oO/ml/agents/tests/test_per_agent_inference.py

"""Per-agent inference tests: momentum (#114), overdue-task (#115), recent-patterns (#116),
and focus-area (#113) preferred_areas wiring."""
from __future__ import annotations

import sys, os
sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "..", ".."))

from datetime import datetime, timezone
import pytest

from ml.agents.inference.history import FeedbackEvent, TaskCompletion, UserHistory
from ml.agents.inference.framework import run_inference
from ml.agents.momentum import MomentumAgent, MANIFEST as MOMENTUM_MANIFEST
from ml.agents.overdue_task import OverdueTaskAgent, MANIFEST as OVERDUE_MANIFEST
from ml.agents.recent_patterns import RecentPatternsAgent, MANIFEST as RECENT_MANIFEST
from ml.agents.focus_area import FocusAreaAgent
from ml.agents.base import AgentInput

_NOW = datetime(2026, 5, 8, 14, 0, 0, tzinfo=timezone.utc)


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, days_ago: float = 1.0) -> FeedbackEvent:
    from datetime import timedelta
    ts = (_NOW - timedelta(days=days_ago)).isoformat()
    dwell = 60_000 if action == "done" else 500
    return FeedbackEvent(action=action, dwell_ms=dwell, created_at=ts)


def _history(*events: FeedbackEvent, completions: list[TaskCompletion] | None = None) -> UserHistory:
    return UserHistory(user_id="u1", events=list(events), task_completions=completions or [])


def _completion(project_id: str | None, lateness_days: float) -> TaskCompletion:
    """Build a TaskCompletion where completed_at is lateness_days after due_at."""
    from datetime import timedelta
    due = _NOW - timedelta(days=30)
    completed = due + timedelta(days=lateness_days)
    return TaskCompletion(
        project_id=project_id,
        completed_at=completed.isoformat(),
        due_at=due.isoformat(),
    )


# ── momentum helpers ─────────────────────────────────────────────────────────

def _neutral_prefs(**extra) -> dict:
    """Prefs that put z-score in the normal range so trend label can show."""
    return {"baseline_completions_per_day": 0.0, "stdev": 1.0, "momentum_window": 7, **extra}


def _feedback_done(n: int, days_ago: float = 1.0) -> list[dict]:
    from datetime import timedelta
    ts = (_NOW - timedelta(days=days_ago)).isoformat()
    return [{"action": "done", "dwell_ms": 60_000, "created_at": ts}] * n


# ── momentum: engagement_trend inference ─────────────────────────────────────

class TestMomentumTrendInference:
    def test_cold_start_below_min_history(self):
        history = _history(*[_event("done", days_ago=i) for i in range(5)])
        result = run_inference(MOMENTUM_MANIFEST, history)
        assert result["engagement_trend"] == "stable"  # cold_start_default

    def test_trend_up_when_recent_done_rate_higher(self):
        recent = [_event("done", days_ago=i) for i in range(1, 9)]
        older = [_event("dismiss", days_ago=i) for i in range(8, 15)]
        older[0] = _event("done", days_ago=8)
        history = _history(*recent, *older)
        result = run_inference(MOMENTUM_MANIFEST, history)
        assert result["engagement_trend"] == "up"

    def test_trend_down_when_recent_done_rate_lower(self):
        recent = [_event("dismiss", days_ago=i) for i in range(1, 8)]
        older = [_event("done", days_ago=i) for i in range(8, 15)]
        history = _history(*recent, *older)
        result = run_inference(MOMENTUM_MANIFEST, history)
        assert result["engagement_trend"] == "down"

    def test_trend_stable_when_similar(self):
        events = [_event("done" if i % 2 == 0 else "dismiss", days_ago=i) for i in range(1, 15)]
        history = _history(*events)
        result = run_inference(MOMENTUM_MANIFEST, history)
        assert result["engagement_trend"] == "stable"

    def test_trend_shown_when_z_score_normal(self):
        # baseline=0 so z≈0 → no z label → trend label falls through
        out = MomentumAgent().compute(_inp(agent_prefs=_neutral_prefs(engagement_trend="up")))
        assert "trending up" in out.prompt_text

    def test_trend_down_shown_when_z_score_normal(self):
        out = MomentumAgent().compute(_inp(agent_prefs=_neutral_prefs(engagement_trend="down")))
        assert "trending down" in out.prompt_text

    def test_snapshot_includes_trend(self):
        out = MomentumAgent().compute(_inp(agent_prefs=_neutral_prefs(engagement_trend="stable")))
        assert "engagement_trend" in out.signals_snapshot


