StatefulCustomMappingEstimator<TSrc,TDst,TState> Class
Definition
Important
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Applies a custom mapping function to the specified input columns, while allowing a per-cursor state. The result will be in output columns.
public sealed class StatefulCustomMappingEstimator<TSrc,TDst,TState> : Microsoft.ML.Data.TrivialEstimator<Microsoft.ML.Transforms.StatefulCustomMappingTransformer<TSrc,TDst,TState>> where TSrc : class, new() where TDst : class, new() where TState : class, new()type StatefulCustomMappingEstimator<'Src, 'Dst, 'State (requires 'Src : null and 'Src : (new : unit -> 'Src) and 'Dst : null and 'Dst : (new : unit -> 'Dst) and 'State : null and 'State : (new : unit -> 'State))> = class
inherit TrivialEstimator<StatefulCustomMappingTransformer<'Src, 'Dst, 'State>>Public NotInheritable Class StatefulCustomMappingEstimator(Of TSrc, TDst, TState)
Inherits TrivialEstimator(Of StatefulCustomMappingTransformer(Of TSrc, TDst, TState))Type Parameters
- TSrc
- TDst
- TState
- Inheritance
-
TrivialEstimator<StatefulCustomMappingTransformer<TSrc,TDst,TState>>StatefulCustomMappingEstimator<TSrc,TDst,TState>
Remarks
Estimator Characteristics
| Does this estimator need to look at the data to train its parameters? | No |
| Input column data type | Any |
| Output column data type | Any |
| Exportable to ONNX | No |
The resulting StatefulCustomMappingTransformer<TSrc,TDst,TState> applies a user defined mapping to one or more input columns and produces one or more output columns. This transformation doesn't change the number of rows, and can be seen as the result of applying the user's function to every row of the input data.
In addition to the input and output objects, the provided custom function is given a state object that it can look at and/or modify.
Check the See Also section for links to usage examples.
Methods
| Fit(IDataView) | (Inherited from TrivialEstimator<TTransformer>) |
| GetOutputSchema(SchemaShape) |
Returns the SchemaShape of the schema which will be produced by the transformer. Used for schema propagation and verification in a pipeline. |
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. |