feat(profile): user-profile feature registry + builder (phase A)

Centralizes user-level features (completion_rate_30d, dismiss_rate_30d,
mean_dwell_ms_30d, preferred_hour, tip_volume_30d) in a TS registry that
owns both definition and SQL aggregation, since the data lives in the
TS-owned SQLite tables (tip_views/tip_feedback). Lazy TTL refresh keeps
recommend latency bounded; values persist in user_profile_features (KV).

ml/serving accepts profile_features on /score + /generate but does not
yet consume them — extending the bandit feature vector changes D and
resets every user's learned state, so that's a deliberate phase-B step.

Includes ml/features/profile_schema.py as a contract mirror with a sync
test that diffs name sets against registry.ts.

ADR-0011 records the data-locality reasoning (registry in TS, not Python
as the issue originally suggested).

Phase B (deferred): event-driven incremental updates, bandit consumption
with state migration, admin per-user profile page, staleness alerts.

Refs #81.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

ml/features/profile_schema.py

@@ -0,0 +1,53 @@
+"""Profile-feature schema mirror (#81 phase A).
+
+The TypeScript registry in ``services/api/src/profile/registry.ts`` is the
+*source of truth* — features are computed there because the data lives in the
+TS-owned SQLite DB. This module is a documentation/typing mirror so Python
+code (ml/serving, eval harnesses, notebooks) knows what fields to expect on
+``profile_features`` payloads without round-tripping the API.
+
+Update this file whenever you add or rename a feature in the TS registry.
+The accompanying test asserts the two stay in sync at the name level.
+"""
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Literal
+
+
+Dtype = Literal["numeric", "categorical"]
+
+
+@dataclass(frozen=True)
+class ProfileFeature:
+    name: str
+    dtype: Dtype
+    description: str
+
+
+PROFILE_FEATURES: tuple[ProfileFeature, ...] = (
+    ProfileFeature(
+        "completion_rate_30d", "numeric",
+        'Fraction of tips served in the last 30 days that received a "done" reaction.',
+    ),
+    ProfileFeature(
+        "dismiss_rate_30d", "numeric",
+        'Fraction of tips served in the last 30 days that received a "dismiss" reaction.',
+    ),
+    ProfileFeature(
+        "mean_dwell_ms_30d", "numeric",
+        "Average dwell time (ms between served and reacted) over the last 30 days.",
+    ),
+    ProfileFeature(
+        "preferred_hour", "numeric",
+        'Hour-of-day with the most "done" reactions in the last 30 days (0-23).',
+    ),
+    ProfileFeature(
+        "tip_volume_30d", "numeric",
+        "Number of tips served to the user in the last 30 days.",
+    ),
+)
+
+
+def feature_names() -> set[str]:
+    return {f.name for f in PROFILE_FEATURES}

ml/features/test_profile_schema.py

@@ -0,0 +1,41 @@
+"""Smoke test for profile_schema mirror (#81 phase A).
+
+The TS registry in services/api/src/profile/registry.ts is the source of truth.
+This test checks the names listed here match the registry by reading the TS
+file and grepping for `name: '...'`. Crude but cheap, and it catches the
+common rename/add-without-mirror failure mode.
+"""
+from __future__ import annotations
+import re
+from pathlib import Path
+
+from ml.features.profile_schema import PROFILE_FEATURES, feature_names
+
+
+REGISTRY_PATH = Path(__file__).resolve().parents[2] / "services" / "api" / "src" / "profile" / "registry.ts"
+
+
+def _ts_registry_names() -> set[str]:
+    text = REGISTRY_PATH.read_text(encoding="utf-8")
+    # Each FEATURES entry has `name: 'something_30d',`. Extract every match.
+    return set(re.findall(r"name:\s*'([a-zA-Z0-9_]+)'", text))
+
+
+def test_python_mirror_matches_ts_registry():
+    py_names = feature_names()
+    ts_names = _ts_registry_names()
+    assert py_names == ts_names, (
+        f"Profile feature names drifted between TS registry and Python mirror.\n"
+        f"  in Python only: {sorted(py_names - ts_names)}\n"
+        f"  in TS only:     {sorted(ts_names - py_names)}"
+    )
+
+
+def test_profile_schema_no_duplicates():
+    names = [f.name for f in PROFILE_FEATURES]
+    assert len(names) == len(set(names)), f"duplicate names: {names}"
+
+
+def test_profile_schema_dtypes_known():
+    for f in PROFILE_FEATURES:
+        assert f.dtype in {"numeric", "categorical"}