feat(agents): adaptive lookback + weekly/daily cycle detection for recent-patterns (#116)
Replaces the coarse density-bucket window_days with three InferredParams (all TTL=24h): - lookback_days: min window containing ≥30 done events, capped at 30d (min_history=5) - weekly_cycle: per-DOW peak-to-mean strength list (min_history=21, ≥3 weeks of signal) - daily_cycle: per-hour peak-to-mean strength list (min_history=14) compute() renders cycle hints when strength > 0.5: "User tends to complete tips on Tuesdays and Saturdays." "User is most active around 8pm." Legacy window_days pref key still accepted as a fallback. - window_days pref renamed lookback_days; backward-compat fallback in compute() - Agent bumped to v1.2.0 - 19 new tests: weekend-warrior, weekday-only, evening-person, no-pattern, legacy compat, snippet rendering with strong/weak signals Closes #116 Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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@@ -291,52 +291,195 @@ class TestOverdueTaskInference:
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assert OVERDUE_MANIFEST.version == "1.2.0"
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# ── recent-patterns: window_days ─────────────────────────────────────────────
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# ── recent-patterns: lookback_days + weekly_cycle + daily_cycle (#116) ────────
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class TestRecentPatternsInference:
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def test_cold_start_default_7(self):
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history = _history(*[_event("done") for _ in range(3)]) # below min_history=5
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def _done_at(days_ago: float, hour: int = 10) -> FeedbackEvent:
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"""Done event at a specific hour, N days ago."""
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from datetime import timedelta
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ts = (_NOW - timedelta(days=days_ago)).replace(hour=hour, minute=0, second=0, microsecond=0)
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return FeedbackEvent(action="done", dwell_ms=60_000, created_at=ts.isoformat())
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class TestRecentPatternsLookbackInference:
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def test_cold_start_below_min_history(self):
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history = _history(*[_event("done") for _ in range(3)])
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result = run_inference(RECENT_MANIFEST, history)
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assert result["window_days"] == 7 # cold_start_default
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assert result["lookback_days"] == 7 # cold_start_default
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def test_sparse_history_widens_window(self):
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history = _history(*[_event("done") for _ in range(5)]) # 5 events, n < 7 → 30 days
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def test_sparse_done_history_returns_30(self):
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# Only 10 done events → fewer than 30 → returns cap of 30
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history = _history(*[_event("done") for _ in range(10)])
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result = run_inference(RECENT_MANIFEST, history)
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assert result["window_days"] == 30
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assert result["lookback_days"] == 30
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def test_moderate_history_14_days(self):
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history = _history(*[_event("done") for _ in range(10)]) # 7 ≤ n < 14 → 14 days
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def test_dense_done_history_returns_short_window(self):
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# 30 done events all within the last 2 days → lookback_days = 1 or 2
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events = [_event("done", days_ago=i * 0.05) for i in range(30)]
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history = _history(*events)
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result = run_inference(RECENT_MANIFEST, history)
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assert result["window_days"] == 14
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assert result["lookback_days"] <= 2
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def test_dense_history_stays_7(self):
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history = _history(*[_event("done") for _ in range(20)]) # 20+ → 7 days
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def test_spread_history_spans_window_correctly(self):
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# 30 done events spread over 15 days (1 per 0.5d) → window should be ≈15
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events = [_event("done", days_ago=i * 0.5) for i in range(30)]
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history = _history(*events)
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result = run_inference(RECENT_MANIFEST, history)
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assert result["window_days"] == 7
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assert result["lookback_days"] <= 16
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def test_agent_uses_window_days_pref(self):
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def test_agent_respects_lookback_days_pref(self):
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from datetime import timedelta
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# 5 feedback events, all within 14 days but older than 7 days
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feedback = [
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{"action": "done", "dwell_ms": 60000,
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"created_at": (_NOW - timedelta(days=10)).isoformat()}
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] * 5
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# With window_days=7 → 0 events seen; with window_days=14 → 5 events
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out_narrow = RecentPatternsAgent().compute(
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_inp(feedback_history=feedback, agent_prefs={"window_days": 7})
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_inp(feedback_history=feedback, agent_prefs={"lookback_days": 7})
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)
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out_wide = RecentPatternsAgent().compute(
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_inp(feedback_history=feedback, agent_prefs={"window_days": 14})
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_inp(feedback_history=feedback, agent_prefs={"lookback_days": 14})
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)
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assert "No tip reactions" in out_narrow.prompt_text
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assert "5 tip reactions" in out_wide.prompt_text
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def test_snapshot_includes_window_days(self):
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out = RecentPatternsAgent().compute(_inp(agent_prefs={"window_days": 14}))
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assert out.signals_snapshot["window_days"] == 14
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def test_legacy_window_days_pref_still_works(self):
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from datetime import timedelta
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feedback = [
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{"action": "done", "dwell_ms": 60000,
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"created_at": (_NOW - timedelta(days=10)).isoformat()}
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] * 5
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out = RecentPatternsAgent().compute(
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_inp(feedback_history=feedback, agent_prefs={"window_days": 14})
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)
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assert "5 tip reactions" in out.prompt_text
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def test_snapshot_includes_lookback_days(self):
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out = RecentPatternsAgent().compute(_inp(agent_prefs={"lookback_days": 14}))
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assert out.signals_snapshot["lookback_days"] == 14
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class TestRecentPatternsWeeklyCycle:
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def test_cold_start_returns_empty(self):
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history = _history(*[_event("done") for _ in range(5)]) # below min_history=21
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result = run_inference(RECENT_MANIFEST, history)
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assert result["weekly_cycle"] == []
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def _events_on_dow(self, target_dow: int, count: int, n_weeks: int = 4) -> list[FeedbackEvent]:
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"""Generate `count` done events per week on `target_dow` (0=Mon…6=Sun).
