CustomMappingTransformer<TSrc,TDst> Class

Definition

ITransformer resulting from fitting an CustomMappingEstimator<TSrc,TDst>.

public sealed class CustomMappingTransformer<TSrc,TDst> : Microsoft.ML.ITransformer where TSrc : class, new() where TDst : class, new()
type CustomMappingTransformer<'Src, 'Dst (requires 'Src : null and 'Src : (new : unit -> 'Src) and 'Dst : null and 'Dst : (new : unit -> 'Dst))> = class
    interface ITransformer
    interface ICanSaveModel
Public NotInheritable Class CustomMappingTransformer(Of TSrc, TDst)
Implements ITransformer

Type Parameters

TSrc

The type that describes what 'source' columns are consumed from the input IDataView.

TDst

The type that describes what new columns are added by this transform.

Inheritance
CustomMappingTransformer<TSrc,TDst>
Implements

Methods

GetOutputSchema(DataViewSchema)

Returns the DataViewSchema which would be produced by the transformer applied to an input data with schema inputSchema.

Transform(IDataView)

Take the data in, make transformations, output the data. Note that IDataView's are lazy, so no actual transformations happen here, just schema validation.

Explicit Interface Implementations

ICanSaveModel.Save(ModelSaveContext)
ITransformer.GetRowToRowMapper(DataViewSchema)

Constructs a row-to-row mapper based on an input schema. If IsRowToRowMapper is false, then an exception is thrown. If the inputSchema is in any way unsuitable for constructing the mapper, an exception is likewise thrown.

ITransformer.IsRowToRowMapper

Whether a call to GetRowToRowMapper(DataViewSchema) should succeed, on an appropriate schema.

Extension Methods

Preview(ITransformer, IDataView, Int32)

Preview an effect of the transformer on a given data.

Append<TTrans>(ITransformer, TTrans)

Create a new transformer chain, by appending another transformer to the end of this transformer chain.

CreateTimeSeriesEngine<TSrc,TDst>(ITransformer, IHostEnvironment, Boolean, SchemaDefinition, SchemaDefinition)

TimeSeriesPredictionEngine<TSrc,TDst> creates a prediction engine for a time series pipeline. It updates the state of time series model with observations seen at prediction phase and allows checkpointing the model.

Applies to