Merge pull request 'v0.6.2: HA-Automationen gleichwertig lernen' (#39) from feature/automation-equality-v0.6.2 into main

CHANGELOG.md

@@ -1,5 +1,27 @@
 # Changelog
 
+## 0.6.2 - 2026-06-14
+- Eindeutig im Home-Assistant-Logbuch erkannte Automationen und Scripts zählen für
+  Lernen und Freigabe gleichwertig wie manuelle Bedienungen
+- Automationsmuster erhalten dieselbe Modellgewichtung wie manuelle Handlungen
+- Oberfläche zeigt die gemeinsame Zahl als `eindeutig geregelt`; eine
+  ausdrückliche Aktivierung pro Aktor bleibt weiterhin erforderlich
+
+## 0.6.1 - 2026-06-14
+- Manuelle Prüfung als `Aktuelle Situation auswerten` eindeutig von Simulation
+  oder Aktorschaltung abgegrenzt
+- Sichtbare Rückmeldung mit Prüfzeitpunkt, vorhergesagtem Zustand und Sicherheit
+  oder klarem Hinweis auf einen fehlenden frischen Sensorwechsel
+
+## 0.6.0 - 2026-06-14
+- Kausales Shadow-Lernen erkennt frische Kontextwechsel unmittelbar vor einer
+  Aktorhandlung, etwa `Tür geschlossen → offen` vor `Licht aus → an`
+- Historische Home-Assistant-Automationen dürfen Vorhersagen begründen, zählen
+  aber weiterhin niemals als eindeutige Benutzerhandlung oder Ausführungsfreigabe
+- Aktuelle `last_changed`-Zeitpunkte verhindern Vorhersagen aus längst
+  unveränderten Sensorzuständen
+- Oberfläche trennt gelernte Benutzerhandlungen und erkannte HA-Automationen
+
 ## 0.5.4 - 2026-06-14
 - Tür-, Bewegungs- und andere belastbare Kontextsensoren werden auch ohne
   numerischen Sensor als vollständige automatische Kontextzuordnung angezeigt

README.md

@@ -109,8 +109,9 @@ Lernentscheidungen erfolgen automatisch.
    System das lokale Modell automatisch.
 5. Vorhersagen laufen zunächst ausschließlich im Shadow-Modus.
 6. Erst nach ausdrücklicher Freigabe pro Aktor werden hochkonfidente,
-   erlaubte Zustände geschaltet. Eigene Schaltungen und erkannte
+   erlaubte Zustände geschaltet. Eindeutig im HA-Logbuch erkannte Automationen
-   HA-Automationen werden nicht als Nutzerhandlungen zurückgelernt.
+   und Scripts zählen dabei gleichwertig wie manuelle Bedienungen. Eigene
+   Schaltungen von SillyHome werden nicht zurückgelernt.
 
 Vor einem Update sollte in Home Assistant unter **Einstellungen → System → Backups**
 eine Teil-Sicherung des Add-ons erstellt werden. Zur Wiederherstellung das gewünschte

addon/config.yaml

@@ -1,5 +1,5 @@
 name: SillyHome Next
-version: "0.5.4"
+version: "0.6.2"
 slug: sillyhome_next
 description: Lernt automatisch aus deinem Verhalten und steuert freigegebene Aktoren
 url: http://192.168.6.31:3000/pino/sillyhome-next

app/actuators/models.py

@@ -92,6 +92,9 @@ class BehaviorPattern(BaseModel):
     minute_of_day: int = Field(ge=0, le=1439)
     weekday: int = Field(ge=0, le=6)
     context_states: dict[str, str] = Field(default_factory=dict)
+    trigger_entity_id: str | None = None
+    trigger_from_state: str | None = None
+    trigger_to_state: str | None = None
     source: str = Field(default="observed", max_length=40)
     weight: float = Field(default=1.0, ge=0.1, le=1.0)
     observed_at: datetime

