// Test the classification result of each map that a user played,
// with the data available as if they were playing through it
public static void classifyTest(String dataString, String playerID)
{
String results = "";
try {
java.io.StringReader stringReader = new java.io.StringReader(dataString);
java.io.BufferedReader buffReader = new java.io.BufferedReader(stringReader);
/* NOTE THAT FOR NAIVE BAYES ALL WEIGHTS CAN BE = 1*/
//weka.core.converters.ConverterUtils.DataSource source = new weka.core.converters.ConverterUtils.DataSource("iris.arff");
weka.core.Instances data = new weka.core.Instances(buffReader); //source.getDataSet();
// setting class attribute if the data format does not provide this information
// For example, the XRFF format saves the class attribute information as well
if (data.classIndex() == -1)
{
data.setClassIndex(data.numAttributes() - 1);
}
weka.classifiers.Classifier cl;
for (int i = 3; i < data.numInstances(); i++)
{
cl = new weka.classifiers.bayes.NaiveBayes();
//cl = new weka.classifiers.trees.J48();
//cl = new weka.classifiers.lazy.IB1();
//cl = new weka.classifiers.functions.MultilayerPerceptron();
((weka.classifiers.functions.MultilayerPerceptron)cl).setHiddenLayers("12");
weka.core.Instances subset = new weka.core.Instances(data, 0, i);
cl.buildClassifier(subset);
weka.classifiers.Evaluation eval = new weka.classifiers.Evaluation(subset);
eval.crossValidateModel(cl, subset, 3, new java.util.Random(1));
results = results + eval.pctCorrect(); // For accuracy measurement
/* For Mathews Correlation Coefficient */
//double TP = eval.numTruePositives(1);
//double FP = eval.numFalsePositives(1);
//double TN = eval.numTrueNegatives(1);
//double FN = eval.numFalseNegatives(1);
//double correlationCoeff = ((TP*TN)-(FP*FN))/Math.Sqrt((TP+FP)*(TP+FN)*(TN+FP)*(TN+FN));
//results = results + correlationCoeff;
if (i != data.numInstances() - 1)
{
results = results + ", ";
}
if (i == data.numInstances() - 1)
{
Debug.Log("Player: " + playerID + ", Num Maps: " + data.numInstances() + ", AUC: " + eval.areaUnderROC(1));
}
}
} catch (java.lang.Exception ex)
{
Debug.LogError(ex.getMessage());
}
// Write values to file for a matlab read
// For accuracy
StreamWriter writer = new StreamWriter("DataForMatlab/" + playerID + "_CrossFoldValidations_NeuralNet.txt");
//StreamWriter writer = new StreamWriter("DataForMatlab/"+playerID+"_CrossFoldCorrCoeff.txt"); // For mathews cc
writer.WriteLine(results);
writer.Close();
Debug.Log(playerID + " has been written to file");
}
public static void Test()
{
weka.core.Instances data = new weka.core.Instances(new java.io.FileReader("./data/Classification/Communication.arff"));
data.setClassIndex(data.numAttributes() - 1);
weka.classifiers.Classifier cls = new weka.classifiers.bayes.BayesNet();
//Save BayesNet results in .txt file
using (System.IO.StreamWriter file = new System.IO.StreamWriter("./data/Classification/Communication_Report.txt"))
{
file.WriteLine("Performing " + percentSplit + "% split evaluation.");
int runs = 1;
// perform cross-validation
for (int i = 0; i < runs; i++)
{
// randomize data
int seed = i + 1;
java.util.Random rand = new java.util.Random(seed);
weka.core.Instances randData = new weka.core.Instances(data);
randData.randomize(rand);
//weka.classifiers.Evaluation eval = new weka.classifiers.Evaluation(randData);
int trainSize = (int)Math.Round((double)data.numInstances() * percentSplit / 100);
int testSize = data.numInstances() - trainSize;
weka.core.Instances train = new weka.core.Instances(data, 0, 0);
weka.core.Instances test = new weka.core.Instances(data, 0, 0);
train.setClassIndex(train.numAttributes() - 1);
