anti
f948e10830
Composite over three [0, 1]-clipped sub-signals (chunking variance, error rate from D.0's Command.errored, pace variability), mean-aggregated and bucketed against COGNITIVE_LOAD_LOW_MAX / COGNITIVE_LOAD_MEDIUM_MAX. Components missing data drop out of the mean rather than zeroing it. v0.1 thresholds; D.8 re-tunes once D.2-D.7 are stable. Confidence held at 0.60 (composite over soft sub-signals) and halved below the 5-command sample-size floor.
89 lines
3.5 KiB
Python
89 lines
3.5 KiB
Python
"""Step D.1: ``cognitive.cognitive_load``.
|
|
|
|
Composite of three [0, 1]-clipped sub-signals (chunking variance, error
|
|
rate, pace variability) → bucketed against COGNITIVE_LOAD_LOW_MAX /
|
|
COGNITIVE_LOAD_MEDIUM_MAX. Tests pin each component at its extremes and
|
|
confirm the bucket falls where the math says.
|
|
"""
|
|
from __future__ import annotations
|
|
|
|
from decnet.profiler.behave_shell import extract_session
|
|
from decnet.profiler.behave_shell._parse import AsciinemaEvent
|
|
|
|
|
|
def _of(observations: list, primitive: str):
|
|
obs = [o for o in observations if o.primitive == primitive]
|
|
assert len(obs) == 1, f"expected exactly one {primitive}, got {len(obs)}"
|
|
return obs[0]
|
|
|
|
|
|
def _typed(text: str, t0: float = 0.0, dt: float = 0.05) -> list[AsciinemaEvent]:
|
|
return [(t0 + i * dt, "i", c) for i, c in enumerate(text)]
|
|
|
|
|
|
def _metronomic_clean_session(n: int = 8) -> list[AsciinemaEvent]:
|
|
"""``n`` commands, perfectly even pacing, zero errors, fluent typing."""
|
|
events: list[AsciinemaEvent] = []
|
|
for i in range(n):
|
|
events.extend(_typed("ls\r", t0=i * 1.0, dt=0.05))
|
|
return events
|
|
|
|
|
|
def test_no_commands_no_emission() -> None:
|
|
events: list[AsciinemaEvent] = [(0.0, "i", "a")]
|
|
out = list(extract_session(events, sid="cl-empty"))
|
|
assert [o for o in out if o.primitive == "cognitive.cognitive_load"] == []
|
|
|
|
|
|
def test_metronomic_clean_session_emits_low() -> None:
|
|
"""Even pacing + clean output + steady typing → low load."""
|
|
out = list(extract_session(_metronomic_clean_session(8), sid="cl-low"))
|
|
obs = _of(out, "cognitive.cognitive_load")
|
|
assert obs.value == "low"
|
|
|
|
|
|
def test_high_error_rate_drives_load_up() -> None:
|
|
"""Every command errored — error_load = 1.0 alone forces load >= 0.33."""
|
|
events: list[AsciinemaEvent] = []
|
|
for i in range(8):
|
|
events.extend(_typed("foo\r", t0=i * 1.0, dt=0.05))
|
|
events.append((i * 1.0 + 0.5, "o", "bash: foo: command not found\n"))
|
|
out = list(extract_session(events, sid="cl-err"))
|
|
obs = _of(out, "cognitive.cognitive_load")
|
|
assert obs.value in ("medium", "high")
|
|
|
|
|
|
def test_all_three_components_high_emits_high() -> None:
|
|
"""Saturate every component → load ≈ 1.0 → high."""
|
|
events: list[AsciinemaEvent] = []
|
|
# Burst-then-gap pacing maximises pace-CV; mid-command jitter
|
|
# maximises chunking-CV; every command errors.
|
|
starts = [0.0, 0.1, 0.2, 30.0, 30.1, 60.0, 90.0, 90.1]
|
|
for i, s in enumerate(starts):
|
|
# Mid-command jitter: 'a' at s, 'b' 0.01s later, 'c' 2s later, '\r' 2.05s later
|
|
events.append((s, "i", "a"))
|
|
events.append((s + 0.01, "i", "b"))
|
|
events.append((s + 2.0, "i", "c"))
|
|
events.append((s + 2.05, "i", "\r"))
|
|
events.append((s + 2.10, "o", "bash: abc: command not found\n"))
|
|
out = list(extract_session(events, sid="cl-high"))
|
|
obs = _of(out, "cognitive.cognitive_load")
|
|
assert obs.value == "high"
|
|
|
|
|
|
def test_low_sample_count_reduces_confidence() -> None:
|
|
short = list(extract_session(_metronomic_clean_session(3), sid="cl-short"))
|
|
full = list(extract_session(_metronomic_clean_session(8), sid="cl-full"))
|
|
s = _of(short, "cognitive.cognitive_load")
|
|
f = _of(full, "cognitive.cognitive_load")
|
|
assert s.confidence < f.confidence
|
|
|
|
|
|
def test_pii_no_command_bodies_in_observation() -> None:
|
|
events: list[AsciinemaEvent] = []
|
|
for i in range(6):
|
|
events.extend(_typed("supersecret\r", t0=i * 1.0, dt=0.05))
|
|
out = list(extract_session(events, sid="cl-pii"))
|
|
obs = _of(out, "cognitive.cognitive_load")
|
|
assert "supersecret" not in obs.model_dump_json()
|