1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2019 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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4 | *
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5 | * This file is part of HeuristicLab.
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6 | *
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7 | * HeuristicLab is free software: you can redistribute it and/or modify
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8 | * it under the terms of the GNU General Public License as published by
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9 | * the Free Software Foundation, either version 3 of the License, or
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10 | * (at your option) any later version.
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11 | *
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12 | * HeuristicLab is distributed in the hope that it will be useful,
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13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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15 | * GNU General Public License for more details.
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16 | *
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17 | * You should have received a copy of the GNU General Public License
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18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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19 | */
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20 | #endregion
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21 |
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22 | using System;
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23 | using System.Collections.Generic;
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24 | using System.Linq;
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25 | using HeuristicLab.Common;
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26 | using HeuristicLab.Core;
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27 | using HeuristicLab.Data;
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28 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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29 | using HeuristicLab.Optimization;
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30 | using HeuristicLab.Parameters;
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31 | using HEAL.Attic;
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32 | using System.Runtime.InteropServices;
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33 |
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34 | namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
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35 | [Item("NLOpt Evaluator (with constraints)", "")]
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36 | [StorableType("5FADAE55-3516-4539-8A36-BC9B0D00880D")]
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37 | public class NLOptEvaluator : SymbolicRegressionSingleObjectiveEvaluator {
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38 | private const string ConstantOptimizationIterationsParameterName = "ConstantOptimizationIterations";
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39 | private const string ConstantOptimizationImprovementParameterName = "ConstantOptimizationImprovement";
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40 | private const string ConstantOptimizationProbabilityParameterName = "ConstantOptimizationProbability";
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41 | private const string ConstantOptimizationRowsPercentageParameterName = "ConstantOptimizationRowsPercentage";
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42 | private const string UpdateConstantsInTreeParameterName = "UpdateConstantsInSymbolicExpressionTree";
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43 | private const string UpdateVariableWeightsParameterName = "Update Variable Weights";
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44 |
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45 | private const string FunctionEvaluationsResultParameterName = "Constants Optimization Function Evaluations";
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46 | private const string GradientEvaluationsResultParameterName = "Constants Optimization Gradient Evaluations";
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47 | private const string CountEvaluationsParameterName = "Count Function and Gradient Evaluations";
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48 |
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49 | public IFixedValueParameter<IntValue> ConstantOptimizationIterationsParameter {
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50 | get { return (IFixedValueParameter<IntValue>)Parameters[ConstantOptimizationIterationsParameterName]; }
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51 | }
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52 | public IFixedValueParameter<DoubleValue> ConstantOptimizationImprovementParameter {
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53 | get { return (IFixedValueParameter<DoubleValue>)Parameters[ConstantOptimizationImprovementParameterName]; }
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54 | }
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55 | public IFixedValueParameter<PercentValue> ConstantOptimizationProbabilityParameter {
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56 | get { return (IFixedValueParameter<PercentValue>)Parameters[ConstantOptimizationProbabilityParameterName]; }
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57 | }
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58 | public IFixedValueParameter<PercentValue> ConstantOptimizationRowsPercentageParameter {
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59 | get { return (IFixedValueParameter<PercentValue>)Parameters[ConstantOptimizationRowsPercentageParameterName]; }
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60 | }
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61 | public IFixedValueParameter<BoolValue> UpdateConstantsInTreeParameter {
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62 | get { return (IFixedValueParameter<BoolValue>)Parameters[UpdateConstantsInTreeParameterName]; }
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63 | }
