RandomRecommender¶

class baybe.recommenders.pure.nonpredictive.sampling.RandomRecommender[source]¶

Recommends experiments randomly.

Public methods

`__init__`(*[, ...])	Method generated by attrs for class NonPredictiveRecommender.
`recommend`(searchspace[, batch_size, ...])	Recommend a batch of points from the given search space.

Public attributes and properties

`allow_recommending_already_measured`	Allow to make recommendations that were measured previously.
`allow_repeated_recommendations`	Allow to make recommendations that were already recommended earlier.
`compatibility`	Class variable reflecting the search space compatibility.

__init__(*, allow_repeated_recommendations: bool = False, allow_recommending_already_measured: bool = True)¶

Method generated by attrs for class NonPredictiveRecommender.

For details on the parameters, see Public attributes and properties.

recommend(searchspace: SearchSpace, batch_size: int = 1, train_x: DataFrame | None = None, train_y: DataFrame | None = None)¶

Recommend a batch of points from the given search space.

Parameters:

searchspace (SearchSpace) – The search space from which to recommend the points.
batch_size (int) – The number of points to be recommended.
train_x (Optional[DataFrame]) – Optional training inputs for training a model.
train_y (Optional[DataFrame]) – Optional training labels for training a model.

Return type:

DataFrame

Returns:

A dataframe containing the recommendations as individual rows.

allow_recommending_already_measured: bool¶: Allow to make recommendations that were measured previously. This only has an influence in discrete search spaces.

allow_repeated_recommendations: bool¶: Allow to make recommendations that were already recommended earlier. This only has an influence in discrete search spaces.

compatibility: ClassVar[SearchSpaceType] = 'HYBRID'¶: Class variable reflecting the search space compatibility.