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source: branches/HeuristicLab.MetaOptimization/HeuristicLab.Problems.MetaOptimization/3.3/Evaluators/AlgorithmRunsAnalyzer.cs @ 6090

Last change on this file since 6090 was 6090, checked in by cneumuel, 13 years ago

#1215

  • added weight parameters for quality, stddev and evaluated solutions
  • lots of fixes
File size: 13.1 KB
Line 
1using System;
2using System.Collections.Generic;
3using System.Linq;
4using HeuristicLab.Common;
5using HeuristicLab.Core;
6using HeuristicLab.Data;
7using HeuristicLab.Operators;
8using HeuristicLab.Optimization;
9using HeuristicLab.Parameters;
10using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
11
12namespace HeuristicLab.Problems.MetaOptimization {
13  [Item("AlgorithmRunsAnalyzer", "")]
14  [StorableClass]
15  public class AlgorithmRunsAnalyzer : SingleSuccessorOperator {
16
17    #region Parameter properties
18    public ILookupParameter<DoubleValue> QualityParameter {
19      get { return (ILookupParameter<DoubleValue>)Parameters["Quality"]; }
20    }
21    public LookupParameter<IntValue> GenerationsParameter {
22      get { return (LookupParameter<IntValue>)Parameters["Generations"]; }
23    }
24    public LookupParameter<IntValue> RepetitionsParameter {
25      get { return (LookupParameter<IntValue>)Parameters[MetaOptimizationProblem.RepetitionsParameterName]; }
26    }
27    public ILookupParameter<ParameterConfigurationTree> ParameterConfigurationParameter {
28      get { return (ILookupParameter<ParameterConfigurationTree>)Parameters["ParameterConfigurationTree"]; }
29    }
30    public ILookupParameter<IItemList<IProblem>> ProblemsParameter {
31      get { return (ILookupParameter<IItemList<IProblem>>)Parameters[MetaOptimizationProblem.ProblemsParameterName]; }
32    }
33    public LookupParameter<DoubleArray> ReferenceQualityAveragesParameter {
34      get { return (LookupParameter<DoubleArray>)Parameters["ReferenceQualityAverages"]; }
35    }
36    public LookupParameter<DoubleArray> ReferenceQualityDeviationsParameter {
37      get { return (LookupParameter<DoubleArray>)Parameters["ReferenceQualityDeviations"]; }
38    }
39    public LookupParameter<DoubleArray> ReferenceEvaluatedSolutionAveragesParameter {
40      get { return (LookupParameter<DoubleArray>)Parameters["ReferenceEvaluatedSolutionAverages"]; }
41    }
42    public LookupParameter<ResultCollection> ResultsParameter {
43      get { return (LookupParameter<ResultCollection>)Parameters["Results"]; }
44    }
45    public ScopeTreeLookupParameter<IAlgorithm> AlgorithmParameter {
46      get { return (ScopeTreeLookupParameter<IAlgorithm>)Parameters["Algorithm"]; }
47    }
48    public ScopeTreeLookupParameter<IntValue> ProblemIndexParameter {
49      get { return (ScopeTreeLookupParameter<IntValue>)Parameters["ProblemIndex"]; }
50    }
51    public ScopeTreeLookupParameter<IntValue> RepetitionIndexParameter {
52      get { return (ScopeTreeLookupParameter<IntValue>)Parameters["RepetitionIndex"]; }
53    }
54    public LookupParameter<BoolValue> MaximizationParameter {
55      get { return (LookupParameter<BoolValue>)Parameters["Maximization"]; }
56    }
57    public LookupParameter<DoubleValue> QualityWeightParameter {
58      get { return (LookupParameter<DoubleValue>)Parameters[MetaOptimizationProblem.QualityWeightParameterName]; }
59    }
60    public LookupParameter<DoubleValue> StandardDeviationWeightParameter {
61      get { return (LookupParameter<DoubleValue>)Parameters[MetaOptimizationProblem.StandardDeviationWeightParameterName]; }
62    }
63    public LookupParameter<DoubleValue> EvaluatedSolutionsWeightParameter {
64      get { return (LookupParameter<DoubleValue>)Parameters[MetaOptimizationProblem.EvaluatedSolutionsWeightParameterName]; }
65    }
66    #endregion
67
68    [StorableConstructor]
69    protected AlgorithmRunsAnalyzer(bool deserializing) : base(deserializing) { }
70    public AlgorithmRunsAnalyzer()
71      : base() {
72      Parameters.Add(new LookupParameter<IRandom>("Random", "The pseudo random number generator which should be used to initialize the new random permutation."));
73      Parameters.Add(new LookupParameter<DoubleValue>("Quality", "The evaluated quality of the ParameterVector."));
74      Parameters.Add(new LookupParameter<IntValue>("Generations", ""));
75      Parameters.Add(new LookupParameter<IntValue>(MetaOptimizationProblem.RepetitionsParameterName, "Number of evaluations on one problem."));
76      Parameters.Add(new LookupParameter<ParameterConfigurationTree>("ParameterConfigurationTree", ""));
77      Parameters.Add(new LookupParameter<IItemList<IProblem>>(MetaOptimizationProblem.ProblemsParameterName, ""));
