"""Empirical multiplier distribution policy."""
from __future__ import annotations
from typing import TYPE_CHECKING
from eclypse.policies.distribution._helpers import (
build_sampled_distribution_policy,
normalize_distributions,
validate_distributions,
)
from eclypse.utils.constants import MIN_FLOAT
if TYPE_CHECKING:
from random import Random
from eclypse.policies._filters import (
EdgeFilter,
NodeFilter,
)
from eclypse.utils.types import UpdatePolicy
[docs]
def empirical(
*,
node_assets: str | list[str] | None = None,
edge_assets: str | list[str] | None = None,
node_distribution: list[float] | tuple[float, ...] = (1.0,),
edge_distribution: list[float] | tuple[float, ...] | None = None,
node_asset_distributions: dict[str, list[float] | tuple[float, ...]] | None = None,
edge_asset_distributions: dict[str, list[float] | tuple[float, ...]] | None = None,
minimum: float = MIN_FLOAT,
node_ids: list[str] | None = None,
node_filter: NodeFilter | None = None,
edge_ids: list[tuple[str, str]] | None = None,
edge_filter: EdgeFilter | None = None,
) -> UpdatePolicy:
"""Sample multiplicative factors from observed values.
Args:
node_assets (str | list[str] | None): Optional node asset key selector.
edge_assets (str | list[str] | None): Optional edge asset key selector.
node_distribution (list[float] | tuple[float, ...]):
Default observed multipliers for selected node assets.
edge_distribution (list[float] | tuple[float, ...] | None):
Default observed multipliers for edge assets. When
omitted, ``node_distribution`` is reused.
node_asset_distributions (dict[str, list[float] | tuple[float, ...]] | None):
Optional per-node-asset observations.
edge_asset_distributions (dict[str, list[float] | tuple[float, ...]] | None):
Optional per-edge-asset observations.
minimum (float): Lower bound after applying the sampled multiplier.
node_ids (list[str] | None): Optional explicit node identifiers to mutate.
node_filter (NodeFilter | None): Optional predicate receiving ``(node_id, data)``.
edge_ids (list[tuple[str, str]] | None): Optional explicit edge identifiers to mutate.
edge_filter (EdgeFilter | None): Optional predicate receiving ``(source, target, data)``.
Returns:
Policy that multiplies selected numeric assets by empirical samples.
"""
effective_edge_distribution = (
node_distribution if edge_distribution is None else edge_distribution
)
validate_distributions(
{
**normalize_distributions("node_distribution", node_distribution),
**normalize_distributions("edge_distribution", effective_edge_distribution),
**normalize_distributions(
"node_asset_distributions", node_asset_distributions
),
**normalize_distributions(
"edge_asset_distributions", edge_asset_distributions
),
},
checks=[(lambda distribution: len(distribution) > 0, "must not be empty.")],
)
return build_sampled_distribution_policy(
kind="empirical",
node_assets=node_assets,
edge_assets=edge_assets,
node_distribution=tuple(node_distribution),
edge_distribution=tuple(effective_edge_distribution),
node_asset_distributions=node_asset_distributions,
edge_asset_distributions=edge_asset_distributions,
minimum=minimum,
node_ids=node_ids,
node_filter=node_filter,
edge_ids=edge_ids,
edge_filter=edge_filter,
sampler=_sample_empirical,
)
def _sample_empirical(rnd: Random, distribution) -> float:
return rnd.choice(tuple(distribution))
