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 })); }