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feat(agents): adaptive lookback + weekly/daily cycle detection for recent-patterns (#116)

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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:
alvis
2026-05-06 05:51:45 +00:00
parent 4cade4868b
commit bc71dc203d
2 changed files with 340 additions and 47 deletions
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198
ml/agents/recent_patterns.py
View File

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