#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;
namespace HeuristicLab.Problems.DataAnalysis {
[StorableClass]
[Item("ClassificationProblemData", "Represents an item containing all data defining a classification problem.")]
public class ClassificationProblemData : DataAnalysisProblemData, IClassificationProblemData {
private const string TargetVariableParameterName = "TargetVariable";
private const string ClassNamesParameterName = "ClassNames";
private const string ClassificationPenaltiesParameterName = "ClassificationPenalties";
#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 Dataset defaultDataset;
private static IEnumerable defaultAllowedInputVariables;
private static string defaultTargetVariable;
static ClassificationProblemData() {
defaultDataset = new Dataset(defaultVariableNames, defaultData);
defaultDataset.Name = "Wisconsin classification problem";
defaultDataset.Description = "subset from to ..";
defaultAllowedInputVariables = defaultVariableNames.Except(new List() { "sample", "class" });
defaultTargetVariable = "class";
}
#endregion
#region parameter properties
public IValueParameter TargetVariableParameter {
get { return (IValueParameter)Parameters[TargetVariableParameterName]; }
}
public IFixedValueParameter ClassNamesParameter {
get { return (IFixedValueParameter)Parameters[ClassNamesParameterName]; }
}
public IFixedValueParameter ClassificationPenaltiesParameter {
get { return (IFixedValueParameter)Parameters[ClassificationPenaltiesParameterName]; }
}
#endregion
#region properties
public string TargetVariable {
get { return TargetVariableParameter.Value.Value; }
}
private List classValues;
public List ClassValues {
get {
if (classValues == null) {
classValues = Dataset.GetEnumeratedVariableValues(TargetVariableParameter.Value.Value).Distinct().ToList();
classValues.Sort();
}
return classValues;
}
}
IEnumerable IClassificationProblemData.ClassValues {
get { return ClassValues; }
}
public int Classes {
get { return ClassValues.Count; }
}
private List classNames;
public List ClassNames {
get {
if (classNames == null) {
classNames = new List();
for (int i = 0; i < ClassNamesParameter.Value.Rows; i++)
classNames.Add(ClassNamesParameter.Value[i, 0]);
}
return classNames;
}
}
IEnumerable IClassificationProblemData.ClassNames {
get { return ClassNames; }
}
private Dictionary, double> classificationPenaltiesCache = new Dictionary, double>();
#endregion
[StorableConstructor]
protected ClassificationProblemData(bool deserializing) : base(deserializing) { }
[StorableHook(HookType.AfterDeserialization)]
private void AfterDeserialization() {
RegisterParameterEvents();
}
protected ClassificationProblemData(ClassificationProblemData original, Cloner cloner)
: base(original, cloner) {
RegisterParameterEvents();
}
public override IDeepCloneable Clone(Cloner cloner) { return new ClassificationProblemData(this, cloner); }
public ClassificationProblemData() : this(defaultDataset, defaultAllowedInputVariables, defaultTargetVariable) { }
public ClassificationProblemData(Dataset dataset, IEnumerable allowedInputVariables, string targetVariable)
: base(dataset, allowedInputVariables) {
var variables = InputVariables.Select(x => x.AsReadOnly()).ToList();
Parameters.Add(new ConstrainedValueParameter(TargetVariableParameterName, new ItemSet(variables), variables.Where(x => x.Value == targetVariable).First()));
Parameters.Add(new FixedValueParameter(ClassNamesParameterName, ""));
Parameters.Add(new FixedValueParameter(ClassificationPenaltiesParameterName, ""));
ResetTargetVariableDependentMembers();
RegisterParameterEvents();
}
private void ResetTargetVariableDependentMembers() {
DergisterParameterEvents();
classNames = null;
((IStringConvertibleMatrix)ClassNamesParameter.Value).Columns = 1;
((IStringConvertibleMatrix)ClassNamesParameter.Value).Rows = ClassValues.Count;
for (int i = 0; i < Classes; i++)
ClassNamesParameter.Value[i, 0] = "Class " + ClassValues[i];