test.setClassIndex(test.numAttributes() - 1);
//Print classifier analytics for all the dataset
file.WriteLine("EVALUATION OF TEST DATASET.");
//int numCorrect = 0;
for (int j = 0; j < data.numInstances(); j++)
{
weka.core.Instance currentInst = randData.instance(j);
if (j < trainSize)
{
train.add(currentInst);
}
else
{
test.add(currentInst);
/*
* double predictedClass = cls.classifyInstance(currentInst);
*
* double[] prediction = cls.distributionForInstance(currentInst);
*
* for (int p = 0; p < prediction.Length; p++)
* {
* file.WriteLine("Probability of class [{0}] for [{1}] is: {2}", currentInst.classAttribute().value(p), currentInst, Math.Round(prediction[p], 4));
* }
* file.WriteLine();
*
* file.WriteLine();
* if (predictedClass == data.instance(j).classValue())
* numCorrect++;*/
}
}
// build and evaluate classifier
cls.buildClassifier(train);
// Test the model
weka.classifiers.Evaluation eval = new weka.classifiers.Evaluation(randData);
eval.evaluateModel(cls, test);
// Print the results as in Weka explorer:
//Print statistics
String strSummaryTest = eval.toSummaryString();
file.WriteLine(strSummaryTest);
file.WriteLine();
//Print detailed class statistics
file.WriteLine(eval.toClassDetailsString());
file.WriteLine();
//Print confusion matrix
file.WriteLine(eval.toMatrixString());
file.WriteLine();
// Get the confusion matrix
double[][] cmMatrixTest = eval.confusionMatrix();
System.Console.WriteLine("Bayesian Network results saved in Communication_Report.txt file successfully.");
}
}
}
public static void BayesTest()
{
try
{
weka.core.Instances insts = new weka.core.Instances(new java.io.FileReader("iris.arff"));
insts.setClassIndex(insts.numAttributes() - 1);
weka.classifiers.Classifier cl = new weka.classifiers.bayes.BayesNet();
System.Console.WriteLine("Performing " + percentSplit + "% split evaluation.");
//randomize the order of the instances in the dataset.
weka.filters.Filter myRandom = new weka.filters.unsupervised.instance.Randomize();
myRandom.setInputFormat(insts);
insts = weka.filters.Filter.useFilter(insts, myRandom);
int trainSize = insts.numInstances() * percentSplit / 100;
int testSize = insts.numInstances() - trainSize;
weka.core.Instances train = new weka.core.Instances(insts, 0, trainSize);
weka.core.Instances test = new weka.core.Instances(insts, 0, 0);
cl.buildClassifier(train);
//print model
System.Console.WriteLine(cl);
int numCorrect = 0;
for (int i = trainSize; i < insts.numInstances(); i++)
{
weka.core.Instance currentInst = insts.instance(i);
double predictedClass = cl.classifyInstance(currentInst);
test.add(currentInst);
double[] prediction = cl.distributionForInstance(currentInst);
for (int x = 0; x < prediction.Length; x++)
{
System.Console.WriteLine("Probability of class [{0}] for [{1}] is: {2}", currentInst.classAttribute().value(x), currentInst, Math.Round(prediction[x], 4));
}
System.Console.WriteLine();
if (predictedClass == insts.instance(i).classValue())
{
numCorrect++;
}
}
System.Console.WriteLine(numCorrect + " out of " + testSize + " correct (" +
(double)((double)numCorrect / (double)testSize * 100.0) + "%)");
// Train the model
weka.classifiers.Evaluation eTrain = new weka.classifiers.Evaluation(train);
eTrain.evaluateModel(cl, train);
// Print the results as in Weka explorer:
//Print statistics
String strSummaryTrain = eTrain.toSummaryString();
System.Console.WriteLine(strSummaryTrain);
//Print detailed class statistics
System.Console.WriteLine(eTrain.toClassDetailsString());
//Print confusion matrix
System.Console.WriteLine(eTrain.toMatrixString());
// Get the confusion matrix
double[][] cmMatrixTrain = eTrain.confusionMatrix();
// Test the model
