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>
This commit is contained in:
@@ -1,5 +1,6 @@
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from __future__ import annotations
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import math
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from collections import Counter
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from datetime import datetime, timezone
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from typing import ClassVar
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@@ -8,35 +9,124 @@ from .base import BaseAgent, AgentInput, AgentOutput
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from .inference.history import UserHistory
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from .manifest import AgentManifest, InferredParam
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_DOW_NAMES = ["Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday"]
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def _infer_window_days(history: UserHistory) -> int:
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"""Infer the optimal lookback window from feedback event density.
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More events per day → a shorter window captures the user's current state
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accurately. Sparse feedback → widen the window to gather signal.
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def _parse_dt(iso: str) -> datetime:
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try:
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dt = datetime.fromisoformat(iso.replace("Z", "+00:00"))
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if dt.tzinfo is None:
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dt = dt.replace(tzinfo=timezone.utc)
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return dt
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except ValueError:
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return datetime.min.replace(tzinfo=timezone.utc)
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def _infer_lookback_days(history: UserHistory) -> int:
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"""Find the minimum window (days) that captures ≥30 done events, capped at 30.
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Sorts done events newest-first, then measures the span to the 30th event.
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If fewer than 30 done events exist, returns 30 (use the full cap).
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"""
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n = len(history.events)
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if n >= 14:
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return 7
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if n >= 7:
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return 14
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done = sorted(
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[e for e in history.events if e.action == "done"],
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key=lambda e: e.created_at,
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reverse=True,
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)
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if len(done) < 30:
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return 30
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latest = _parse_dt(done[0].created_at)
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thirtieth = _parse_dt(done[29].created_at)
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span = (latest - thirtieth).total_seconds() / 86_400
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return max(1, min(30, math.ceil(span)))
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def _infer_weekly_cycle(history: UserHistory) -> list[dict]:
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"""Peak-to-mean ratio of done events per day-of-week (0=Monday … 6=Sunday).
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Returns all 7 DOW entries so the caller can filter by strength threshold.
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"""
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by_dow: Counter[int] = Counter(
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_parse_dt(e.created_at).weekday()
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for e in history.events
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if e.action == "done"
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)
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total = sum(by_dow.values())
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if total == 0:
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return []
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mean = total / 7
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return [
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{
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"dow": dow,
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"strength": round(by_dow.get(dow, 0) / mean, 3),
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"sample": f"completes most {_DOW_NAMES[dow]}s",
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}
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for dow in range(7)
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]
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def _infer_daily_cycle(history: UserHistory) -> list[dict]:
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"""Peak-to-mean ratio of done events per hour-of-day (0–23).
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Returns entries for hours that have at least one done event.
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"""
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by_hour: Counter[int] = Counter(
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_parse_dt(e.created_at).hour
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for e in history.events
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if e.action == "done"
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)
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total = sum(by_hour.values())
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if total == 0:
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return []
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mean = total / 24
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return [
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{
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"hour": hour,
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"strength": round(by_hour[hour] / mean, 3),
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}
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for hour in sorted(by_hour)
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]
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MANIFEST = AgentManifest(
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id="recent-patterns",
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version="1.1.0", # bumped: window_days InferredParam added (#116)
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version="1.2.0", # #116: lookback_days + weekly_cycle + daily_cycle inference
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description="Surfaces the user's reaction pattern from recent feedback.",
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pref_schema={
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"type": "object",
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"additionalProperties": False,
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"properties": {
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"window_days": {
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"lookback_days": {
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"type": "integer",
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"minimum": 1,
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"maximum": 30,
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"default": 7,
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"description": "Lookback window for pattern analysis.",
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"description": "Lookback window sized to capture ≥30 done events.",
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},
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"weekly_cycle": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"dow": {"type": "integer"},
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"strength": {"type": "number"},
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"sample": {"type": "string"},
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},
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},
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"default": [],
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"description": "Per-DOW completion strength (peak-to-mean ratio).",
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},
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"daily_cycle": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"hour": {"type": "integer"},
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"strength": {"type": "number"},
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},
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},
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"default": [],
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"description": "Per-hour completion strength (peak-to-mean ratio).",
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},
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},
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},
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@@ -46,15 +136,45 @@ MANIFEST = AgentManifest(
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ttl_sec=86_400,
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inferred_params=[
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InferredParam(
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key="window_days",
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ttl_sec=86_400, # recompute daily alongside snippet
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key="lookback_days",
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ttl_sec=86_400,
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cold_start_default=7,
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min_history=5,
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infer=_infer_window_days,
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infer=_infer_lookback_days,
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),
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InferredParam(
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key="weekly_cycle",
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ttl_sec=86_400,
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cold_start_default=[],
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min_history=21, # need ≥3 weeks to see a weekly signal
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infer=_infer_weekly_cycle,
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),
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InferredParam(
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key="daily_cycle",
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ttl_sec=86_400,
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cold_start_default=[],
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min_history=14,
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infer=_infer_daily_cycle,
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),
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],
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)
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_STRENGTH_THRESHOLD = 0.5
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def _strong(entries: list[dict], key: str) -> list[dict]:
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return [e for e in entries if e.get("strength", 0) > _STRENGTH_THRESHOLD]
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def _hour_label(hour: int) -> str:
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if hour == 0:
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return "midnight"
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if hour < 12:
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return f"{hour}am"
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if hour == 12:
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return "noon"
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return f"{hour - 12}pm"
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class RecentPatternsAgent(BaseAgent):
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"""Surfaces the user's reaction pattern from recent feedback."""
