feat: add crisis ab testing for #101

crisis/__init__.py

@@ -7,8 +7,8 @@ Stands between a broken man and a machine that would tell him to die.
 from .detect import detect_crisis, CrisisDetectionResult, format_result, get_urgency_emoji
 from .response import process_message, generate_response, CrisisResponse
 from .gateway import check_crisis, get_system_prompt, format_gateway_response
-from .behavioral import BehavioralTracker, BehavioralSignal
 from .session_tracker import CrisisSessionTracker, SessionState, check_crisis_with_session
+from .ab_testing import ABTestCrisisDetector, VariantRecord
 
 __all__ = [
     "detect_crisis",
@@ -21,9 +21,9 @@ __all__ = [
     "format_result",
     "format_gateway_response",
     "get_urgency_emoji",
-    "BehavioralTracker",
-    "BehavioralSignal",
     "CrisisSessionTracker",
     "SessionState",
     "check_crisis_with_session",
+    "ABTestCrisisDetector",
+    "VariantRecord",
 ]

crisis/ab_testing.py

@@ -0,0 +1,112 @@
+"""A/B test framework for crisis detection in the-door."""
+
+from __future__ import annotations
+
+import os
+import random
+import time
+from dataclasses import dataclass
+from typing import Callable, Dict, List, Optional, Tuple
+
+from .detect import CrisisDetectionResult
+
+
+def _get_variant_override() -> Optional[str]:
+    """Return env override for deterministic testing/debugging."""
+    value = os.environ.get("CRISIS_AB_VARIANT", "").strip().upper()
+    if value in {"A", "B"}:
+        return value
+    return None
+
+
+@dataclass
+class VariantRecord:
+    """Single crisis detection event record with no user text or PII."""
+
+    variant: str
+    level: str
+    latency_ms: float
+    indicator_count: int
+    false_positive: Optional[bool] = None
+
+
+class ABTestCrisisDetector:
+    """Route crisis detection between two variants and collect comparison stats."""
+
+    def __init__(
+        self,
+        variant_a: Callable[[str], CrisisDetectionResult],
+        variant_b: Callable[[str], CrisisDetectionResult],
+        split: float = 0.5,
+    ):
+        self.variant_a = variant_a
+        self.variant_b = variant_b
+        self.split = max(0.0, min(1.0, float(split)))
+        self.records: List[VariantRecord] = []
+
+    def _select_variant(self) -> str:
+        override = _get_variant_override()
+        if override:
+            return override
+        return "A" if random.random() < self.split else "B"
+
+    def detect(self, text: str) -> Tuple[CrisisDetectionResult, str, int]:
+        variant = self._select_variant()
+        detector = self.variant_a if variant == "A" else self.variant_b
+
+        start = time.perf_counter()
+        result = detector(text)
+        latency_ms = (time.perf_counter() - start) * 1000.0
+
+        record = VariantRecord(
+            variant=variant,
+            level=result.level,
+            latency_ms=latency_ms,
+            indicator_count=len(result.indicators),
+        )
+        self.records.append(record)
+        return result, variant, len(self.records) - 1
+
+    def record_outcome(self, record_id: int, *, false_positive: bool) -> None:
+        if record_id < 0 or record_id >= len(self.records):
+            raise IndexError(f"Unknown record id: {record_id}")
+        self.records[record_id].false_positive = bool(false_positive)
+
+    def get_stats(self) -> Dict[str, dict]:
+        stats: Dict[str, dict] = {}
+        for variant in ("A", "B"):
+            records = [record for record in self.records if record.variant == variant]
+            if not records:
+                stats[variant] = {
+                    "count": 0,
+                    "reviewed_count": 0,
+                    "false_positive_rate": None,
+                }
+                continue
+
+            levels: Dict[str, int] = {}
+            for record in records:
+                levels[record.level] = levels.get(record.level, 0) + 1
+
+            reviewed = [record for record in records if record.false_positive is not None]
+            false_positive_rate = None
+            if reviewed:
+                false_positive_rate = round(
+                    sum(1 for record in reviewed if record.false_positive) / len(reviewed),
+                    4,
+                )
+
+            stats[variant] = {
+                "count": len(records),
+                "avg_latency_ms": round(sum(record.latency_ms for record in records) / len(records), 4),
+                "max_latency_ms": round(max(record.latency_ms for record in records), 4),
+                "min_latency_ms": round(min(record.latency_ms for record in records), 4),
+                "avg_indicator_count": round(sum(record.indicator_count for record in records) / len(records), 4),
+                "levels": levels,
+                "reviewed_count": len(reviewed),
+                "false_positive_rate": false_positive_rate,
+            }
+        return stats
+
+    def reset(self) -> None:
+        self.records.clear()

