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 SdcaNonCalibratedMulticlassTrainer.Options
{
// Add custom loss
Loss = new HingeLoss(),
// Make the convergence tolerance tighter.
ConvergenceTolerance = 0.05f,
// Increase the maximum number of passes over training data.
MaximumNumberOfIterations = 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.SdcaNonCalibrated(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.Data.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
}
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);
// ML.NET doesn't cache data set by default. Therefore, if one reads a data set from a file and accesses it many times,
// it can be slow due to expensive featurization and disk operations. When the considered data can fit into memory,
// a solution is to cache the data in memory. Caching is especially helpful when working with iterative algorithms
// which needs many data passes.
trainingData = mlContext.Data.Cache(trainingData);
// Define trainer options.
var options = new SdcaNonCalibratedMulticlassTrainer.Options
{
Loss = new HingeLoss(),
L1Regularization = 0.1f,
BiasLearningRate = 0.01f,
NumberOfThreads = 1
};
// Define the trainer.
var pipeline =
// Convert the string labels into key types.
mlContext.Transforms.Conversion.MapValueToKey("Label")
// Apply SdcaNonCalibrated multiclass trainer.
.Append(mlContext.MulticlassClassification.Trainers.SdcaNonCalibrated(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
}
SdcaNonCalibreated(this SweepableMultiClassificationTrainers trainer, string labelColumnName = "Label", string featureColumnName = "Features", SweepableOption <SdcaNonCalibratedMulticlassTrainer.Options> optionBuilder = null, SdcaNonCalibratedMulticlassTrainer.Options defaultOption = null)
{
var context = trainer.Context;
if (optionBuilder == null)
{
optionBuilder = SdcaNonCalibratedMulticlassTrainerSweepableOptions.Default;
}
optionBuilder.SetDefaultOption(defaultOption);
return(context.AutoML().CreateSweepableEstimator(
(context, option) =>
{
option.LabelColumnName = labelColumnName;
option.FeatureColumnName = featureColumnName;
return context.MulticlassClassification.Trainers.SdcaNonCalibrated(option);
},
optionBuilder,
trainerName: nameof(SdcaNonCalibratedMulticlassTrainer),
inputs: new string[] { featureColumnName },
outputs: new string[] { PredictedLabel }));
}
