optional#
- skrub.optional(value, *, name=None, default=value)[source]#
A choice between
value
andNone
.When a pipeline is used without hyperparameter tuning, the outcome of this choice is
value
. Passdefault=None
to makeNone
the default outcome.- Parameters:
- valueobject
The outcome (when
None
is not chosen).- name
str
, optional (default=None) If not
None
,name
is used when displaying search results and can also be used to override the choice’s value by setting it in the environment containing a pipeline’s inputs.- defaultNoneType, optional
An
optional
is a choice between the providedvalue
andNone
. Normally, the default outcome when a pipeline is used without hyperparameter tuning is the providedvalue
. Passdefault=None
to make the alternative outcome,None
, the default.None
is the only allowed value for this parameter.
- Returns:
- Choice
An object representing this choice, which can be used in a skrub pipeline.
Examples
optional
is useful for optional steps in a pipeline. If we want to try our pipeline with or without dimensionality reduction, we can add a step such as:>>> from sklearn.decomposition import PCA >>> from skrub import optional >>> optional(PCA(), name='use dim reduction') optional(PCA(), name='use dim reduction')
The constructed parameter grid will include a version of the pipeline with the PCA and one without:
>>> print(optional(PCA(), name='dim reduction').as_expr().skb.describe_param_grid()) - dim reduction: [PCA(), None]
When a pipeline containing an
optional
step is used without hyperparameter tuning, the default outcome is the providedvalue
.>>> print(optional(PCA()).default()) PCA()
This can be overridden by passing
default=None
:>>> print(optional(PCA(), default=None).default()) None