers();
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[15968] | 188 | InitAllParameters();
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[16152] | 189 |
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[16215] | 190 | // TODO: use training range as default training episode
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[16398] | 191 | // TODO: optimization of starting values for latent variables in CVODES solver
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[16601] | 192 | // TODO: allow to specify the name for the time variable in the dataset and allow variable step-sizes
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[15964] | 193 | }
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| 194 |
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[15968] | 195 | public override double Evaluate(Individual individual, IRandom random) {
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| 196 | var trees = individual.Values.Select(v => v.Value).OfType<ISymbolicExpressionTree>().ToArray(); // extract all trees from individual
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| 197 |
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[16601] | 198 | var problemData = ProblemData;
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[16399] | 199 | var targetVars = TargetVariables.CheckedItems.OrderBy(i => i.Index).Select(i => i.Value.Value).ToArray();
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| 200 | var latentVariables = Enumerable.Range(1, NumberOfLatentVariables).Select(i => "λ" + i).ToArray(); // TODO: must coincide with the variables which are actually defined in the grammar and also for which we actually have trees
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[16215] | 201 | if (OptimizeParametersForEpisodes) {
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[16601] | 202 | throw new NotImplementedException();
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[16215] | 203 | int eIdx = 0;
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[16155] | 204 | double totalNMSE = 0.0;
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| 205 | int totalSize = 0;
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[16215] | 206 | foreach (var episode in TrainingEpisodes) {
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[16602] | 207 | // double[] optTheta;
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| 208 | double nmse = OptimizeForEpisodes(trees, problemData, targetVars, latentVariables, random, new[] { episode }, MaximumParameterOptimizationIterations, NumericIntegrationSteps, OdeSolver);
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| 209 | // individual["OptTheta_" + eIdx] = new DoubleArray(optTheta); // write back optimized parameters so that we can use them in the Analysis method
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[16155] | 210 | eIdx++;
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| 211 | totalNMSE += nmse * episode.Size;
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| 212 | totalSize += episode.Size;
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| 213 | }
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| 214 | return totalNMSE / totalSize;
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| 215 | } else {
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[16602] | 216 | // double[] optTheta;
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| 217 | double nmse = OptimizeForEpisodes(trees, problemData, targetVars, latentVariables, random, TrainingEpisodes, MaximumParameterOptimizationIterations, NumericIntegrationSteps, OdeSolver);
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| 218 | // individual["OptTheta"] = new DoubleArray(optTheta); // write back optimized parameters so that we can use them in the Analysis method
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[16155] | 219 | return nmse;
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| 220 | }
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| 221 | }
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| 222 |
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[16602] | 223 | public static double OptimizeForEpisodes(
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[16250] | 224 | ISymbolicExpressionTree[] trees,
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[16399] | 225 | IRegressionProblemData problemData,
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| 226 | string[] targetVars,
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| 227 | string[] latentVariables,
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[16250] | 228 | IRandom random,
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| 229 | IEnumerable<IntRange> episodes,
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[16399] | 230 | int maxParameterOptIterations,
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| 231 | int numericIntegrationSteps,
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[16602] | 232 | string odeSolver) {
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[15970] | 233 |
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[16660] | 234 | // extract constants from trees (without trees for latent variables)
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| 235 | var targetVariableTrees = trees.Take(targetVars.Length).ToArray();
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| 236 | var latentVariableTrees = trees.Skip(targetVars.Length).ToArray();
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| 237 | var constantNodes = targetVariableTrees.Select(t => t.IterateNodesPrefix().OfType<ConstantTreeNode>().ToArray()).ToArray();
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[16602] | 238 | var initialTheta = constantNodes.Select(nodes => nodes.Select(n => n.Value).ToArray()).ToArray();
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[16600] | 239 |
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[16601] | 240 | // optimize parameters by fitting f(x,y) to calculated differences dy/dt(t)
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[16602] | 241 | double nmse = PreTuneParameters(trees, problemData, targetVars, latentVariables, random, episodes, maxParameterOptIterations,
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| 242 | initialTheta, out double[] pretunedParameters);
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[15964] | 243 |
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[16660] | 244 | // extend parameter vector to include parameters for latent variable trees
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| 245 | pretunedParameters = pretunedParameters
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| 246 | .Concat(latentVariableTrees
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| 247 | .SelectMany(t => t.IterateNodesPrefix().OfType<ConstantTreeNode>().Select(n => n.Value)))
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| 248 | .ToArray();
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| 249 |
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[16601] | 250 | // optimize parameters using integration of f(x,y) to calculate y(t)
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[16602] | 251 | nmse = OptimizeParameters(trees, problemData, targetVars, latentVariables, episodes, maxParameterOptIterations, pretunedParameters, numericIntegrationSteps, odeSolver,
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| 252 | out double[] optTheta);
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[16616] | 253 | // var optTheta = pretunedParameters;
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[16601] | 254 |
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[16602] | 255 | if (double.IsNaN(nmse) ||
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| 256 | double.IsInfinity(nmse) ||
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| 257 | nmse > 100 * trees.Length * episodes.Sum(ep => ep.Size))
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| 258 | return 100 * trees.Length * episodes.Sum(ep => ep.Size);
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| 259 |
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| 260 | // update tree nodes with optimized values
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| 261 | var paramIdx = 0;
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| 262 | for (var treeIdx = 0; treeIdx < constantNodes.Length; treeIdx++) {
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| 263 | for (int i = 0; i < constantNodes[treeIdx].Length; i++)
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| 264 | constantNodes[treeIdx][i].Value = optTheta[paramIdx++];
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| 265 | }
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| 266 | return nmse;
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[16601] | 267 | }
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| 268 |
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| 269 | private static double PreTuneParameters(
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| 270 | ISymbolicExpressionTree[] trees,
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| 271 | IRegressionProblemData problemData,
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| 272 | string[] targetVars,
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| 273 | string[] latentVariables,
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| 274 | IRandom random,
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| 275 | IEnumerable<IntRange> episodes,
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| 276 | int maxParameterOptIterations,
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[16602] | 277 | double[][] initialTheta,
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[16601] | 278 | out double[] optTheta) {
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| 279 | var thetas = new List<double>();
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| 280 | double nmse = 0.0;
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[16602] | 281 | var maxTreeNmse = 100 * episodes.Sum(ep => ep.Size);
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| 282 |
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[16660] | 283 | var targetTrees = trees.Take(targetVars.Length).ToArray();
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| 284 | var latentTrees = trees.Take(latentVariables.Length).ToArray();
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| 285 |
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| 286 | {
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| 287 | // first calculate values of latent variables by integration
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| 288 | var inputVariables = targetVars.Concat(latentTrees.SelectMany(t => t.IterateNodesPrefix().OfType<VariableTreeNode>().Select(n => n.VariableName))).Except(latentVariables).Distinct();
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| 289 | var myState = new OptimizationData(latentTrees, targetVars, inputVariables.ToArray(), problemData, null, episodes.ToArray(), 10, latentVariables, "HeuristicLab");
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| 290 |
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| 291 | var fi = new double[myState.rows.Length * targetVars.Length];
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| 292 | var jac = new double[myState.rows.Length * targetVars.Length, myState.nodeValueLookup.ParameterCount];
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| 293 | var latentValues = new double[myState.rows.Length, latentVariables.Length];
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| 294 | Integrate(myState, fi, jac, latentValues);
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| 295 |
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| 296 | // add integrated latent variables to dataset
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| 297 | var modifiedDataset = ((Dataset)problemData.Dataset).ToModifiable();
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| 298 | foreach (var variable in latentVariables) {
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| 299 | modifiedDataset.AddVariable(variable, Enumerable.Repeat(0.0, modifiedDataset.Rows).ToList()); // empty column
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| 300 | }
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| 301 | int predIdx = 0;
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| 302 | foreach (var ep in episodes) {
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| 303 | for (int r = ep.Start; r < ep.End; r++) {
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| 304 | for (int latVarIdx = 0; latVarIdx < latentVariables.Length; latVarIdx++) {
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| 305 | modifiedDataset.SetVariableValue(latentValues[predIdx, latVarIdx], latentVariables[latVarIdx], r);
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| 306 | }
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| 307 | predIdx++;
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| 308 | }
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| 309 | }
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| 310 |
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| 311 | problemData = new RegressionProblemData(modifiedDataset, problemData.AllowedInputVariables, problemData.TargetVariable);
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| 312 | }
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[16251] | 313 | // NOTE: the order of values in parameter matches prefix order of constant nodes in trees
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[16660] | 314 | for (int treeIdx = 0; treeIdx < targetTrees.Length; treeIdx++) {
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| 315 | var t = targetTrees[treeIdx];
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[16601] | 316 |
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[16610] | 317 | var targetValuesDiff = new List<double>();
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| 318 | foreach (var ep in episodes) {
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| 319 | var episodeRows = Enumerable.Range(ep.Start, ep.Size);
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| 320 | var targetValues = problemData.Dataset.GetDoubleValues(targetVars[treeIdx], episodeRows).ToArray();
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| 321 | targetValuesDiff.AddRange(targetValues.Skip(1).Zip(targetValues, (t1, t0) => t1 - t0));// TODO: smoothing or multi-pole);
