skfolio.preprocessing.BaseCSTransformer#

class skfolio.preprocessing.BaseCSTransformer[source]#

Base class for all cross-sectional transformers in skfolio.

Cross-sectional transformers process each observation of a 2D input array using values from the same observation only.

These transformers are stateless. The default fit method validates the estimator parameters, validates X, and records n_features_in_ for scikit-learn compatibility.

Methods

fit(X[, y, cs_weights, cs_groups])

Fit the transformer.

fit_transform(X[, y, cs_weights, cs_groups])

Fit to X and return the transformed values.

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_fit_request(*[, cs_groups, cs_weights])

Configure whether metadata should be requested to be passed to the fit method.

set_params(**params)

Set the parameters of this estimator.

set_transform_request(*[, cs_groups, cs_weights])

Configure whether metadata should be requested to be passed to the transform method.

transform(X[, cs_weights, cs_groups])

Transform X observation by observation.

Notes

All estimators should specify all the parameters that can be set at the class level in their __init__ as explicit keyword arguments (no *args or **kwargs).

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

Fit the transformer.

Cross-sectional transformers are stateless and do not learn data-dependent parameters. This method validates the estimator parameters, validates X, and records n_features_in_ for scikit-learn compatibility.

Parameters:
Xarray-like of shape (n_observations, n_assets)

Input matrix where each row is an observation and each column is an asset.

yIgnored

Not used, present for API consistency by convention.

cs_weightsarray-like of shape (n_observations, n_assets), optional

Optional cross-sectional weights accepted for API consistency with transform. They are ignored during fitting.

cs_groupsarray-like of shape (n_observations, n_assets), optional

Optional cross-sectional group labels accepted for API consistency with transform. They are ignored during fitting.

Returns:
selfBaseCSTransformer

Fitted estimator.

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

Fit to X and return the transformed values.

Parameters:
Xarray-like of shape (n_observations, n_assets)

Input matrix where each row is an observation and each column is an asset.

yIgnored

Not used, present for API consistency by convention.

cs_weightsarray-like of shape (n_observations, n_assets), optional

Optional cross-sectional weights forwarded to transform.

cs_groupsarray-like of shape (n_observations, n_assets), optional

Optional cross-sectional group labels forwarded to transform.

Returns:
X_newndarray of shape (n_observations, n_assets)

Transformed array.

get_feature_names_out(input_features=None)#

Get output feature names for transformation.

Parameters:
input_featuresarray-like of str or None, default=None

Input features.

  • If input_features is None, then feature_names_in_ is used as feature names in. If feature_names_in_ is not defined, then the following input feature names are generated: ["x0", "x1", ..., "x(n_features_in_ - 1)"].

  • If input_features is an array-like, then input_features must match feature_names_in_ if feature_names_in_ is defined.

Returns:
feature_names_outndarray of str objects

Same as input features.

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_fit_request(*, cs_groups='$UNCHANGED$', cs_weights='$UNCHANGED$')#

Configure whether metadata should be requested to be passed to the fit method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to fit.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Parameters:
cs_groupsstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for cs_groups parameter in fit.

cs_weightsstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for cs_weights parameter in fit.

Returns:
selfobject

The updated object.

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.

set_transform_request(*, cs_groups='$UNCHANGED$', cs_weights='$UNCHANGED$')#

Configure whether metadata should be requested to be passed to the transform method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to transform if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to transform.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Parameters:
cs_groupsstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for cs_groups parameter in transform.

cs_weightsstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for cs_weights parameter in transform.

Returns:
selfobject

The updated object.

abstractmethod transform(X, cs_weights=None, cs_groups=None)[source]#

Transform X observation by observation.

Parameters:
Xarray-like of shape (n_observations, n_assets)

Input matrix where each row is an observation and each column is an asset.

cs_weightsarray-like of shape (n_observations, n_assets), optional

Optional cross-sectional weights used by the concrete transformer.

cs_groupsarray-like of shape (n_observations, n_assets), optional

Optional cross-sectional group labels used by the concrete transformer.

Returns:
X_transformedndarray of shape (n_observations, n_assets)

Transformed values.