/// <summary>
/// Predict a target using a linear multiclass classification model trained with the <see cref="Microsoft.ML.Learners.MulticlassLogisticRegression"/> trainer.
/// </summary>
/// <param name="catalog">The <see cref="MulticlassClassificationCatalog.MulticlassClassificationTrainers"/>.</param>
/// <param name="labelColumn">The labelColumn, or dependent variable.</param>
/// <param name="featureColumn">The features, or independent variables.</param>
/// <param name="weights">The optional example weights.</param>
/// <param name="enforceNoNegativity">Enforce non-negative weights.</param>
/// <param name="l1Weight">Weight of L1 regularization term.</param>
/// <param name="l2Weight">Weight of L2 regularization term.</param>
/// <param name="memorySize">Memory size for <see cref="Microsoft.ML.Learners.LogisticRegression"/>. Low=faster, less accurate.</param>
/// <param name="optimizationTolerance">Threshold for optimizer convergence.</param>
public static MulticlassLogisticRegression LogisticRegression(this MulticlassClassificationCatalog.MulticlassClassificationTrainers catalog,
string labelColumn = DefaultColumnNames.Label,
string featureColumn = DefaultColumnNames.Features,
string weights = null,
float l1Weight = LROptions.Defaults.L1Weight,
float l2Weight = LROptions.Defaults.L2Weight,
float optimizationTolerance = LROptions.Defaults.OptTol,
int memorySize = LROptions.Defaults.MemorySize,
bool enforceNoNegativity = LROptions.Defaults.EnforceNonNegativity)
{
Contracts.CheckValue(catalog, nameof(catalog));
var env = CatalogUtils.GetEnvironment(catalog);
return(new MulticlassLogisticRegression(env, labelColumn, featureColumn, weights, l1Weight, l2Weight, optimizationTolerance, memorySize, enforceNoNegativity));
}
/// <summary>
/// Predicts a target using a linear multiclass classification model trained with the <see cref="Pkpd"/>.
/// </summary>
/// <remarks>
/// <para>
/// In the Pairwise coupling (PKPD) strategy, a binary classification algorithm is used to train one classifier for each pair of classes.
/// Prediction is then performed by running these binary classifiers, and computing a score for each class by counting how many of the binary
/// classifiers predicted it. The prediction is the class with the highest score.
/// </para>
/// </remarks>
/// <param name="catalog">The <see cref="MulticlassClassificationCatalog.MulticlassClassificationTrainers"/>.</param>
/// <param name="binaryEstimator">An instance of a binary <see cref="ITrainerEstimator{TTransformer, TPredictor}"/> used as the base trainer.</param>
/// <param name="calibrator">The calibrator. If a calibrator is not explicitely provided, it will default to <see cref="PlattCalibratorTrainer"/></param>
/// <param name="labelColumn">The name of the label colum.</param>
/// <param name="imputeMissingLabelsAsNegative">Whether to treat missing labels as having negative labels, instead of keeping them missing.</param>
/// <param name="maxCalibrationExamples">Number of instances to train the calibrator.</param>
/// <typeparam name="TModel">The type of the model. This type parameter will usually be inferred automatically from <paramref name="binaryEstimator"/>.</typeparam>
public static Pkpd PairwiseCoupling <TModel>(this MulticlassClassificationCatalog.MulticlassClassificationTrainers catalog,
ITrainerEstimator <ISingleFeaturePredictionTransformer <TModel>, TModel> binaryEstimator,
string labelColumn = DefaultColumnNames.Label,
bool imputeMissingLabelsAsNegative = false,
IEstimator <ISingleFeaturePredictionTransformer <ICalibrator> > calibrator = null,
int maxCalibrationExamples = 1_000_000_000)
where TModel : class
{
Contracts.CheckValue(catalog, nameof(catalog));
var env = CatalogUtils.GetEnvironment(catalog);
if (!(binaryEstimator is ITrainerEstimator <ISingleFeaturePredictionTransformer <IPredictorProducing <float> >, IPredictorProducing <float> > est))
{
throw env.ExceptParam(nameof(binaryEstimator), "Trainer estimator does not appear to produce the right kind of model.");
}
return(new Pkpd(env, est, labelColumn, imputeMissingLabelsAsNegative, GetCalibratorTrainerOrThrow(env, calibrator), maxCalibrationExamples));
}
/// <summary>
/// Performs image classification using transfer learning.
/// Usage of this API requires additional NuGet dependencies on TensorFlow redist, see linked document
/// for more information.
/// <format type="text/markdown">
/// <]
/// ]]>
/// </format>
/// </summary>
/// <param name="catalog">Catalog</param>
/// <param name="options">An <see cref="ImageClassificationTrainer.Options"/> object specifying advanced
/// options for <see cref="ImageClassificationTrainer"/>.</param>
public static ImageClassificationTrainer ImageClassification(
this MulticlassClassificationCatalog.MulticlassClassificationTrainers catalog,
ImageClassificationTrainer.Options options) =>
new ImageClassificationTrainer(CatalogUtils.GetEnvironment(catalog), options);