# ── momentum: baseline + stdev inference (#114) ───────────────────────────────

class TestMomentumBaselineInference:
    def _events_n_per_day(self, done_per_day: int, n_days: int) -> list[FeedbackEvent]:
        """Generate done events spread across n_days."""
        events = []
        for d in range(n_days):
            for _ in range(done_per_day):
                events.append(_event("done", days_ago=d + 0.5))
        return events

    def test_cold_start_when_few_events(self):
        history = _history(*[_event("done", days_ago=i) for i in range(5)])
        result = run_inference(MOMENTUM_MANIFEST, history)
        assert result["baseline_completions_per_day"] == 1.0
        assert result["stdev"] == 1.0

    def test_power_user_baseline_high(self):
        # 5 done events per day for 20 days → baseline ≈ 5/day (over 28d window, zeros fill rest)
        events = self._events_n_per_day(5, 20)
        history = _history(*events)
        result = run_inference(MOMENTUM_MANIFEST, history)
        assert result["baseline_completions_per_day"] > 2.0

    def test_casual_user_baseline_low(self):
        # 1 done every 3 days + dismiss filler to clear min_history=14 → baseline ≈ 0.33/day
        done_events = [_event("done", days_ago=d * 3 + 0.5) for d in range(7)]
        filler = [_event("dismiss", days_ago=d + 0.5) for d in range(10)]
        history = _history(*done_events, *filler)
        result = run_inference(MOMENTUM_MANIFEST, history)
        assert result["baseline_completions_per_day"] < 0.5

    def test_stdev_reflects_variability(self):
        # Alternating 0 and 4 done events → high stdev
        events = []
        for d in range(14):
            if d % 2 == 0:
                for _ in range(4):
                    events.append(_event("done", days_ago=d + 0.5))
        history = _history(*events)
        result = run_inference(MOMENTUM_MANIFEST, history)
        assert result["stdev"] > 1.0

    def test_consistent_user_lower_stdev_than_variable(self):
        # Consistent 2/day for 28 days has lower stdev than alternating 0/4
        consistent = self._events_n_per_day(2, 28)
        variable = []
        for d in range(14):
            if d % 2 == 0:
                for _ in range(4):
                    variable.append(_event("done", days_ago=d + 0.5))
            else:
                variable.append(_event("dismiss", days_ago=d + 0.5))
        r_consistent = run_inference(MOMENTUM_MANIFEST, _history(*consistent))
        r_variable = run_inference(MOMENTUM_MANIFEST, _history(*variable))
        assert r_consistent["stdev"] < r_variable["stdev"]


# ── momentum: z-score snippet language ───────────────────────────────────────

class TestMomentumZScore:
    def _prefs(self, baseline: float, stdev: float = 1.0) -> dict:
        return {"baseline_completions_per_day": baseline, "stdev": stdev,
                "momentum_window": 7, "engagement_trend": "stable"}

    def test_power_user_above_baseline_says_above_usual(self):
        # baseline=3/day, stdev=1.0, window=7 → expected rate=3; user did 35 → rate=5, z=2
        prefs = self._prefs(baseline=3.0, stdev=1.0)
        feedback = _feedback_done(35, days_ago=1.0)
        out = MomentumAgent().compute(_inp(feedback_history=feedback, agent_prefs=prefs))
        assert "above your usual" in out.prompt_text

    def test_casual_user_slowing_down(self):
        # baseline=1/day, user did 0 in 7d → z = (0 - 1) / 1 = -1 → below usual
        prefs = self._prefs(baseline=1.0, stdev=1.0)
        out = MomentumAgent().compute(_inp(feedback_history=[], agent_prefs=prefs))
        assert "below your usual" in out.prompt_text

    def test_returning_from_break_at_normal_rate(self):
        # User just came back: 1 done, baseline=1/day, window=7 → z=(1/7-1)/1≈-0.86, within normal
        prefs = self._prefs(baseline=1.0, stdev=1.0)
        feedback = _feedback_done(1, days_ago=0.5)
        out = MomentumAgent().compute(_inp(feedback_history=feedback, agent_prefs=prefs))
        # z ≈ -0.86 → no z label, falls back to trend (stable → no extra sentence)
        assert "above your usual" not in out.prompt_text
        assert "below your usual" not in out.prompt_text

    def test_snapshot_includes_z_score(self):
        prefs = self._prefs(baseline=1.0)
        out = MomentumAgent().compute(_inp(agent_prefs=prefs))
        assert "z_score" in out.signals_snapshot
        assert "recent_done_count" in out.signals_snapshot

    def test_version_bumped(self):
        assert MOMENTUM_MANIFEST.version == "1.2.0"


