source: branches/3136_Structural_GP/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/StructuredSymbolicRegressionSingleObjectiveProblem.cs @ 18081

using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
using HeuristicLab.Core;
using HeuristicLab.Optimization;
using HEAL.Attic;
using HeuristicLab.Common;
using HeuristicLab.Problems.Instances;
using HeuristicLab.Parameters;
using HeuristicLab.Data;
using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
using HeuristicLab.PluginInfrastructure;

namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
  [StorableType("7464E84B-65CC-440A-91F0-9FA920D730F9")]
  [Item(Name = "Structured Symbolic Regression Single Objective Problem (single-objective)", Description = "A problem with a structural definition and unfixed subfunctions.")]
  [Creatable(CreatableAttribute.Categories.GeneticProgrammingProblems, Priority = 150)]
  public class StructuredSymbolicRegressionSingleObjectiveProblem : SingleObjectiveBasicProblem<MultiEncoding>, IRegressionProblem, IProblemInstanceConsumer<IRegressionProblemData> {

    #region Constants
    private const string TreeEvaluatorParameterName = "TreeEvaluator";
    private const string ProblemDataParameterName = "ProblemData";
    private const string StructureTemplateParameterName = "Structure Template";
    private const string InterpreterParameterName = "Interpreter";
    private const string EstimationLimitsParameterName = "EstimationLimits";
    private const string BestTrainingSolutionParameterName = "Best Training Solution";

    private const string SymbolicExpressionTreeName = "SymbolicExpressionTree";

    private const string StructureTemplateDescriptionText =
      "Enter your expression as string in infix format into the empty input field.\n" +
      "By checking the \"Apply Linear Scaling\" checkbox you can add the relevant scaling terms to your expression.\n" +
      "After entering the expression click parse to build the tree.\n" +
      "To edit the defined sub-functions, click on the coressponding colored node in the tree view.";
    #endregion

    #region Parameters
    public IConstrainedValueParameter<SymbolicRegressionSingleObjectiveEvaluator> TreeEvaluatorParameter => (IConstrainedValueParameter<SymbolicRegressionSingleObjectiveEvaluator>)Parameters[TreeEvaluatorParameterName];
    public IValueParameter<IRegressionProblemData> ProblemDataParameter => (IValueParameter<IRegressionProblemData>)Parameters[ProblemDataParameterName];
    public IFixedValueParameter<StructureTemplate> StructureTemplateParameter => (IFixedValueParameter<StructureTemplate>)Parameters[StructureTemplateParameterName];
    public IValueParameter<ISymbolicDataAnalysisExpressionTreeInterpreter> InterpreterParameter => (IValueParameter<ISymbolicDataAnalysisExpressionTreeInterpreter>)Parameters[InterpreterParameterName];
    public IFixedValueParameter<DoubleLimit> EstimationLimitsParameter => (IFixedValueParameter<DoubleLimit>)Parameters[EstimationLimitsParameterName];
    public IResultParameter<ISymbolicRegressionSolution> BestTrainingSolutionParameter => (IResultParameter<ISymbolicRegressionSolution>)Parameters[BestTrainingSolutionParameterName];
    #endregion

    #region Properties

    public IRegressionProblemData ProblemData {
      get => ProblemDataParameter.Value;
      set {
        ProblemDataParameter.Value = value;
        ProblemDataChanged?.Invoke(this, EventArgs.Empty);
      }
    }

    public StructureTemplate StructureTemplate => StructureTemplateParameter.Value;

    public ISymbolicDataAnalysisExpressionTreeInterpreter Interpreter => InterpreterParameter.Value;

    IParameter IDataAnalysisProblem.ProblemDataParameter => ProblemDataParameter;
    IDataAnalysisProblemData IDataAnalysisProblem.ProblemData => ProblemData;

    public DoubleLimit EstimationLimits => EstimationLimitsParameter.Value;

    public override bool Maximization => false;
    #endregion

    #region EventHandlers
    public event EventHandler ProblemDataChanged;
    #endregion

    #region Constructors & Cloning
    public StructuredSymbolicRegressionSingleObjectiveProblem() {
      var problemData = new ShapeConstrainedRegressionProblemData();
      var targetInterval = problemData.VariableRanges.GetInterval(problemData.TargetVariable);
      var estimationWidth = targetInterval.Width * 10;


      var structureTemplate = new StructureTemplate();
      structureTemplate.Changed += OnTemplateChanged;

      var evaluators = new ItemSet<SymbolicRegressionSingleObjectiveEvaluator>(
        ApplicationManager.Manager.GetInstances<SymbolicRegressionSingleObjectiveEvaluator>()
        .Where(x => x.Maximization == Maximization));

      Parameters.Add(new ConstrainedValueParameter<SymbolicRegressionSingleObjectiveEvaluator>(
        TreeEvaluatorParameterName, 
        evaluators,
        evaluators.First()));

      Parameters.Add(new ValueParameter<IRegressionProblemData>(
        ProblemDataParameterName,
        problemData));
      ProblemDataParameter.ValueChanged += ProblemDataParameterValueChanged;

