anti
d90c8b70ce
BEHAVE-EXTRACTOR.md Phase B Step B.1.
* SessionContext gains typing_bursts: tuple[tuple[float, ...], ...]
built by _split_typing_bursts(iats) — splits at gaps > IKI_THINK_MAX_S
(1.5s) and drops bursts of fewer than 3 IATs. Mirrors prototype's
_split_into_bursts at BEHAVE/prototype_extractors/shell/extract.py:275.
* _features/motor.py:keystroke_cadence(ctx) emits one Observation
in {steady, bursty, hunt_and_peck, machine}. Median CV across
typing bursts; mean IKI < IKI_MACHINE_MAX_S paired with CV <
CV_MACHINE_MAX → machine. Confidence 0.85/0.70/0.65/0.60 per the
prototype's calibration history.
* < MIN_INPUTS_FOR_CADENCE inputs or zero typing bursts → skip
emission. v0.1 emits only the burst-CV variant; the prototype's
NAIVE session-CV variant is parked for v0.2.
* Calibration grid widened (PHASE_A_PRIMITIVES → PHASE_AB_PRIMITIVES)
to include motor.keystroke_cadence. Grid green across all five
shards.
Tests: too-few-inputs → no emit, all-think-pauses → no burst → no
emit, uniform IATs → steady, sub-5ms → machine, mixed-pace → bursty,
extreme bimodal → hunt_and_peck.
86 lines
3.0 KiB
Python
86 lines
3.0 KiB
Python
"""Step B.1: ``motor.keystroke_cadence``."""
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from __future__ import annotations
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import random
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from decnet.profiler.behave_shell import extract_session
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from decnet.profiler.behave_shell._parse import AsciinemaEvent
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def _of(observations: list, primitive: str):
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obs = [o for o in observations if o.primitive == primitive]
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assert len(obs) == 1, f"expected exactly one {primitive}, got {len(obs)}"
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return obs[0]
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def _typed_events(iats: list[float], terminator: bool = True) -> list[AsciinemaEvent]:
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"""Build a typed input stream where consecutive single-char events are
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separated by ``iats``."""
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events: list[AsciinemaEvent] = []
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t = 0.0
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events.append((t, "i", "a"))
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for x in iats:
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t += x
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events.append((t, "i", "b"))
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if terminator:
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events.append((t + 0.1, "i", "\r"))
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return events
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def test_too_few_inputs_no_emission() -> None:
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out = list(extract_session(_typed_events([0.1, 0.1]), sid="cad-low"))
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assert [o for o in out if o.primitive == "motor.keystroke_cadence"] == []
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def test_huge_think_pauses_yield_no_typing_bursts() -> None:
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# Two events 5s apart → no IAT under IKI_THINK_MAX_S, and only 1
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# IAT total — below the 3-IAT-per-burst minimum. No burst, no emit.
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events: list[AsciinemaEvent] = [
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(0.0, "i", "a"),
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(5.0, "i", "b"),
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(10.0, "i", "c"),
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(15.0, "i", "d"),
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(20.0, "i", "e"),
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]
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out = list(extract_session(events, sid="cad-no-bursts"))
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assert [o for o in out if o.primitive == "motor.keystroke_cadence"] == []
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def test_uniform_iats_emit_steady() -> None:
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iats = [0.15] * 12
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out = list(extract_session(_typed_events(iats), sid="cad-steady"))
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obs = _of(out, "motor.keystroke_cadence")
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assert obs.value == "steady"
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assert obs.confidence == 0.70
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def test_machine_iats_emit_machine() -> None:
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# Sub-5ms IATs with near-zero CV — no terminator IAT to inflate the
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# variance away from machine
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iats = [0.002] * 20
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out = list(extract_session(_typed_events(iats, terminator=False), sid="cad-machine"))
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obs = _of(out, "motor.keystroke_cadence")
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assert obs.value == "machine"
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assert obs.confidence == 0.85
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def test_bursty_iats_emit_bursty() -> None:
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# Mean ~0.15 with moderate variance, CV between 0.5 and 1.5
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rng = random.Random(42)
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iats = []
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for _ in range(20):
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# Mostly fast, occasionally slow → CV in the bursty band
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iats.append(rng.choice([0.05, 0.05, 0.05, 0.30, 0.50]))
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out = list(extract_session(_typed_events(iats), sid="cad-bursty"))
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obs = _of(out, "motor.keystroke_cadence")
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assert obs.value == "bursty"
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def test_hunt_and_peck_iats_emit_hunt_and_peck() -> None:
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# CV >= 1.5: extreme bimodal (very-fast + very-slow within burst).
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# Most IATs are tiny; a few are ~10x the mean — drives stdev/mean above 1.5.
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iats = [0.01] * 15 + [1.4] * 5
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out = list(extract_session(_typed_events(iats, terminator=False), sid="cad-hp"))
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obs = _of(out, "motor.keystroke_cadence")
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assert obs.value == "hunt_and_peck"
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