swectral.IdentityResampler

class swectral.IdentityResampler

A passthrough imblearn-style resampler that returns the input data unchanged.

This resampler is useful as a placeholder in pipelines or for enforcing a consistent resampler interface without modifying data.

__init__(*args, **kwargs)

Methods

__init__(*args, **kwargs)
fit_resample(X[, y])	Fit the transformer and return the input data unchanged.
get_metadata_routing()	Get metadata routing of this object.
get_params([deep])	Get parameters for this estimator.
set_params(**params)	Set the parameters of this estimator.

fit_resample(X, y=None)

Fit the transformer and return the input data unchanged.

Parameters:

Xarray_like of shape (n_samples, n_features)

Input data.

yarray_like of shape (n_samples,), optional

Target values. Ignored.

Returns:

tuple[Any, Any]

The input X and y.

Return type:

tuple[Any, Any]

get_metadata_routing()

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)

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_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:

**paramsdict

Estimator parameters.

Returns:

selfestimator instance

Estimator instance.