public IEnumerable <StudentPredictionModel> Predict(StudentTrainingModel model)
{
foreach (var trainedModel in trainedModels)
{
var predictedModel = Predict(trainedModel.Value, model);
predictedModel.TrainerName = trainedModel.Key;
yield return(predictedModel);
}
}
static IEnumerable <StudentTrainingModel> ReadPredictionSamples()
{
object ConvertValue(string val, Type toType)
{
if (toType == typeof(float))
{
return(Convert.ToSingle(val));
}
return(val);
}
var predictionSamples = new List <StudentTrainingModel>();
using (var fileStream = new FileStream("student-mat-test.txt", FileMode.Open, FileAccess.Read))
{
using (var textReader = new StreamReader(fileStream))
{
var lineNumber = 0;
var line = string.Empty;
var columns = new List <string>();
while (!textReader.EndOfStream)
{
line = textReader.ReadLine();
if (lineNumber == 0)
{
columns.AddRange(line.Split('\t'));
}
else
{
var values = line.Split('\t');
var sample = new StudentTrainingModel();
for (var idx = 0; idx < values.Length; idx++)
{
var column = columns[idx];
var fieldInfo = typeof(StudentTrainingModel)
.GetFields()
.FirstOrDefault(x => string.Equals(x.Name, column, StringComparison.InvariantCultureIgnoreCase));
if (fieldInfo != null)
{
fieldInfo.SetValue(sample, ConvertValue(values[idx], fieldInfo.FieldType));
}
}
predictionSamples.Add(sample);
}
lineNumber++;
}
}
}
return(predictionSamples);
}
public StudentPredictionModel Predict(ITransformer trainedModel, StudentTrainingModel model)
{
var engine = trainedModel.CreatePredictionEngine <StudentTrainingModel, StudentPredictionModel>(mlContext);
return(engine.Predict(model));
}
public IActionResult Predict([FromBody] StudentTrainingModel model) => Ok(predictionEngine.Predict(model));
