using Microsoft.ML; namespace RhSolutions.ML.Builder { public class Program { private static string _appPath = Path.GetDirectoryName(Environment.GetCommandLineArgs()[0]) ?? "."; private static MLContext _mlContext = new MLContext(seed: 0); public static void Main() { var _trainDataView = _mlContext.Data.LoadFromTextFile( Path.Combine(_appPath, "..", "..", "..", "Data", "train.tsv"), hasHeader: true); var pipeline = ProcessData(); BuildAndTrainModel(_trainDataView, pipeline, out ITransformer trainedModel); SaveModelAsFile(_mlContext, _trainDataView.Schema, trainedModel); } private static IEstimator ProcessData() { var pipeline = _mlContext.Transforms.Conversion.MapValueToKey(inputColumnName: "Type", outputColumnName: "Label") .Append(_mlContext.Transforms.Text.FeaturizeText(inputColumnName: "Name", outputColumnName: "NameFeaturized")) .Append(_mlContext.Transforms.Concatenate("Features", "NameFeaturized")) .AppendCacheCheckpoint(_mlContext); return pipeline; } private static IEstimator BuildAndTrainModel(IDataView trainingDataView, IEstimator pipeline, out ITransformer trainedModel) { var trainingPipeline = pipeline.Append(_mlContext.MulticlassClassification.Trainers.SdcaMaximumEntropy("Label", "Features")) .Append(_mlContext.Transforms.Conversion.MapKeyToValue("PredictedLabel")); trainedModel = trainingPipeline.Fit(trainingDataView); return trainingPipeline; } private static void SaveModelAsFile(MLContext mlContext, DataViewSchema trainingDataViewSchema, ITransformer model) { mlContext.Model.Save(model, trainingDataViewSchema, Path.Combine(_appPath, "..", "..", "..", "Models", "model.zip")); } } }