1 | #region License Information
|
---|
2 |
|
---|
3 | /* HeuristicLab
|
---|
4 | * Copyright (C) 2002-2018 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
|
---|
5 | *
|
---|
6 | * This file is part of HeuristicLab.
|
---|
7 | *
|
---|
8 | * HeuristicLab is free software: you can redistribute it and/or modify
|
---|
9 | * it under the terms of the GNU General Public License as published by
|
---|
10 | * the Free Software Foundation, either version 3 of the License, or
|
---|
11 | * (at your option) any later version.
|
---|
12 | *
|
---|
13 | * HeuristicLab is distributed in the hope that it will be useful,
|
---|
14 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
|
---|
15 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
---|
16 | * GNU General Public License for more details.
|
---|
17 | *
|
---|
18 | * You should have received a copy of the GNU General Public License
|
---|
19 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
|
---|
20 | */
|
---|
21 |
|
---|
22 | #endregion License Information
|
---|
23 |
|
---|
24 | using System;
|
---|
25 | using System.Collections.Generic;
|
---|
26 | using System.Linq;
|
---|
27 | using HEAL.Attic;
|
---|
28 | using HeuristicLab.Algorithms.DataAnalysis;
|
---|
29 | using HeuristicLab.Common;
|
---|
30 | using HeuristicLab.Core;
|
---|
31 | using HeuristicLab.Data;
|
---|
32 | using HeuristicLab.Encodings.IntegerVectorEncoding;
|
---|
33 | using HeuristicLab.Encodings.RealVectorEncoding;
|
---|
34 | using HeuristicLab.Optimization;
|
---|
35 | using HeuristicLab.Parameters;
|
---|
36 | using HeuristicLab.Problems.DataAnalysis;
|
---|
37 |
|
---|
38 | namespace HeuristicLab.Problems.Modifiers {
|
---|
39 |
|
---|
40 | [StorableType("A0E33EDB-04F6-48B6-BB10-7E3753841AEA")]
|
---|
41 | [Item("AnalysisRunningPredictionQualityProblemModifier", " A problem modifier that provides extended Analysis by creating running models (models trained on the evaluations of previous iterations) and analyzing their performance over time")]
|
---|
42 | public class AnalysisRunningPredictionQualityProblemModifier : ProblemModifier {
|
---|
43 |
|
---|
44 | #region Properties
|
---|
45 | [Storable]
|
---|
46 | private ModifiableDataset data;
|
---|
47 |
|
---|
48 | [Storable]
|
---|
49 | private Dictionary<string, List<double[]>> evaluationsLookUp;
|
---|
50 |
|
---|
51 | [Storable]
|
---|
52 | private List<Tuple<double[], double[]>> evaluatedThisIteration;
|
---|
53 |
|
---|
54 | [Storable]
|
---|
55 | private List<Tuple<double[], double[]>> lastPopulation;
|
---|
56 |
|
---|
57 | [Storable]
|
---|
58 | private int iteration;
|
---|
59 |
|
---|
60 | [Storable]
|
---|
61 | private int trainingLength;
|
---|
62 |
|
---|
63 | public const string ModelBuilderParameterName = "ModelBuilder";
|
---|
64 |
|
---|
65 | public IValueParameter<IAlgorithm> ModelBuilderParameter => (IValueParameter<IAlgorithm>)Parameters[ModelBuilderParameterName];
|
---|
66 |
|
---|
67 | public IAlgorithm ModelBuilder => ModelBuilderParameter.Value;
|
---|
68 |
|
---|
69 | #endregion
|
---|
70 | [StorableConstructor]
|
---|
71 | protected AnalysisRunningPredictionQualityProblemModifier(StorableConstructorFlag _) : base(_) { }
|
---|
72 |
|
---|
73 | protected AnalysisRunningPredictionQualityProblemModifier(AnalysisRunningPredictionQualityProblemModifier original, Cloner cloner) : base(original, cloner) {
|
---|
74 | data = cloner.Clone(original?.data);
|
---|
