source: branches/3076_IA_evaluators_analyzers_reintegration/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/MultiObjective/NMSEConstraintsEvaluator.cs @ 17899

#region License Information

/* HeuristicLab
 * Copyright (C) Heuristic and Evolutionary Algorithms Laboratory (HEAL)
 *
 * This file is part of HeuristicLab.
 *
 * HeuristicLab is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * HeuristicLab is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
 */

#endregion

using System;
using System.Collections.Generic;
using System.Linq;
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.MultiObjective {
  [Item("NMSE Evaluator (multi-objective, with shape-constraints)",
    "Calculates the NMSE and the constraints violations of a symbolic regression solution as objectives.")]
  [StorableType("8E9D76B7-ED9C-43E7-9898-01FBD3633880")]
  public class NMSEConstraintsEvaluator : SymbolicRegressionMultiObjectiveEvaluator {
    public const string NumObjectivesParameterName = "NumObjectives";
    private const string BoundsEstimatorParameterName = "BoundsEstimator";

    public IFixedValueParameter<IntValue> NumObjectivesParameter =>
      (IFixedValueParameter<IntValue>)Parameters[NumObjectivesParameterName];

    public IValueParameter<IBoundsEstimator> BoundsEstimatorParameter =>
      (IValueParameter<IBoundsEstimator>)Parameters[BoundsEstimatorParameterName];

    [Storable]
    private bool[] maximization;

    public int NumObjectives {
      get => NumObjectivesParameter.Value.Value;
      set {
        NumObjectivesParameter.Value.Value = value;

        /*
         * First objective is to minimize the NMSE
         * All following objectives have to be minimized ==> Constraints
         */
        this.maximization = new bool[value];
      }
    }

    public IBoundsEstimator BoundsEstimator {
      get => BoundsEstimatorParameter.Value;
      set => BoundsEstimatorParameter.Value = value;
    }

    #region Constructors

    public NMSEConstraintsEvaluator() {
      Parameters.Add(new FixedValueParameter<IntValue>(NumObjectivesParameterName, new IntValue(2)));
      Parameters.Add(new ValueParameter<IBoundsEstimator>(BoundsEstimatorParameterName, new IntervalArithBoundsEstimator()));
      maximization = new bool[2];
    }

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

    protected NMSEConstraintsEvaluator(NMSEConstraintsEvaluator original, Cloner cloner) : base(original, cloner) {
      this.maximization = (bool[])original.maximization.Clone();
    }

    #endregion

    [StorableHook(HookType.AfterDeserialization)]
    private void AfterDeserialization() { }

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


    public override IOperation InstrumentedApply() {
      var rows = GenerateRowsToEvaluate();
      var tree = SymbolicExpressionTreeParameter.ActualValue;
      var problemData = ProblemDataParameter.ActualValue;
      var interpreter = SymbolicDataAnalysisTreeInterpreterParameter.ActualValue;
      var estimationLimits = EstimationLimitsParameter.ActualValue;
      var applyLinearScaling = ApplyLinearScalingParameter.ActualValue.Value;

      if (UseConstantOptimization) {
        SymbolicRegressionConstantOptimizationEvaluator.OptimizeConstants(interpreter, tree, problemData, rows,
          false,
          ConstantOptimizationIterations,
          ConstantOptimizationUpdateVariableWeights,
          estimationLimits.Lower,
          estimationLimits.Upper);
      } else {
        if (applyLinearScaling) {
          //Check for interval arithmetic grammar
          if (!(tree.Root.Grammar is IntervalArithmeticGrammar))
            throw new ArgumentException($"{ItemName} can only be used with IntervalArithmeticGrammar.");

          var rootNode = new ProgramRootSymbol().CreateTreeNode();
          var startNode = new StartSymbol().CreateTreeNode();
          var offset = tree.Root.GetSubtree(0) //Start
                                .GetSubtree(0); //Offset
          var scaling = offset.GetSubtree(0);
          var t = (ISymbolicExpressionTreeNode)scaling.GetSubtree(0).Clone();
          rootNode.AddSubtree(startNode);
          startNode.AddSubtree(t);
          var newTree = new SymbolicExpressionTree(rootNode);

          //calculate alpha and beta for scaling
          var estimatedValues = interpreter.GetSymbolicExpressionTreeValues(newTree, problemData.Dataset, rows);

          var targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
          OnlineLinearScalingParameterCalculator.Calculate(estimatedValues, targetValues, out var alpha, out var beta,
            out var errorState);
          if (errorState == OnlineCalculatorError.None) {
            //Set alpha and beta to the scaling nodes from ia grammar
            var offsetParameter = offset.GetSubtree(1) as ConstantTreeNode;
            offsetParameter.Value = alpha;
            var scalingParameter = scaling.GetSubtree(1) as ConstantTreeNode;
            scalingParameter.Value = beta;
          }
        }
      }

      var qualities = Calculate(interpreter, tree, estimationLimits.Lower, estimationLimits.Upper, problemData,
        rows, BoundsEstimator);
      QualitiesParameter.ActualValue = new DoubleArray(qualities);
      return base.InstrumentedApply();
    }

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

      var quality = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree,
        EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper,
        problemData, rows, BoundsEstimator);

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

      return quality;
    }


    public static double[] Calculate(
      ISymbolicDataAnalysisExpressionTreeInterpreter interpreter,
      ISymbolicExpressionTree solution, double lowerEstimationLimit,
      double upperEstimationLimit,
      IRegressionProblemData problemData, IEnumerable<int> rows, IBoundsEstimator estimator) {
      OnlineCalculatorError errorState;
      var estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, rows);
      var targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
      var constraints = problemData.ShapeConstraints.EnabledConstraints;
      var intervalCollection = problemData.VariableRanges;

      double nmse;

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

      if (nmse > 1)
        nmse = 1.0;

      var objectives = new List<double> { nmse };
      var violations = IntervalUtil.GetConstraintViolations(constraints, estimator, intervalCollection, solution);
      foreach (var violation in violations) {
        if (double.IsNaN(violation) || double.IsInfinity(violation)) {
          objectives.Add(double.MaxValue);
        } else {
          objectives.Add(violation);
        }
      }

      return objectives.ToArray();
    }

    public override IEnumerable<bool> Maximization => maximization;
  }
}