chore(ml): remove bandit endpoints + helpers (ADR-0013 step 9)
Deletes all LinUCB and ε-greedy code from ml/serving: score, reward, stats, reset, features endpoints; feature vector builders; per-user state file helpers; related Pydantic models; numpy/math/time imports. Removes test_score.py (pure bandit unit tests). 40 remaining tests pass. STATE_DIR kept — nats_consumer still writes sync metadata there. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
@@ -1,41 +1,24 @@
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"""
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oO ML Serving — Phase 1: LinUCB contextual bandit.
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oO ML Serving — multi-agent orchestrator (ADR-0013).
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Contract:
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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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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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POST /agents/{agent_id}/compute run a sub-agent, return prompt snippet
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POST /recommend orchestrate agent snippets → one tip via LiteLLM
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POST /generate LLM tip candidates (legacy; kept for bench/eval)
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GET /health { ok, agents: [...] }
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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 sys
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import time
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from collections import deque
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from contextlib import asynccontextmanager
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from datetime import datetime, timezone
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from pathlib import Path
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from typing import Optional, Deque
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from typing import Optional
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import httpx
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import numpy as np
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import sentry_sdk
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import structlog
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import structlog.contextvars
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@@ -93,242 +76,12 @@ app.add_middleware(_TracingMiddleware)
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LITELLM_URL = os.getenv("LITELLM_URL", "http://localhost:4000")
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LITELLM_MASTER_KEY = os.getenv("LITELLM_MASTER_KEY", "sk-oo-dev")
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STATE_DIR = Path(os.getenv("STATE_DIR", "/tmp/oo-bandit-state"))
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STATE_DIR = Path(os.getenv("STATE_DIR", "/tmp/oo-serving-state"))
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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 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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# ── Per-user in-memory feature history ────────────────────────────────────
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_feature_history: dict[str, deque] = {}
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def get_feature_history(user_id: str) -> deque:
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if user_id not in _feature_history:
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_feature_history[user_id] = deque(maxlen=FEATURE_HISTORY_SIZE)
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return _feature_history[user_id]
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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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# ── LinUCB state helpers ───────────────────────────────────────────────────
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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, dict]:
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"""Returns (A, b, meta). 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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meta = raw.get("meta", {})
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return A, b, meta
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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, meta: dict) -> 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(), "meta": meta}))
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# ── ε-greedy state helpers (d=7, extended features) ───────────────────────
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def build_feature_vector_7(features: dict, day_of_week: int = 0) -> np.ndarray:
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"""d=7: base 5 features + day-of-week cyclical encoding."""
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base = build_feature_vector(features)
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dow_sin = math.sin(2 * math.pi * day_of_week / 7)
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dow_cos = math.cos(2 * math.pi * day_of_week / 7)
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return np.append(base, [dow_sin, dow_cos])
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def state7_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.json"
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def load_state7(user_id: str) -> tuple[np.ndarray, np.ndarray, dict]:
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"""Returns (A, b, meta) for ε-greedy d=7 policy."""
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p = state7_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(D7, dtype=np.float64), np.zeros(D7, dtype=np.float64), {}
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def save_state7(user_id: str, A: np.ndarray, b: np.ndarray, meta: dict) -> None:
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p = state7_path(user_id)
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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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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: 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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# User-level features computed by the API (#81 phase A). Accepted, logged,
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# but not yet consumed by the bandit — extending the feature vector
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# changes `D` and resets every user's learned state, which is a deliberate
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# follow-up (phase B), not a side effect of this PR.
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profile_features: Optional[dict] = None
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class ScoreResponse(BaseModel):
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tip_id: str
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score: float
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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, +0.5 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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ok: bool
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class PromptContext(BaseModel):
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tasks: list[dict] = []
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hour_of_day: int = 12
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@@ -652,364 +405,3 @@ async def generate(req: GenerateRequest) -> GenerateResponse:
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)
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@app.post("/score", response_model=ScoreResponse)
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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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A, b, meta = 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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best_features: dict = {}
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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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best_features = feat_dict
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# Log to feature history ring buffer
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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": best_features,
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"score": best_score,
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"tip_id": best_id,
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})
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# Update meta stats
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meta["pulls"] = meta.get("pulls", 0) + 1
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meta["last_updated"] = time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime())
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save_state(req.user_id, A, b, meta)
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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, meta = 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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# Track cumulative reward in meta
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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_state(req.user_id, A, b, meta)
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return RewardResponse(ok=True)
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@app.post("/score/egreedy", response_model=ScoreResponse)
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def score_egreedy(req: ScoreRequest) -> ScoreResponse:
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"""ε-greedy policy with d=7 features (adds day-of-week encoding).
