LdSvm(
this SweepableBinaryClassificationTrainers trainer,
string labelColumnName = "Label",
string featureColumnName = "Features",
SweepableOption <LdSvmTrainer.Options> optionBuilder = null,
LdSvmTrainer.Options defaultOption = null)
{
var context = trainer.Context;
if (optionBuilder == null)
{
optionBuilder = LdSvmBinaryTrainerSweepableOptions.Default;
}
optionBuilder.SetDefaultOption(defaultOption);
return(context.AutoML().CreateSweepableEstimator(
(context, option) =>
{
option.LabelColumnName = labelColumnName;
option.FeatureColumnName = featureColumnName;
return context.BinaryClassification.Trainers.LdSvm(option);
},
optionBuilder,
new string[] { labelColumnName, featureColumnName },
new string[] { PredictedLabel },
nameof(LdSvmTrainer)));
}
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 LdSvmTrainer.Options
{
TreeDepth = 5,
NumberOfIterations = 10000,
Sigma = 0.1f,
};
// Define the trainer.
var pipeline = mlContext.BinaryClassification.Trainers
.LdSvm(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();
// Print 5 predictions.
foreach (var p in predictions.Take(5))
{
Console.WriteLine($"Label: {p.Label}, "
+ $"Prediction: {p.PredictedLabel}");
}
// Expected output:
// Label: True, Prediction: True
// Label: False, Prediction: True
// Label: True, Prediction: True
// Label: True, Prediction: True
// Label: False, Prediction: False
// Evaluate the overall metrics.
var metrics = mlContext.BinaryClassification
.EvaluateNonCalibrated(transformedTestData);
PrintMetrics(metrics);
// Expected output:
// Accuracy: 0.80
// AUC: 0.89
// F1 Score: 0.79
// Negative Precision: 0.81
// Negative Recall: 0.81
// Positive Precision: 0.79
// Positive Recall: 0.79
// TEST POSITIVE RATIO: 0.4760 (238.0/(238.0+262.0))
// Confusion table
// ||======================
// PREDICTED || positive | negative | Recall
// TRUTH ||======================
// positive || 189 | 49 | 0.7941
// negative || 50 | 212 | 0.8092
// ||======================
// Precision || 0.7908 | 0.8123 |
}