app/behavior/engine.py

@@ -22,6 +22,7 @@ from app.ha.reader import HaReader
 _MAX_PATTERNS = 500
 _MAX_EXECUTION_EVENTS = 100
 _ACTION_LOGBOOK_TOLERANCE = timedelta(seconds=10)
+_CONTEXT_TRIGGER_TOLERANCE = timedelta(seconds=3)
 _OWN_ACTION_TOLERANCE = timedelta(seconds=20)
 _SAFE_ACTIVE_DOMAINS = frozenset({"cover", "fan", "humidifier", "light", "switch"})
 _AUTOMATION_CONTEXT_DOMAINS = frozenset({"automation", "script"})
@@ -123,7 +124,9 @@ class BehaviorEngine:
             logbook=logbook,
             own_executions=record.behavior.execution_events,
         )
-        high_confidence = sum(1 for pattern in patterns if pattern.source == "user")
+        trusted_actions = sum(
+            1 for pattern in patterns if pattern.source in {"user", "automation"}
+        )
         status = (
             BehaviorStatus.TRAINED
             if len(patterns) >= self._settings.min_behavior_actions
@@ -141,7 +144,7 @@ class BehaviorEngine:
             update={
                 "status": status,
                 "sample_count": len(patterns),
-                "high_confidence_sample_count": high_confidence,
+                "high_confidence_sample_count": trusted_actions,
                 "patterns": patterns[-_MAX_PATTERNS:],
                 "last_trained_at": now,
                 "reason": reason,
@@ -198,12 +201,18 @@ class BehaviorEngine:
             )
             if entity_id and entity_id in entities and entities[entity_id].state is not None
         }
+        current_context_changed_at = {
+            entity_id: entities[entity_id].last_changed
+            for entity_id in current_context
+        }
         prediction = predict_behavior(
             record.behavior.patterns,
             current_context=current_context,
+            current_context_changed_at=current_context_changed_at,
             now=now,
             min_support=self._settings.min_behavior_actions,
             window_minutes=self._settings.prediction_window_minutes,
+            causal_window_seconds=self._settings.prediction_interval_seconds * 2,
             timezone_name=self._settings.timezone,
         )
         behavior = record.behavior.model_copy(
@@ -289,8 +298,8 @@ class BehaviorEngine:
                 < self._settings.min_behavior_actions
             ):
                 raise ValueError(
-                    "Für die Freigabe fehlen noch eindeutig dir zugeordnete Handlungen. "
+                    "Für die Freigabe fehlen noch eindeutig zugeordnete manuelle "
-                    "Bediene den Aktor einige Male über Home Assistant."
+                    "oder automatisierte Handlungen."
                 )
             mode = BehaviorMode.ACTIVE
             approved_at = now
@@ -326,8 +335,11 @@ class BehaviorEngine:
             if _matches_own_execution(point, own_executions):
                 continue
             source, weight = _action_source(point, logbook)
-            if source == "automation":
+            trigger = _recent_context_transition(
-                continue
+                context_history,
+                context_ids,
+                point.timestamp,
+            )
             contexts = {
                 entity_id: state
                 for entity_id in context_ids
@@ -340,6 +352,9 @@ class BehaviorEngine:
                     minute_of_day=local.hour * 60 + local.minute,
                     weekday=local.weekday(),
                     context_states=contexts,
+                    trigger_entity_id=trigger[0] if trigger else None,
+                    trigger_from_state=trigger[1] if trigger else None,
+                    trigger_to_state=trigger[2] if trigger else None,
                     source=source,
                     weight=weight,
                     observed_at=point.timestamp,
@@ -373,14 +388,52 @@ def predict_behavior(
     now: datetime,
     min_support: int,
     window_minutes: int,
+    current_context_changed_at: dict[str, datetime | None] | None = None,
+    causal_window_seconds: int = 120,
     timezone_name: str = "Europe/Berlin",
 ) -> BehaviorPrediction | None:
     if not patterns:
         return None
     local = now.astimezone(ZoneInfo(timezone_name))
     minute_of_day = local.hour * 60 + local.minute
+    changed_at = current_context_changed_at or {}
     by_state: dict[str, list[float]] = {}
+    causal_support_by_state: dict[str, int] = {}
     for pattern in patterns:
+        if pattern.trigger_entity_id and pattern.trigger_to_state:
+            trigger_changed_at = changed_at.get(pattern.trigger_entity_id)
+            trigger_age = (
+                (now - trigger_changed_at).total_seconds()
+                if trigger_changed_at is not None
+                else None
+            )
+            if not (
+                current_context.get(pattern.trigger_entity_id)
+                == pattern.trigger_to_state
+                and trigger_age is not None
+                and 0 <= trigger_age <= causal_window_seconds
+            ):
+                continue
+            comparable = [
+                (entity_id, expected)
+                for entity_id, expected in pattern.context_states.items()
+                if entity_id in current_context
+            ]
+            context_score = (
+                sum(
+                    current_context[entity_id] == expected
+                    for entity_id, expected in comparable
+                )
+                / len(comparable)
+                if comparable
+                else 0.5
+            )
+            score = pattern.weight * (0.85 + 0.15 * context_score)
+            by_state.setdefault(pattern.target_state, []).append(score)
+            causal_support_by_state[pattern.target_state] = (
+                causal_support_by_state.get(pattern.target_state, 0) + 1
+            )
+            continue
         distance = _circular_minute_distance(minute_of_day, pattern.minute_of_day)
         if distance > window_minutes:
             continue
@@ -414,6 +467,7 @@ def predict_behavior(
         key=lambda item: (sum(item[1]), len(item[1]), item[0]),
     )
     support = len(scores)
+    causal_support = causal_support_by_state.get(target_state, 0)
     confidence = min(1.0, (sum(scores) / support) * min(1.0, support / min_support))
     if confidence <= 0:
         return None
@@ -423,7 +477,12 @@ def predict_behavior(
         generated_at=now,
         matching_patterns=support,
         reason=(
-            f"{support} ähnliche Handlungsmuster passen zu Zeit und aktuellem Kontext."
+            (
+                f"{causal_support} historische Handlungen folgten demselben "
+                "frischen Sensorwechsel."
+            )
+            if causal_support
+            else f"{support} ähnliche Handlungsmuster passen zu Zeit und aktuellem Kontext."
         ),
     )
 
