LearningPipelineExtensions Class

Definition

Namespace:

Microsoft.ML

Assembly:

Microsoft.ML.Data.dll

Package:

Microsoft.ML v3.0.1

Extension methods that allow chaining of estimator and transformer pipelines.

public static class LearningPipelineExtensions

type LearningPipelineExtensions = class

Public Module LearningPipelineExtensions

Inheritance

Object

LearningPipelineExtensions

Methods

Append<TSource,TTrans>(IDataLoader<TSource>, IEstimator<TTrans>)	Create a new composite loader estimator, by appending an estimator to this data loader.
Append<TSource,TTrans>(IDataLoader<TSource>, TTrans)	Create a new composite loader, by appending a transformer to this data loader.
Append<TSource,TTrans>(IDataLoaderEstimator<TSource,IDataLoader<TSource>>, IEstimator<TTrans>)	Create a new composite loader estimator, by appending another estimator to the end of this data loader estimator.
Append<TTrans>(IEstimator<ITransformer>, IEstimator<TTrans>, TransformerScope)	Create a new estimator chain, by appending another estimator to the end of this estimator.
Append<TTrans>(ITransformer, TTrans)	Create a new transformer chain, by appending another transformer to the end of this transformer chain.
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.