public override IEstimator <ITransformer> BuildFromOption(MLContext context, LbfgsOption param)
{
var option = new LbfgsMaximumEntropyMulticlassTrainer.Options()
{
L1Regularization = param.L1Regularization,
L2Regularization = param.L2Regularization,
LabelColumnName = param.LabelColumnName,
FeatureColumnName = param.FeatureColumnName,
ExampleWeightColumnName = param.ExampleWeightColumnName,
NumberOfThreads = AutoMlUtils.GetNumberOfThreadFromEnvrionment(),
};
return(context.MulticlassClassification.Trainers.LbfgsMaximumEntropy(option));
}
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 training data points.
var dataPoints = GenerateRandomDataPoints(1000);
// Convert the list of data points to an IDataView object, which is consumable by ML.NET API.
var trainingData = mlContext.Data.LoadFromEnumerable(dataPoints);
// Define trainer options.
var options = new LbfgsMaximumEntropyMulticlassTrainer.Options
{
HistorySize = 50,
L1Regularization = 0.1f,
NumberOfThreads = 1
};
// Define the trainer.
var pipeline =
// Convert the string labels into key types.
mlContext.Transforms.Conversion.MapValueToKey("Label")
// Apply LbfgsMaximumEntropy multiclass trainer.
.Append(mlContext.MulticlassClassification.Trainers.LbfgsMaximumEntropy(options));
// Train the model.
var model = pipeline.Fit(trainingData);
// Create testing data. Use different random seed to make it different from training data.
var testData = mlContext.Data.LoadFromEnumerable(GenerateRandomDataPoints(500, seed: 123));
// Run the model on test data set.
var transformedTestData = model.Transform(testData);
// Convert IDataView object to a list.
var predictions = mlContext.Data.CreateEnumerable <Prediction>(transformedTestData, reuseRowObject: false).ToList();
// Look at 5 predictions
foreach (var p in predictions.Take(5))
{
Console.WriteLine($"Label: {p.Label}, Prediction: {p.PredictedLabel}");
}
// Expected output:
// Label: 1, Prediction: 1
// Label: 2, Prediction: 2
// Label: 3, Prediction: 2
// Label: 2, Prediction: 2
// Label: 3, Prediction: 3
// Evaluate the overall metrics
var metrics = mlContext.MulticlassClassification.Evaluate(transformedTestData);
PrintMetrics(metrics);
// Expected output:
// Micro Accuracy: 0.91
// Macro Accuracy: 0.91
// Log Loss: 0.22
// Log Loss Reduction: 0.80
}
LbfgsMaximumEntropy(this SweepableMultiClassificationTrainers trainer, string labelColumnName = "Label", string featureColumnName = "Features", SweepableOption <LbfgsMaximumEntropyMulticlassTrainer.Options> optionBuilder = null, LbfgsMaximumEntropyMulticlassTrainer.Options defaultOption = null)
{
var context = trainer.Context;
if (optionBuilder == null)
{
optionBuilder = LbfgsMaximumEntropyMulticlassTrainerSweepableOptions.Default;
}
optionBuilder.SetDefaultOption(defaultOption);
return(context.AutoML().CreateSweepableEstimator(
(context, option) =>
{
option.LabelColumnName = labelColumnName;
option.FeatureColumnName = featureColumnName;
return context.MulticlassClassification.Trainers.LbfgsMaximumEntropy(option);
},
optionBuilder,
trainerName: nameof(LbfgsMaximumEntropyMulticlassTrainer),
inputs: new string[] { featureColumnName },
outputs: new string[] { PredictedLabel }));
}
