alvis
faf44c18fc
- Promote egreedy-v1 to active serving policy (ADR-0007): /score/egreedy + /reward/egreedy
replaces linucb-v1 endpoints after offline sim shows +10.7% mean reward (−0.548 vs −0.606)
- Replace explicit helpful/not_helpful feedback with dwell-time inferred reward (inferReward):
dismiss=−1.0, snooze=+0.1, done<15s=−0.3, done 15s–2min=+1.0, done 2–10min=+0.6, done>10min=+0.3
- Add ml/serving ε-greedy endpoints: /score/egreedy, /reward/egreedy, /stats/egreedy/{user_id}
with d=7 feature vector (base 5 + sin/cos day-of-week encoding)
- Add offline simulation framework (ml/experiments/sim): rule/LLM/claude-code judges,
two-phase score+reward, synthetic personas, task generator; results stored in sim_runs/sim_events
- Add /admin/simulations page: start runs, live-poll status, reward curve SVG, action/persona tables
- Fix egreedy day_of_week training skew: reward endpoint now uses actual dow instead of hardcoded 0
- Fix runner.py proxy bypass: httpx.Client(trust_env=False) for localhost ML calls
- Add dwellMs to TipFeedbackEvent contract and bus.test.ts fixture
- Schema: sim_runs, sim_events tables; tip_feedback gains dwell_ms, reward_milli columns
- ADR-0006: admin console framework; ADR-0007: egreedy-v1 policy selection rationale
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
80 lines
2.3 KiB
Python
80 lines
2.3 KiB
Python
"""Synthetic user personas for simulation."""
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from dataclasses import dataclass
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@dataclass
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class Persona:
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name: str
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description: str
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# Feature preference weights — used by deterministic judge
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prefers_high_priority: float # 0–1: scales response to priority
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prefers_overdue: float # 0–1: scales response to overdue tasks
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morning_active: bool # higher engagement hours 6–10
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evening_active: bool # higher engagement hours 18–22
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recency_bias: float # 0–1: prefers recently-due tasks
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PERSONAS: list[Persona] = [
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Persona(
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name="deadline-driven",
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description=(
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"Responds urgently to overdue and high-priority tasks. "
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"Most active in the morning. Dismisses low-priority tips."
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),
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prefers_high_priority=0.9,
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prefers_overdue=0.85,
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morning_active=True,
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evening_active=False,
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recency_bias=0.3,
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),
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Persona(
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name="evening-relaxed",
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description=(
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"Reviews tasks in the evenings. Neutral on priority. "
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"Snoozes morning recommendations."
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),
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prefers_high_priority=0.5,
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prefers_overdue=0.4,
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morning_active=False,
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evening_active=True,
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recency_bias=0.5,
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),
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Persona(
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name="low-priority-first",
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description=(
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"Clears small tasks first. Snoozes urgent items until deadline. "
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"Morning person."
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),
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prefers_high_priority=0.2,
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prefers_overdue=0.6,
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morning_active=True,
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evening_active=False,
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recency_bias=0.7,
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),
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Persona(
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name="consistent-responder",
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description=(
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"Engages consistently across hours and days. "
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"Acts on helpful tips regardless of priority."
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),
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prefers_high_priority=0.6,
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prefers_overdue=0.6,
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morning_active=True,
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evening_active=True,
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recency_bias=0.5,
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),
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Persona(
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name="overdue-ignorer",
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description=(
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"Avoids overdue tasks (stress avoidance). "
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"Focuses on future-due, high-priority items. Evening person."
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),
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prefers_high_priority=0.8,
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prefers_overdue=0.1,
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morning_active=False,
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evening_active=True,
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recency_bias=0.2,
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),
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]
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