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9e96540bcc9cda45eea1c87f92bf6129191e9e29
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4 Commits

Author SHA1 Message Date
alvis
9e96540bcc feat(admin): per-user profile view + rebuild action (#81 phase B.1) 
Surfaces phase A's profile features in /admin/users/:id so we can verify
they're actually computing useful values before investing in bandit
consumption. The detail GET now includes profile rows joined with registry
metadata (name, value, age, fresh badge, ttlSec, description). Read does
NOT trigger compute — staleness must be visible. A new POST
.../profile/rebuild button force-recomputes and is audit-logged like
reset-bandit.

Refs #81.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
 2026-04-25 00:27:08 +00:00
alvis
aa4bdd8f09 feat(admin): LLM tip quality dashboard — per-model/prompt/kind breakdowns 
/admin/reward-analytics now surfaces served count, reaction rate, and avg
reward grouped by llm_model, prompt_version, and tip_kind — closing the
loop so model/prompt iterations in M2 are legible next to the bandit
policy view. Data comes from the tip_scores columns added in ffdf707 and
tip_feedback.reward_milli; bandit-only tips show as "(bandit-only)".

Closes #92.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
 2026-04-24 15:24:52 +00:00
alvis
bb879c5f0f refactor(admin): drop simulations/experiments/models pages; group nav into sections 
Removes the in-shell MLOps pages (experiments, models, simulations) and their
client API helpers in favour of external MLflow/Airflow links. Nav is regrouped
into Signals / Recommender status / Ops sections for clarity.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
 2026-04-18 14:41:17 +00:00
alvis
faf44c18fc feat: ε-greedy v1 as active policy; dwell-time reward inference; offline sim framework 
- 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>
 2026-04-16 07:44:37 +00:00