private static void SVMWrapperTrainingPipeline()
{
Preferences.Instance.modelLoaded = false;
TrainingTesting_SharedVariables.training = new LibSVM_TrainTest();
TrainingTesting_SharedVariables.training.setSvmType();
GuiPreferences.Instance.setLog("NOTE: we shouldnt balance labels count. it should be balanced in the protocol");
GuiPreferences.Instance.setLog("Default Training MultiClass on all data");
TrainingTesting_SharedVariables._trainingProblem = TrainingTesting_SharedVariables._trialProblem;
// show some statistics regarding the labels
Training_MultiRunProcessing.labelStats(TrainingTesting_SharedVariables._trainingProblem);
//train
GuiPreferences.Instance.setLog("Training on Data");
TrainingTesting_SharedVariables.training.TrainOnAllData(TrainingTesting_SharedVariables._trainingProblem);
GuiPreferences.Instance.setLog("Finished Training.");
//test on self should get 100%
TrainingTesting_SharedVariables.training.TestOnAllData(TrainingTesting_SharedVariables._trainingProblem);
GuiPreferences.Instance.setLog("SVM Model Tested on Training Data, check that you get 100%.");
TrainingTesting_SharedVariables.training.saveModel(GuiPreferences.Instance.WorkDirectory + "TrainSet_" + GuiPreferences.Instance.NudClassifyUsingTR.ToString() + "th_vectors_scaledCS_filteredIG.libsvm.model");
GuiPreferences.Instance.setLog("SVM Model saved.");
TrainingTesting_SharedVariables.training.loadModel(GuiPreferences.Instance.WorkDirectory + "TrainSet_" + GuiPreferences.Instance.NudClassifyUsingTR.ToString() + "th_vectors_scaledCS_filteredIG.libsvm.model");
GuiPreferences.Instance.setLog("SVM Model loaded.");
//test loaded model - DOES IT REALY USE THE LOADED MODEL? THIS IS UNVERIFIED, DONT RELY UNTIL TESTED.
TrainingTesting_SharedVariables.training.TestOnAllData(TrainingTesting_SharedVariables._trainingProblem);
GuiPreferences.Instance.setLog("SVM 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.");
}
/// <summary>
/// when train button pushed, we add jobs, pre, process, post them.
/// </summary>
public static void Train()
{
GC.Collect();
if (TrainingTesting_SharedVariables._trialProblem != null)
{
if (TrainingTesting_SharedVariables._trialProblem.samples != null)
{
//temprarily the weka pipeline is the first thing we want to do, as it doesnt need all the C# wrapper functions and configuration
if (GuiPreferences.Instance.TrainType == TrainingType.Weka)
{
if (GuiPreferences.Instance.CbSVMChecked)
{
GuiPreferences.Instance.setLog("SVM C# Wrapper Training..");
SVMWrapperTrainingPipeline();
}
else if (GuiPreferences.Instance.CbSMOChecked)
{
GuiPreferences.Instance.setLog("SMO Training..");
WekaTrainingPipeline(TrainingTesting_SharedVariables._trialWekaData);
}
return;
}
GuiPreferences.Instance.setLog("TODO: if any models were loaded we ignore then as they would be replaced by training again.\n" +
"actually this should only be for rfe/grid/90-10, as cfv doesnt provide a model.");
Preferences.Instance.modelLoaded = false;
TrainingTesting_SharedVariables.training = new LibSVM_TrainTest();
TrainingTesting_SharedVariables.training.setSvmType();
GuiPreferences.Instance.setLog("NOTE: we shouldnt balance labels count. it should be balanced in the protocol");
//if not multiclass, keep only the two binary classes
if (!GuiPreferences.Instance.CbMultiClassChecked)
{
TrainingTesting_SharedVariables._trainingProblem = Training_MultiRunProcessing.getBinaryClases(TrainingTesting_SharedVariables._trialProblem);
GuiPreferences.Instance.setLog("Using only Binary Classes");
}
else
{
TrainingTesting_SharedVariables._trainingProblem = TrainingTesting_SharedVariables._trialProblem;
}
// show some statistics regarding the labels
Training_MultiRunProcessing.labelStats(TrainingTesting_SharedVariables._trainingProblem);
if (GuiPreferences.Instance.TrainType == TrainingType.TrainTestSplit)
{
GuiPreferences.Instance.setLog("Starting Training, splitting folds 90%/10%.");
libSVM_ExtendedProblem train_problem = new libSVM_ExtendedProblem();
libSVM_ExtendedProblem test_problem = new libSVM_ExtendedProblem();
int[] test_indices = ChronologicalSplitTrainTest(TrainingTesting_SharedVariables._trainingProblem, ref train_problem, ref test_problem);
TrainingTesting_SharedVariables.training.TrainTestSplit(train_problem, test_problem, test_indices);
GuiPreferences.Instance.setLog("Finished Training.");
}
else if (GuiPreferences.Instance.TrainType == TrainingType.CrossValidation)
{
GuiPreferences.Instance.setLog("Starting Cross Validation, using " + GuiPreferences.Instance.NudCVFolds.ToString() + " folds.");
TrainingTesting_SharedVariables.training.TrainFolds((int)GuiPreferences.Instance.NudCVFolds, TrainingTesting_SharedVariables._trainingProblem, LibSVM_TrainTest.trainingType.cfv);
GuiPreferences.Instance.setLog("Finished Cross Validation.");
}
else if (GuiPreferences.Instance.TrainType == TrainingType.GridSearch)
{
GuiPreferences.Instance.setLog("Starting Grid Search, using " + GuiPreferences.Instance.NudGridFolds.ToString() + " folds.");
TrainingTesting_SharedVariables.training.TrainFolds((int)GuiPreferences.Instance.NudGridFolds, TrainingTesting_SharedVariables._trainingProblem, LibSVM_TrainTest.trainingType.bestTotal);
GuiPreferences.Instance.setLog("Finished Grid Search.");
}
else if (GuiPreferences.Instance.TrainType == TrainingType.RFE)
{
GuiPreferences.Instance.setLog("Not Implemented yet.");
}
///copy model into main model variable
if (GuiPreferences.Instance.TrainType != TrainingType.CrossValidation)
{
//training.
Preferences.Instance.svmModel = (libSVM)Preferences.Instance.svmWrapper;
GuiPreferences.Instance.setLog("Model saved into memory");
}
}
else
{
GuiPreferences.Instance.setLog("Training Failed: Original(unprocessed) or Final Problem(processed) Samples empty!");
}
}
else
{
GuiPreferences.Instance.setLog("Training Failed: Original(unprocessed) or Final Problem(processed) null!");
}
}
