Add handle_missing and handle_unknown options to OrdinalEncoder

category_encoders.ordinal.OrdinalEncoder in scikit-learn-contrib/category_encoders has 2 really useful options:

1. handle_unknown, options are ‘error’, ‘return_nan’ and ‘value’, defaults to ‘value’, which will impute the category -1.
2. handle_missing, options are ‘error’, ‘return_nan’, and ‘value, default to ‘value’, which treat nan as a category at fit time, or -2 at transform time if nan is not a category during fit.

These 2 options are really, really useful for handling real-world data

Describe the workflow you want to enable

1. Handle new categories at predict time in OrdinalEncoder (OneHotEncoder already has this opion).
2. Handle NaNs at fit and predict time in OrdinalEncoder

Describe your proposed solution

Port the logic for handle_unknown and handle_missing from category_encoders.ordinal.OrdinalEncoder

Describe alternatives you’ve considered, if relevant

Just using scikit-learn-contrib/category_encoders instead

Additional context

Every encoder in scikit-learn-contrib/category_encoders has the option handle_unknown and handle_missing, giving users the flexibility to decide how to handle unknown or new values. This consistency in the API makes it really easy to switch between different encoders and try them out in your workflow.

[thomasjpfan]

With 1.1, OrdinalEncoder now handles unknown values and missing values.

[NicolasHug]

no need to IMO, we can just wait for #18968 to be solved for now.