# skfolio.factor_exposure.GlobalFactor ### *class* skfolio.factor_exposure.GlobalFactor(\*, family='market') Constant factor exposure equal to one for every asset. `GlobalFactor` represents a cross-sectional regression intercept or broad market factor in a characteristics factor model. It does not depend on any characteristic field. It uses the panel dimensions to return an exposure matrix of ones with shape `(n_observations, n_assets)`. For each observation $t$ and asset $i$, the exposure is: $$ x_{t,i} = 1 $$ This factor is typically used when the cross-sectional regression should estimate a common return component in addition to characteristic-based style industry or country factors. * **Parameters:** **family** *str, default=”market”* : The factor family this exposure belongs to. Factor families group related factors for basket-neutral constraints, neutralization, attribution and reporting. The default is `"market"`. * **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`](#skfolio.factor_exposure.GlobalFactor.fit_transform)(X[, y]) | Return a constant exposure matrix. | |--------------------------------------------------------------------------------|------------------------------------------------| | [`get_metadata_routing`](#skfolio.factor_exposure.GlobalFactor.get_metadata_routing)() | Get metadata routing of this object. | | [`get_params`](#skfolio.factor_exposure.GlobalFactor.get_params)([deep]) | Get parameters for this estimator. | | [`partial_fit_transform`](#skfolio.factor_exposure.GlobalFactor.partial_fit_transform)(X[, y]) | Stateless class delegation to `fit_transform`. | | [`set_params`](#skfolio.factor_exposure.GlobalFactor.set_params)(\*\*params) | Set the parameters of this estimator. | ### Examples ```pycon >>> from skfolio.factor_exposure import GlobalFactor >>> from skfolio.prior import CharacteristicsFactorModel >>> >>> model = CharacteristicsFactorModel( ... factors=[("market", GlobalFactor())] ... ) ``` #### fit_transform(X, y=None, \*\*fit_params) Return a constant exposure matrix. * **Parameters:** **X** *AssetPanel* : Input panel used to determine the number of observations and assets. **y** *None* : Ignored. Present for compatibility with scikit-learn’s API. **\*\*fit_params** *dict* : Additional fit parameters. They are ignored. * **Returns:** **exposure** *ndarray of shape (n_observations, n_assets)* : Constant factor exposure equal to one for every asset in every observation. #### 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.