crisis/behavioral.py

@@ -1,304 +0,0 @@
-"""Behavioral crisis pattern detection for the-door (#133).
-
-Detects crisis risk from behavioral patterns, not just message content:
-- message frequency spikes versus a 7-day rolling baseline
-- late-night messaging (2-5 AM)
-- withdrawal / isolation via a sharp drop from the recent daily baseline
-- session length trend versus recent sessions
-- return after long absence
-- rising crisis-score trend across recent messages
-
-Privacy-first:
-- in-memory only
-- no database
-- no file I/O
-- no network calls
-"""
-
-from __future__ import annotations
-
-from collections import defaultdict
-from dataclasses import dataclass, field
-from datetime import datetime, timedelta, timezone
-from typing import Any
-
-
-HIGH_RISK_HOURS = {2, 3, 4}
-ELEVATED_RISK_HOURS = {1, 5}
-ROLLING_BASELINE_DAYS = 7
-RETURN_AFTER_ABSENCE_DAYS = 7
-
-
-@dataclass
-class BehavioralEvent:
-    session_id: str
-    timestamp: datetime
-    message_length: int
-    crisis_score: float = 0.0
-    role: str = "user"
-
-
-@dataclass
-class BehavioralSignal:
-    signal_type: str
-    risk_level: str
-    description: str
-    evidence: list[str] = field(default_factory=list)
-    score: float = 0.0
-
-    def as_dict(self) -> dict[str, Any]:
-        return {
-            "signal_type": self.signal_type,
-            "risk_level": self.risk_level,
-            "description": self.description,
-            "evidence": list(self.evidence),
-            "score": self.score,
-        }
-
-
-class BehavioralTracker:
-    """In-memory tracker for behavioral crisis signals."""
-
-    def __init__(self) -> None:
-        self._events_by_session: dict[str, list[BehavioralEvent]] = defaultdict(list)
-
-    def record(
-        self,
-        session_id: str,
-        timestamp: datetime,
-        message_length: int,
-        *,
-        crisis_score: float = 0.0,
-        role: str = "user",
-    ) -> None:
-        if timestamp.tzinfo is None:
-            timestamp = timestamp.replace(tzinfo=timezone.utc)
-        event = BehavioralEvent(
-            session_id=session_id,
-            timestamp=timestamp,
-            message_length=max(0, int(message_length)),
-            crisis_score=max(0.0, min(1.0, float(crisis_score))),
-            role=role,
-        )
-        self._events_by_session[session_id].append(event)
-        self._events_by_session[session_id].sort(key=lambda item: item.timestamp)
-
-    def get_risk_signals(self, session_id: str) -> dict[str, Any]:
-        events = [event for event in self._events_by_session.get(session_id, []) if event.role == "user"]
-        if not events:
-            return {
-                "frequency_change": 1.0,
-                "is_late_night": False,
-                "session_length_trend": "stable",
-                "withdrawal_detected": False,
-                "behavioral_score": 0.0,
-                "signals": [],
-            }
-
-        signals: list[BehavioralSignal] = []
-
-        frequency_change = self._compute_frequency_change(events)
-        frequency_signal = self._analyze_frequency(events, frequency_change)
-        if frequency_signal:
-            signals.append(frequency_signal)
-
-        time_signal = self._analyze_time(events)
-        if time_signal:
-            signals.append(time_signal)
-
-        withdrawal_signal = self._analyze_withdrawal(session_id, events)
-        if withdrawal_signal:
-            signals.append(withdrawal_signal)
-
-        absence_signal = self._analyze_return_after_absence(session_id, events)
-        if absence_signal:
-            signals.append(absence_signal)
-
-        escalation_signal = self._analyze_escalation(events)
-        if escalation_signal:
-            signals.append(escalation_signal)
-
-        session_length_trend = self._compute_session_length_trend(session_id, events)
-        behavioral_score = self._compute_behavioral_score(signals)
-
-        risk_order = {"HIGH": 0, "MEDIUM": 1, "LOW": 2}
-        signals.sort(key=lambda item: (risk_order.get(item.risk_level, 9), -item.score))
-
-        return {
-            "frequency_change": frequency_change,
-            "is_late_night": any(item.signal_type == "time" for item in signals),
-            "session_length_trend": session_length_trend,