ClassNamesParameter.Value.ColumnNames = new List() { "ClassNames" };
ClassNamesParameter.Value.RowNames = ClassValues.Select(s => "ClassValue: " + s);
classificationPenaltiesCache.Clear();
((ValueParameter)ClassificationPenaltiesParameter).ReactOnValueToStringChangedAndValueItemImageChanged = false;
((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;
}
}
((ValueParameter)ClassificationPenaltiesParameter).ReactOnValueToStringChangedAndValueItemImageChanged = true;
RegisterParameterEvents();
}
public string GetClassName(double classValue) {
if (!ClassValues.Contains(classValue)) throw new ArgumentException();
int index = ClassValues.IndexOf(classValue);
return ClassNames[index];
}
public double GetClassValue(string className) {
if (!ClassNames.Contains(className)) throw new ArgumentException();
int index = ClassNames.IndexOf(className);
return ClassValues[index];
}
public void SetClassName(double classValue, string className) {
if (!classValues.Contains(classValue)) throw new ArgumentException();
int index = ClassValues.IndexOf(classValue);
ClassNames[index] = className;
ClassNamesParameter.Value[index, 0] = className;
}
public double GetClassificationPenalty(string correctClassName, string estimatedClassName) {
return GetClassificationPenalty(GetClassValue(correctClassName), GetClassValue(estimatedClassName));
}
public double GetClassificationPenalty(double correctClassValue, double estimatedClassValue) {
var key = Tuple.Create(correctClassValue, estimatedClassValue);
if (!classificationPenaltiesCache.ContainsKey(key)) {
int correctClassIndex = ClassValues.IndexOf(correctClassValue);
int estimatedClassIndex = ClassValues.IndexOf(estimatedClassValue);
classificationPenaltiesCache[key] = ClassificationPenaltiesParameter.Value[correctClassIndex, estimatedClassIndex];
}
return classificationPenaltiesCache[key];
}
public void SetClassificationPenalty(string correctClassName, string estimatedClassName, double penalty) {
SetClassificationPenalty(GetClassValue(correctClassName), GetClassValue(estimatedClassName), penalty);
}
public void SetClassificationPenalty(double correctClassValue, double estimatedClassValue, double penalty) {
var key = Tuple.Create(correctClassValue, estimatedClassValue);
int correctClassIndex = ClassValues.IndexOf(correctClassValue);
int estimatedClassIndex = ClassValues.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>(MatrixParameter_ItemChanged);
ClassificationPenaltiesParameter.Value.Reset += new EventHandler(Parameter_ValueChanged);
ClassificationPenaltiesParameter.Value.ItemChanged += new EventHandler>(MatrixParameter_ItemChanged);
}
private void DergisterParameterEvents() {
TargetVariableParameter.ValueChanged -= new EventHandler(TargetVariableParameter_ValueChanged);
ClassNamesParameter.Value.Reset -= new EventHandler(Parameter_ValueChanged);
ClassNamesParameter.Value.ItemChanged -= new EventHandler>(MatrixParameter_ItemChanged);
ClassificationPenaltiesParameter.Value.Reset -= new EventHandler(Parameter_ValueChanged);
ClassificationPenaltiesParameter.Value.ItemChanged -= new EventHandler>(MatrixParameter_ItemChanged);
}
private void TargetVariableParameter_ValueChanged(object sender, EventArgs e) {
classValues = null;
ResetTargetVariableDependentMembers();
OnChanged();
}
private void Parameter_ValueChanged(object sender, EventArgs e) {
OnChanged();
}
private void MatrixParameter_ItemChanged(object sender, EventArgs e) {
OnChanged();
}
#endregion
#region Import from file
public static ClassificationProblemData ImportFromFile(string fileName) {
TableFileParser csvFileParser = new TableFileParser();
csvFileParser.Parse(fileName);
Dataset dataset = new Dataset(csvFileParser.VariableNames, csvFileParser.Values);
dataset.Name = Path.GetFileName(fileName);
ClassificationProblemData problemData = new ClassificationProblemData(dataset, dataset.VariableNames.Skip(1), dataset.VariableNames.First());
problemData.Name = "Data imported from " + Path.GetFileName(fileName);
return problemData;
}
#endregion
}
}