public static void Example()
{
// Create a new context for ML.NET operations. It can be used for exception tracking and logging,
// as a catalog of available operations and as the source of randomness.
// Setting the seed to a fixed number in this example to make outputs deterministic.
var mlContext = new MLContext(seed: 0);
// Create a list of data examples.
var examples = DatasetUtils.GenerateRandomMulticlassClassificationExamples(1000);
// Convert the examples list to an IDataView object, which is consumable by ML.NET API.
var dataView = mlContext.Data.LoadFromEnumerable(examples);
//////////////////// Data Preview ////////////////////
// Label Features
// AA 0.7262433,0.8173254,0.7680227,0.5581612,0.2060332,0.5588848,0.9060271,0.4421779,0.9775497,0.2737045
// BB 0.4919063,0.6673147,0.8326591,0.6695119,1.182151,0.230367,1.06237,1.195347,0.8771811,0.5145918
// CC 1.216908,1.248052,1.391902,0.4326252,1.099942,0.9262842,1.334019,1.08762,0.9468155,0.4811099
// DD 0.7871246,1.053327,0.8971719,1.588544,1.242697,1.362964,0.6303943,0.9810045,0.9431419,1.557455
var options = new SdcaMultiClassTrainer.Options
{
// Add custom loss
LossFunction = new HingeLoss.Options(),
// Make the convergence tolerance tighter.
ConvergenceTolerance = 0.05f,
// Increase the maximum number of passes over training data.
NumberOfIterations = 30,
};
// Create a pipeline.
var pipeline =
// Convert the string labels into key types.
mlContext.Transforms.Conversion.MapValueToKey("Label")
// Apply StochasticDualCoordinateAscent multiclass trainer.
.Append(mlContext.MulticlassClassification.Trainers.StochasticDualCoordinateAscent(options));
// Split the data into training and test sets. Only training set is used in fitting
// the created pipeline. Metrics are computed on the test.
var split = mlContext.MulticlassClassification.TrainTestSplit(dataView, testFraction: 0.1);
// Train the model.
var model = pipeline.Fit(split.TrainSet);
// Do prediction on the test set.
var dataWithPredictions = model.Transform(split.TestSet);
// Evaluate the trained model using the test set.
var metrics = mlContext.MulticlassClassification.Evaluate(dataWithPredictions);
SamplesUtils.ConsoleUtils.PrintMetrics(metrics);
// Expected output:
// Micro Accuracy: 0.82
// Macro Accuracy: 0.81
// Log Loss: 0.64
// Log Loss Reduction: 52.51
}
/// <summary>
/// Predict a target using a linear multiclass classification model trained with the SDCA trainer.
/// </summary>
/// <param name="catalog">The multiclass classification catalog trainer object.</param>
/// <param name="options">Advanced arguments to the algorithm.</param>
public static SdcaMultiClassTrainer StochasticDualCoordinateAscent(this MulticlassClassificationCatalog.MulticlassClassificationTrainers catalog,
SdcaMultiClassTrainer.Options options)
{
Contracts.CheckValue(catalog, nameof(catalog));
Contracts.CheckValue(options, nameof(options));
var env = CatalogUtils.GetEnvironment(catalog);
return(new SdcaMultiClassTrainer(env, options));
}
