feat(profile): /api/profile + eligibility filter + inference framework (ADR-0014 steps 4-6)

Step 4 — /api/profile read-through API:
  GET  /api/profile          → { user, prefs, consents, contexts }
  PATCH /api/profile/prefs/:scope  upsert user_preferences (source='user')
  PATCH /api/profile/consents      grant / revoke consent keys
  PATCH /api/profile/contexts      create / activate / deactivate contexts
  Legacy consentGiven bit folded in as data:core fallback.

Step 5 — registry-driven eligibility filter:
  fetchRegistry() exported from agent-registry.ts.
  profile/eligibility.ts: getEligibleAgentIds(userId) — filters by required
  consents, silenced_in_contexts, and user_preferences[enabled=false].
  fetchOrchestratorTip filters agent_outputs to eligible set before calling
  ml/serving /recommend. Fail-closed: registry unavailable → empty set.

Step 6 — shared context-inference framework (#111) + time-of-day proof (#112):
  ml/agents/inference/: UserHistory, FeedbackEvent, run_inference().
  Framework: cold-start, min_history gating, error fallback, structured logs.
  TimeOfDayAgent v1.1.0: inferred_params=[preferred_hour]; also reads
  quiet_start/quiet_end from agent_prefs. agent_prefs injected by TS caller.
  AgentInput gains agent_prefs field.
  ml/serving: POST /agents/{agent_id}/infer endpoint.
  agent-outputs.ts computeAndStore: loads prefs before compute, calls /infer
  after, persists results (source='inferred'); user overrides never touched.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
2026-05-05 11:14:25 +00:00
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"""Tests for the inference framework and time-of-day #112 proof."""
from __future__ import annotations
import sys, os
sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "..", ".."))
import pytest
from datetime import datetime, timezone
from ml.agents.inference.history import FeedbackEvent, UserHistory
from ml.agents.inference.framework import run_inference
from ml.agents.time_of_day import TimeOfDayAgent, MANIFEST as TOD_MANIFEST, MANIFEST
from ml.agents.base import AgentInput
_NOW = datetime(2026, 5, 1, 14, 0, 0, tzinfo=timezone.utc) # Thursday 14:00
def _inp(**kwargs) -> AgentInput:
defaults = dict(user_id="u1", tasks=[], profile={}, now=_NOW, agent_prefs={})
defaults.update(kwargs)
return AgentInput(**defaults)
def _event(action: str, hour: int) -> FeedbackEvent:
ts = f"2026-05-01T{hour:02d}:00:00+00:00"
return FeedbackEvent(action=action, dwell_ms=60_000 if action == "done" else 500, created_at=ts)
class TestRunInference:
def test_cold_start_when_below_min_history(self):
history = UserHistory(user_id="u1", events=[_event("done", 9)] * 5) # only 5 < 10
result = run_inference(TOD_MANIFEST, history)
assert result["preferred_hour"] is None # cold_start_default
def test_infers_preferred_hour_as_mode(self):
# 7 events at 09:00, 3 at 17:00 → preferred_hour should be 9
events = [_event("done", 9)] * 7 + [_event("done", 17)] * 3
history = UserHistory(user_id="u1", events=events)
result = run_inference(TOD_MANIFEST, history)
assert result["preferred_hour"] == 9
def test_infers_preferred_hour_from_majority_hour(self):
events = [_event("done", 20)] * 6 + [_event("done", 8)] * 4
history = UserHistory(user_id="u1", events=events)
result = run_inference(TOD_MANIFEST, history)
assert result["preferred_hour"] == 20
def test_no_inferred_params_returns_empty(self):
from ml.agents.manifest import AgentManifest
bare = AgentManifest(
id="bare", version="1.0.0", description="", pref_schema={},
context_schema=[], required_consents=[], output_contract={}, ttl_sec=300,
)
history = UserHistory(user_id="u1", events=[_event("done", 9)] * 20)
result = run_inference(bare, history)
assert result == {}
def test_cold_start_fallback_on_infer_error(self):
"""infer() raising should fall back to cold_start_default, not crash."""
from ml.agents.manifest import InferredParam, AgentManifest
def _bad_infer(h):
raise RuntimeError("oops")
m = AgentManifest(
id="boom", version="1.0.0", description="", pref_schema={},
context_schema=[], required_consents=[], output_contract={}, ttl_sec=300,
inferred_params=[InferredParam(key="x", ttl_sec=60, cold_start_default=42, min_history=1, infer=_bad_infer)],
)
history = UserHistory(user_id="u1", events=[_event("done", 9)] * 5)
result = run_inference(m, history)
assert result["x"] == 42
class TestTimeOfDayAgentWithInference:
agent = TimeOfDayAgent()
def test_uses_preferred_hour_from_agent_prefs(self):
inp = _inp(agent_prefs={"preferred_hour": 9}, now=datetime(2026, 5, 1, 9, 0, 0, tzinfo=timezone.utc))
out = self.agent.compute(inp)
assert "peak productivity hour" in out.prompt_text.lower() or "peak" in out.prompt_text
def test_quiet_window_noon_suppressed(self):
inp = _inp(
agent_prefs={"quiet_start": "22:00", "quiet_end": "07:00"},
now=datetime(2026, 5, 1, 23, 0, 0, tzinfo=timezone.utc),
)
out = self.agent.compute(inp)
assert "quiet window" in out.prompt_text
def test_quiet_window_not_in_window(self):
inp = _inp(
agent_prefs={"quiet_start": "22:00", "quiet_end": "07:00"},
now=datetime(2026, 5, 1, 14, 0, 0, tzinfo=timezone.utc),
)
out = self.agent.compute(inp)
assert "quiet window" not in out.prompt_text
def test_agent_prefs_override_profile(self):
# agent_prefs.preferred_hour wins over profile.preferred_hour
inp = _inp(
profile={"preferred_hour": 8},
agent_prefs={"preferred_hour": 14},
now=datetime(2026, 5, 1, 14, 0, 0, tzinfo=timezone.utc),
)
out = self.agent.compute(inp)
assert "peak productivity hour (14:00)" in out.prompt_text
def test_no_prefs_falls_back_to_profile(self):
inp = _inp(profile={"preferred_hour": 10}, now=datetime(2026, 5, 1, 10, 0, 0, tzinfo=timezone.utc))
out = self.agent.compute(inp)
assert "peak" in out.prompt_text
def test_version_bumped(self):
assert MANIFEST.version == "1.1.0"
def test_manifest_has_preferred_hour_param(self):
keys = {p.key for p in MANIFEST.inferred_params}
assert "preferred_hour" in keys