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source: branches/DataAnalysis/HeuristicLab.Problems.DataAnalysis.Regression/3.3/Symbolic/Analyzers/FixedValidationBestScaledSymbolicRegressionSolutionAnalyzer.cs @ 4244

Last change on this file since 4244 was 4244, checked in by gkronber, 12 years ago

Use FastRandom instead of Mersenne twister for partial evaluation of training samples. #1082

File size: 20.6 KB
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1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2010 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
4 *
5 * This file is part of HeuristicLab.
6 *
7 * HeuristicLab is free software: you can redistribute it and/or modify
8 * it under the terms of the GNU General Public License as published by
9 * the Free Software Foundation, either version 3 of the License, or
10 * (at your option) any later version.
11 *
12 * HeuristicLab is distributed in the hope that it will be useful,
13 * but WITHOUT ANY WARRANTY; without even the implied warranty of
14 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
15 * GNU General Public License for more details.
16 *
17 * You should have received a copy of the GNU General Public License
18 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
19 */
20#endregion
21
22using System.Collections.Generic;
23using System.Linq;
24using HeuristicLab.Analysis;
25using HeuristicLab.Core;
26using HeuristicLab.Data;
27using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
28using HeuristicLab.Operators;
29using HeuristicLab.Optimization;
30using HeuristicLab.Parameters;
31using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
32using HeuristicLab.Problems.DataAnalysis.Evaluators;
33using HeuristicLab.Problems.DataAnalysis.Symbolic;
34using System;
35
36namespace HeuristicLab.Problems.DataAnalysis.Regression.Symbolic.Analyzers {
37  /// <summary>
38  /// An operator that analyzes the validation best scaled symbolic regression solution.
39  /// </summary>
40  [Item("FixedValidationBestScaledSymbolicRegressionSolutionAnalyzer", "An operator that analyzes the validation best scaled symbolic regression solution.")]
41  [StorableClass]
42  public sealed class FixedValidationBestScaledSymbolicRegressionSolutionAnalyzer : SingleSuccessorOperator, ISymbolicRegressionAnalyzer {
43    private const string RandomParameterName = "Random";
44    private const string SymbolicExpressionTreeParameterName = "SymbolicExpressionTree";
45    private const string SymbolicExpressionTreeInterpreterParameterName = "SymbolicExpressionTreeInterpreter";
46    private const string ProblemDataParameterName = "ProblemData";
47    private const string ValidationSamplesStartParameterName = "SamplesStart";
48    private const string ValidationSamplesEndParameterName = "SamplesEnd";
49    // private const string QualityParameterName = "Quality";
50    private const string UpperEstimationLimitParameterName = "UpperEstimationLimit";
51    private const string LowerEstimationLimitParameterName = "LowerEstimationLimit";
52    private const string EvaluatorParameterName = "Evaluator";
53    private const string MaximizationParameterName = "Maximization";
54    private const string BestSolutionParameterName = "Best solution (validation)";
55    private const string BestSolutionQualityParameterName = "Best solution quality (validation)";
56    private const string CurrentBestValidationQualityParameterName = "Current best validation quality";
57    private const string BestSolutionQualityValuesParameterName = "Validation Quality";
58    private const string ResultsParameterName = "Results";
59    private const string VariableFrequenciesParameterName = "VariableFrequencies";
60    private const string BestKnownQualityParameterName = "BestKnownQuality";
61    private const string GenerationsParameterName = "Generations";
62    private const string RelativeNumberOfEvaluatedSamplesParameterName = "RelativeNumberOfEvaluatedSamples";
63
64    private const string TrainingMeanSquaredErrorQualityParameterName = "Mean squared error (training)";
65    private const string MinTrainingMeanSquaredErrorQualityParameterName = "Min mean squared error (training)";
66    private const string MaxTrainingMeanSquaredErrorQualityParameterName = "Max mean squared error (training)";
67    private const string AverageTrainingMeanSquaredErrorQualityParameterName = "Average mean squared error (training)";