@@ -461,7 +520,7 @@ def _action_source(
     if nearest.context_user_id:
         return "user", 1.0
     if nearest.context_domain in _AUTOMATION_CONTEXT_DOMAINS:
-        return "automation", 0.1
+        return "automation", 1.0
     return "physical_or_unknown", 0.7
 
 
@@ -476,6 +535,32 @@ def _matches_own_execution(
     )
 
 
+def _recent_context_transition(
+    history: dict[str, StateHistorySeries],
+    context_ids: list[str],
+    timestamp: datetime,
+) -> tuple[str, str, str] | None:
+    nearest: tuple[timedelta, str, str, str] | None = None
+    for entity_id in context_ids:
+        series = history.get(entity_id)
+        if series is None:
+            continue
+        previous_state: str | None = None
+        for point in series.points:
+            if point.timestamp > timestamp:
+                break
+            if previous_state is not None and point.state != previous_state:
+                age = timestamp - point.timestamp
+                if age <= _CONTEXT_TRIGGER_TOLERANCE and (
+                    nearest is None or age < nearest[0]
+                ):
+                    nearest = (age, entity_id, previous_state, point.state)
+            previous_state = point.state
+    if nearest is None:
+        return None
+    return nearest[1], nearest[2], nearest[3]
+
+
 def _circular_minute_distance(left: int, right: int) -> int:
     direct = abs(left - right)
     return min(direct, 1440 - direct)

