swectral.IdentityTransformer#

class swectral.IdentityTransformer[source]#

A passthrough scikit-learn-style transformer that returns the input data unchanged.

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

__init__(*args, **kwargs)#

Methods

`__init__`(args, *kwargs)
`fit`(X[, y])	Fit the transformer.
`fit_transform`(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_output`(*[, transform])	Set output container.
`set_params`(**params)	Set the parameters of this estimator.
`transform`(X)	Return the input data unchanged.

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

Fit the transformer.

This method performs no computation and exists to satisfy the scikit-learn estimator interface.

Parameters:

Xarray_like of shape (n_samples, n_features): Input data.
yarray_like of shape (n_samples,), optional: Target values. Ignored.

Returns:

IdentityTransformer: The fitted transformer.

Return type:

IdentityTransformer

transform(X)[source]#

Return the input data unchanged.

Parameters:

Xarray_like of shape (n_samples, n_features): Input data.

Returns:

numpy.ndarray: The input data converted to a NumPy array.

Return type:

ndarray

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

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:

numpy.ndarray: The input data converted to a NumPy array.

Return type:

ndarray

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_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”, “polars”}, 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

Added in version 1.4: “polars” option was added.

Returns:

selfestimator instance: 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:

**paramsdict: Estimator parameters.

Returns:

selfestimator instance: Estimator instance.