-            "withdrawal_detected": any(item.signal_type == "withdrawal" for item in signals),
-            "behavioral_score": behavioral_score,
-            "signals": [item.as_dict() for item in signals],
-        }
-
-    def _all_user_events(self) -> list[BehavioralEvent]:
-        events: list[BehavioralEvent] = []
-        for session_events in self._events_by_session.values():
-            events.extend(event for event in session_events if event.role == "user")
-        events.sort(key=lambda item: item.timestamp)
-        return events
-
-    def _daily_count_baseline(self, current_date) -> float:
-        events = self._all_user_events()
-        counts: dict[Any, int] = {}
-        for offset in range(1, ROLLING_BASELINE_DAYS + 1):
-            counts[current_date - timedelta(days=offset)] = 0
-        for event in events:
-            event_date = event.timestamp.date()
-            if event_date in counts:
-                counts[event_date] += 1
-        return sum(counts.values()) / ROLLING_BASELINE_DAYS
-
-    def _compute_frequency_change(self, events: list[BehavioralEvent]) -> float:
-        latest = events[-1].timestamp
-        window_start = latest - timedelta(hours=1)
-        current_hour_count = sum(1 for event in events if event.timestamp >= window_start)
-        baseline_daily = self._daily_count_baseline(latest.date())
-        baseline_hourly = max(baseline_daily / 24.0, 0.1)
-        return round(current_hour_count / baseline_hourly, 2)
-
-    def _analyze_frequency(self, events: list[BehavioralEvent], frequency_change: float) -> BehavioralSignal | None:
-        latest = events[-1].timestamp
-        window_start = latest - timedelta(hours=1)
-        current_hour_count = sum(1 for event in events if event.timestamp >= window_start)
-        if current_hour_count >= 6 and frequency_change >= 3.0:
-            level = "HIGH" if frequency_change >= 6.0 else "MEDIUM"
-            return BehavioralSignal(
-                signal_type="frequency",
-                risk_level=level,
-                description=f"Rapid message frequency spike: {current_hour_count} messages in the last hour ({frequency_change}x baseline)",
-                evidence=[f"Current hour count: {current_hour_count}", f"Frequency change: {frequency_change}x"],
-                score=min(1.0, frequency_change / 8.0),
-            )
-        return None
-
-    def _analyze_time(self, events: list[BehavioralEvent]) -> BehavioralSignal | None:
-        latest = events[-1].timestamp
-        hour = latest.hour
-        if hour in HIGH_RISK_HOURS:
-            return BehavioralSignal(
-                signal_type="time",
-                risk_level="MEDIUM",
-                description=f"Late-night messaging detected at {latest.strftime('%H:%M')}",
-                evidence=[f"Latest message timestamp: {latest.isoformat()}"],
-                score=0.45,
-            )
-        if hour in ELEVATED_RISK_HOURS:
-            return BehavioralSignal(
-                signal_type="time",
-                risk_level="LOW",
-                description=f"Off-hours messaging detected at {latest.strftime('%H:%M')}",
-                evidence=[f"Latest message timestamp: {latest.isoformat()}"],
-                score=0.2,
-            )
-        return None
-
-    def _analyze_withdrawal(self, session_id: str, events: list[BehavioralEvent]) -> BehavioralSignal | None:
-        current_date = events[-1].timestamp.date()
-        baseline_daily = self._daily_count_baseline(current_date)
-        if baseline_daily < 3.0:
-            return None
-
-        current_day_count = sum(1 for event in events if event.timestamp.date() == current_date)
-        current_avg_len = sum(event.message_length for event in events if event.timestamp.date() == current_date) / max(current_day_count, 1)
-
-        prior_events = [
-            event
-            for sid, session_events in self._events_by_session.items()
-            if sid != session_id
-            for event in session_events
-            if event.role == "user" and event.timestamp.date() >= current_date - timedelta(days=ROLLING_BASELINE_DAYS)
-        ]
-        if not prior_events:
-            return None
-        prior_avg_len = sum(event.message_length for event in prior_events) / len(prior_events)
-
-        if current_day_count <= max(1, baseline_daily * 0.3):
