ApproximatedKernelMappingEstimator Class

  • Reference

Definition

Namespace:
Microsoft.ML.Transforms
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

Maps vector columns to a low -dimensional feature space.

public sealed class ApproximatedKernelMappingEstimator : Microsoft.ML.IEstimator<Microsoft.ML.Transforms.ApproximatedKernelTransformer>
type ApproximatedKernelMappingEstimator = class
    interface IEstimator<ApproximatedKernelTransformer>
Public NotInheritable Class ApproximatedKernelMappingEstimator
Implements IEstimator(Of 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