75 | evaluationsLookUp = original?.evaluationsLookUp.ToDictionary(e => e.Key, e => e.Value.Select(o => o.ToArray()).ToList());
|
---|
76 | iteration = original?.iteration ?? 0;
|
---|
77 | trainingLength = original?.trainingLength ?? 0;
|
---|
78 | evaluatedThisIteration = original?.evaluatedThisIteration.Select(x => Tuple.Create(x.Item1.ToArray(), x.Item2.ToArray())).ToList();
|
---|
79 | lastPopulation = original?.lastPopulation.Select(x => Tuple.Create(x.Item1.ToArray(), x.Item2.ToArray())).ToList();
|
---|
80 | Parameters.Add(new ValueParameter<IAlgorithm>(ModelBuilderParameterName, "The model builder", new GaussianProcessRegression()));
|
---|
81 | }
|
---|
82 |
|
---|
83 | protected AnalysisRunningPredictionQualityProblemModifier() {
|
---|
84 | evaluationsLookUp = new Dictionary<string, List<double[]>>();
|
---|
85 | }
|
---|
86 |
|
---|
87 | public override void Initialize() {
|
---|
88 | data = new ModifiableDataset();
|
---|
89 | if (evaluationsLookUp == null) evaluationsLookUp = new Dictionary<string, List<double[]>>();
|
---|
90 | evaluationsLookUp.Clear();
|
---|
91 | iteration = 0;
|
---|
92 | trainingLength = 0;
|
---|
93 | evaluatedThisIteration = new List<Tuple<double[], double[]>>();
|
---|
94 | lastPopulation = new List<Tuple<double[], double[]>>();
|
---|
95 | }
|
---|
96 |
|
---|
97 | public override void ModifiedAnalyze(Individual[] individuals, double[][] qualities, ResultCollection results, IRandom random) {
|
---|
98 | var models = new ResultCollection(qualities.First().Length);
|
---|
99 | for (var i = 0; i < qualities.First().Length; i++) {
|
---|
100 | var pd = new RegressionProblemData(data, data.VariableNames.Where(v => v.Contains("X")), TargetVariableName(i));
|
---|
101 | pd.TrainingPartition.Start = 0;
|
---|
102 | pd.TrainingPartition.End = pd.TestPartition.Start = trainingLength;
|
---|
103 | pd.TestPartition.End = data.Rows;
|
---|
104 | models.AddOrUpdateResult(TargetVariableName(i), BuildRunningModel(pd, random));
|
---|
105 | }
|
---|
106 | results.AddOrUpdateResult("Running Models", models);
|
---|
107 | trainingLength = data.Rows;
|
---|
108 | lastPopulation = individuals.Zip(qualities, (i, q) => Tuple.Create(ExtractInputs(i), q)).ToList();
|
---|
109 | evaluatedThisIteration.Clear();
|
---|
110 | iteration++;
|
---|
111 | base.ModifiedAnalyze(individuals, qualities, results, random);
|
---|
112 | }
|
---|
113 |
|
---|
114 | public override double[] ModifiedEvaluate(Individual individual, IRandom random) {
|
---|
115 | var q = base.ModifiedEvaluate(individual, random);
|
---|
116 | lock (data) {
|
---|
117 | ExtendDatasetWithoutDuplicates(new[] { individual }, new[] { q });
|
---|
118 | evaluatedThisIteration.Add(Tuple.Create(ExtractInputs(individual), q.ToArray()));
|
---|
119 | }
|
---|
120 | return q;
|
---|
121 | }
|
---|
122 |
|
---|
123 | private IRegressionSolution BuildRunningModel(RegressionProblemData pd, IRandom random) {
|
---|
124 | if (pd.TrainingPartition.Size <= 0) return null;
|
---|
125 | try {
|
---|
126 | ModelBuilder.Problem = new RegressionProblem() { ProblemData = pd };
|
---|
127 | if (ModelBuilder.Parameters.ContainsKey("Seed") && (ModelBuilder.Parameters["Seed"] is IValueParameter<IntValue> seedParam)) seedParam.Value.Value = random.Next();
|
---|
128 | if (ModelBuilder.Parameters.ContainsKey("SetSeedRandomly") && (ModelBuilder.Parameters["SetSeedRandomly"] is IValueParameter<BoolValue> setSeedParam)) setSeedParam.Value.Value = false;