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64 | public IFixedValueParameter<BoolValue> UpdateVariableWeightsParameter {
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65 | get { return (IFixedValueParameter<BoolValue>)Parameters[UpdateVariableWeightsParameterName]; }
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66 | }
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67 |
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68 | public IResultParameter<IntValue> FunctionEvaluationsResultParameter {
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69 | get { return (IResultParameter<IntValue>)Parameters[FunctionEvaluationsResultParameterName]; }
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70 | }
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71 | public IResultParameter<IntValue> GradientEvaluationsResultParameter {
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72 | get { return (IResultParameter<IntValue>)Parameters[GradientEvaluationsResultParameterName]; }
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73 | }
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74 | public IFixedValueParameter<BoolValue> CountEvaluationsParameter {
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75 | get { return (IFixedValueParameter<BoolValue>)Parameters[CountEvaluationsParameterName]; }
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76 | }
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77 | public IConstrainedValueParameter<StringValue> SolverParameter {
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78 | get { return (IConstrainedValueParameter<StringValue>)Parameters["Solver"]; }
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79 | }
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80 |
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81 |
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82 | public IntValue ConstantOptimizationIterations {
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83 | get { return ConstantOptimizationIterationsParameter.Value; }
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84 | }
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85 | public DoubleValue ConstantOptimizationImprovement {
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86 | get { return ConstantOptimizationImprovementParameter.Value; }
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87 | }
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88 | public PercentValue ConstantOptimizationProbability {
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89 | get { return ConstantOptimizationProbabilityParameter.Value; }
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90 | }
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91 | public PercentValue ConstantOptimizationRowsPercentage {
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92 | get { return ConstantOptimizationRowsPercentageParameter.Value; }
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93 | }
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94 | public bool UpdateConstantsInTree {
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95 | get { return UpdateConstantsInTreeParameter.Value.Value; }
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96 | set { UpdateConstantsInTreeParameter.Value.Value = value; }
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97 | }
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98 |
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99 | public bool UpdateVariableWeights {
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100 | get { return UpdateVariableWeightsParameter.Value.Value; }
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101 | set { UpdateVariableWeightsParameter.Value.Value = value; }
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102 | }
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103 |
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104 | public bool CountEvaluations {
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105 | get { return CountEvaluationsParameter.Value.Value; }
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106 | set { CountEvaluationsParameter.Value.Value = value; }
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107 | }
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108 |
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109 | public string Solver {
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110 | get { return SolverParameter.Value.Value; }
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111 | }
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112 | public override bool Maximization {
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113 | get { return false; }
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114 | }
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115 |
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116 | [StorableConstructor]
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117 | protected NLOptEvaluator(StorableConstructorFlag _) : base(_) { }
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118 | protected NLOptEvaluator(NLOptEvaluator original, Cloner cloner)
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119 | : base(original, cloner) {
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120 | }
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121 | public NLOptEvaluator()
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122 | : base() {
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123 | Parameters.Add(new FixedValueParameter<IntValue>(ConstantOptimizationIterationsParameterName, "Determines how many iterations should be calculated while optimizing the constant of a symbolic expression tree (0 indicates other or default stopping criterion).", new IntValue(10)));
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124 | Parameters.Add(new FixedValueParameter<DoubleValue>(ConstantOptimizationImprovementParameterName, "Determines the relative improvement which must be achieved in the constant optimization to continue with it (0 indicates other or default stopping criterion).", new DoubleValue(0)) { Hidden = true });
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125 | Parameters.Add(new FixedValueParameter<PercentValue>(ConstantOptimizationProbabilityParameterName, "Determines the probability that the constants are optimized", new PercentValue(1)));
