skfolio.factor_exposure.OneHotCategoricalFactors#

class skfolio.factor_exposure.OneHotCategoricalFactors(category, *, family)[source]#

One-hot factor exposures from a categorical field.

Expands a categorical field into one factor per category level. The result is an exposure tensor with shape (n_observations, n_assets, n_factors), where n_factors is the number of category levels.

For each observation \(t\), asset \(i\) and category factor \(k\), the exposure is:

\[\begin{split}x_{t,i,k} = \begin{cases} 1 & \text{if asset } i \text{ belongs to category } k \\ 0 & \text{otherwise} \end{cases}\end{split}\]

Missing category codes produce NaN exposures for all category factors of that asset-observation pair.

Parameters:

categorystr: Name of the categorical field in the AssetPanel to one-hot encode. The field must be a FieldCategorical.
familystr: The factor family this exposure belongs to (e.g., “industry”, “country”). Factor families group related factors for basket-neutral constraints, neutralization, attribution and reporting.

Attributes:

factor_names_ndarray of shape (n_factors,): The category labels corresponding to each one-hot column.
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])	One-hot encode the categorical 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.

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

One-hot encode the categorical field.

Parameters:

XAssetPanel: Input panel containing the categorical field as integer codes.
yNone: Ignored. Present for compatibility with scikit-learn’s API.
**fit_paramsdict: Additional fit parameters. They are ignored.

Returns:

exposuresndarray of shape (n_observations, n_assets, n_factors): One-hot encoded exposures. Column order matches X.fields[category].levels. Entries with missing codes (MISSING_CATEGORY_CODE == -1) are filled with NaN.

Raises:

IndexError: If any valid code is >= n_levels (indicates data corruption).

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.