public static void BuildAndTrainModel(string DataSetLocation, string ModelPath)
{
// Create MLContext to be shared across the model creation workflow objects
// Set a random seed for repeatable/deterministic results across multiple trainings.
var mlContext = new MLContext(seed: 0);
// STEP 1: Common data loading configuration
DataLoader dataLoader = new DataLoader(mlContext);
var trainingDataView = dataLoader.GetDataView(DataSetLocation);
// STEP 2: Common data process configuration with pipeline data transformations
var dataProcessor = new DataProcessor(mlContext);
var dataProcessPipeline = dataProcessor.DataProcessPipeline;
// (OPTIONAL) Peek data (such as 2 records) in training DataView after applying the ProcessPipeline's transformations into "Features"
Common.ConsoleHelper.PeekDataViewInConsole <GitHubIssue>(mlContext, trainingDataView, dataProcessPipeline, 2);
//Common.ConsoleHelper.PeekVectorColumnDataInConsole(mlContext, "Features", trainingDataView, dataProcessPipeline, 2);
// STEP 3: Set the selected training algorithm into the modelBuilder
var modelBuilder = new Common.ModelBuilder <GitHubIssue, GitHubIssuePrediction>(mlContext, dataProcessPipeline);
var trainer = mlContext.MulticlassClassification.Trainers.StochasticDualCoordinateAscent("Label", "Features");
modelBuilder.AddTrainer(trainer);
modelBuilder.AddEstimator(new KeyToValueEstimator(mlContext, "PredictedLabel"));
// STEP 4: Cross-Validate with single dataset (since we don't have two datasets, one for training and for evaluate)
// in order to evaluate and get the model's accuracy metrics
Console.WriteLine("=============== Cross-validating to get model's accuracy metrics ===============");
var crossValResults = modelBuilder.CrossValidateAndEvaluateMulticlassClassificationModel(trainingDataView, 6, "Label");
ConsoleHelper.PrintMulticlassClassificationFoldsAverageMetrics("SdcaMultiClassTrainer", crossValResults);
// STEP 5: Train the model fitting to the DataSet
Console.WriteLine("=============== Training the model ===============");
modelBuilder.Train(trainingDataView);
// STEP 6: Save/persist the trained model to a .ZIP file
Console.WriteLine("=============== Saving the model to a file ===============");
modelBuilder.SaveModelAsFile(ModelPath);
// (OPTIONAL) Try/test a single prediction by loding the model from the file, first.
GitHubIssue issue = new GitHubIssue()
{
ID = "Any-ID", Title = "Entity Framework crashes", Description = "When connecting to the database, EF is crashing"
};
var modelScorer = new ModelScorer <GitHubIssue, GitHubIssuePrediction>(mlContext);
modelScorer.LoadModelFromZipFile(ModelPath);
var prediction = modelScorer.PredictSingle(issue);
Console.WriteLine($"=============== Single Prediction - Result: {prediction.Area} ===============");
//
Common.ConsoleHelper.ConsoleWriteHeader("Training process finalized");
}
public static void BuildAndTrainModel(string DataSetLocation, string ModelPath, MyTrainerStrategy selectedStrategy)
{
// Create MLContext to be shared across the model creation workflow objects
// Set a random seed for repeatable/deterministic results across multiple trainings.
var mlContext = new MLContext(seed: 0);
// STEP 1: Common data loading configuration
var textLoader = GitHubLabelerTextLoaderFactory.CreateTextLoader(mlContext);
var trainingDataView = textLoader.Read(DataSetLocation);
// STEP 2: Common data process configuration with pipeline data transformations
var dataProcessPipeline = GitHubLabelerDataProcessPipelineFactory.CreateDataProcessPipeline(mlContext);
// (OPTIONAL) Peek data (such as 2 records) in training DataView after applying the ProcessPipeline's transformations into "Features"
Common.ConsoleHelper.PeekDataViewInConsole <GitHubIssue>(mlContext, trainingDataView, dataProcessPipeline, 2);
//Common.ConsoleHelper.PeekVectorColumnDataInConsole(mlContext, "Features", trainingDataView, dataProcessPipeline, 2);
// STEP 3: Create the selected training algorithm/trainer
IEstimator <ITransformer> trainer = null;
switch (selectedStrategy)
{
case MyTrainerStrategy.SdcaMultiClassTrainer:
trainer = mlContext.MulticlassClassification.Trainers.StochasticDualCoordinateAscent(DefaultColumnNames.Label,
DefaultColumnNames.Features);
break;
case MyTrainerStrategy.OVAAveragedPerceptronTrainer:
{
// Create a binary classification trainer.
