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($"Under these conditions, the most likely outcome is a {outcome.Outcome.ToLower()}."));
}
public void Setup()
{
mlContext = new MLContext(seed: 9997);
bmt = new BillsModelTrainer();
var data = bmt.GetRawData(mlContext, "Resources\\2018Bills.csv");
var split = mlContext.Data.TrainTestSplit(data, testFraction: 0.25);
trainer = mlContext.MulticlassClassification.Trainers.NaiveBayes(labelColumnName: "Label", featureColumnName: "Features");
model = bmt.TrainModel(mlContext, split.TrainSet, trainer);
predictor = mlContext.Model.CreatePredictionEngine <RawInput, Prediction>(model);
}
public void BasicEvaluationTest(string trainerToUse)
{
mlContext = new MLContext(seed: 9997);
bmt = new BillsModelTrainer();
var data = bmt.GetRawData(mlContext, "Resources\\2018Bills.csv");
var split = mlContext.Data.TrainTestSplit(data, testFraction: 0.4);
// If we wish to review the split data, we can run these.
var trainSet = mlContext.Data.CreateEnumerable <RawInput>(split.TrainSet, reuseRowObject: false);
var testSet = mlContext.Data.CreateEnumerable <RawInput>(split.TestSet, reuseRowObject: false);
IEstimator <ITransformer> newTrainer;
switch (trainerToUse)
{
case "Naive Bayes":
newTrainer = mlContext.MulticlassClassification.Trainers.NaiveBayes(labelColumnName: "Label", featureColumnName: "Features");
break;
case "L-BFGS":
newTrainer = mlContext.MulticlassClassification.Trainers.LbfgsMaximumEntropy(labelColumnName: "Label", featureColumnName: "Features");
break;
case "SDCA Non-Calibrated":
newTrainer = mlContext.MulticlassClassification.Trainers.SdcaNonCalibrated(labelColumnName: "Label", featureColumnName: "Features");
break;
default:
newTrainer = mlContext.MulticlassClassification.Trainers.NaiveBayes(labelColumnName: "Label", featureColumnName: "Features");
break;
}
var newModel = bmt.TrainModel(mlContext, split.TrainSet, newTrainer);
var metrics = mlContext.MulticlassClassification.Evaluate(newModel.Transform(split.TestSet));
Console.WriteLine($"Macro Accuracy = {metrics.MacroAccuracy}; Micro Accuracy = {metrics.MicroAccuracy}");
Console.WriteLine($"Confusion Matrix with {metrics.ConfusionMatrix.NumberOfClasses} classes.");
Console.WriteLine($"{metrics.ConfusionMatrix.GetFormattedConfusionTable()}");
Assert.AreNotEqual(0, metrics.MacroAccuracy);
}