/// <summary>
/// Initializes a new instance of <see cref="MultiClassNaiveBayesTrainer"/>
/// </summary>
/// <param name="env">The environment to use.</param>
/// <param name="labelColumn">The name of the label column.</param>
/// <param name="featureColumn">The name of the feature column.</param>
public MultiClassNaiveBayesTrainer(IHostEnvironment env, string featureColumn, string labelColumn)
: base(Contracts.CheckRef(env, nameof(env)).Register(LoadName), TrainerUtils.MakeR4VecFeature(featureColumn),
TrainerUtils.MakeU4ScalarColumn(labelColumn))
{
Host.CheckNonEmpty(featureColumn, nameof(featureColumn));
Host.CheckNonEmpty(labelColumn, nameof(labelColumn));
}
/// <summary>
/// Initializes a new instance of <see cref="NaiveBayesMulticlassTrainer"/>
/// </summary>
/// <param name="env">The environment to use.</param>
/// <param name="labelColumn">The name of the label column.</param>
/// <param name="featureColumn">The name of the feature column.</param>
internal NaiveBayesMulticlassTrainer(IHostEnvironment env,
string labelColumn = DefaultColumnNames.Label,
string featureColumn = DefaultColumnNames.Features)
: base(Contracts.CheckRef(env, nameof(env)).Register(LoadName), TrainerUtils.MakeR4VecFeature(featureColumn),
TrainerUtils.MakeU4ScalarColumn(labelColumn))
{
Host.CheckNonEmpty(featureColumn, nameof(featureColumn));
Host.CheckNonEmpty(labelColumn, nameof(labelColumn));
}
internal SdcaMultiClassTrainer(IHostEnvironment env, Options options,
string featureColumn, string labelColumn, string weightColumn = null)
: base(env, options, TrainerUtils.MakeU4ScalarColumn(labelColumn), TrainerUtils.MakeR4ScalarWeightColumn(weightColumn))
{
Host.CheckValue(labelColumn, nameof(labelColumn));
Host.CheckValue(featureColumn, nameof(featureColumn));
_loss = options.LossFunction.CreateComponent(env);
Loss = _loss;
}
/// <summary>
/// Initializes a new instance of <see cref="SdcaMultiClassTrainer"/>
/// </summary>
/// <param name="env">The environment to use.</param>
/// <param name="labelColumn">The label, or dependent variable.</param>
/// <param name="featureColumn">The features, or independent variables.</param>
/// <param name="weights">The optional example weights.</param>
/// <param name="loss">The custom loss.</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>
internal SdcaMultiClassTrainer(IHostEnvironment env,
string labelColumn = DefaultColumnNames.Label,
string featureColumn = DefaultColumnNames.Features,
string weights = null,
ISupportSdcaClassificationLoss loss = null,
float?l2Const = null,
float?l1Threshold = null,
int?maxIterations = null)
: base(env, featureColumn, TrainerUtils.MakeU4ScalarColumn(labelColumn), TrainerUtils.MakeR4ScalarWeightColumn(weights),
l2Const, l1Threshold, maxIterations)
{
Host.CheckNonEmpty(featureColumn, nameof(featureColumn));
Host.CheckNonEmpty(labelColumn, nameof(labelColumn));
_loss = loss ?? SdcaTrainerOptions.LossFunction.CreateComponent(env);
Loss = _loss;
}
/// <summary>
/// Initializes a new instance of <see cref="MulticlassLogisticRegression"/>
/// </summary>
/// <param name="env">The environment to use.</param>
/// <param name="labelColumn">The name of the label column.</param>
/// <param name="featureColumn">The name of the feature column.</param>
/// <param name="weights">The name for the example weight column.</param>
/// <param name="enforceNoNegativity">Enforce non-negative weights.</param>
/// <param name="l1Weight">Weight of L1 regularizer term.</param>
/// <param name="l2Weight">Weight of L2 regularizer term.</param>
/// <param name="memorySize">Memory size for <see cref="LogisticRegression"/>. Low=faster, less accurate.</param>
