IEstimator<TTransformer> Interface
Definition
Important
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The estimator (in Spark terminology) is an 'untrained transformer'. It needs to 'fit' on the data to manufacture a transformer. It also provides the 'schema propagation' like transformers do, but over SchemaShape instead of DataViewSchema.
public interface IEstimator<out TTransformer> where TTransformer : ITransformertype IEstimator<'ransformer (requires 'ransformer :> ITransformer)> = interfacePublic Interface IEstimator(Of Out TTransformer)Type Parameters
- TTransformer
- Derived
Methods
| Fit(IDataView) |
Train and return a transformer. |
| GetOutputSchema(SchemaShape) |
Schema propagation for estimators. Returns the output schema shape of the estimator, if the input schema shape is like the one provided. |
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. |