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126 | Parameters.Add(new FixedValueParameter<PercentValue>(ConstantOptimizationRowsPercentageParameterName, "Determines the percentage of the rows which should be used for constant optimization", new PercentValue(1)));
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127 | Parameters.Add(new FixedValueParameter<BoolValue>(UpdateConstantsInTreeParameterName, "Determines if the constants in the tree should be overwritten by the optimized constants.", new BoolValue(true)) { Hidden = true });
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128 | Parameters.Add(new FixedValueParameter<BoolValue>(UpdateVariableWeightsParameterName, "Determines if the variable weights in the tree should be optimized.", new BoolValue(true)) { Hidden = true });
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129 |
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130 | Parameters.Add(new FixedValueParameter<BoolValue>(CountEvaluationsParameterName, "Determines if function and gradient evaluation should be counted.", new BoolValue(false)));
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131 |
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132 | var validSolvers = new ItemSet<StringValue>(new[] { "MMA", "COBYLA", "CCSAQ", "ISRES" }.Select(s => new StringValue(s).AsReadOnly()));
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133 | Parameters.Add(new ConstrainedValueParameter<StringValue>("Solver", "The solver algorithm", validSolvers, validSolvers.First()));
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134 | Parameters.Add(new ResultParameter<IntValue>(FunctionEvaluationsResultParameterName, "The number of function evaluations performed by the constants optimization evaluator", "Results", new IntValue()));
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135 | Parameters.Add(new ResultParameter<IntValue>(GradientEvaluationsResultParameterName, "The number of gradient evaluations performed by the constants optimization evaluator", "Results", new IntValue()));
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136 | }
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137 |
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138 | public override IDeepCloneable Clone(Cloner cloner) {
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139 | return new NLOptEvaluator(this, cloner);
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140 | }
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141 |
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142 | [StorableHook(HookType.AfterDeserialization)]
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143 | private void AfterDeserialization() { }
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144 |
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145 | private static readonly object locker = new object();
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146 |
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147 | public override IOperation InstrumentedApply() {
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148 | var solution = SymbolicExpressionTreeParameter.ActualValue;
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149 | double quality;
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150 | if (RandomParameter.ActualValue.NextDouble() < ConstantOptimizationProbability.Value) {
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151 | IEnumerable<int> constantOptimizationRows = GenerateRowsToEvaluate(ConstantOptimizationRowsPercentage.Value);
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152 | var counter = new EvaluationsCounter();
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153 | quality = OptimizeConstants(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution, ProblemDataParameter.ActualValue,
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154 | constantOptimizationRows, ApplyLinearScalingParameter.ActualValue.Value, Solver, ConstantOptimizationIterations.Value, updateVariableWeights: UpdateVariableWeights, lowerEstimationLimit: EstimationLimitsParameter.ActualValue.Lower, upperEstimationLimit: EstimationLimitsParameter.ActualValue.Upper, updateConstantsInTree: UpdateConstantsInTree, counter: counter);
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155 |
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156 | if (ConstantOptimizationRowsPercentage.Value != RelativeNumberOfEvaluatedSamplesParameter.ActualValue.Value) {
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157 | throw new NotSupportedException();
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158 | }
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159 |
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160 | if (CountEvaluations) {
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161 | lock (locker) {
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162 | FunctionEvaluationsResultParameter.ActualValue.Value += counter.FunctionEvaluations;
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163 | GradientEvaluationsResultParameter.ActualValue.Value += counter.GradientEvaluations;
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164 | }
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165 | }
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166 |
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167 | } else {
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168 | throw new NotSupportedException();
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169 | }
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170 | QualityParameter.ActualValue = new DoubleValue(quality);
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171 |
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172 | return base.InstrumentedApply();
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173 | }
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174 |
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175 | public override double Evaluate(IExecutionContext context, ISymbolicExpressionTree tree, IRegressionProblemData problemData, IEnumerable<int> rows) {
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176 | SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
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177 | EstimationLimitsParameter.ExecutionContext = context;
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178 | ApplyLinearScalingParameter.ExecutionContext = context;
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179 | FunctionEvaluationsResultParameter.ExecutionContext = context;
