StochasticTrainerBase<TTransformer,TModel> Class

Definition

Namespace:

Microsoft.ML.Trainers

Assembly:

Microsoft.ML.StandardTrainers.dll

Package:

Microsoft.ML v3.0.1

public abstract class StochasticTrainerBase<TTransformer,TModel> : Microsoft.ML.Trainers.TrainerEstimatorBase<TTransformer,TModel> where TTransformer : ISingleFeaturePredictionTransformer<TModel> where TModel : class

type StochasticTrainerBase<'ransformer, 'Model (requires 'ransformer :> ISingleFeaturePredictionTransformer<'Model> and 'Model : null)> = class
    inherit TrainerEstimatorBase<'ransformer, 'Model (requires 'ransformer :> ISingleFeaturePredictionTransformer<'Model> and 'Model : null)>

Public MustInherit Class StochasticTrainerBase(Of TTransformer, TModel)
Inherits TrainerEstimatorBase(Of TTransformer, TModel)

Type Parameters

TTransformer

TModel

Inheritance

Object

TrainerEstimatorBase<TTransformer,TModel>

StochasticTrainerBase<TTransformer,TModel>

Derived

Microsoft.ML.Trainers.SdcaTrainerBase<TOptions,TTransformer,TModel>

Constructors

StochasticTrainerBase<TTransformer,TModel>(IHost, SchemaShape+Column, SchemaShape+Column, SchemaShape+Column)

Fields

FeatureColumn	The feature column that the trainer expects. (Inherited from TrainerEstimatorBase<TTransformer,TModel>)
LabelColumn	The label column that the trainer expects. Can be null, which indicates that label is not used for training. (Inherited from TrainerEstimatorBase<TTransformer,TModel>)
WeightColumn	The weight column that the trainer expects. Can be null, which indicates that weight is not used for training. (Inherited from TrainerEstimatorBase<TTransformer,TModel>)

Properties

Info

Methods

Fit(IDataView)	Trains and returns a ITransformer. (Inherited from TrainerEstimatorBase<TTransformer,TModel>)
GetOutputSchema(SchemaShape)	(Inherited from TrainerEstimatorBase<TTransformer,TModel>)

Extension Methods

AppendCacheCheckpoint<TTrans>(IEstimator<TTrans>, IHostEnvironment)

Append a 'caching checkpoint' to the estimator chain. This will ensure that the downstream estimators will be trained against cached data. It is helpful to have a caching checkpoint before trainers that take multiple data passes.

WithOnFitDelegate<TTransformer>(IEstimator<TTransformer>, Action<TTransformer>)

Given an estimator, return a wrapping object that will call a delegate once Fit(IDataView) is called. It is often important for an estimator to return information about what was fit, which is why the Fit(IDataView) method returns a specifically typed object, rather than just a general ITransformer. However, at the same time, IEstimator<TTransformer> are often formed into pipelines with many objects, so we may need to build a chain of estimators via EstimatorChain<TLastTransformer> where the estimator for which we want to get the transformer is buried somewhere in this chain. For that scenario, we can through this method attach a delegate that will be called once fit is called.