anti
d4dc7dff81
high_agitated when any of: * caps_run_max ≥ 5 * bang_run_max ≥ 3 * fastest typing burst median IAT < 0.06s with ≥ 30 IATs total low_calm when slowest qualifying burst median IAT > 0.30s with ≥ 30 IATs. Else medium_engaged. Confidence hard-capped at 0.5; 0.30 below AROUSAL_MIN_IATS.
120 lines
4.1 KiB
Python
120 lines
4.1 KiB
Python
"""``emotional_valence.*`` feature functions (Phase G, soft block).
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All four primitives in this module ride a hard 0.5 confidence cap
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(:data:`EMOTIONAL_VALENCE_CONFIDENCE_CAP`). Cap is enforced inside
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the feature functions, *not* via :func:`make_observation` — sample-size
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honesty may still pull confidence below 0.5.
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Step G.5: ``emotional_valence.valence``.
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Step G.6: ``emotional_valence.arousal`` (lands later).
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Step G.7: ``emotional_valence.stress_response`` (lands later).
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Step G.8: ``emotional_valence.frustration_venting`` (lands later).
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"""
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from __future__ import annotations
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import statistics
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from typing import Iterator
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from decnet_behave_core.spec.envelope import Observation
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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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AROUSAL_BANG_RUN_MIN,
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AROUSAL_CALM_IAT_S,
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AROUSAL_CAPS_RUN_MIN,
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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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VALENCE_FULL_CONFIDENCE_MIN,
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VALENCE_MIN_HITS,
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VALENCE_MIN_TYPED_CHARS,
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)
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def _cap_soft(c: float) -> float:
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"""Clamp confidence to the soft-primitive ceiling."""
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return min(c, EMOTIONAL_VALENCE_CONFIDENCE_CAP)
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def valence(ctx: SessionContext) -> Iterator[Observation]:
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"""Emit ``emotional_valence.valence`` ∈ {positive, neutral, negative}.
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Pure ratio over the lexical counters built in G.0:
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* ``positive`` — ``positive_lex_hits > negative_lex_hits +
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obscenity_hits`` AND ``positive_lex_hits ≥ VALENCE_MIN_HITS`` (2).
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* ``negative`` — ``negative_lex_hits + obscenity_hits >
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positive_lex_hits`` AND that sum ≥ ``VALENCE_MIN_HITS``.
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* ``neutral`` — fall-through.
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Skip emission below ``VALENCE_MIN_TYPED_CHARS`` (80) typed letters.
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Confidence hard-capped at 0.50 (registry convention); 0.30 below
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``VALENCE_FULL_CONFIDENCE_MIN`` (200).
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"""
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if ctx.typed_letter_count < VALENCE_MIN_TYPED_CHARS:
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return
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pos = ctx.positive_lex_hits
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neg_total = ctx.negative_lex_hits + ctx.obscenity_hits
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if pos > neg_total and pos >= VALENCE_MIN_HITS:
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value = "positive"
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elif neg_total > pos and neg_total >= VALENCE_MIN_HITS:
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value = "negative"
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else:
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value = "neutral"
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raw = 0.50 if ctx.typed_letter_count >= VALENCE_FULL_CONFIDENCE_MIN else 0.30
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yield make_observation(
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ctx,
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primitive="emotional_valence.valence",
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value=value,
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confidence=_cap_soft(raw),
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)
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def arousal(ctx: SessionContext) -> Iterator[Observation]:
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"""Emit ``emotional_valence.arousal`` ∈ {low_calm, medium_engaged,
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high_agitated}.
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Three signals (any of which fires ``high_agitated``):
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* ``ctx.caps_run_max ≥ AROUSAL_CAPS_RUN_MIN`` (5) — capslock rant.
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* ``ctx.bang_run_max ≥ AROUSAL_BANG_RUN_MIN`` (3) — repeated bangs.
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* The fastest typing burst's median IAT < ``AROUSAL_FAST_IAT_S``
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(0.06) over a burst of ≥ ``AROUSAL_MIN_IATS`` (30) IATs.
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``low_calm`` — slowest qualifying burst's median IAT >
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``AROUSAL_CALM_IAT_S`` (0.30).
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``medium_engaged`` — fall-through.
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Skip emission when no qualifying typing bursts. Confidence hard-
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capped at 0.50; 0.30 below ``AROUSAL_MIN_IATS`` total typed IATs.
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"""
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qualifying = [b for b in ctx.typing_bursts if len(b) >= 3]
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if not qualifying:
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return
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fastest_med = min(statistics.median(b) for b in qualifying)
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slowest_med = max(statistics.median(b) for b in qualifying)
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total_iats = sum(len(b) for b in qualifying)
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if (
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ctx.caps_run_max >= AROUSAL_CAPS_RUN_MIN
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or ctx.bang_run_max >= AROUSAL_BANG_RUN_MIN
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or (
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total_iats >= AROUSAL_MIN_IATS
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and fastest_med < AROUSAL_FAST_IAT_S
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)
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):
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value = "high_agitated"
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elif total_iats >= AROUSAL_MIN_IATS and slowest_med > AROUSAL_CALM_IAT_S:
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value = "low_calm"
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else:
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value = "medium_engaged"
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raw = 0.50 if total_iats >= AROUSAL_MIN_IATS else 0.30
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yield make_observation(
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ctx,
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primitive="emotional_valence.arousal",
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value=value,
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confidence=_cap_soft(raw),
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)
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