78      Parameters.Add(new LookupParameter<DoubleArray>("ReferenceQualityAverages", ""));
79      Parameters.Add(new LookupParameter<DoubleArray>("ReferenceQualityDeviations", ""));
80      Parameters.Add(new LookupParameter<DoubleArray>("ReferenceEvaluatedSolutionAverages", ""));
81      Parameters.Add(new LookupParameter<ResultCollection>("Results", ""));
82      Parameters.Add(new ScopeTreeLookupParameter<IAlgorithm>("Algorithm", "The finished algorithms containing Runs."));
83      Parameters.Add(new ScopeTreeLookupParameter<IntValue>("ProblemIndex", "The index of the problem an algorithm was executed with."));
84      Parameters.Add(new ScopeTreeLookupParameter<IntValue>("RepetitionIndex", "The index of the repetition"));
85      Parameters.Add(new LookupParameter<BoolValue>("Maximization", "Set to false if the problem should be minimized."));
86      Parameters.Add(new LookupParameter<DoubleValue>(MetaOptimizationProblem.QualityWeightParameterName));
87      Parameters.Add(new LookupParameter<DoubleValue>(MetaOptimizationProblem.StandardDeviationWeightParameterName));
88      Parameters.Add(new LookupParameter<DoubleValue>(MetaOptimizationProblem.EvaluatedSolutionsWeightParameterName));
89    }
90    protected AlgorithmRunsAnalyzer(AlgorithmRunsAnalyzer original, Cloner cloner)
91      : base(original, cloner) {
92    }
93    public override IDeepCloneable Clone(Cloner cloner) {
94      return new AlgorithmRunsAnalyzer(this, cloner);
95    }
96
97    public override IOperation Apply() {
98      ParameterConfigurationTree parameterConfiguration = ParameterConfigurationParameter.ActualValue;
99      ItemArray<IAlgorithm> algorithms = AlgorithmParameter.ActualValue;
100      ItemArray<IntValue> problemIndices = ProblemIndexParameter.ActualValue;
101      ItemArray<IntValue> repetitionIndices = RepetitionIndexParameter.ActualValue;
102      IEnumerable<string> parameterNames = parameterConfiguration.GetOptimizedParameterNames();
103      IItemList<IProblem> problems = ProblemsParameter.ActualValue;
104      bool maximization = MaximizationParameter.ActualValue.Value;
105      int repetitions = RepetitionsParameter.ActualValue.Value;
106      double qualityWeight = QualityWeightParameter.ActualValue.Value;
107      double standardDeviationWeight = StandardDeviationWeightParameter.ActualValue.Value;
108      double evaluatedSolutionsWeight = EvaluatedSolutionsWeightParameter.ActualValue.Value;
109      var resultNames = new List<string> { "BestQuality", "Execution Time", "EvaluatedSolutions" };
110      int currentGeneration = GenerationsParameter.ActualValue != null ? GenerationsParameter.ActualValue.Value : 0;
111      double[] referenceQualityAverages;
112      double[] referenceQualityDeviations;
113      double[] referenceEvaluatedSolutionAverages;
114      GetReferenceValues(problems.Count, out referenceQualityAverages, out referenceQualityDeviations, out referenceEvaluatedSolutionAverages);
115
116      ResultCollection results = ResultsParameter.ActualValue;
117
118      if (algorithms.All(x => x.Runs.Count == 1)) {
119        var runs = new RunCollection();
120        var qualities = new double[problems.Count][];
121        var executionTimes = new TimeSpan[problems.Count][];
122        var evaluatedSolutions = new int[problems.Count][];
123
124        for (int i = 0; i < problems.Count; i++) {
125          qualities[i] = new double[repetitions];
126          evaluatedSolutions[i] = new int[repetitions];
127          executionTimes[i] = new TimeSpan[repetitions];
128        }
129
130        for (int i = 0; i < algorithms.Length; i++) {
131          int problemIndex = problemIndices[i].Value;
132          int repetitionIndex = repetitionIndices[i].Value;
133
134          IRun run = algorithms[i].Runs.Single();
135          MetaOptimizationUtil.ClearResults(run, resultNames);
136          MetaOptimizationUtil.ClearParameters(run, parameterNames);
137          run.Results.Add("Meta.FromCache", new BoolValue(false));
138          run.Results.Add("Meta.Generation", new IntValue(currentGeneration));
139          run.Results.Add("Meta.ProblemIndex", new IntValue(problemIndex));
140          run.Name = string.Format("{0} Problem {1} Run {2}", parameterConfiguration.ParameterInfoString, problemIndex, repetitionIndex);
141          qualities[problemIndex][repetitionIndex] = (((DoubleValue)run.Results["BestQuality"]).Value);
142          executionTimes[problemIndex][repetitionIndex] = (((TimeSpanValue)run.Results["Execution Time"]).Value);
143          evaluatedSolutions[problemIndex][repetitionIndex] = (((IntValue)run.Results["EvaluatedSolutions"]).Value);
144          runs.Add((IRun)run.Clone());
145        }
146
