Pipeline Extensions Class
Extension methods that allow chaining of estimator and transformer pipelines.
public static class LearningPipelineExtensions
type LearningPipelineExtensions = class
Public Module LearningPipelineExtensions
Create a new composite loader estimator, by appending an estimator to this data loader.
Create a new composite loader, by appending a transformer to this data loader.
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.
Create a new transformer chain, by appending another transformer to the end of this transformer chain.
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.
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.