lenskit.tuning.BasePipelineTuner

class lenskit.tuning.BasePipelineTuner(spec, out_dir=None, rng=None)

Bases: abc.ABC

Base class for hyperparameter tuning.

Parameters:

• spec (lenskit.tuning.spec.TuningSpec) – The tuning specification.

• out_dir (pathlib.Path | None)

• rng (lenskit.random.RNGInput)

settings: lenskit.config.TuneSettings

spec: lenskit.tuning.spec.TuningSpec

out_dir: pathlib.Path

pipe_name: str | None

random_seed: numpy.random.SeedSequence

iterative: bool

data: lenskit.splitting.TTSplit

log

set_data(train, test, *, name=None)

Set the data to be used for tuning.

Parameters:

• train (lenskit.data.Dataset | pathlib.Path)

• test (lenskit.data.ItemListCollection | pathlib.Path)

• name (str | None)

property metric

property mode: Literal['min', 'max']

Return type:

Literal[‘min’, ‘max’]

property pipeline: lenskit.pipeline.config.PipelineConfig

Return type:

lenskit.pipeline.config.PipelineConfig

abstractmethod run()

Run the tuning job, returning the results.

Return type:

TuneResults