private void Results_Analysis()
{
this.Invoke((MethodInvoker) delegate()
{
ChoosenCriteria = ddlCriteris.Text;
});
for (int i = 0; i < TestResults.Count; i++)
{
Hashtable htclass = new Hashtable();
foreach (DictionaryEntry de in DistinctClasses)
{
PredictedClassLabelAnalysis p = new PredictedClassLabelAnalysis();
htclass.Add(de.Key, p);
}
FoundItemsetsProperties t = (FoundItemsetsProperties)TestResults[i];
foreach (string rule in t.itemsetIndexInTrainingSetClassLabel)
{
string Category = StringOperations.GetAttrVal("Category", rule);
string Oc = StringOperations.GetAttrVal("Oc", rule);
string Fc = StringOperations.GetAttrVal("Fc", rule);
string Rids = StringOperations.GetAttrVal("Rid", rule);
//1- Get DistinctClasses and iterate over them
//2- Get All mutual Rows for each class
//3- Get Max(Oc/M)
//4- Get Max(Oc/c) where c = number of rows for this class
foreach (DictionaryEntry str in DistinctClasses)
{
if (Category == str.Key.ToString())
{
PredictedClassLabelAnalysis p = (PredictedClassLabelAnalysis)htclass[Category];
p.rowIDs += Rids;
p.Ocs.Add(Oc);
p.className = Category;
p.Supp_c_to_i.Add(double.Parse(CategoriesN[Category].ToString()) / double.Parse(NrOfInstances.ToString()));
p.Supp_i_to_c.Add(double.Parse(Oc.ToString()) / double.Parse(NrOfInstances.ToString()));
p.Conf_c_to_i.Add(double.Parse(Oc.ToString()) / double.Parse(CategoriesN[Category].ToString()));
htclass[Category] = p;
break;
}
}
}
if (ChoosenCriteria == "MaxFcXOc" || true)
{
StringBuilder sb2 = new StringBuilder();
PredictedClassLabelAnalysis f = new PredictedClassLabelAnalysis();
foreach (DictionaryEntry de in htclass)
{
//if (f.GetMutualRowsIds().Count < ((PredictedClassLabelAnalysis)de.Value).GetMutualRowsIds().Count) // swap to get max
//{
f.rowIDs = ((PredictedClassLabelAnalysis)de.Value).rowIDs;
f.className = ((PredictedClassLabelAnalysis)de.Value).className;
f.Ocs = ((PredictedClassLabelAnalysis)de.Value).Ocs;
f.Conf_c_to_i = ((PredictedClassLabelAnalysis)de.Value).Conf_c_to_i;
f.Supp_c_to_i = ((PredictedClassLabelAnalysis)de.Value).Supp_c_to_i;
f.Supp_i_to_c = ((PredictedClassLabelAnalysis)de.Value).Supp_i_to_c;
f.DistinctRowsIDs = ((PredictedClassLabelAnalysis)de.Value).GetMutualRowsIds();
f.rowIDsCount = f.DistinctRowsIDs.Count;
// }
sb2.Append(f.className + "," + f.rowIDsCount.ToString() + "," + f.GetMax_Conf_c_to_i().ToString() + "," + f.GetMax_Supp_c_to_i().ToString() + "," + f.GetMax_Supp_i_to_c().ToString() + " | ");
}
SetTxtText(Environment.NewLine + i.ToString() + "," + sb2.ToString());
}
// SetTxtText(Environment.NewLine + i.ToString()+"," + f.className);
//f = new PredictedClassLabelAnalysis();
// FinalObjectsClassLabels = new ArrayList();
}
}
public void LoadTrainingFile(string path)
{
try
{
string line;
// First File Lists
line = "";
bool isFirst = true;
string[] strCn = new string[5];
using (StreamReader reader = new StreamReader(path))
{
while ((line = reader.ReadLine()) != null)
{
if (!String.IsNullOrWhiteSpace(line) && !String.IsNullOrEmpty(line))
{
string s = line.Replace(" =", "=").Replace("'", "\"");
line = s;
if (line.IndexOf("<Rule ") != -1)
{
string RuleClass = StringOperations.GetAttrVal("Category", line);
if (!DistinctClasses.ContainsKey(RuleClass))