|
---|
129 | ModelBuilder.Start();
|
---|
130 | var res = ModelBuilder.Results.Select(x => x.Value).OfType<IRegressionSolution>().Single();
|
---|
131 | ModelBuilder.Prepare();
|
---|
132 | ModelBuilder.Runs.Clear();
|
---|
133 | return res;
|
---|
134 | } catch (Exception) {
|
---|
135 | return null;
|
---|
136 | }
|
---|
137 | }
|
---|
138 |
|
---|
139 |
|
---|
140 | #region DataHandling
|
---|
141 | private void ExtendDatasetWithoutDuplicates(IReadOnlyList<Individual> individuals, IReadOnlyList<double[]> qualities) {
|
---|
142 | if (data.Rows == 0) {
|
---|
143 | for (var i = 0; i < ExtractInputs(individuals[0]).Length; i++) {
|
---|
144 | var v = InputVariableName(i);
|
---|
145 | if (!data.DoubleVariables.Contains(v))
|
---|
146 | data.AddVariable(v, new List<double>());
|
---|
147 | }
|
---|
148 | for (var i = 0; i < qualities[0].Length; i++) {
|
---|
149 | var v = TargetVariableName(i);
|
---|
150 | if (!data.DoubleVariables.Contains(v))
|
---|
151 | data.AddVariable(v, new List<double>());
|
---|
152 | }
|
---|
153 | }
|
---|
154 | for (var i = 0; i < individuals.Count; i++) {
|
---|
155 | var ins = ExtractInputs(individuals[i]);
|
---|
156 | var id = ToIdentifier(ins);
|
---|
157 | var outs = qualities[i];
|
---|
158 | if (outs.Any(x => double.IsNaN(x) || double.IsInfinity(x) || double.MaxValue / 100 < x || double.MinValue / 100 > x)) continue;
|
---|
159 | if (evaluationsLookUp.ContainsKey(id) && evaluationsLookUp[id].Any(o => Equals(o, outs))) continue;
|
---|
160 | if (ins.Length + outs.Length != data.DoubleVariables.Count()) throw new ArgumentException("length of individuals and outputs does not match existing data");
|
---|
161 | data.AddRow(ins.Concat(qualities[i]).Select(x => (object)x));
|
---|
162 | if (!evaluationsLookUp.ContainsKey(id)) evaluationsLookUp.Add(id, new List<double[]>() { outs });
|
---|
163 | else { evaluationsLookUp[id].Add(outs); }
|
---|
164 | }
|
---|
165 | }
|
---|
166 |
|
---|
167 | private static double[] ExtractInputs(Individual individual) {
|
---|
168 | if (!(individual is SingleEncodingIndividual si)) throw new ArgumentException("Multi encodings are not supported with this problem modifier");
|
---|
169 | switch (si[si.Name]) {
|
---|
170 | case RealVector rv:
|
---|
171 | return rv.CloneAsArray();
|
---|
172 | case IntegerVector iv:
|
---|
173 | return iv.Select(i => (double)i).ToArray();
|
---|
174 | default:
|
---|
175 | throw new ArgumentException("Only Integer and Real Vector Individuals can be transformed to input values");
|
---|
176 | }
|
---|
177 | }
|
---|
178 | #endregion DataHandling
|
---|
179 |
|
---|
180 | #region Naming
|
---|
181 | public static string ToIdentifier(IEnumerable<double> inputs) {
|
---|
182 | return string.Join(";", inputs);
|
---|
183 | }
|
---|
184 |
|
---|
185 | public static string ToIdentifier(Individual i) {
|
---|
186 | return string.Join(";", ExtractInputs(i));
|
---|
187 | }
|
---|
188 |
|
---|
189 | public static string TargetVariableName(int targetNumber) {
|
---|
190 | return "Y" + targetNumber;
|
---|
191 | }
|
---|
192 |
|
---|
193 | public static string InputVariableName(int inputNumber) {
|
---|
194 | return "X" + inputNumber;
|
---|
195 | }
|
---|
196 | #endregion Naming
|
---|
197 | public override IDeepCloneable Clone(Cloner cloner) {
|
---|
198 | return new AnalysisRunningPredictionQualityProblemModifier(this, cloner);
|
---|
199 | }
|
---|
200 | }
|
---|
201 | } |
---|