public static bool[] AccordDecide(LogisticRegression lrModel, IDataView input, double threshold)
{
var convertedInput = IDataViewToAccord(input).inputs;
var lrProbability = lrModel.Probabilities(convertedInput);
bool[] output = new bool[lrProbability.Length];
for (int i = 0; i < lrProbability.Length; i++)
{
output[i] = lrProbability[i][1] >= threshold;
}
return(output);
}
private static List <ROCDataPoint> ROCCurve(LogisticRegression accordModel, IDataView data)
{
var convertedInput = IDataViewToAccord(data).inputs;
var lrProbability = accordModel.Probabilities(convertedInput);
double[] probabilities = new double[lrProbability.Length];
for (int i = 0; i < probabilities.Length; i++)
{
probabilities[i] = lrProbability[i][1];
}
double[] correctLabels = IDataViewToAccord(data).labels;
return(ROCCurve(correctLabels, probabilities));
}
private static void ShowAccordIntermediateData(IDataView input, LogisticRegression accordModel)
{
var convertedInput = IDataViewToAccord(input).inputs;
bool[] lrOutput = accordModel.Decide(convertedInput);
var lrScore = accordModel.Scores(convertedInput);
var lrProbability = accordModel.Probabilities(convertedInput);
var inputEnumerable = mlContext.Data.CreateEnumerable <IDataViewWrapper>(input, true);
sw.WriteLine("Label\tScore\tProbability\tPrediction");
int count = 0;
foreach (IDataViewWrapper pp in inputEnumerable)
{
int label = pp.Label ? 1 : 0;
int prediction = lrOutput[count] ? 1 : 0;
double score = lrScore[count][1];
double probability = lrProbability[count][1];
sw.WriteLine("{0}\t{1}\t{2}\t{3}", label, score, probability, prediction);
count++;
}
sw.Flush();
}