      Parameters.Add(new FixedValueParameter<StructureTemplate>(
        StructureTemplateParameterName,
        StructureTemplateDescriptionText,
        structureTemplate));
      
      Parameters.Add(new ValueParameter<ISymbolicDataAnalysisExpressionTreeInterpreter>(
        InterpreterParameterName,
        new SymbolicDataAnalysisExpressionTreeInterpreter()) { Hidden = true });
      
      Parameters.Add(new FixedValueParameter<DoubleLimit>(
        EstimationLimitsParameterName,
        new DoubleLimit(targetInterval.LowerBound - estimationWidth, targetInterval.UpperBound + estimationWidth)));
      EstimationLimitsParameter.Hidden = true;

      Parameters.Add(new ResultParameter<ISymbolicRegressionSolution>(BestTrainingSolutionParameterName, ""));
      this.BestTrainingSolutionParameter.Hidden = true;

      this.EvaluatorParameter.Hidden = true;

     

      Operators.Add(new SymbolicDataAnalysisVariableFrequencyAnalyzer());
      Operators.Add(new MinAverageMaxSymbolicExpressionTreeLengthAnalyzer());
      //TODO change to value lookup
      //Operators.Add(new SymbolicExpressionTreeLengthAnalyzer());
      Operators.Add(new SymbolicExpressionSymbolFrequencyAnalyzer());

    }

    public StructuredSymbolicRegressionSingleObjectiveProblem(StructuredSymbolicRegressionSingleObjectiveProblem original,
      Cloner cloner) : base(original, cloner) { }

    [StorableConstructor]
    protected StructuredSymbolicRegressionSingleObjectiveProblem(StorableConstructorFlag _) : base(_) { }
    #endregion

    #region Cloning
    public override IDeepCloneable Clone(Cloner cloner) =>
      new StructuredSymbolicRegressionSingleObjectiveProblem(this, cloner);
    #endregion

    private void ProblemDataParameterValueChanged(object sender, EventArgs e) {
      StructureTemplate.Reset();
      // InfoBox for Reset?
    }

    private void OnTemplateChanged(object sender, EventArgs args) {
      SetupStructureTemplate();
    }

    private void SetupStructureTemplate() {
      foreach (var e in Encoding.Encodings.ToArray())
        Encoding.Remove(e);

      foreach (var f in StructureTemplate.SubFunctions.Values) {
        SetupVariables(f);
        if (!Encoding.Encodings.Any(x => x.Name == f.Name)) // to prevent the same encoding twice
          Encoding.Add(new SymbolicExpressionTreeEncoding(
            f.Name,
            f.Grammar,
            f.MaximumSymbolicExpressionTreeLength,
            f.MaximumSymbolicExpressionTreeDepth));
      }
    }

    public override void Analyze(Individual[] individuals, double[] qualities, ResultCollection results, IRandom random) {
      base.Analyze(individuals, qualities, results, random);

      var orderedIndividuals = individuals.Zip(qualities, (i, q) => new { Individual = i, Quality = q }).OrderBy(z => z.Quality);
      var best = Maximization ? orderedIndividuals.Last().Individual : orderedIndividuals.First().Individual;

      if (!results.ContainsKey(BestTrainingSolutionParameter.ActualName)) {
        results.Add(new Result(BestTrainingSolutionParameter.ActualName, typeof(SymbolicRegressionSolution)));
      }

      var tree = (ISymbolicExpressionTree)best[SymbolicExpressionTreeName];

      var model = new SymbolicRegressionModel(ProblemData.TargetVariable, tree, Interpreter);
      var solution = model.CreateRegressionSolution(ProblemData);

      results[BestTrainingSolutionParameter.ActualName].Value = solution;
    }


    public override double Evaluate(Individual individual, IRandom random) {
      var tree = BuildTree(individual);

      if (StructureTemplate.ApplyLinearScaling)
        AdjustLinearScalingParams(ProblemData, tree, Interpreter);

      individual[SymbolicExpressionTreeName] = tree;

      //TreeEvaluatorParameter.Value.EstimationLimitsParameter.ActualValue = EstimationLimits;
      //TreeEvaluatorParameter.Value.EstimationLimitsParameter.Value = EstimationLimits;
      //var quality = TreeEvaluatorParameter.Value.Evaluate(new ExecutionContext(null, this, new Scope("Test")), tree, ProblemData, ProblemData.TrainingIndices);

      var quality = double.MaxValue;
      var evaluatorGUID = TreeEvaluatorParameter.Value.GetType().GUID;

