feat: M1 — LinUCB bandit, RemotePolicy, Web Push, event bus
ML serving: - LinUCB contextual bandit (disjoint, d=5 features: hour_sin/cos, is_overdue, task_age, priority) - /score endpoint replaces stub random; /reward endpoint for online learning - Per-user model state persisted to disk as JSON (survives restarts) - venv at ml/serving/.venv; start with pnpm dev from ml/serving Recommender: - Todoist fetch now extracts features (is_overdue, task_age_days, priority) - RemotePolicy calls ml/serving with 3s timeout; falls back to RandomPolicy - Reward sent to /reward on feedback (done=+1, snooze=0, dismiss=-1) Web Push: - VAPID keys in config; push_subscriptions table in DB - POST/DELETE /api/push/subscribe; GET /api/push/vapid-public-key - Service worker (public/sw.js): push → showNotification, notificationclick → focus/open - "notify me" button on tip page; registers SW + subscribes on permission grant Event bus: - services/api/src/events/bus.ts: typed EventEmitter wrapper - Subjects: signals.tip.served, signals.tip.feedback, signals.task.synced - Same publish/subscribe API NATS JetStream will implement — swap is mechanical Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
@@ -1,32 +1,101 @@
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"""
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oO ML Serving — Phase 0 stub.
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Returns a placeholder response that matches the interface the real scorer will implement.
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The recommender service calls this via RemotePolicy (not yet wired in Phase 0).
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oO ML Serving — Phase 1: LinUCB contextual bandit.
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Contract:
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POST /score
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Body: { user_id: str, candidates: [{ id: str, content: str, source: str, source_id?: str }] }
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Response: { tip_id: str, score: float }
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POST /score { user_id, candidates, context } → { tip_id, score, policy }
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POST /reward { user_id, tip_id, reward, features } → { ok }
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GET /health → { ok }
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Features (d=5):
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hour_sin, hour_cos — cyclical time-of-day encoding
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is_overdue — 0 or 1
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task_age_days — days since due date (clipped 0–30, normalised 0–1)
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priority_norm — Todoist priority 1–4, normalised to 0–1
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"""
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from __future__ import annotations
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import json
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import math
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import os
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import random
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from pathlib import Path
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from typing import Optional
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import numpy as np
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from fastapi import FastAPI, HTTPException
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from pydantic import BaseModel
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import random
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app = FastAPI(title="oO ML Serving", version="0.0.0")
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app = FastAPI(title="oO ML Serving", version="1.0.0")
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STATE_DIR = Path(os.getenv("STATE_DIR", "/tmp/oo-bandit-state"))
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STATE_DIR.mkdir(parents=True, exist_ok=True)
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ALPHA = 1.0 # exploration coefficient
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D = 5 # feature dimension
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# ── Feature helpers ────────────────────────────────────────────────────────
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def build_feature_vector(features: dict) -> np.ndarray:
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hour = features.get("hour_of_day", 12)
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hour_sin = math.sin(2 * math.pi * hour / 24)
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hour_cos = math.cos(2 * math.pi * hour / 24)
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is_overdue = float(bool(features.get("is_overdue", False)))
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age = min(float(features.get("task_age_days", 0)), 30.0) / 30.0
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priority = (float(features.get("priority", 1)) - 1.0) / 3.0
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return np.array([hour_sin, hour_cos, is_overdue, age, priority], dtype=np.float64)
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# ── Per-user bandit state (disjoint LinUCB, global arm) ───────────────────
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def state_path(user_id: str) -> Path:
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safe = "".join(c if c.isalnum() else "_" for c in user_id)
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return STATE_DIR / f"{safe}.json"
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def load_state(user_id: str) -> tuple[np.ndarray, np.ndarray]:
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"""Returns (A, b). A is DxD, b is D-vector."""
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p = state_path(user_id)
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if p.exists():
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raw = json.loads(p.read_text())
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A = np.array(raw["A"], dtype=np.float64)
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b = np.array(raw["b"], dtype=np.float64)
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return A, b
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return np.identity(D, dtype=np.float64), np.zeros(D, dtype=np.float64)
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def save_state(user_id: str, A: np.ndarray, b: np.ndarray) -> None:
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p = state_path(user_id)
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p.write_text(json.dumps({"A": A.tolist(), "b": b.tolist()}))
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# ── API models ─────────────────────────────────────────────────────────────
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class CandidateFeatures(BaseModel):
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hour_of_day: int = 12
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is_overdue: bool = False
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task_age_days: float = 0.0
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priority: int = 1
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class Candidate(BaseModel):
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id: str
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content: str
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source: str
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source_id: str | None = None
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source_id: Optional[str] = None
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features: CandidateFeatures = CandidateFeatures()
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class Context(BaseModel):
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hour_of_day: int = 12
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day_of_week: int = 0
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class ScoreRequest(BaseModel):
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user_id: str
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candidates: list[Candidate]
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context: Context = Context()
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class ScoreResponse(BaseModel):
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@@ -35,15 +104,69 @@ class ScoreResponse(BaseModel):
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policy: str
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class RewardRequest(BaseModel):
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user_id: str
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tip_id: str
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reward: float # +1 done, 0 snooze, -1 dismiss
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features: CandidateFeatures
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class RewardResponse(BaseModel):
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ok: bool
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# ── Endpoints ──────────────────────────────────────────────────────────────
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@app.get("/health")
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def health():
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return {"ok": True}
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@app.post("/score", response_model=ScoreResponse)
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def score(req: ScoreRequest):
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def score(req: ScoreRequest) -> ScoreResponse:
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if not req.candidates:
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raise HTTPException(status_code=422, detail="No candidates")
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# Stub: random uniform scoring — real model slots in here
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chosen = random.choice(req.candidates)
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return ScoreResponse(tip_id=chosen.id, score=1.0, policy="stub-random")
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A, b = load_state(req.user_id)
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try:
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A_inv = np.linalg.inv(A)
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except np.linalg.LinAlgError:
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A_inv = np.identity(D, dtype=np.float64)
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theta = A_inv @ b
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best_id = None
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best_score = -float("inf")
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for candidate in req.candidates:
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feat_dict = {
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"hour_of_day": req.context.hour_of_day,
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"is_overdue": candidate.features.is_overdue,
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"task_age_days": candidate.features.task_age_days,
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"priority": candidate.features.priority,
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}
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x = build_feature_vector(feat_dict)
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exploit = float(theta @ x)
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explore = ALPHA * math.sqrt(float(x @ A_inv @ x))
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ucb = exploit + explore
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if ucb > best_score:
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best_score = ucb
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best_id = candidate.id
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return ScoreResponse(tip_id=best_id, score=best_score, policy="linucb-v1")
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@app.post("/reward", response_model=RewardResponse)
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def reward(req: RewardRequest) -> RewardResponse:
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A, b = load_state(req.user_id)
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feat_dict = {
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"hour_of_day": req.features.hour_of_day,
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"is_overdue": req.features.is_overdue,
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"task_age_days": req.features.task_age_days,
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"priority": req.features.priority,
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}
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x = build_feature_vector(feat_dict)
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A += np.outer(x, x)
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b += req.reward * x
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save_state(req.user_id, A, b)
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return RewardResponse(ok=True)
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