`sklearn.kernel_approximation`.PolynomialCountSketch#

class sklearn.kernel_approximation.PolynomialCountSketch(*, gamma=1.0, degree=2, coef0=0, n_components=100, random_state=None)[source]#

Polynomial kernel approximation via Tensor Sketch.

Implements Tensor Sketch, which approximates the feature map of the polynomial kernel:

K(X, Y) = (gamma * <X, Y> + coef0)^degree

by efficiently computing a Count Sketch of the outer product of a vector with itself using Fast Fourier Transforms (FFT). Read more in the User Guide.

New in version 0.24.

Parameters:

gammafloat, default=1.0: Parameter of the polynomial kernel whose feature map will be approximated.
degreeint, default=2: Degree of the polynomial kernel whose feature map will be approximated.
coef0int, default=0: Constant term of the polynomial kernel whose feature map will be approximated.
n_componentsint, default=100: Dimensionality of the output feature space. Usually, n_components should be greater than the number of features in input samples in order to achieve good performance. The optimal score / run time balance is typically achieved around n_components = 10 * n_features, but this depends on the specific dataset being used.
random_stateint, RandomState instance, default=None: Determines random number generation for indexHash and bitHash initialization. Pass an int for reproducible results across multiple function calls. See Glossary.

Attributes:

indexHash_ndarray of shape (degree, n_features), dtype=int64: Array of indexes in range [0, n_components) used to represent the 2-wise independent hash functions for Count Sketch computation.
bitHash_ndarray of shape (degree, n_features), dtype=float32: Array with random entries in {+1, -1}, used to represent the 2-wise independent hash functions for Count Sketch computation.
n_features_in_int: Number of features seen during fit.

New in version 0.24.
feature_names_in_ndarray of shape (n_features_in_,): Names of features seen during fit. Defined only when X has feature names that are all strings.

New 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.
RBFSampler: Approximate a RBF kernel feature map using random Fourier features.
SkewedChi2Sampler: Approximate feature map for “skewed chi-squared” kernel.
sklearn.metrics.pairwise.kernel_metrics: List of built-in kernels.

Examples

>>> from sklearn.kernel_approximation import PolynomialCountSketch
>>> from sklearn.linear_model import SGDClassifier
>>> X = [[0, 0], [1, 1], [1, 0], [0, 1]]
>>> y = [0, 0, 1, 1]
>>> ps = PolynomialCountSketch(degree=3, random_state=1)
>>> X_features = ps.fit_transform(X)
>>> clf = SGDClassifier(max_iter=10, tol=1e-3)
>>> clf.fit(X_features, y)
SGDClassifier(max_iter=10)
>>> clf.score(X_features, y)
1.0

For a more detailed example of usage, see Scalable learning with polynomial kernel approximation

Methods

`fit`(X[, y])	Fit the model with X.
`fit_transform`(X[, y])	Fit to data, then transform it.
`get_feature_names_out`([input_features])	Get output feature names for transformation.
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`set_output`(*[, transform])	Set output container.
`set_params`(**params)	Set the parameters of this estimator.
`transform`(X)	Generate the feature map approximation for X.

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

Fit the model with X.

Initializes the internal variables. The method needs no information about the distribution of data, so we only care about n_features in X.

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_features): Training data, where n_samples is the number of samples and n_features is the number of features.
yarray-like of 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 to X and y with optional parameters fit_params and returns a transformed version of X.

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. For example, if the transformer outputs 3 features, then the feature names out 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 in fit.

Returns:

feature_names_outndarray of str objects: Transformed feature names.

get_metadata_routing()[source]#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest: A MetadataRequest encapsulating routing information.

get_params(deep=True)[source]#

Get parameters for this estimator.

Parameters:

deepbool, default=True: If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsdict: Parameter names mapped to their values.

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

Set output container.

See Introducing the set_output API for an example on how to use the API.

Parameters:

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

Configure output of transform and fit_transform.

"default": Default output format of a transformer
"pandas": DataFrame output
"polars": Polars output
None: Transform configuration is unchanged

New 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 as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**paramsdict: Estimator parameters.

Returns:

selfestimator instance: Estimator instance.

transform(X)[source]#

Generate the feature map approximation for X.

Parameters:

X{array-like}, shape (n_samples, n_features): New data, where n_samples is the number of samples and n_features is the number of features.

Returns:

X_newarray-like, shape (n_samples, n_components): Returns the instance itself.

Examples using `sklearn.kernel_approximation.PolynomialCountSketch`#

Release Highlights for scikit-learn 0.24

Scalable learning with polynomial kernel approximation

sklearn.kernel_approximation.PolynomialCountSketch#

Examples using sklearn.kernel_approximation.PolynomialCountSketch#

`sklearn.kernel_approximation`.PolynomialCountSketch#

Examples using `sklearn.kernel_approximation.PolynomialCountSketch`#