/// <summary> /// Predict a target using logistic regression trained with the <see cref="SgdBinaryTrainer"/> trainer. /// </summary> /// <param name="catalog">The binary classification catalog trainer object.</param> /// <param name="label">The name of the label column.</param> /// <param name="features">The name of the feature column.</param> /// <param name="weights">The name for the example weight column.</param> /// <param name="options">Advanced arguments to the algorithm.</param> /// <param name="onFit">A delegate that is called every time the /// <see cref="Estimator{TTupleInShape, TTupleOutShape, TTransformer}.Fit(DataView{TTupleInShape})"/> method is called on the /// <see cref="Estimator{TTupleInShape, TTupleOutShape, TTransformer}"/> instance created out of this. This delegate will receive /// the linear model that was trained. Note that this action cannot change the result in any way; it is only a way for the caller to /// be informed about what was learnt.</param> /// <returns>The predicted output.</returns> public static (Scalar <float> score, Scalar <float> probability, Scalar <bool> predictedLabel) StochasticGradientDescentClassificationTrainer( this BinaryClassificationCatalog.BinaryClassificationTrainers catalog, Scalar <bool> label, Vector <float> features, Scalar <float> weights, SgdBinaryTrainer.Options options, Action <CalibratedModelParametersBase <LinearBinaryModelParameters, PlattCalibrator> > onFit = null) { var rec = new TrainerEstimatorReconciler.BinaryClassifier( (env, labelName, featuresName, weightsName) => { options.FeatureColumnName = featuresName; options.LabelColumnName = labelName; options.ExampleWeightColumnName = weightsName; var trainer = new SgdBinaryTrainer(env, options); if (onFit != null) { return(trainer.WithOnFitDelegate(trans => onFit(trans.Model))); } return(trainer); }, label, features, weights); return(rec.Output); }
/// <summary> /// Predict a target using logistic regression trained with the <see cref="SgdBinaryTrainer"/> trainer. /// </summary> /// <param name="catalog">The binary classificaiton catalog trainer object.</param> /// <param name="options">Advanced arguments to the algorithm.</param> public static SgdBinaryTrainer StochasticGradientDescent(this BinaryClassificationCatalog.BinaryClassificationTrainers catalog, SgdBinaryTrainer.Options options) { Contracts.CheckValue(catalog, nameof(catalog)); Contracts.CheckValue(options, nameof(options)); var env = CatalogUtils.GetEnvironment(catalog); return(new SgdBinaryTrainer(env, options)); }
// In this examples we will use the adult income dataset. The goal is to predict // if a person's income is above $50K or not, based on demographic information about that person. // For more details about this dataset, please see https://archive.ics.uci.edu/ml/datasets/adult. 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); // Download and featurize the dataset. var data = SamplesUtils.DatasetUtils.LoadFeaturizedAdultDataset(mlContext); // Leave out 10% of data for testing. var trainTestData = mlContext.BinaryClassification.TrainTestSplit(data, testFraction: 0.1); // Define the trainer options. var options = new SgdBinaryTrainer.Options() { // Make the convergence tolerance tighter. ConvergenceTolerance = 5e-5, // Increase the maximum number of passes over training data. NumberOfIterations = 30, // Give the instances of the positive class slightly more weight. PositiveInstanceWeight = 1.2f, }; // Create data training pipeline. var pipeline = mlContext.BinaryClassification.Trainers.StochasticGradientDescent(options); // Fit this pipeline to the training data. var model = pipeline.Fit(trainTestData.TrainSet); // Evaluate how the model is doing on the test data. var dataWithPredictions = model.Transform(trainTestData.TestSet); var metrics = mlContext.BinaryClassification.Evaluate(dataWithPredictions); SamplesUtils.ConsoleUtils.PrintMetrics(metrics); // Expected output: // Accuracy: 0.85 // AUC: 0.90 // F1 Score: 0.67 // Negative Precision: 0.91 // Negative Recall: 0.89 // Positive Precision: 0.65 // Positive Recall: 0.70 // LogLoss: 0.48 // LogLossReduction: 37.52 // Entropy: 0.78 }