app/ha/models.py

@@ -1,5 +1,7 @@
 from __future__ import annotations
 
+from datetime import datetime
+
 from pydantic import BaseModel
 
 
@@ -15,6 +17,7 @@ class HaEntitySummary(BaseModel):
     entity_id: str
     domain: str
     state: str | None = None
+    last_changed: datetime | None = None
     state_class: str | None = None
     device_class: str | None = None
     unit_of_measurement: str | None = None

app/ha/reader.py

@@ -53,6 +53,7 @@ class HaReader:
                     entity_id=entity_id,
                     domain=domain,
                     state=_optional_str(item.get("state")),
+                    last_changed=_optional_datetime(item.get("last_changed")),
                     state_class=_optional_str(attributes.get("state_class")),
                     device_class=_optional_str(attributes.get("device_class")),
                     unit_of_measurement=_optional_str(attributes.get("unit_of_measurement")),
@@ -116,3 +117,13 @@ def _optional_str(value: object) -> str | None:
     if value is None or value == "":
         return None
     return str(value)
+
+
+def _optional_datetime(value: object) -> datetime | None:
+    if not isinstance(value, str) or not value:
+        return None
+    try:
+        parsed = datetime.fromisoformat(value.replace("Z", "+00:00"))
+    except ValueError:
+        return None
+    return parsed if parsed.tzinfo is not None else None

app/static/index.html

@@ -225,7 +225,7 @@ async function loadConfiguredActuators() {
   }
 }
 
-async function showActuator(actuatorId) {
+async function showActuator(actuatorId, evaluationMessage = "") {
   currentActuatorId = actuatorId;
   const box = document.getElementById("actuator-detail");
   try {
@@ -239,17 +239,20 @@ async function showActuator(actuatorId) {
       .map(candidate => `<li><strong>${escapeHtml(candidate.friendly_name || candidate.entity_id)}</strong>: ${candidate.evidence.map(escapeHtml).join(", ") || "statistisch relevanter Kandidat"}</li>`)
       .join("");
     const prediction = record.behavior.prediction;
-    const requiredUserActions = 3;
+    const requiredTrustedActions = 3;
-    const missingUserActions = Math.max(
+    const learnedAutomationActions = record.behavior.patterns.filter(
+      pattern => pattern.source === "automation",
+    ).length;
+    const missingTrustedActions = Math.max(
       0,
-      requiredUserActions - record.behavior.high_confidence_sample_count,
+      requiredTrustedActions - record.behavior.high_confidence_sample_count,
     );
     const activationButton = record.behavior.mode === "active"
       ? `<button class="danger" onclick="setActivation('${escapeHtml(record.actuator_entity_id)}', false)">Autonomes Schalten stoppen</button>`
-      : record.behavior.status === "trained" && missingUserActions === 0
+      : record.behavior.status === "trained" && missingTrustedActions === 0
         ? `<button onclick="setActivation('${escapeHtml(record.actuator_entity_id)}', true)">Lernen und Schalten freigeben</button>`
         : record.behavior.status === "trained"
-          ? `<p class='muted'>Freigabe noch gesperrt: ${missingUserActions} eindeutig manuelle Bedienung${missingUserActions === 1 ? "" : "en"} fehlen. Bediene das Licht dafür direkt über Home Assistant.</p>`
+          ? `<p class='muted'>Freigabe noch gesperrt: ${missingTrustedActions} eindeutig zugeordnete manuelle oder automatisierte Handlung${missingTrustedActions === 1 ? "" : "en"} fehlen.</p>`
           : "<p class='muted'>Freigabe wird möglich, sobald genügend Handlungen gelernt wurden.</p>";
     box.innerHTML = `
       <div class="grid-two">
@@ -265,11 +268,14 @@ async function showActuator(actuatorId) {
           <h3>Lernfortschritt</h3>
           <p><strong>Betriebsart:</strong> ${escapeHtml(behaviorLabel(record))}</p>
           <p><strong>Gelernte Handlungen:</strong> ${record.behavior.sample_count}</p>
-          <p><strong>Davon eindeutig Benutzer:</strong> ${record.behavior.high_confidence_sample_count}</p>
+          <p><strong>Davon eindeutig geregelt:</strong> ${record.behavior.high_confidence_sample_count}</p>
+          <p><strong>Davon erkannte HA-Automationen:</strong> ${learnedAutomationActions}</p>
           <p><strong>Letztes Training:</strong> ${escapeHtml(record.behavior.last_trained_at || "noch nicht")}</p>
           <p><strong>Was noch passiert:</strong> ${escapeHtml(record.behavior.reason)}</p>
           ${activationButton}
-          <button class="secondary" onclick="evaluateActuator('${escapeHtml(record.actuator_entity_id)}')">Vorhersage jetzt prüfen</button>
+          <button class="secondary" onclick="evaluateActuator('${escapeHtml(record.actuator_entity_id)}')">Aktuelle Situation auswerten</button>
+          <p class="muted">Die Prüfung simuliert keinen Sensorwechsel und schaltet keinen Aktor.</p>
+          ${evaluationMessage ? `<p class="ok">${escapeHtml(evaluationMessage)}</p>` : ""}
         </div>
       </div>
       <h3>Was SillyHome aktuell vorhersagt</h3>
@@ -286,9 +292,18 @@ async function showActuator(actuatorId) {
 