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180 | GradientEvaluationsResultParameter.ExecutionContext = context;
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181 |
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182 | // MSE evaluator is used on purpose instead of the const-opt evaluator,
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183 | // because Evaluate() is used to get the quality of evolved models on
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184 | // different partitions of the dataset (e.g., best validation model)
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185 | double mse = SymbolicRegressionSingleObjectiveMeanSquaredErrorEvaluator.Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, double.MinValue, double.MaxValue, problemData, rows, applyLinearScaling: false);
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186 |
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187 | SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
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188 | EstimationLimitsParameter.ExecutionContext = null;
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189 | ApplyLinearScalingParameter.ExecutionContext = null;
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190 | FunctionEvaluationsResultParameter.ExecutionContext = null;
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191 | GradientEvaluationsResultParameter.ExecutionContext = null;
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192 |
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193 | return mse;
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194 | }
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195 |
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196 | public class EvaluationsCounter {
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197 | public int FunctionEvaluations = 0;
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198 | public int GradientEvaluations = 0;
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199 | }
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200 |
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201 | private static void GetParameterNodes(ISymbolicExpressionTree tree, out List<ISymbolicExpressionTreeNode> thetaNodes, out List<double> thetaValues) {
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202 | thetaNodes = new List<ISymbolicExpressionTreeNode>();
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203 | thetaValues = new List<double>();
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204 |
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205 | var nodes = tree.IterateNodesPrefix().ToArray();
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206 | for (int i = 0; i < nodes.Length; ++i) {
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207 | var node = nodes[i];
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208 | if (node is VariableTreeNode variableTreeNode) {
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209 | thetaValues.Add(variableTreeNode.Weight);
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210 | thetaNodes.Add(node);
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211 | } else if (node is ConstantTreeNode constantTreeNode) {
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212 | thetaNodes.Add(node);
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213 | thetaValues.Add(constantTreeNode.Value);
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214 | }
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215 | }
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216 | }
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217 |
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218 | // for data exchange to/from optimizer in native code
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219 | [StructLayout(LayoutKind.Sequential)]
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220 | private struct ConstraintData {
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221 | public ISymbolicExpressionTree Tree;
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222 | public ISymbolicExpressionTreeNode[] ParameterNodes;
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223 | }
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224 |
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225 | public static double OptimizeConstants(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter,
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226 | ISymbolicExpressionTree tree, IRegressionProblemData problemData, IEnumerable<int> rows, bool applyLinearScaling,
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227 | string solver,
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228 | int maxIterations, bool updateVariableWeights = true,
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229 | double lowerEstimationLimit = double.MinValue, double upperEstimationLimit = double.MaxValue,
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230 | bool updateConstantsInTree = true, Action<double[], double, object> iterationCallback = null, EvaluationsCounter counter = null) {
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231 |
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232 | if (!updateVariableWeights) throw new NotSupportedException("not updating variable weights is not supported");
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233 | if (!updateConstantsInTree) throw new NotSupportedException("not updating tree parameters is not supported");
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234 | if (!applyLinearScaling) throw new NotSupportedException("application without linear scaling is not supported");
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235 |
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236 | // we always update constants, so we don't need to calculate initial quality
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237 | // double originalQuality = SymbolicRegressionSingleObjectiveMeanSquaredErrorEvaluator.Calculate(interpreter, tree, lowerEstimationLimit, upperEstimationLimit, problemData, rows, applyLinearScaling: false);
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238 |
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239 | if (counter == null) counter = new EvaluationsCounter();
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240 | var rowEvaluationsCounter = new EvaluationsCounter();
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241 |
