anti
842b7de950
BEHAVE-EXTRACTOR.md Phase A Step 8. Dispersion / bimodality of
inter-command pauses. HUMAN-bimodal vs LLM-metronomic.
* _features/cognitive.py:inter_command_consistency(ctx) emits one
Observation in {metronomic, variable, bimodal}.
* CV = stdev / mean of ctx.inter_cmd_iats. CV < 0.40 → metronomic
(LLM-pure; corpus anchor 0.24); CV ≥ 1.50 → bimodal heuristic
(LLM-assisted human; v0.1 placeholder, true bimodal via Hartigan
dip is registry-flagged for v0.2); else → variable (human;
corpus anchor 0.94).
* < 2 IATs or zero mean → skip emission. < 5 commands halves
confidence (0.40 vs 0.75) per sample-size honesty.
Tests: too-few IATs → no emission, uniform → metronomic,
human-like dispersion → variable, extreme bursts+gaps → bimodal,
low-sample-count → reduced confidence.
Step 8 closes the six-primitive calibration floor for Phase A.
Step 9 (calibration grid lockdown) is the gate that pins it.
58 lines
2.1 KiB
Python
58 lines
2.1 KiB
Python
"""Step 8: ``cognitive.inter_command_consistency``."""
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from __future__ import annotations
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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 _commands_at(starts: list[float]) -> list[AsciinemaEvent]:
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events: list[AsciinemaEvent] = []
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for s in starts:
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events.append((s, "i", "x\r"))
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return events
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def test_too_few_iats_no_emission() -> None:
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out = list(extract_session(_commands_at([0.0, 1.0]), sid="cv-low"))
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assert [o for o in out if o.primitive == "cognitive.inter_command_consistency"] == []
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def test_uniform_pace_emits_metronomic() -> None:
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# Constant 1s gap → CV 0
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out = list(extract_session(
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_commands_at([i * 1.0 for i in range(8)]), sid="cv-metro",
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))
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obs = _of(out, "cognitive.inter_command_consistency")
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assert obs.value == "metronomic"
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def test_human_like_dispersion_emits_variable() -> None:
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# Pauses around 1s mean with CV ≈ 0.9 (human empirical)
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starts = [0.0, 0.4, 1.4, 1.6, 4.0, 4.4, 7.5]
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out = list(extract_session(_commands_at(starts), sid="cv-var"))
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obs = _of(out, "cognitive.inter_command_consistency")
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assert obs.value == "variable"
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def test_extreme_dispersion_emits_bimodal() -> None:
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# Mix of very tight bursts and very long gaps → CV well above 1.5
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starts = [0.0, 0.1, 0.2, 30.0, 30.1, 30.2, 60.0]
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out = list(extract_session(_commands_at(starts), sid="cv-bi"))
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obs = _of(out, "cognitive.inter_command_consistency")
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assert obs.value == "bimodal"
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def test_low_sample_count_reduces_confidence() -> None:
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# 3 commands → 2 IATs; below the floor of 5
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short = list(extract_session(_commands_at([0.0, 1.0, 2.0]), sid="cv-short"))
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full = list(extract_session(_commands_at([i * 1.0 for i in range(8)]), sid="cv-full"))
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s = _of(short, "cognitive.inter_command_consistency")
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f = _of(full, "cognitive.inter_command_consistency")
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assert s.confidence < f.confidence
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