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| 322 | }
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| 323 | var adjustedEpisodes = episodes.Select(ep => new IntRange(ep.Start, ep.End - 1)); // because we lose the last row in the differencing step
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[16653] | 324 |
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| 325 | // data for input variables is assumed to be known
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| 326 | // input variables in pretuning are all target variables and all variable names that occur in the tree
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[16660] | 327 | var inputVariables = targetVars.Concat(t.IterateNodesPrefix().OfType<VariableTreeNode>().Select(n => n.VariableName)).Distinct();
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[16653] | 328 |
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[16610] | 329 | var myState = new OptimizationData(new[] { t },
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| 330 | targetVars,
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[16653] | 331 | inputVariables.ToArray(),
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[16610] | 332 | problemData, new[] { targetValuesDiff.ToArray() }, adjustedEpisodes.ToArray(), -99, latentVariables, string.Empty); // TODO
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[16601] | 333 | var paramCount = myState.nodeValueLookup.ParameterCount;
|
---|
| 334 |
|
---|
| 335 | optTheta = new double[0];
|
---|
[16602] | 336 | if (initialTheta[treeIdx].Length > 0) {
|
---|
| 337 | try {
|
---|
| 338 | alglib.minlmstate state;
|
---|
| 339 | alglib.minlmreport report;
|
---|
| 340 | var p = new double[initialTheta[treeIdx].Length];
|
---|
[16616] | 341 | var lowerBounds = Enumerable.Repeat(-1000.0, p.Length).ToArray();
|
---|
| 342 | var upperBounds = Enumerable.Repeat(1000.0, p.Length).ToArray();
|
---|
[16602] | 343 | Array.Copy(initialTheta[treeIdx], p, p.Length);
|
---|
[16610] | 344 | alglib.minlmcreatevj(targetValuesDiff.Count, p, out state);
|
---|
[16602] | 345 | alglib.minlmsetcond(state, 0.0, 0.0, 0.0, maxParameterOptIterations);
|
---|
| 346 | alglib.minlmsetbc(state, lowerBounds, upperBounds);
|
---|
[16616] | 347 | #if DEBUG
|
---|
| 348 | //alglib.minlmsetgradientcheck(state, 1.0e-7);
|
---|
| 349 | #endif
|
---|
[16602] | 350 | alglib.minlmoptimize(state, EvaluateObjectiveVector, EvaluateObjectiveVectorAndJacobian, null, myState);
|
---|
[16601] | 351 |
|
---|
[16602] | 352 | alglib.minlmresults(state, out optTheta, out report);
|
---|
[16616] | 353 | if (report.terminationtype < 0) {
|
---|
| 354 | #if DEBUG
|
---|
| 355 | if (report.terminationtype == -7) throw new InvalidProgramException("gradient calculation fail!");
|
---|
| 356 | #endif
|
---|
| 357 | optTheta = initialTheta[treeIdx];
|
---|
| 358 | }
|
---|
[16602] | 359 | } catch (alglib.alglibexception) {
|
---|
| 360 | optTheta = initialTheta[treeIdx];
|
---|
| 361 | }
|
---|
| 362 | }
|
---|
| 363 | var tree_nmse = EvaluateMSE(optTheta, myState);
|
---|
| 364 | if (double.IsNaN(tree_nmse) || double.IsInfinity(tree_nmse) || tree_nmse > maxTreeNmse) {
|
---|
| 365 | nmse += maxTreeNmse;
|
---|
| 366 | thetas.AddRange(initialTheta[treeIdx]);
|
---|
| 367 | } else {
|
---|
| 368 | nmse += tree_nmse;
|
---|
[16601] | 369 | thetas.AddRange(optTheta);
|
---|
[15968] | 370 | }
|
---|
[16601] | 371 | } // foreach tree
|
---|
| 372 | optTheta = thetas.ToArray();
|
---|
| 373 |
|
---|
| 374 | return nmse;
|
---|
| 375 | }
|
---|
| 376 |
|
---|
| 377 |
|
---|
| 378 | // similar to above but this time we integrate and optimize all parameters for all targets concurrently
|
---|
| 379 | private static double OptimizeParameters(ISymbolicExpressionTree[] trees, IRegressionProblemData problemData, string[] targetVars, string[] latentVariables,
|
---|
| 380 | IEnumerable<IntRange> episodes, int maxParameterOptIterations, double[] initialTheta, int numericIntegrationSteps, string odeSolver, out double[] optTheta) {
|
---|
[16610] | 381 | var rowsForDataExtraction = episodes.SelectMany(e => Enumerable.Range(e.Start, e.Size)).ToArray();
|
---|
[16660] | 382 | var targetValues = new double[targetVars.Length][];
|
---|
| 383 | for (int treeIdx = 0; treeIdx < targetVars.Length; treeIdx++) {
|
---|
[16601] | 384 | var t = trees[treeIdx];
|
---|
| 385 |
|
---|
| 386 | targetValues[treeIdx] = problemData.Dataset.GetDoubleValues(targetVars[treeIdx], rowsForDataExtraction).ToArray();
|
---|
[15964] | 387 | }
|
---|
| 388 |
|
---|
[16653] | 389 | // data for input variables is assumed to be known
|
---|
| 390 | // input variables are all variable names that occur in the trees except for target variables (we assume that trees have been generated correctly)
|
---|
[16660] | 391 | var inputVariables = trees.SelectMany(t => t.IterateNodesPrefix().OfType<VariableTreeNode>().Select(n => n.VariableName))
|
---|
| 392 | .Except(targetVars)
|
---|
| 393 | .Except(latentVariables)
|
---|
| 394 | .Distinct();
|
---|
[16653] | 395 |
|
---|
| 396 | var myState = new OptimizationData(trees, targetVars, inputVariables.ToArray(), problemData, targetValues, episodes.ToArray(), numericIntegrationSteps, latentVariables, odeSolver);
|
---|
[16601] | 397 | optTheta = initialTheta;
|
---|
[16250] | 398 |
|
---|
[16601] | 399 | if (initialTheta.Length > 0) {
|
---|
[16616] | 400 | var lowerBounds = Enumerable.Repeat(-1000.0, initialTheta.Length).ToArray();
|
---|
| 401 | var upperBounds = Enumerable.Repeat(1000.0, initialTheta.Length).ToArray();
|
---|
[16601] | 402 | try {
|
---|
| 403 | alglib.minlmstate state;
|
---|
| 404 | alglib.minlmreport report;
|
---|
| 405 | alglib.minlmcreatevj(rowsForDataExtraction.Length * trees.Length, initialTheta, out state);
|
---|
[16602] | 406 | alglib.minlmsetbc(state, lowerBounds, upperBounds);
|
---|
[16601] | 407 | alglib.minlmsetcond(state, 0.0, 0.0, 0.0, maxParameterOptIterations);
|
---|
[16616] | 408 | #if DEBUG
|
---|
| 409 | //alglib.minlmsetgradientcheck(state, 1.0e-7);
|
---|
| 410 | #endif
|
---|
[16601] | 411 | alglib.minlmoptimize(state, IntegrateAndEvaluateObjectiveVector, IntegrateAndEvaluateObjectiveVectorAndJacobian, null, myState);
|
---|
[15964] | 412 |
|
---|
[16601] | 413 | alglib.minlmresults(state, out optTheta, out report);
|
---|
[15964] | 414 |
|
---|
[16601] | 415 | if (report.terminationtype < 0) {
|
---|
[16616] | 416 | #if DEBUG
|
---|
| 417 | if (report.terminationtype == -7) throw new InvalidProgramException("gradient calculation fail!");
|
---|
| 418 | #endif // there was a problem: reset theta and evaluate for inital values
|
---|
[16601] | 419 | optTheta = initialTheta;
|
---|
| 420 | }
|
---|
| 421 | } catch (alglib.alglibexception) {
|
---|
| 422 | optTheta = initialTheta;
|
---|
| 423 | }
|
---|
[15964] | 424 | }
|
---|
[16601] | 425 | var nmse = EvaluateIntegratedMSE(optTheta, myState);
|
---|
| 426 | var maxNmse = 100 * targetValues.Length * rowsForDataExtraction.Length;
|
---|
| 427 | if (double.IsNaN(nmse) || double.IsInfinity(nmse) || nmse > maxNmse) nmse = maxNmse;
|
---|
| 428 | return nmse;
|
---|
[16599] | 429 | }
|
---|
[15964] | 430 |
|
---|
[16599] | 431 |
|
---|
[16601] | 432 | // helper
|
---|
| 433 | public static double EvaluateMSE(double[] x, OptimizationData optimizationData) {
|
---|
| 434 | var fi = new double[optimizationData.rows.Count()];
|
---|
| 435 | EvaluateObjectiveVector(x, fi, optimizationData);
|
---|
| 436 | return fi.Sum(fii => fii * fii) / fi.Length;
|
---|
| 437 | }
|
---|
[16600] | 438 | public static void EvaluateObjectiveVector(double[] x, double[] fi, object optimizationData) { EvaluateObjectiveVector(x, fi, (OptimizationData)optimizationData); } // for alglib
|
---|
| 439 | public static void EvaluateObjectiveVector(double[] x, double[] fi, OptimizationData optimizationData) {
|
---|
[16603] | 440 | var rows = optimizationData.rows;
|
---|
[16601] | 441 | var problemData = optimizationData.problemData;
|
---|
| 442 | var nodeValueLookup = optimizationData.nodeValueLookup;
|
---|
| 443 | var ds = problemData.Dataset;
|
---|
[16610] | 444 | var variables = optimizationData.variables;
|
---|
[16601] | 445 |
|
---|
| 446 | nodeValueLookup.UpdateParamValues(x);
|
---|
| 447 |
|
---|
[16610] | 448 | int outputIdx = 0;
|
---|
[16601] | 449 | for (int trainIdx = 0; trainIdx < rows.Length; trainIdx++) {
|
---|
| 450 | // update variable values
|
---|
[16610] | 451 | foreach (var variable in variables) {
|
---|
[16653] | 452 | // in this problem we also allow fixed numeric parameters (represented as variables with the value as name)
|
---|
| 453 | if (double.TryParse(variable, NumberStyles.Float, CultureInfo.InvariantCulture, out double value)) {
|
---|
| 454 | nodeValueLookup.SetVariableValue(variable, value); // TODO: Perf we don't need to set this for each index
|
---|
| 455 | } else {
|
---|
| 456 | nodeValueLookup.SetVariableValue(variable, ds.GetDoubleValue(variable, rows[trainIdx])); // TODO: perf
|
---|
| 457 | }
|
---|
[16601] | 458 | }
|
---|
| 459 | // interpret all trees
|
---|
| 460 | for (int treeIdx = 0; treeIdx < optimizationData.trees.Length; treeIdx++) {
|
---|
| 461 | var tree = optimizationData.trees[treeIdx];
|
---|
| 462 | var pred = InterpretRec(tree.Root.GetSubtree(0).GetSubtree(0), nodeValueLookup);
|
---|
| 463 | var y = optimizationData.targetValues[treeIdx][trainIdx];
|
---|
[16616] | 464 | fi[outputIdx++] = (y - pred) * optimizationData.inverseStandardDeviation[treeIdx];
|
---|
[16601] | 465 | }
|
---|
| 466 | }
|
---|
[15964] | 467 | }
|
---|
| 468 |
|
---|
[16600] | 469 | public static void EvaluateObjectiveVectorAndJacobian(double[] x, double[] fi, double[,] jac, object optimizationData) { EvaluateObjectiveVectorAndJacobian(x, fi, jac, (OptimizationData)optimizationData); } // for alglib
|
---|
| 470 | public static void EvaluateObjectiveVectorAndJacobian(double[] x, double[] fi, double[,] jac, OptimizationData optimizationData) {
|
---|
[16601] | 471 | // extract variable values from dataset
|
---|
| 472 | var variableValues = new Dictionary<string, Tuple<double, Vector>>();
|
---|
| 473 | var problemData = optimizationData.problemData;
|
---|
| 474 | var ds = problemData.Dataset;
|
---|
[16603] | 475 | var rows = optimizationData.rows;
|
---|
[16610] | 476 | var variables = optimizationData.variables;
|
---|
[15964] | 477 |
|
---|
[16601] | 478 | var nodeValueLookup = optimizationData.nodeValueLookup;
|
---|
| 479 | nodeValueLookup.UpdateParamValues(x);
|
---|
[15964] | 480 |
|
---|
[16601] | 481 | int termIdx = 0;
|
---|
[15968] | 482 |
|
---|
[16601] | 483 | for (int trainIdx = 0; trainIdx < rows.Length; trainIdx++) {
|
---|
| 484 | // update variable values
|
---|
[16610] | 485 | foreach (var variable in variables) {
|
---|
[16653] | 486 | // in this problem we also allow fixed numeric parameters (represented as variables with the value as name)
|
---|
| 487 | if (double.TryParse(variable, NumberStyles.Float, CultureInfo.InvariantCulture, out double value)) {
|
---|
| 488 | nodeValueLookup.SetVariableValue(variable, value); // TODO: Perf we don't need to set this for each index
|
---|
| 489 | } else {
|
---|
| 490 | nodeValueLookup.SetVariableValue(variable, ds.GetDoubleValue(variable, rows[trainIdx])); // TODO: perf
|
---|
| 491 | }
|
---|
[16601] | 492 | }
|
---|
[16599] | 493 |
|
---|
[16601] | 494 | var calculatedVariables = optimizationData.targetVariables;
|
---|
| 495 |
|
---|
| 496 | var trees = optimizationData.trees;
|
---|
| 497 | for (int i = 0; i < trees.Length; i++) {
|
---|
| 498 | var tree = trees[i];
|
---|
| 499 | var targetVarName = calculatedVariables[i];
|
---|
| 500 |
|
---|
| 501 | double f; Vector g;
|
---|
| 502 | InterpretRec(tree.Root.GetSubtree(0).GetSubtree(0), nodeValueLookup, out f, out g);
|
---|
| 503 |
|
---|
| 504 | var y = optimizationData.targetValues[i][trainIdx];
|
---|
[16603] | 505 | fi[termIdx] = (y - f) * optimizationData.inverseStandardDeviation[i]; // scale of NMSE
|
---|
[16604] | 506 | if (jac != null && g != Vector.Zero) for (int j = 0; j < g.Length; j++) jac[termIdx, j] = -g[j] * optimizationData.inverseStandardDeviation[i];
|
---|
[16601] | 507 |
|
---|
| 508 | termIdx++;
|
---|
| 509 | }
|
---|
[16251] | 510 | }
|
---|
[16250] | 511 |
|
---|
[16601] | 512 | }
|
---|
[15968] | 513 |
|
---|
[16601] | 514 | // helper
|
---|
| 515 | public static double EvaluateIntegratedMSE(double[] x, OptimizationData optimizationData) {
|
---|
| 516 | var fi = new double[optimizationData.rows.Count() * optimizationData.targetVariables.Length];
|
---|
| 517 | IntegrateAndEvaluateObjectiveVector(x, fi, optimizationData);
|
---|
| 518 | return fi.Sum(fii => fii * fii) / fi.Length;
|
---|
| 519 | }
|
---|
| 520 | public static void IntegrateAndEvaluateObjectiveVector(double[] x, double[] fi, object optimizationData) { IntegrateAndEvaluateObjectiveVector(x, fi, (OptimizationData)optimizationData); } // for alglib
|
---|
| 521 | public static void IntegrateAndEvaluateObjectiveVector(double[] x, double[] fi, OptimizationData optimizationData) {
|
---|
| 522 | IntegrateAndEvaluateObjectiveVectorAndJacobian(x, fi, null, optimizationData);
|
---|
| 523 | }
|
---|
[16597] | 524 |
|
---|
[16601] | 525 | public static void IntegrateAndEvaluateObjectiveVectorAndJacobian(double[] x, double[] fi, double[,] jac, object optimizationData) { IntegrateAndEvaluateObjectiveVectorAndJacobian(x, fi, jac, (OptimizationData)optimizationData); } // for alglib
|
---|
| 526 | public static void IntegrateAndEvaluateObjectiveVectorAndJacobian(double[] x, double[] fi, double[,] jac, OptimizationData optimizationData) {
|
---|
| 527 | var rows = optimizationData.rows.ToArray();
|
---|
| 528 | var problemData = optimizationData.problemData;
|
---|
| 529 | var nodeValueLookup = optimizationData.nodeValueLookup;
|
---|
| 530 | var ds = problemData.Dataset;
|
---|
| 531 | int outputIdx = 0;
|
---|
[16597] | 532 |
|
---|
[16603] | 533 | nodeValueLookup.UpdateParamValues(x);
|
---|
| 534 |
|
---|
[16660] | 535 | Integrate(optimizationData, fi, jac, null);
|
---|
[16601] | 536 | var trees = optimizationData.trees;
|
---|
[15970] | 537 |
|
---|
[16604] | 538 | // update result with error
|
---|
[16601] | 539 | for (int trainIdx = 0; trainIdx < rows.Length; trainIdx++) {
|
---|
[16660] | 540 | for (int i = 0; i < optimizationData.targetVariables.Length; i++) {
|
---|
[16601] | 541 | var tree = trees[i];
|
---|
| 542 | var y = optimizationData.targetValues[i][trainIdx];
|
---|
[16604] | 543 | fi[outputIdx] = (y - fi[outputIdx]) * optimizationData.inverseStandardDeviation[i]; // scale for normalized squared error
|
---|
| 544 | if (jac != null) for (int j = 0; j < x.Length; j++) jac[outputIdx, j] = -jac[outputIdx, j] * optimizationData.inverseStandardDeviation[i];
