RBFSampler#

classsklearn.kernel_approximation.RBFSampler(*,gamma=1.0,n_components=100,random_state=None)[source]#

Approximate a RBF kernel feature map using random Fourier features.

It implements a variant of Random Kitchen Sinks.[1]

Read more in theUser Guide.

Parameters:

gamma‘scale’ or float, default=1.0

Parameter of RBF kernel: exp(-gamma * x^2).Ifgamma='scale' is passed then it uses1 / (n_features * X.var()) as value of gamma.

Added in version 1.2:The option"scale" was added in 1.2.

n_componentsint, default=100

Number of Monte Carlo samples per original feature.Equals the dimensionality of the computed feature space.

random_stateint, RandomState instance or None, default=None

Pseudo-random number generator to control the generation of the randomweights and random offset when fitting the training data.Pass an int for reproducible output across multiple function calls.SeeGlossary.

Attributes:

random_offset_ndarray of shape (n_components,), dtype={np.float64, np.float32}

Random offset used to compute the projection in then_componentsdimensions of the feature space.

random_weights_ndarray of shape (n_features, n_components), dtype={np.float64, np.float32}

Random projection directions drawn from the Fourier transformof the RBF kernel.

n_features_in_int

Number of features seen duringfit.

Added in version 0.24.

feature_names_in_ndarray of shape (n_features_in_,)

Names of features seen duringfit. Defined only whenXhas feature names that are all strings.

Added in version 1.0.

See also

AdditiveChi2Sampler

Approximate feature map for additive chi2 kernel.

Nystroem

Approximate a kernel map using a subset of the training data.

PolynomialCountSketch

Polynomial kernel approximation via Tensor Sketch.

SkewedChi2Sampler

Approximate feature map for “skewed chi-squared” kernel.

sklearn.metrics.pairwise.kernel_metrics

List of built-in kernels.

Notes

See “Random Features for Large-Scale Kernel Machines” by A. Rahimi andBenjamin Recht.

[1] “Weighted Sums of Random Kitchen Sinks: Replacingminimization with randomization in learning” by A. Rahimi andBenjamin Recht.(https://people.eecs.berkeley.edu/~brecht/papers/08.rah.rec.nips.pdf)

Examples

>>>fromsklearn.kernel_approximationimportRBFSampler>>>fromsklearn.linear_modelimportSGDClassifier>>>X=[[0,0],[1,1],[1,0],[0,1]]>>>y=[0,0,1,1]>>>rbf_feature=RBFSampler(gamma=1,random_state=1)>>>X_features=rbf_feature.fit_transform(X)>>>clf=SGDClassifier(max_iter=5,tol=1e-3)>>>clf.fit(X_features,y)SGDClassifier(max_iter=5)>>>clf.score(X_features,y)1.0

fit(X,y=None)[source]#

Fit the model with X.

Samples random projection according to n_features.

Parameters:

X{array-like, sparse matrix}, shape (n_samples, n_features)

Training data, wheren_samples is the number of samplesandn_features is the number of features.

yarray-like, shape (n_samples,) or (n_samples, n_outputs), default=None

Target values (None for unsupervised transformations).

Returns:

selfobject

Returns the instance itself.

fit_transform(X,y=None,**fit_params)[source]#

Fit to data, then transform it.

Fits transformer toX andy with optional parametersfit_paramsand returns a transformed version ofX.

Parameters:

Xarray-like of shape (n_samples, n_features)

Input samples.

yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None

Target values (None for unsupervised transformations).

**fit_paramsdict

Additional fit parameters.

Returns:

X_newndarray array of shape (n_samples, n_features_new)

Transformed array.

get_feature_names_out(input_features=None)[source]#

Get output feature names for transformation.

The feature names out will prefixed by the lowercased class name. Forexample, if the transformer outputs 3 features, then the feature namesout are:["class_name0","class_name1","class_name2"].

Parameters:

input_featuresarray-like of str or None, default=None

Only used to validate feature names with the names seen infit.

Returns:

feature_names_outndarray of str objects

Transformed feature names.

get_metadata_routing()[source]#

Get metadata routing of this object.

Please checkUser Guide on how the routingmechanism works.

Returns:

routingMetadataRequest

AMetadataRequest encapsulatingrouting information.

get_params(deep=True)[source]#

Get parameters for this estimator.

Parameters:

deepbool, default=True

If True, will return the parameters for this estimator andcontained subobjects that are estimators.

Returns:

paramsdict

Parameter names mapped to their values.

set_output(*,transform=None)[source]#

Set output container.

SeeIntroducing the set_output APIfor an example on how to use the API.

Parameters:

transform{“default”, “pandas”, “polars”}, default=None

Configure output oftransform andfit_transform.

• "default": Default output format of a transformer

• "pandas": DataFrame output

• "polars": Polars output

• None: Transform configuration is unchanged

Added in version 1.4:"polars" option was added.

Returns:

selfestimator instance

Estimator instance.

set_params(**params)[source]#

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects(such asPipeline). The latter haveparameters of the form<component>__<parameter> so that it’spossible to update each component of a nested object.

Parameters:

**paramsdict

Estimator parameters.

Returns:

selfestimator instance

Estimator instance.

transform(X)[source]#

Apply the approximate feature map to X.

Parameters:

X{array-like, sparse matrix}, shape (n_samples, n_features)

New data, wheren_samples is the number of samplesandn_features is the number of features.

Returns:

X_newarray-like, shape (n_samples, n_components)

Returns the instance itself.

Gallery examples#

Explicit feature map approximation for RBF kernels

Explicit feature map approximation for RBF kernels