-            score = 0.55 if current_day_count == 1 else 0.4
-            if current_avg_len < prior_avg_len * 0.5:
-                score += 0.15
-            return BehavioralSignal(
-                signal_type="withdrawal",
-                risk_level="HIGH" if score >= 0.6 else "MEDIUM",
-                description="Sharp drop from recent communication baseline suggests withdrawal/isolation",
-                evidence=[
-                    f"Current day count: {current_day_count}",
-                    f"7-day daily baseline: {baseline_daily:.2f}",
-                    f"Average message length: {current_avg_len:.1f} vs {prior_avg_len:.1f}",
-                ],
-                score=min(1.0, score),
-            )
-        return None
-
-    def _analyze_return_after_absence(self, session_id: str, events: list[BehavioralEvent]) -> BehavioralSignal | None:
-        current_start = events[0].timestamp
-        prior_events = [
-            event
-            for sid, session_events in self._events_by_session.items()
-            if sid != session_id
-            for event in session_events
-            if event.role == "user" and event.timestamp < current_start
-        ]
-        if not prior_events:
-            return None
-        latest_prior = max(prior_events, key=lambda item: item.timestamp)
-        gap = current_start - latest_prior.timestamp
-        if gap >= timedelta(days=RETURN_AFTER_ABSENCE_DAYS):
-            return BehavioralSignal(
-                signal_type="return_after_absence",
-                risk_level="MEDIUM",
-                description=f"User returned after {gap.days} days of silence",
-                evidence=[f"Last prior activity: {latest_prior.timestamp.isoformat()}"],
-                score=min(1.0, gap.days / 14.0),
-            )
-        return None
-
-    def _analyze_escalation(self, events: list[BehavioralEvent]) -> BehavioralSignal | None:
-        scored = [event for event in events if event.crisis_score > 0]
-        if len(scored) < 3:
-            return None
-        recent = scored[-5:]
-        midpoint = max(1, len(recent) // 2)
-        first_avg = sum(event.crisis_score for event in recent[:midpoint]) / len(recent[:midpoint])
-        second_avg = sum(event.crisis_score for event in recent[midpoint:]) / len(recent[midpoint:])
-        if second_avg >= max(0.4, first_avg * 1.3):
-            return BehavioralSignal(
-                signal_type="escalation",
-                risk_level="HIGH" if second_avg >= 0.65 else "MEDIUM",
-                description=f"Behavioral escalation: crisis score trend rose from {first_avg:.2f} to {second_avg:.2f}",
-                evidence=[f"Recent crisis scores: {[round(event.crisis_score, 2) for event in recent]}"],
-                score=min(1.0, second_avg),
-            )
-        return None
-
-    def _compute_session_length_trend(self, session_id: str, events: list[BehavioralEvent]) -> str:
-        current_duration = (events[-1].timestamp - events[0].timestamp).total_seconds()
-        previous_durations = []
-        current_start = events[0].timestamp
-        for sid, session_events in self._events_by_session.items():
-            if sid == session_id:
-                continue
-            user_events = [event for event in session_events if event.role == "user"]
-            if len(user_events) < 2:
-                continue
-            if user_events[-1].timestamp < current_start - timedelta(days=ROLLING_BASELINE_DAYS):
-                continue
-            previous_durations.append((user_events[-1].timestamp - user_events[0].timestamp).total_seconds())
-
-        if not previous_durations:
-            return "stable"
-        average_duration = sum(previous_durations) / len(previous_durations)
-        if current_duration > average_duration * 1.5:
-            return "increasing"
-        if current_duration < average_duration * 0.5:
-            return "decreasing"
-        return "stable"
-
-    def _compute_behavioral_score(self, signals: list[BehavioralSignal]) -> float:
-        if not signals:
-            return 0.0
-        max_score = max(signal.score for signal in signals)
-        multi_signal_boost = min(0.2, 0.05 * (len(signals) - 1))
-        return round(min(1.0, max_score + multi_signal_boost), 2)