68    private const string BestTrainingMeanSquaredErrorQualityParameterName = "Best mean squared error (training)";
69
70    private const string TrainingAverageRelativeErrorQualityParameterName = "Average relative error (training)";
71    private const string MinTrainingAverageRelativeErrorQualityParameterName = "Min average relative error (training)";
72    private const string MaxTrainingAverageRelativeErrorQualityParameterName = "Max average relative error (training)";
73    private const string AverageTrainingAverageRelativeErrorQualityParameterName = "Average average relative error (training)";
74    private const string BestTrainingAverageRelativeErrorQualityParameterName = "Best average relative error (training)";
75
76    private const string TrainingRSquaredQualityParameterName = "R² (training)";
77    private const string MinTrainingRSquaredQualityParameterName = "Min R² (training)";
78    private const string MaxTrainingRSquaredQualityParameterName = "Max R² (training)";
79    private const string AverageTrainingRSquaredQualityParameterName = "Average R² (training)";
80    private const string BestTrainingRSquaredQualityParameterName = "Best R² (training)";
81
82    private const string TestMeanSquaredErrorQualityParameterName = "Mean squared error (test)";
83    private const string MinTestMeanSquaredErrorQualityParameterName = "Min mean squared error (test)";
84    private const string MaxTestMeanSquaredErrorQualityParameterName = "Max mean squared error (test)";
85    private const string AverageTestMeanSquaredErrorQualityParameterName = "Average mean squared error (test)";
86    private const string BestTestMeanSquaredErrorQualityParameterName = "Best mean squared error (test)";
87
88    private const string TestAverageRelativeErrorQualityParameterName = "Average relative error (test)";
89    private const string MinTestAverageRelativeErrorQualityParameterName = "Min average relative error (test)";
90    private const string MaxTestAverageRelativeErrorQualityParameterName = "Max average relative error (test)";
91    private const string AverageTestAverageRelativeErrorQualityParameterName = "Average average relative error (test)";
92    private const string BestTestAverageRelativeErrorQualityParameterName = "Best average relative error (test)";
93
94    private const string TestRSquaredQualityParameterName = "R² (test)";
95    private const string MinTestRSquaredQualityParameterName = "Min R² (test)";
96    private const string MaxTestRSquaredQualityParameterName = "Max R² (test)";
97    private const string AverageTestRSquaredQualityParameterName = "Average R² (test)";
98    private const string BestTestRSquaredQualityParameterName = "Best R² (test)";
99
100    private const string RSquaredValuesParameterName = "R²";
101    private const string MeanSquaredErrorValuesParameterName = "Mean squared error";
102    private const string RelativeErrorValuesParameterName = "Average relative error";
103
104    #region parameter properties
105    public ILookupParameter<IRandom> RandomParameter {
106      get { return (ILookupParameter<IRandom>)Parameters[RandomParameterName]; }
107    }
108    public ScopeTreeLookupParameter<SymbolicExpressionTree> SymbolicExpressionTreeParameter {
109      get { return (ScopeTreeLookupParameter<SymbolicExpressionTree>)Parameters[SymbolicExpressionTreeParameterName]; }
110    }
111    public IValueLookupParameter<ISymbolicExpressionTreeInterpreter> SymbolicExpressionTreeInterpreterParameter {
112      get { return (IValueLookupParameter<ISymbolicExpressionTreeInterpreter>)Parameters[SymbolicExpressionTreeInterpreterParameterName]; }
113    }
114    public ILookupParameter<ISymbolicRegressionEvaluator> EvaluatorParameter {
115      get { return (ILookupParameter<ISymbolicRegressionEvaluator>)Parameters[EvaluatorParameterName]; }
116    }
117    public ILookupParameter<BoolValue> MaximizationParameter {
118      get { return (ILookupParameter<BoolValue>)Parameters[MaximizationParameterName]; }
119    }
120    public IValueLookupParameter<DataAnalysisProblemData> ProblemDataParameter {
121      get { return (IValueLookupParameter<DataAnalysisProblemData>)Parameters[ProblemDataParameterName]; }
122    }
123    public IValueLookupParameter<IntValue> ValidationSamplesStartParameter {
124      get { return (IValueLookupParameter<IntValue>)Parameters[ValidationSamplesStartParameterName]; }
125    }