 async function evaluateActuator(actuatorId) {
   try {
-    await api(`v1/actuators/${encodeURIComponent(actuatorId)}/evaluate`, {method: "POST"});
+    const record = await api(
+      `v1/actuators/${encodeURIComponent(actuatorId)}/evaluate`,
+      {method: "POST"},
+    );
+    const checkedAt = new Date(
+      record.behavior.last_evaluated_at || Date.now(),
+    ).toLocaleString("de-DE");
+    const message = record.behavior.prediction
+      ? `Prüfung ${checkedAt}: ${record.behavior.prediction.target_state} mit ${Math.round(record.behavior.prediction.confidence * 100)} % vorhergesagt.`
+      : `Prüfung ${checkedAt}: Kein frischer passender Sensorwechsel erkannt; aktuell ist keine Aktion fällig.`;
     await loadConfiguredActuators();
-    await showActuator(actuatorId);
+    await showActuator(actuatorId, message);
   } catch (error) {
     alert(error.message);
   }

docs/ml_training.md

@@ -12,10 +12,10 @@ Für jeden Aktor lädt SillyHome Next:
 - automatisch zugeordnete Mess- und Kontext-Entities
 - deren Zustand zum Zeitpunkt der Handlung
 
-Eindeutig einem Home-Assistant-Benutzer zugeordnete Handlungen erhalten das
+Eindeutig einem Home-Assistant-Benutzer zugeordnete Handlungen und im Logbuch
-höchste Gewicht. Erkannte Automations- und Script-Aktionen werden verworfen.
+erkannte Automations- oder Script-Aktionen erhalten das höchste Gewicht.
-Physische oder nicht eindeutig zuordenbare Bedienungen dürfen das
+Physische oder nicht eindeutig zuordenbare Bedienungen dürfen das Shadow-Modell
-Shadow-Modell ergänzen, reichen allein aber nicht zur Aktivierung.
+ergänzen, reichen allein aber nicht zur Aktivierung.
 
 ## Modell
 
@@ -36,8 +36,8 @@ Kontext. Mehrere passende historische Handlungen erhöhen die Confidence.
 2. `shadow`: Modell ist trainiert; Vorhersagen werden angezeigt, aber nicht ausgeführt.
 3. `active`: Nutzer hat den Aktor ausdrücklich freigegeben.
 
