SequentialMetaRecommender

class baybe.recommenders.meta.sequential.SequentialMetaRecommender

Bases: BaseSequentialMetaRecommender

A meta recommender that uses a pre-defined sequence of recommenders.

A new recommender is taken from the sequence whenever at least one new measurement is available, until all recommenders are exhausted. More precisely, a recommender change is triggered whenever the size of the training dataset increases; the actual content of the dataset is ignored.

Note

The provided sequence of recommenders will be internally pre-collected into a list. If this is not acceptable, consider using baybe.recommenders.meta.sequential.StreamingSequentialMetaRecommender instead.

Raises:

• RuntimeError – If the training dataset size decreased compared to the previous call.

• NoRecommendersLeftError – If more recommenders are requested than there are recommenders available and mode="raise".

Public methods

__init__(recommenders[, mode, _step, ...])	Method generated by attrs for class SequentialMetaRecommender.
from_dict(dictionary)	Create an object from its dictionary representation.
from_json(string)	Create an object from its JSON representation.
get_current_recommender()	Deprecated! Use select_recommender() or get_non_meta_recommender() instead.
get_next_recommender()	Deprecated! Use select_recommender() or get_non_meta_recommender() instead.
get_non_meta_recommender([batch_size, ...])	Follow the meta recommender chain to the selected non-meta recommender.
recommend(batch_size, searchspace[, ...])	See baybe.recommenders.base.RecommenderProtocol.recommend().
select_recommender([batch_size, ...])	Select a 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	A finite-length sequence of recommenders to be used.
mode	Defines what shall happen when the last recommender in the sequence has been consumed but additional recommender changes are triggered:
is_stateful	Boolean indicating if the meta recommender is stateful.

__init__(recommenders, mode: Literal['raise', 'reuse_last', 'cyclic'] = 'raise', *, _step: int = 0, _was_used: bool = False, _n_last_measurements: int = 0)

Method generated by attrs for class SequentialMetaRecommender.

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

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()

Deprecated! Use select_recommender() or get_non_meta_recommender() instead.

Return type:

PureRecommender

get_next_recommender()

Deprecated! Use select_recommender() or get_non_meta_recommender() instead.

Return type:

PureRecommender

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

Follow the meta recommender chain to the selected non-meta recommender.

Recursively calls MetaRecommender.select_recommender() until a non-meta recommender is encountered, which is then returned. Effectively, this extracts the recommender responsible for generating the recommendations for the specified context.

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

Return type:

RecommenderProtocol

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 | None = None, searchspace: SearchSpace | None = None, objective: Objective | None = None, measurements: DataFrame | None = None, pending_experiments: DataFrame | None = None)

Select a recommender for the given experimentation context.

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

Return type:

RecommenderProtocol

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.

property is_stateful: bool

Boolean indicating if the meta recommender is stateful.

mode: Literal['raise', 'reuse_last', 'cyclic']

Defines what shall happen when the last recommender in the sequence has been consumed but additional recommender changes are triggered:

• "raise": An error is raised.

• "reuse_last": The last recommender in the sequence is used indefinitely.

• "cycle": The selection restarts from the beginning of the sequence.

recommenders: list[RecommenderProtocol]

A finite-length sequence of recommenders to be used. For infinite-length iterables, see baybe.recommenders.meta.sequential.StreamingSequentialMetaRecommender.