poprox_recommender.components.diversifiers.calibration

Functions

compute_kl_divergence(interacted_distr, ...)

KL (p || q), the lower the better.

Classes

Calibrator([config])
CalibratorConfig(*[, theta, num_slots])

class poprox_recommender.components.diversifiers.calibration.CalibratorConfig(*, theta=0.1, num_slots=10)

Bases: BaseModel

Parameters:

• theta (float)

• num_slots (int)

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class poprox_recommender.components.diversifiers.calibration.Calibrator(config=None, **kwargs)

Bases: Component

Parameters:

• config (CalibratorConfig)

• kwargs (Any)

poprox_recommender.components.diversifiers.calibration.compute_kl_divergence(interacted_distr, reco_distr, kl_div=0.0, alpha=0.01)

KL (p || q), the lower the better.

alpha is not really a tuning parameter, it’s just there to make the computation more numerically stable.