126    public IValueLookupParameter<IntValue> ValidationSamplesEndParameter {
127      get { return (IValueLookupParameter<IntValue>)Parameters[ValidationSamplesEndParameterName]; }
128    }
129    public IValueParameter<PercentValue> RelativeNumberOfEvaluatedSamplesParameter {
130      get { return (IValueParameter<PercentValue>)Parameters[RelativeNumberOfEvaluatedSamplesParameterName]; }
131    }
132
133    public IValueLookupParameter<DoubleValue> UpperEstimationLimitParameter {
134      get { return (IValueLookupParameter<DoubleValue>)Parameters[UpperEstimationLimitParameterName]; }
135    }
136    public IValueLookupParameter<DoubleValue> LowerEstimationLimitParameter {
137      get { return (IValueLookupParameter<DoubleValue>)Parameters[LowerEstimationLimitParameterName]; }
138    }
139    public ILookupParameter<SymbolicRegressionSolution> BestSolutionParameter {
140      get { return (ILookupParameter<SymbolicRegressionSolution>)Parameters[BestSolutionParameterName]; }
141    }
142    public ILookupParameter<IntValue> GenerationsParameter {
143      get { return (ILookupParameter<IntValue>)Parameters[GenerationsParameterName]; }
144    }
145    public ILookupParameter<DoubleValue> BestSolutionQualityParameter {
146      get { return (ILookupParameter<DoubleValue>)Parameters[BestSolutionQualityParameterName]; }
147    }
148    public ILookupParameter<ResultCollection> ResultsParameter {
149      get { return (ILookupParameter<ResultCollection>)Parameters[ResultsParameterName]; }
150    }
151    public ILookupParameter<DoubleValue> BestKnownQualityParameter {
152      get { return (ILookupParameter<DoubleValue>)Parameters[BestKnownQualityParameterName]; }
153    }
154    public ILookupParameter<DoubleValue> CurrentBestValidationQualityParameter {
155      get { return (ILookupParameter<DoubleValue>)Parameters[CurrentBestValidationQualityParameterName]; }
156    }
157
158    public ILookupParameter<DataTable> VariableFrequenciesParameter {
159      get { return (ILookupParameter<DataTable>)Parameters[VariableFrequenciesParameterName]; }
160    }
161
162    #endregion
163    #region properties
164    public IRandom Random {
165      get { return RandomParameter.ActualValue; }
166    }
167    public ItemArray<SymbolicExpressionTree> SymbolicExpressionTree {
168      get { return SymbolicExpressionTreeParameter.ActualValue; }
169    }
170    public ISymbolicExpressionTreeInterpreter SymbolicExpressionTreeInterpreter {
171      get { return SymbolicExpressionTreeInterpreterParameter.ActualValue; }
172    }
173    public ISymbolicRegressionEvaluator Evaluator {
174      get { return EvaluatorParameter.ActualValue; }
175    }
176    public BoolValue Maximization {
177      get { return MaximizationParameter.ActualValue; }
178    }
179    public DataAnalysisProblemData ProblemData {
180      get { return ProblemDataParameter.ActualValue; }
181    }
182    public IntValue ValidiationSamplesStart {
183      get { return ValidationSamplesStartParameter.ActualValue; }
184    }
185    public IntValue ValidationSamplesEnd {
186      get { return ValidationSamplesEndParameter.ActualValue; }
187    }
188    public PercentValue RelativeNumberOfEvaluatedSamples {
189      get { return RelativeNumberOfEvaluatedSamplesParameter.Value; }
190    }
191
192    public DoubleValue UpperEstimationLimit {
193      get { return UpperEstimationLimitParameter.ActualValue; }
194    }
195    public DoubleValue LowerEstimationLimit {
196      get { return LowerEstimationLimitParameter.ActualValue; }
197    }
198    public ResultCollection Results {
199      get { return ResultsParameter.ActualValue; }
200    }
201    public DataTable VariableFrequencies {
202      get { return VariableFrequenciesParameter.ActualValue; }
203    }
204    public IntValue Generations {
205      get { return GenerationsParameter.ActualValue; }
206    }
207    public DoubleValue BestSolutionQuality {
208      get { return BestSolutionQualityParameter.ActualValue; }
209    }
210
211    #endregion
212
213    public FixedValidationBestScaledSymbolicRegressionSolutionAnalyzer()
214      : base() {
215      Parameters.Add(new LookupParameter<IRandom>(RandomParameterName, "The random generator to use."));
216      Parameters.Add(new LookupParameter<ISymbolicRegressionEvaluator>(EvaluatorParameterName, "The evaluator which should be used to evaluate the solution on the validation set."));
