#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2018 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 HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;

namespace HeuristicLab.Problems.DataAnalysis {
  [StorableClass]
  [Item("ClassificationProblemData", "Represents an item containing all data defining a classification problem.")]
  public class ClassificationProblemData : DataAnalysisProblemData, IClassificationProblemData, IStorableContent {
    protected const string TargetVariableParameterName = "TargetVariable";
    protected const string ClassNamesParameterName = "ClassNames";
    protected const string ClassificationPenaltiesParameterName = "ClassificationPenalties";
    protected const string PositiveClassParameterName = "PositiveClass";
    protected const int MaximumNumberOfClasses = 100;
    protected const int InspectedRowsToDetermineTargets = 2000;

    public string Filename { get; set; }

    #region default data
    private static string[] defaultVariableNames = new string[] { "sample", "clump thickness", "cell size", "cell shape", "marginal adhesion", "epithelial cell size", "bare nuclei", "chromatin", "nucleoli", "mitoses", "class" };
    private static double[,] defaultData = new double[,]{
     {1000025,5,1,1,1,2,1,3,1,1,2      },
     {1002945,5,4,4,5,7,10,3,2,1,2     },
     {1015425,3,1,1,1,2,2,3,1,1,2      },
     {1016277,6,8,8,1,3,4,3,7,1,2      },
     {1017023,4,1,1,3,2,1,3,1,1,2      },
     {1017122,8,10,10,8,7,10,9,7,1,4   },
     {1018099,1,1,1,1,2,10,3,1,1,2     },
     {1018561,2,1,2,1,2,1,3,1,1,2      },
     {1033078,2,1,1,1,2,1,1,1,5,2      },
     {1033078,4,2,1,1,2,1,2,1,1,2      },
     {1035283,1,1,1,1,1,1,3,1,1,2      },
     {1036172,2,1,1,1,2,1,2,1,1,2      },
     {1041801,5,3,3,3,2,3,4,4,1,4      },
     {1043999,1,1,1,1,2,3,3,1,1,2      },
     {1044572,8,7,5,10,7,9,5,5,4,4     },
     {1047630,7,4,6,4,6,1,4,3,1,4      },
     {1048672,4,1,1,1,2,1,2,1,1,2      },
     {1049815,4,1,1,1,2,1,3,1,1,2      },
     {1050670,10,7,7,6,4,10,4,1,2,4    },
     {1050718,6,1,1,1,2,1,3,1,1,2      },
     {1054590,7,3,2,10,5,10,5,4,4,4    },
     {1054593,10,5,5,3,6,7,7,10,1,4    },
     {1056784,3,1,1,1,2,1,2,1,1,2      },
     {1057013,8,4,5,1,2,2,7,3,1,4      },
     {1059552,1,1,1,1,2,1,3,1,1,2      },
     {1065726,5,2,3,4,2,7,3,6,1,4      },
     {1066373,3,2,1,1,1,1,2,1,1,2      },
     {1066979,5,1,1,1,2,1,2,1,1,2      },
     {1067444,2,1,1,1,2,1,2,1,1,2      },
     {1070935,1,1,3,1,2,1,1,1,1,2      },
     {1070935,3,1,1,1,1,1,2,1,1,2      },
     {1071760,2,1,1,1,2,1,3,1,1,2      },
     {1072179,10,7,7,3,8,5,7,4,3,4     },
     {1074610,2,1,1,2,2,1,3,1,1,2      },
     {1075123,3,1,2,1,2,1,2,1,1,2      },
     {1079304,2,1,1,1,2,1,2,1,1,2      },
     {1080185,10,10,10,8,6,1,8,9,1,4   },
     {1081791,6,2,1,1,1,1,7,1,1,2      },
     {1084584,5,4,4,9,2,10,5,6,1,4     },
     {1091262,2,5,3,3,6,7,7,5,1,4      },
     {1096800,6,6,6,9,6,4,7,8,1,2      },
     {1099510,10,4,3,1,3,3,6,5,2,4     },
     {1100524,6,10,10,2,8,10,7,3,3,4   },
     {1102573,5,6,5,6,10,1,3,1,1,4     },
     {1103608,10,10,10,4,8,1,8,10,1,4  },
     {1103722,1,1,1,1,2,1,2,1,2,2      },
     {1105257,3,7,7,4,4,9,4,8,1,4      },
     {1105524,1,1,1,1,2,1,2,1,1,2      },
     {1106095,4,1,1,3,2,1,3,1,1,2      },
     {1106829,7,8,7,2,4,8,3,8,2,4      },
     {1108370,9,5,8,1,2,3,2,1,5,4      },