|
---|
[16601] | 545 | outputIdx++;
|
---|
[15968] | 546 | }
|
---|
[15964] | 547 | }
|
---|
| 548 | }
|
---|
| 549 |
|
---|
[15968] | 550 | public override void Analyze(Individual[] individuals, double[] qualities, ResultCollection results, IRandom random) {
|
---|
| 551 | base.Analyze(individuals, qualities, results, random);
|
---|
[15964] | 552 |
|
---|
[16215] | 553 | if (!results.ContainsKey("Prediction (training)")) {
|
---|
[15968] | 554 | results.Add(new Result("Prediction (training)", typeof(ReadOnlyItemList<DataTable>)));
|
---|
| 555 | }
|
---|
[16215] | 556 | if (!results.ContainsKey("Prediction (test)")) {
|
---|
[15968] | 557 | results.Add(new Result("Prediction (test)", typeof(ReadOnlyItemList<DataTable>)));
|
---|
| 558 | }
|
---|
[16215] | 559 | if (!results.ContainsKey("Models")) {
|
---|
[16153] | 560 | results.Add(new Result("Models", typeof(VariableCollection)));
|
---|
[15968] | 561 | }
|
---|
[16399] | 562 | if (!results.ContainsKey("SNMSE")) {
|
---|
[16398] | 563 | results.Add(new Result("SNMSE", typeof(DoubleValue)));
|
---|
| 564 | }
|
---|
[16399] | 565 | if (!results.ContainsKey("Solution")) {
|
---|
| 566 | results.Add(new Result("Solution", typeof(Solution)));
|
---|
| 567 | }
|
---|
[16597] | 568 | if (!results.ContainsKey("Squared error and gradient")) {
|
---|
| 569 | results.Add(new Result("Squared error and gradient", typeof(DataTable)));
|
---|
| 570 | }
|
---|
[15968] | 571 |
|
---|
| 572 | var bestIndividualAndQuality = this.GetBestIndividual(individuals, qualities);
|
---|
| 573 | var trees = bestIndividualAndQuality.Item1.Values.Select(v => v.Value).OfType<ISymbolicExpressionTree>().ToArray(); // extract all trees from individual
|
---|
[16155] | 574 |
|
---|
[16398] | 575 | results["SNMSE"].Value = new DoubleValue(bestIndividualAndQuality.Item2);
|
---|
| 576 |
|
---|
[16601] | 577 | var problemData = ProblemData;
|
---|
[16268] | 578 | var targetVars = TargetVariables.CheckedItems.OrderBy(i => i.Index).Select(i => i.Value.Value).ToArray();
|
---|
[15970] | 579 | var latentVariables = Enumerable.Range(1, NumberOfLatentVariables).Select(i => "λ" + i).ToArray(); // TODO: must coincide with the variables which are actually defined in the grammar and also for which we actually have trees
|
---|
[15968] | 580 |
|
---|
| 581 | var trainingList = new ItemList<DataTable>();
|
---|
| 582 |
|
---|
[16215] | 583 | if (OptimizeParametersForEpisodes) {
|
---|
[16602] | 584 | throw new NotSupportedException();
|
---|
[16155] | 585 | var eIdx = 0;
|
---|
| 586 | var trainingPredictions = new List<Tuple<double, Vector>[][]>();
|
---|
[16215] | 587 | foreach (var episode in TrainingEpisodes) {
|
---|
[16155] | 588 | var episodes = new[] { episode };
|
---|
[16610] | 589 | var optimizationData = new OptimizationData(trees, targetVars, problemData.AllowedInputVariables.ToArray(), problemData, null, episodes, NumericIntegrationSteps, latentVariables, OdeSolver);
|
---|
[16603] | 590 | var trainingPrediction = Integrate(optimizationData).ToArray();
|
---|
[16155] | 591 | trainingPredictions.Add(trainingPrediction);
|
---|
| 592 | eIdx++;
|
---|
| 593 | }
|
---|
[15968] | 594 |
|
---|
[16329] | 595 | // only for target values
|
---|
[16155] | 596 | var trainingRows = TrainingEpisodes.SelectMany(e => Enumerable.Range(e.Start, e.End - e.Start));
|
---|
[16215] | 597 | for (int colIdx = 0; colIdx < targetVars.Length; colIdx++) {
|
---|
[16155] | 598 | var targetVar = targetVars[colIdx];
|
---|
| 599 | var trainingDataTable = new DataTable(targetVar + " prediction (training)");
|
---|
| 600 | var actualValuesRow = new DataRow(targetVar, "The values of " + targetVar, problemData.Dataset.GetDoubleValues(targetVar, trainingRows));
|
---|
| 601 | var predictedValuesRow = new DataRow(targetVar + " pred.", "Predicted values for " + targetVar, trainingPredictions.SelectMany(arr => arr.Select(row => row[colIdx].Item1)).ToArray());
|
---|
| 602 | trainingDataTable.Rows.Add(actualValuesRow);
|
---|
| 603 | trainingDataTable.Rows.Add(predictedValuesRow);
|
---|
| 604 | trainingList.Add(trainingDataTable);
|
---|
| 605 | }
|
---|
| 606 | results["Prediction (training)"].Value = trainingList.AsReadOnly();
|
---|
[15968] | 607 |
|
---|
| 608 |
|
---|
[16155] | 609 | var models = new VariableCollection();
|
---|
[16126] | 610 |
|
---|
[16215] | 611 | foreach (var tup in targetVars.Zip(trees, Tuple.Create)) {
|
---|
[16155] | 612 | var targetVarName = tup.Item1;
|
---|
| 613 | var tree = tup.Item2;
|
---|
[16126] | 614 |
|
---|
[16155] | 615 | var origTreeVar = new HeuristicLab.Core.Variable(targetVarName + "(original)");
|
---|
| 616 | origTreeVar.Value = (ISymbolicExpressionTree)tree.Clone();
|
---|
| 617 | models.Add(origTreeVar);
|
---|
| 618 | }
|
---|
| 619 | results["Models"].Value = models;
|
---|
| 620 | } else {
|
---|
[16653] | 621 | // data for input variables is assumed to be known
|
---|
| 622 | // input variables are all variable names that occur in the trees except for target variables (we assume that trees have been generated correctly)
|
---|
[16660] | 623 | var inputVariables = trees
|
---|
| 624 | .SelectMany(t => t.IterateNodesPrefix().OfType<VariableTreeNode>().Select(n => n.VariableName))
|
---|
| 625 | .Except(targetVars)
|
---|
| 626 | .Except(latentVariables)
|
---|
| 627 | .Distinct();
|
---|
[16653] | 628 |
|
---|
| 629 | var optimizationData = new OptimizationData(trees, targetVars, inputVariables.ToArray(), problemData, null, TrainingEpisodes.ToArray(), NumericIntegrationSteps, latentVariables, OdeSolver);
|
---|
[16660] | 630 | var numParams = optimizationData.nodeValueLookup.ParameterCount;
|
---|
[16603] | 631 |
|
---|
[16660] | 632 | var fi = new double[optimizationData.rows.Length * targetVars.Length];
|
---|
| 633 | var jac = new double[optimizationData.rows.Length * targetVars.Length, numParams];
|
---|
| 634 | var latentValues = new double[optimizationData.rows.Length, latentVariables.Length];
|
---|
| 635 | Integrate(optimizationData, fi, jac, latentValues);
|
---|
[16603] | 636 |
|
---|
[16660] | 637 |
|
---|
[16329] | 638 | // for target values and latent variables
|
---|
[16610] | 639 | var trainingRows = optimizationData.rows;
|
---|
[16329] | 640 | for (int colIdx = 0; colIdx < trees.Length; colIdx++) {
|
---|
| 641 | // is target variable
|
---|
| 642 | if (colIdx < targetVars.Length) {
|
---|
| 643 | var targetVar = targetVars[colIdx];
|
---|
| 644 | var trainingDataTable = new DataTable(targetVar + " prediction (training)");
|
---|
| 645 | var actualValuesRow = new DataRow(targetVar, "The values of " + targetVar, problemData.Dataset.GetDoubleValues(targetVar, trainingRows));
|
---|
[16660] | 646 | var idx = Enumerable.Range(0, trainingRows.Length).Select(i => i * targetVars.Length + colIdx);
|
---|
| 647 | var pred = idx.Select(i => fi[i]);
|
---|
| 648 | var predictedValuesRow = new DataRow(targetVar + " pred.", "Predicted values for " + targetVar, pred.ToArray());
|
---|
[16329] | 649 | trainingDataTable.Rows.Add(actualValuesRow);
|
---|
| 650 | trainingDataTable.Rows.Add(predictedValuesRow);
|
---|
[16597] | 651 |
|
---|
[16603] | 652 | for (int paramIdx = 0; paramIdx < numParams; paramIdx++) {
|
---|
[16660] | 653 | var paramSensitivityRow = new DataRow($"∂{targetVar}/∂θ{paramIdx}", $"Sensitivities of parameter {paramIdx}", idx.Select(i => jac[i, paramIdx]).ToArray());
|
---|
[16597] | 654 | paramSensitivityRow.VisualProperties.SecondYAxis = true;
|
---|
| 655 | trainingDataTable.Rows.Add(paramSensitivityRow);
|
---|
| 656 | }
|
---|
[16329] | 657 | trainingList.Add(trainingDataTable);
|
---|
| 658 | } else {
|
---|
| 659 | var latentVar = latentVariables[colIdx - targetVars.Length];
|
---|
| 660 | var trainingDataTable = new DataTable(latentVar + " prediction (training)");
|
---|
[16660] | 661 | var idx = Enumerable.Range(0, trainingRows.Length);
|
---|
| 662 | var pred = idx.Select(i => latentValues[i, colIdx - targetVars.Length]);
|
---|
| 663 | var predictedValuesRow = new DataRow(latentVar + " pred.", "Predicted values for " + latentVar, pred.ToArray());
|
---|
[16329] | 664 | var emptyRow = new DataRow(latentVar);
|
---|
| 665 | trainingDataTable.Rows.Add(emptyRow);
|
---|
| 666 | trainingDataTable.Rows.Add(predictedValuesRow);
|
---|
| 667 | trainingList.Add(trainingDataTable);
|
---|
| 668 | }
|
---|
[16155] | 669 | }
|
---|
[16597] | 670 |
|
---|
| 671 | var errorTable = new DataTable("Squared error and gradient");
|
---|
| 672 | var seRow = new DataRow("Squared error");
|
---|
[16603] | 673 | var gradientRows = Enumerable.Range(0, numParams).Select(i => new DataRow($"∂SE/∂θ{i}")).ToArray();
|
---|
[16597] | 674 | errorTable.Rows.Add(seRow);
|
---|
| 675 | foreach (var gRow in gradientRows) {
|
---|
| 676 | gRow.VisualProperties.SecondYAxis = true;
|
---|
| 677 | errorTable.Rows.Add(gRow);
|
---|
| 678 | }
|
---|
| 679 | var targetValues = targetVars.Select(v => problemData.Dataset.GetDoubleValues(v, trainingRows).ToArray()).ToArray();
|
---|
| 680 | int r = 0;
|
---|
[16610] | 681 |
|
---|
[16660] | 682 | // foreach (var y_pred in trainingPrediction) {
|
---|
| 683 | // // calculate objective function gradient
|
---|
| 684 | // double f_i = 0.0;
|
---|
| 685 | // Vector g_i = Vector.CreateNew(new double[numParams]);
|
---|
| 686 | // for (int colIdx = 0; colIdx < targetVars.Length; colIdx++) {
|
---|
| 687 | // var y_pred_f = y_pred[colIdx].Item1;
|
---|
| 688 | // var y = targetValues[colIdx][r];
|
---|
| 689 | //
|
---|
| 690 | // var res = (y - y_pred_f) * optimizationData.inverseStandardDeviation[colIdx];
|
---|
| 691 | // var ressq = res * res;
|
---|
| 692 | // f_i += ressq;
|
---|
| 693 | // g_i.Add(y_pred[colIdx].Item2.Scale(-2.0 * res));
|
---|
| 694 | // }
|
---|
| 695 | // seRow.Values.Add(f_i);
|
---|
| 696 | // for (int j = 0; j < g_i.Length; j++) gradientRows[j].Values.Add(g_i[j]);
|
---|
| 697 | // r++;
|
---|
| 698 | // }
|
---|
| 699 | // results["Squared error and gradient"].Value = errorTable;
|
---|
[16597] | 700 |
|
---|
[16155] | 701 | // TODO: DRY for training and test
|
---|
| 702 | var testList = new ItemList<DataTable>();
|
---|
| 703 | var testRows = ProblemData.TestIndices.ToArray();
|
---|
[16610] | 704 | var testOptimizationData = new OptimizationData(trees, targetVars, problemData.AllowedInputVariables.ToArray(), problemData, null, new IntRange[] { ProblemData.TestPartition }, NumericIntegrationSteps, latentVariables, OdeSolver);
|
---|
[16603] | 705 | var testPrediction = Integrate(testOptimizationData).ToArray();
|
---|
[16126] | 706 |
|
---|
[16329] | 707 | for (int colIdx = 0; colIdx < trees.Length; colIdx++) {
|
---|
| 708 | // is target variable
|
---|
| 709 | if (colIdx < targetVars.Length) {
|
---|
| 710 | var targetVar = targetVars[colIdx];
|
---|
| 711 | var testDataTable = new DataTable(targetVar + " prediction (test)");
|
---|
| 712 | var actualValuesRow = new DataRow(targetVar, "The values of " + targetVar, problemData.Dataset.GetDoubleValues(targetVar, testRows));
|
---|
| 713 | var predictedValuesRow = new DataRow(targetVar + " pred.", "Predicted values for " + targetVar, testPrediction.Select(arr => arr[colIdx].Item1).ToArray());
|
---|
| 714 | testDataTable.Rows.Add(actualValuesRow);
|
---|
| 715 | testDataTable.Rows.Add(predictedValuesRow);
|
---|
| 716 | testList.Add(testDataTable);
|
---|
| 717 |
|
---|
| 718 | } else {
|
---|
[16660] | 719 | // var latentVar = latentVariables[colIdx - targetVars.Length];
|
---|
| 720 | // var testDataTable = new DataTable(latentVar + " prediction (test)");
|
---|
| 721 | // var predictedValuesRow = new DataRow(latentVar + " pred.", "Predicted values for " + latentVar, testPrediction.Select(arr => arr[colIdx].Item1).ToArray());
|
---|
| 722 | // var emptyRow = new DataRow(latentVar);
|
---|
| 723 | // testDataTable.Rows.Add(emptyRow);
|
---|
| 724 | // testDataTable.Rows.Add(predictedValuesRow);
|
---|
| 725 | // testList.Add(testDataTable);
|
---|
[16329] | 726 | }
|
---|
[16155] | 727 | }
|
---|
[16126] | 728 |
|
---|
[16155] | 729 | results["Prediction (training)"].Value = trainingList.AsReadOnly();
|
---|
| 730 | results["Prediction (test)"].Value = testList.AsReadOnly();
|
---|
[16399] | 731 |
|
---|
| 732 |
|
---|
[16155] | 733 | #region simplification of models
|
---|
| 734 | // TODO the dependency of HeuristicLab.Problems.DataAnalysis.Symbolic is not ideal
|
---|
| 735 | var models = new VariableCollection(); // to store target var names and original version of tree
|
---|
[16126] | 736 |
|
---|
[16603] | 737 | var clonedTrees = new List<ISymbolicExpressionTree>();
|
---|
[16329] | 738 | for (int idx = 0; idx < trees.Length; idx++) {
|
---|
[16604] | 739 | clonedTrees.Add((ISymbolicExpressionTree)trees[idx].Clone());
|
---|
[16399] | 740 | }
|
---|
| 741 | var ds = problemData.Dataset;
|
---|
[16603] | 742 | var newProblemData = new RegressionProblemData((IDataset)ds.Clone(), problemData.AllowedInputVariables, problemData.TargetVariable);
|
---|
| 743 | results["Solution"].Value = new Solution(clonedTrees.ToArray(),
|
---|
[16399] | 744 | // optTheta,
|
---|
| 745 | newProblemData,
|
---|
| 746 | targetVars,
|
---|
| 747 | latentVariables,
|
---|
| 748 | TrainingEpisodes,
|
---|
| 749 | OdeSolver,
|
---|
| 750 | NumericIntegrationSteps);
|
---|
| 751 |
|
---|
| 752 |
|
---|
| 753 | for (int idx = 0; idx < trees.Length; idx++) {
|
---|
[16329] | 754 | var varName = string.Empty;
|
---|
| 755 | if (idx < targetVars.Length) {
|
---|
| 756 | varName = targetVars[idx];
|
---|
| 757 | } else {
|
---|
| 758 | varName = latentVariables[idx - targetVars.Length];
|
---|
| 759 | }
|
---|
| 760 | var tree = trees[idx];
|
---|
[16153] | 761 |
|
---|
[16329] | 762 | var origTreeVar = new HeuristicLab.Core.Variable(varName + "(original)");
|
---|
[16155] | 763 | origTreeVar.Value = (ISymbolicExpressionTree)tree.Clone();
|
---|
| 764 | models.Add(origTreeVar);
|
---|
[16329] | 765 | var simplifiedTreeVar = new HeuristicLab.Core.Variable(varName + "(simplified)");
|
---|
[16602] | 766 | simplifiedTreeVar.Value = TreeSimplifier.Simplify(tree);
|
---|
[16155] | 767 | models.Add(simplifiedTreeVar);
|
---|
| 768 |
|
---|
| 769 | }
|
---|
[16399] | 770 |
|
---|
[16155] | 771 | results["Models"].Value = models;
|
---|
| 772 | #endregion
|
---|
[16126] | 773 | }
|
---|
[15968] | 774 | }
|
---|
| 775 |
|
---|
| 776 | #region interpretation
|
---|
[16222] | 777 |
|
---|
| 778 | // the following uses auto-diff to calculate the gradient w.r.t. the parameters forward in time.
|
---|
| 779 | // this is basically the method described in Gronwall T. Note on the derivatives with respect to a parameter of the solutions of a system of differential equations. Ann. Math. 1919;20:292–296.