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242 | var intervalConstraints = problemData.IntervalConstraints;
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243 | var dataIntervals = problemData.VariableRanges.GetIntervals();
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244 | var trainingRows = problemData.TrainingIndices.ToArray();
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245 | // buffers
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246 | var target = problemData.TargetVariableTrainingValues.ToArray();
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247 | var targetStDev = target.StandardDeviationPop();
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248 | var targetVariance = targetStDev * targetStDev;
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249 | var targetMean = target.Average();
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250 | var pred = interpreter.GetSymbolicExpressionTreeValues(tree, problemData.Dataset, trainingRows).ToArray();
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251 | if (pred.Any(pi => double.IsInfinity(pi) || double.IsNaN(pi))) return targetVariance;
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252 |
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253 | #region linear scaling
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254 | var predStDev = pred.StandardDeviationPop();
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255 | if (predStDev == 0) return targetVariance; // constant expression
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256 | var predMean = pred.Average();
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257 |
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258 | var scalingFactor = targetStDev / predStDev;
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259 | var offset = targetMean - predMean * scalingFactor;
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260 |
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261 | ISymbolicExpressionTree scaledTree = null;
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262 | if (applyLinearScaling) scaledTree = CopyAndScaleTree(tree, scalingFactor, offset);
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263 | #endregion
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264 |
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265 | // convert constants to variables named theta...
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266 | var treeForDerivation = ReplaceConstWithVar(scaledTree, out List<string> thetaNames, out List<double> thetaValues); // copies the tree
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267 |
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268 | // create trees for relevant derivatives
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269 | Dictionary<string, ISymbolicExpressionTree> derivatives = new Dictionary<string, ISymbolicExpressionTree>();
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270 | var allThetaNodes = thetaNames.Select(_ => new List<ConstantTreeNode>()).ToArray();
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271 | var constraintTrees = new List<ISymbolicExpressionTree>();
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272 | foreach (var constraint in intervalConstraints.Constraints) {
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273 | if (constraint.IsDerivation) {
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274 | if (!problemData.AllowedInputVariables.Contains(constraint.Variable))
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275 | throw new ArgumentException($"Invalid constraint: the variable {constraint.Variable} does not exist in the dataset.");
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276 | var df = DerivativeCalculator.Derive(treeForDerivation, constraint.Variable);
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277 |
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278 | // alglib requires constraint expressions of the form c(x) <= 0
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279 | // -> we make two expressions, one for the lower bound and one for the upper bound
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280 |
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281 | if (constraint.Interval.UpperBound < double.PositiveInfinity) {
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282 | var df_smaller_upper = Subtract((ISymbolicExpressionTree)df.Clone(), CreateConstant(constraint.Interval.UpperBound));
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283 | // convert variables named theta back to constants
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284 | var df_prepared = ReplaceVarWithConst(df_smaller_upper, thetaNames, thetaValues, allThetaNodes);
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285 | constraintTrees.Add(df_prepared);
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286 | }
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287 | if (constraint.Interval.LowerBound > double.NegativeInfinity) {
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288 | var df_larger_lower = Subtract(CreateConstant(constraint.Interval.LowerBound), (ISymbolicExpressionTree)df.Clone());
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289 | // convert variables named theta back to constants
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290 | var df_prepared = ReplaceVarWithConst(df_larger_lower, thetaNames, thetaValues, allThetaNodes);
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291 | constraintTrees.Add(df_prepared);
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292 | }
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293 | } else {
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294 | if (constraint.Interval.UpperBound < double.PositiveInfinity) {
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295 | var f_smaller_upper = Subtract((ISymbolicExpressionTree)treeForDerivation.Clone(), CreateConstant(constraint.Interval.UpperBound));
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296 | // convert variables named theta back to constants
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297 | var df_prepared = ReplaceVarWithConst(f_smaller_upper, thetaNames, thetaValues, allThetaNodes);
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298 | constraintTrees.Add(df_prepared);
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299 | }
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300 | if (constraint.Interval.LowerBound > double.NegativeInfinity) {
