public IActionResult Index(BillsViewModel bvm)
{
MLContext mlContext = new MLContext(seed: 9997);
BillsModelTrainer bmt = new BillsModelTrainer();
var data = bmt.GetRawData(mlContext, "2018Bills.csv");
var trainer = mlContext.MulticlassClassification.Trainers.NaiveBayes(labelColumnName: "Label", featureColumnName: "Features");
var model = bmt.TrainModel(mlContext, data, trainer);
PredictionEngineBase <RawInput, Prediction> predictor = mlContext.Model.CreatePredictionEngine <RawInput, Prediction>(model);
var outcome = predictor.Predict(new RawInput
{
Game = 0,
Quarterback = bvm.Quarterback,
Location = bvm.Location.ToString(),
NumberOfPointsScored = bvm.NumberOfPointsScored,
TopReceiver = bvm.TopReceiver,
TopRunner = bvm.TopRunner,
NumberOfSacks = 0,
NumberOfDefensiveTurnovers = 0,
MinutesPossession = 0,
Outcome = "WHO KNOWS?"
});
return(Content($"Under these conditions, the most likely outcome is a {outcome.Outcome.ToLower()}."));
}
public static IEnumerable <TPredict> Predict <TBase, TPredict>(this PredictionEngineBase <TBase, TPredict> engine, IEnumerable <TBase> testData) where TBase : class, new() where TPredict : class, new()
{
List <TPredict> predicts = new List <TPredict>();
foreach (var data in testData)
{
predicts.Add(engine.Predict(data));
}
return(predicts);
}
public void Setup()
{
mlContext = new MLContext(seed: 9997);
bmt = new BillsModelTrainer();
var data = bmt.GetRawData(mlContext, "Resources\\2018Bills.csv");
var split = mlContext.Data.TrainTestSplit(data, testFraction: 0.25);
trainer = mlContext.MulticlassClassification.Trainers.NaiveBayes(labelColumnName: "Label", featureColumnName: "Features");
model = bmt.TrainModel(mlContext, split.TrainSet, trainer);
predictor = mlContext.Model.CreatePredictionEngine <RawInput, Prediction>(model);
}
private Dictionary <uint, FileTypes> GetClusterToMap(PredictionEngineBase <FileData, FileTypePrediction> predictionEngine)
{
var map = new Dictionary <uint, FileTypes>();
var fileTypes = Enum.GetValues(typeof(FileTypes)).Cast <FileTypes>();
foreach (var fileType in fileTypes)
{
var fileData = new FileData(fileType);
var prediction = predictionEngine.Predict(fileData);
map.Add(prediction.PredictedClusterId, fileType);
}
return(map);
}
private string GenerateOutcome(PredictionEngineBase <RawInput, Prediction> pe)
{
return(pe.Predict(new RawInput
{
Game = 0,
Quarterback = "Josh Allen",
Location = "Home",
NumberOfPointsScored = 17,
TopReceiver = "Robert Foster",
TopRunner = "Josh Allen",
NumberOfSacks = 0,
NumberOfDefensiveTurnovers = 0,
MinutesPossession = 0,
Outcome = "WHO KNOWS?"
}).Outcome);
}
private static float GetPrediction(PredictionEngineBase <EmployeeDto, SalaryPrediction> predictionEngine, Employee employee)
{
return(predictionEngine.Predict(new EmployeeDto(employee)).Salary);
}