TrivialEstimator<TTransformer> Class

Definition

Namespace:

Microsoft.ML.Data

Assembly:

Microsoft.ML.Data.dll

Package:

Microsoft.ML v3.0.1

The trivial implementation of IEstimator<TTransformer> that already has the transformer and returns it on every call to Fit(IDataView).

Concrete implementations still have to provide the schema propagation mechanism, since there is no easy way to infer it from the transformer.

public abstract class TrivialEstimator<TTransformer> : Microsoft.ML.IEstimator<TTransformer> where TTransformer : class, ITransformer

type TrivialEstimator<'ransformer (requires 'ransformer : null and 'ransformer :> ITransformer)> = class
    interface IEstimator<'ransformer (requires 'ransformer : null and 'ransformer :> ITransformer)>

Public MustInherit Class TrivialEstimator(Of TTransformer)
Implements IEstimator(Of TTransformer)

Type Parameters

TTransformer

Inheritance

Object

TrivialEstimator<TTransformer>

Derived

Microsoft.ML.Data.ImageLoadingEstimator

Microsoft.ML.Transforms.ColumnCopyingEstimator

Microsoft.ML.Transforms.ColumnSelectingEstimator

Microsoft.ML.Transforms.CustomMappingEstimator<TSrc,TDst>

Microsoft.ML.Transforms.FeatureContributionCalculatingEstimator

Microsoft.ML.Transforms.Image.ImageGrayscalingEstimator

Microsoft.ML.Transforms.Image.ImagePixelExtractingEstimator

Microsoft.ML.Transforms.Image.ImageResizingEstimator

Microsoft.ML.Transforms.Image.VectorToImageConvertingEstimator

Microsoft.ML.Transforms.KeyToBinaryVectorMappingEstimator

Microsoft.ML.Transforms.KeyToValueMappingEstimator

Microsoft.ML.Transforms.KeyToVectorMappingEstimator

Microsoft.ML.Transforms.LpNormNormalizingEstimatorBase

Microsoft.ML.Transforms.MissingValueIndicatorEstimator

Microsoft.ML.Transforms.Onnx.OnnxScoringEstimator

Microsoft.ML.Transforms.StatefulCustomMappingEstimator<TSrc,TDst,TState>

Microsoft.ML.Transforms.Text.CustomStopWordsRemovingEstimator

Microsoft.ML.Transforms.Text.StopWordsRemovingEstimator

Microsoft.ML.Transforms.Text.TextNormalizingEstimator

Microsoft.ML.Transforms.Text.TokenizingByCharactersEstimator

Microsoft.ML.Transforms.Text.WordTokenizingEstimator

Microsoft.ML.Transforms.TimeSeries.IidChangePointEstimator

Microsoft.ML.Transforms.TimeSeries.IidSpikeEstimator

Microsoft.ML.Transforms.TimeSeries.SrCnnAnomalyEstimator

Microsoft.ML.Transforms.TypeConvertingEstimator

Microsoft.ML.Transforms.ValueMappingEstimator

Implements

IEstimator<TTransformer>

Methods

Fit(IDataView)

GetOutputSchema(SchemaShape)

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.