feat(ml): egreedy-v2 shadow policy — D=12 with profile features (#99)
Ship the scaffolding for #99 (phase B.3 of #81): - ml/serving: add /score/egreedy/v2, /reward/egreedy/v2, /stats/egreedy/v2 endpoints (D=12). New feature dims: completion/dismiss rates, mean dwell (clipped 10min), preferred-hour alignment (cosine, 1-dim), tip volume (log). Separate state file per user (_egreedy_v2.json). /reset clears v2 state too. - ADR-0012: documents D=7→12 dimension change, normalization choices, shadow rollout protocol, and promotion gate (offline sim win per ADR-0002). - recommender.ts: register egreedy-v2-shadow in shadow-policy map (disabled by default). When enabled, calls /score/egreedy/v2 fire-and-forget and publishes shadow:egreedy-v2-shadow serve signal. No reward to shadow — sim is the gate. - sim runner/personas: personas carry synthetic profile_features per persona; _call_score/_call_reward thread profile_features through (None-safe for v1/linucb). - 18 new Python tests; all 56 Python + 170 TS tests pass. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
@@ -2,12 +2,17 @@
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oO ML Serving — Phase 1: LinUCB contextual bandit.
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Contract:
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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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POST /reset/{user_id} → { ok }
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GET /stats/{user_id} → { pulls, cumulative_reward, estimated_mean, last_updated }
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GET /features/{user_id} → { history: [{ ts, features, score }] }
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GET /health → { ok }
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POST /score LinUCB d=5 (baseline, kept as shadow-eligible)
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POST /score/egreedy ε-greedy v1, d=7 (active — ADR-0007)
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POST /score/egreedy/v2 ε-greedy v2, d=12, profile features (shadow — ADR-0012)
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POST /reward, /reward/egreedy, /reward/egreedy/v2
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GET /stats/{user_id} LinUCB stats
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GET /stats/egreedy/{user_id} ε-greedy v1 stats
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GET /stats/egreedy/v2/{user_id} ε-greedy v2 stats
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POST /reset/{user_id} clear all per-user bandit state
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GET /features/{user_id} last 100 scored feature vectors
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POST /generate LLM tip candidates via LiteLLM
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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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@@ -43,7 +48,8 @@ STATE_DIR.mkdir(parents=True, exist_ok=True)
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ALPHA = 1.0 # LinUCB exploration coefficient
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D = 5 # LinUCB feature dimension
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D7 = 7 # ε-greedy feature dimension (adds day-of-week cyclical encoding)
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D7 = 7 # ε-greedy v1 feature dimension (adds day-of-week cyclical encoding)
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D12 = 12 # ε-greedy v2 feature dimension (adds 5 profile features — ADR-0012)
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EPSILON = 0.1 # ε-greedy exploration rate
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FEATURE_HISTORY_SIZE = 100 # per-user ring buffer
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@@ -126,6 +132,98 @@ def save_state7(user_id: str, A: np.ndarray, b: np.ndarray, meta: dict) -> None:
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p.write_text(json.dumps({"A": A.tolist(), "b": b.tolist(), "meta": meta}))
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# ── ε-greedy v2 state helpers (d=12, profile features — ADR-0012) ─────────
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#
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# Normalization choices (see ADR-0012):
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# completion_rate_30d — already 0–1, passthrough; null → 0
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# dismiss_rate_30d — already 0–1, passthrough; null → 0
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# mean_dwell_ms_30d — clipped to [0, 600_000 ms] (10 min), then /600_000
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# preferred_hour — circular alignment with context hour:
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# (cos(2π·(now − pref)/24) + 1) / 2 → 0–1
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# captures "is the user's habitual peak near now?"