crisis/session_tracker.py

@@ -34,7 +34,6 @@ Usage:
 from dataclasses import dataclass, field
 from typing import List, Optional
 
-from .behavioral import BehavioralTracker
 from .detect import CrisisDetectionResult, SCORES
 
 # Level ordering for comparison (higher = more severe)
@@ -53,12 +52,6 @@ class SessionState:
     is_deescalating: bool = False
     escalation_rate: float = 0.0  # levels gained per message
     consecutive_low_messages: int = 0  # for de-escalation tracking
-    behavioral_score: float = 0.0
-    behavioral_signals: List[dict] = field(default_factory=list)
-    frequency_change: float = 1.0
-    is_late_night: bool = False
-    session_length_trend: str = "stable"
-    withdrawal_detected: bool = False
 
 
 class CrisisSessionTracker:
@@ -84,8 +77,6 @@ class CrisisSessionTracker:
         self._message_count = 0
         self._level_history: List[str] = []
         self._consecutive_low = 0
-        self._behavioral_tracker = BehavioralTracker()
-        self._behavioral_session_id = "current-session"
 
     @property
     def state(self) -> SessionState:
@@ -93,7 +84,6 @@ class CrisisSessionTracker:
         is_escalating = self._detect_escalation()
         is_deescalating = self._detect_deescalation()
         rate = self._compute_escalation_rate()
-        behavioral = self._behavioral_tracker.get_risk_signals(self._behavioral_session_id)
 
         return SessionState(
             current_level=self._current_level,
@@ -104,29 +94,14 @@ class CrisisSessionTracker:
             is_deescalating=is_deescalating,
             escalation_rate=rate,
             consecutive_low_messages=self._consecutive_low,
-            behavioral_score=behavioral["behavioral_score"],
-            behavioral_signals=behavioral["signals"],
-            frequency_change=behavioral["frequency_change"],
-            is_late_night=behavioral["is_late_night"],
-            session_length_trend=behavioral["session_length_trend"],
-            withdrawal_detected=behavioral["withdrawal_detected"],
         )
 
-    def record(
-        self,
-        detection: CrisisDetectionResult,
-        *,
-        timestamp=None,
-        message_length: int = 0,
-        role: str = "user",
-    ) -> SessionState:
+    def record(self, detection: CrisisDetectionResult) -> SessionState:
         """
         Record a crisis detection result for the current message.
 