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Exploration: pick uniformly at random with probability ε.
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Exploitation: pick argmax of linear payoff estimate θ·x.
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Differs from LinUCB in: no UCB bonus, richer feature space.
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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_state7(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(D7, 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_7(feat_dict, dow)
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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_7(fd, dow)
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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()),
|
||||
"features": {**feat_dict, "day_of_week": dow, "exploring": exploring},
|
||||
"score": best_score,
|
||||
"tip_id": best_id,
|
||||
"policy": "egreedy-v1",
|
||||
})
|
||||
|
||||
meta["pulls"] = meta.get("pulls", 0) + 1
|
||||
meta["explore_count"] = meta.get("explore_count", 0) + int(exploring)
|
||||
meta["last_updated"] = time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime())
|
||||
save_state7(req.user_id, A, b, meta)
|
||||
|
||||
return ScoreResponse(tip_id=best_id, score=best_score, policy="egreedy-v1")
|
||||
|
||||
|
||||
@app.post("/reward/egreedy", response_model=RewardResponse)
|
||||
def reward_egreedy(req: RewardRequest) -> RewardResponse:
|
||||
"""Update ε-greedy ridge estimator with observed reward."""
|
||||
A, b, meta = load_state7(req.user_id)
|
||||
feat_dict = {
|
||||
"hour_of_day": req.features.hour_of_day,
|
||||
"is_overdue": req.features.is_overdue,
|
||||
"task_age_days": req.features.task_age_days,
|
||||
"priority": req.features.priority,
|
||||
}
|
||||
x = build_feature_vector_7(feat_dict, day_of_week=req.day_of_week)
|
||||
A += np.outer(x, x)
|
||||
b += req.reward * x
|
||||
|
||||
meta["cumulative_reward"] = meta.get("cumulative_reward", 0.0) + req.reward
|
||||
meta["reward_count"] = meta.get("reward_count", 0) + 1
|
||||
meta["last_updated"] = time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime())
|
||||
save_state7(req.user_id, A, b, meta)
|
||||
return RewardResponse(ok=True)
|
||||
|
||||
|
||||
@app.post("/score/egreedy/v2", response_model=ScoreResponse)
|
||||
def score_egreedy_v2(req: ScoreRequest) -> ScoreResponse:
|
||||
"""ε-greedy v2 — d=12, adds 5 normalized profile features (ADR-0012).
|
||||
|
||||
Shadow-only until offline sim + rollout per ADR-0002 completes.
|
||||
Accepts the same ScoreRequest shape as v1; `profile_features` drives the
|
||||
extra 5 dims (defaults: zeros for rates/volume/dwell, 0.5 neutral for
|
||||
preferred_hour alignment).
