private static void BuildTrainEvaluateAndSaveModel(MLContext mlContext)
{
// STEP 1: Common data loading configuration
var textLoader = IrisTextLoaderFactory.CreateTextLoader(mlContext);
var trainingDataView = textLoader.Read(TrainDataPath);
var testDataView = textLoader.Read(TestDataPath);
// STEP 2: Common data process configuration with pipeline data transformations
var dataProcessPipeline = mlContext.Transforms.Concatenate("Features", "SepalLength",
"SepalWidth",
"PetalLength",
"PetalWidth");
// STEP 3: Set the training algorithm, then create and config the modelBuilder
var modelBuilder = new Common.ModelBuilder <IrisData, IrisPrediction>(mlContext, dataProcessPipeline);
// We apply our selected Trainer
var trainer = mlContext.MulticlassClassification.Trainers.StochasticDualCoordinateAscent(labelColumn: "Label", featureColumn: "Features");
modelBuilder.AddTrainer(trainer);
// STEP 4: Train the model fitting to the DataSet
//The pipeline is trained on the dataset that has been loaded and transformed.
Console.WriteLine("=============== Training the model ===============");
modelBuilder.Train(trainingDataView);
// STEP 5: Evaluate the model and show accuracy stats
Console.WriteLine("===== Evaluating Model's accuracy with Test data =====");
var metrics = modelBuilder.EvaluateMultiClassClassificationModel(testDataView, "Label");
Common.ConsoleHelper.PrintMultiClassClassificationMetrics(trainer.ToString(), metrics);
// STEP 6: Save/persist the trained model to a .ZIP file
Console.WriteLine("=============== Saving the model to a file ===============");
modelBuilder.SaveModelAsFile(ModelPath);
}
private static void BuildTrainEvaluateAndSaveModel(MLContext mlContext)
{
// STEP 1: Common data loading configuration
DataLoader dataLoader = new DataLoader(mlContext);
var trainingDataView = dataLoader.GetDataView(TrainDataPath);
var testDataView = dataLoader.GetDataView(TestDataPath);
// STEP 2: Common data process configuration with pipeline data transformations
var dataProcessor = new DataProcessor(mlContext);
var dataProcessPipeline = dataProcessor.DataProcessPipeline;
// (OPTIONAL) Peek data (such as 5 records) in training DataView after applying the ProcessPipeline's transformations into "Features"
Common.ConsoleHelper.PeekDataViewInConsole <IrisData>(mlContext, trainingDataView, dataProcessPipeline, 5);
Common.ConsoleHelper.PeekVectorColumnDataInConsole(mlContext, "Features", trainingDataView, dataProcessPipeline, 5);
// STEP 3: Set the training algorithm, then create and config the modelBuilder
var modelBuilder = new Common.ModelBuilder <IrisData, IrisPrediction>(mlContext, dataProcessPipeline);
// We apply our selected Trainer
var trainer = mlContext.MulticlassClassification.Trainers.StochasticDualCoordinateAscent(label: "Label", features: "Features");
modelBuilder.AddTrainer(trainer);
// STEP 4: Train the model fitting to the DataSet
//The pipeline is trained on the dataset that has been loaded and transformed.
Console.WriteLine("=============== Training the model ===============");
modelBuilder.Train(trainingDataView);
// STEP 5: Evaluate the model and show accuracy stats
Console.WriteLine("===== Evaluating Model's accuracy with Test data =====");
var metrics = modelBuilder.EvaluateMultiClassClassificationModel(testDataView, "Label");
Common.ConsoleHelper.PrintMultiClassClassificationMetrics("StochasticDualCoordinateAscent", metrics);
// STEP 6: Save/persist the trained model to a .ZIP file
Console.WriteLine("=============== Saving the model to a file ===============");
modelBuilder.SaveModelAsFile(ModelPath);
}