Files
DECNET/decnet/correlation/attribution/aggregate.py
anti c2891d6cca feat(correlation/attribution): substrate + idle handler (Phase 1)
v0 Phase 1 of ATTRIBUTION-ENGINE.md:

* AttributionStateRow SQLModel keyed on (identity_uuid, primitive)
  per ANTI direction — re-keying state rows when the v1 clusterer
  merges attackers is the migration debt v0 should not bake in.
  ATTRIBUTION-ENGINE.md updated with the deviation note.
* AttributionMixin: ensure_stub_identity_for_attacker, idempotent
  upsert_attribution_state, get_attribution_state[_for_identity],
  list_multi_actor_identities (the Phase 5 correlator's read).
* attribution.profile.{state_changed,multi_actor_suspected} bus
  topics + builder; wiki Service-Bus.md updated separately.
* attribution_worker.py: subscribes to attacker.observation.>,
  ensures stub identity per event, logs and continues. No merger,
  no state writes, no derived events — Phase 4 wires those.
* attribution/{aggregate.py,_thresholds.py} skeletons: Phase 2
  fills _aggregate_categorical, Phase 3 adds numeric+hash+dispatcher.
2026-05-08 23:16:13 -04:00

88 lines
3.1 KiB
Python

"""Per-(identity, primitive) state-machine — the attribution engine's
core merge logic.
Pure: given a list of BEHAVE observations for one
``(identity_uuid, primitive)`` pair, returns the derived state and
mirror metadata. No DB, no bus, no I/O. The worker
(``decnet.correlation.attribution_worker``) is responsible for loading
the observations and writing the state row.
State vocabulary is frozen at five values (see
``ATTRIBUTION-ENGINE.md``):
* ``unknown`` — < 3 observations (insufficient signal)
* ``stable`` — recent N agree
* ``drifting`` — recent N stable but disagree with older N
* ``conflicted`` — recent N split
* ``multi_actor`` — conflicted + cross-session alternation pattern
Phase 2 ships :func:`_aggregate_categorical`. Phase 3 will add
:func:`_aggregate_numeric` and :func:`_aggregate_hash` and the
ValueKind dispatcher.
"""
from __future__ import annotations
from dataclasses import dataclass
from typing import Any, Iterable, Sequence
__all__ = ["AttributionState", "aggregate_observations"]
@dataclass(frozen=True)
class AttributionState:
"""Output of the merger for one ``(identity, primitive)`` pair.
The fields map 1:1 onto :class:`AttributionStateRow` columns —
callers compose the final dict for ``upsert_attribution_state``
by adding ``identity_uuid`` and ``primitive`` (the merger does not
own the natural key).
"""
current_value: Any
state: str
confidence: float
observation_count: int
last_observation_ts: float
def aggregate_observations(
observations: Sequence[dict[str, Any]],
) -> AttributionState:
"""Run the merger over *observations* and return the derived state.
*observations* is a list of dicts with at minimum ``value``,
``ts``, and ``confidence`` fields (matching the BEHAVE
``Observation`` envelope shape that
``ObservationRow.observations_time_series`` returns). They MUST
arrive ordered by ``ts`` ascending; the merger assumes that.
Phase 2 only supports categorical values. Phase 3 will dispatch
on the BEHAVE primitive's ``ValueKind`` and pick the right merger.
"""
if not observations:
return AttributionState(
current_value=None,
state="unknown",
confidence=0.0,
observation_count=0,
last_observation_ts=0.0,
)
# Phase 2 stub — categorical only. Phase 3 will inspect
# ``primitive`` (passed in alongside observations) to pick a
# merger; for now defer to the categorical implementation
# (``_aggregate_categorical``) which Phase 2 lands.
raise NotImplementedError(
"aggregate_observations is implemented in Phase 2 (categorical) "
"and Phase 3 (numeric + hash). v0 Phase 1 ships the substrate "
"only; the worker logs without invoking the merger.",
)
def _coerce_obs_iter(
observations: Iterable[dict[str, Any]],
) -> list[dict[str, Any]]:
"""Defensive: accept any iterable, return a list. Used by the
worker which pulls observations off the bus + DB into mixed
iterables."""
return list(observations)