|
||||
"""
|
||||
if not req.candidates:
|
||||
raise HTTPException(status_code=422, detail="No candidates")
|
||||
|
||||
A, b, meta = load_state12(req.user_id)
|
||||
try:
|
||||
A_inv = np.linalg.inv(A)
|
||||
except np.linalg.LinAlgError:
|
||||
A_inv = np.identity(D12, dtype=np.float64)
|
||||
theta = A_inv @ b
|
||||
|
||||
dow = req.context.day_of_week
|
||||
exploring = np.random.random() < EPSILON
|
||||
|
||||
if exploring:
|
||||
chosen = req.candidates[np.random.randint(len(req.candidates))]
|
||||
feat_dict = {
|
||||
"hour_of_day": req.context.hour_of_day,
|
||||
"is_overdue": chosen.features.is_overdue,
|
||||
"task_age_days": chosen.features.task_age_days,
|
||||
"priority": chosen.features.priority,
|
||||
}
|
||||
x = build_feature_vector_12(feat_dict, dow, req.profile_features)
|
||||
best_score = float(theta @ x)
|
||||
best_id = chosen.id
|
||||
else:
|
||||
best_id = None
|
||||
best_score = -float("inf")
|
||||
feat_dict = {}
|
||||
for candidate in req.candidates:
|
||||
fd = {
|
||||
"hour_of_day": req.context.hour_of_day,
|
||||
"is_overdue": candidate.features.is_overdue,
|
||||
"task_age_days": candidate.features.task_age_days,
|
||||
"priority": candidate.features.priority,
|
||||
}
|
||||
x = build_feature_vector_12(fd, dow, req.profile_features)
|
||||
s = float(theta @ x)
|
||||
if s > best_score:
|
||||
best_score = s
|
||||
best_id = candidate.id
|
||||
feat_dict = fd
|
||||
|
||||
history = get_feature_history(req.user_id)
|
||||
history.append({
|
||||
"ts": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()),
|
||||
"features": {**feat_dict, "day_of_week": dow, "exploring": exploring},
|
||||
"score": best_score,
|
||||
"tip_id": best_id,
|
||||
"policy": "egreedy-v2",
|
||||
})
|
||||
|
||||
meta["pulls"] = meta.get("pulls", 0) + 1
|
||||
meta["explore_count"] = meta.get("explore_count", 0) + int(exploring)
|
||||
meta["last_updated"] = time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime())
|
||||
save_state12(req.user_id, A, b, meta)
|
||||
|
||||
return ScoreResponse(tip_id=best_id, score=best_score, policy="egreedy-v2")
|
||||
|
||||
|
||||
@app.post("/reward/egreedy/v2", response_model=RewardResponse)
|
||||
def reward_egreedy_v2(req: RewardRequest) -> RewardResponse:
|
||||
"""Update ε-greedy v2 ridge estimator using the d=12 feature vector."""
|
||||
A, b, meta = load_state12(req.user_id)
|
||||
feat_dict = {
|
||||
"hour_of_day": req.features.hour_of_day,
|
||||
"is_overdue": req.features.is_overdue,
|
||||
"task_age_days": req.features.task_age_days,
|
||||
"priority": req.features.priority,
|
||||
}
|
||||
x = build_feature_vector_12(feat_dict, req.day_of_week, req.profile_features)
|
||||
A += np.outer(x, x)
|
||||
b += req.reward * x
|
||||
|
||||
meta["cumulative_reward"] = meta.get("cumulative_reward", 0.0) + req.reward
|
||||
meta["reward_count"] = meta.get("reward_count", 0) + 1
|
||||
meta["last_updated"] = time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime())
|
||||
save_state12(req.user_id, A, b, meta)
|
||||
return RewardResponse(ok=True)
|
||||
|
||||
|
||||
@app.get("/stats/egreedy/v2/{user_id}")
|
||||
def stats_egreedy_v2(user_id: str):
|
||||
"""ε-greedy v2 policy stats — pulls, cumulative reward, θ vector."""
|
||||
A, b, meta = load_state12(user_id)
|
||||
try:
|
||||
theta = (np.linalg.inv(A) @ b).tolist()
|
||||
except np.linalg.LinAlgError:
|
||||
theta = [0.0] * D12
|
||||
|
||||
pulls = meta.get("pulls", 0)
|
||||
cumulative_reward = meta.get("cumulative_reward", 0.0)
|
||||
reward_count = meta.get("reward_count", 0)
|
||||
explore_count = meta.get("explore_count", 0)
|
||||
|
||||
return {
|
||||
"user_id": user_id,
|
||||
"policy": "egreedy-v2",
|
||||
"pulls": pulls,
|
||||
"reward_count": reward_count,
|
||||
"cumulative_reward": cumulative_reward,
|
||||
"estimated_mean_reward": cumulative_reward / reward_count if reward_count > 0 else 0.0,
|
||||
"exploration_rate": explore_count / pulls if pulls > 0 else 0.0,
|
||||
"theta": theta,
|
||||
"feature_labels": [
|
||||
"hour_sin", "hour_cos", "is_overdue", "task_age", "priority",
|
||||
"dow_sin", "dow_cos",
|
||||
"completion_rate_30d", "dismiss_rate_30d", "mean_dwell_norm",
|
||||
"preferred_hour_alignment", "tip_volume_norm",
|
||||
],
|
||||
"last_updated": meta.get("last_updated"),
|
||||
}
|
||||
|
||||
|
||||
@app.get("/stats/egreedy/{user_id}")
|
||||
def stats_egreedy(user_id: str):
|
||||
"""ε-greedy policy stats — pulls, cumulative reward, θ vector."""
