# skfolio.descriptor.Passthrough ### *class* skfolio.descriptor.Passthrough(field) Passthrough descriptor for an [`AssetPanel`](https://skfolio.org/generated/skfolio.containers.AssetPanel.html.md#skfolio.containers.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:** **field** *str* : Name of the field to read from the input [`AssetPanel`](https://skfolio.org/generated/skfolio.containers.AssetPanel.html.md#skfolio.containers.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`](#skfolio.descriptor.Passthrough.fit_transform)(X[, y]) | Return the configured panel field. | |--------------------------------------------------------------------------------|------------------------------------------------| | [`get_metadata_routing`](#skfolio.descriptor.Passthrough.get_metadata_routing)() | Get metadata routing of this object. | | [`get_params`](#skfolio.descriptor.Passthrough.get_params)([deep]) | Get parameters for this estimator. | | [`partial_fit_transform`](#skfolio.descriptor.Passthrough.partial_fit_transform)(X[, y]) | Stateless class delegation to `fit_transform`. | | [`set_params`](#skfolio.descriptor.Passthrough.set_params)(\*\*params) | Set the parameters of this estimator. | ### Examples ```pycon >>> 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) Return the configured panel field. * **Parameters:** **X** *AssetPanel* : Input panel containing the `field` characteristic configured at construction. **y** *None* : Ignored. Present for compatibility with scikit-learn’s API. **\*\*fit_params** *dict* : Additional fit parameters. Ignored. * **Returns:** **values** *ndarray 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](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.