skfolio.descriptor.Passthrough#

class skfolio.descriptor.Passthrough(field)[source]#

Passthrough descriptor for an AssetPanel field.

Returns the selected panel field without numerical transformation. This is useful for raw vendor fields or for values computed upstream that should enter a factor exposure model unchanged.

Parameters:

fieldstr: Name of the field to read from the input AssetPanel.

Attributes:

n_assets_int: Number of assets seen during fitting.
asset_names_ndarray of shape (n_assets,): Asset names seen during fitting.

Methods

`fit_transform`(X[, y])	Return the configured panel field.
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`partial_fit_transform`(X[, y])	Stateless class delegation to `fit_transform`.
`set_params`(**params)	Set the parameters of this estimator.

Examples

>>> from skfolio.datasets import make_synthetic_characteristics
>>> from skfolio.descriptor import Passthrough
>>>
>>> X = make_synthetic_characteristics()
>>>
>>> descriptor = Passthrough("eps_ntm")
>>> eps_ntm = descriptor.fit_transform(X)

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

Return the configured panel field.

Parameters:

XAssetPanel: Input panel containing the field characteristic configured at construction.
yNone: Ignored. Present for compatibility with scikit-learn’s API.
**fit_paramsdict: Additional fit parameters. Ignored.

Returns:

valuesndarray of shape (n_observations, n_assets): Raw values of field for each observation and asset.

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.

partial_fit_transform(X, y=None, **fit_params)#: Stateless class delegation to fit_transform.

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.