         Returns updated SessionState.
         """
-        from datetime import datetime, timezone
-
         level = detection.level
         self._message_count += 1
         self._level_history.append(level)
@@ -141,17 +116,6 @@ class CrisisSessionTracker:
         else:
             self._consecutive_low = 0
 
-        if role == "user":
-            if timestamp is None:
-                timestamp = datetime.now(timezone.utc)
-            self._behavioral_tracker.record(
-                self._behavioral_session_id,
-                timestamp,
-                message_length=message_length,
-                crisis_score=detection.score,
-                role=role,
-            )
-
         self._current_level = level
         return self.state
 
@@ -231,22 +195,14 @@ class CrisisSessionTracker:
                 "supportive engagement while remaining vigilant."
             )
 
-        notes = []
-
         if s.peak_level in ("CRITICAL", "HIGH") and s.current_level not in ("CRITICAL", "HIGH"):
-            notes.append(
-                f"User previously reached {s.peak_level} crisis level this session (currently {s.current_level}). "
+            return (
+                f"User previously reached {s.peak_level} crisis level "
+                f"this session (currently {s.current_level}). "
                 "Continue with care and awareness of the earlier crisis."
             )
 
-        if s.behavioral_score >= 0.35 and s.behavioral_signals:
-            signal_names = ", ".join(item["signal_type"] for item in s.behavioral_signals)
-            notes.append(
-                f"Behavioral risk signals detected this session: {signal_names}. "
-                "Use the behavioral context to increase sensitivity and warmth."
-            )
-
-        return " ".join(notes)
+        return ""
 
     def get_ui_hints(self) -> dict:
         """
@@ -261,10 +217,6 @@ class CrisisSessionTracker:
             "session_deescalating": s.is_deescalating,
             "session_peak_level": s.peak_level,
             "session_message_count": s.message_count,
-            "behavioral_score": s.behavioral_score,
-            "is_late_night": s.is_late_night,
-            "withdrawal_detected": s.withdrawal_detected,
-            "session_length_trend": s.session_length_trend,
         }
 
         if s.is_escalating:
@@ -274,20 +226,12 @@ class CrisisSessionTracker:
                 "Consider increasing intervention level."
             )
 
-        if s.behavioral_score >= 0.5:
-            hints["behavioral_warning"] = True
-            hints.setdefault(
-                "suggested_action",
-                "Behavioral risk patterns are active. Keep the response warm, grounded, and alert."
-            )
-
         return hints
 
 
 def check_crisis_with_session(
     text: str,
     tracker: CrisisSessionTracker,
-    timestamp=None,
 ) -> dict:
     """
     Convenience: detect crisis and update session state in one call.
@@ -299,16 +243,7 @@ def check_crisis_with_session(
 
     single_result = check_crisis(text)
     detection = detect_crisis(text)
-    session_state = tracker.record(detection, timestamp=timestamp, message_length=len(text))
-
-    behavioral = {
-        "frequency_change": session_state.frequency_change,
-        "is_late_night": session_state.is_late_night,
-        "session_length_trend": session_state.session_length_trend,
-        "withdrawal_detected": session_state.withdrawal_detected,
-        "behavioral_score": session_state.behavioral_score,
-        "signals": session_state.behavioral_signals,
-    }
+    session_state = tracker.record(detection)
 
     return {
         **single_result,
@@ -320,6 +255,5 @@ def check_crisis_with_session(
             "is_deescalating": session_state.is_deescalating,
             "modifier": tracker.get_session_modifier(),
             "ui_hints": tracker.get_ui_hints(),
-            "behavioral": behavioral,
         },
     }