-Die Aktivierung verlangt genügend eindeutig einem Benutzer zugeordnete
+Die Aktivierung verlangt genügend eindeutig zugeordnete manuelle oder
-Handlungen. Ausgeführt werden nur erlaubte Zustände reversibler Domains:
+automatisierte Handlungen. Ausgeführt werden nur erlaubte Zustände reversibler Domains:
 `light`, `switch`, `fan`, `humidifier` und `cover`.
 
 ## Schutzmechanismen
@@ -48,4 +48,4 @@ Handlungen. Ausgeführt werden nur erlaubte Zustände reversibler Domains:
 - keine Ausführung bei bereits erreichtem Zielzustand
 - keine Ausführung unbekannter Zustände oder riskanter Domains
 - eigene Schaltungen werden beim nächsten Training herausgefiltert
-- bekannte Automation-/Script-Aktionen werden nicht als Nutzerverhalten gelernt
+- Automation-/Script-Aktionen zählen nur bei eindeutiger Herkunft im HA-Logbuch

tests/api/test_entities.py

@@ -76,6 +76,7 @@ def test_entities_returns_reader_data() -> None:
                 "entity_id": "sensor.temperature",
                 "domain": "sensor",
                 "state": None,
+                "last_changed": None,
                 "state_class": None,
             "device_class": None,
             "unit_of_measurement": None,

tests/behavior/test_engine.py

@@ -5,7 +5,7 @@ from pathlib import Path
 
 import pytest
 
-from app.actuators.models import BehaviorMode, BehaviorStatus
+from app.actuators.models import BehaviorMode, BehaviorPattern, BehaviorStatus
 from app.actuators.store import ActuatorStore
 from app.behavior.engine import BehaviorEngine, predict_behavior, service_for_state
 from app.config import Settings
@@ -161,8 +161,8 @@ def test_engine_trains_predicts_in_shadow_and_executes_only_after_approval(
     shadow = engine.evaluate("light.office")
 
     assert trained.behavior.status is BehaviorStatus.TRAINED
-    assert trained.behavior.sample_count == 3
+    assert trained.behavior.sample_count == 6
-    assert trained.behavior.high_confidence_sample_count == 3
+    assert trained.behavior.high_confidence_sample_count == 6
     assert shadow.behavior.mode is BehaviorMode.SHADOW
     assert shadow.behavior.prediction is not None
     assert shadow.behavior.prediction.target_state == "on"
@@ -179,14 +179,121 @@ def test_engine_trains_predicts_in_shadow_and_executes_only_after_approval(
     ]
 
 
-def test_engine_excludes_known_automation_actions(tmp_path: Path) -> None:
+def test_engine_counts_known_automation_actions_like_manual_actions(
+    tmp_path: Path,
+) -> None:
     now = datetime.now(timezone.utc).replace(second=0, microsecond=0)
     engine, _ = _engine(tmp_path, now)
 
     trained = engine.train("light.office")
 