/// <param name="optimizationTolerance">Threshold for optimizer convergence.</param>
internal MulticlassLogisticRegression(IHostEnvironment env,
string labelColumn = DefaultColumnNames.Label,
string featureColumn = DefaultColumnNames.Features,
string weights = null,
float l1Weight = Options.Defaults.L1Weight,
float l2Weight = Options.Defaults.L2Weight,
float optimizationTolerance = Options.Defaults.OptTol,
int memorySize = Options.Defaults.MemorySize,
bool enforceNoNegativity = Options.Defaults.EnforceNonNegativity)
: base(env, featureColumn, TrainerUtils.MakeU4ScalarColumn(labelColumn), weights, l1Weight, l2Weight, optimizationTolerance, memorySize, enforceNoNegativity)
{
Host.CheckNonEmpty(featureColumn, nameof(featureColumn));
Host.CheckNonEmpty(labelColumn, nameof(labelColumn));
ShowTrainingStats = Args.ShowTrainingStats;
}
/// <summary>
/// Initializes a new instance of <see cref="SdcaMultiClassTrainer"/>
/// </summary>
/// <param name="env">The environment to use.</param>
/// <param name="featureColumn">The features, or independent variables.</param>
/// <param name="labelColumn">The label, or dependent variable.</param>
/// <param name="loss">The custom loss.</param>
/// <param name="weightColumn">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>
/// <param name="advancedSettings">A delegate to set more settings.
/// The settings here will override the ones provided in the direct method 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 SdcaMultiClassTrainer(IHostEnvironment env,
string featureColumn,
string labelColumn,
string weightColumn = null,
ISupportSdcaClassificationLoss loss = null,
float?l2Const = null,
float?l1Threshold = null,
int?maxIterations = null,
Action <Arguments> advancedSettings = null)
: base(env, featureColumn, TrainerUtils.MakeU4ScalarColumn(labelColumn), TrainerUtils.MakeR4ScalarWeightColumn(weightColumn), advancedSettings,
l2Const, l1Threshold, maxIterations)
{
Host.CheckNonEmpty(featureColumn, nameof(featureColumn));
Host.CheckNonEmpty(labelColumn, nameof(labelColumn));
_loss = loss ?? Args.LossFunction.CreateComponent(env);
Loss = _loss;
}
/// <summary>
/// Initializes a new instance of <see cref="MultiClassNaiveBayesTrainer"/>
/// </summary>
internal MultiClassNaiveBayesTrainer(IHostEnvironment env, Arguments args)
: base(Contracts.CheckRef(env, nameof(env)).Register(LoadName), TrainerUtils.MakeR4VecFeature(args.FeatureColumn),
TrainerUtils.MakeU4ScalarColumn(args.LabelColumn))
{
Host.CheckValue(args, nameof(args));
}
/// <summary>
/// Initializes a new instance of <see cref="NaiveBayesMulticlassTrainer"/>
/// </summary>
internal NaiveBayesMulticlassTrainer(IHostEnvironment env, Options options)
: base(Contracts.CheckRef(env, nameof(env)).Register(LoadName), TrainerUtils.MakeR4VecFeature(options.FeatureColumnName),
TrainerUtils.MakeU4ScalarColumn(options.LabelColumnName))
{
Host.CheckValue(options, nameof(options));
}
/// <summary>
/// Initializes a new instance of <see cref="MulticlassLogisticRegression"/>
/// </summary>
internal MulticlassLogisticRegression(IHostEnvironment env, Options options)
: base(env, options, TrainerUtils.MakeU4ScalarColumn(options.LabelColumn))
{
ShowTrainingStats = Args.ShowTrainingStats;
}
/// <summary>
/// Initializes a new instance of <see cref="LogisticRegressionMulticlassClassificationTrainer"/>
/// </summary>
internal LogisticRegressionMulticlassClassificationTrainer(IHostEnvironment env, Options options)
: base(env, options, TrainerUtils.MakeU4ScalarColumn(options.LabelColumnName))
{
ShowTrainingStats = LbfgsTrainerOptions.ShowTrainingStatistics;
}
