source: branches/2886_SymRegGrammarEnumeration/HeuristicLab.Algorithms.DataAnalysis.SymRegGrammarEnumeration/Analysis/RSquaredEvaluator.cs @ 15824

using System;
using HeuristicLab.Algorithms.DataAnalysis.SymRegGrammarEnumeration.GrammarEnumeration;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
using HeuristicLab.Optimization;
using HeuristicLab.Problems.DataAnalysis;
using HeuristicLab.Problems.DataAnalysis.Symbolic;
using HeuristicLab.Problems.DataAnalysis.Symbolic.Regression;

namespace HeuristicLab.Algorithms.DataAnalysis.SymRegGrammarEnumeration {
  public class RSquaredEvaluator : Item, IGrammarEnumerationAnalyzer {
    private readonly string BestTrainingQualityResultName = "Best R² (Training)";
    private readonly string BestTrainingModelResultName = "Best model (Training)";
    private readonly string BestTrainingSolutionResultName = "Best solution (Training)";

    private readonly ISymbolicDataAnalysisExpressionTreeInterpreter expressionTreeLinearInterpreter = new SymbolicDataAnalysisExpressionTreeLinearInterpreter();

    public RSquaredEvaluator() { }

    protected RSquaredEvaluator(RSquaredEvaluator original, Cloner cloner) { }

    public override IDeepCloneable Clone(Cloner cloner) {
      return new RSquaredEvaluator(this, cloner);
    }

    public void Register(GrammarEnumerationAlgorithm algorithm) {
      algorithm.Started += OnStarted;
      algorithm.Stopped += OnStopped;

      algorithm.DistinctSentenceGenerated += AlgorithmOnDistinctSentenceGenerated;
    }

    public void Deregister(GrammarEnumerationAlgorithm algorithm) {
      algorithm.Started -= OnStarted;
      algorithm.Stopped -= OnStopped;

      algorithm.DistinctSentenceGenerated -= AlgorithmOnDistinctSentenceGenerated;
    }

    private void AlgorithmOnDistinctSentenceGenerated(object sender, PhraseAddedEventArgs phraseAddedEventArgs) {
      GrammarEnumerationAlgorithm algorithm = (GrammarEnumerationAlgorithm)sender;
      EvaluateSentence(algorithm, phraseAddedEventArgs.NewPhrase);
    }

    private void OnStarted(object sender, EventArgs eventArgs) {
      GrammarEnumerationAlgorithm algorithm = (GrammarEnumerationAlgorithm)sender;
      algorithm.Results.Add(new Result(BestTrainingQualityResultName, new DoubleValue(-1.0)));

      algorithm.BestTrainingSentence = null;
    }

    private void OnStopped(object sender, EventArgs eventArgs) {
      GrammarEnumerationAlgorithm algorithm = (GrammarEnumerationAlgorithm)sender;
      if (algorithm.Results.ContainsKey(BestTrainingModelResultName)) {
        SymbolicRegressionModel model = (SymbolicRegressionModel)algorithm.Results[BestTrainingModelResultName].Value;
        IRegressionSolution bestTrainingSolution = new RegressionSolution(model, algorithm.Problem.ProblemData);

        algorithm.Results.AddOrUpdateResult(BestTrainingSolutionResultName, bestTrainingSolution);
      }
    }

    private void EvaluateSentence(GrammarEnumerationAlgorithm algorithm, SymbolString symbolString) {
      SymbolicExpressionTree tree = algorithm.Grammar.ParseSymbolicExpressionTree(symbolString);

      var problemData = algorithm.Problem.ProblemData;
      SymbolicRegressionModel model = new SymbolicRegressionModel(
        problemData.TargetVariable,
        tree,
        expressionTreeLinearInterpreter);

      var target = problemData.TargetVariableTrainingValues;
      var estVals = model.GetEstimatedValues(problemData.Dataset, problemData.TrainingIndices);
      OnlineCalculatorError error;
      var r2 = OnlinePearsonsRCalculator.Calculate(target, estVals, out error);
      if (error != OnlineCalculatorError.None) r2 = 0.0;

      var bestR2Result = (DoubleValue)algorithm.Results[BestTrainingQualityResultName].Value;
      if (r2 > bestR2Result.Value) {
        bestR2Result.Value = r2;
        algorithm.Results.AddOrUpdateResult(BestTrainingModelResultName, model);

        algorithm.BestTrainingSentence = symbolString;
      }
    }
  }
}