weka.classifiers.Evaluation eTest = new weka.classifiers.Evaluation(test);
eTest.evaluateModel(cl, test);
// Print the results as in Weka explorer:
//Print statistics
String strSummaryTest = eTest.toSummaryString();
System.Console.WriteLine(strSummaryTest);
//Print detailed class statistics
System.Console.WriteLine(eTest.toClassDetailsString());
//Print confusion matrix
System.Console.WriteLine(eTest.toMatrixString());
// Get the confusion matrix
double[][] cmMatrixTest = eTest.confusionMatrix();
}
catch (java.lang.Exception ex)
{
ex.printStackTrace();
}
}
public static void cvdTest()
{
weka.core.Instances data = new weka.core.Instances(new java.io.FileReader("./data/Classification/Communication.arff"));
data.setClassIndex(data.numAttributes() - 1);
weka.classifiers.Classifier cls = new weka.classifiers.bayes.NaiveBayes();
//Save BayesNet results in .txt file
using (System.IO.StreamWriter file = new System.IO.StreamWriter("./data/Classification/Communication_Report.txt"))
{
int runs = 1;
int folds = 10;
// perform cross-validation
for (int i = 0; i < runs; i++)
{
// randomize data
int seed = i + 1;
java.util.Random rand = new java.util.Random(seed);
weka.core.Instances randData = new weka.core.Instances(data);
randData.randomize(rand);
if (randData.classAttribute().isNominal())
{
randData.stratify(folds);
}
weka.classifiers.Evaluation eval = new weka.classifiers.Evaluation(randData);
for (int n = 0; n < folds; n++)
{
weka.core.Instances train = randData.trainCV(folds, n);
weka.core.Instances test = randData.testCV(folds, n);
// build and evaluate classifier
//weka.classifiers.Classifier clsCopy = weka.classifiers.Classifier.makeCopy(cls);
cls.buildClassifier(train);
//eval.evaluateModel(cls, test);
//Print classifier analytics for all the dataset
file.WriteLine("EVALUATION OF TEST DATASET.");
// Test the model
weka.classifiers.Evaluation eTest = new weka.classifiers.Evaluation(test);
eTest.evaluateModel(cls, test);
// Print the results as in Weka explorer:
//Print statistics
String strSummaryTest = eTest.toSummaryString();
file.WriteLine(strSummaryTest);
file.WriteLine();
//Print detailed class statistics
file.WriteLine(eTest.toClassDetailsString());
file.WriteLine();
//Print confusion matrix
file.WriteLine(eTest.toMatrixString());
file.WriteLine();
// Get the confusion matrix
double[][] cmMatrixTest = eTest.confusionMatrix();
System.Console.WriteLine("Bayesian Network results saved in Communication_Report.txt file successfully.");
}
//Print classifier analytics for all the dataset
file.WriteLine("EVALUATION OF ALL DATASET.");
cls.buildClassifier(data);
// Train the model
weka.classifiers.Evaluation eAlldata = new weka.classifiers.Evaluation(data);
eAlldata.evaluateModel(cls, data);
// Print the results as in Weka explorer:
//Print statistics
String strSummaryAlldata = eAlldata.toSummaryString();
file.WriteLine(strSummaryAlldata);
file.WriteLine();
//Print detailed class statistics
file.WriteLine(eAlldata.toClassDetailsString());
file.WriteLine();
//Print confusion matrix
file.WriteLine(eAlldata.toMatrixString());
file.WriteLine("----------------");
//print model
file.WriteLine(cls);
file.WriteLine();
}
}
}
private void buttonNewClassificationProcess_Click(object sender, EventArgs e)
{
cMachineLearning MachineLearningForClassif = new cMachineLearning(this.GlobalInfo);
//cParamAlgo AlgoAndParameters = MachineLearningForClassif.AskAndGetClassifAlgo();
// List<bool> ListClassSelected = ((PanelForClassSelection)panelForClassSelectionClustering.Controls[0]).GetListSelectedClass();
// cInfoClass InfoClass = new cInfoClass();
//Instances ListInstances = CompleteScreening.GetCurrentDisplayPlate().CreateInstancesWithClasses(ListClassSelected);
