static void Main(string[] args)
{
var hackernewsWorkspace = new Guid("2480be4a-ca1a-4cae-8bb3-654aa0ba8740");
var scienceWorkspace = new Guid("698ff939-3eb4-4e65-b93b-ccde24c2838e");
var newsAndGossipWorkspace = new Guid("0e4622a6-ef4b-4308-b18d-9a4a8a71a50c");
var randomWorkspace = new Guid("24f51e4e-951e-453b-957c-1a1dfe475672");
var schema = new MLModelSchema("news.mlmodel");
schema.AddDataSource(new FetchoWorkspaceMLModelSchemaCategoryDataSource("news", newsAndGossipWorkspace)
{
UseNameForCategory = true
});
schema.AddDataSource(new FetchoWorkspaceMLModelSchemaCategoryDataSource("random", randomWorkspace)
{
UseNameForCategory = true
});
schema.BalanceResults = false;
var trainer = new MultiClassifierModelTrainer();
trainer.TrainAndSave(schema);
var fi = new FileInfo(schema.FilePath);
Console.WriteLine("Size: {0}kb", fi.Length / 1024);
// var r = new RedditMLModelSchemaCategoryDataSource("science", "science");
// var a = r.GetData(5000);
}
public void TrainAndSave(MLModelSchema schema)
{
var data = schema.GetAllData();
foreach (var category in data.Select(x => x.Category).Distinct())
{
Console.WriteLine("{0}:{1}", category, data.Count(x => x.Category == category));
}
MLContext mlContext = new MLContext(null, 1); // 1 concurrency, leave me some CPUs plz
IDataView trainingDataView = mlContext.Data.LoadFromEnumerable(data);
var pipeline = mlContext.Transforms.Conversion.MapValueToKey("Category")
.Append(mlContext.Transforms.Text.FeaturizeText(inputColumnName: "TextData", outputColumnName: "TextDataFeaturized"))
.Append(mlContext.Transforms.Concatenate("Features", "TextDataFeaturized"))
.AppendCacheCheckpoint(mlContext)
.Append(mlContext.MulticlassClassification.Trainers.StochasticDualCoordinateAscent("Category", DefaultColumnNames.Features))
.Append(mlContext.Transforms.Conversion.MapKeyToValue("PredictedLabel"));
// STEP 4: Train your model based on the data set
var model = pipeline.Fit(trainingDataView);
using (var fs = File.OpenWrite(schema.FilePath))
mlContext.Model.Save(model, fs);
}