public ModelController(IConfiguration configuration)
{
_configuration = configuration;
_context = new MLContext();
var modelPath = System.IO.Path.Combine(Environment.GetFolderPath(Environment.SpecialFolder.MyDocuments), "wine.zip");
if (!System.IO.File.Exists(modelPath))
{
var blob = BlobConnection.GetBlobReference(_configuration["blobConnectionString"], "models", "wine.zip");
blob.DownloadToFileAsync(modelPath, System.IO.FileMode.CreateNew).RunSynchronously();
}
using (var stream = System.IO.File.OpenRead(modelPath))
{
_model = _context.Model.Load(stream);
}
}
static async Task Main(string[] args)
{
var builder = new ConfigurationBuilder()
.SetBasePath(Directory.GetCurrentDirectory())
.AddJsonFile("config.json");
var configuration = builder.Build();
_sqlConnectionString = configuration["connectionString"];
var fileData = ReadFromFile("./winequality.csv");
AddDataToDatabase(fileData);
var dbData = ReadFromDatabase();
var context = new MLContext();
var mlData = context.Data.LoadFromEnumerable(dbData);
var trainTestData = context.Regression.TrainTestSplit(mlData, testFraction: 0.2);
var dataPreview = trainTestData.TrainSet.Preview();
var pipeline = context.Transforms.Categorical.OneHotEncoding("TypeOneHot", "Type")
.Append(context.Transforms.Concatenate("Features", "FixedAcidity", "VolatileAcidity", "CitricAcid",
"ResidualSugar", "Chlorides", "FreeSulfurDioxide", "TotalSulfurDioxide", "Density", "Ph", "Sulphates",
"Alcohol"))
.Append(context.Transforms.CopyColumns(("Label", "Quality")))
.Append(context.Regression.Trainers.FastTree());
var model = pipeline.Fit(trainTestData.TrainSet);
var blob = BlobConnection.GetBlobReference(configuration["blobConnectionString"], "models", fileName);
using (var stream = File.Create(fileName))
{
context.Model.Save(model, stream);
}
await blob.UploadFromFileAsync(fileName);
}