|
---|
| 780 |
|
---|
| 781 | // a comparison of three potential calculation methods for the gradient is given in:
|
---|
| 782 | // Sengupta, B., Friston, K. J., & Penny, W. D. (2014). Efficient gradient computation for dynamical models. Neuroimage, 98(100), 521–527. http://doi.org/10.1016/j.neuroimage.2014.04.040
|
---|
| 783 | // "Our comparison establishes that the adjoint method is computationally more efficient for numerical estimation of parametric gradients
|
---|
| 784 | // for state-space models — both linear and non-linear, as in the case of a dynamical causal model (DCM)"
|
---|
| 785 |
|
---|
| 786 | // for a solver with the necessary features see: https://computation.llnl.gov/projects/sundials/cvodes
|
---|
[16253] | 787 |
|
---|
[16603] | 788 | public static IEnumerable<Tuple<double, Vector>[]> Integrate(OptimizationData optimizationData) {
|
---|
[16604] | 789 | var nTargets = optimizationData.targetVariables.Length;
|
---|
| 790 | var n = optimizationData.rows.Length * optimizationData.targetVariables.Length;
|
---|
| 791 | var d = optimizationData.nodeValueLookup.ParameterCount;
|
---|
| 792 | double[] fi = new double[n];
|
---|
| 793 | double[,] jac = new double[n, d];
|
---|
[16660] | 794 | Integrate(optimizationData, fi, jac, null);
|
---|
[16610] | 795 | for (int i = 0; i < optimizationData.rows.Length; i++) {
|
---|
[16604] | 796 | var res = new Tuple<double, Vector>[nTargets];
|
---|
[16610] | 797 | for (int j = 0; j < nTargets; j++) {
|
---|
[16604] | 798 | res[j] = Tuple.Create(fi[i * nTargets + j], Vector.CreateFromMatrixRow(jac, i * nTargets + j));
|
---|
| 799 | }
|
---|
| 800 | yield return res;
|
---|
| 801 | }
|
---|
| 802 | }
|
---|
[15964] | 803 |
|
---|
[16660] | 804 | public static void Integrate(OptimizationData optimizationData, double[] fi, double[,] jac, double[,] latentValues) {
|
---|
[16600] | 805 | var trees = optimizationData.trees;
|
---|
| 806 | var dataset = optimizationData.problemData.Dataset;
|
---|
[16653] | 807 | var inputVariables = optimizationData.variables;
|
---|
[16600] | 808 | var targetVariables = optimizationData.targetVariables;
|
---|
| 809 | var latentVariables = optimizationData.latentVariables;
|
---|
| 810 | var episodes = optimizationData.episodes;
|
---|
| 811 | var odeSolver = optimizationData.odeSolver;
|
---|
| 812 | var numericIntegrationSteps = optimizationData.numericIntegrationSteps;
|
---|
[16601] | 813 | var calculatedVariables = targetVariables.Concat(latentVariables).ToArray(); // TODO: must conincide with the order of trees in the encoding
|
---|
[16600] | 814 |
|
---|
[16610] | 815 |
|
---|
| 816 |
|
---|
[16601] | 817 | var nodeValues = optimizationData.nodeValueLookup;
|
---|
| 818 |
|
---|
[16250] | 819 | // TODO: numericIntegrationSteps is only relevant for the HeuristicLab solver
|
---|
[16610] | 820 | var outputRowIdx = 0;
|
---|
[16329] | 821 | var episodeIdx = 0;
|
---|
[16600] | 822 | foreach (var episode in optimizationData.episodes) {
|
---|
[16601] | 823 | var rows = Enumerable.Range(episode.Start, episode.End - episode.Start).ToArray();
|
---|
[15968] | 824 |
|
---|
[16601] | 825 | var t0 = rows.First();
|
---|
[15964] | 826 |
|
---|
[16601] | 827 | // initialize values for inputs and targets from dataset
|
---|
[16604] | 828 | foreach (var varName in inputVariables) {
|
---|
[16653] | 829 | // in this problem we also allow fixed numeric parameters (represented as variables with the value as name)
|
---|
| 830 | if (double.TryParse(varName, NumberStyles.Float, CultureInfo.InvariantCulture, out double value)) {
|
---|
| 831 | nodeValues.SetVariableValue(varName, value, Vector.Zero);
|
---|
| 832 | } else {
|
---|
| 833 | var y0 = dataset.GetDoubleValue(varName, t0);
|
---|
| 834 | nodeValues.SetVariableValue(varName, y0, Vector.Zero);
|
---|
| 835 | }
|
---|
[16153] | 836 | }
|
---|
[16610] | 837 | foreach (var varName in targetVariables) {
|
---|
[16604] | 838 | var y0 = dataset.GetDoubleValue(varName, t0);
|
---|
| 839 | nodeValues.SetVariableValue(varName, y0, Vector.Zero);
|
---|
[16601] | 840 |
|
---|
[16604] | 841 | // output starting value
|
---|
| 842 | fi[outputRowIdx] = y0;
|
---|
| 843 | Vector.Zero.CopyTo(jac, outputRowIdx);
|
---|
| 844 |
|
---|
| 845 | outputRowIdx++;
|
---|
| 846 | }
|
---|
| 847 |
|
---|
[16660] | 848 | var latentValueRowIdx = 0;
|
---|
| 849 | var latentValueColIdx = 0;
|
---|
| 850 | foreach (var varName in latentVariables) {
|
---|
| 851 | var y0 = 0.0; // assume we start at zero
|
---|
| 852 | nodeValues.SetVariableValue(varName, y0, Vector.Zero);
|
---|
| 853 |
|
---|
| 854 | if (latentValues != null) {
|
---|
| 855 | latentValues[latentValueRowIdx, latentValueColIdx++] = y0;
|
---|
| 856 | }
|
---|
| 857 | }
|
---|
| 858 | latentValueColIdx = 0; latentValueRowIdx++;
|
---|
| 859 |
|
---|
[16603] | 860 | { // CODE BELOW DOESN'T WORK ANYMORE
|
---|
| 861 | // if (latentVariables.Length > 0) throw new NotImplementedException();
|
---|
| 862 | //
|
---|
| 863 | // // add value entries for latent variables which are also integrated
|
---|
| 864 | // // initial values are at the end of the parameter vector
|
---|
| 865 | // // separate initial values for each episode
|
---|
| 866 | // var initialValueIdx = parameterValues.Length - episodes.Count() * latentVariables.Length + episodeIdx * latentVariables.Length;
|
---|
| 867 | // foreach (var latentVar in latentVariables) {
|
---|
| 868 | // var arr = new double[parameterValues.Length]; // backing array
|
---|
| 869 | // arr[initialValueIdx] = 1.0;
|
---|
| 870 | // var g = new Vector(arr);
|
---|
| 871 | // nodeValues.SetVariableValue(latentVar, parameterValues[initialValueIdx], g); // we don't have observations for latent variables therefore we optimize the initial value for each episode
|
---|
| 872 | // initialValueIdx++;
|
---|
| 873 | // }
|
---|
[16153] | 874 | }
|
---|
[16329] | 875 |
|
---|
[16601] | 876 | var prevT = t0; // TODO: here we should use a variable for t if it is available. Right now we assume equidistant measurements.
|
---|
[16215] | 877 | foreach (var t in rows.Skip(1)) {
|
---|
[16250] | 878 | if (odeSolver == "HeuristicLab")
|
---|
[16601] | 879 | IntegrateHL(trees, calculatedVariables, nodeValues, numericIntegrationSteps); // integrator updates nodeValues
|
---|
[16250] | 880 | else if (odeSolver == "CVODES")
|
---|
[16597] | 881 | throw new NotImplementedException();
|
---|
| 882 | // IntegrateCVODES(trees, calculatedVariables, variableValues, parameterValues, t - prevT);
|
---|
[16250] | 883 | else throw new InvalidOperationException("Unknown ODE solver " + odeSolver);
|
---|
[16253] | 884 | prevT = t;
|
---|
[15964] | 885 |
|
---|
[16660] | 886 | // update output for target variables (TODO: if we want to visualize the latent variables then we need to provide a separate output)
|
---|
| 887 | for (int i = 0; i < targetVariables.Length; i++) {
|
---|
| 888 | var targetVar = targetVariables[i];
|
---|
[16604] | 889 | var yt = nodeValues.GetVariableValue(targetVar);
|
---|
[16601] | 890 |
|
---|
[16604] | 891 | // fill up remaining rows with last valid value if there are invalid values
|
---|
| 892 | if (double.IsNaN(yt.Item1) || double.IsInfinity(yt.Item1)) {
|
---|
| 893 | for (; outputRowIdx < fi.Length; outputRowIdx++) {
|
---|
[16660] | 894 | var prevIdx = outputRowIdx - targetVariables.Length;
|
---|
[16604] | 895 | fi[outputRowIdx] = fi[prevIdx]; // current <- prev
|
---|
| 896 | if (jac != null) for (int j = 0; j < jac.GetLength(1); j++) jac[outputRowIdx, j] = jac[prevIdx, j];
|
---|
| 897 | }
|
---|
| 898 | return;
|
---|
| 899 | };
|
---|
[16601] | 900 |
|
---|
[16604] | 901 | fi[outputRowIdx] = yt.Item1;
|
---|
| 902 | var g = yt.Item2;
|
---|
| 903 | g.CopyTo(jac, outputRowIdx);
|
---|
| 904 | outputRowIdx++;
|
---|
| 905 | }
|
---|
[16660] | 906 | if (latentValues != null) {
|
---|
| 907 | foreach (var latentVariable in latentVariables) {
|
---|
| 908 | var lt = nodeValues.GetVariableValue(latentVariable).Item1;
|
---|
| 909 | latentValues[latentValueRowIdx, latentValueColIdx++] = lt;
|
---|
| 910 | }
|
---|
| 911 | latentValueRowIdx++; latentValueColIdx = 0;
|
---|
| 912 | }
|
---|
[15964] | 913 |
|
---|
[16601] | 914 | // update for next time step (only the inputs)
|
---|
[16215] | 915 | foreach (var varName in inputVariables) {
|
---|
[16653] | 916 | // in this problem we also allow fixed numeric parameters (represented as variables with the value as name)
|
---|
| 917 | if (double.TryParse(varName, NumberStyles.Float, CultureInfo.InvariantCulture, out double value)) {
|
---|
| 918 | // value is unchanged
|
---|
| 919 | } else {
|
---|
| 920 | nodeValues.SetVariableValue(varName, dataset.GetDoubleValue(varName, t), Vector.Zero);
|
---|
| 921 | }
|
---|
[16153] | 922 | }
|
---|
[15964] | 923 | }
|
---|
[16329] | 924 | episodeIdx++;
|
---|
[15964] | 925 | }
|
---|
| 926 | }
|
---|
| 927 |
|
---|
[16398] | 928 | #region CVODES
|
---|
[16253] | 929 |
|
---|
[16597] | 930 | /*
|
---|
[16253] | 931 | /// <summary>
|
---|
| 932 | /// Here we use CVODES to solve the ODE. Forward sensitivities are used to calculate the gradient for parameter optimization
|
---|
| 933 | /// </summary>
|
---|
| 934 | /// <param name="trees">Each equation in the ODE represented as a tree</param>
|
---|
| 935 | /// <param name="calculatedVariables">The names of the calculated variables</param>
|
---|
| 936 | /// <param name="variableValues">The start values of the calculated variables as well as their sensitivites over parameters</param>
|
---|
| 937 | /// <param name="parameterValues">The current parameter values</param>
|
---|
| 938 | /// <param name="t">The time t up to which we need to integrate.</param>
|
---|
[16250] | 939 | private static void IntegrateCVODES(
|
---|
[16251] | 940 | ISymbolicExpressionTree[] trees, // f(y,p) in tree representation
|
---|
| 941 | string[] calculatedVariables, // names of elements of y
|
---|
| 942 | Dictionary<string, Tuple<double, Vector>> variableValues, // y (intput and output) input: y(t0), output: y(t0+t)
|
---|
| 943 | double[] parameterValues, // p
|
---|
| 944 | double t // duration t for which we want to integrate
|
---|
[16250] | 945 | ) {
|
---|
[16251] | 946 |
|
---|
[16250] | 947 | // the RHS of the ODE
|
---|
[16251] | 948 | // dy/dt = f(y_t,x_t,p)
|
---|
| 949 | CVODES.CVRhsFunc f = CreateOdeRhs(trees, calculatedVariables, parameterValues);
|
---|
| 950 | // the Jacobian ∂f/∂y
|
---|
| 951 | CVODES.CVDlsJacFunc jac = CreateJac(trees, calculatedVariables, parameterValues);
|
---|
| 952 |
|
---|
| 953 | // the RHS for the forward sensitivities (∂f/∂y)s_i(t) + ∂f/∂p_i
|
---|
| 954 | CVODES.CVSensRhsFn sensF = CreateSensitivityRhs(trees, calculatedVariables, parameterValues);
|
---|
| 955 |
|
---|
| 956 | // setup solver
|
---|
| 957 | int numberOfEquations = trees.Length;
|
---|
| 958 | IntPtr y = IntPtr.Zero;
|
---|
| 959 | IntPtr cvode_mem = IntPtr.Zero;
|
---|