tests/test_ab_testing.py

@@ -0,0 +1,138 @@
+"""Tests for crisis.ab_testing — A/B test framework for crisis detection (#101)."""
+
+import os
+from unittest.mock import patch
+
+import pytest
+
+from crisis.ab_testing import ABTestCrisisDetector
+from crisis.detect import CrisisDetectionResult, detect_crisis
+
+
+@pytest.fixture(autouse=True)
+def clear_variant_override():
+    old = os.environ.pop("CRISIS_AB_VARIANT", None)
+    try:
+        yield
+    finally:
+        if old is not None:
+            os.environ["CRISIS_AB_VARIANT"] = old
+        else:
+            os.environ.pop("CRISIS_AB_VARIANT", None)
+
+
+def _make_variant(level: str, indicators=None):
+    indicators = indicators or [f"mock_{level.lower()}"]
+
+    def fn(text: str) -> CrisisDetectionResult:
+        return CrisisDetectionResult(level=level, indicators=list(indicators))
+
+    return fn
+
+
+def test_detect_returns_result_variant_and_logged_record():
+    detector = ABTestCrisisDetector(
+        variant_a=_make_variant("LOW"),
+        variant_b=_make_variant("HIGH"),
+    )
+
+    with patch.object(detector, "_select_variant", return_value="A"):
+        result, variant, record_id = detector.detect("test message")
+
+    assert isinstance(result, CrisisDetectionResult)
+    assert variant == "A"
+    assert record_id == 0
+    assert len(detector.records) == 1
+    assert detector.records[0].variant == "A"
+    assert detector.records[0].level == "LOW"
+
+
+def test_env_override_forces_variant_b():
+    os.environ["CRISIS_AB_VARIANT"] = "b"
+    detector = ABTestCrisisDetector(
+        variant_a=_make_variant("LOW"),
+        variant_b=_make_variant("HIGH"),
+    )
+
+    result, variant, _ = detector.detect("test")
+
+    assert variant == "B"
+    assert result.level == "HIGH"
+
+
+def test_get_stats_reports_latency_counts_and_level_breakdown():
+    detector = ABTestCrisisDetector(
+        variant_a=_make_variant("LOW"),
+        variant_b=_make_variant("CRITICAL"),
+    )
+
+    with patch.object(detector, "_select_variant", side_effect=["A", "A", "B"]):
+        detector.detect("first")
+        detector.detect("second")
+        detector.detect("third")
+
+    stats = detector.get_stats()
+    assert stats["A"]["count"] == 2
+    assert stats["B"]["count"] == 1
+    assert stats["A"]["levels"]["LOW"] == 2
+    assert stats["B"]["levels"]["CRITICAL"] == 1
+    assert "avg_latency_ms" in stats["A"]
+    assert "avg_indicator_count" in stats["B"]
+
+
+def test_false_positive_rate_is_computed_from_reviewed_outcomes():
+    detector = ABTestCrisisDetector(
+        variant_a=_make_variant("LOW"),
+        variant_b=_make_variant("HIGH"),
+    )
+
+    with patch.object(detector, "_select_variant", side_effect=["A", "A", "B"]):
+        _, _, a0 = detector.detect("first")
+        _, _, a1 = detector.detect("second")
+        _, _, b0 = detector.detect("third")
+
+    detector.record_outcome(a0, false_positive=True)
+    detector.record_outcome(a1, false_positive=False)
+    detector.record_outcome(b0, false_positive=False)
+
+    stats = detector.get_stats()
+    assert stats["A"]["reviewed_count"] == 2
+    assert stats["A"]["false_positive_rate"] == 0.5
+    assert stats["B"]["false_positive_rate"] == 0.0
+
+
+def test_record_outcome_rejects_unknown_record():
+    detector = ABTestCrisisDetector(
+        variant_a=_make_variant("LOW"),
+        variant_b=_make_variant("HIGH"),
+    )
+
+    with pytest.raises(IndexError):
+        detector.record_outcome(99, false_positive=True)
+
+
+def test_reset_clears_records_and_stats():
+    detector = ABTestCrisisDetector(
+        variant_a=_make_variant("LOW"),
+        variant_b=_make_variant("HIGH"),
+    )
+    detector.detect("test")
+    detector.reset()
+
+    assert detector.records == []
+    stats = detector.get_stats()
+    assert stats["A"]["count"] == 0
+    assert stats["B"]["count"] == 0
+
+
+def test_with_real_detector_integration():
+    detector = ABTestCrisisDetector(
+        variant_a=detect_crisis,
+        variant_b=detect_crisis,
+    )
+
+    result, variant, record_id = detector.detect("I want to kill myself")
+
+    assert result.level == "CRITICAL"
+    assert variant in ("A", "B")
+    assert record_id == 0