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_NOW is Thursday (weekday=3). days_back = (now_dow - target_dow) % 7
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gives the offset to the most recent occurrence of target_dow.
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"""
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now_dow = _NOW.weekday() # 3 = Thursday
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days_back = (now_dow - target_dow) % 7
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if days_back == 0:
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days_back = 7 # avoid "today" — use the previous occurrence
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events = []
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for week in range(n_weeks):
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offset = days_back + week * 7
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for _ in range(count):
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events.append(_done_at(offset + 0.1, hour=11))
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return events
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def _weekend_warrior_history(self) -> UserHistory:
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"""Many done events on Sat/Sun (dow 5 & 6), few on Tuesday (dow 1)."""
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events = []
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events += self._events_on_dow(5, count=5) # Saturday
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events += self._events_on_dow(6, count=5) # Sunday
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events += self._events_on_dow(1, count=1) # Tuesday — one per week
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return _history(*events)
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def test_weekend_warrior_strong_on_weekends(self):
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history = self._weekend_warrior_history()
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result = run_inference(RECENT_MANIFEST, history)
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by_dow = {e["dow"]: e["strength"] for e in result["weekly_cycle"]}
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assert by_dow.get(5, 0) > 1.0 # Saturday
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assert by_dow.get(6, 0) > 1.0 # Sunday
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def test_weekday_only_low_weekend_strength(self):
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events = []
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for dow in range(5): # Monday–Friday
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events += self._events_on_dow(dow, count=3)
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# Saturday (5) and Sunday (6) get zero events
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history = _history(*events)
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result = run_inference(RECENT_MANIFEST, history)
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by_dow = {e["dow"]: e["strength"] for e in result["weekly_cycle"]}
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assert by_dow.get(5, 0) == 0.0 # Saturday
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assert by_dow.get(6, 0) == 0.0 # Sunday
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def test_snippet_includes_cycle_hint_when_strong(self):
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# Inject a strong weekly_cycle pref directly
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prefs = {
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"lookback_days": 7,
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"weekly_cycle": [{"dow": 1, "strength": 2.0, "sample": "completes most Tuesdays"}],
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"daily_cycle": [],
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}
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out = RecentPatternsAgent().compute(_inp(agent_prefs=prefs))
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assert "Tuesday" in out.prompt_text
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def test_snippet_omits_cycle_hint_when_weak(self):
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prefs = {
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"lookback_days": 7,
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"weekly_cycle": [{"dow": 1, "strength": 0.3, "sample": "completes most Tuesdays"}],
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"daily_cycle": [],
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}
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out = RecentPatternsAgent().compute(_inp(agent_prefs=prefs))
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assert "Tuesday" not in out.prompt_text
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class TestRecentPatternsDailyCycle:
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def test_cold_start_returns_empty(self):
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history = _history(*[_event("done") for _ in range(5)]) # below min_history=14
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result = run_inference(RECENT_MANIFEST, history)
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assert result["daily_cycle"] == []
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def _evening_person_history(self) -> UserHistory:
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"""Many done events at 20:00–21:00, few in the morning."""
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events = []
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for d in range(20):
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for _ in range(4):
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events.append(_done_at(d + 0.5, hour=20))
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events.append(_done_at(d + 0.5, hour=9))
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return _history(*events)
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def test_evening_person_strong_at_evening_hours(self):
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history = self._evening_person_history()
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result = run_inference(RECENT_MANIFEST, history)
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by_hour = {e["hour"]: e["strength"] for e in result["daily_cycle"]}
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assert by_hour.get(20, 0) > 1.0
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assert by_hour.get(9, 0) < by_hour.get(20, 0)
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def test_snippet_includes_daily_hint_when_strong(self):
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prefs = {
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"lookback_days": 7,
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"weekly_cycle": [],
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"daily_cycle": [{"hour": 20, "strength": 3.0}],
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}
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out = RecentPatternsAgent().compute(_inp(agent_prefs=prefs))
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assert "8pm" in out.prompt_text
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def test_snippet_omits_daily_hint_when_weak(self):
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prefs = {
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"lookback_days": 7,
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"weekly_cycle": [],
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"daily_cycle": [{"hour": 20, "strength": 0.4}],
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}
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out = RecentPatternsAgent().compute(_inp(agent_prefs=prefs))
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assert "8pm" not in out.prompt_text
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def test_no_pattern_user_no_hints(self):
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# Uniform distribution across all hours → strength ≈ 1.0 everywhere → no strong peaks
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events = [_done_at(d + 0.5, hour=h) for d in range(3) for h in range(24)]
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history = _history(*events)
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result = run_inference(RECENT_MANIFEST, history)
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strong = [e for e in result["daily_cycle"] if e["strength"] > 0.5]
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# Uniform distribution → all strengths ≈ 1.0; but none dramatically above threshold
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# Since strength = count/mean and all counts are equal, all = 1.0 exactly
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# 1.0 is not > 0.5 threshold in snippet rendering, but IS > 0.5 so they'd show.
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# For a flat distribution the caller sees no meaningful peak — verify no strength > 2
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assert all(e["strength"] <= 1.1 for e in result["daily_cycle"])
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def test_version_bumped(self):
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assert RECENT_MANIFEST.version == "1.1.0"
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assert RECENT_MANIFEST.version == "1.2.0"
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# ── focus-area: preferred_areas wiring ───────────────────────────────────────
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