Filter autopilot actuator candidates

addon/Dockerfile

@@ -2,7 +2,7 @@ FROM python:3.13-slim
 
 WORKDIR /app
 
-ARG SILLYHOME_FUTURE_REF=v2.0.0-alpha.11
+ARG SILLYHOME_FUTURE_REF=v2.0.0-alpha.15
 RUN python -m pip install --no-cache-dir \
     "http://192.168.6.31:3000/Otto/sillyhome-future/archive/${SILLYHOME_FUTURE_REF}.tar.gz"
 

addon/config.yaml

@@ -1,5 +1,5 @@
 name: SillyHome Future
-version: "2.0.0-alpha.11"
+version: "2.0.0-alpha.15"
 slug: sillyhome_future
 description: Event-first SillyHome v2 test controller
 url: http://192.168.6.31:3000/Otto/sillyhome-future

app/core/ha_client.py

@@ -72,6 +72,46 @@ class FutureHaClient:
             )
             response.raise_for_status()
 
+    def read_history(
+        self,
+        entity_ids: list[str],
+        start_time: datetime,
+        end_time: datetime,
+    ) -> dict[str, list[StateEvent]]:
+        params = {
+            "filter_entity_id": ",".join(entity_ids),
+            "end_time": end_time.isoformat(),
+            "minimal_response": "1",
+        }
+        with httpx.Client(timeout=self._config.timeout_seconds) as client:
+            response = client.get(
+                f"{self._core_url}/api/history/period/{start_time.isoformat()}",
+                headers=self._headers,
+                params=params,
+            )
+            response.raise_for_status()
+            payload = response.json()
+        result: dict[str, list[StateEvent]] = {entity_id: [] for entity_id in entity_ids}
+        for series in payload if isinstance(payload, list) else []:
+            if not isinstance(series, list):
+                continue
+            for item in series:
+                if not isinstance(item, dict):
+                    continue
+                entity_id = item.get("entity_id")
+                if not isinstance(entity_id, str):
+                    continue
+                result.setdefault(entity_id, []).append(
+                    StateEvent(
+                        entity_id=entity_id,
+                        new_state=item.get("state") if isinstance(item.get("state"), str) else None,
+                        changed_at=_parse_datetime(
+                            item.get("last_changed") or item.get("last_updated")
+                        ),
+                    )
+                )
+        return result
+
     async def listen_state_events(self) -> AsyncIterator[StateEvent]:
         websocket_url = self._config.websocket_url or _default_websocket_url(self._core_url)
         async with websockets.connect(websocket_url, ping_interval=None) as websocket:
@@ -160,4 +200,3 @@ def _parse_datetime(value: object) -> datetime:
     if parsed.tzinfo is None:
         return parsed.replace(tzinfo=timezone.utc)
     return parsed
-

app/core/models.py

@@ -34,6 +34,12 @@ class JobStatus(StrEnum):
     FAILED = "failed"
 
 
+class CandidateStatus(StrEnum):
+    PROPOSED = "proposed"
+    ACCEPTED = "accepted"
+    DISMISSED = "dismissed"
+
+
 class EntityState(BaseModel):
     entity_id: str = Field(pattern=r"^[a-z0-9_]+\.[a-z0-9_]+$")
     domain: str
@@ -123,6 +129,19 @@ class ModelRecord(BaseModel):
     trained_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
 
 
+class ModelEvaluation(BaseModel):
+    evaluation_id: str
+    model_id: str
+    actuator_entity_id: str
+    score: float = Field(default=0.0, ge=0.0, le=1.0)
+    coverage: float = Field(default=0.0, ge=0.0, le=1.0)
+    dry_run_success_rate: float = Field(default=0.0, ge=0.0, le=1.0)
+    feedback_score: float = Field(default=0.0, ge=0.0, le=1.0)
+    verdict: str
+    reasons: list[str] = Field(default_factory=list)
+    evaluated_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
+
+
 class JobQueueItem(BaseModel):
     job_id: str
     kind: str
@@ -139,11 +158,35 @@ class SensorWeightOverride(BaseModel):
     updated_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
 
