feat(profiler/behave_shell): emit motor.command_chunking
BEHAVE-EXTRACTOR.md Phase B Step B.4. First implementation —
prototype doesn't ship this primitive.
* SessionContext gains intra_command_iats: per-command tuple of
IATs between consecutive input events whose timestamps fall
inside [cmd.start_ts, cmd.end_ts). Excludes the terminator IAT.
Built by _per_command_iats.
* _features/motor.py:command_chunking(ctx) emits one Observation
in {fluent, fragmented, single_command}.
- 0 commands → skip emit
- 1 command → single_command (registry-allowed point)
- ≥2 commands → median CV across per-command typed-IATs;
< CMD_CHUNKING_FLUENT_CV_MAX (0.50) → fluent, else fragmented
- paste-only sessions (no command has ≥3 typed IATs) → skip emit
(no honest within-command rhythm to measure)
Confidence 0.80 / 0.65 / 0.60.
* Calibration grid widened to include motor.command_chunking;
green across all five shards. Phase B primitive set complete.
Tests: no commands → skip, 1 command → single_command, uniform
typing → fluent, alternating fast/slow → fragmented, paste-only
multi-command → skip emit.
This commit is contained in:
@@ -56,6 +56,9 @@ class SessionContext:
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backspace_iats: tuple[float, ...] = field(default_factory=tuple)
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kill_line_count: int = 0
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# Step B.4 derivations — per-command intra-typing IATs
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intra_command_iats: tuple[tuple[float, ...], ...] = field(default_factory=tuple)
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def _detect_paste_bursts(
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inputs: list[AsciinemaEvent],
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@@ -191,6 +194,30 @@ def _segment_commands(inputs: list[AsciinemaEvent]) -> tuple[Command, ...]:
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return tuple(cmds)
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def _per_command_iats(
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commands: tuple[Command, ...],
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inputs: list[AsciinemaEvent],
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) -> tuple[tuple[float, ...], ...]:
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"""Per-command IATs between consecutive input events whose
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timestamps fall in ``[cmd.start_ts, cmd.end_ts)``.
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Excludes the terminator IAT (the last event at ``cmd.end_ts`` is
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the ``\\r``/``\\n`` itself). Returns one tuple per command.
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"""
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out: list[tuple[float, ...]] = []
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for cmd in commands:
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prev_t: float | None = None
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cmd_iats: list[float] = []
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for t, _kind, _data in inputs:
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if t < cmd.start_ts or t >= cmd.end_ts:
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continue
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if prev_t is not None:
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cmd_iats.append(max(0.0, t - prev_t))
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prev_t = t
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out.append(tuple(cmd_iats))
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return tuple(out)
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def _output_bytes_between(
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outputs: list[AsciinemaEvent],
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start: float,
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@@ -246,6 +273,7 @@ def build_session_context(
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_output_bytes_between(outputs, commands[i].end_ts, commands[i + 1].start_ts)
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for i in range(len(commands) - 1)
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)
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intra_command_iats = _per_command_iats(commands, inputs)
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return SessionContext(
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sid=sid,
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@@ -266,4 +294,5 @@ def build_session_context(
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backspace_count=backspace_count,
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backspace_iats=backspace_iats,
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kill_line_count=kill_line_count,
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intra_command_iats=intra_command_iats,
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)
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@@ -18,6 +18,7 @@ from decnet.profiler.behave_shell._features.cognitive import (
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inter_command_latency_class,
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)
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from decnet.profiler.behave_shell._features.motor import (
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command_chunking,
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error_correction,
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input_modality,
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keystroke_cadence,
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@@ -33,6 +34,7 @@ FEATURES: tuple[FeatureFn, ...] = (
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keystroke_cadence,
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motor_stability,
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error_correction,
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command_chunking,
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inter_command_latency_class,
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command_branch_diversity,
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feedback_loop_engagement,
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@@ -16,6 +16,7 @@ from decnet.profiler.behave_shell._ctx import SessionContext
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from decnet.profiler.behave_shell._features._emit import make_observation
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from decnet.profiler.behave_shell._thresholds import (
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BACKSPACE_IMMEDIATE_MAX_S,
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CMD_CHUNKING_FLUENT_CV_MAX,
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CV_BURSTY_MAX,
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CV_MACHINE_MAX,
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CV_STEADY_MAX,
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@@ -205,3 +206,49 @@ def error_correction(ctx: SessionContext) -> Iterator[Observation]:
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value=value,
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confidence=confidence,
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)
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def command_chunking(ctx: SessionContext) -> Iterator[Observation]:
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"""Emit ``motor.command_chunking`` ∈ {fluent, fragmented, single_command}.
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* 0 commands → skip (no honest answer).
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* 1 command → ``single_command`` (registry-allowed, distinct from
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the fluent/fragmented continuum that needs multiple commands).
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* ≥2 commands → median CV across per-command intra-typing IATs;
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below ``CMD_CHUNKING_FLUENT_CV_MAX`` → fluent, else fragmented.
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Skips emission if no command has ≥3 typed IATs to compute a CV
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over (paste-driven sessions where every command arrived as one
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bulk write — no honest within-command rhythm to measure).
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"""
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n = len(ctx.commands)
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if n == 0:
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return
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if n == 1:
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yield make_observation(
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ctx,
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primitive="motor.command_chunking",
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value="single_command",
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confidence=0.80,
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)
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return
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cvs: list[float] = []
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for iats in ctx.intra_command_iats:
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if len(iats) < 3:
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continue
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m = statistics.fmean(iats)
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if m > 0:
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cvs.append(statistics.pstdev(iats) / m)
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if not cvs:
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return
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cv = statistics.median(cvs)
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if cv < CMD_CHUNKING_FLUENT_CV_MAX:
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value, confidence = "fluent", 0.65
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else:
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value, confidence = "fragmented", 0.60
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yield make_observation(
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ctx,
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primitive="motor.command_chunking",
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value=value,
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confidence=confidence,
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)
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@@ -104,3 +104,8 @@ TREMOR_RATE_MIN: float = 0.10 # ≥10% sub-floor → tremor
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# typo mid-keystroke" (immediate). Beyond this = the operator paused,
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# noticed, then went back (deferred).
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BACKSPACE_IMMEDIATE_MAX_S: float = 0.50
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# ── motor.command_chunking (Step B.4) ───────────────────────────────────────
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# Median CV of within-command IATs. Below this → fluent (steady within
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# each command); above → fragmented (operator pauses mid-command).
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CMD_CHUNKING_FLUENT_CV_MAX: float = 0.50
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