source: branches/EfficientGlobalOptimization/HeuristicLab.Algorithms.EGO/DiscreteEGO/DiscreteCorrelationAnalyzer.cs @ 16101

#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2016 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.Collections.Generic;
using System.Linq;
using HeuristicLab.Analysis;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Encodings.IntegerVectorEncoding;
using HeuristicLab.Operators;
using HeuristicLab.Optimization;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.Problems.DataAnalysis;

namespace HeuristicLab.Algorithms.EGO {
  [Item("DiscreteCorrelationAnalyzer", "Analyzes the correlation between perdictions and actual fitness values")]
  [StorableClass]
  public class DiscreteCorrelationAnalyzer : SingleSuccessorOperator, IAnalyzer, IResultsOperator {
    public override bool CanChangeName => true;
    public bool EnabledByDefault => false;

    public IScopeTreeLookupParameter<IntegerVector> IntegerVectorParameter => (IScopeTreeLookupParameter<IntegerVector>)Parameters["IntegerVector"];
    public IScopeTreeLookupParameter<DoubleValue> QualityParameter => (IScopeTreeLookupParameter<DoubleValue>)Parameters["Quality"];
    public ILookupParameter<IRegressionSolution> ModelParameter => (ILookupParameter<IRegressionSolution>)Parameters["Model"];
    public ILookupParameter<ResultCollection> ResultsParameter => (ILookupParameter<ResultCollection>)Parameters["Results"];

    private const string PlotName = "Prediction";
    private const string RowName = "Samples";

    [StorableConstructor]
    protected DiscreteCorrelationAnalyzer(bool deserializing) : base(deserializing) { }
    protected DiscreteCorrelationAnalyzer(DiscreteCorrelationAnalyzer original, Cloner cloner) : base(original, cloner) { }
    public DiscreteCorrelationAnalyzer() {
      Parameters.Add(new ScopeTreeLookupParameter<IntegerVector>("IntegerVector", "The vector which should be collected."));
      Parameters.Add(new ScopeTreeLookupParameter<DoubleValue>("Quality", "The quality associated which this vector"));
      Parameters.Add(new LookupParameter<IRegressionSolution>("Model", "The model of this iteration"));
      Parameters.Add(new LookupParameter<ResultCollection>("Results", "The collection to store the results in."));
    }

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

    public sealed override IOperation Apply() {
      var model = ModelParameter.ActualValue;
      var results = ResultsParameter.ActualValue;
      var q = QualityParameter.ActualValue.Select(x => x.Value).ToArray();
      var p = IntegerVectorParameter.ActualValue.ToArray();
      if (model == null) return base.Apply();
      var plot = CreateScatterPlotResult(results);
      for (var i = 0; i < q.Length; i++) plot.Rows[RowName].Points.Add(new Point2D<double>(model.Model.GetEstimation(p[i]), q[i], p[i]));
      return base.Apply();
    }

    private static ScatterPlot CreateScatterPlotResult(ResultCollection results) {
      ScatterPlot plot;
      if (!results.ContainsKey(PlotName)) {
        plot = new ScatterPlot("Fitness-Prediction-Correlation", "The correlation between the predicted and actual fitness values") {
          VisualProperties = {
            XAxisTitle = "Predicted Objective Value",
            YAxisTitle = "True Objective Value"
          }
        };
        results.Add(new Result(PlotName, plot));
      } else { plot = (ScatterPlot)results[PlotName].Value; }
      if (!plot.Rows.ContainsKey(RowName)) {
        plot.Rows.Add(new ScatterPlotDataRow(RowName, RowName, new List<Point2D<double>>()));
        plot.Rows[RowName].VisualProperties.PointSize = 5;
      }
      return plot;
    }

  }
}