 
+class AutopilotSettings(BaseModel):
+    enabled: bool = True
+    interval_seconds: int = Field(default=3600, ge=300)
+    min_candidate_confidence: float = Field(default=0.65, ge=0.0, le=1.0)
+    auto_train: bool = True
+    auto_evaluate: bool = True
+    auto_activate: bool = False
+    last_run_at: datetime | None = None
+
+
+class CandidateRecommendation(BaseModel):
+    candidate_id: str
+    actuator_entity_id: str
+    trigger_entity_id: str
+    trigger_state: str | None = None
+    target_state: str
+    confidence: float = Field(default=0.0, ge=0.0, le=1.0)
+    reason: str
+    status: CandidateStatus = CandidateStatus.PROPOSED
+    created_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
+
+
 class HistoryAnalysis(BaseModel):
     entity_id: str
     samples: int
     last_state: str | None = None
     changed_at: datetime | None = None
+    unique_states: int = 0
+    transitions: int = 0
     recommendation: str
 
 
@@ -181,13 +224,16 @@ class LearningState(BaseModel):
     scenes: dict[str, SceneProfile] = Field(default_factory=dict)
     automation_proposals: dict[str, AutomationProposal] = Field(default_factory=dict)
     models: dict[str, ModelRecord] = Field(default_factory=dict)
+    model_evaluations: dict[str, ModelEvaluation] = Field(default_factory=dict)
     jobs: dict[str, JobQueueItem] = Field(default_factory=dict)
     weight_overrides: dict[str, SensorWeightOverride] = Field(default_factory=dict)
+    candidates: dict[str, CandidateRecommendation] = Field(default_factory=dict)
 
 
 class ControlState(BaseModel):
     profiles: dict[str, ControlProfile] = Field(default_factory=dict)
     global_enabled: bool = True
+    autopilot: AutopilotSettings = Field(default_factory=AutopilotSettings)
 
 
 class BackupBundle(BaseModel):

app/main.py

@@ -16,9 +16,12 @@ from app.core.ha_client import FutureHaClient, HaClientConfig, service_for_state
 from app.core.handoff import HandoffMatrix
 from app.core.models import (
     AuditEvent,
+    AutopilotSettings,
     AutomationProposal,
     BackupBundle,
     BehaviorPatternV2,
+    CandidateRecommendation,
+    CandidateStatus,
     ControlProfile,
     ControlState,
     EntityState,
@@ -28,6 +31,7 @@ from app.core.models import (
     JobStatus,
     LearningProfile,
     LearningState,
+    ModelEvaluation,
     ModelRecord,
     ProposalStatus,
     RuntimeState,
@@ -104,38 +108,54 @@ class AutomationProposalRequest(BaseModel):
 
 class HistoryAnalysisRequest(BaseModel):
     entity_ids: list[str] = Field(default_factory=list)
+    start_time: datetime | None = None
+    end_time: datetime | None = None
 
 
 class TrainModelRequest(BaseModel):
     actuator_entity_id: str | None = None
 
 
+class EvaluateModelRequest(BaseModel):
+    model_id: str | None = None
+    actuator_entity_id: str | None = None
+
+
 class WeightOverrideRequest(BaseModel):
     sensor_weights: dict[str, float] = Field(default_factory=dict)
     note: str | None = Field(default=None, max_length=500)
 
 
+class AutopilotRunResult(BaseModel):
+    candidates: list[CandidateRecommendation]
+    trained_models: list[ModelRecord]
+    evaluations: list[ModelEvaluation]
+
+
 @asynccontextmanager
 async def lifespan(app: FastAPI) -> AsyncIterator[None]:
     global ha_client
     listener_task: asyncio.Task[None] | None = None
+    autopilot_task: asyncio.Task[None] | None = None
     ha_client = _ha_client_from_env()
     if ha_client is not None:
         _load_initial_ha_states(ha_client)
         listener_task = asyncio.create_task(_ha_listener_loop(ha_client))
+    autopilot_task = asyncio.create_task(_autopilot_loop())
     try:
         yield
     finally:
-        if listener_task is not None:
-            listener_task.cancel()
-            with suppress(asyncio.CancelledError):
-                await listener_task
+        for task in (listener_task, autopilot_task):
+            if task is not None:
+                task.cancel()
+                with suppress(asyncio.CancelledError):
+                    await task
 
 
 app = FastAPI(
     title="SillyHome Future API",
     description="SillyHome v2 event-core side project.",
-    version="2.0.0-alpha.11",
+    version="2.0.0-alpha.15",
     lifespan=lifespan,
 )
 
