IEstimator<TTransformer> Interface

Definition

Namespace:

Microsoft.ML

Assembly:

Microsoft.ML.Core.dll

Package:

Microsoft.ML v3.0.1

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 : ITransformer

type IEstimator<'ransformer (requires 'ransformer :> ITransformer)> = interface

Public Interface IEstimator(Of Out TTransformer)

Type Parameters

TTransformer

This type parameter is covariant. That is, you can use either the type you specified or any type that is more derived. For more information about covariance and contravariance, see Covariance and Contravariance in Generics.

Derived

Microsoft.ML.Calibrators.CalibratorEstimatorBase<TICalibrator>

Microsoft.ML.Data.EstimatorChain<TLastTransformer>

Microsoft.ML.Data.TrivialEstimator<TTransformer>

Microsoft.ML.TorchSharp.AutoFormerV2.ObjectDetectionTrainer

Microsoft.ML.TorchSharp.Roberta.QATrainer

Microsoft.ML.TorchSharp.TorchSharpBaseTrainer

Microsoft.ML.Trainers.FastTree.TreeEnsembleFeaturizationEstimatorBase

Microsoft.ML.Trainers.FieldAwareFactorizationMachineTrainer

Microsoft.ML.Trainers.ITrainerEstimator<TTransformer,TModel>

Microsoft.ML.Trainers.MatrixFactorizationTrainer

Microsoft.ML.Trainers.MetaMulticlassTrainer<TTransformer,TModel>

Microsoft.ML.Trainers.PriorTrainer

Microsoft.ML.Trainers.TrainerEstimatorBase<TTransformer,TModel>

Microsoft.ML.Transforms.ApproximatedKernelMappingEstimator

Microsoft.ML.Transforms.ColumnConcatenatingEstimator

Microsoft.ML.Transforms.CountFeatureSelectingEstimator

Microsoft.ML.Transforms.ExpressionEstimator

Microsoft.ML.Transforms.HashingEstimator

Microsoft.ML.Transforms.MissingValueReplacingEstimator

Microsoft.ML.Transforms.MutualInformationFeatureSelectingEstimator

Microsoft.ML.Transforms.NormalizingEstimator

Microsoft.ML.Transforms.OneHotEncodingEstimator

Microsoft.ML.Transforms.OneHotHashEncodingEstimator

Microsoft.ML.Transforms.Onnx.DnnImageFeaturizerEstimator

Microsoft.ML.Transforms.PrincipalComponentAnalyzer

Microsoft.ML.Transforms.TensorFlowEstimator

Microsoft.ML.Transforms.Text.LatentDirichletAllocationEstimator

Microsoft.ML.Transforms.Text.NgramExtractingEstimator

Microsoft.ML.Transforms.Text.NgramHashingEstimator

Microsoft.ML.Transforms.Text.TextFeaturizingEstimator

Microsoft.ML.Transforms.Text.WordBagEstimator

Microsoft.ML.Transforms.Text.WordEmbeddingEstimator

Microsoft.ML.Transforms.Text.WordHashBagEstimator

Microsoft.ML.Transforms.TimeSeries.SsaChangePointEstimator

Microsoft.ML.Transforms.TimeSeries.SsaForecastingEstimator

Microsoft.ML.Transforms.TimeSeries.SsaSpikeEstimator

Microsoft.ML.Transforms.ValueToKeyMappingEstimator

Microsoft.ML.Transforms.VectorWhiteningEstimator

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.