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301 | var f_larger_lower = Subtract(CreateConstant(constraint.Interval.LowerBound), (ISymbolicExpressionTree)treeForDerivation.Clone());
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302 | // convert variables named theta back to constants
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303 | var df_prepared = ReplaceVarWithConst(f_larger_lower, thetaNames, thetaValues, allThetaNodes);
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304 | constraintTrees.Add(df_prepared);
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305 | }
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306 | }
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307 | }
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308 |
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309 | var preparedTree = ReplaceVarWithConst(treeForDerivation, thetaNames, thetaValues, allThetaNodes);
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310 | var preparedTreeParameterNodes = GetParameterNodes(preparedTree, allThetaNodes);
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311 |
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312 | // local function
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313 | void UpdateThetaValues(double[] theta) {
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314 | for (int i = 0; i < theta.Length; ++i) {
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315 | foreach (var constNode in allThetaNodes[i]) constNode.Value = theta[i];
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316 | }
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317 | }
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318 |
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319 | var fi_eval = new double[target.Length];
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320 | var jac_eval = new double[target.Length, thetaValues.Count];
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321 |
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322 | double calculate_obj(uint dim, double[] curX, double[] grad, IntPtr data) {
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323 | UpdateThetaValues(curX);
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324 |
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325 | if (grad != null) {
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326 | var autoDiffEval = new VectorAutoDiffEvaluator();
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327 | autoDiffEval.Evaluate(preparedTree, problemData.Dataset, trainingRows,
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328 | preparedTreeParameterNodes, fi_eval, jac_eval);
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329 |
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330 | // calc sum of squared errors and gradient
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331 | var sse = 0.0;
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332 | for (int j = 0; j < grad.Length; j++) grad[j] = 0;
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333 | for (int i = 0; i < target.Length; i++) {
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334 | var r = target[i] - fi_eval[i];
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335 | sse += 0.5 * r * r;
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336 | for (int j = 0; j < grad.Length; j++) {
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337 | grad[j] -= r * jac_eval[i, j];
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338 | }
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339 | }
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340 | if (double.IsNaN(sse)) return double.MaxValue;
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341 | // average
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342 | for (int j = 0; j < grad.Length; j++) { grad[j] /= target.Length; }
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343 | return sse / target.Length;
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344 | } else {
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345 | var eval = new VectorEvaluator();
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346 | var prediction = eval.Evaluate(preparedTree, problemData.Dataset, trainingRows);
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347 |
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348 | // calc sum of squared errors and gradient
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349 | var sse = 0.0;
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350 | for (int i = 0; i < target.Length; i++) {
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351 | var r = target[i] - prediction[i];
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352 | sse += 0.5 * r * r;
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353 | }
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354 | if (double.IsNaN(sse)) return double.MaxValue;
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355 | // average
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356 | return sse / target.Length;
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357 | }
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358 |
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359 | }
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360 |
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361 | double calculate_constraint(uint dim, double[] curX, double[] grad, IntPtr data) {
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362 | UpdateThetaValues(curX);
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363 | var intervalEvaluator = new IntervalEvaluator();
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364 | var constraintData = Marshal.PtrToStructure<ConstraintData>(data);
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365 |
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366 | if (grad != null) for (int j = 0; j < grad.Length; j++) grad[j] = 0; // clear grad
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367 |
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368 | var interval = intervalEvaluator.Evaluate(constraintData.Tree, dataIntervals, constraintData.ParameterNodes,
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369 | out double[] lowerGradient, out double[] upperGradient);
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370 |