var averagedPerceptronBinaryTrainer = mlContext.BinaryClassification.Trainers.AveragedPerceptron(DefaultColumnNames.Label,
DefaultColumnNames.Features,
numIterations: 10);
// Compose an OVA (One-Versus-All) trainer with the BinaryTrainer.
// In this strategy, a binary classification algorithm is used to train one classifier for each class, "
// which distinguishes that class from all other classes. Prediction is then performed by running these binary classifiers, "
// and choosing the prediction with the highest confidence score.
trainer = new Ova(mlContext, averagedPerceptronBinaryTrainer);
break;
}
default:
break;
}
//Set the trainer/algorithm
var modelBuilder = new Common.ModelBuilder <GitHubIssue, GitHubIssuePrediction>(mlContext, dataProcessPipeline);
modelBuilder.AddTrainer(trainer);
modelBuilder.AddEstimator(mlContext.Transforms.Conversion.MapKeyToValue("PredictedLabel"));
// STEP 4: Cross-Validate with single dataset (since we don't have two datasets, one for training and for evaluate)
// in order to evaluate and get the model's accuracy metrics
Console.WriteLine("=============== Cross-validating to get model's accuracy metrics ===============");
var crossValResults = modelBuilder.CrossValidateAndEvaluateMulticlassClassificationModel(trainingDataView, 6, "Label");
ConsoleHelper.PrintMulticlassClassificationFoldsAverageMetrics(trainer.ToString(), crossValResults);
// STEP 5: Train the model fitting to the DataSet
Console.WriteLine("=============== Training the model ===============");
modelBuilder.Train(trainingDataView);
// (OPTIONAL) Try/test a single prediction with the "just-trained model" (Before saving the model)
GitHubIssue issue = new GitHubIssue()
{
ID = "Any-ID", Title = "WebSockets communication is slow in my machine", Description = "The WebSockets communication used under the covers by SignalR looks like is going slow in my development machine.."
};
var modelScorer = new ModelScorer <GitHubIssue, GitHubIssuePrediction>(mlContext, modelBuilder.TrainedModel);
var prediction = modelScorer.PredictSingle(issue);
Console.WriteLine($"=============== Single Prediction just-trained-model - Result: {prediction.Area} ===============");
//
// STEP 6: Save/persist the trained model to a .ZIP file
Console.WriteLine("=============== Saving the model to a file ===============");
modelBuilder.SaveModelAsFile(ModelPath);
Common.ConsoleHelper.ConsoleWriteHeader("Training process finalized");
}
private static ITransformer BuildTrainEvaluateAndSaveModel(MLContext mlContext)
{
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 ===============");
var 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);
return(model);
// // STEP 1: Common data loading configuration
// var textLoader = IrisTextLoaderFactory.CreateTextLoader(mlContext);
// var trainingDataView = textLoader.Read(TrainDataPath);
// // STEP 2: Common data process configuration with pipeline data transformations
// var dataProcessPipeline = mlContext.Transforms.Concatenate("Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth")
// // Note that the label is text, so it needs to be converted to key.
// .Append(mlContext.Transforms.Conversion.MapValueToKey("Label"), TransformerScope.TrainTest)
// // Use the multi-class SDCA model to predict the label using features.
//.Append(mlContext.MulticlassClassification.Trainers.StochasticDualCoordinateAscent())
// // Apply the inverse conversion from 'PredictedLabel' column back to string value.
// .Append(mlContext.Transforms.Conversion.MapKeyToValue(("PredictedLabel", "PredictedLabel")));
// ;
// // STEP 3: Set the training algorithm, then create and config the modelBuilder
// var modelBuilder = new 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 ===============");
// var 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);
// return model;
}