# ── overdue-task: lateness_tolerance_days + project_realness (#115) ──────────

class TestOverdueTaskInference:
    # -- lateness_tolerance_days inference --

    def test_cold_start_returns_zero_when_few_completions(self):
        # Below min_history=10 task completions → cold start
        cs = [_completion("p1", 2.0) for _ in range(5)]
        history = _history(*[_event("done")] * 5, completions=cs)
        result = run_inference(OVERDUE_MANIFEST, history)
        assert result["lateness_tolerance_days"] == 0.0

    def test_punctual_user_zero_tolerance(self):
        # User always finishes early or on time (negative lateness) → tolerance 0
        cs = [_completion("p1", -1.0) for _ in range(12)]
        history = _history(*[_event("done")] * 12, completions=cs)
        result = run_inference(OVERDUE_MANIFEST, history)
        assert result["lateness_tolerance_days"] == 0.0

    def test_chronic_late_user_positive_tolerance(self):
        # User consistently finishes 5 days late → p50 = 5
        cs = [_completion("p1", 5.0) for _ in range(12)]
        history = _history(*[_event("done")] * 12, completions=cs)
        result = run_inference(OVERDUE_MANIFEST, history)
        assert result["lateness_tolerance_days"] == pytest.approx(5.0)

    def test_mixed_lateness_uses_median(self):
        # 6 tasks at +1d, 6 tasks at +3d → median = 2
        cs = [_completion("p1", 1.0)] * 6 + [_completion("p1", 3.0)] * 6
        history = _history(*[_event("done")] * 12, completions=cs)
        result = run_inference(OVERDUE_MANIFEST, history)
        assert result["lateness_tolerance_days"] == pytest.approx(2.0)

    # -- project_realness inference --

    def test_project_realness_cold_start_empty(self):
        cs = [_completion("p1", 1.0) for _ in range(5)]  # below min_history
        history = _history(*[_event("done")] * 5, completions=cs)
        result = run_inference(OVERDUE_MANIFEST, history)
        assert result["project_realness"] == {}

    def test_project_realness_punctual_project_scores_high(self):
        # p1 always on time (0d late), p2 always 10d late → p1 should be realness ≈ 1
        cs = [_completion("p1", 0.0)] * 6 + [_completion("p2", 10.0)] * 6
        history = _history(*[_event("done")] * 12, completions=cs)
        result = run_inference(OVERDUE_MANIFEST, history)
        assert result["project_realness"]["p1"] > result["project_realness"]["p2"]

    def test_project_realness_values_clipped_01(self):
        cs = [_completion("p1", 0.0)] * 6 + [_completion("p2", 100.0)] * 6
        history = _history(*[_event("done")] * 12, completions=cs)
        result = run_inference(OVERDUE_MANIFEST, history)
        for v in result["project_realness"].values():
            assert 0.0 <= v <= 1.0

    # -- compute() reads inferred prefs --

    def test_tolerance_filters_tasks(self):
        tasks = [
            {"content": "Fresh overdue", "is_overdue": True, "task_age_days": 0.5},
            {"content": "Old overdue", "is_overdue": True, "task_age_days": 3.0},
        ]
        out = OverdueTaskAgent().compute(_inp(tasks=tasks, agent_prefs={"lateness_tolerance_days": 2}))
        assert "1 overdue task" in out.prompt_text
        assert "Old overdue" in out.prompt_text

    def test_low_realness_softens_language(self):
        tasks = [{"content": "Wishlist", "is_overdue": True, "task_age_days": 3.0,
                  "project_id": "aspirational"}]
        prefs = {"lateness_tolerance_days": 0, "project_realness": {"aspirational": 0.2}}
        out = OverdueTaskAgent().compute(_inp(tasks=tasks, agent_prefs=prefs))
        assert "target date" in out.prompt_text

