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>

ml/experiments/sim/personas.py

@@ -1,5 +1,6 @@
 """Synthetic user personas for simulation."""
 
+import math
 from dataclasses import dataclass
 
 
@@ -13,6 +14,24 @@ class Persona:
     morning_active: bool          # higher engagement hours 6–10
     evening_active: bool          # higher engagement hours 18–22
     recency_bias: float           # 0–1: prefers recently-due tasks
+    # Synthetic profile features for egreedy-v2 sim (ADR-0012).
+    # Values represent what a typical user of this persona would have
+    # accumulated after a few weeks of app use.
+    _completion_rate: float = 0.3
+    _dismiss_rate: float = 0.2
+    _mean_dwell_ms: float = 60_000.0   # ms
+    _preferred_hour: float = 12.0      # 0–23
+    _tip_volume_30d: float = 15.0
+
+    def profile_features(self, now_hour: int | None = None) -> dict:
+        """Return profile_features dict compatible with the ml/serving API."""
+        return {
+            "completion_rate_30d": self._completion_rate,
+            "dismiss_rate_30d": self._dismiss_rate,
+            "mean_dwell_ms_30d": self._mean_dwell_ms,
+            "preferred_hour": self._preferred_hour,
+            "tip_volume_30d": self._tip_volume_30d,
+        }
 
 
 PERSONAS: list[Persona] = [
@@ -27,6 +46,11 @@ PERSONAS: list[Persona] = [
         morning_active=True,
         evening_active=False,
         recency_bias=0.3,
+        _completion_rate=0.55,
+        _dismiss_rate=0.10,
+        _mean_dwell_ms=45_000.0,
+        _preferred_hour=8.0,
+        _tip_volume_30d=22.0,
     ),
     Persona(
         name="evening-relaxed",
@@ -39,6 +63,11 @@ PERSONAS: list[Persona] = [
         morning_active=False,
         evening_active=True,
         recency_bias=0.5,
+        _completion_rate=0.30,
+        _dismiss_rate=0.25,
+        _mean_dwell_ms=90_000.0,
+        _preferred_hour=20.0,
+        _tip_volume_30d=12.0,
     ),
     Persona(
         name="low-priority-first",
@@ -51,6 +80,11 @@ PERSONAS: list[Persona] = [
         morning_active=True,
         evening_active=False,
         recency_bias=0.7,
+        _completion_rate=0.40,
+        _dismiss_rate=0.15,
+        _mean_dwell_ms=30_000.0,
+        _preferred_hour=9.0,
+        _tip_volume_30d=18.0,
     ),
     Persona(
         name="consistent-responder",
@@ -63,6 +97,11 @@ PERSONAS: list[Persona] = [
         morning_active=True,
         evening_active=True,
         recency_bias=0.5,
+        _completion_rate=0.50,
+        _dismiss_rate=0.10,
+        _mean_dwell_ms=60_000.0,
+        _preferred_hour=12.0,
+        _tip_volume_30d=30.0,
     ),
     Persona(
         name="overdue-ignorer",
@@ -75,5 +114,10 @@ PERSONAS: list[Persona] = [
         morning_active=False,
         evening_active=True,
         recency_bias=0.2,
+        _completion_rate=0.20,
+        _dismiss_rate=0.40,
+        _mean_dwell_ms=120_000.0,
+        _preferred_hour=19.0,
+        _tip_volume_30d=10.0,
     ),
 ]