     {1108449,5,3,3,4,2,4,3,4,1,4      },
     {1110102,10,3,6,2,3,5,4,10,2,4    },
     {1110503,5,5,5,8,10,8,7,3,7,4     },
     {1110524,10,5,5,6,8,8,7,1,1,4     },
     {1111249,10,6,6,3,4,5,3,6,1,4     },
     {1112209,8,10,10,1,3,6,3,9,1,4    },
     {1113038,8,2,4,1,5,1,5,4,4,4      },
     {1113483,5,2,3,1,6,10,5,1,1,4     },
     {1113906,9,5,5,2,2,2,5,1,1,4      },
     {1115282,5,3,5,5,3,3,4,10,1,4     },
     {1115293,1,1,1,1,2,2,2,1,1,2      },
     {1116116,9,10,10,1,10,8,3,3,1,4   },
     {1116132,6,3,4,1,5,2,3,9,1,4      },
     {1116192,1,1,1,1,2,1,2,1,1,2      },
     {1116998,10,4,2,1,3,2,4,3,10,4    },
     {1117152,4,1,1,1,2,1,3,1,1,2      },
     {1118039,5,3,4,1,8,10,4,9,1,4     },
     {1120559,8,3,8,3,4,9,8,9,8,4      },
     {1121732,1,1,1,1,2,1,3,2,1,2      },
     {1121919,5,1,3,1,2,1,2,1,1,2      },
     {1123061,6,10,2,8,10,2,7,8,10,4   },
     {1124651,1,3,3,2,2,1,7,2,1,2      },
     {1125035,9,4,5,10,6,10,4,8,1,4    },
     {1126417,10,6,4,1,3,4,3,2,3,4     },
     {1131294,1,1,2,1,2,2,4,2,1,2      },
     {1132347,1,1,4,1,2,1,2,1,1,2      },
     {1133041,5,3,1,2,2,1,2,1,1,2      },
     {1133136,3,1,1,1,2,3,3,1,1,2      },
     {1136142,2,1,1,1,3,1,2,1,1,2      },
     {1137156,2,2,2,1,1,1,7,1,1,2      },
     {1143978,4,1,1,2,2,1,2,1,1,2      },
     {1143978,5,2,1,1,2,1,3,1,1,2      },
     {1147044,3,1,1,1,2,2,7,1,1,2      },
     {1147699,3,5,7,8,8,9,7,10,7,4     },
     {1147748,5,10,6,1,10,4,4,10,10,4  },
     {1148278,3,3,6,4,5,8,4,4,1,4      },
     {1148873,3,6,6,6,5,10,6,8,3,4     },
     {1152331,4,1,1,1,2,1,3,1,1,2      },
     {1155546,2,1,1,2,3,1,2,1,1,2      },
     {1156272,1,1,1,1,2,1,3,1,1,2      },
     {1156948,3,1,1,2,2,1,1,1,1,2      },
     {1157734,4,1,1,1,2,1,3,1,1,2      },
     {1158247,1,1,1,1,2,1,2,1,1,2      },
     {1160476,2,1,1,1,2,1,3,1,1,2      },
     {1164066,1,1,1,1,2,1,3,1,1,2      },
     {1165297,2,1,1,2,2,1,1,1,1,2      },
     {1165790,5,1,1,1,2,1,3,1,1,2      },
     {1165926,9,6,9,2,10,6,2,9,10,4    },
     {1166630,7,5,6,10,5,10,7,9,4,4    },
     {1166654,10,3,5,1,10,5,3,10,2,4   },
     {1167439,2,3,4,4,2,5,2,5,1,4      },
     {1167471,4,1,2,1,2,1,3,1,1,2      },
     {1168359,8,2,3,1,6,3,7,1,1,4      },
     {1168736,10,10,10,10,10,1,8,8,8,4 },
     {1169049,7,3,4,4,3,3,3,2,7,4      },
     {1170419,10,10,10,8,2,10,4,1,1,4  },
     {1170420,1,6,8,10,8,10,5,7,1,4    },
     {1171710,1,1,1,1,2,1,2,3,1,2      },
     {1171710,6,5,4,4,3,9,7,8,3,4      },
     {1171795,1,3,1,2,2,2,5,3,2,2      },
     {1171845,8,6,4,3,5,9,3,1,1,4      },
     {1172152,10,3,3,10,2,10,7,3,3,4   },
     {1173216,10,10,10,3,10,8,8,1,1,4  },
     {1173235,3,3,2,1,2,3,3,1,1,2      },
     {1173347,1,1,1,1,2,5,1,1,1,2      },
     {1173347,8,3,3,1,2,2,3,2,1,2      },
     {1173509,4,5,5,10,4,10,7,5,8,4    },
     {1173514,1,1,1,1,4,3,1,1,1,2      },
     {1173681,3,2,1,1,2,2,3,1,1,2      },
     {1174057,1,1,2,2,2,1,3,1,1,2      },
     {1174057,4,2,1,1,2,2,3,1,1,2      },
     {1174131,10,10,10,2,10,10,5,3,3,4 },
     {1174428,5,3,5,1,8,10,5,3,1,4     },
     {1175937,5,4,6,7,9,7,8,10,1,4     },
     {1176406,1,1,1,1,2,1,2,1,1,2      },
     {1176881,7,5,3,7,4,10,7,5,5,4        }
};
    private static readonly Dataset defaultDataset;
    private static readonly IEnumerable<string> defaultAllowedInputVariables;
    private static readonly string defaultTargetVariable;

