protected override AbstractClassifier GenerateClassifier()
{
AbstractClassifier ac;
ac = new J48();
//ac = new DecisionStump();
//ac = new LMT();
//ac = new M5P();
//ac = new NaiveBayes();
//ac = new BayesNet();
//bn.setUseADTree(true);
//bn.setSearchAlgorithm(new weka.classifiers.bayes.net.search.ci.CISearchAlgorithm());
//ac = new LWL();
//lwl.setKNN(3);
//ac = new RandomForest();
//rf.setMaxDepth(20);
//rf.setNumFeatures(100);
//rf.setNumTrees(100);
//ac = new REPTree();
//ac = new IBk(3);
//ac = new REPTree();
//ac.setUseMDLcorrection(false);
//ac.setSubtreeRaising(true);
//ac.setUseLaplace(true);
//ac.setReducedErrorPruning(true);
return ac;
}
public FormForClassificationTree DisplayTree(cGlobalInfo GlobalInfo, J48 J48Model, bool IsCellular)
{
FormForClassificationTree WindowForTree = new FormForClassificationTree();
string StringForTree = J48Model.graph().Remove(0, J48Model.graph().IndexOf("{") + 2);
WindowForTree.gViewerForTreeClassif.Graph = cGlobalInfo.WindowHCSAnalyzer.ComputeAndDisplayGraph(StringForTree.Remove(StringForTree.Length - 3, 3), IsCellular);
return WindowForTree;
}
public void EvaluateIncrementalExamples()
{
//Calculate the number of increments for the training data based on the increment size
int numberIncrements = (int)Math.Ceiling((double)(this.numExamples * (DEFAULT_FOLDS - 1) / DEFAULT_FOLDS) / (double)TRAINING_INCRMENETS);
this.trainingSizeMatrix = new double[this.classCount, DEFAULT_FOLDS, numberIncrements];
for (int i = 0; (i < this.classCount); i++)
for (int j = 0; (j < DEFAULT_FOLDS); j++)
for (int k = 0; (k < numberIncrements); k++)
this.trainingSizeMatrix[i, j, k] = 0.0;
//Randomize the data
Randomize randomizeFilter = new Randomize();
randomizeFilter.setInputFormat(this.data);
Instances randomData = Filter.useFilter(this.data, randomizeFilter);
//Run incremental training data for each fold and store the results for each activity
for (int i = 1; (i <= DEFAULT_FOLDS); i++)
{
//Training folds filter
RemoveFolds trainingFoldsFilter = new RemoveFolds();
trainingFoldsFilter.set_NumFolds(DEFAULT_FOLDS);
trainingFoldsFilter.inputFormat(randomData);
trainingFoldsFilter.set_InvertSelection(true);
trainingFoldsFilter.set_Fold(i);
Instances alltraining = Filter.useFilter(randomData, trainingFoldsFilter);
RemoveFolds testFoldsFilter = new RemoveFolds();
testFoldsFilter.set_NumFolds(DEFAULT_FOLDS);
testFoldsFilter.inputFormat(randomData);
testFoldsFilter.set_InvertSelection(false);
testFoldsFilter.set_Fold(i);
Instances test = Filter.useFilter(randomData, testFoldsFilter);
for (int j = 1; (j <= numberIncrements); j++)
{
//Range Filter
RemoveRange rangeFilter = new RemoveRange();
rangeFilter.setInputFormat(alltraining);
int first = 1;
int last = j * TRAINING_INCRMENETS;
if (last > (alltraining.m_Instances.size()))
last = alltraining.m_Instances.size();
string range = first.ToString() + "-" + last.ToString();
rangeFilter.set_InstancesIndices(range);
rangeFilter.set_InvertSelection(true);
Instances training = Filter.useFilter(alltraining, rangeFilter);
//ready for training and testing
J48 tree = new J48(); // new instance of tree
tree.set_MinNumObj(10);
tree.set_ConfidenceFactor((float)0.25);
tree.buildClassifier(training); // build classifier
Evaluation eval = new Evaluation(training);