      // TODO: use Evaluate method instead of static Calculate -> a fake ExecutionContext is needed
      if (evaluatorGUID == typeof(NMSESingleObjectiveConstraintsEvaluator).GUID) {
        quality = NMSESingleObjectiveConstraintsEvaluator.Calculate(
        Interpreter, tree,
        EstimationLimits.Lower, EstimationLimits.Upper,
        ProblemData, ProblemData.TrainingIndices, new IntervalArithBoundsEstimator());
      } else if (evaluatorGUID == typeof(SymbolicRegressionLogResidualEvaluator).GUID) {
        quality = SymbolicRegressionLogResidualEvaluator.Calculate(
        Interpreter, tree,
        EstimationLimits.Lower, EstimationLimits.Upper,
        ProblemData, ProblemData.TrainingIndices);
      } else if (evaluatorGUID == typeof(SymbolicRegressionMeanRelativeErrorEvaluator).GUID) {
        quality = SymbolicRegressionMeanRelativeErrorEvaluator.Calculate(
        Interpreter, tree,
        EstimationLimits.Lower, EstimationLimits.Upper,
        ProblemData, ProblemData.TrainingIndices);
      } else if (evaluatorGUID == typeof(SymbolicRegressionSingleObjectiveMaxAbsoluteErrorEvaluator).GUID) {
        quality = SymbolicRegressionSingleObjectiveMaxAbsoluteErrorEvaluator.Calculate(
        Interpreter, tree,
        EstimationLimits.Lower, EstimationLimits.Upper,
        ProblemData, ProblemData.TrainingIndices, false);
      } else if (evaluatorGUID == typeof(SymbolicRegressionSingleObjectiveMeanAbsoluteErrorEvaluator).GUID) {
        quality = SymbolicRegressionSingleObjectiveMeanAbsoluteErrorEvaluator.Calculate(
        Interpreter, tree,
        EstimationLimits.Lower, EstimationLimits.Upper,
        ProblemData, ProblemData.TrainingIndices, false);
      } else { // SymbolicRegressionSingleObjectiveMeanSquaredErrorEvaluator
        quality = SymbolicRegressionSingleObjectiveMeanSquaredErrorEvaluator.Calculate(
        Interpreter, tree,
        EstimationLimits.Lower, EstimationLimits.Upper,
        ProblemData, ProblemData.TrainingIndices, false);
      }
    
      return quality;
    }

    private static void AdjustLinearScalingParams(IRegressionProblemData problemData, ISymbolicExpressionTree tree, ISymbolicDataAnalysisExpressionTreeInterpreter interpreter) {
      var offsetNode = tree.Root.GetSubtree(0).GetSubtree(0);
      var scalingNode = offsetNode.Subtrees.Where(x => !(x is ConstantTreeNode)).First();

      var offsetConstantNode = (ConstantTreeNode)offsetNode.Subtrees.Where(x => x is ConstantTreeNode).First();
      var scalingConstantNode = (ConstantTreeNode)scalingNode.Subtrees.Where(x => x is ConstantTreeNode).First();

      var estimatedValues = interpreter.GetSymbolicExpressionTreeValues(tree, problemData.Dataset, problemData.TrainingIndices);
      var targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, problemData.TrainingIndices);

      OnlineLinearScalingParameterCalculator.Calculate(estimatedValues, targetValues, out double a, out double b, out OnlineCalculatorError error);
      if (error == OnlineCalculatorError.None) {
        offsetConstantNode.Value = a;
        scalingConstantNode.Value = b;
      }
    }

    private ISymbolicExpressionTree BuildTree(Individual individual) {
      if (StructureTemplate.Tree == null)
        throw new ArgumentException("No structure template defined!");

      var templateTree = (ISymbolicExpressionTree)StructureTemplate.Tree.Clone();

      // build main tree
      foreach (var subFunctionTreeNode in templateTree.IterateNodesPrefix().OfType<SubFunctionTreeNode>()) {
        var subFunctionTree = individual.SymbolicExpressionTree(subFunctionTreeNode.Name);

        // add new tree
        var subTree = subFunctionTree.Root.GetSubtree(0)  // Start
                                          .GetSubtree(0); // Offset
        subTree = (ISymbolicExpressionTreeNode)subTree.Clone();
        subFunctionTreeNode.AddSubtree(subTree);

      }
      return templateTree;
    }

    private void SetupVariables(SubFunction subFunction) {
      var varSym = (Variable)subFunction.Grammar.GetSymbol("Variable");
      if (varSym == null) {
        varSym = new Variable();
        subFunction.Grammar.AddSymbol(varSym);
      }

      var allVariables = ProblemData.InputVariables.Select(x => x.Value);
      var allInputs = allVariables.Where(x => x != ProblemData.TargetVariable);

      // set all variables
      varSym.AllVariableNames = allVariables;

      // set all allowed variables
      if (subFunction.Arguments.Contains("_")) {
        varSym.VariableNames = allInputs;
      } else {
        var vars = new List<string>();
        var exceptions = new List<Exception>();
        foreach (var arg in subFunction.Arguments) {
          if (allInputs.Contains(arg))
            vars.Add(arg);
          else
            exceptions.Add(new ArgumentException($"The argument '{arg}' for sub-function '{subFunction.Name}' is not a valid variable."));
        }
        if (exceptions.Any())
          throw new AggregateException(exceptions);
        varSym.VariableNames = vars;
      }

      varSym.Enabled = true;
    }

    public void Load(IRegressionProblemData data) => ProblemData = data;
  }
}