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371 | // we transformed this to a constraint c(x) <= 0, so only the upper bound is relevant for us
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372 | if (grad != null) for (int j = 0; j < grad.Length; j++) { grad[j] = upperGradient[j]; }
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373 | if (double.IsNaN(interval.UpperBound)) return double.MaxValue;
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374 | else return interval.UpperBound;
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375 | }
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376 |
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377 | var minVal = Math.Min(-1000.0, thetaValues.Min());
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378 | var maxVal = Math.Max(1000.0, thetaValues.Max());
|
---|
379 | var lb = Enumerable.Repeat(minVal, thetaValues.Count).ToArray();
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380 | var up = Enumerable.Repeat(maxVal, thetaValues.Count).ToArray();
|
---|
381 | IntPtr nlopt_opt = NLOpt.nlopt_create(GetAlgFromIdentifier(solver), (uint)thetaValues.Count); /* algorithm and dimensionality */
|
---|
382 |
|
---|
383 | NLOpt.nlopt_set_lower_bounds(nlopt_opt, lb);
|
---|
384 | NLOpt.nlopt_set_upper_bounds(nlopt_opt, up);
|
---|
385 | var calculateObjectiveDelegate = new NLOpt.nlopt_func(calculate_obj); // keep a reference to the delegate (see below)
|
---|
386 | NLOpt.nlopt_set_min_objective(nlopt_opt, calculateObjectiveDelegate, IntPtr.Zero);
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---|
387 |
|
---|
388 |
|
---|
389 | var constraintDataPtr = new IntPtr[constraintTrees.Count];
|
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390 | var calculateConstraintDelegates = new NLOpt.nlopt_func[constraintTrees.Count]; // make sure we keep a reference to the delegates (otherwise GC will free delegate objects see https://stackoverflow.com/questions/7302045/callback-delegates-being-collected#7302258)
|
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391 | for (int i = 0; i < constraintTrees.Count; i++) {
|
---|
392 | var constraintData = new ConstraintData() { Tree = constraintTrees[i], ParameterNodes = GetParameterNodes(constraintTrees[i], allThetaNodes) };
|
---|
393 | constraintDataPtr[i] = Marshal.AllocHGlobal(Marshal.SizeOf<ConstraintData>());
|
---|
394 | Marshal.StructureToPtr(constraintData, constraintDataPtr[i], fDeleteOld: false);
|
---|
395 | calculateConstraintDelegates[i] = new NLOpt.nlopt_func(calculate_constraint);
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---|
396 | NLOpt.nlopt_add_inequality_constraint(nlopt_opt, calculateConstraintDelegates[i], constraintDataPtr[i], 1e-8);
|
---|
397 | }
|
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398 |
|
---|
399 | NLOpt.nlopt_set_xtol_rel(nlopt_opt, 1e-4);
|
---|
400 | NLOpt.nlopt_set_maxtime(nlopt_opt, 10.0); // 10 secs
|
---|
401 | NLOpt.nlopt_set_maxeval(nlopt_opt, maxIterations);
|
---|
402 |
|
---|
403 | var x = thetaValues.ToArray(); /* initial guess */
|
---|
404 | double minf = double.MaxValue; /* minimum objective value upon return */
|
---|
405 | var res = NLOpt.nlopt_optimize(nlopt_opt, x, ref minf);
|
---|
406 | if (res < 0) {
|
---|
407 | throw new InvalidOperationException($"NLOpt failed {res} {NLOpt.nlopt_get_errmsg(nlopt_opt)}");
|
---|
408 | } else {
|
---|
409 | // update parameters in tree
|
---|
410 | var pIdx = 0;
|
---|
411 | // here we lose the two last parameters (for linear scaling)
|
---|
412 | foreach (var node in tree.IterateNodesPostfix()) {
|
---|
413 | if (node is ConstantTreeNode constTreeNode) {
|
---|
414 | constTreeNode.Value = x[pIdx++];
|
---|
415 | } else if (node is VariableTreeNode varTreeNode) {
|
---|
416 | varTreeNode.Weight = x[pIdx++];
|
---|
417 | }
|
---|
418 | }
|
---|
419 | // note: we keep the optimized constants even when the tree is worse.
|
---|
420 | // assert that we lose the last two parameters
|
---|
421 | if (pIdx != x.Length - 2) throw new InvalidProgramException();
|
---|
422 | }
|
---|
423 |
|
---|
424 | NLOpt.nlopt_destroy(nlopt_opt);
|
---|
425 | for (int i = 0; i < constraintDataPtr.Length; i++)
|
---|
426 | Marshal.FreeHGlobal(constraintDataPtr[i]);
|
---|
427 |
|
---|
428 | counter.FunctionEvaluations += NLOpt.nlopt_get_numevals(nlopt_opt);
|
---|
429 | // counter.GradientEvaluations += NLOpt.nlopt_get; // TODO
|
---|
430 |
|
---|
431 |
|
---|
432 |
|
---|
433 | return Math.Min(minf, targetVariance);
|
---|
434 | }
|
---|
435 |
|
---|
436 | private static NLOpt.nlopt_algorithm GetAlgFromIdentifier(string solver) {
|
---|
437 | if (solver.Contains("MMA")) return NLOpt.nlopt_algorithm.NLOPT_LD_MMA;
|
---|
438 | if (solver.Contains("COBYLA")) return NLOpt.nlopt_algorithm.NLOPT_LN_COBYLA;
|
---|
439 | if (solver.Contains("CCSAQ")) return NLOpt.nlopt_algorithm.NLOPT_LD_CCSAQ;
|
---|
440 | if (solver.Contains("ISRES")) return NLOpt.nlopt_algorithm.NLOPT_GN_ISRES;
|
---|
441 | throw new ArgumentException($"Unknown solver {solver}");
|
---|
442 | }
|
---|
443 |
|
---|
444 | private static ISymbolicExpressionTree CopyAndScaleTree(ISymbolicExpressionTree tree, double scalingFactor, double offset) {
|
---|
445 | var m = (ISymbolicExpressionTree)tree.Clone();
|
---|
446 |
|
---|
447 | var add = MakeNode<Addition>(MakeNode<Multiplication>(m.Root.GetSubtree(0).GetSubtree(0), CreateConstant(scalingFactor)), CreateConstant(offset));
|
---|
448 | m.Root.GetSubtree(0).RemoveSubtree(0);
|
---|
449 | m.Root.GetSubtree(0).AddSubtree(add);
|
---|
450 | return m;
|
---|
451 | }
|
---|
452 |
|
---|
453 | #region helper
|
---|
454 | private static ISymbolicExpressionTreeNode[] GetParameterNodes(ISymbolicExpressionTree tree, List<ConstantTreeNode>[] allNodes) {
|
---|
455 | // TODO better solution necessary
|
---|
456 | var treeConstNodes = tree.IterateNodesPostfix().OfType<ConstantTreeNode>().ToArray();
|
---|
457 | var paramNodes = new ISymbolicExpressionTreeNode[allNodes.Length];
|
---|
458 | for (int i = 0; i < paramNodes.Length; i++) {
|
---|
459 | paramNodes[i] = allNodes[i].SingleOrDefault(n => treeConstNodes.Contains(n));
|
---|
460 | }
|
---|
461 | return paramNodes;
|
---|
462 | }
|
---|
463 |
|
---|
464 | private static ISymbolicExpressionTree ReplaceVarWithConst(ISymbolicExpressionTree tree, List<string> thetaNames, List<double> thetaValues, List<ConstantTreeNode>[] thetaNodes) {
|
---|
465 | var copy = (ISymbolicExpressionTree)tree.Clone();
|
---|
466 | var nodes = copy.IterateNodesPostfix().ToList();
|
---|
467 | for (int i = 0; i < nodes.Count; i++) {
|
---|