| 960 | IntPtr A = IntPtr.Zero;
|
---|
| 961 | IntPtr yS0 = IntPtr.Zero;
|
---|
| 962 | IntPtr linearSolver = IntPtr.Zero;
|
---|
| 963 | var ns = parameterValues.Length; // number of parameters
|
---|
| 964 |
|
---|
| 965 | try {
|
---|
| 966 | y = CVODES.N_VNew_Serial(numberOfEquations);
|
---|
| 967 | // init y to current values of variables
|
---|
| 968 | // y must be initialized before calling CVodeInit
|
---|
| 969 | for (int i = 0; i < calculatedVariables.Length; i++) {
|
---|
| 970 | CVODES.NV_Set_Ith_S(y, i, variableValues[calculatedVariables[i]].Item1);
|
---|
| 971 | }
|
---|
| 972 |
|
---|
| 973 | cvode_mem = CVODES.CVodeCreate(CVODES.MultistepMethod.CV_ADAMS, CVODES.NonlinearSolverIteration.CV_FUNCTIONAL);
|
---|
| 974 |
|
---|
| 975 | var flag = CVODES.CVodeInit(cvode_mem, f, 0.0, y);
|
---|
[16616] | 976 | Assert(CVODES.CV_SUCCESS == flag);
|
---|
[16251] | 977 |
|
---|
| 978 | double relTol = 1.0e-2;
|
---|
| 979 | double absTol = 1.0;
|
---|
| 980 | flag = CVODES.CVodeSStolerances(cvode_mem, relTol, absTol); // TODO: probably need to adjust absTol per variable
|
---|
[16616] | 981 | Assert(CVODES.CV_SUCCESS == flag);
|
---|
[16251] | 982 |
|
---|
| 983 | A = CVODES.SUNDenseMatrix(numberOfEquations, numberOfEquations);
|
---|
[16616] | 984 | Assert(A != IntPtr.Zero);
|
---|
[16251] | 985 |
|
---|
| 986 | linearSolver = CVODES.SUNDenseLinearSolver(y, A);
|
---|
[16616] | 987 | Assert(linearSolver != IntPtr.Zero);
|
---|
[16251] | 988 |
|
---|
| 989 | flag = CVODES.CVDlsSetLinearSolver(cvode_mem, linearSolver, A);
|
---|
[16616] | 990 | Assert(CVODES.CV_SUCCESS == flag);
|
---|
[16251] | 991 |
|
---|
| 992 | flag = CVODES.CVDlsSetJacFn(cvode_mem, jac);
|
---|
[16616] | 993 | Assert(CVODES.CV_SUCCESS == flag);
|
---|
[16251] | 994 |
|
---|
| 995 | yS0 = CVODES.N_VCloneVectorArray_Serial(ns, y); // clone the output vector for each parameter
|
---|
| 996 | unsafe {
|
---|
| 997 | // set to initial sensitivities supplied by caller
|
---|
| 998 | for (int pIdx = 0; pIdx < ns; pIdx++) {
|
---|
| 999 | var yS0_i = *((IntPtr*)yS0.ToPointer() + pIdx);
|
---|
| 1000 | for (var varIdx = 0; varIdx < calculatedVariables.Length; varIdx++) {
|
---|
| 1001 | CVODES.NV_Set_Ith_S(yS0_i, varIdx, variableValues[calculatedVariables[varIdx]].Item2[pIdx]);
|
---|
| 1002 | }
|
---|
| 1003 | }
|
---|
| 1004 | }
|
---|
| 1005 |
|
---|
| 1006 | flag = CVODES.CVodeSensInit(cvode_mem, ns, CVODES.CV_SIMULTANEOUS, sensF, yS0);
|
---|
[16616] | 1007 | Assert(CVODES.CV_SUCCESS == flag);
|
---|
[16251] | 1008 |
|
---|
| 1009 | flag = CVODES.CVodeSensEEtolerances(cvode_mem);
|
---|
[16616] | 1010 | Assert(CVODES.CV_SUCCESS == flag);
|
---|
[16251] | 1011 |
|
---|
| 1012 | // make one forward integration step
|
---|
| 1013 | double tout = 0.0; // first output time
|
---|
| 1014 | flag = CVODES.CVode(cvode_mem, t, y, ref tout, CVODES.CV_NORMAL);
|
---|
| 1015 | if (flag == CVODES.CV_SUCCESS) {
|
---|
[16616] | 1016 | Assert(t == tout);
|
---|
[16251] | 1017 |
|
---|
| 1018 | // get sensitivities
|
---|
| 1019 | flag = CVODES.CVodeGetSens(cvode_mem, ref tout, yS0);
|
---|
[16616] | 1020 | Assert(CVODES.CV_SUCCESS == flag);
|
---|
[16251] | 1021 |
|
---|
| 1022 | // update variableValues based on integration results
|
---|
| 1023 | for (int varIdx = 0; varIdx < calculatedVariables.Length; varIdx++) {
|
---|
| 1024 | var yi = CVODES.NV_Get_Ith_S(y, varIdx);
|
---|
| 1025 | var gArr = new double[parameterValues.Length];
|
---|
| 1026 | for (var pIdx = 0; pIdx < parameterValues.Length; pIdx++) {
|
---|
| 1027 | unsafe {
|
---|
| 1028 | var yS0_pi = *((IntPtr*)yS0.ToPointer() + pIdx);
|
---|
| 1029 | gArr[pIdx] = CVODES.NV_Get_Ith_S(yS0_pi, varIdx);
|
---|
| 1030 | }
|
---|
| 1031 | }
|
---|
| 1032 | variableValues[calculatedVariables[varIdx]] = Tuple.Create(yi, new Vector(gArr));
|
---|
| 1033 | }
|
---|
| 1034 | } else {
|
---|
| 1035 | variableValues.Clear(); // indicate problems by not returning new values
|
---|
| 1036 | }
|
---|
| 1037 |
|
---|
| 1038 | // cleanup all allocated objects
|
---|
| 1039 | } finally {
|
---|
| 1040 | if (y != IntPtr.Zero) CVODES.N_VDestroy_Serial(y);
|
---|
[16253] | 1041 | if (cvode_mem != IntPtr.Zero) CVODES.CVodeFree(ref cvode_mem);
|
---|
[16251] | 1042 | if (linearSolver != IntPtr.Zero) CVODES.SUNLinSolFree(linearSolver);
|
---|
| 1043 | if (A != IntPtr.Zero) CVODES.SUNMatDestroy(A);
|
---|
| 1044 | if (yS0 != IntPtr.Zero) CVODES.N_VDestroyVectorArray_Serial(yS0, ns);
|
---|
| 1045 | }
|
---|
[16250] | 1046 | }
|
---|
| 1047 |
|
---|
[16251] | 1048 |
|
---|
[16250] | 1049 | private static CVODES.CVRhsFunc CreateOdeRhs(
|
---|
| 1050 | ISymbolicExpressionTree[] trees,
|
---|
| 1051 | string[] calculatedVariables,
|
---|
| 1052 | double[] parameterValues) {
|
---|
[16398] | 1053 | // we don't need to calculate a gradient here
|
---|
[16250] | 1054 | return (double t,
|
---|
| 1055 | IntPtr y, // N_Vector, current value of y (input)
|
---|
| 1056 | IntPtr ydot, // N_Vector (calculated value of y' (output)
|
---|
| 1057 | IntPtr user_data // optional user data, (unused here)
|
---|
| 1058 | ) => {
|
---|
[16251] | 1059 | // TODO: perf
|
---|
| 1060 | var nodeValues = new Dictionary<ISymbolicExpressionTreeNode, Tuple<double, Vector>>();
|
---|
| 1061 |
|
---|
| 1062 | int pIdx = 0;
|
---|
| 1063 | foreach (var tree in trees) {
|
---|
| 1064 | foreach (var n in tree.IterateNodesPrefix()) {
|
---|
| 1065 | if (IsConstantNode(n)) {
|
---|
| 1066 | nodeValues.Add(n, Tuple.Create(parameterValues[pIdx], Vector.Zero)); // here we do not need a gradient
|
---|
| 1067 | pIdx++;
|
---|
| 1068 | } else if (n.SubtreeCount == 0) {
|
---|
| 1069 | // for variables and latent variables get the value from variableValues
|
---|
| 1070 | var varName = n.Symbol.Name;
|
---|
| 1071 | var varIdx = Array.IndexOf(calculatedVariables, varName); // TODO: perf!
|
---|
[16268] | 1072 | if (varIdx < 0) throw new InvalidProgramException();
|
---|
[16251] | 1073 | var y_i = CVODES.NV_Get_Ith_S(y, (long)varIdx);
|
---|
| 1074 | nodeValues.Add(n, Tuple.Create(y_i, Vector.Zero)); // no gradient needed
|
---|
| 1075 | }
|
---|
| 1076 | }
|
---|
[16250] | 1077 | }
|
---|
| 1078 | for (int i = 0; i < trees.Length; i++) {
|
---|
| 1079 | var tree = trees[i];
|
---|
[16251] | 1080 | var res_i = InterpretRec(tree.Root.GetSubtree(0).GetSubtree(0), nodeValues);
|
---|
[16250] | 1081 | CVODES.NV_Set_Ith_S(ydot, i, res_i.Item1);
|
---|
| 1082 | }
|
---|
| 1083 | return 0;
|
---|
| 1084 | };
|
---|
| 1085 | }
|
---|
| 1086 |
|
---|
[16251] | 1087 | private static CVODES.CVDlsJacFunc CreateJac(
|
---|
| 1088 | ISymbolicExpressionTree[] trees,
|
---|
[16250] | 1089 | string[] calculatedVariables,
|
---|
[16251] | 1090 | double[] parameterValues) {
|
---|
| 1091 |
|
---|
| 1092 | return (
|
---|
| 1093 | double t, // current time (input)
|
---|
| 1094 | IntPtr y, // N_Vector, current value of y (input)
|
---|
| 1095 | IntPtr fy, // N_Vector, current value of f (input)
|
---|
| 1096 | IntPtr Jac, // SUNMatrix ∂f/∂y (output, rows i contains are ∂f_i/∂y vector)
|
---|
| 1097 | IntPtr user_data, // optional (unused here)
|
---|
| 1098 | IntPtr tmp1, // N_Vector, optional (unused here)
|
---|
| 1099 | IntPtr tmp2, // N_Vector, optional (unused here)
|
---|
| 1100 | IntPtr tmp3 // N_Vector, optional (unused here)
|
---|
| 1101 | ) => {
|
---|
| 1102 | // here we need to calculate partial derivatives for the calculated variables y
|
---|
| 1103 | var nodeValues = new Dictionary<ISymbolicExpressionTreeNode, Tuple<double, Vector>>();
|
---|
| 1104 | int pIdx = 0;
|
---|
| 1105 | foreach (var tree in trees) {
|
---|
| 1106 | foreach (var n in tree.IterateNodesPrefix()) {
|
---|
| 1107 | if (IsConstantNode(n)) {
|
---|
| 1108 | nodeValues.Add(n, Tuple.Create(parameterValues[pIdx], Vector.Zero)); // here we need a gradient over y which is zero for parameters
|
---|
| 1109 | pIdx++;
|
---|
| 1110 | } else if (n.SubtreeCount == 0) {
|
---|
| 1111 | // for variables and latent variables we use values supplied in y and init gradient vectors accordingly
|
---|
| 1112 | var varName = n.Symbol.Name;
|
---|
| 1113 | var varIdx = Array.IndexOf(calculatedVariables, varName); // TODO: perf!
|
---|
[16268] | 1114 | if (varIdx < 0) throw new InvalidProgramException();
|
---|
| 1115 |
|
---|
[16251] | 1116 | var y_i = CVODES.NV_Get_Ith_S(y, (long)varIdx);
|
---|
| 1117 | var gArr = new double[CVODES.NV_LENGTH_S(y)]; // backing array
|
---|
| 1118 | gArr[varIdx] = 1.0;
|
---|
| 1119 | var g = new Vector(gArr);
|
---|
| 1120 | nodeValues.Add(n, Tuple.Create(y_i, g));
|
---|
| 1121 | }
|
---|
| 1122 | }
|
---|
| 1123 | }
|
---|
| 1124 |
|
---|
| 1125 | for (int i = 0; i < trees.Length; i++) {
|
---|
| 1126 | var tree = trees[i];
|
---|
| 1127 | var res = InterpretRec(tree.Root.GetSubtree(0).GetSubtree(0), nodeValues);
|
---|
| 1128 | var g = res.Item2;
|
---|
| 1129 | for (int j = 0; j < calculatedVariables.Length; j++) {
|
---|
| 1130 | CVODES.SUNDenseMatrix_Set(Jac, i, j, g[j]);
|
---|
| 1131 | }
|
---|
| 1132 | }
|
---|
| 1133 | return 0; // on success
|
---|
| 1134 | };
|
---|
| 1135 | }
|
---|
| 1136 |
|
---|
| 1137 |
|
---|
| 1138 | // to calculate sensitivities RHS for all equations at once
|
---|
| 1139 | // must compute (∂f/∂y)s_i(t) + ∂f/∂p_i and store in ySdot.
|
---|
| 1140 | // Index i refers to parameters, dimensionality of matrix and vectors is number of equations
|
---|
| 1141 | private static CVODES.CVSensRhsFn CreateSensitivityRhs(ISymbolicExpressionTree[] trees, string[] calculatedVariables, double[] parameterValues) {
|
---|
| 1142 | return (
|
---|
| 1143 | int Ns, // number of parameters
|
---|
| 1144 | double t, // current time
|
---|
| 1145 | IntPtr y, // N_Vector y(t) (input)
|
---|
| 1146 | IntPtr ydot, // N_Vector dy/dt(t) (input)
|
---|
| 1147 | IntPtr yS, // N_Vector*, one vector for each parameter (input)
|
---|
| 1148 | IntPtr ySdot, // N_Vector*, one vector for each parameter (output)
|
---|
| 1149 | IntPtr user_data, // optional (unused here)
|
---|
| 1150 | IntPtr tmp1, // N_Vector, optional (unused here)
|
---|
| 1151 | IntPtr tmp2 // N_Vector, optional (unused here)
|
---|
| 1152 | ) => {
|
---|
| 1153 | // here we need to calculate partial derivatives for the calculated variables y as well as for the parameters
|
---|
| 1154 | var nodeValues = new Dictionary<ISymbolicExpressionTreeNode, Tuple<double, Vector>>();
|
---|
| 1155 | var d = calculatedVariables.Length + parameterValues.Length; // dimensionality of gradient
|
---|
| 1156 | // first collect variable values
|
---|
| 1157 | foreach (var tree in trees) {
|
---|
| 1158 | foreach (var n in tree.IterateNodesPrefix()) {
|
---|
| 1159 | if (IsVariableNode(n)) {
|
---|
| 1160 | // for variables and latent variables we use values supplied in y and init gradient vectors accordingly
|
---|
| 1161 | var varName = n.Symbol.Name;
|
---|
| 1162 | var varIdx = Array.IndexOf(calculatedVariables, varName); // TODO: perf!