|
||||
A, b, meta = load_state7(user_id)
|
||||
try:
|
||||
theta = (np.linalg.inv(A) @ b).tolist()
|
||||
except np.linalg.LinAlgError:
|
||||
theta = [0.0] * D7
|
||||
|
||||
pulls = meta.get("pulls", 0)
|
||||
cumulative_reward = meta.get("cumulative_reward", 0.0)
|
||||
reward_count = meta.get("reward_count", 0)
|
||||
explore_count = meta.get("explore_count", 0)
|
||||
|
||||
return {
|
||||
"user_id": user_id,
|
||||
"policy": "egreedy-v1",
|
||||
"pulls": pulls,
|
||||
"reward_count": reward_count,
|
||||
"cumulative_reward": cumulative_reward,
|
||||
"estimated_mean_reward": cumulative_reward / reward_count if reward_count > 0 else 0.0,
|
||||
"exploration_rate": explore_count / pulls if pulls > 0 else 0.0,
|
||||
"theta": theta,
|
||||
"feature_labels": ["hour_sin", "hour_cos", "is_overdue", "task_age", "priority", "dow_sin", "dow_cos"],
|
||||
"last_updated": meta.get("last_updated"),
|
||||
}
|
||||
|
||||
|
||||
@app.post("/reset/{user_id}", response_model=RewardResponse)
|
||||
def reset(user_id: str) -> RewardResponse:
|
||||
"""Reset per-user bandit state (admin action)."""
|
||||
p = state_path(user_id)
|
||||
if p.exists():
|
||||
p.unlink()
|
||||
p7 = state7_path(user_id)
|
||||
if p7.exists():
|
||||
p7.unlink()
|
||||
p12 = state12_path(user_id)
|
||||
if p12.exists():
|
||||
p12.unlink()
|
||||
if user_id in _feature_history:
|
||||
_feature_history[user_id].clear()
|
||||
return RewardResponse(ok=True)
|
||||
|
||||
|
||||
@app.get("/stats/{user_id}")
|
||||
def stats(user_id: str):
|
||||
"""Return current LinUCB state summary for a user."""
|
||||
A, b, meta = load_state(user_id)
|
||||
try:
|
||||
A_inv = np.linalg.inv(A)
|
||||
theta = (A_inv @ b).tolist()
|
||||
except np.linalg.LinAlgError:
|
||||
theta = [0.0] * D
|
||||
|
||||
pulls = meta.get("pulls", 0)
|
||||
cumulative_reward = meta.get("cumulative_reward", 0.0)
|
||||
reward_count = meta.get("reward_count", 0)
|
||||
estimated_mean = cumulative_reward / reward_count if reward_count > 0 else 0.0
|
||||
|
||||
return {
|
||||
"user_id": user_id,
|
||||
"pulls": pulls,
|
||||
"reward_count": reward_count,
|
||||
"cumulative_reward": cumulative_reward,
|
||||
"estimated_mean_reward": estimated_mean,
|
||||
"theta": theta,
|
||||
"last_updated": meta.get("last_updated"),
|
||||
}
|
||||
|
||||
|
||||
@app.get("/features/{user_id}")
|
||||
def features(user_id: str):
|
||||
"""Return recent feature vectors logged at scoring time."""
|
||||
history = get_feature_history(user_id)
|
||||
return {
|
||||
"user_id": user_id,
|
||||
"history": list(history),
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user