#region License Information
/* HeuristicLab
* Copyright (C) 2002-2011 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 .
*/
#endregion
using System;
using System.Collections.Generic;
using System.IO;
using System.Linq;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.PluginInfrastructure;
namespace HeuristicLab.Problems.DataAnalysis.Classification {
[Item("ClassificationProblemData", "Represents an item containing all data defining a classification problem.")]
[StorableClass]
[NonDiscoverableType]
public class ClassificationProblemData : DataAnalysisProblemData {
#region default data
private static string[] defaultInputs = 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 }
};
#endregion
private const int MaximumClasses = 20;
private const string ClassNamesParameterName = "ClassNames";
private const string MisclassificationMatrixParameterName = "MisClassificationMatrix";
public StringArray ClassNames {
get { return ClassNamesParameter.Value; }
protected set { ClassNamesParameter.Value = value; }
}
public IValueParameter ClassNamesParameter {
get { return (IValueParameter)Parameters[ClassNamesParameterName]; }
}
public DoubleMatrix MisclassificationMatrix {
get { return MisclassificationMatrixParameter.Value; }
protected set { MisclassificationMatrixParameter.Value = value; }
}
public IValueParameter MisclassificationMatrixParameter {
get { return (IValueParameter)Parameters[MisclassificationMatrixParameterName]; }
}
[Storable]
private List sortedClassValues;
public IEnumerable SortedClassValues {
get { return sortedClassValues; }
}
public int NumberOfClasses {
get { return sortedClassValues.Count; }
}
[StorableConstructor]
protected ClassificationProblemData(bool deserializing) : base(deserializing) { }
protected ClassificationProblemData(ClassificationProblemData original, Cloner cloner)
: base(original, cloner) {
RegisterParameterEvents();
UpdateClassValues();
}
public ClassificationProblemData()
: base(new Dataset(defaultInputs, defaultData), defaultInputs, defaultInputs[defaultInputs.Length - 1], 0, 60, 60, 120) {
Parameters.Add(new ValueParameter(ClassNamesParameterName, "An array of the names for all class values."));
Parameters.Add(new ValueParameter(MisclassificationMatrixParameterName, "A matrix that describles the penalties for misclassifaction between the single classes."));
sortedClassValues = new List();
InputVariables.SetItemCheckedState(InputVariables[InputVariables.Count - 1], false);
RegisterParameterEvents();
UpdateClassValues();
}
public override IDeepCloneable Clone(Cloner cloner) {
return new ClassificationProblemData(this, cloner);
}
[StorableHook(HookType.AfterDeserialization)]
private void AfterDeserialization() {
RegisterParameterEvents();
RegisterParameterValueEvents();
}
public override void ImportFromFile(string fileName) {
var csvFileParser = new TableFileParser();
csvFileParser.Parse(fileName);
suppressEvents = true;
Name = "Data imported from " + Path.GetFileName(fileName);
Dataset = new Dataset(csvFileParser.VariableNames, csvFileParser.Values);
Dataset.Name = Path.GetFileName(fileName);
var variableNames = Dataset.VariableNames.Select(x => new StringValue(x).AsReadOnly()).ToList();
var validTargetVariables = from v in variableNames
let DistinctValues = Dataset.Rows > 50 ? Dataset.GetVariableValues(v.Value, 0, 50).Distinct().Count()
: Dataset.GetVariableValues(v.Value).Distinct().Count()
where DistinctValues < MaximumClasses
select v;
if (!validTargetVariables.Any())
throw new ArgumentException("Import of classification problem data was not successfull, because no target variable was found." +
" A target variable must have at most " + MaximumClasses + " distinct values to be applicable to classification.");
((ConstrainedValueParameter)TargetVariableParameter).ValidValues.Clear();
foreach (var variableName in validTargetVariables)
((ConstrainedValueParameter)TargetVariableParameter).ValidValues.Add(variableName);
TargetVariable = validTargetVariables.FirstOrDefault();
InputVariables = new CheckedItemList(variableNames).AsReadOnly();
if (TargetVariable != null) InputVariables.SetItemCheckedState(TargetVariable, false);
int middle = (int)(csvFileParser.Rows * 0.5);
TrainingSamplesEnd = new IntValue(middle);
TrainingSamplesStart = new IntValue(0);
TestSamplesEnd = new IntValue(csvFileParser.Rows);
TestSamplesStart = new IntValue(middle);
UpdateClassValues();
suppressEvents = false;
OnProblemDataChanged(EventArgs.Empty);
}
protected override void OnProblemDataChanged(EventArgs e) {
base.OnProblemDataChanged(e);
if (!suppressEvents)
UpdateClassValues();
}
private void UpdateClassValues() {
sortedClassValues = Dataset.GetVariableValues(TargetVariable.Value).Distinct().ToList();
sortedClassValues.Sort();
ResetMisclassificationMatrix();
UpdateClassNames();
}
private void UpdateClassNames() {
if (ClassNames != null) DeregisterParameterValueEvents();
StringArray array = new StringArray(NumberOfClasses);
int i = 0;
foreach (double classValue in SortedClassValues) {
array[i] = "Class " + classValue;
i++;
}
ClassNames = array;
UpdateMisclassifciationMatrixHeaders();
RegisterParameterValueEvents();
}
private void RegisterParameterEvents() {
ClassNamesParameter.ValueChanged += new EventHandler(ClassNamesChanged);
}
private void RegisterParameterValueEvents() {
ClassNames.ItemChanged += new EventHandler>(ClassNamesChanged);
}
private void DeregisterParameterValueEvents() {
ClassNames.ItemChanged -= new EventHandler>(ClassNamesChanged);
}
private void ClassNamesChanged(object sender, EventArgs e) {
UpdateMisclassifciationMatrixHeaders();
}
private void ResetMisclassificationMatrix() {
double[,] matrix = new double[NumberOfClasses, NumberOfClasses];
for (int i = 0; i < NumberOfClasses; i++) {
for (int j = 0; j < NumberOfClasses; j++)
if (i != j) matrix[i, j] = 1;
}
if (MisclassificationMatrix == null)
MisclassificationMatrix = new DoubleMatrix(matrix);
if (MisclassificationMatrix.Rows != NumberOfClasses)
MisclassificationMatrix = new DoubleMatrix(matrix);
if (MisclassificationMatrix.Columns != NumberOfClasses)
MisclassificationMatrix = new DoubleMatrix(matrix);
}
private void UpdateMisclassifciationMatrixHeaders() {
MisclassificationMatrix.RowNames = ClassNames.Select(name => "Estimated " + name).ToList();
MisclassificationMatrix.ColumnNames = ClassNames.Select(name => "Actual " + name).ToList();
}
}
}