StreamingSequentialMetaRecommender

class baybe.recommenders.meta.sequential.StreamingSequentialMetaRecommender

Bases: MetaRecommender

A meta recommender that switches between recommenders from an iterable.

Similar to baybe.recommenders.meta.sequential.SequentialMetaRecommender but without explicit list conversion. Consequently, it supports arbitrary iterables, possibly of infinite length. The downside is that serialization is not supported.

Raises:

NoRecommendersLeftError – If more recommenders are requested than there are recommenders available.

Public methods

__init__(recommenders)	Method generated by attrs for class StreamingSequentialMetaRecommender.
default_iterator()	Initialize the recommender iterator.
from_dict(dictionary)	Create an object from its dictionary representation.
from_json(string)	Create an object from its JSON representation.
get_current_recommender()	Get the current recommender, if available.
get_next_recommender(batch_size, searchspace)	Get the recommender for the next recommendation.
recommend(batch_size, searchspace[, ...])	See baybe.recommenders.base.RecommenderProtocol.recommend().
select_recommender(batch_size[, ...])	Select a pure recommender for the given experimentation context.
to_dict()	Create an object's dictionary representation.
to_json()	Create an object's JSON representation.

Public attributes and properties

recommenders

An iterable providing the recommenders to be used.

__init__(recommenders: Iterable[PureRecommender])

Method generated by attrs for class StreamingSequentialMetaRecommender.

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

default_iterator()

Initialize the recommender iterator.

classmethod from_dict(dictionary: dict)

Create an object from its dictionary representation.

Parameters:

dictionary (dict) – The dictionary representation.

Return type:

TypeVar(_T)

Returns:

The reconstructed object.

classmethod from_json(string: str)

Create an object from its JSON representation.

Parameters:

string (str) – The JSON representation of the object.

Return type:

TypeVar(_T)

Returns:

The reconstructed object.

get_current_recommender()

Get the current recommender, if available.

Return type:

PureRecommender

get_next_recommender(batch_size: int, searchspace: SearchSpace, objective: Objective | None = None, measurements: DataFrame | None = None, pending_experiments: DataFrame | None = None)

Get the recommender for the next recommendation.

Returns the next recommender in row that has not yet been used for generating recommendations. In case of multiple consecutive calls, this means that the same recommender instance is returned until its recommend() method is called.

See baybe.recommenders.base.RecommenderProtocol.recommend() for details on the method arguments.

Return type:

PureRecommender

recommend(batch_size: int, searchspace: SearchSpace, objective: Objective | None = None, measurements: DataFrame | None = None, pending_experiments: DataFrame | None = None)

See baybe.recommenders.base.RecommenderProtocol.recommend().

Return type:

DataFrame

select_recommender(batch_size: int, searchspace: SearchSpace | None = None, objective: Objective | None = None, measurements: DataFrame | None = None, pending_experiments: DataFrame | None = None)

Select a pure recommender for the given experimentation context.

See baybe.recommenders.base.RecommenderProtocol.recommend() for details on the method arguments.

Return type:

PureRecommender

to_dict()

Create an object’s dictionary representation.

Return type:

dict

to_json()

Create an object’s JSON representation.

Return type:

str

Returns:

The JSON representation as a string.

recommenders: Iterable[PureRecommender]

An iterable providing the recommenders to be used.