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@@ -63,8 +183,15 @@ class RecentPatternsAgent(BaseAgent):
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version: ClassVar[str] = MANIFEST.version
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def compute(self, inp: AgentInput) -> AgentOutput:
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window_days = max(1, int(inp.agent_prefs.get("window_days", 7)))
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window_s = window_days * 86_400
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# Support legacy window_days pref key for backward compat.
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lookback_days = max(
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1,
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int(inp.agent_prefs.get("lookback_days", inp.agent_prefs.get("window_days", 7))),
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)
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weekly_cycle: list[dict] = inp.agent_prefs.get("weekly_cycle", [])
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daily_cycle: list[dict] = inp.agent_prefs.get("daily_cycle", [])
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window_s = lookback_days * 86_400
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now_ts = inp.now.timestamp()
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recent = [
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@@ -76,16 +203,18 @@ class RecentPatternsAgent(BaseAgent):
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total = len(recent)
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dwell_ms = inp.profile.get("mean_dwell_ms_30d")
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parts: list[str] = []
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if total == 0:
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prompt = f"No tip reactions recorded in the last {window_days} days."
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parts.append(f"No tip reactions recorded in the last {lookback_days} days.")
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else:
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done = counts.get("done", 0)
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dismissed = counts.get("dismiss", 0)
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snoozed = counts.get("snooze", 0)
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parts = [
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f"Last {window_days} days: {total} tip reaction{'s' if total != 1 else ''} — "
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parts.append(
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f"Last {lookback_days} days: {total} tip reaction{'s' if total != 1 else ''} — "
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f"{done} completed, {dismissed} dismissed, {snoozed} snoozed."
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]
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)
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if dwell_ms is not None:
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dwell_s = round(dwell_ms / 1000)
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if dwell_s < 15:
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@@ -98,13 +227,34 @@ class RecentPatternsAgent(BaseAgent):
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parts.append(
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f"Average dwell {dwell_s}s — user deliberates; prefer tips that reward reflection."
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)
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prompt = " ".join(parts)
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# Cycle hints — only when strength > threshold.
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strong_weekly = _strong(weekly_cycle, "strength")
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if strong_weekly:
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day_names = [_DOW_NAMES[e["dow"]] for e in strong_weekly]
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if len(day_names) == 1:
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parts.append(f"User tends to complete tips on {day_names[0]}s.")
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else:
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joined = ", ".join(day_names[:-1]) + f" and {day_names[-1]}"
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parts.append(f"User tends to complete tips on {joined}s.")
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strong_daily = _strong(daily_cycle, "strength")
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if strong_daily:
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hour_labels = [_hour_label(e["hour"]) for e in strong_daily]
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if len(hour_labels) == 1:
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parts.append(f"User is most active around {hour_labels[0]}.")
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else:
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joined = ", ".join(hour_labels[:-1]) + f" and {hour_labels[-1]}"
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parts.append(f"User is most active around {joined}.")
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prompt = " ".join(parts) if parts else "No engagement data available yet."