147        parameterConfiguration.AverageExecutionTimes = new ItemList<TimeSpanValue>(executionTimes.Select(t => new TimeSpanValue(TimeSpan.FromMilliseconds(t.Average(ts => ts.TotalMilliseconds)))));
148        parameterConfiguration.AverageEvaluatedSolutions = new DoubleArray(evaluatedSolutions.Select(x => x.Average()).ToArray());
149        parameterConfiguration.Repetitions = new IntValue(repetitions);
150        parameterConfiguration.AverageQualities = new DoubleArray(qualities.Select(q => q.Average()).ToArray());
151
152        if (maximization)
153          parameterConfiguration.BestQualities = new DoubleArray(qualities.Select(q => q.Max()).ToArray());
154        else
155          parameterConfiguration.BestQualities = new DoubleArray(qualities.Select(q => q.Min()).ToArray());
156
157        if (maximization)
158          parameterConfiguration.WorstQualities = new DoubleArray(qualities.Select(q => q.Min()).ToArray());
159        else
160          parameterConfiguration.WorstQualities = new DoubleArray(qualities.Select(q => q.Max()).ToArray());
161
162        parameterConfiguration.QualityVariances = new DoubleArray(qualities.Select(q => q.Variance()).ToArray());
163        parameterConfiguration.QualityStandardDeviations = new DoubleArray(qualities.Select(q => q.StandardDeviation()).ToArray());
164        parameterConfiguration.Runs = runs;
165
166        this.QualityParameter.ActualValue = new DoubleValue(MetaOptimizationUtil.Normalize(parameterConfiguration, referenceQualityAverages, referenceQualityDeviations, referenceEvaluatedSolutionAverages, qualityWeight, standardDeviationWeight, evaluatedSolutionsWeight, maximization));
167      } else {
168        // something terrible happened -> most probably due to invalid parameters.
169        // penalty with worst quality from latest generation!
170        double penaltyValue;
171        if (maximization)
172          penaltyValue = results.ContainsKey("CurrentWorstQuality") ? ((DoubleValue)results["CurrentWorstQuality"].Value).Value : referenceQualityAverages.Min();
173        else
174          penaltyValue = results.ContainsKey("CurrentWorstQuality") ? ((DoubleValue)results["CurrentWorstQuality"].Value).Value : referenceQualityAverages.Max();
175       
176        this.QualityParameter.ActualValue = new DoubleValue(penaltyValue);
177        parameterConfiguration.Quality = new DoubleValue(penaltyValue);
178
179        parameterConfiguration.AverageExecutionTimes = new ItemList<TimeSpanValue>(Enumerable.Repeat(new TimeSpanValue(TimeSpan.Zero), problems.Count));
180        parameterConfiguration.AverageEvaluatedSolutions = new DoubleArray(Enumerable.Repeat(0.0, problems.Count).ToArray());
181        parameterConfiguration.Repetitions = new IntValue(repetitions);
182        parameterConfiguration.AverageQualities = new DoubleArray(Enumerable.Repeat(0.0, problems.Count).ToArray());
183        parameterConfiguration.BestQualities = new DoubleArray(Enumerable.Repeat(0.0, problems.Count).ToArray());
184        parameterConfiguration.WorstQualities = new DoubleArray(Enumerable.Repeat(0.0, problems.Count).ToArray());
185        parameterConfiguration.QualityVariances = new DoubleArray(Enumerable.Repeat(0.0, problems.Count).ToArray());
186        parameterConfiguration.QualityStandardDeviations = new DoubleArray(Enumerable.Repeat(0.0, problems.Count).ToArray());
187        parameterConfiguration.Runs = null;
188      }
189
190      return base.Apply();
191    }
192
193    private void GetReferenceValues(int problemsCount, out double[] referenceQualityAverages, out double[] referenceQualityDeviations, out double[] referenceEvaluatedSolutionAverages) {
194      if (ReferenceQualityAveragesParameter.ActualValue == null) {
195        // this is generation zero. no reference qualities for normalization have been calculated yet. in this special case the ReferenceQualityAnalyzer will do the normalization
196        referenceQualityAverages = new double[problemsCount];
197        referenceQualityDeviations = new double[problemsCount];
198        referenceEvaluatedSolutionAverages = new double[problemsCount];
199        for (int i = 0; i < referenceQualityAverages.Length; i++) {
200          referenceQualityAverages[i] = 1;
201          referenceQualityDeviations[i] = 1;
202          referenceEvaluatedSolutionAverages[i] = 1;
203        }
204      } else {
205        referenceQualityAverages = ReferenceQualityAveragesParameter.ActualValue.ToArray();
206        referenceQualityDeviations = ReferenceQualityDeviationsParameter.ActualValue.ToArray();
207        referenceEvaluatedSolutionAverages = ReferenceEvaluatedSolutionAveragesParameter.ActualValue.ToArray();
208      }
209    }
210  }
211}
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