cInfoClass InfoClass = cGlobalInfo.CurrentScreening.GetCurrentDisplayPlate().GetNumberOfClassesBut(comboBoxNeutralClassForClassif.SelectedIndex);
Instances ListInstances = cGlobalInfo.CurrentScreening.GetCurrentDisplayPlate().CreateInstancesWithClasses(InfoClass, comboBoxNeutralClassForClassif.SelectedIndex);
//panelForClassSelectionClustering
weka.classifiers.Evaluation EvalClassif = new weka.classifiers.Evaluation(ListInstances);
MachineLearningForClassif.PerformTraining(MachineLearningForClassif.AskAndGetClassifAlgo(),
ListInstances,
//InfoClass.NumberOfClass,
richTextBoxInfoClustering,
panelTMPForFeedBack,
out EvalClassif,
false);
}
private static void WekaTrainingPipeline(Instances data)
{
//grab SMO, config
TrainingTesting_SharedVariables.smo = new SMO();
TrainingTesting_SharedVariables.smo.setOptions(weka.core.Utils.splitOptions(" -C 1.0 -L 0.001 -P 1.0E-12 -N 0 -V -1 -W 1 -K \"weka.classifiers.functions.supportVector.PolyKernel -C 250007 -E 1.0\""));
GuiPreferences.Instance.setLog("SMO Assigned.");
//train
TrainingTesting_SharedVariables.smo.buildClassifier(data);
GuiPreferences.Instance.setLog("Training on Data");
//test on self should get 100%
weka.classifiers.Evaluation eval = new weka.classifiers.Evaluation(data);
eval.evaluateModel(TrainingTesting_SharedVariables.smo, data);
Training_Output.printWekaResults(eval.toSummaryString("\nResults\n======\n", false));
GuiPreferences.Instance.setLog("SMO Model Tested on Training Data, check that you get 100%.");
//save model serialize model
weka.core.SerializationHelper.write(GuiPreferences.Instance.WorkDirectory + "TrainSet_" + GuiPreferences.Instance.NudClassifyUsingTR.ToString() + "th_vectors_scaledCS_filteredIG.libsvm.arff.model", TrainingTesting_SharedVariables.smo);
GuiPreferences.Instance.setLog("SMO Model Serialized and saved.");
//load model deserialize model
TrainingTesting_SharedVariables.smo = (weka.classifiers.functions.SMO)weka.core.SerializationHelper.read(GuiPreferences.Instance.WorkDirectory + "TrainSet_" + GuiPreferences.Instance.NudClassifyUsingTR.ToString() + "th_vectors_scaledCS_filteredIG.libsvm.arff.model");
GuiPreferences.Instance.setLog("SMO Model DeSerialized and loaded.");
//test loaded model
eval = new weka.classifiers.Evaluation(data);
eval.evaluateModel(TrainingTesting_SharedVariables.smo, data);
Training_Output.printWekaResults(eval.toSummaryString("\nResults\n======\n", false));
GuiPreferences.Instance.setLog("SMO Model Tested on data (sanity check for loaded model).");
//display top IG on dicom view
if (Preferences.Instance.attsel == null)
{
GuiPreferences.Instance.setLog("there are no ranked IG attributes or selected attr, continuing but please fix this possible bug.");
}
GuiPreferences.Instance.setLog("Dicom Viewer Displaying..");
string dicomDir = GuiPreferences.Instance.WorkDirectory;
dicomDir = dicomDir.Substring(0, dicomDir.Length - 4) + @"master\";
string[] files = System.IO.Directory.GetFiles(dicomDir, "*.dcm");
string firstFile = files[0].Substring(files[0].LastIndexOf(@"\") + 1);
bool thresholdOrVoxelAmount;
if (GuiPreferences.Instance.IgSelectionType == IGType.Threshold)
{
thresholdOrVoxelAmount = true;
}
else
{
thresholdOrVoxelAmount = false;
}
Form plotForm = new DicomImageViewer.MainForm(dicomDir + firstFile, firstFile,
Preferences.Instance.attsel.rankedAttributes(),
Convert.ToDouble(GuiPreferences.Instance.NudIGThreshold),
Convert.ToInt32(GuiPreferences.Instance.NudIGVoxelAmount),
thresholdOrVoxelAmount);// _trainTopIGFeatures);
plotForm.StartPosition = FormStartPosition.CenterParent;
plotForm.ShowDialog();
plotForm.Close();
GuiPreferences.Instance.setLog("Dicom Viewer Closed.");
}