217      Parameters.Add(new ScopeTreeLookupParameter<SymbolicExpressionTree>(SymbolicExpressionTreeParameterName, "The symbolic expression trees to analyze."));
218      Parameters.Add(new LookupParameter<BoolValue>(MaximizationParameterName, "The direction of optimization."));
219      Parameters.Add(new ValueLookupParameter<ISymbolicExpressionTreeInterpreter>(SymbolicExpressionTreeInterpreterParameterName, "The interpreter that should be used for the analysis of symbolic expression trees."));
220      Parameters.Add(new ValueLookupParameter<DataAnalysisProblemData>(ProblemDataParameterName, "The problem data for which the symbolic expression tree is a solution."));
221      Parameters.Add(new ValueLookupParameter<IntValue>(ValidationSamplesStartParameterName, "The first index of the validation partition of the data set."));
222      Parameters.Add(new ValueLookupParameter<IntValue>(ValidationSamplesEndParameterName, "The last index of the validation partition of the data set."));
223      Parameters.Add(new ValueParameter<PercentValue>(RelativeNumberOfEvaluatedSamplesParameterName, "The relative number of samples of the dataset partition, which should be randomly chosen for evaluation between the start and end index.", new PercentValue(1)));
224      Parameters.Add(new ValueLookupParameter<DoubleValue>(UpperEstimationLimitParameterName, "The upper estimation limit that was set for the evaluation of the symbolic expression trees."));
225      Parameters.Add(new ValueLookupParameter<DoubleValue>(LowerEstimationLimitParameterName, "The lower estimation limit that was set for the evaluation of the symbolic expression trees."));
226      Parameters.Add(new LookupParameter<SymbolicRegressionSolution>(BestSolutionParameterName, "The best symbolic regression solution."));
227      Parameters.Add(new LookupParameter<IntValue>(GenerationsParameterName, "The number of generations calculated so far."));
228      Parameters.Add(new LookupParameter<DoubleValue>(BestSolutionQualityParameterName, "The quality of the best symbolic regression solution."));
229      Parameters.Add(new LookupParameter<ResultCollection>(ResultsParameterName, "The result collection where the best symbolic regression solution should be stored."));
230      Parameters.Add(new LookupParameter<DoubleValue>(BestKnownQualityParameterName, "The best known (validation) quality achieved on the data set."));
231      Parameters.Add(new LookupParameter<DoubleValue>(CurrentBestValidationQualityParameterName, "The quality of the best solution (on the validation set) of the current generation."));
232      Parameters.Add(new LookupParameter<DataTable>(VariableFrequenciesParameterName, "The variable frequencies table to use for the calculation of variable impacts"));
233    }
234
235    [StorableConstructor]
236    private FixedValidationBestScaledSymbolicRegressionSolutionAnalyzer(bool deserializing) : base() { }
237
238    [StorableHook(HookType.AfterDeserialization)]
239    private void AfterDeserialization() {
240      #region compatibility remove before releasing 3.3.1
241      if (!Parameters.ContainsKey(EvaluatorParameterName)) {
242        Parameters.Add(new LookupParameter<ISymbolicRegressionEvaluator>(EvaluatorParameterName, "The evaluator which should be used to evaluate the solution on the validation set."));
243      }
244      if (!Parameters.ContainsKey(MaximizationParameterName)) {
245        Parameters.Add(new LookupParameter<BoolValue>(MaximizationParameterName, "The direction of optimization."));
246      }
247      #endregion
248    }
249
250    public override IOperation Apply() {
251      var trees = SymbolicExpressionTree;
252
253      string targetVariable = ProblemData.TargetVariable.Value;
254
255      // select a random subset of rows in the validation set
256      int validationStart = ValidiationSamplesStart.Value;
257      int validationEnd = ValidationSamplesEnd.Value;
258      int seed = Random.Next();
259      int count = (int)((validationEnd - validationStart) * RelativeNumberOfEvaluatedSamples.Value);
260      if (count == 0) count = 1;
261      IEnumerable<int> rows = RandomEnumerable.SampleRandomNumbers(seed, validationStart, validationEnd, count);
262
263      double upperEstimationLimit = UpperEstimationLimit != null ? UpperEstimationLimit.Value : double.PositiveInfinity;
264      double lowerEstimationLimit = LowerEstimationLimit != null ? LowerEstimationLimit.Value : double.NegativeInfinity;
