#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2017 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.Linq;
using System.Threading;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Core.Networks;
using HeuristicLab.Data;
using HeuristicLab.Encodings.BinaryVectorEncoding;
using HeuristicLab.Optimization;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.Problems.DataAnalysis;

namespace HeuristicLab.Networks.IntegratedOptimization.MachineLearning {
  [StorableClass]
  public sealed class FeatureSelectionOrchestrator : OrchestratorNode {
    private const string REGRESSION_ORCHESTRATION_PORT_NAME = "Regression algorithm orchestration port";
    private const string FEATURE_SELECTION_EVALUATION_PORT_NAME = "Feature selection evaluation port";
    private const string REGRESSION_PROBLEM_PARAMETER_NAME = "Regression Problem Data";

    public IMessagePort RegressionOrchestrationPort {
      get { return (IMessagePort)Ports[REGRESSION_ORCHESTRATION_PORT_NAME]; }
    }
    public IMessagePort FeatureSelectionEvaluationPort {
      get { return (IMessagePort)Ports[FEATURE_SELECTION_EVALUATION_PORT_NAME]; }
    }

    public IValueParameter<IRegressionProblemData> ProblemDataParameter {
      get { return (IValueParameter<IRegressionProblemData>)Parameters[REGRESSION_PROBLEM_PARAMETER_NAME]; }
    }

    public IRegressionProblemData RegressionProblemData {
      get { return ProblemDataParameter.Value; }
      set { ProblemDataParameter.Value = value; }
    }

    [StorableConstructor]
    private FeatureSelectionOrchestrator(bool deserializing) : base(deserializing) { }
    [StorableHook(HookType.AfterDeserialization)]
    private void AfterDeserialization() {
      RegisterPortEvents();
    }

    private FeatureSelectionOrchestrator(FeatureSelectionOrchestrator original, Cloner cloner)
      : base(original, cloner) {
      network = cloner.Clone(original.network);
      RegisterPortEvents();
    }
    public override IDeepCloneable Clone(Cloner cloner) { return new FeatureSelectionOrchestrator(this, cloner); }

    //TODO remove network reference;
    //TODO move regression problem to network
    [Storable]
    private readonly FeatureSelectionNetwork network;

    public FeatureSelectionOrchestrator(FeatureSelectionNetwork network)
      : base() {
      var featureSelectionPort = CreateEvaluationPort<BinaryVector>(FEATURE_SELECTION_EVALUATION_PORT_NAME, "BinaryVector", "Quality");
      Ports.Add(featureSelectionPort);

      var regressionPort = CreateOrchestrationPort<IRegressionProblem>(REGRESSION_ORCHESTRATION_PORT_NAME);
      Ports.Add(regressionPort);

      this.network = network;

      Parameters.Add(new ValueParameter<IRegressionProblemData>(REGRESSION_PROBLEM_PARAMETER_NAME, "", new RegressionProblemData()));
      RegisterPortEvents();
    }

    private void RegisterPortEvents() {
      FeatureSelectionEvaluationPort.MessageReceived += (s, e) => FeatureSelectionEvaluationPort_MessageReceived(e.Value, e.Value2);
    }

    private void FeatureSelectionEvaluationPort_MessageReceived(IMessage evaluationMessage, CancellationToken token) {
      var problemData = (IRegressionProblemData)RegressionProblemData.Clone();
      var binaryVector = (BinaryVector)evaluationMessage["BinaryVector"];
      binaryVector.ElementNames = problemData.InputVariables.CheckedItems.Select(variable => variable.Value.Value);

      var allowedVariables = problemData.InputVariables.CheckedItems.Zip(binaryVector,
        (variable, allowed) => new { VariableName = variable.Value, Allowed = allowed });

      foreach (var allowedVariable in allowedVariables)
        problemData.InputVariables.SetItemCheckedState(allowedVariable.VariableName, allowedVariable.Allowed);

      var orchestrationMessage = RegressionOrchestrationPort.PrepareMessage();
      orchestrationMessage["Problem"] = new RegressionProblem() { ProblemData = problemData };
      orchestrationMessage["OrchestrationMessage"] = new EnumValue<OrchestrationMessage>(OrchestrationMessage.Prepare);
      RegressionOrchestrationPort.SendMessage(orchestrationMessage, token);

      var startMessage = RegressionOrchestrationPort.PrepareMessage();
      startMessage["OrchestrationMessage"] = new EnumValue<OrchestrationMessage>(OrchestrationMessage.Start);
      RegressionOrchestrationPort.SendMessage(startMessage, token);

      var results = (ResultCollection)startMessage["Results"];

      var regressionSolution = results.Select(r => r.Value).OfType<IRegressionSolution>().First();

      UpdatedResults(binaryVector, regressionSolution);


      double quality = regressionSolution.TestMeanAbsoluteError;
      evaluationMessage["Quality"] = new DoubleValue(quality);
    }

    private void UpdatedResults(BinaryVector binaryVector, IRegressionSolution solution) {
      if (!Results.ContainsKey("Best Solution Vector")) {
        Results.Add(new Result("Best Solution Vector", typeof(BinaryVector)));
        //Results.Add(new Result("Best Solution Variables", typeof(DoubleArray)));
        Results.Add(new Result("Best Symbolic Solution", typeof(IRegressionSolution)));
      }

      var previousBestVector = (BinaryVector)Results["Best Solution Vector"].Value;

      //check if better vector has been found
      if (previousBestVector != null && binaryVector.SequenceEqual(previousBestVector))
        return;

      Results["Best Solution Vector"].Value = binaryVector;
      Results["Best Symbolic Solution"].Value = solution;

      //var variableNames = solution.ProblemData.AllowedInputVariables;
      //alglib.linearmodel lm = BuildModel(best, problemData);

      //double[] coefficients = null;
      //int nFeatures = -1;
      //alglib.lrunpack(lm, out coefficients, out nFeatures);

      //var doubleArray = new DoubleArray(coefficients.ToArray());
      //doubleArray.ElementNames = variableNames;
      //Results["Best Solution Variables"].Value = doubleArray;


    }


    //TODO Remove methods
    public override void Pause() { network.Pause(); }
    public override void Prepare(bool clearRuns = false) { network.Prepare(clearRuns); }
    public override void Start() { network.Start(); }
    public override void Stop() { network.Stop(); }
  }
}