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# null → 0.5 (neutral)
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# tip_volume_30d — log1p(n) / log1p(100), clipped to [0, 1]
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_DWELL_CLIP_MS = 600_000.0 # 10 minutes
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_VOLUME_LOG_MAX = math.log1p(100.0)
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def _profile_value(profile: Optional[dict], key: str) -> Optional[float]:
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if not profile:
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return None
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v = profile.get(key)
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if v is None:
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return None
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try:
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return float(v)
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except (TypeError, ValueError):
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return None
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def _norm_rate(v: Optional[float]) -> float:
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return 0.0 if v is None else max(0.0, min(1.0, v))
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def _norm_dwell(v: Optional[float]) -> float:
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if v is None:
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return 0.0
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return max(0.0, min(1.0, v / _DWELL_CLIP_MS))
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def _norm_volume(v: Optional[float]) -> float:
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if v is None or v <= 0:
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return 0.0
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return min(1.0, math.log1p(float(v)) / _VOLUME_LOG_MAX)
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def _norm_preferred_hour(pref: Optional[float], now_hour: int) -> float:
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if pref is None:
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return 0.5 # neutral when the user has no established peak yet
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delta = (float(pref) - float(now_hour)) * (2.0 * math.pi / 24.0)
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return (math.cos(delta) + 1.0) / 2.0
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def build_feature_vector_12(
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features: dict,
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day_of_week: int = 0,
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profile: Optional[dict] = None,
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) -> np.ndarray:
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"""d=12: egreedy-v1's 7 dims + 5 normalized profile features (ADR-0012)."""
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base7 = build_feature_vector_7(features, day_of_week)
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now_hour = int(features.get("hour_of_day", 12))
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profile_dims = np.array(
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[
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_norm_rate(_profile_value(profile, "completion_rate_30d")),
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_norm_rate(_profile_value(profile, "dismiss_rate_30d")),
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_norm_dwell(_profile_value(profile, "mean_dwell_ms_30d")),
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_norm_preferred_hour(_profile_value(profile, "preferred_hour"), now_hour),
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_norm_volume(_profile_value(profile, "tip_volume_30d")),
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],
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dtype=np.float64,
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)
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return np.concatenate([base7, profile_dims])
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def state12_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}_egreedy_v2.json"
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def load_state12(user_id: str) -> tuple[np.ndarray, np.ndarray, dict]:
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p = state12_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, raw.get("meta", {})
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return np.identity(D12, dtype=np.float64), np.zeros(D12, dtype=np.float64), {}
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def save_state12(user_id: str, A: np.ndarray, b: np.ndarray, meta: dict) -> None:
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p = state12_path(user_id)
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p.write_text(json.dumps({"A": A.tolist(), "b": b.tolist(), "meta": meta}))
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# ── API models ─────────────────────────────────────────────────────────────
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class CandidateFeatures(BaseModel):
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@@ -171,6 +269,10 @@ class RewardRequest(BaseModel):
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reward: float # +1 done, +0.5 helpful, 0 snooze, -0.5 not_helpful, -1 dismiss
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features: CandidateFeatures
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day_of_week: int = 0 # included so egreedy can train dow features correctly
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# Profile features at the time the tip was served. Ignored by /reward and
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# /reward/egreedy; consumed by /reward/egreedy/v2 so the ridge update uses
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# the same feature vector as the matching /score/egreedy/v2 call.
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profile_features: Optional[dict] = None
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class RewardResponse(BaseModel):
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@@ -472,6 +574,128 @@ def reward_egreedy(req: RewardRequest) -> RewardResponse:
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return RewardResponse(ok=True)
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@app.post("/score/egreedy/v2", response_model=ScoreResponse)
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def score_egreedy_v2(req: ScoreRequest) -> ScoreResponse:
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"""ε-greedy v2 — d=12, adds 5 normalized profile features (ADR-0012).
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Shadow-only until offline sim + rollout per ADR-0002 completes.
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Accepts the same ScoreRequest shape as v1; `profile_features` drives the
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extra 5 dims (defaults: zeros for rates/volume/dwell, 0.5 neutral for
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preferred_hour alignment).