|
---|
[16268] | 1163 | if (varIdx < 0) throw new InvalidProgramException();
|
---|
| 1164 |
|
---|
[16251] | 1165 | var y_i = CVODES.NV_Get_Ith_S(y, (long)varIdx);
|
---|
| 1166 | var gArr = new double[d]; // backing array
|
---|
| 1167 | gArr[varIdx] = 1.0;
|
---|
| 1168 | var g = new Vector(gArr);
|
---|
| 1169 | nodeValues.Add(n, Tuple.Create(y_i, g));
|
---|
| 1170 | }
|
---|
| 1171 | }
|
---|
| 1172 | }
|
---|
| 1173 | // then collect constants
|
---|
| 1174 | int pIdx = 0;
|
---|
| 1175 | foreach (var tree in trees) {
|
---|
| 1176 | foreach (var n in tree.IterateNodesPrefix()) {
|
---|
| 1177 | if (IsConstantNode(n)) {
|
---|
| 1178 | var gArr = new double[d];
|
---|
| 1179 | gArr[calculatedVariables.Length + pIdx] = 1.0;
|
---|
| 1180 | var g = new Vector(gArr);
|
---|
| 1181 | nodeValues.Add(n, Tuple.Create(parameterValues[pIdx], g));
|
---|
| 1182 | pIdx++;
|
---|
| 1183 | }
|
---|
| 1184 | }
|
---|
| 1185 | }
|
---|
| 1186 | // gradient vector is [∂f/∂y_1, ∂f/∂y_2, ... ∂f/∂yN, ∂f/∂p_1 ... ∂f/∂p_K]
|
---|
| 1187 |
|
---|
| 1188 |
|
---|
| 1189 | for (pIdx = 0; pIdx < Ns; pIdx++) {
|
---|
| 1190 | unsafe {
|
---|
| 1191 | var sDot_pi = *((IntPtr*)ySdot.ToPointer() + pIdx);
|
---|
| 1192 | CVODES.N_VConst_Serial(0.0, sDot_pi);
|
---|
| 1193 | }
|
---|
| 1194 | }
|
---|
| 1195 |
|
---|
| 1196 | for (int i = 0; i < trees.Length; i++) {
|
---|
| 1197 | var tree = trees[i];
|
---|
| 1198 | var res = InterpretRec(tree.Root.GetSubtree(0).GetSubtree(0), nodeValues);
|
---|
| 1199 | var g = res.Item2;
|
---|
| 1200 |
|
---|
| 1201 |
|
---|
| 1202 | // update ySdot = (∂f/∂y)s_i(t) + ∂f/∂p_i
|
---|
| 1203 |
|
---|
| 1204 | for (pIdx = 0; pIdx < Ns; pIdx++) {
|
---|
| 1205 | unsafe {
|
---|
| 1206 | var sDot_pi = *((IntPtr*)ySdot.ToPointer() + pIdx);
|
---|
| 1207 | var s_pi = *((IntPtr*)yS.ToPointer() + pIdx);
|
---|
| 1208 |
|
---|
| 1209 | var v = CVODES.NV_Get_Ith_S(sDot_pi, i);
|
---|
| 1210 | // (∂f/∂y)s_i(t)
|
---|
| 1211 | var p = 0.0;
|
---|
| 1212 | for (int yIdx = 0; yIdx < calculatedVariables.Length; yIdx++) {
|
---|
| 1213 | p += g[yIdx] * CVODES.NV_Get_Ith_S(s_pi, yIdx);
|
---|
| 1214 | }
|
---|
| 1215 | // + ∂f/∂p_i
|
---|
| 1216 | CVODES.NV_Set_Ith_S(sDot_pi, i, v + p + g[calculatedVariables.Length + pIdx]);
|
---|
| 1217 | }
|
---|
| 1218 | }
|
---|
| 1219 |
|
---|
| 1220 | }
|
---|
| 1221 | return 0; // on success
|
---|
| 1222 | };
|
---|
| 1223 | }
|
---|
[16597] | 1224 | */
|
---|
[16398] | 1225 | #endregion
|
---|
[16251] | 1226 |
|
---|
| 1227 | private static void IntegrateHL(
|
---|
| 1228 | ISymbolicExpressionTree[] trees,
|
---|
| 1229 | string[] calculatedVariables, // names of integrated variables
|
---|
[16601] | 1230 | NodeValueLookup nodeValues,
|
---|
[16250] | 1231 | int numericIntegrationSteps) {
|
---|
[16251] | 1232 |
|
---|
| 1233 |
|
---|
[16597] | 1234 | double[] deltaF = new double[calculatedVariables.Length];
|
---|
| 1235 | Vector[] deltaG = new Vector[calculatedVariables.Length];
|
---|
[16251] | 1236 |
|
---|
[16250] | 1237 | double h = 1.0 / numericIntegrationSteps;
|
---|
| 1238 | for (int step = 0; step < numericIntegrationSteps; step++) {
|
---|
[16601] | 1239 |
|
---|
| 1240 | // evaluate all trees
|
---|
[16251] | 1241 | for (int i = 0; i < trees.Length; i++) {
|
---|
| 1242 | var tree = trees[i];
|
---|
| 1243 |
|
---|
| 1244 | // Root.GetSubtree(0).GetSubtree(0) skips programRoot and startSymbol
|
---|
[16597] | 1245 | double f; Vector g;
|
---|
| 1246 | InterpretRec(tree.Root.GetSubtree(0).GetSubtree(0), nodeValues, out f, out g);
|
---|
| 1247 | deltaF[i] = f;
|
---|
| 1248 | deltaG[i] = g;
|
---|
[16250] | 1249 | }
|
---|
| 1250 |
|
---|
[16251] | 1251 | // update variableValues for next step, trapezoid integration
|
---|
[16597] | 1252 | for (int i = 0; i < trees.Length; i++) {
|
---|
| 1253 | var varName = calculatedVariables[i];
|
---|
[16601] | 1254 | var oldVal = nodeValues.GetVariableValue(varName);
|
---|
[16604] | 1255 | nodeValues.SetVariableValue(varName, oldVal.Item1 + h * deltaF[i], oldVal.Item2.Add(deltaG[i].Scale(h)));
|
---|
[16250] | 1256 | }
|
---|
[16601] | 1257 | }
|
---|
| 1258 | }
|
---|
[16398] | 1259 |
|
---|
[16602] | 1260 | // TODO: use an existing interpreter implementation instead
|
---|
[16601] | 1261 | private static double InterpretRec(ISymbolicExpressionTreeNode node, NodeValueLookup nodeValues) {
|
---|
[16603] | 1262 | if (node is ConstantTreeNode) {
|
---|
| 1263 | return ((ConstantTreeNode)node).Value;
|
---|
[16604] | 1264 | } else if (node is VariableTreeNode) {
|
---|
[16602] | 1265 | return nodeValues.NodeValue(node);
|
---|
| 1266 | } else if (node.Symbol is Addition) {
|
---|
| 1267 | var f = InterpretRec(node.GetSubtree(0), nodeValues);
|
---|
[16652] | 1268 | for (int i = 1; i < node.SubtreeCount; i++) {
|
---|
| 1269 | f += InterpretRec(node.GetSubtree(i), nodeValues);
|
---|
| 1270 | }
|
---|
| 1271 | return f;
|
---|
[16602] | 1272 | } else if (node.Symbol is Multiplication) {
|
---|
| 1273 | var f = InterpretRec(node.GetSubtree(0), nodeValues);
|
---|
[16652] | 1274 | for (int i = 1; i < node.SubtreeCount; i++) {
|
---|
| 1275 | f *= InterpretRec(node.GetSubtree(i), nodeValues);
|
---|
| 1276 | }
|
---|
| 1277 | return f;
|
---|
[16602] | 1278 | } else if (node.Symbol is Subtraction) {
|
---|
| 1279 | if (node.SubtreeCount == 1) {
|
---|
[16652] | 1280 | return -InterpretRec(node.GetSubtree(0), nodeValues);
|
---|
[16602] | 1281 | } else {
|
---|
| 1282 | var f = InterpretRec(node.GetSubtree(0), nodeValues);
|
---|
[16652] | 1283 | for (int i = 1; i < node.SubtreeCount; i++) {
|
---|
| 1284 | f -= InterpretRec(node.GetSubtree(i), nodeValues);
|
---|
| 1285 | }
|
---|
| 1286 | return f;
|
---|
[16602] | 1287 | }
|
---|
| 1288 | } else if (node.Symbol is Division) {
|
---|
[16610] | 1289 | if (node.SubtreeCount == 1) {
|
---|
| 1290 | var f = InterpretRec(node.GetSubtree(0), nodeValues);
|
---|
| 1291 | // protected division
|
---|
| 1292 | if (f.IsAlmost(0.0)) {
|
---|
| 1293 | return 0;
|
---|
| 1294 | } else {
|
---|
| 1295 | return 1.0 / f;
|
---|
| 1296 | }
|
---|
[16602] | 1297 | } else {
|
---|
[16610] | 1298 | var f = InterpretRec(node.GetSubtree(0), nodeValues);
|
---|
[16652] | 1299 | for (int i = 1; i < node.SubtreeCount; i++) {
|
---|
| 1300 | var g = InterpretRec(node.GetSubtree(i), nodeValues);
|
---|
| 1301 | // protected division
|
---|
| 1302 | if (g.IsAlmost(0.0)) {
|
---|
| 1303 | return 0;
|
---|
| 1304 | } else {
|
---|
| 1305 | f /= g;
|
---|
| 1306 | }
|
---|
[16610] | 1307 | }
|
---|
[16652] | 1308 | return f;
|
---|
[16602] | 1309 | }
|
---|
| 1310 | } else if (node.Symbol is Sine) {
|
---|
[16616] | 1311 | Assert(node.SubtreeCount == 1);
|
---|
[16610] | 1312 |
|
---|
[16602] | 1313 | var f = InterpretRec(node.GetSubtree(0), nodeValues);
|
---|
| 1314 | return Math.Sin(f);
|
---|
| 1315 | } else if (node.Symbol is Cosine) {
|
---|
[16616] | 1316 | Assert(node.SubtreeCount == 1);
|
---|
[16610] | 1317 |
|
---|
[16602] | 1318 | var f = InterpretRec(node.GetSubtree(0), nodeValues);
|
---|
| 1319 | return Math.Cos(f);
|
---|
| 1320 | } else if (node.Symbol is Square) {
|
---|
[16616] | 1321 | Assert(node.SubtreeCount == 1);
|
---|
[16610] | 1322 |
|
---|
[16602] | 1323 | var f = InterpretRec(node.GetSubtree(0), nodeValues);
|
---|
| 1324 | return f * f;
|
---|
[16610] | 1325 | } else if (node.Symbol is Exponential) {
|
---|
[16616] | 1326 | Assert(node.SubtreeCount == 1);
|
---|
[16610] | 1327 |
|
---|
| 1328 | var f = InterpretRec(node.GetSubtree(0), nodeValues);
|
---|
| 1329 | return Math.Exp(f);
|
---|
| 1330 | } else if (node.Symbol is Logarithm) {
|
---|
[16616] | 1331 | Assert(node.SubtreeCount == 1);
|
---|
[16610] | 1332 |
|
---|
| 1333 | var f = InterpretRec(node.GetSubtree(0), nodeValues);
|
---|
| 1334 | return Math.Log(f);
|
---|
[16602] | 1335 | } else throw new NotSupportedException("unsupported symbol");
|
---|
[16250] | 1336 | }
|
---|
| 1337 |
|
---|
[16616] | 1338 | private static void Assert(bool cond) {
|
---|
| 1339 | #if DEBUG
|
---|
| 1340 | if (!cond) throw new InvalidOperationException("Assertion failed");
|
---|
| 1341 | #endif
|
---|
| 1342 | }
|
---|
| 1343 |
|
---|
[16597] | 1344 | private static void InterpretRec(
|
---|
[15964] | 1345 | ISymbolicExpressionTreeNode node,
|
---|
[16601] | 1346 | NodeValueLookup nodeValues, // contains value and gradient vector for a node (variables and constants only)
|
---|
[16600] | 1347 | out double z,
|
---|
| 1348 | out Vector dz
|
---|
[16597] | 1349 | ) {
|
---|
[16600] | 1350 | double f, g;
|
---|
| 1351 | Vector df, dg;
|
---|
[16602] | 1352 | if (node.Symbol is Constant || node.Symbol is Variable) {
|
---|
| 1353 | z = nodeValues.NodeValue(node);
|
---|
[16604] | 1354 | dz = Vector.CreateNew(nodeValues.NodeGradient(node)); // original gradient vectors are never changed by evaluation
|
---|
[16602] | 1355 | } else if (node.Symbol is Addition) {
|
---|
| 1356 | InterpretRec(node.GetSubtree(0), nodeValues, out f, out df);
|
---|
[16652] | 1357 | for (int i = 1; i < node.SubtreeCount; i++) {
|
---|
| 1358 | InterpretRec(node.GetSubtree(i), nodeValues, out g, out dg);
|
---|
| 1359 | f = f + g;
|
---|
| 1360 | df = df.Add(dg);
|
---|
| 1361 | }
|
---|
| 1362 | z = f;
|
---|
| 1363 | dz = df;
|
---|
[16602] | 1364 | } else if (node.Symbol is Multiplication) {
|
---|
| 1365 | InterpretRec(node.GetSubtree(0), nodeValues, out f, out df);
|
---|
[16652] | 1366 | for (int i = 1; i < node.SubtreeCount; i++) {
|
---|
| 1367 | InterpretRec(node.GetSubtree(i), nodeValues, out g, out dg);
|
---|
| 1368 | f = f * g;
|
---|
| 1369 | df = df.Scale(g).Add(dg.Scale(f)); // f'*g + f*g'
|
---|
| 1370 | }
|
---|
| 1371 | z = f;
|
---|
| 1372 | dz = df;
|
---|
[16602] | 1373 | } else if (node.Symbol is Subtraction) {
|
---|
| 1374 | if (node.SubtreeCount == 1) {
|
---|
| 1375 | InterpretRec(node.GetSubtree(0), nodeValues, out f, out df);
|
---|
| 1376 | z = -f;
|
---|
[16604] | 1377 | dz = df.Scale(-1.0);
|
---|
[16602] | 1378 | } else {
|
---|
| 1379 | InterpretRec(node.GetSubtree(0), nodeValues, out f, out df);
|
---|
[16652] | 1380 | for (int i = 1; i < node.SubtreeCount; i++) {
|
---|
| 1381 | InterpretRec(node.GetSubtree(i), nodeValues, out g, out dg);
|
---|
| 1382 | f = f - g;
|
---|
| 1383 | df = df.Subtract(dg);
|
---|
| 1384 | }
|
---|
| 1385 | z = f;
|
---|
| 1386 | dz = df;
|
---|
[16602] | 1387 | }
|
---|
| 1388 | } else if (node.Symbol is Division) {
|
---|
[16610] | 1389 | if (node.SubtreeCount == 1) {
|
---|
| 1390 | InterpretRec(node.GetSubtree(0), nodeValues, out f, out df);
|
---|
| 1391 | // protected division
|
---|
| 1392 | if (f.IsAlmost(0.0)) {
|
---|
| 1393 | z = 0;
|
---|
| 1394 | dz = Vector.Zero;
|
---|
| 1395 | } else {
|
---|
| 1396 | z = 1.0 / f;
|
---|
[16652] | 1397 | dz = df.Scale(-1 * z * z);
|
---|
[16610] | 1398 | }
|
---|
[16602] | 1399 | } else {
|
---|
[16610] | 1400 | InterpretRec(node.GetSubtree(0), nodeValues, out f, out df);
|
---|
[16652] | 1401 | for (int i = 1; i < node.SubtreeCount; i++) {
|
---|
| 1402 | InterpretRec(node.GetSubtree(i), nodeValues, out g, out dg);
|
---|
| 1403 | // protected division
|
---|
| 1404 | if (g.IsAlmost(0.0)) {
|
---|
| 1405 | z = 0;
|
---|
| 1406 | dz = Vector.Zero;
|
---|
| 1407 | return;
|
---|
| 1408 | } else {
|
---|
| 1409 | var inv_g = 1.0 / g;
|
---|
| 1410 | f = f * inv_g;
|
---|
| 1411 | df = dg.Scale(-f * inv_g * inv_g).Add(df.Scale(inv_g));
|
---|
| 1412 | }
|
---|
[16610] | 1413 | }
|
---|
[16652] | 1414 | z = f;
|
---|
| 1415 | dz = df;
|
---|
[16602] | 1416 | }
|
---|
| 1417 | } else if (node.Symbol is Sine) {
|
---|
[16616] | 1418 | Assert(node.SubtreeCount == 1);
|
---|
[16602] | 1419 | InterpretRec(node.GetSubtree(0), nodeValues, out f, out df);
|
---|
| 1420 | z = Math.Sin(f);
|
---|
[16604] | 1421 | dz = df.Scale(Math.Cos(f));
|
---|
[16602] | 1422 | } else if (node.Symbol is Cosine) {
|
---|
