lenskit.batch.BatchPipelineRunner#

class lenskit.batch.BatchPipelineRunner(*, n_jobs=None)#

Bases: object

Apply a pipeline to a collection of test users.

Argss:

pipeline:: The pipeline to evaluate.
n_jobs:: The number of parallel processes to use, or None for the default (defined by lenskit.parallel.config.initialize()).

Parameters:: n_jobs (int | None)

__init__(*, n_jobs=None)#

Parameters:: n_jobs (int | None)

Methods

`__init__`(*[, n_jobs])
`add_invocation`(inv)
`predict`([component, output])	Request the batch run to generate test item scores or rating predictins.
`recommend`([component, output])	Request the batch run to generate recomendations.
`run`(pipeline, test_data)	Run the pipeline and return its results.

Attributes

`n_jobs`
`invocations`

predict(component='rating-predictor', *, output='predictions')#

Request the batch run to generate test item scores or rating predictins.

Parameters:

component (str) – The name of the rating predictor component to run.
output (str) – The name of the results in the output dictionary.

recommend(component='recommender', *, output='recommendations', **extra)#

Request the batch run to generate recomendations.

Parameters:

component (str) – The name of the recommender component to run.
output (str) – The name of the results in the output dictionary.
extra (Any) – Extra inputs to the recommender. A common option is n, the number of recommendations to return (a default may be baked into the pipeline).

run(pipeline, test_data)#

Run the pipeline and return its results.

Parameters:

test_data (ItemListCollection[tuple[int | str | bytes | integer[Any] | str_ | bytes_ | object_, ...]] | Mapping[int | str | bytes | integer[Any] | str_ | bytes_ | object_, ItemList] | Iterable[int | str | bytes | integer[Any] | str_ | bytes_ | object_ | tuple[int | str | bytes | integer[Any] | str_ | bytes_ | object_, ...]]) – The collection of test data, as an ItemListCollection, a mapping of user IDs to test data, or as a sequence of item IDs for recommendation.
pipeline (Pipeline)

Returns:

The results, as a nested dictionary. The outer dictionary maps component output names to inner dictionaries of result data. These inner dictionaries map user IDs to

Return type:

BatchResults