eval.evaluateModel(tree, test);
//store the results for each activity
for (int k = 0; (k < this.classCount); k++)
{
double tpRate = eval.truePositiveRate(k);
trainingSizeMatrix[k, i - 1, j - 1] = +eval.truePositiveRate(k);
}
}
}
TextWriter tw = new StreamWriter("evaluation.txt");
for (int i = 0; (i < this.classCount); i++)
{
string line = randomData.attribute(this.data.numAttributes() - 1).value_Renamed(i);
for (int k = 0; (k < numberIncrements); k++)
{
double percentage = 0.0;
for (int j = 0; (j < DEFAULT_FOLDS); j++)
percentage += this.trainingSizeMatrix[i, j, k];
percentage /= DEFAULT_FOLDS;
percentage *= 100;
line += "," + percentage.ToString("0.00");
}
tw.WriteLine(line);
}
tw.Close();
for (int i = 0; (i < this.classCount); i++)
{
string activity = randomData.attribute(this.data.numAttributes() - 1).value_Renamed(i);
tw = new StreamWriter("evaluation-" + activity + ".txt");
for (int j = 0; (j < DEFAULT_FOLDS); j++)
{
string line = j.ToString();
for (int k = 0; (k < numberIncrements); k++)
{
double percentage = this.trainingSizeMatrix[i, j, k];
percentage /= DEFAULT_FOLDS;
percentage *= 100;
line += "\t" + percentage.ToString("0.00");
}
tw.WriteLine(line);
}
tw.Close();
}
}
public static void Test_predictClass(string classifierFileName)
{
FileReader javaFileReader = new FileReader(classifierFileName);
weka.core.Instances insts = new weka.core.Instances(javaFileReader);
javaFileReader.close();
insts.setClassIndex(insts.numAttributes() - 1);
weka.classifiers.Classifier cl = new weka.classifiers.trees.J48();
System.Console.WriteLine("Performing " + percentSplit + "% split evaluation.");
#region Manual Cross Fold
Instances foldsData = new Instances(insts);
int folds = 10;
for (int n = 0; n < folds; n++)
{
Instances trainFold = foldsData.trainCV(folds, n);
Instances testFold = foldsData.testCV(folds, n);
}
#endregion
#region
int trainSize = insts.numInstances() * percentSplit / 100;
int testSize = insts.numInstances() - trainSize;
weka.core.Instances train = new weka.core.Instances(insts, 0, trainSize);
cl.buildClassifier(train);
#endregion
//Classifier cls = new J48();
Evaluation eval = new Evaluation(insts);
java.util.Random rand = new java.util.Random(1); // using seed = 1
int fold = 10;
eval.crossValidateModel(cl, insts, fold, rand);
System.Console.WriteLine("toClassDetailsString" + eval.toClassDetailsString());
System.Console.WriteLine("toMatrixString\n" + eval.toMatrixString());
System.Console.WriteLine("toCumulativeMarginDistributionString\n" + eval.toCumulativeMarginDistributionString());
//System.Console.WriteLine("predictions\n" + eval.predictions());
System.Console.ReadKey();
//var numnerOfInst = insts.numInstances();
//for (int i = trainSize; i < numnerOfInst; i++)
//{
// weka.core.Instance currentInst = insts.instance(i);
// double pred = cl.classifyInstance(currentInst);
// System.Console.WriteLine("class Index: " + insts.instance(i).classIndex());
// System.Console.WriteLine(", class value: " + insts.instance(i).classValue());
// System.Console.WriteLine(", ID: " + insts.instance(i).value(0));
// System.Console.WriteLine(", actual: " + insts.classAttribute().value((int)insts.instance(i).classValue()));
// System.Console.WriteLine(", predicted: " + insts.classAttribute().value((int)pred));
//}
}