ApproximatedKernelMappingEstimator Class

Definition

Namespace:

Microsoft.ML.Transforms

Assembly:

Microsoft.ML.Transforms.dll

Package:

Microsoft.ML v5.0.0-preview.1.25125.4

Source:

RandomFourierFeaturizing.cs

Maps vector columns to a low -dimensional feature space.

C#

public sealed class ApproximatedKernelMappingEstimator : Microsoft.ML.IEstimator<Microsoft.ML.Transforms.ApproximatedKernelTransformer>

Inheritance

Object

ApproximatedKernelMappingEstimator

Implements

IEstimator<ApproximatedKernelTransformer>

Remarks

Estimator Characteristics

Does this estimator need to look at the data to train its parameters?	Yes
Input column data type	Known-sized vector of Single
Output column data type	Known-sized vector of Single
Exportable to ONNX	No

The resulting ApproximatedKernelTransformer creates a new column, named as specified in the output column name parameters, where each input vector is mapped to a feature space where inner products approximate one of two shift-invariant kernel functions: The Gaussian kernel, or the Laplacian kernel. By mapping features to a space that approximate non-linear kernels, linear methods can be used to approximate more complex kernel SVM models. This mapping is based on the paper Random Features for Large-Scale Kernel Machines by Rahimi and Recht.

Check the See Also section for links to usage examples.

Methods

Fit(IDataView)	Trains and returns a ApproximatedKernelTransformer.
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

• ApproximatedKernelMap(TransformsCatalog, String, String, Int32, Boolean, KernelBase, Nullable<Int32>)