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"""
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if not req.candidates:
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raise HTTPException(status_code=422, detail="No candidates")
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A, b, meta = load_state12(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(D12, dtype=np.float64)
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theta = A_inv @ b
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dow = req.context.day_of_week
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exploring = np.random.random() < EPSILON
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if exploring:
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chosen = req.candidates[np.random.randint(len(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": chosen.features.is_overdue,
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"task_age_days": chosen.features.task_age_days,
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"priority": chosen.features.priority,
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}
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x = build_feature_vector_12(feat_dict, dow, req.profile_features)
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best_score = float(theta @ x)
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best_id = chosen.id
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else:
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best_id = None
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best_score = -float("inf")
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feat_dict = {}
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for candidate in req.candidates:
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fd = {
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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_12(fd, dow, req.profile_features)
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s = float(theta @ x)
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if s > best_score:
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best_score = s
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best_id = candidate.id
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feat_dict = fd
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history = get_feature_history(req.user_id)
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history.append({
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"ts": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()),
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"features": {**feat_dict, "day_of_week": dow, "exploring": exploring},
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"score": best_score,
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"tip_id": best_id,
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"policy": "egreedy-v2",
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})
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meta["pulls"] = meta.get("pulls", 0) + 1
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meta["explore_count"] = meta.get("explore_count", 0) + int(exploring)
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meta["last_updated"] = time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime())
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save_state12(req.user_id, A, b, meta)
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return ScoreResponse(tip_id=best_id, score=best_score, policy="egreedy-v2")
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@app.post("/reward/egreedy/v2", response_model=RewardResponse)
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def reward_egreedy_v2(req: RewardRequest) -> RewardResponse:
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"""Update ε-greedy v2 ridge estimator using the d=12 feature vector."""
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A, b, meta = load_state12(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_12(feat_dict, req.day_of_week, req.profile_features)
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A += np.outer(x, x)
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b += req.reward * x
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meta["cumulative_reward"] = meta.get("cumulative_reward", 0.0) + req.reward
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meta["reward_count"] = meta.get("reward_count", 0) + 1
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meta["last_updated"] = time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime())
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save_state12(req.user_id, A, b, meta)
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return RewardResponse(ok=True)
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@app.get("/stats/egreedy/v2/{user_id}")
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def stats_egreedy_v2(user_id: str):
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"""ε-greedy v2 policy stats — pulls, cumulative reward, θ vector."""
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A, b, meta = load_state12(user_id)
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try:
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theta = (np.linalg.inv(A) @ b).tolist()
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except np.linalg.LinAlgError:
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theta = [0.0] * D12
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pulls = meta.get("pulls", 0)
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cumulative_reward = meta.get("cumulative_reward", 0.0)
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reward_count = meta.get("reward_count", 0)
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explore_count = meta.get("explore_count", 0)
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return {
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"user_id": user_id,
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"policy": "egreedy-v2",
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"pulls": pulls,
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"reward_count": reward_count,
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"cumulative_reward": cumulative_reward,
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"estimated_mean_reward": cumulative_reward / reward_count if reward_count > 0 else 0.0,
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"exploration_rate": explore_count / pulls if pulls > 0 else 0.0,
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"theta": theta,
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"feature_labels": [
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"hour_sin", "hour_cos", "is_overdue", "task_age", "priority",
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"dow_sin", "dow_cos",
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"completion_rate_30d", "dismiss_rate_30d", "mean_dwell_norm",
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"preferred_hour_alignment", "tip_volume_norm",
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],
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"last_updated": meta.get("last_updated"),
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}
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@app.get("/stats/egreedy/{user_id}")
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def stats_egreedy(user_id: str):
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"""ε-greedy policy stats — pulls, cumulative reward, θ vector."""
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@@ -509,6 +733,9 @@ def reset(user_id: str) -> RewardResponse:
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p7 = state7_path(user_id)
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if p7.exists():
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p7.unlink()
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p12 = state12_path(user_id)
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if p12.exists():
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p12.unlink()
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if user_id in _feature_history:
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_feature_history[user_id].clear()
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return RewardResponse(ok=True)
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