lenskit.basic.random#

Classes

RandomSelector([n, rng])

Randomly select items from a candidate list.

SoftmaxRanker([n, rng])

Stochastic top-N ranking with softmax sampling.
class lenskit.basic.random.RandomSelector(n=-1, rng=None)#

Bases: Component

Randomly select items from a candidate list.

Parameters:

  • n (int) – The number of items to select, or -1 to randomly permute the items.

  • rng (int | integer[Any] | Sequence[int] | SeedSequence | Literal['user'] | tuple[int | ~numpy.integer[~typing.Any] | ~typing.Sequence[int] | ~numpy.random.bit_generator.SeedSequence, ~typing.Literal['user']] | None) – The random number generator or specification (see Random Seeds). This class supports derivable RNGs.

class lenskit.basic.random.SoftmaxRanker(n=-1, rng=None)#

Bases: Component

Stochastic top-N ranking with softmax sampling.

This uses the “softmax” sampling method, a more efficient approximation of Plackett-Luce sampling than even the Gumbell trick, as documented by `Tim Vieira`_. It expects a scored list of input items, and samples n items, with selection probabilities proportional to their scores.

Note

Negative scores are clamped to (approximately) zero.

Parameters:

  • n (int) – The number of items to return (-1 to return unlimited).

  • rng (int | integer[Any] | Sequence[int] | SeedSequence | Literal['user'] | tuple[int | ~numpy.integer[~typing.Any] | ~typing.Sequence[int] | ~numpy.random.bit_generator.SeedSequence, ~typing.Literal['user']] | None) – The random number generator or specification (see Random Seeds). This class supports derivable RNGs.