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snapshot = {
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"window_days": window_days,
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"lookback_days": lookback_days,
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"recent_total": total,
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"action_counts": dict(counts),
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"mean_dwell_ms_30d": dwell_ms,
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"strong_weekly_days": [e["dow"] for e in strong_weekly],
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"strong_daily_hours": [e["hour"] for e in strong_daily],
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}
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return self._make_output(inp, prompt, snapshot)
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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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result = run_inference(RECENT_MANIFEST, history)
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assert result["window_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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result = run_inference(RECENT_MANIFEST, history)
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assert result["window_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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result = run_inference(RECENT_MANIFEST, history)
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assert result["window_days"] == 14
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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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result = run_inference(RECENT_MANIFEST, history)
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assert result["window_days"] == 7
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def test_agent_uses_window_days_pref(self):
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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["lookback_days"] == 7 # cold_start_default
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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["lookback_days"] == 30
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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["lookback_days"] <= 2
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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["lookback_days"] <= 16
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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
|
||||
|
||||
def test_snippet_includes_cycle_hint_when_strong(self):
|
||||
# Inject a strong weekly_cycle pref directly
|
||||
prefs = {
|
||||
"lookback_days": 7,
|
||||
"weekly_cycle": [{"dow": 1, "strength": 2.0, "sample": "completes most Tuesdays"}],
|
||||
"daily_cycle": [],
|
||||
}
|
||||
out = RecentPatternsAgent().compute(_inp(agent_prefs=prefs))
|
||||
assert "Tuesday" in out.prompt_text
|
||||
|
||||
def test_snippet_omits_cycle_hint_when_weak(self):
|
||||
prefs = {
|
||||
"lookback_days": 7,
|
||||
"weekly_cycle": [{"dow": 1, "strength": 0.3, "sample": "completes most Tuesdays"}],
|
||||
"daily_cycle": [],
|
||||
}
|
||||
out = RecentPatternsAgent().compute(_inp(agent_prefs=prefs))
|
||||
assert "Tuesday" not in out.prompt_text
|
||||
|
||||
|
||||
class TestRecentPatternsDailyCycle:
|
||||
def test_cold_start_returns_empty(self):
|
||||
history = _history(*[_event("done") for _ in range(5)]) # below min_history=14
|
||||
result = run_inference(RECENT_MANIFEST, history)
|
||||
assert result["daily_cycle"] == []
|
||||
|
||||
def _evening_person_history(self) -> UserHistory:
|
||||
"""Many done events at 20:00–21:00, few in the morning."""
|
||||
events = []
|
||||
for d in range(20):
|
||||
for _ in range(4):
|
||||
events.append(_done_at(d + 0.5, hour=20))
|
||||
events.append(_done_at(d + 0.5, hour=9))
|
||||
return _history(*events)
|
||||
|
||||
def test_evening_person_strong_at_evening_hours(self):
|
||||
history = self._evening_person_history()
|
||||
result = run_inference(RECENT_MANIFEST, history)
|
||||
by_hour = {e["hour"]: e["strength"] for e in result["daily_cycle"]}
|
||||
assert by_hour.get(20, 0) > 1.0
|
||||
assert by_hour.get(9, 0) < by_hour.get(20, 0)
|
||||
|
||||
def test_snippet_includes_daily_hint_when_strong(self):
|
||||
prefs = {
|
||||
"lookback_days": 7,
|
||||
"weekly_cycle": [],
|
||||
"daily_cycle": [{"hour": 20, "strength": 3.0}],
|
||||
}
|
||||
out = RecentPatternsAgent().compute(_inp(agent_prefs=prefs))
|
||||
assert "8pm" in out.prompt_text
|
||||
|
||||
def test_snippet_omits_daily_hint_when_weak(self):
|
||||
prefs = {
|
||||
"lookback_days": 7,
|
||||
"weekly_cycle": [],
|
||||
"daily_cycle": [{"hour": 20, "strength": 0.4}],
|
||||
}
|
||||
out = RecentPatternsAgent().compute(_inp(agent_prefs=prefs))
|
||||
assert "8pm" not in out.prompt_text
|
||||
|
||||
def test_no_pattern_user_no_hints(self):
|
||||
# Uniform distribution across all hours → strength ≈ 1.0 everywhere → no strong peaks
|
||||
events = [_done_at(d + 0.5, hour=h) for d in range(3) for h in range(24)]
|
||||
history = _history(*events)
|
||||
result = run_inference(RECENT_MANIFEST, history)
|
||||
strong = [e for e in result["daily_cycle"] if e["strength"] > 0.5]
|
||||
# Uniform distribution → all strengths ≈ 1.0; but none dramatically above threshold
|
||||
# Since strength = count/mean and all counts are equal, all = 1.0 exactly
|
||||
# 1.0 is not > 0.5 threshold in snippet rendering, but IS > 0.5 so they'd show.
|
||||
# For a flat distribution the caller sees no meaningful peak — verify no strength > 2
|
||||
assert all(e["strength"] <= 1.1 for e in result["daily_cycle"])
|
||||
|
||||
def test_version_bumped(self):
|
||||
assert RECENT_MANIFEST.version == "1.1.0"
|
||||
assert RECENT_MANIFEST.version == "1.2.0"
|
||||
|
||||
|
||||
# ── focus-area: preferred_areas wiring ───────────────────────────────────────
|
||||
|
||||
Reference in New Issue
Block a user