NgramHashingEstimator Class

  • Reference

Definition

Namespace:
Microsoft.ML.Transforms.Text
Assembly:
Microsoft.ML.Transforms.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

IEstimator<TTransformer> for the NgramHashingTransformer.

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

Remarks

Estimator Characteristics
Does this estimator need to look at the data to train its parameters? Yes
Input column data type Vector of key type.
Output column data type Vector of known size of Single
Exportable to ONNX No

The resulting NgramHashingTransformer creates a new column, named as specified in the output column name parameters, and produces a vector of n-gram counts (sequences of consecutive words of length 1-n) from a given data. It does so by hashing each n-gram and using the hash value as the index in the bag.

NgramHashingEstimator is different from WordHashBagEstimator in a way that The former takes tokenized text as input while the latter tokenizes text internally.

Check the See Also section for links to usage examples.

Methods

Fit(IDataView)

Trains and returns a NgramHashingTransformer.
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