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>
63 lines
1.9 KiB
Python
63 lines
1.9 KiB
Python
"""Generate synthetic task pools for simulation."""
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from __future__ import annotations
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import random
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_TEMPLATES = [
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"Send weekly report to team",
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"Review pull request #{n}",
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"Schedule meeting with {name}",
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"Update project documentation",
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"Fix bug in authentication module",
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"Prepare presentation for stakeholders",
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"Call back {name}",
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"Submit expense report",
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"Review quarterly goals",
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"Clean up inbox",
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"Follow up on proposal to {name}",
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"Complete onboarding checklist",
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"Write tests for feature #{n}",
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"Deploy hotfix to production",
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"Respond to support ticket #{n}",
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"Draft release notes",
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"Update dependencies",
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"Review design mockups",
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"Archive old tickets",
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"Check in with {name}",
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]
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_NAMES = ["Alice", "Bob", "Carol", "David", "Eve", "Frank", "Grace"]
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def generate_task_pool(n: int = 10, seed: int | None = None) -> list[dict]:
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"""Return n synthetic tasks with randomly sampled features."""
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rng = random.Random(seed)
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tasks = []
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for i in range(n):
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priority = rng.choices([1, 2, 3, 4], weights=[0.3, 0.3, 0.25, 0.15])[0]
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# age_days: most tasks fresh, a few stale
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age_days = rng.choices(
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[0.0, 0.5, 1.0, 3.0, 7.0, 14.0],
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weights=[0.35, 0.20, 0.20, 0.12, 0.08, 0.05],
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)[0] + rng.random() * 0.5
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# is_overdue only meaningful when age > 0
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is_overdue = age_days > 0.5 and rng.random() < 0.65
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template = rng.choice(_TEMPLATES)
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content = template.format(n=rng.randint(100, 999), name=rng.choice(_NAMES))
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tasks.append({
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"id": f"sim:{i}",
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"content": content,
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"source": "sim",
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"features": {
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"is_overdue": is_overdue,
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"task_age_days": age_days if is_overdue else 0.0,
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"priority": priority,
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},
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})
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return tasks
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