265
266      double bestQuality = Maximization.Value ? double.NegativeInfinity : double.PositiveInfinity;
267      SymbolicExpressionTree bestTree = null;
268
269      foreach (var tree in trees) {
270        double quality = Evaluator.Evaluate(SymbolicExpressionTreeInterpreter, tree,
271          lowerEstimationLimit, upperEstimationLimit,
272          ProblemData.Dataset, targetVariable,
273         rows);
274
275        if ((Maximization.Value && quality > bestQuality) ||
276            (!Maximization.Value && quality < bestQuality)) {
277          bestQuality = quality;
278          bestTree = tree;
279        }
280      }
281
282      // if the best validation tree is better than the current best solution => update
283      bool newBest =
284        BestSolutionQuality == null ||
285        (Maximization.Value && bestQuality > BestSolutionQuality.Value) ||
286        (!Maximization.Value && bestQuality < BestSolutionQuality.Value);
287      if (newBest) {
288        // calculate scaling parameters and only for the best tree using the full training set
289        double alpha, beta;
290        int trainingStart = ProblemData.TrainingSamplesStart.Value;
291        int trainingEnd = ProblemData.TrainingSamplesEnd.Value;
292        IEnumerable<int> trainingRows = Enumerable.Range(trainingStart, trainingEnd - trainingStart);
293        IEnumerable<double> originalValues = ProblemData.Dataset.GetEnumeratedVariableValues(targetVariable, trainingRows);
294        IEnumerable<double> estimatedValues = SymbolicExpressionTreeInterpreter.GetSymbolicExpressionTreeValues(bestTree, ProblemData.Dataset, trainingRows);
295
296        SymbolicRegressionScaledMeanSquaredErrorEvaluator.CalculateScalingParameters(originalValues, estimatedValues, out beta, out alpha);
297
298        // scale tree for solution
299        var scaledTree = SymbolicRegressionSolutionLinearScaler.Scale(bestTree, alpha, beta);
300        var model = new SymbolicRegressionModel((ISymbolicExpressionTreeInterpreter)SymbolicExpressionTreeInterpreter.Clone(),
301          scaledTree);
302        var solution = new SymbolicRegressionSolution(ProblemData, model, lowerEstimationLimit, upperEstimationLimit);
303        solution.Name = BestSolutionParameterName;
304        solution.Description = "Best solution on validation partition found over the whole run.";
305
306        BestSolutionParameter.ActualValue = solution;
307        BestSolutionQualityParameter.ActualValue = new DoubleValue(bestQuality);
308
309        BestSymbolicRegressionSolutionAnalyzer.UpdateBestSolutionResults(solution, ProblemData, Results, Generations, VariableFrequencies);
310      }
311
312      CurrentBestValidationQualityParameter.ActualValue = new DoubleValue(bestQuality);
313
314      if (!Results.ContainsKey(BestSolutionQualityValuesParameterName)) {
315        Results.Add(new Result(BestSolutionQualityValuesParameterName, new DataTable(BestSolutionQualityValuesParameterName, BestSolutionQualityValuesParameterName)));
316        Results.Add(new Result(BestSolutionQualityParameterName, new DoubleValue()));
317        Results.Add(new Result(CurrentBestValidationQualityParameterName, new DoubleValue()));
318      }
319      Results[BestSolutionQualityParameterName].Value = new DoubleValue(BestSolutionQualityParameter.ActualValue.Value);
320      Results[CurrentBestValidationQualityParameterName].Value = new DoubleValue(bestQuality);
321
322      DataTable validationValues = (DataTable)Results[BestSolutionQualityValuesParameterName].Value;
323      AddValue(validationValues, BestSolutionQualityParameter.ActualValue.Value, BestSolutionQualityParameterName, BestSolutionQualityParameterName);
324      AddValue(validationValues, bestQuality, CurrentBestValidationQualityParameterName, CurrentBestValidationQualityParameterName);
325      return base.Apply();
326    }
327
328    [StorableHook(HookType.AfterDeserialization)]
329    private void Initialize() { }
330
331    private static void AddValue(DataTable table, double data, string name, string description) {
332      DataRow row;
333      table.Rows.TryGetValue(name, out row);
334      if (row == null) {
335        row = new DataRow(name, description);
336        row.Values.Add(data);
337        table.Rows.Add(row);
338      } else {
339        row.Values.Add(data);
340      }
341    }
342  }
343}
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