/// <summary>
/// Predicts a target using a linear multiclass classification model trained with the <see cref="MultiClassNaiveBayesTrainer"/>.
/// The <see cref="MultiClassNaiveBayesTrainer"/> trains a multiclass Naive Bayes predictor that supports binary feature values.
/// </summary>
/// <param name="ctx">The <see cref="MulticlassClassificationContext.MulticlassClassificationTrainers"/>.</param>
/// <param name="labelColumn">The name of the label column.</param>
/// <param name="featureColumn">The name of the feature column.</param>
public static MultiClassNaiveBayesTrainer NaiveBayes(this MulticlassClassificationContext.MulticlassClassificationTrainers ctx,
string labelColumn = DefaultColumnNames.Label,
string featureColumn = DefaultColumnNames.Features)
{
Contracts.CheckValue(ctx, nameof(ctx));
return(new MultiClassNaiveBayesTrainer(CatalogUtils.GetEnvironment(ctx), labelColumn, featureColumn));
}
/// <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="ctx">The <see cref="MulticlassClassificationContext.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>
public static Pkpd PairwiseCoupling(this MulticlassClassificationContext.MulticlassClassificationTrainers ctx,
ITrainerEstimator <ISingleFeaturePredictionTransformer <IPredictorProducing <float> >, IPredictorProducing <float> > binaryEstimator,
string labelColumn = DefaultColumnNames.Label,
bool imputeMissingLabelsAsNegative = false,
ICalibratorTrainer calibrator = null,
int maxCalibrationExamples = 1000000000)
{
Contracts.CheckValue(ctx, nameof(ctx));
return(new Pkpd(CatalogUtils.GetEnvironment(ctx), binaryEstimator, labelColumn, imputeMissingLabelsAsNegative, calibrator, maxCalibrationExamples));
}
Sdca <TVal>(this MulticlassClassificationContext.MulticlassClassificationTrainers ctx,
Key <uint, TVal> label,
Vector <float> features,
ISupportSdcaClassificationLoss loss = null,
Scalar <float> weights = null,
float?l2Const = null,
float?l1Threshold = null,
int?maxIterations = null,
Action <MulticlassLogisticRegressionModelParameters> onFit = null)
{
/// <summary>
/// Predict a target using a linear multiclass classification model trained with the <see cref="Microsoft.ML.Learners.MulticlassLogisticRegression"/> trainer.
/// </summary>
/// <param name="ctx">The <see cref="MulticlassClassificationContext.MulticlassClassificationTrainers"/>.</param>
/// <param name="options">Advanced arguments to the algorithm.</param>
public static MulticlassLogisticRegression LogisticRegression(this MulticlassClassificationContext.MulticlassClassificationTrainers ctx,
MulticlassLogisticRegression.Options options)
{
Contracts.CheckValue(ctx, nameof(ctx));
Contracts.CheckValue(options, nameof(options));
var env = CatalogUtils.GetEnvironment(ctx);
return(new MulticlassLogisticRegression(env, options));
}
/// <summary>
/// Predict a target using a linear multiclass classification model trained with the SDCA trainer.
/// </summary>
/// <param name="ctx">The multiclass classification context trainer object.</param>
/// <param name="options">Advanced arguments to the algorithm.</param>
public static SdcaMultiClassTrainer StochasticDualCoordinateAscent(this MulticlassClassificationContext.MulticlassClassificationTrainers ctx,
SdcaMultiClassTrainer.Options options)
{
Contracts.CheckValue(ctx, nameof(ctx));
Contracts.CheckValue(options, nameof(options));
var env = CatalogUtils.GetEnvironment(ctx);
return(new SdcaMultiClassTrainer(env, options));
}
Sdca <TVal>(this MulticlassClassificationContext.MulticlassClassificationTrainers ctx,
Key <uint, TVal> label,
Vector <float> features,
ISupportSdcaClassificationLoss loss = null,
Scalar <float> weights = null,
float?l2Const = null,
float?l1Threshold = null,
int?maxIterations = null,
Action <SdcaMultiClassTrainer.Arguments> advancedSettings = null,
Action <MulticlassLogisticRegressionPredictor> onFit = null)
{
LightGbm <TVal>(this MulticlassClassificationContext.MulticlassClassificationTrainers ctx,
Key <uint, TVal> label,
Vector <float> features,
Scalar <float> weights = null,
int?numLeaves = null,
int?minDataPerLeaf = null,
double?learningRate = null,
int numBoostRound = LightGbmArguments.Defaults.NumBoostRound,
Action <LightGbmArguments> advancedSettings = null,
Action <OvaPredictor> onFit = null)
{
MultiClassLogisticRegression <TVal>(this MulticlassClassificationContext.MulticlassClassificationTrainers ctx,
Key <uint, TVal> label,
Vector <float> features,
Scalar <float> weights = null,
float l1Weight = Arguments.Defaults.L1Weight,
float l2Weight = Arguments.Defaults.L2Weight,
float optimizationTolerance = Arguments.Defaults.OptTol,
int memorySize = Arguments.Defaults.MemorySize,
bool enoforceNoNegativity = Arguments.Defaults.EnforceNonNegativity,
Action <MulticlassLogisticRegressionPredictor> onFit = null)
{
/// <summary>
/// Predict a target using a linear multiclass classification model trained with the SDCA trainer.