    def test_high_realness_uses_overdue_language(self):
        tasks = [{"content": "Critical", "is_overdue": True, "task_age_days": 3.0,
                  "project_id": "work"}]
        prefs = {"lateness_tolerance_days": 0, "project_realness": {"work": 0.9}}
        out = OverdueTaskAgent().compute(_inp(tasks=tasks, agent_prefs=prefs))
        assert "overdue" in out.prompt_text

    def test_snapshot_includes_realness(self):
        tasks = [{"content": "T", "is_overdue": True, "task_age_days": 1.0, "project_id": "p1"}]
        prefs = {"lateness_tolerance_days": 0, "project_realness": {"p1": 0.8}}
        out = OverdueTaskAgent().compute(_inp(tasks=tasks, agent_prefs=prefs))
        assert "realness" in out.signals_snapshot["top_overdue"][0]

    def test_version_bumped(self):
        assert OVERDUE_MANIFEST.version == "1.2.0"


# ── recent-patterns: window_days ─────────────────────────────────────────────

class TestRecentPatternsInference:
    def test_cold_start_default_7(self):
        history = _history(*[_event("done") for _ in range(3)])  # below min_history=5
        result = run_inference(RECENT_MANIFEST, history)
        assert result["window_days"] == 7  # cold_start_default

    def test_sparse_history_widens_window(self):
        history = _history(*[_event("done") for _ in range(5)])  # 5 events, n < 7 → 30 days
        result = run_inference(RECENT_MANIFEST, history)
        assert result["window_days"] == 30

    def test_moderate_history_14_days(self):
        history = _history(*[_event("done") for _ in range(10)])  # 7 ≤ n < 14 → 14 days
        result = run_inference(RECENT_MANIFEST, history)
        assert result["window_days"] == 14

    def test_dense_history_stays_7(self):
        history = _history(*[_event("done") for _ in range(20)])  # 20+ → 7 days
        result = run_inference(RECENT_MANIFEST, history)
        assert result["window_days"] == 7

    def test_agent_uses_window_days_pref(self):
        from datetime import timedelta
        # 5 feedback events, all within 14 days but older than 7 days
        feedback = [
            {"action": "done", "dwell_ms": 60000,
             "created_at": (_NOW - timedelta(days=10)).isoformat()}
        ] * 5
        # With window_days=7 → 0 events seen; with window_days=14 → 5 events
        out_narrow = RecentPatternsAgent().compute(
            _inp(feedback_history=feedback, agent_prefs={"window_days": 7})
        )
        out_wide = RecentPatternsAgent().compute(
            _inp(feedback_history=feedback, agent_prefs={"window_days": 14})
        )
        assert "No tip reactions" in out_narrow.prompt_text
        assert "5 tip reactions" in out_wide.prompt_text

    def test_snapshot_includes_window_days(self):
        out = RecentPatternsAgent().compute(_inp(agent_prefs={"window_days": 14}))
        assert out.signals_snapshot["window_days"] == 14

    def test_version_bumped(self):
        assert RECENT_MANIFEST.version == "1.1.0"


# ── focus-area: preferred_areas wiring ───────────────────────────────────────

class TestFocusAreaPreferredAreas:
    agent = FocusAreaAgent()

    def _task(self, content: str, project_id: str, is_overdue: bool = False) -> dict:
        return {"id": "t1", "content": content, "is_overdue": is_overdue,
                "task_age_days": 2.0, "priority": 1, "project_id": project_id}

    def test_preferred_area_wins_tie(self):
        tasks = [
            self._task("Work thing", "work"),
            self._task("Home thing", "home"),
        ]
        out = self.agent.compute(_inp(tasks=tasks, agent_prefs={"preferred_areas": ["work"]}))
        assert "work" in out.prompt_text
        assert "matches the user's stated focus preferences" in out.prompt_text

    def test_no_preferred_areas_uses_congestion_score(self):
        tasks = [
            self._task("W1", "work"),
            self._task("H1", "home"),
            self._task("H2", "home"),
        ]
        out = self.agent.compute(_inp(tasks=tasks))
        # home has more tasks → wins without any preference
        assert "home" in out.prompt_text

    def test_snapshot_includes_preferred_areas(self):
        tasks = [self._task("T", "work")]
        out = self.agent.compute(_inp(tasks=tasks, agent_prefs={"preferred_areas": ["work"]}))
        assert out.signals_snapshot["preferred_areas"] == ["work"]

    def test_version_bumped(self):
        from ml.agents.focus_area import MANIFEST as FA_MANIFEST
        assert FA_MANIFEST.version == "1.1.0"