feat(profiler/behave_shell): G.8 emotional_valence.frustration_venting
Binary read of ctx.obscenity_hits (G.0 lexical counter): * detected — obscenity_hits ≥ 1 * none — zero hits Skip below FRUST_VENT_MIN_TYPED_CHARS (30). Confidence hard-capped at 0.5: 0.40 when detected, 0.50 only when cleanly absent over ≥ 200 typed letters, 0.30 otherwise.
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@@ -26,6 +26,7 @@ from decnet.profiler.behave_shell._features.cognitive import (
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)
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from decnet.profiler.behave_shell._features.emotional_valence import (
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arousal,
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frustration_venting,
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stress_response,
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valence,
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)
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@@ -99,4 +100,5 @@ FEATURES: tuple[FeatureFn, ...] = (
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valence,
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arousal,
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stress_response,
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frustration_venting,
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)
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@@ -26,6 +26,8 @@ from decnet.profiler.behave_shell._thresholds import (
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AROUSAL_FAST_IAT_S,
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AROUSAL_MIN_IATS,
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EMOTIONAL_VALENCE_CONFIDENCE_CAP,
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FRUST_VENT_FULL_CONFIDENCE_MIN,
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FRUST_VENT_MIN_TYPED_CHARS,
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STRESS_DISTRESS_RATIO_MIN,
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STRESS_EUSTRESS_RATIO_MIN,
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STRESS_MIN_ERRORED_WITH_IATS,
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@@ -186,3 +188,36 @@ def stress_response(ctx: SessionContext) -> Iterator[Observation]:
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value=value,
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confidence=_cap_soft(raw),
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)
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def frustration_venting(ctx: SessionContext) -> Iterator[Observation]:
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"""Emit ``emotional_valence.frustration_venting`` ∈ {none, detected}.
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Pure read of ``ctx.obscenity_hits`` (G.0 lexical counter):
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* ``detected`` — ``obscenity_hits ≥ 1``.
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* ``none`` — zero hits.
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Skip emission below ``FRUST_VENT_MIN_TYPED_CHARS`` (30) typed
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letters — too thin to call cleanly absent. Confidence hard-capped
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at 0.50; 0.40 when ``detected``; 0.50 only when ``none`` AND
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typed_letter_count ≥ ``FRUST_VENT_FULL_CONFIDENCE_MIN`` (200);
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0.30 otherwise.
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"""
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if ctx.typed_letter_count < FRUST_VENT_MIN_TYPED_CHARS:
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return
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if ctx.obscenity_hits >= 1:
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value = "detected"
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raw = 0.40
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else:
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value = "none"
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if ctx.typed_letter_count >= FRUST_VENT_FULL_CONFIDENCE_MIN:
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raw = 0.50
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else:
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raw = 0.30
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yield make_observation(
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ctx,
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primitive="emotional_valence.frustration_venting",
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value=value,
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confidence=_cap_soft(raw),
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)
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@@ -0,0 +1,52 @@
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"""Step G.8: ``emotional_valence.frustration_venting`` ∈ {none, detected}.
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Hard 0.5 confidence cap.
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"""
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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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PRIMITIVE = "emotional_valence.frustration_venting"
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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(text: str, t0: float = 0.0, dt: float = 0.05) -> list[AsciinemaEvent]:
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return [(t0 + i * dt, "i", c) for i, c in enumerate(text)]
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def test_too_little_text_no_emission() -> None:
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out = list(extract_session(_typed("hi"), sid="g8-thin"))
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assert [o for o in out if o.primitive == PRIMITIVE] == []
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def test_detected_when_obscenity_present() -> None:
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text = "hostname date hostname date oh fuck this is broken really "
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obs = _of(list(extract_session(_typed(text), sid="g8-yes")), PRIMITIVE)
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assert obs.value == "detected"
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assert obs.confidence == 0.40
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def test_none_when_clean() -> None:
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text = "hostname date hostname date hostname date hostname date "
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obs = _of(list(extract_session(_typed(text), sid="g8-no")), PRIMITIVE)
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assert obs.value == "none"
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def test_high_confidence_when_long_clean() -> None:
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text = "hostname date " * 30
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obs = _of(list(extract_session(_typed(text), sid="g8-long")), PRIMITIVE)
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assert obs.value == "none"
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assert obs.confidence == 0.50
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def test_cap_never_exceeded() -> None:
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text = "fuck shit damn " * 30
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obs = _of(list(extract_session(_typed(text), sid="g8-cap")), PRIMITIVE)
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assert obs.confidence <= 0.50
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