468 | var n = nodes[i] as VariableTreeNode;
|
---|
469 | if (n != null) {
|
---|
470 | var thetaIdx = thetaNames.IndexOf(n.VariableName);
|
---|
471 | if (thetaIdx >= 0) {
|
---|
472 | var parent = n.Parent;
|
---|
473 | if (thetaNodes[thetaIdx].Any()) {
|
---|
474 | // HACK: REUSE CONSTANT TREE NODE IN SEVERAL TREES
|
---|
475 | // we use this trick to allow autodiff over thetas when thetas occurr multiple times in the tree (e.g. in derived trees)
|
---|
476 | var constNode = thetaNodes[thetaIdx].First();
|
---|
477 | var childIdx = parent.IndexOfSubtree(n);
|
---|
478 | parent.RemoveSubtree(childIdx);
|
---|
479 | parent.InsertSubtree(childIdx, constNode);
|
---|
480 | } else {
|
---|
481 | var constNode = (ConstantTreeNode)CreateConstant(thetaValues[thetaIdx]);
|
---|
482 | var childIdx = parent.IndexOfSubtree(n);
|
---|
483 | parent.RemoveSubtree(childIdx);
|
---|
484 | parent.InsertSubtree(childIdx, constNode);
|
---|
485 | thetaNodes[thetaIdx].Add(constNode);
|
---|
486 | }
|
---|
487 | }
|
---|
488 | }
|
---|
489 | }
|
---|
490 | return copy;
|
---|
491 | }
|
---|
492 |
|
---|
493 | private static ISymbolicExpressionTree ReplaceConstWithVar(ISymbolicExpressionTree tree, out List<string> thetaNames, out List<double> thetaValues) {
|
---|
494 | thetaNames = new List<string>();
|
---|
495 | thetaValues = new List<double>();
|
---|
496 | var copy = (ISymbolicExpressionTree)tree.Clone();
|
---|
497 | var nodes = copy.IterateNodesPostfix().ToList();
|
---|
498 |
|
---|
499 | int n = 1;
|
---|
500 | for (int i = 0; i < nodes.Count; ++i) {
|
---|
501 | var node = nodes[i];
|
---|
502 | if (node is ConstantTreeNode constantTreeNode) {
|
---|
503 | var thetaVar = (VariableTreeNode)new Problems.DataAnalysis.Symbolic.Variable().CreateTreeNode();
|
---|
504 | thetaVar.Weight = 1;
|
---|
505 | thetaVar.VariableName = $"θ{n++}";
|
---|
506 |
|
---|
507 | thetaNames.Add(thetaVar.VariableName);
|
---|
508 | thetaValues.Add(constantTreeNode.Value);
|
---|
509 |
|
---|
510 | var parent = constantTreeNode.Parent;
|
---|
511 | if (parent != null) {
|
---|
512 | var index = constantTreeNode.Parent.IndexOfSubtree(constantTreeNode);
|
---|
513 | parent.RemoveSubtree(index);
|
---|
514 | parent.InsertSubtree(index, thetaVar);
|
---|
515 | }
|
---|
516 | }
|
---|
517 | if (node is VariableTreeNode varTreeNode) {
|
---|
518 | var thetaVar = (VariableTreeNode)new Problems.DataAnalysis.Symbolic.Variable().CreateTreeNode();
|
---|
519 | thetaVar.Weight = 1;
|
---|
520 | thetaVar.VariableName = $"θ{n++}";
|
---|
521 |
|
---|
522 | thetaNames.Add(thetaVar.VariableName);
|
---|
523 | thetaValues.Add(varTreeNode.Weight);
|
---|
524 |
|
---|
525 | var parent = varTreeNode.Parent;
|
---|
526 | if (parent != null) {
|
---|
527 | var index = varTreeNode.Parent.IndexOfSubtree(varTreeNode);
|
---|
528 | parent.RemoveSubtree(index);
|
---|
529 | var prodNode = MakeNode<Multiplication>();
|
---|
530 | varTreeNode.Weight = 1.0;
|
---|
531 | prodNode.AddSubtree(varTreeNode);
|
---|
532 | prodNode.AddSubtree(thetaVar);
|
---|
533 | parent.InsertSubtree(index, prodNode);
|
---|
534 | }
|
---|
535 | }
|
---|
536 | }
|
---|
537 | return copy;
|
---|
538 | }
|
---|
539 |
|
---|
540 | private static ISymbolicExpressionTreeNode CreateConstant(double value) {
|
---|
541 | var constantNode = (ConstantTreeNode)new Constant().CreateTreeNode();
|
---|
542 | constantNode.Value = value;
|
---|
543 | return constantNode;
|
---|
544 | }
|
---|
545 |
|
---|
546 | private static ISymbolicExpressionTree Subtract(ISymbolicExpressionTree t, ISymbolicExpressionTreeNode b) {
|
---|
547 | var sub = MakeNode<Subtraction>(t.Root.GetSubtree(0).GetSubtree(0), b);
|
---|
548 | t.Root.GetSubtree(0).RemoveSubtree(0);
|
---|
549 | t.Root.GetSubtree(0).InsertSubtree(0, sub);
|
---|
550 | return t;
|
---|
551 | }
|
---|
552 | private static ISymbolicExpressionTree Subtract(ISymbolicExpressionTreeNode b, ISymbolicExpressionTree t) {
|
---|
553 | var sub = MakeNode<Subtraction>(b, t.Root.GetSubtree(0).GetSubtree(0));
|
---|
554 | t.Root.GetSubtree(0).RemoveSubtree(0);
|
---|
555 | t.Root.GetSubtree(0).InsertSubtree(0, sub);
|
---|
556 | return t;
|
---|
557 | }
|
---|
558 |
|
---|
559 | private static ISymbolicExpressionTreeNode MakeNode<T>(params ISymbolicExpressionTreeNode[] fs) where T : ISymbol, new() {
|
---|
560 | var node = new T().CreateTreeNode();
|
---|
561 | foreach (var f in fs) node.AddSubtree(f);
|
---|
562 | return node;
|
---|
563 | }
|
---|
564 | #endregion
|
---|
565 |
|
---|
566 | private static void UpdateConstants(ISymbolicExpressionTreeNode[] nodes, double[] constants) {
|
---|
567 | if (nodes.Length != constants.Length) throw new InvalidOperationException();
|
---|
568 | for (int i = 0; i < nodes.Length; i++) {
|
---|
569 | if (nodes[i] is VariableTreeNode varNode) varNode.Weight = constants[i];
|
---|
570 | else if (nodes[i] is ConstantTreeNode constNode) constNode.Value = constants[i];
|
---|
571 | }
|
---|
572 | }
|
---|
573 |
|
---|
574 | private static alglib.ndimensional_fvec CreateFunc(ISymbolicExpressionTree tree, VectorEvaluator eval, ISymbolicExpressionTreeNode[] parameterNodes, IDataset ds, string targetVar, int[] rows) {
|
---|
575 | var y = ds.GetDoubleValues(targetVar, rows).ToArray();
|
---|
576 | return (double[] c, double[] fi, object o) => {
|
---|
577 | UpdateConstants(parameterNodes, c);
|
---|
578 | var pred = eval.Evaluate(tree, ds, rows);
|
---|
579 | for (int i = 0; i < fi.Length; i++)
|
---|
580 | fi[i] = pred[i] - y[i];
|
---|
581 |
|
---|
582 | var counter = (EvaluationsCounter)o;
|
---|
583 | counter.FunctionEvaluations++;
|
---|
584 | };
|
---|
585 | }
|
---|
586 |
|
---|
587 | private static alglib.ndimensional_jac CreateJac(ISymbolicExpressionTree tree, VectorAutoDiffEvaluator eval, ISymbolicExpressionTreeNode[] parameterNodes, IDataset ds, string targetVar, int[] rows) {
|
---|
588 | var y = ds.GetDoubleValues(targetVar, rows).ToArray();
|
---|
589 | return (double[] c, double[] fi, double[,] jac, object o) => {
|
---|
590 | UpdateConstants(parameterNodes, c);
|
---|
591 | eval.Evaluate(tree, ds, rows, parameterNodes, fi, jac);
|
---|
592 |
|
---|
593 | for (int i = 0; i < fi.Length; i++)
|
---|
594 | fi[i] -= y[i];
|
---|
595 |
|
---|
596 | var counter = (EvaluationsCounter)o;
|
---|
597 | counter.GradientEvaluations++;
|
---|
598 | };
|
---|
599 | }
|
---|
600 | public static bool CanOptimizeConstants(ISymbolicExpressionTree tree) {
|
---|
601 | return TreeToAutoDiffTermConverter.IsCompatible(tree);
|
---|
602 | }
|
---|
603 | }
|
---|
604 | }
|
---|