# skfolio.factor_exposure.OneHotCategoricalFactors ### *class* skfolio.factor_exposure.OneHotCategoricalFactors(category, \*, family) 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: $$ x_{t,i,k} = \begin{cases} 1 & \text{if asset } i \text{ belongs to category } k \\ 0 & \text{otherwise} \end{cases} $$ Missing category codes produce NaN exposures for all category factors of that asset-observation pair. * **Parameters:** **category** *str* : Name of the categorical field in the AssetPanel to one-hot encode. The field must be a `FieldCategorical`. **family** *str* : 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`](#skfolio.factor_exposure.OneHotCategoricalFactors.fit_transform)(X[, y]) | One-hot encode the categorical field. | |--------------------------------------------------------------------------------|------------------------------------------------| | [`get_metadata_routing`](#skfolio.factor_exposure.OneHotCategoricalFactors.get_metadata_routing)() | Get metadata routing of this object. | | [`get_params`](#skfolio.factor_exposure.OneHotCategoricalFactors.get_params)([deep]) | Get parameters for this estimator. | | [`partial_fit_transform`](#skfolio.factor_exposure.OneHotCategoricalFactors.partial_fit_transform)(X[, y]) | Stateless class delegation to `fit_transform`. | | [`set_params`](#skfolio.factor_exposure.OneHotCategoricalFactors.set_params)(\*\*params) | Set the parameters of this estimator. | #### fit_transform(X, y=None, \*\*fit_params) One-hot encode the categorical field. * **Parameters:** **X** *AssetPanel* : Input panel containing the categorical field as integer codes. **y** *None* : Ignored. Present for compatibility with scikit-learn’s API. **\*\*fit_params** *dict* : Additional fit parameters. They are ignored. * **Returns:** **exposures** *ndarray 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](https://skfolio.org/user_guide/metadata_routing.html.md#metadata-routing) on how the routing mechanism works. * **Returns:** **routing** *MetadataRequest* : A `MetadataRequest` encapsulating routing information. #### get_params(deep=True) Get parameters for this estimator. * **Parameters:** **deep** *bool, default=True* : If True, will return the parameters for this estimator and contained subobjects that are estimators. * **Returns:** **params** *dict* : 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 `__` so that it’s possible to update each component of a nested object. * **Parameters:** **\*\*params** *dict* : Estimator parameters. * **Returns:** **self** *estimator instance* : Estimator instance.