    private static readonly ClassificationProblemData emptyProblemData;
    public static ClassificationProblemData EmptyProblemData {
      get { return EmptyProblemData; }
    }

    static ClassificationProblemData() {
      defaultDataset = new Dataset(defaultVariableNames, defaultData);
      defaultDataset.Name = "Wisconsin classification problem";
      defaultDataset.Description = "subset from to ..";

      defaultAllowedInputVariables = defaultVariableNames.Except(new List<string>() { "sample", "class" });
      defaultTargetVariable = "class";

      var problemData = new ClassificationProblemData();
      problemData.Parameters.Clear();
      problemData.Name = "Empty Classification ProblemData";
      problemData.Description = "This ProblemData acts as place holder before the correct problem data is loaded.";
      problemData.isEmpty = true;

      problemData.Parameters.Add(new FixedValueParameter<Dataset>(DatasetParameterName, "", new Dataset()));
      problemData.Parameters.Add(new FixedValueParameter<ReadOnlyCheckedItemList<StringValue>>(InputVariablesParameterName, ""));
      problemData.Parameters.Add(new FixedValueParameter<IntRange>(TrainingPartitionParameterName, "", (IntRange)new IntRange(0, 0).AsReadOnly()));
      problemData.Parameters.Add(new FixedValueParameter<IntRange>(TestPartitionParameterName, "", (IntRange)new IntRange(0, 0).AsReadOnly()));
      problemData.Parameters.Add(new ConstrainedValueParameter<StringValue>(TargetVariableParameterName, new ItemSet<StringValue>()));
      problemData.Parameters.Add(new FixedValueParameter<StringMatrix>(ClassNamesParameterName, "", new StringMatrix(0, 0).AsReadOnly()));
      problemData.Parameters.Add(new FixedValueParameter<DoubleMatrix>(ClassificationPenaltiesParameterName, "", (DoubleMatrix)new DoubleMatrix(0, 0).AsReadOnly()));
      emptyProblemData = problemData;
    }
    #endregion