-    assert {pattern.target_state for pattern in trained.behavior.patterns} == {"on"}
+    assert {pattern.target_state for pattern in trained.behavior.patterns} == {
-    assert {pattern.source for pattern in trained.behavior.patterns} == {"user"}
+        "on",
+        "off",
+    }
+    assert {pattern.source for pattern in trained.behavior.patterns} == {
+        "user",
+        "automation",
+    }
+    assert trained.behavior.high_confidence_sample_count == 6
+    assert {pattern.weight for pattern in trained.behavior.patterns} == {1.0}
+
+
+def test_engine_learns_causal_automation_with_activation_credit(
+    tmp_path: Path,
+) -> None:
+    now = datetime.now(timezone.utc).replace(second=0, microsecond=0)
+    actuator_points: list[StateHistoryPoint] = []
+    door_points: list[StateHistoryPoint] = []
+    logbook: list[LogbookEntry] = []
+    for days_ago in (3, 2, 1):
+        action_at = now - timedelta(days=days_ago)
+        actuator_points.extend(
+            [
+                StateHistoryPoint(
+                    timestamp=action_at - timedelta(minutes=1),
+                    state="off",
+                ),
+                StateHistoryPoint(timestamp=action_at, state="on"),
+            ]
+        )
+        door_points.extend(
+            [
+                StateHistoryPoint(
+                    timestamp=action_at - timedelta(minutes=1),
+                    state="off",
+                ),
+                StateHistoryPoint(
+                    timestamp=action_at - timedelta(seconds=1),
+                    state="on",
+                ),
+            ]
+        )
+        logbook.append(
+            LogbookEntry(
+                entity_id="light.storage",
+                timestamp=action_at,
+                message="turned on",
+                context_domain="automation",
+                context_service="trigger",
+            )
+        )
+    actuator_points.sort(key=lambda point: point.timestamp)
+    door_points.sort(key=lambda point: point.timestamp)
+    settings = _settings(tmp_path)
+    store = ActuatorStore(settings.actuator_store)
+    record = store.configure("light.storage")
+    store.upsert(
+        record.model_copy(
+            update={
+                "assignment": record.assignment.model_copy(
+                    update={
+                        "selected_context_entity_ids": [
+                            "binary_sensor.storage_door"
+                        ],
+                    }
+                )
+            }
+        )
+    )
+    reader = FakeBehaviorReader(
+        entities=[],
+        history=[
+            StateHistorySeries(
+                entity_id="light.storage",
+                points=actuator_points,
+            ),
+            StateHistorySeries(
+                entity_id="binary_sensor.storage_door",
+                points=door_points,
+            ),
+        ],
+        logbook=logbook,
+    )
+    engine = BehaviorEngine(ha_reader=reader, store=store, settings=settings)
+
+    trained = engine.train("light.storage")
+    automation_patterns = [
+        pattern
+        for pattern in trained.behavior.patterns
+        if pattern.source == "automation"
+    ]
+
+    assert len(automation_patterns) == 3
+    assert trained.behavior.high_confidence_sample_count == 3
+    assert {pattern.weight for pattern in automation_patterns} == {1.0}
+    assert {
+        (
+            pattern.trigger_entity_id,
+            pattern.trigger_from_state,
+            pattern.trigger_to_state,
+        )
+        for pattern in automation_patterns
+    } == {("binary_sensor.storage_door", "off", "on")}
+
+    active = engine.set_active("light.storage", active=True)
+
+    assert active.behavior.mode is BehaviorMode.ACTIVE
 
 
 def test_active_mode_rejects_unsafe_domains(tmp_path: Path) -> None:
@@ -209,7 +316,7 @@ def test_active_mode_rejects_unsafe_domains(tmp_path: Path) -> None:
         engine.set_active("lock.front_door", active=True)
 
 
-def test_active_mode_requires_user_attributed_actions(tmp_path: Path) -> None:
+def test_active_mode_requires_trusted_manual_or_automation_actions(tmp_path: Path) -> None:
     settings = _settings(tmp_path)
     store = ActuatorStore(settings.actuator_store)
     record = store.configure("light.office")
@@ -229,7 +336,7 @@ def test_active_mode_requires_user_attributed_actions(tmp_path: Path) -> None:
     reader = FakeBehaviorReader(entities=[], history=[], logbook=[])
     engine = BehaviorEngine(ha_reader=reader, store=store, settings=settings)
 
-    with pytest.raises(ValueError, match="eindeutig dir zugeordnete"):
+    with pytest.raises(ValueError, match="manuelle oder automatisierte"):
         engine.set_active("light.office", active=True)
 