[16616] | 1423 | Assert(node.SubtreeCount == 1);
|
---|
[16602] | 1424 | InterpretRec(node.GetSubtree(0), nodeValues, out f, out df);
|
---|
| 1425 | z = Math.Cos(f);
|
---|
[16604] | 1426 | dz = df.Scale(-Math.Sin(f));
|
---|
[16602] | 1427 | } else if (node.Symbol is Square) {
|
---|
[16616] | 1428 | Assert(node.SubtreeCount == 1);
|
---|
[16602] | 1429 | InterpretRec(node.GetSubtree(0), nodeValues, out f, out df);
|
---|
| 1430 | z = f * f;
|
---|
[16604] | 1431 | dz = df.Scale(2.0 * f);
|
---|
[16610] | 1432 | } else if (node.Symbol is Exponential) {
|
---|
[16616] | 1433 | Assert(node.SubtreeCount == 1);
|
---|
[16610] | 1434 | InterpretRec(node.GetSubtree(0), nodeValues, out f, out df);
|
---|
| 1435 | z = Math.Exp(f);
|
---|
| 1436 | dz = df.Scale(Math.Exp(f));
|
---|
| 1437 | } else if (node.Symbol is Logarithm) {
|
---|
[16616] | 1438 | Assert(node.SubtreeCount == 1);
|
---|
[16610] | 1439 | InterpretRec(node.GetSubtree(0), nodeValues, out f, out df);
|
---|
| 1440 | z = Math.Log(f);
|
---|
| 1441 | dz = df.Scale(1.0 / f);
|
---|
[16602] | 1442 | } else {
|
---|
| 1443 | throw new NotSupportedException("unsupported symbol");
|
---|
[15964] | 1444 | }
|
---|
| 1445 | }
|
---|
[16602] | 1446 |
|
---|
[15968] | 1447 | #endregion
|
---|
[15964] | 1448 |
|
---|
| 1449 | #region events
|
---|
[15968] | 1450 | /*
|
---|
| 1451 | * Dependencies between parameters:
|
---|
| 1452 | *
|
---|
| 1453 | * ProblemData
|
---|
| 1454 | * |
|
---|
| 1455 | * V
|
---|
[15970] | 1456 | * TargetVariables FunctionSet MaximumLength NumberOfLatentVariables
|
---|
| 1457 | * | | | |
|
---|
| 1458 | * V V | |
|
---|
| 1459 | * Grammar <---------------+-------------------
|
---|
[15968] | 1460 | * |
|
---|
| 1461 | * V
|
---|
| 1462 | * Encoding
|
---|
| 1463 | */
|
---|
[15964] | 1464 | private void RegisterEventHandlers() {
|
---|
[15968] | 1465 | ProblemDataParameter.ValueChanged += ProblemDataParameter_ValueChanged;
|
---|
[16215] | 1466 | if (ProblemDataParameter.Value != null) ProblemDataParameter.Value.Changed += ProblemData_Changed;
|
---|
[15968] | 1467 |
|
---|
| 1468 | TargetVariablesParameter.ValueChanged += TargetVariablesParameter_ValueChanged;
|
---|
[16215] | 1469 | if (TargetVariablesParameter.Value != null) TargetVariablesParameter.Value.CheckedItemsChanged += CheckedTargetVariablesChanged;
|
---|
[15968] | 1470 |
|
---|
| 1471 | FunctionSetParameter.ValueChanged += FunctionSetParameter_ValueChanged;
|
---|
[16215] | 1472 | if (FunctionSetParameter.Value != null) FunctionSetParameter.Value.CheckedItemsChanged += CheckedFunctionsChanged;
|
---|
[15968] | 1473 |
|
---|
| 1474 | MaximumLengthParameter.Value.ValueChanged += MaximumLengthChanged;
|
---|
[15970] | 1475 |
|
---|
| 1476 | NumberOfLatentVariablesParameter.Value.ValueChanged += NumLatentVariablesChanged;
|
---|
[15964] | 1477 | }
|
---|
| 1478 |
|
---|
[15970] | 1479 | private void NumLatentVariablesChanged(object sender, EventArgs e) {
|
---|
| 1480 | UpdateGrammarAndEncoding();
|
---|
| 1481 | }
|
---|
| 1482 |
|
---|
[15968] | 1483 | private void MaximumLengthChanged(object sender, EventArgs e) {
|
---|
| 1484 | UpdateGrammarAndEncoding();
|
---|
| 1485 | }
|
---|
| 1486 |
|
---|
| 1487 | private void FunctionSetParameter_ValueChanged(object sender, EventArgs e) {
|
---|
| 1488 | FunctionSetParameter.Value.CheckedItemsChanged += CheckedFunctionsChanged;
|
---|
| 1489 | }
|
---|
| 1490 |
|
---|
[16268] | 1491 | private void CheckedFunctionsChanged(object sender, CollectionItemsChangedEventArgs<IndexedItem<StringValue>> e) {
|
---|
[15968] | 1492 | UpdateGrammarAndEncoding();
|
---|
| 1493 | }
|
---|
| 1494 |
|
---|
| 1495 | private void TargetVariablesParameter_ValueChanged(object sender, EventArgs e) {
|
---|
| 1496 | TargetVariablesParameter.Value.CheckedItemsChanged += CheckedTargetVariablesChanged;
|
---|
| 1497 | }
|
---|
| 1498 |
|
---|
[16268] | 1499 | private void CheckedTargetVariablesChanged(object sender, CollectionItemsChangedEventArgs<IndexedItem<StringValue>> e) {
|
---|
[15968] | 1500 | UpdateGrammarAndEncoding();
|
---|
| 1501 | }
|
---|
| 1502 |
|
---|
[15964] | 1503 | private void ProblemDataParameter_ValueChanged(object sender, EventArgs e) {
|
---|
[15968] | 1504 | ProblemDataParameter.Value.Changed += ProblemData_Changed;
|
---|
[15964] | 1505 | OnProblemDataChanged();
|
---|
| 1506 | OnReset();
|
---|
| 1507 | }
|
---|
| 1508 |
|
---|
| 1509 | private void ProblemData_Changed(object sender, EventArgs e) {
|
---|
[15968] | 1510 | OnProblemDataChanged();
|
---|
[15964] | 1511 | OnReset();
|
---|
| 1512 | }
|
---|
| 1513 |
|
---|
| 1514 | private void OnProblemDataChanged() {
|
---|
[15968] | 1515 | UpdateTargetVariables(); // implicitly updates other dependent parameters
|
---|
[15964] | 1516 | var handler = ProblemDataChanged;
|
---|
[16215] | 1517 | if (handler != null) handler(this, EventArgs.Empty);
|
---|
[15964] | 1518 | }
|
---|
| 1519 |
|
---|
[15968] | 1520 | #endregion
|
---|
| 1521 |
|
---|
| 1522 | #region helper
|
---|
| 1523 |
|
---|
[16660] | 1524 | private static IEnumerable<T> EveryNth<T>(IEnumerable<T> xs, int step) {
|
---|
| 1525 | var e = xs.GetEnumerator();
|
---|
| 1526 | while (e.MoveNext()) {
|
---|
| 1527 | for (int i = 0; i < step; i++) {
|
---|
| 1528 | if (!e.MoveNext()) yield break;
|
---|
| 1529 | }
|
---|
| 1530 | yield return e.Current;
|
---|
| 1531 | }
|
---|
| 1532 | }
|
---|
| 1533 |
|
---|
[15968] | 1534 | private void InitAllParameters() {
|
---|
[16602] | 1535 | UpdateTargetVariables(); // implicitly updates the grammar and the encoding
|
---|
[15968] | 1536 | }
|
---|
| 1537 |
|
---|
[16268] | 1538 | private ReadOnlyCheckedItemList<StringValue> CreateFunctionSet() {
|
---|
| 1539 | var l = new CheckedItemList<StringValue>();
|
---|
[16602] | 1540 | l.Add(new StringValue("Addition").AsReadOnly());
|
---|
| 1541 | l.Add(new StringValue("Multiplication").AsReadOnly());
|
---|
| 1542 | l.Add(new StringValue("Division").AsReadOnly());
|
---|
| 1543 | l.Add(new StringValue("Subtraction").AsReadOnly());
|
---|
| 1544 | l.Add(new StringValue("Sine").AsReadOnly());
|
---|
| 1545 | l.Add(new StringValue("Cosine").AsReadOnly());
|
---|
| 1546 | l.Add(new StringValue("Square").AsReadOnly());
|
---|
[15968] | 1547 | return l.AsReadOnly();
|
---|
| 1548 | }
|
---|
| 1549 |
|
---|
| 1550 | private static bool IsConstantNode(ISymbolicExpressionTreeNode n) {
|
---|
[16602] | 1551 | // return n.Symbol.Name[0] == 'θ';
|
---|
| 1552 | return n is ConstantTreeNode;
|
---|
[15968] | 1553 | }
|
---|
[16601] | 1554 | private static double GetConstantValue(ISymbolicExpressionTreeNode n) {
|
---|
[16602] | 1555 | return ((ConstantTreeNode)n).Value;
|
---|
[16601] | 1556 | }
|
---|
[15970] | 1557 | private static bool IsLatentVariableNode(ISymbolicExpressionTreeNode n) {
|
---|
[16399] | 1558 | return n.Symbol.Name[0] == 'λ';
|
---|
[15970] | 1559 | }
|
---|
[16251] | 1560 | private static bool IsVariableNode(ISymbolicExpressionTreeNode n) {
|
---|
| 1561 | return (n.SubtreeCount == 0) && !IsConstantNode(n) && !IsLatentVariableNode(n);
|
---|
| 1562 | }
|
---|
[16601] | 1563 | private static string GetVariableName(ISymbolicExpressionTreeNode n) {
|
---|
[16602] | 1564 | return ((VariableTreeNode)n).VariableName;
|
---|
[16601] | 1565 | }
|
---|
[15968] | 1566 |
|
---|
| 1567 | private void UpdateTargetVariables() {
|
---|
[16268] | 1568 | var currentlySelectedVariables = TargetVariables.CheckedItems
|
---|
| 1569 | .OrderBy(i => i.Index)
|
---|
| 1570 | .Select(i => i.Value.Value)
|
---|
| 1571 | .ToArray();
|
---|
[15968] | 1572 |
|
---|
[16268] | 1573 | var newVariablesList = new CheckedItemList<StringValue>(ProblemData.Dataset.VariableNames.Select(str => new StringValue(str).AsReadOnly()).ToArray()).AsReadOnly();
|
---|
[15968] | 1574 | var matchingItems = newVariablesList.Where(item => currentlySelectedVariables.Contains(item.Value)).ToArray();
|
---|
[16597] | 1575 | foreach (var item in newVariablesList) {
|
---|
| 1576 | if (currentlySelectedVariables.Contains(item.Value)) {
|
---|
| 1577 | newVariablesList.SetItemCheckedState(item, true);
|
---|
| 1578 | } else {
|
---|
| 1579 | newVariablesList.SetItemCheckedState(item, false);
|
---|
| 1580 | }
|
---|
[15968] | 1581 | }
|
---|
| 1582 | TargetVariablesParameter.Value = newVariablesList;
|
---|
| 1583 | }
|
---|
| 1584 |
|
---|
| 1585 | private void UpdateGrammarAndEncoding() {
|
---|
| 1586 | var encoding = new MultiEncoding();
|
---|
| 1587 | var g = CreateGrammar();
|
---|
[16215] | 1588 | foreach (var targetVar in TargetVariables.CheckedItems) {
|
---|
[16603] | 1589 | var e = new SymbolicExpressionTreeEncoding(targetVar + "_tree", g, MaximumLength, MaximumLength);
|
---|
| 1590 | var multiManipulator = e.Operators.Where(op => op is MultiSymbolicExpressionTreeManipulator).First();
|
---|
| 1591 | var filteredOperators = e.Operators.Where(op => !(op is IManipulator)).ToArray();
|
---|
| 1592 | // make sure our multi-manipulator is the only manipulator
|
---|
| 1593 | e.Operators = new IOperator[] { multiManipulator }.Concat(filteredOperators);
|
---|
[16616] | 1594 |
|
---|
| 1595 | // set the crossover probability to reduce likelihood that multiple trees are crossed at the same time
|
---|
| 1596 | var subtreeCrossovers = e.Operators.OfType<SubtreeCrossover>();
|
---|
| 1597 | foreach (var xover in subtreeCrossovers) {
|
---|
| 1598 | xover.CrossoverProbability.Value = 0.3;
|
---|
| 1599 | }
|
---|
| 1600 |
|
---|
[16603] | 1601 | encoding = encoding.Add(e); // only limit by length
|
---|
[15968] | 1602 | }
|
---|
[16215] | 1603 | for (int i = 1; i <= NumberOfLatentVariables; i++) {
|
---|
[16603] | 1604 | var e = new SymbolicExpressionTreeEncoding("λ" + i + "_tree", g, MaximumLength, MaximumLength);
|
---|
| 1605 | var multiManipulator = e.Operators.Where(op => op is MultiSymbolicExpressionTreeManipulator).First();
|
---|
| 1606 | var filteredOperators = e.Operators.Where(op => !(op is IManipulator)).ToArray();
|
---|
| 1607 | // make sure our multi-manipulator is the only manipulator
|
---|
| 1608 | e.Operators = new IOperator[] { multiManipulator }.Concat(filteredOperators);
|
---|
[16616] | 1609 |
|
---|
| 1610 | // set the crossover probability to reduce likelihood that multiple trees are crossed at the same time
|
---|
| 1611 | var subtreeCrossovers = e.Operators.OfType<SubtreeCrossover>();
|
---|
| 1612 | foreach (var xover in subtreeCrossovers) {
|
---|
| 1613 | xover.CrossoverProbability.Value = 0.3;
|
---|
| 1614 | }
|
---|
| 1615 |
|
---|
[16603] | 1616 | encoding = encoding.Add(e);
|
---|
[15970] | 1617 | }
|
---|
[15968] | 1618 | Encoding = encoding;
|
---|
| 1619 | }
|
---|
| 1620 |
|
---|
| 1621 | private ISymbolicExpressionGrammar CreateGrammar() {
|
---|
[16602] | 1622 | var grammar = new TypeCoherentExpressionGrammar();
|
---|
| 1623 | grammar.StartGrammarManipulation();
|
---|
| 1624 |
|
---|
| 1625 | var problemData = ProblemData;
|
---|
| 1626 | var ds = problemData.Dataset;
|
---|
| 1627 | grammar.MaximumFunctionArguments = 0;
|
---|
| 1628 | grammar.MaximumFunctionDefinitions = 0;
|
---|
| 1629 | var allowedVariables = problemData.AllowedInputVariables.Concat(TargetVariables.CheckedItems.Select(chk => chk.Value.Value));
|
---|
| 1630 | foreach (var varSymbol in grammar.Symbols.OfType<HeuristicLab.Problems.DataAnalysis.Symbolic.VariableBase>()) {
|
---|
| 1631 | if (!varSymbol.Fixed) {
|
---|
| 1632 | varSymbol.AllVariableNames = problemData.InputVariables.Select(x => x.Value).Where(x => ds.VariableHasType<double>(x));
|
---|
| 1633 | varSymbol.VariableNames = allowedVariables.Where(x => ds.VariableHasType<double>(x));
|
---|
| 1634 | }
|
---|
[16597] | 1635 | }
|
---|
[16602] | 1636 | foreach (var factorSymbol in grammar.Symbols.OfType<BinaryFactorVariable>()) {
|
---|
| 1637 | if (!factorSymbol.Fixed) {
|
---|
| 1638 | factorSymbol.AllVariableNames = problemData.InputVariables.Select(x => x.Value).Where(x => ds.VariableHasType<string>(x));