{
PredictedClassLabelAnalysis p = new PredictedClassLabelAnalysis();
p.className = RuleClass;
p.Rules_ClassCount++;
DistinctClasses.Add(RuleClass, p);
}
else
{
PredictedClassLabelAnalysis p = (PredictedClassLabelAnalysis)DistinctClasses[RuleClass];
//p.className = RuleClass;
p.Rules_ClassCount++;
DistinctClasses[RuleClass] = p;
}
FirstFileAsSubsetsClasses.Add(line);
}
if (line.IndexOf("</Rule>") > -1)
{
FirstFileAsSubsets.Add((ArrayList)SubSet.Clone());
SubSet.Clear();
}
if (line.IndexOf("<Tuple") > -1)
{
if (!SubSet.Contains(line))
{
SubSet.Add(line);
}
}
if (line.IndexOf("<Root") > -1)
{
NrOfInstances = Int32.Parse(StringOperations.GetAttrVal("M", line));
string z = StringOperations.GetAttrVal("CategoriesN", line);// In this section Get each category with nr of cases in training set
strCn = z.Split(',');
if (strCn.Length > 0)
{
for (int ii = 0; ii < strCn.Length - 1; ii += 2)
{
CategoriesN.Add(strCn[ii], strCn[ii + 1]);
}
}
}
}
}
}
for (int g = 0; g < strCn.Length - 1; g += 2)
{
PredictedClassLabelAnalysis p = (PredictedClassLabelAnalysis)DistinctClasses[strCn[g]];
p.ds_ClassCount = Int32.Parse(strCn[g + 1]);
DistinctClasses[strCn[g]] = p;
}
int NumberOfRules = FirstFileAsSubsetsClasses.Count;
for (int i = 0; i < NumberOfRules; i++)
{
//if (!FirstFileDs.Contains(FirstFileAsSubsetsClasses[i].ToString()))
//{
// FirstFileDs.Add(FirstFileAsSubsetsClasses[i].ToString());
//}
for (int j = 0; j < ((ArrayList)FirstFileAsSubsets[i]).Count; j++)
{
if (!FirstFileFs.Contains(((ArrayList)FirstFileAsSubsets[i])[j].ToString()))
{
FirstFileFs.Add(((ArrayList)FirstFileAsSubsets[i])[j].ToString());
}
}
}
ArrayList tmpFs = new ArrayList();
foreach (string x in FirstFileFs) // need to extract all features
{
string y = StringOperations.GetAttrVal("Prop", x); // x.Substring(x.IndexOf("<Rule ") + ("<Rule ").Length + 2, x.IndexOf("=")-(x.IndexOf("<Rule ") + ("<Rule ").Length)-2);
string v = StringOperations.GetAttrVal("Val", x);
if (!tmpFs.Contains(y))
{
tmpFs.Add(y);
FeaturesCharacteristics fc = new FeaturesCharacteristics();
fc.FeatureName = y;
fc.FeatureValues.Add(v);
TrainingSetMetaData.Add(y, fc);
}
else
{
((FeaturesCharacteristics)TrainingSetMetaData[y]).FeatureValues.Add(v);
}
if (((FeaturesCharacteristics)TrainingSetMetaData[y]).isContinuous != false)
{
if (IsNumber(v))
{
((FeaturesCharacteristics)TrainingSetMetaData[y]).isContinuous = true;
}
}
}
foreach (DictionaryEntry de in TrainingSetMetaData)
{
TrainingSetMetaDataList.Add((FeaturesCharacteristics)de.Value);
}
for (int i = 0; i < FirstFileAsSubsets.Count; i++)
{
ArrayList tmp = new ArrayList();
ArrayList itemset = new ArrayList();
tmp = (ArrayList)(FirstFileAsSubsets[i]);
foreach (string tuple in tmp)
{
int featureIndex = 0;
foreach (FeaturesCharacteristics fc in TrainingSetMetaDataList)
{
if (StringOperations.GetAttrVal("Prop", tuple) == fc.FeatureName) // recoginze feature value
{
int ValueIndex = 0;
foreach (string val in fc.FeatureValues)
{
if (StringOperations.GetAttrVal("Val", tuple) == val)
{
itemset.Add(featureIndex.ToString() + "," + ValueIndex.ToString());
break;
}
ValueIndex++;
}
}
featureIndex++;
}
}
TrainingSubsets.Add((ArrayList)itemset.Clone());
}
}
catch
{ }
}
