pykoop.KernelApproximation

class KernelApproximation

Bases: BaseEstimator, TransformerMixin

Base class for all kernel approximations.

All attributes with a trailing underscore must be set in the subclass’ fit().

n_features_in_

Number of features input.

Type:

int

n_features_out_

Number of features output. This attribute is not available in estimators from sklearn.kernel_approximation.

Type:

int

__init__()

Methods

__init__()
fit(X[, y])	Fit kernel approximation.
fit_transform(X[, y])	Fit to data, then transform it.
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)	Transform data.

abstract fit(X, y=None)

Fit kernel approximation.

Parameters:

• X (np.ndarray) – Data matrix.

• y (Optional[np.ndarray]) – Ignored.

Returns:

Instance of itself.

Return type:

KernelApproximation

Raises:

ValueError – If any of the constructor parameters are incorrect.

fit_transform(X, y=None, **fit_params)

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:

• X (array-like of shape (n_samples, n_features)) – Input samples.

• y (array-like of shape (n_samples,) or (n_samples, n_outputs), default=None) – Target values (None for unsupervised transformations).

• **fit_params (dict) – Additional fit parameters.

Returns:

X_new – Transformed array.

Return type:

ndarray array of shape (n_samples, n_features_new)

get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routing – A MetadataRequest encapsulating routing information.

Return type:

MetadataRequest

get_params(deep=True)

Get parameters for this estimator.

Parameters:

deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

params – Parameter names mapped to their values.

Return type:

dict

set_output(*, transform=None)

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

• None: Transform configuration is unchanged

Returns:

self – Estimator instance.

Return type:

estimator instance

set_params(**params)

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:

**params (dict) – Estimator parameters.

Returns:

self – Estimator instance.

Return type:

estimator instance

abstract transform(X)

Transform data.

Parameters:

X (np.ndarray) – Data matrix.

Returns:

Transformed data matrix.

Return type:

np.ndarray