/// </summary>
/// <param name="ctx">The multiclass classification context trainer object.</param>
/// <param name="labelColumn">The labelColumn, or dependent variable.</param>
/// <param name="featureColumn">The features, or independent variables.</param>
/// <param name="loss">The optional custom loss.</param>
/// <param name="weights">The optional example weights.</param>
/// <param name="l2Const">The L2 regularization hyperparameter.</param>
/// <param name="l1Threshold">The L1 regularization hyperparameter. Higher values will tend to lead to more sparse model.</param>
/// <param name="maxIterations">The maximum number of passes to perform over the data.</param>
public static SdcaMultiClassTrainer StochasticDualCoordinateAscent(this MulticlassClassificationContext.MulticlassClassificationTrainers ctx,
string labelColumn = DefaultColumnNames.Label,
string featureColumn = DefaultColumnNames.Features,
string weights = null,
ISupportSdcaClassificationLoss loss = null,
float?l2Const = null,
float?l1Threshold = null,
int?maxIterations = null)
{
Contracts.CheckValue(ctx, nameof(ctx));
var env = CatalogUtils.GetEnvironment(ctx);
return(new SdcaMultiClassTrainer(env, labelColumn, featureColumn, weights, loss, l2Const, l1Threshold, maxIterations));
}
/// <summary>
/// Predict a target using a decision tree binary classification model trained with the <see cref="LightGbmRankingTrainer"/>.
/// </summary>
/// <param name="ctx">The <see cref="RankingContext"/>.</param>
/// <param name="labelColumn">The labelColumn column.</param>
/// <param name="featureColumn">The features column.</param>
/// <param name="weights">The weights column.</param>
/// <param name="numLeaves">The number of leaves to use.</param>
/// <param name="numBoostRound">Number of iterations.</param>
/// <param name="minDataPerLeaf">The minimal number of documents allowed in a leaf of the tree, out of the subsampled data.</param>
/// <param name="learningRate">The learning rate.</param>
/// <param name="advancedSettings">A delegate to set more settings.
/// The settings here will override the ones provided in the direct signature,
/// if both are present and have different values.
/// The columns names, however need to be provided directly, not through the <paramref name="advancedSettings"/>.</param>
public static LightGbmMulticlassTrainer LightGbm(this MulticlassClassificationContext.MulticlassClassificationTrainers ctx,
string labelColumn = DefaultColumnNames.Label,
string featureColumn = DefaultColumnNames.Features,
string weights = null,
int?numLeaves = null,
int?minDataPerLeaf = null,
double?learningRate = null,
int numBoostRound = LightGbmArguments.Defaults.NumBoostRound,
Action <LightGbmArguments> advancedSettings = null)
{
Contracts.CheckValue(ctx, nameof(ctx));
var env = CatalogUtils.GetEnvironment(ctx);
return(new LightGbmMulticlassTrainer(env, labelColumn, featureColumn, weights, numLeaves, minDataPerLeaf, learningRate, numBoostRound, advancedSettings));
}
/// <summary>
/// Predict a target using a linear multiclass classification model trained with the <see cref="Microsoft.ML.Learners.MulticlassLogisticRegression"/> trainer.
/// </summary>
/// <param name="ctx">The <see cref="MulticlassClassificationContext.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 MulticlassClassificationContext.MulticlassClassificationTrainers ctx,
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(ctx, nameof(ctx));
var env = CatalogUtils.GetEnvironment(ctx);
return(new MulticlassLogisticRegression(env, labelColumn, featureColumn, weights, l1Weight, l2Weight, optimizationTolerance, memorySize, enforceNoNegativity));
}
MultiClassNaiveBayesTrainer <TVal>(this MulticlassClassificationContext.MulticlassClassificationTrainers ctx,
Key <uint, TVal> label,
Vector <float> features,
Action <MultiClassNaiveBayesModelParameters> onFit = null)
{
