private static void JudgePerformance(ref MLPerformance mlp, bool label, bool prediction)
{
if (label)
{
if (prediction)
{
mlp.tp++;
}
else
{
mlp.fn++;
}
}
else
{
if (prediction)
{
mlp.fp++;
}
else
{
mlp.tn++;
}
}
}
private static MLPerformance EvaluateResults(double[] label, double[] probabilities, double threshold)
{
MLPerformance mlp = new MLPerformance();
for (int i = 0; i < label.Length; i++)
{
JudgePerformance(ref mlp, (int)label[i] == 1, probabilities[i] >= threshold);
}
return(mlp);
}
private static MLPerformance EvaluateResults(double[] label, bool[] predictions)
{
MLPerformance mlp = new MLPerformance();
for (int i = 0; i < label.Length; i++)
{
JudgePerformance(ref mlp, (int)label[i] == 1, predictions[i]);
}
return(mlp);
}
public static void TestLRModel(String lrSavedCoeff, String trainingFile, String testingFile)
{
var lr = Loader.LoadAccordNetLogisticRegressionModel(lrSavedCoeff);
IDataView trainingSet = LoadData(mlContext, trainingFile);
IDataView testingSet = LoadData(mlContext, testingFile);
double[] correctLabels = IDataViewToAccord(testingSet).labels;
List <ROCDataPoint> AccordROC = ROCCurve(lr, trainingSet);
double settedThreshold = ThresholdFromROC(AccordROC, minimumSpecificity: 0.95);
double lrAUC = GetAUCFromROC(AccordROC);
bool[] lrOutput = AccordDecide(lr, testingSet, settedThreshold);
MLPerformance accordEvaluation = EvaluateResults(correctLabels, lrOutput);
Console.WriteLine("AUC:{0}\tSpecificity:{1}\tAccuracy:{2}\tThreshold:{3}", lrAUC, accordEvaluation.getSpecificity(), accordEvaluation.getAccuracy(), settedThreshold);
}
public static void DoJob()
{
//String testingFile = InputFileOrganizer.DataRoot + "DCN_TestingSet120_NoDecoy.csv";
//String trainingFile = InputFileOrganizer.DataRoot + "240minTestingSetDCN_NoDecoy.csv";
String testingFile = "C:\\Coding\\2019LavalleeLab\\GitProjectRealTimeMS\\TestData\\240minTestingSetDCN_NoDecoy.csv";
String trainingFile = "C:\\Coding\\2019LavalleeLab\\GitProjectRealTimeMS\\TestData\\240minTestingSetDCN_NoDecoy.csv";
IDataView trainingSet = LoadData(mlContext, trainingFile);
IDataView testingSet = LoadData(mlContext, testingFile);
sw = new StreamWriter(Path.Combine(InputFileOrganizer.OutputFolderOfTheRun, "LogisticRegressionTrainerOutput.txt"));
StreamWriter sw2 = new StreamWriter(Path.Combine(InputFileOrganizer.OutputFolderOfTheRun, "AccordWeight.txt"));
double[] correctLabels = IDataViewToAccord(testingSet).labels;
var lrAccord = TrainAccordModel(trainingSet);
Console.WriteLine("Accord weights");
foreach (double w in lrAccord.Weights)
{
Console.WriteLine(w);
sw2.Write(w + "\t");
}
Console.WriteLine("intercept: {0}", lrAccord.Intercept);
sw2.WriteLine();
sw2.WriteLine(lrAccord.Intercept);
sw2.Close();
Console.WriteLine("--------------------");
//LogisticRegression lrAccord = new LogisticRegression();
////spark weights
//lrAccord.Weights = new double[] { -0.014101223713988448, 0.40498899120575244, -0.4050931006103277, -0.6514251562095439, -1.4199639211914807, -0.00154170434120518, -0.0017589165180070616, -0.001427050540781882, -0.006890591731651152, 0.23434955458842885, 0.24386505335051745, 0.25265687551174654, 0.34976191542247076, 0.17989186249395828, 0.15598728100439885 };
//lrAccord.Intercept = -2.0771355924182346;
List <ROCDataPoint> AccordROC = ROCCurve(lrAccord, trainingSet);
double lrAUC = GetAUCFromROC(AccordROC);
Console.WriteLine("AUC= " + lrAUC);
//ShowAccordIntermediateData(trainingSet, lrAccord);
double settedThreshold = ThresholdFromROC(AccordROC, minimumSpecificity: 0.95);
Console.WriteLine("Accord threshold: " + settedThreshold);
bool[] lrOutput = AccordDecide(lrAccord, testingSet, settedThreshold);
//bool[] lrOutput = AccordDecide(lrAccord, testingSet,0.3);
//var kk = EvaluateResults(IDataViewToAccord(trainingSet).labels, AccordDecide(lrAccord, trainingSet, settedThreshold));
//double AccordTrainingSpecificity = kk.getSpecificity();
MLPerformance accordEvaluation = EvaluateResults(correctLabels, lrOutput);
//BuildMLModel(0.95, trainingSet,testingSet);
if (false)
{
var MLNetModel = BuildAndTrainModel(trainingSet);
//ViewIntermediateData(model.Transform(testingSet));
IDataView predictions = MLNetModel.Transform(testingSet);
bool[] mlOutput = ExtractSVMPrediction(predictions);
//CalibratedBinaryClassificationMetrics MLMetrics = GetMetrics(MLNetModel, testingSet);
MLPerformance mlEvaluation = EvaluateResults(correctLabels, mlOutput);
}
SupportVectorMachine <Gaussian> svm;
//SVMSaveFile = Path.Combine(InputFileOrganizer.DataRoot, "SVMParams_trainedOn240DCN.txt");
SVMSaveFile = "";
if (SVMSaveFile.Equals(""))
{
SVMSaveFile = Path.Combine(InputFileOrganizer.OutputFolderOfTheRun, "SVMParams.txt");
svm = TrainSVMModel(trainingSet);
Serializer.Save(obj: svm, path: SVMSaveFile);
}
else
{
svm = Serializer.Load(SVMSaveFile, out svm);
}
bool[] svmOutput = SVMDecide(svm, testingSet);
MLPerformance svmEvaluation = EvaluateResults(correctLabels, svmOutput);
Output("Training Set: {0}\nTesting Set: {1}", trainingFile, testingFile);
Output("Accord\t ML.Net \tSVM:");
//Console.WriteLine("AUC: {0}\t {1}", lrAUC, MLMetrics.AreaUnderRocCurve);
Output("AUC: {0}\t {1}\t {2}", lrAUC, 0, 0);
//Console.WriteLine("Specificity: {0}\t {1}\t {2}", AccordTrainingSpecificity, mlEvaluation.getSpecificity(), -1);
//Console.WriteLine("Accuracy: {0}\t {1}\t {2}", accordEvaluation.getAccuracy(), mlEvaluation.getAccuracy(),-1);
Output("Specificity: {0}\t {1}\t {2}", accordEvaluation.getSpecificity(), 0, /* mlEvaluation.getSpecificity(),*/ svmEvaluation.getSpecificity());
Output("Accuracy: {0}\t {1}\t {2}", accordEvaluation.getAccuracy(), 0, /*mlEvaluation.getAccuracy(), */ svmEvaluation.getAccuracy());
//model = SetThreshold( model, testingSet);
//UseModelWithSingleItem(mlContext, model);
sw.Close();
}