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HashingEstimator Class

  • Reference

Definition

Namespace:
Microsoft.ML.Transforms
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

Estimator for HashingTransformer, which hashes either single valued columns or vector columns. For vector columns, it hashes each slot separately.

public sealed class HashingEstimator : Microsoft.ML.IEstimator<Microsoft.ML.Transforms.HashingTransformer>
type HashingEstimator = class
    interface IEstimator<HashingTransformer>
Public NotInheritable Class HashingEstimator
Implements IEstimator(Of HashingTransformer)
Inheritance
Object
HashingEstimator
Implements
IEstimator<HashingTransformer>

Remarks

Estimator Characteristics
Does this estimator need to look at the data to train its parameters? Yes, if the mapping of the hashes to the values is required.
Input column data type Vector or scalars of numeric, boolean, text, DateTime and key type.
Output column data type Vector or scalar key type.
Exportable to ONNX Yes - on estimators trained on v1.5 and up. Int64, UInt64, Single, Double and OrderedHashing are not supported.

Check the See Also section for links to usage examples.

Methods

Fit(IDataView)

Trains and returns a HashingTransformer.
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.

Applies to

See also