SequentialMetaRecommender¶

class baybe.recommenders.meta.sequential.SequentialMetaRecommender[source]¶

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, ...])	Method generated by attrs for class SequentialMetaRecommender.
`from_dict`(dictionary)	Create an object from its dictionary representation.
`from_json`(source, /)	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`([sink, overwrite])	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', *, _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, bound= SerialMixin)
Returns:: The reconstructed object.

classmethod from_json(source: str | Path | SupportsRead[str], /)¶

Create an object from its JSON representation.

Parameters:

source (str | Path | SupportsRead[str]) –

The JSON source. Can be:

A string containing JSON content.
A file path or Path object pointing to a JSON file.
A file-like object with a read() method.

Raises:

ValueError – If source is not one of the allowed types.

Return type:

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

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 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
Returns:: The dictionary representation of the object.

to_json(sink: str | Path | SupportsWrite[str] | None = None, /, *, overwrite: bool = False, **kwargs: Any)¶

Create an object’s JSON representation.

Parameters:

sink (str | Path | SupportsWrite[str] | None) –
The JSON sink. Can be:
- None (only returns the JSON string).
- A file path or Path object pointing to a location where to write the JSON content.
- A file-like object with a write() method.
overwrite (bool) – Boolean flag indicating if to overwrite the file if it already exists. Only relevant if sink is a file path or Path object.
**kwargs (Any) – Additional keyword arguments to pass to json.dumps().

Raises:

FileExistsError – If sink points to an already existing file but overwrite is False.

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.