source: branches/3076_IA_evaluators_analyzers/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionSingleObjectiveConstraintConstOptNmseEvaluator.cs @ 17705

using System;
using System.Collections.Generic;
using HEAL.Attic;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
using HeuristicLab.Parameters;

namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression.SingleObjective.Evaluators {
  [Item("Constraint ConstOpt NMSE Evaluator", "")]
  [StorableType("DED36D85-A4BA-4019-B12D-C523F7327899")]
  public class SymbolicRegressionSingleObjectiveConstraintConstOptNmseEvaluator
    : SymbolicRegressionSingleObjectiveEvaluator {
    private const string UseConstOptParameterName = "Use ConstOpt";
    private const string ConstOptIterationsParameterName = "ConstOpt Iterations";

    public IFixedValueParameter<BoolValue> UseConstOptParamerter =>
      (IFixedValueParameter<BoolValue>) Parameters[UseConstOptParameterName];

    public IFixedValueParameter<IntValue> ConstOpterIterationsParameter =>
      (IFixedValueParameter<IntValue>) Parameters[ConstOptIterationsParameterName];

    public bool UseConstOpt {
      get => UseConstOptParamerter.Value.Value;
      set => UseConstOptParamerter.Value.Value = value;
    }

    public int ConstOptIterations {
      get => ConstOpterIterationsParameter.Value.Value;
      set => ConstOpterIterationsParameter.Value.Value = value;
    }

    [StorableConstructor]
    protected SymbolicRegressionSingleObjectiveConstraintConstOptNmseEvaluator(StorableConstructorFlag _) : base(_) {}

    protected SymbolicRegressionSingleObjectiveConstraintConstOptNmseEvaluator(
      SymbolicRegressionSingleObjectiveConstraintConstOptNmseEvaluator original, Cloner cloner) :
      base(original, cloner) { }

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

    public SymbolicRegressionSingleObjectiveConstraintConstOptNmseEvaluator() {
      Parameters.Add(new FixedValueParameter<BoolValue>(UseConstOptParameterName,
        "Define whether constOpt is active or not.", new BoolValue(true)));
      Parameters.Add(new FixedValueParameter<IntValue>(ConstOptIterationsParameterName,
        "Define how many constOpt iterations should be performed.", new IntValue(10)));
    }

    public override bool Maximization => false;

    [StorableHook(HookType.AfterDeserialization)]
    private void AfterDeserialization() {
      if (!Parameters.ContainsKey(UseConstOptParameterName)) {
        Parameters.Add(new FixedValueParameter<BoolValue>(UseConstOptParameterName,
          "Define whether constOpt is active or not.", new BoolValue(true)));
      }

      if (!Parameters.ContainsKey(ConstOptIterationsParameterName)) {
        Parameters.Add(new FixedValueParameter<IntValue>(ConstOptIterationsParameterName,
          "Define how many constOpt iterations should be performed.", new IntValue(10)));
      }
    }

    public override IOperation InstrumentedApply() {
      var rows                                      = GenerateRowsToEvaluate();
      var solution                                  = SymbolicExpressionTreeParameter.ActualValue;
      var problemData                               = ProblemDataParameter.ActualValue;
      var interpreter                               = SymbolicDataAnalysisTreeInterpreterParameter.ActualValue;
      var estimationLimits                          = EstimationLimitsParameter.ActualValue;
      var applyLinearScaling                        = false;
      var constantOptimizationIterations            = ConstOptIterations;
      var constantOptimizationUpdateVariableWeights = true;

      //Use Const Opt
      if(UseConstOpt)
        SymbolicRegressionConstantOptimizationEvaluator.OptimizeConstants(interpreter, solution, problemData, rows,
          applyLinearScaling, constantOptimizationIterations, constantOptimizationUpdateVariableWeights,
          estimationLimits.Lower, estimationLimits.Upper);

      var quality = Calculate(interpreter, solution, estimationLimits.Lower, estimationLimits.Upper, problemData, rows,
        applyLinearScaling);
      QualityParameter.ActualValue = new DoubleValue(quality);
      return base.InstrumentedApply();
    }

    public static double Calculate(
      ISymbolicDataAnalysisExpressionTreeInterpreter interpreter,
      ISymbolicExpressionTree solution, double lowerEstimationLimit,
      double upperEstimationLimit,
      IRegressionProblemData problemData, IEnumerable<int> rows, bool applyLinearScaling) {
      var estimatedValues =
        interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, rows);
      var targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
      var constraints = problemData.IntervalConstraints.EnabledConstraints;
      var variableRanges = problemData.VariableRanges.GetReadonlyDictionary();

      if (!SymbolicRegressionConstraintAnalyzer.ConstraintsSatisfied(constraints, variableRanges, solution))
        return 1.0;

      var boundedEstimatedValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit);
      var nmse = OnlineNormalizedMeanSquaredErrorCalculator.Calculate(targetValues, boundedEstimatedValues, out var errorState);
      if (errorState != OnlineCalculatorError.None) nmse = 1.0;

      return nmse;
    }
    
    public override double Evaluate(
      IExecutionContext context, ISymbolicExpressionTree tree, IRegressionProblemData problemData,
      IEnumerable<int> rows) {
      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
      EstimationLimitsParameter.ExecutionContext                    = context;
      ApplyLinearScalingParameter.ExecutionContext                  = context;

      var nmse = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree,
        EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper,
        problemData, rows,
        ApplyLinearScalingParameter.ActualValue.Value);

      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
      EstimationLimitsParameter.ExecutionContext                    = null;
      ApplyLinearScalingParameter.ExecutionContext                  = null;

      return nmse;
    }
  }
}