 
@@ -259,3 +366,66 @@ def test_prediction_requires_temporal_support() -> None:
         min_support=3,
         window_minutes=30,
     ) is None
+
+
+def test_prediction_uses_fresh_causal_context_transition_outside_time_window() -> None:
+    now = datetime.now(timezone.utc).replace(second=0, microsecond=0)
+    patterns = [
+        BehaviorPattern(
+            target_state="on",
+            minute_of_day=60,
+            weekday=0,
+            context_states={"binary_sensor.storage_door": "on"},
+            trigger_entity_id="binary_sensor.storage_door",
+            trigger_from_state="off",
+            trigger_to_state="on",
+            source="automation",
+            weight=0.7,
+            observed_at=now - timedelta(days=days_ago),
+        )
+        for days_ago in (3, 2, 1)
+    ]
+
+    prediction = predict_behavior(
+        patterns,
+        current_context={"binary_sensor.storage_door": "on"},
+        current_context_changed_at={
+            "binary_sensor.storage_door": now - timedelta(seconds=10)
+        },
+        now=now,
+        min_support=3,
+        window_minutes=30,
+    )
+
+    assert prediction is not None
+    assert prediction.target_state == "on"
+    assert prediction.matching_patterns == 3
+    assert prediction.confidence == 0.7
+    assert "frischen Sensorwechsel" in prediction.reason
+
+
+def test_prediction_ignores_stale_causal_context_state() -> None:
+    now = datetime.now(timezone.utc).replace(second=0, microsecond=0)
+    pattern = BehaviorPattern(
+        target_state="on",
+        minute_of_day=60,
+        weekday=0,
+        context_states={"binary_sensor.storage_door": "on"},
+        trigger_entity_id="binary_sensor.storage_door",
+        trigger_from_state="off",
+        trigger_to_state="on",
+        source="automation",
+        weight=0.7,
+        observed_at=now - timedelta(days=1),
+    )
+
+    assert predict_behavior(
+        [pattern],
+        current_context={"binary_sensor.storage_door": "on"},
+        current_context_changed_at={
+            "binary_sensor.storage_door": now - timedelta(minutes=5)
+        },
+        now=now,
+        min_support=1,
+        window_minutes=30,
+    ) is None

tests/ha/test_ha_reader.py

@@ -15,6 +15,7 @@ class FakeHaClient(HaClient):
             {
                 "entity_id": "sensor.temperature",
                 "state": "21.5",
+                "last_changed": "2026-06-14T12:00:00+00:00",
                 "attributes": {
                     "state_class": "measurement",
                     "device_class": "temperature",
@@ -87,6 +88,7 @@ def test_ha_reader_returns_summaries() -> None:
     sensor = next(item for item in summaries if item.entity_id == "sensor.temperature")
     assert sensor.unit_of_measurement == "°C"
     assert sensor.state == "21.5"
+    assert sensor.last_changed == datetime(2026, 6, 14, 12, 0, tzinfo=timezone.utc)
     assert sensor.area_name == "Kueche"
     assert sensor.device_name == "Thermometer"
 

tests/test_dashboard.py

@@ -15,7 +15,15 @@ def test_dashboard_is_served_at_root() -> None:
     assert "Ohne deine spätere Freigabe wird nichts geschaltet" in response.text
     assert "Du wählst keine Sensoren und erstellst keine Regeln" in response.text
     assert "Freigabe noch gesperrt" in response.text
-    assert "Bediene das Licht dafür direkt über Home Assistant" in response.text
+    assert "manuelle oder automatisierte Handlung" in response.text
-    assert 'record.behavior.status === "trained" && missingUserActions === 0' in response.text
+    assert "Davon erkannte HA-Automationen" in response.text
+    assert "Aktuelle Situation auswerten" in response.text
+    assert "Die Prüfung simuliert keinen Sensorwechsel" in response.text
+    assert "Kein frischer passender Sensorwechsel erkannt" in response.text
+    assert "Vorhersage jetzt prüfen" not in response.text
+    assert (
+        'record.behavior.status === "trained" && missingTrustedActions === 0'
+        in response.text
+    )
     assert "Automation-Entwurf" not in response.text
     assert "Manuelle Overrides" not in response.text