NgramExtractingEstimator 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

Produces a vector of counts of n-grams (sequences of consecutive words) encountered in the input text.

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

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 Known-sized vector of Single
Exportable to ONNX Yes

The resulting NgramExtractingTransformer creates a new column, named as specified in the output column name parameters, where each input vector is mapped to a vector of counts of n-grams (sequences of consecutive words) encountered in the input text.

The estimator builds a dictionary of n-grams and the NgramExtractingTransformer uses the id in the dictionary as the index in the count vector that it produces.

Check the See Also section for links to usage examples.

Methods

Fit(IDataView)

Trains and returns a NgramExtractingTransformer.
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