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 | }