GamTrainerBase<TOptions,TTransformer,TPredictor> Class
Definition
Important
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Base class for GAM trainers.
public abstract class GamTrainerBase<TOptions,TTransformer,TPredictor> : Microsoft.ML.Trainers.TrainerEstimatorBase<TTransformer,TPredictor> where TOptions : GamTrainerBase<TOptions,TTransformer,TPredictor>.OptionsBase, new() where TTransformer : ISingleFeaturePredictionTransformer<TPredictor> where TPredictor : class
type GamTrainerBase<'Options, 'ransformer, 'Predictor (requires 'Options :> GamTrainerBase<'Options, 'ransformer, 'Predictor>.OptionsBase and 'Options : (new : unit -> 'Options) and 'ransformer :> ISingleFeaturePredictionTransformer<'Predictor> and 'Predictor : null)> = class
inherit TrainerEstimatorBase<'ransformer, 'Predictor (requires 'ransformer :> ISingleFeaturePredictionTransformer<'Predictor> and 'Predictor : null)>
Public MustInherit Class GamTrainerBase(Of TOptions, TTransformer, TPredictor)
Inherits TrainerEstimatorBase(Of TTransformer, TPredictor)
Type Parameters
- TOptions
- TTransformer
- TPredictor
- Inheritance
-
TrainerEstimatorBase<TTransformer,TPredictor>GamTrainerBase<TOptions,TTransformer,TPredictor>
- Derived
Fields
FeatureColumn |
The feature column that the trainer expects. (Inherited from TrainerEstimatorBase<TTransformer,TModel>) |
LabelColumn |
The label column that the trainer expects. Can be |
WeightColumn |
The weight column that the trainer expects. Can be |
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. |