|
---|
| 1639 | factorSymbol.VariableNames = problemData.AllowedInputVariables.Where(x => ds.VariableHasType<string>(x));
|
---|
| 1640 | factorSymbol.VariableValues = factorSymbol.VariableNames
|
---|
| 1641 | .ToDictionary(varName => varName, varName => ds.GetStringValues(varName).Distinct().ToList());
|
---|
| 1642 | }
|
---|
[16597] | 1643 | }
|
---|
[16602] | 1644 | foreach (var factorSymbol in grammar.Symbols.OfType<FactorVariable>()) {
|
---|
| 1645 | if (!factorSymbol.Fixed) {
|
---|
| 1646 | factorSymbol.AllVariableNames = problemData.InputVariables.Select(x => x.Value).Where(x => ds.VariableHasType<string>(x));
|
---|
| 1647 | factorSymbol.VariableNames = problemData.AllowedInputVariables.Where(x => ds.VariableHasType<string>(x));
|
---|
| 1648 | factorSymbol.VariableValues = factorSymbol.VariableNames
|
---|
| 1649 | .ToDictionary(varName => varName,
|
---|
| 1650 | varName => ds.GetStringValues(varName).Distinct()
|
---|
| 1651 | .Select((n, i) => Tuple.Create(n, i))
|
---|
| 1652 | .ToDictionary(tup => tup.Item1, tup => tup.Item2));
|
---|
| 1653 | }
|
---|
[15964] | 1654 | }
|
---|
[15970] | 1655 |
|
---|
[16602] | 1656 | grammar.ConfigureAsDefaultRegressionGrammar();
|
---|
| 1657 | grammar.GetSymbol("Logarithm").Enabled = false; // not supported yet
|
---|
| 1658 | grammar.GetSymbol("Exponential").Enabled = false; // not supported yet
|
---|
[15970] | 1659 |
|
---|
[16602] | 1660 | // configure initialization of constants
|
---|
| 1661 | var constSy = (Constant)grammar.GetSymbol("Constant");
|
---|
| 1662 | // max and min are only relevant for initialization
|
---|
| 1663 | constSy.MaxValue = +1.0e-1; // small initial values for constant opt
|
---|
| 1664 | constSy.MinValue = -1.0e-1;
|
---|
| 1665 | constSy.MultiplicativeManipulatorSigma = 1.0; // allow large jumps for manipulation
|
---|
| 1666 | constSy.ManipulatorMu = 0.0;
|
---|
| 1667 | constSy.ManipulatorSigma = 1.0; // allow large jumps
|
---|
[15968] | 1668 |
|
---|
[16602] | 1669 | // configure initialization of variables
|
---|
| 1670 | var varSy = (Variable)grammar.GetSymbol("Variable");
|
---|
| 1671 | // fix variable weights to 1.0
|
---|
| 1672 | varSy.WeightMu = 1.0;
|
---|
| 1673 | varSy.WeightSigma = 0.0;
|
---|
| 1674 | varSy.WeightManipulatorMu = 0.0;
|
---|
| 1675 | varSy.WeightManipulatorSigma = 0.0;
|
---|
| 1676 | varSy.MultiplicativeWeightManipulatorSigma = 0.0;
|
---|
[16251] | 1677 |
|
---|
[16602] | 1678 | foreach (var f in FunctionSet) {
|
---|
| 1679 | grammar.GetSymbol(f.Value).Enabled = FunctionSet.ItemChecked(f);
|
---|
[16399] | 1680 | }
|
---|
| 1681 |
|
---|
[16602] | 1682 | grammar.FinishedGrammarManipulation();
|
---|
| 1683 | return grammar;
|
---|
| 1684 | // // whenever ProblemData is changed we create a new grammar with the necessary symbols
|
---|
| 1685 | // var g = new SimpleSymbolicExpressionGrammar();
|
---|
| 1686 | // var unaryFunc = new string[] { "sin", "cos", "sqr" };
|
---|
| 1687 | // var binaryFunc = new string[] { "+", "-", "*", "%" };
|
---|
| 1688 | // foreach (var func in unaryFunc) {
|
---|
| 1689 | // if (FunctionSet.CheckedItems.Any(ci => ci.Value.Value == func)) g.AddSymbol(func, 1, 1);
|
---|
| 1690 | // }
|
---|
| 1691 | // foreach (var func in binaryFunc) {
|
---|
| 1692 | // if (FunctionSet.CheckedItems.Any(ci => ci.Value.Value == func)) g.AddSymbol(func, 2, 2);
|
---|
| 1693 | // }
|
---|
| 1694 | //
|
---|
| 1695 | // foreach (var variableName in ProblemData.AllowedInputVariables.Union(TargetVariables.CheckedItems.Select(i => i.Value.Value)))
|
---|
| 1696 | // g.AddTerminalSymbol(variableName);
|
---|
| 1697 | //
|
---|
| 1698 | // // generate symbols for numeric parameters for which the value is optimized using AutoDiff
|
---|
| 1699 | // // we generate multiple symbols to balance the probability for selecting a numeric parameter in the generation of random trees
|
---|
| 1700 | // var numericConstantsFactor = 2.0;
|
---|
| 1701 | // for (int i = 0; i < numericConstantsFactor * (ProblemData.AllowedInputVariables.Count() + TargetVariables.CheckedItems.Count()); i++) {
|
---|
| 1702 | // g.AddTerminalSymbol("θ" + i); // numeric parameter for which the value is optimized using AutoDiff
|
---|
| 1703 | // }
|
---|
| 1704 | //
|
---|
| 1705 | // // generate symbols for latent variables
|
---|
| 1706 | // for (int i = 1; i <= NumberOfLatentVariables; i++) {
|
---|
| 1707 | // g.AddTerminalSymbol("λ" + i); // numeric parameter for which the value is optimized using AutoDiff
|
---|
| 1708 | // }
|
---|
| 1709 | //
|
---|
| 1710 | // return g;
|
---|
[16251] | 1711 | }
|
---|
[15964] | 1712 | #endregion
|
---|
| 1713 |
|
---|
[16601] | 1714 |
|
---|
[15964] | 1715 | #region Import & Export
|
---|
| 1716 | public void Load(IRegressionProblemData data) {
|
---|
| 1717 | Name = data.Name;
|
---|
| 1718 | Description = data.Description;
|
---|
| 1719 | ProblemData = data;
|
---|
| 1720 | }
|
---|
| 1721 |
|
---|
| 1722 | public IRegressionProblemData Export() {
|
---|
| 1723 | return ProblemData;
|
---|
| 1724 | }
|
---|
[16601] | 1725 | #endregion
|
---|
[16600] | 1726 |
|
---|
[16601] | 1727 |
|
---|
| 1728 | // TODO: for integration we only need a part of the data that we need for optimization
|
---|
| 1729 |
|
---|
[16600] | 1730 | public class OptimizationData {
|
---|
| 1731 | public readonly ISymbolicExpressionTree[] trees;
|
---|
| 1732 | public readonly string[] targetVariables;
|
---|
| 1733 | public readonly IRegressionProblemData problemData;
|
---|
[16601] | 1734 | public readonly double[][] targetValues;
|
---|
[16603] | 1735 | public readonly double[] inverseStandardDeviation;
|
---|
[16600] | 1736 | public readonly IntRange[] episodes;
|
---|
| 1737 | public readonly int numericIntegrationSteps;
|
---|
| 1738 | public readonly string[] latentVariables;
|
---|
| 1739 | public readonly string odeSolver;
|
---|
[16601] | 1740 | public readonly NodeValueLookup nodeValueLookup;
|
---|
[16603] | 1741 | public readonly int[] rows;
|
---|
[16610] | 1742 | internal readonly string[] variables;
|
---|
[16600] | 1743 |
|
---|
[16610] | 1744 | public OptimizationData(ISymbolicExpressionTree[] trees, string[] targetVars, string[] inputVariables,
|
---|
| 1745 | IRegressionProblemData problemData,
|
---|
[16601] | 1746 | double[][] targetValues,
|
---|
| 1747 | IntRange[] episodes,
|
---|
| 1748 | int numericIntegrationSteps, string[] latentVariables, string odeSolver) {
|
---|
[16600] | 1749 | this.trees = trees;
|
---|
| 1750 | this.targetVariables = targetVars;
|
---|
| 1751 | this.problemData = problemData;
|
---|
| 1752 | this.targetValues = targetValues;
|
---|
[16610] | 1753 | this.variables = inputVariables;
|
---|
[16616] | 1754 | if (targetValues != null) {
|
---|
| 1755 | this.inverseStandardDeviation = new double[targetValues.Length];
|
---|
| 1756 | for (int i = 0; i < targetValues.Length; i++) {
|
---|
| 1757 | // calculate variance for each episode separately and calc the average
|
---|
| 1758 | var epStartIdx = 0;
|
---|
| 1759 | var stdevs = new List<double>();
|
---|
| 1760 | foreach (var ep in episodes) {
|
---|
| 1761 | var epValues = targetValues[i].Skip(epStartIdx).Take(ep.Size);
|
---|
| 1762 | stdevs.Add(epValues.StandardDeviation());
|
---|
| 1763 | epStartIdx += ep.Size;
|
---|
| 1764 | }
|
---|
| 1765 | inverseStandardDeviation[i] = 1.0 / stdevs.Average();
|
---|
| 1766 | }
|
---|
| 1767 | } else
|
---|
| 1768 | this.inverseStandardDeviation = Enumerable.Repeat(1.0, trees.Length).ToArray();
|
---|
[16600] | 1769 | this.episodes = episodes;
|
---|
| 1770 | this.numericIntegrationSteps = numericIntegrationSteps;
|
---|
| 1771 | this.latentVariables = latentVariables;
|
---|
| 1772 | this.odeSolver = odeSolver;
|
---|
[16601] | 1773 | this.nodeValueLookup = new NodeValueLookup(trees);
|
---|
[16604] | 1774 | this.rows = episodes.SelectMany(ep => Enumerable.Range(ep.Start, ep.Size)).ToArray();
|
---|
[16600] | 1775 | }
|
---|
| 1776 | }
|
---|
[15964] | 1777 |
|
---|
[16601] | 1778 | public class NodeValueLookup {
|
---|
| 1779 | private readonly Dictionary<ISymbolicExpressionTreeNode, Tuple<double, Vector>> node2val = new Dictionary<ISymbolicExpressionTreeNode, Tuple<double, Vector>>();
|
---|
| 1780 | private readonly Dictionary<string, List<ISymbolicExpressionTreeNode>> name2nodes = new Dictionary<string, List<ISymbolicExpressionTreeNode>>();
|
---|
[16603] | 1781 | private readonly ConstantTreeNode[] constantNodes;
|
---|
| 1782 | private readonly Vector[] constantGradientVectors;
|
---|
[16601] | 1783 |
|
---|
[16603] | 1784 | // private readonly Dictionary<int, ISymbolicExpressionTreeNode> paramIdx2node = new Dictionary<int, ISymbolicExpressionTreeNode>();
|
---|
| 1785 |
|
---|
[16601] | 1786 | public double NodeValue(ISymbolicExpressionTreeNode node) => node2val[node].Item1;
|
---|
| 1787 | public Vector NodeGradient(ISymbolicExpressionTreeNode node) => node2val[node].Item2;
|
---|
| 1788 |
|
---|
| 1789 | public NodeValueLookup(ISymbolicExpressionTree[] trees) {
|
---|
| 1790 |
|
---|
[16603] | 1791 | this.constantNodes = trees.SelectMany(t => t.IterateNodesPrefix().OfType<ConstantTreeNode>()).ToArray();
|
---|
| 1792 | constantGradientVectors = new Vector[constantNodes.Length];
|
---|
[16604] | 1793 | for (int paramIdx = 0; paramIdx < constantNodes.Length; paramIdx++) {
|
---|
[16603] | 1794 | constantGradientVectors[paramIdx] = Vector.CreateIndicator(length: constantNodes.Length, idx: paramIdx);
|
---|
| 1795 |
|
---|
| 1796 | var node = constantNodes[paramIdx];
|
---|
| 1797 | node2val[node] = Tuple.Create(node.Value, constantGradientVectors[paramIdx]);
|
---|
[16601] | 1798 | }
|
---|
| 1799 |
|
---|
| 1800 | foreach (var tree in trees) {
|
---|
| 1801 | foreach (var node in tree.IterateNodesPrefix().Where(IsVariableNode)) {
|
---|
| 1802 | var varName = GetVariableName(node);
|
---|
| 1803 | if (!name2nodes.TryGetValue(varName, out List<ISymbolicExpressionTreeNode> nodes)) {
|
---|
| 1804 | nodes = new List<ISymbolicExpressionTreeNode>();
|
---|
| 1805 | name2nodes.Add(varName, nodes);
|
---|
| 1806 | }
|
---|
| 1807 | nodes.Add(node);
|
---|
[16602] | 1808 | SetVariableValue(varName, 0.0); // this value is updated in the prediction loop
|
---|
[16601] | 1809 | }
|
---|
| 1810 | }
|
---|
| 1811 | }
|
---|
| 1812 |
|
---|
[16603] | 1813 | public int ParameterCount => constantNodes.Length;
|
---|
[16601] | 1814 |
|
---|
| 1815 | public void SetVariableValue(string variableName, double val) {
|
---|
| 1816 | SetVariableValue(variableName, val, Vector.Zero);
|
---|
| 1817 | }
|
---|
| 1818 | public Tuple<double, Vector> GetVariableValue(string variableName) {
|
---|
| 1819 | return node2val[name2nodes[variableName].First()];
|
---|
| 1820 | }
|
---|
| 1821 | public void SetVariableValue(string variableName, double val, Vector dVal) {
|
---|
| 1822 | if (name2nodes.TryGetValue(variableName, out List<ISymbolicExpressionTreeNode> nodes)) {
|
---|
| 1823 | nodes.ForEach(n => node2val[n] = Tuple.Create(val, dVal));
|
---|
| 1824 | } else {
|
---|
[16602] | 1825 | var fakeNode = new VariableTreeNode(new Variable());
|
---|
[16610] | 1826 | fakeNode.Weight = 1.0;
|
---|
| 1827 | fakeNode.VariableName = variableName;
|
---|
[16601] | 1828 | var newNodeList = new List<ISymbolicExpressionTreeNode>();
|
---|
| 1829 | newNodeList.Add(fakeNode);
|
---|
| 1830 | name2nodes.Add(variableName, newNodeList);
|
---|
| 1831 | node2val[fakeNode] = Tuple.Create(val, dVal);
|
---|
| 1832 | }
|
---|
| 1833 | }
|
---|
| 1834 |
|
---|
| 1835 | internal void UpdateParamValues(double[] x) {
|
---|
[16603] | 1836 | for (int i = 0; i < x.Length; i++) {
|
---|
| 1837 | constantNodes[i].Value = x[i];
|
---|
| 1838 | node2val[constantNodes[i]] = Tuple.Create(x[i], constantGradientVectors[i]);
|
---|
[16601] | 1839 | }
|
---|
| 1840 | }
|
---|
| 1841 | }
|
---|
[15964] | 1842 | }
|
---|
| 1843 | }
|
---|