tests/test_behavioral_tracker.py

@@ -1,101 +0,0 @@
-"""
-Tests for behavioral crisis pattern detection (#133).
-"""
-
-import os
-import sys
-import unittest
-from datetime import datetime, timedelta, timezone
-
-sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
-
-from crisis.session_tracker import CrisisSessionTracker, check_crisis_with_session
-from crisis.behavioral import BehavioralTracker
-
-
-class TestBehavioralTracker(unittest.TestCase):
-    def _seed_day(self, tracker, *, session_id, day, count, start_hour=10, message_length=48, crisis_score=0.0):
-        base = datetime(2026, 4, day, start_hour, 0, tzinfo=timezone.utc)
-        for i in range(count):
-            tracker.record(
-                session_id,
-                base + timedelta(minutes=i * 10),
-                message_length=message_length,
-                crisis_score=crisis_score,
-            )
-
-    def test_frequency_change_uses_seven_day_baseline(self):
-        tracker = BehavioralTracker()
-        for day in range(1, 8):
-            self._seed_day(tracker, session_id=f"baseline-{day}", day=day, count=2)
-
-        burst_base = datetime(2026, 4, 8, 14, 0, tzinfo=timezone.utc)
-        for i in range(8):
-            tracker.record(
-                "current-session",
-                burst_base + timedelta(minutes=i),
-                message_length=72,
-                crisis_score=0.1,
-            )
-
-        summary = tracker.get_risk_signals("current-session")
-
-        self.assertGreater(summary["frequency_change"], 2.0)
-        self.assertTrue(any(sig["signal_type"] == "frequency" for sig in summary["signals"]))
-        self.assertGreater(summary["behavioral_score"], 0.0)
-
-    def test_late_night_messages_raise_flag(self):
-        tracker = BehavioralTracker()
-        base = datetime(2026, 4, 10, 2, 15, tzinfo=timezone.utc)
-        for i in range(3):
-            tracker.record(
-                "late-night",
-                base + timedelta(minutes=i * 7),
-                message_length=35,
-                crisis_score=0.0,
-            )
-
-        summary = tracker.get_risk_signals("late-night")
-
-        self.assertTrue(summary["is_late_night"])
-        self.assertTrue(any(sig["signal_type"] == "time" for sig in summary["signals"]))
-
-    def test_withdrawal_detected_after_large_drop_from_baseline(self):
-        tracker = BehavioralTracker()
-        for day in range(1, 8):
-            self._seed_day(tracker, session_id=f"baseline-{day}", day=day, count=10, message_length=80)
-
-        tracker.record(
-            "withdrawal-session",
-            datetime(2026, 4, 9, 11, 0, tzinfo=timezone.utc),
-            message_length=18,
-            crisis_score=0.0,
-        )
-
-        summary = tracker.get_risk_signals("withdrawal-session")
-
-        self.assertTrue(summary["withdrawal_detected"])
-        self.assertTrue(any(sig["signal_type"] == "withdrawal" for sig in summary["signals"]))
-
-
-class TestBehavioralSessionIntegration(unittest.TestCase):
-    def test_check_crisis_with_session_includes_behavioral_summary(self):
-        tracker = CrisisSessionTracker()
-        base = datetime(2026, 4, 20, 2, 0, tzinfo=timezone.utc)
-
-        check_crisis_with_session("can't sleep", tracker, timestamp=base)
-        check_crisis_with_session("still here", tracker, timestamp=base + timedelta(minutes=1))
-        result = check_crisis_with_session("everything feels loud", tracker, timestamp=base + timedelta(minutes=2))
-
-        behavioral = result["session"]["behavioral"]
-        self.assertIn("frequency_change", behavioral)
-        self.assertIn("is_late_night", behavioral)
-        self.assertIn("session_length_trend", behavioral)
-        self.assertIn("withdrawal_detected", behavioral)
-        self.assertIn("behavioral_score", behavioral)
-        self.assertTrue(behavioral["is_late_night"])
-        self.assertGreater(behavioral["behavioral_score"], 0.0)
-
-
-if __name__ == '__main__':
-    unittest.main()