    #region parameter properties
    public IConstrainedValueParameter<StringValue> TargetVariableParameter {
      get { return (IConstrainedValueParameter<StringValue>)Parameters[TargetVariableParameterName]; }
    }
    public IFixedValueParameter<StringMatrix> ClassNamesParameter {
      get { return (IFixedValueParameter<StringMatrix>)Parameters[ClassNamesParameterName]; }
    }
    public IConstrainedValueParameter<StringValue> PositiveClassParameter {
      get { return (IConstrainedValueParameter<StringValue>)Parameters[PositiveClassParameterName]; }
    }
    public IFixedValueParameter<DoubleMatrix> ClassificationPenaltiesParameter {
      get { return (IFixedValueParameter<DoubleMatrix>)Parameters[ClassificationPenaltiesParameterName]; }
    }
    #endregion

    #region properties
    public string TargetVariable {
      get { return TargetVariableParameter.Value.Value; }
      set {
        if (value == null) throw new ArgumentNullException("targetVariable", "The provided value for the targetVariable is null.");
        if (value == TargetVariable) return;


        var matchingParameterValue = TargetVariableParameter.ValidValues.FirstOrDefault(v => v.Value == value);
        if (matchingParameterValue == null) throw new ArgumentException("The provided value is not valid as the targetVariable.", "targetVariable");
        TargetVariableParameter.Value = matchingParameterValue;
      }
    }

    private List<double> classValuesCache;
    private List<double> ClassValuesCache {
      get {
        if (classValuesCache == null) {
          classValuesCache = Dataset.GetDoubleValues(TargetVariableParameter.Value.Value).Distinct().OrderBy(x => x).ToList();
        }
        return classValuesCache;
      }
    }
    public IEnumerable<double> ClassValues {
      get { return ClassValuesCache; }
    }
    public int Classes {
      get { return ClassValuesCache.Count; }
    }

    private List<string> classNamesCache;
    private List<string> ClassNamesCache {
      get {
        if (classNamesCache == null) {
          classNamesCache = new List<string>();
          for (int i = 0; i < ClassNamesParameter.Value.Rows; i++)
            classNamesCache.Add(ClassNamesParameter.Value[i, 0]);
        }
        return classNamesCache;
      }
    }
    public IEnumerable<string> ClassNames {
      get { return ClassNamesCache; }
    }

    public string PositiveClass {
      get { return PositiveClassParameter.Value.Value; }
      set {
        var matchingValue = PositiveClassParameter.ValidValues.SingleOrDefault(x => x.Value == value);
        if (matchingValue == null) throw new ArgumentException(string.Format("{0} cannot be set as positive class.", value));
        PositiveClassParameter.Value = matchingValue;
      }
    }
    #endregion


    [StorableConstructor]
    protected ClassificationProblemData(bool deserializing) : base(deserializing) { }
    [StorableHook(HookType.AfterDeserialization)]
    private void AfterDeserialization() {
      RegisterParameterEvents();

      classNamesCache = new List<string>();
      for (int i = 0; i < ClassNamesParameter.Value.Rows; i++)
        classNamesCache.Add(ClassNamesParameter.Value[i, 0]);

      // BackwardsCompatibility3.4
      #region Backwards compatible code, remove with 3.5
      if (!Parameters.ContainsKey(PositiveClassParameterName)) {
        var validValues = new ItemSet<StringValue>(ClassNames.Select(s => new StringValue(s).AsReadOnly()));
        Parameters.Add(new ConstrainedValueParameter<StringValue>(PositiveClassParameterName,
          "The positive class which is used for quality measure calculation (e.g., specifity, sensitivity,...)", validValues, validValues.First()));
      }
      #endregion

