ColumnConcatenatingTransformer Class

Reference

Definition

Namespace:: Microsoft.ML.Data

Assembly:: Microsoft.ML.Data.dll

Package:: Microsoft.ML v3.0.1

Package:: Microsoft.ML v1.0.0

Package:: Microsoft.ML v1.1.0

Package:: Microsoft.ML v1.2.0

Package:: Microsoft.ML v1.3.1

Package:: Microsoft.ML v1.4.0

Package:: Microsoft.ML v1.5.5

Package:: Microsoft.ML v1.6.0

Package:: Microsoft.ML v1.7.0

Package:: Microsoft.ML v2.0.0

Important

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ITransformer resulting from fitting an ColumnConcatenatingEstimator.

public sealed class ColumnConcatenatingTransformer : Microsoft.ML.Data.RowToRowTransformerBase

type ColumnConcatenatingTransformer = class
    inherit RowToRowTransformerBase

Public NotInheritable Class ColumnConcatenatingTransformer
Inherits RowToRowTransformerBase

Inheritance: Object

RowToRowTransformerBase
ColumnConcatenatingTransformer

Methods

GetOutputSchema(DataViewSchema)	(Inherited from RowToRowTransformerBase)
Transform(IDataView)	(Inherited from RowToRowTransformerBase)

Explicit Interface Implementations

ICanSaveModel.Save(ModelSaveContext)	(Inherited from RowToRowTransformerBase)
ITransformer.GetRowToRowMapper(DataViewSchema)	(Inherited from RowToRowTransformerBase)
ITransformer.IsRowToRowMapper	(Inherited from RowToRowTransformerBase)

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, PredictionEngineOptions)	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.
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

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