    }

    protected ClassificationProblemData(ClassificationProblemData original, Cloner cloner)
      : base(original, cloner) {
      RegisterParameterEvents();
      classNamesCache = new List<string>();
      for (int i = 0; i < ClassNamesParameter.Value.Rows; i++)
        classNamesCache.Add(ClassNamesParameter.Value[i, 0]);
    }
    public override IDeepCloneable Clone(Cloner cloner) {
      if (this == emptyProblemData) return emptyProblemData;
      return new ClassificationProblemData(this, cloner);
    }

    public ClassificationProblemData() : this(defaultDataset, defaultAllowedInputVariables, defaultTargetVariable) { }

    public ClassificationProblemData(IClassificationProblemData classificationProblemData)
      : this(classificationProblemData.Dataset, classificationProblemData.AllowedInputVariables, classificationProblemData.TargetVariable) {
      TrainingPartition.Start = classificationProblemData.TrainingPartition.Start;
      TrainingPartition.End = classificationProblemData.TrainingPartition.End;
      TestPartition.Start = classificationProblemData.TestPartition.Start;
      TestPartition.End = classificationProblemData.TestPartition.End;

      for (int i = 0; i < classificationProblemData.ClassNames.Count(); i++)
        ClassNamesParameter.Value[i, 0] = classificationProblemData.ClassNames.ElementAt(i);

      //mkommend: The positive class depends on the class names and as a result must only be set after the classe names parameter.
      PositiveClass = classificationProblemData.PositiveClass;

      for (int i = 0; i < Classes; i++) {
        for (int j = 0; j < Classes; j++) {
          ClassificationPenaltiesParameter.Value[i, j] = classificationProblemData.GetClassificationPenalty(ClassValuesCache[i], ClassValuesCache[j]);
        }
      }
    }

    public ClassificationProblemData(IDataset dataset, IEnumerable<string> allowedInputVariables, string targetVariable, IEnumerable<ITransformation> transformations = null)
      : base(dataset, allowedInputVariables, transformations ?? Enumerable.Empty<ITransformation>()) {
      var validTargetVariableValues = CheckVariablesForPossibleTargetVariables(dataset).Select(x => new StringValue(x).AsReadOnly()).ToList();
      var target = validTargetVariableValues.Where(x => x.Value == targetVariable).DefaultIfEmpty(validTargetVariableValues.First()).First();

      Parameters.Add(new ConstrainedValueParameter<StringValue>(TargetVariableParameterName, new ItemSet<StringValue>(validTargetVariableValues), target));
      Parameters.Add(new FixedValueParameter<StringMatrix>(ClassNamesParameterName, ""));
      Parameters.Add(new ConstrainedValueParameter<StringValue>(PositiveClassParameterName, "The positive class which is used for quality measure calculation (e.g., specifity, sensitivity,...)"));
      Parameters.Add(new FixedValueParameter<DoubleMatrix>(ClassificationPenaltiesParameterName, ""));

      RegisterParameterEvents();
      ResetTargetVariableDependentMembers();
    }

    public static IEnumerable<string> CheckVariablesForPossibleTargetVariables(IDataset dataset) {
      int maxSamples = Math.Min(InspectedRowsToDetermineTargets, dataset.Rows);
      var validTargetVariables = (from v in dataset.DoubleVariables
                                  let distinctValues = dataset.GetDoubleValues(v)
                                    .Take(maxSamples)
                                    .Distinct()
                                    .Count()
                                  where distinctValues <= MaximumNumberOfClasses
                                  select v).ToArray();

      if (!validTargetVariables.Any())
        throw new ArgumentException("Import of classification problem data was not successful, because no target variable was found." +
          " A target variable must have at most " + MaximumNumberOfClasses + " distinct values to be applicable to classification.");
      return validTargetVariables;
    }


    private void ResetTargetVariableDependentMembers() {
      DeregisterParameterEvents();

      ((IStringConvertibleMatrix)ClassNamesParameter.Value).Columns = 1;
      ((IStringConvertibleMatrix)ClassNamesParameter.Value).Rows = ClassValuesCache.Count;
      for (int i = 0; i < Classes; i++)
        ClassNamesParameter.Value[i, 0] = "Class " + ClassValuesCache[i];
      ClassNamesParameter.Value.ColumnNames = new List<string>() { "ClassNames" };
      ClassNamesParameter.Value.RowNames = ClassValues.Select(s => "ClassValue: " + s);

      PositiveClassParameter.ValidValues.Clear();
      foreach (var className in ClassNames) {
        PositiveClassParameter.ValidValues.Add(new StringValue(className).AsReadOnly());
      }

      ((IStringConvertibleMatrix)ClassificationPenaltiesParameter.Value).Rows = Classes;
      ((IStringConvertibleMatrix)ClassificationPenaltiesParameter.Value).Columns = Classes;
      ClassificationPenaltiesParameter.Value.RowNames = ClassNames.Select(name => "Actual " + name);
      ClassificationPenaltiesParameter.Value.ColumnNames = ClassNames.Select(name => "Estimated " + name);
      for (int i = 0; i < Classes; i++) {
        for (int j = 0; j < Classes; j++) {
          if (i != j) ClassificationPenaltiesParameter.Value[i, j] = 1;
          else ClassificationPenaltiesParameter.Value[i, j] = 0;
        }
      }
      RegisterParameterEvents();
    }

    public string GetClassName(double classValue) {
      if (!ClassValuesCache.Contains(classValue)) throw new ArgumentException();
      int index = ClassValuesCache.IndexOf(classValue);
      return ClassNamesCache[index];
    }
    public double GetClassValue(string className) {
      if (!ClassNamesCache.Contains(className)) throw new ArgumentException();
      int index = ClassNamesCache.IndexOf(className);
      return ClassValuesCache[index];
    }
    public void SetClassName(double classValue, string className) {
      if (!ClassValuesCache.Contains(classValue)) throw new ArgumentException();
      int index = ClassValuesCache.IndexOf(classValue);
      ClassNamesParameter.Value[index, 0] = className;
      // updating of class names cache is not necessary here as the parameter value fires a changed event which updates the cache
    }

    public double GetClassificationPenalty(string correctClassName, string estimatedClassName) {
      return GetClassificationPenalty(GetClassValue(correctClassName), GetClassValue(estimatedClassName));
    }
    public double GetClassificationPenalty(double correctClassValue, double estimatedClassValue) {
      int correctClassIndex = ClassValuesCache.IndexOf(correctClassValue);
      int estimatedClassIndex = ClassValuesCache.IndexOf(estimatedClassValue);
      return ClassificationPenaltiesParameter.Value[correctClassIndex, estimatedClassIndex];
    }
    public void SetClassificationPenalty(string correctClassName, string estimatedClassName, double penalty) {
      SetClassificationPenalty(GetClassValue(correctClassName), GetClassValue(estimatedClassName), penalty);
    }
    public void SetClassificationPenalty(double correctClassValue, double estimatedClassValue, double penalty) {
      int correctClassIndex = ClassValuesCache.IndexOf(correctClassValue);
      int estimatedClassIndex = ClassValuesCache.IndexOf(estimatedClassValue);

      ClassificationPenaltiesParameter.Value[correctClassIndex, estimatedClassIndex] = penalty;
    }

    #region events
    private void RegisterParameterEvents() {
      TargetVariableParameter.ValueChanged += new EventHandler(TargetVariableParameter_ValueChanged);
      ClassNamesParameter.Value.Reset += new EventHandler(Parameter_ValueChanged);
      ClassNamesParameter.Value.ItemChanged += new EventHandler<EventArgs<int, int>>(Parameter_ValueChanged);
      ClassificationPenaltiesParameter.Value.ItemChanged += new EventHandler<EventArgs<int, int>>(Parameter_ValueChanged);
      ClassificationPenaltiesParameter.Value.Reset += new EventHandler(Parameter_ValueChanged);
    }
    private void DeregisterParameterEvents() {
      TargetVariableParameter.ValueChanged -= new EventHandler(TargetVariableParameter_ValueChanged);
      ClassNamesParameter.Value.Reset -= new EventHandler(Parameter_ValueChanged);
      ClassNamesParameter.Value.ItemChanged -= new EventHandler<EventArgs<int, int>>(Parameter_ValueChanged);
      ClassificationPenaltiesParameter.Value.ItemChanged -= new EventHandler<EventArgs<int, int>>(Parameter_ValueChanged);
      ClassificationPenaltiesParameter.Value.Reset -= new EventHandler(Parameter_ValueChanged);
    }

    private void TargetVariableParameter_ValueChanged(object sender, EventArgs e) {
      classValuesCache = null;
      classNamesCache = null;
      ResetTargetVariableDependentMembers();
      OnChanged();
    }
    private void Parameter_ValueChanged(object sender, EventArgs e) {
      var oldPositiveClass = PositiveClass;
      var oldClassNames = classNamesCache;
      var index = oldClassNames.IndexOf(oldPositiveClass);

      classNamesCache = null;
      ClassificationPenaltiesParameter.Value.RowNames = ClassNames.Select(name => "Actual " + name);
      ClassificationPenaltiesParameter.Value.ColumnNames = ClassNames.Select(name => "Estimated " + name);

      PositiveClassParameter.ValidValues.Clear();
      foreach (var className in ClassNames) {
        PositiveClassParameter.ValidValues.Add(new StringValue(className).AsReadOnly());
      }
      PositiveClassParameter.Value = PositiveClassParameter.ValidValues.ElementAt(index);

      OnChanged();
    }
    #endregion

    protected override bool IsProblemDataCompatible(IDataAnalysisProblemData problemData, out string errorMessage) {
      if (problemData == null) throw new ArgumentNullException("problemData", "The provided problemData is null.");
      IClassificationProblemData classificationProblemData = problemData as IClassificationProblemData;
      if (classificationProblemData == null)
        throw new ArgumentException("The problem data is no classification problem data. Instead a " + problemData.GetType().GetPrettyName() + " was provided.", "problemData");

      var returnValue = base.IsProblemDataCompatible(classificationProblemData, out errorMessage);
      //check targetVariable
      if (classificationProblemData.InputVariables.All(var => var.Value != TargetVariable)) {
        errorMessage = string.Format("The target variable {0} is not present in the new problem data.", TargetVariable)
                       + Environment.NewLine + errorMessage;
        return false;
      }

      var newClassValues = classificationProblemData.Dataset.GetDoubleValues(TargetVariable).Distinct().OrderBy(x => x);
      if (!newClassValues.SequenceEqual(ClassValues)) {
        errorMessage = errorMessage + string.Format("The class values differ in the provided classification problem data.");
        returnValue = false;
      }

      var newPositivieClassName = classificationProblemData.PositiveClass;
      if (newPositivieClassName != PositiveClass) {
        errorMessage = errorMessage + string.Format("The positive class differs in the provided classification problem data.");
        returnValue = false;
      }

      return returnValue;
    }

    public override void AdjustProblemDataProperties(IDataAnalysisProblemData problemData) {
      if (problemData == null) throw new ArgumentNullException("problemData", "The provided problemData is null.");
      ClassificationProblemData classificationProblemData = problemData as ClassificationProblemData;
      if (classificationProblemData == null)
        throw new ArgumentException("The problem data is not a classification problem data. Instead a " + problemData.GetType().GetPrettyName() + " was provided.", "problemData");

      base.AdjustProblemDataProperties(problemData);
      TargetVariable = classificationProblemData.TargetVariable;
      for (int i = 0; i < classificationProblemData.ClassNames.Count(); i++)
        ClassNamesParameter.Value[i, 0] = classificationProblemData.ClassNames.ElementAt(i);

      PositiveClass = classificationProblemData.PositiveClass;

      for (int i = 0; i < Classes; i++) {
        for (int j = 0; j < Classes; j++) {
          ClassificationPenaltiesParameter.Value[i, j] = classificationProblemData.GetClassificationPenalty(ClassValuesCache[i], ClassValuesCache[j]);
        }
      }
    }
  }
}