@@ -179,7 +199,7 @@ def dashboard_data() -> dict[str, object]:
     actuator_entities = [
         entity
         for entity in runtime.entities.values()
-        if entity.domain in {"light", "switch", "fan", "cover", "humidifier"}
+        if _is_good_actuator(entity.entity_id, entity.friendly_name)
     ]
     return {
         "websocket_status": runtime.websocket_status,
@@ -191,6 +211,9 @@ def dashboard_data() -> dict[str, object]:
         "automation_proposals": len(learning.automation_proposals),
         "models": len(learning.models),
         "jobs": len(learning.jobs),
+        "candidates": len(learning.candidates),
+        "autopilot_enabled": control.autopilot.enabled,
+        "autopilot_last_run_at": control.autopilot.last_run_at,
         "rooms": list(learning.rooms.values()),
         "scenes": list(learning.scenes.values()),
         "audit": latest_audit,
@@ -223,11 +246,87 @@ def feature_parity() -> dict[str, object]:
             "lokales Modelltraining",
             "Sensor-Gewichte",
             "Job-Queue",
+            "Autopilot light",
+            "Kandidaten-Vorschlaege",
+            "periodisches Training/Bewertung",
         ],
-        "next_to_expand": ["echte Langzeit-History aus HA", "fortgeschrittene Modellbewertung"],
+        "next_to_expand": ["Dashboard-Flaechen fuer Autopilot/History/Modelle"],
     }
 
 
+@app.get("/v2/autopilot/settings", response_model=AutopilotSettings)
+def get_autopilot_settings() -> AutopilotSettings:
+    return stores.control().autopilot
+
+
+@app.put("/v2/autopilot/settings", response_model=AutopilotSettings)
+def put_autopilot_settings(settings: AutopilotSettings) -> AutopilotSettings:
+    control = stores.control().model_copy(update={"autopilot": settings})
+    stores.save_control(control)
+    _append_audit(
+        "autopilot",
+        None,
+        "Autopilot light aktiviert." if settings.enabled else "Autopilot light deaktiviert.",
+    )
+    return settings
+
+
+@app.post("/v2/autopilot/run", response_model=AutopilotRunResult)
+def run_autopilot() -> AutopilotRunResult:
+    return _run_autopilot_once()
+
+
+@app.get("/v2/autopilot/candidates", response_model=list[CandidateRecommendation])
+def list_candidates() -> list[CandidateRecommendation]:
+    return list(stores.learning().candidates.values())
+
+
+@app.post("/v2/autopilot/candidates/{candidate_id}/accept", response_model=LearningProfile)
+def accept_candidate(candidate_id: str) -> LearningProfile:
+    state = stores.learning()
+    candidate = state.candidates[candidate_id].model_copy(
+        update={"status": CandidateStatus.ACCEPTED}
+    )
+    state.candidates[candidate_id] = candidate
+    profile = state.profiles.get(
+        candidate.actuator_entity_id,
+        LearningProfile(actuator_entity_id=candidate.actuator_entity_id),
+    )
+    profile = profile.model_copy(
+        update={
+            "patterns": [
+                *profile.patterns,
+                BehaviorPatternV2(
+                    actuator_entity_id=candidate.actuator_entity_id,
+                    target_state=candidate.target_state,
+                    trigger_entity_id=candidate.trigger_entity_id,
+                    trigger_state=candidate.trigger_state,
+                    support=3,
+                    confidence=candidate.confidence,
+                    source="autopilot",
+                ),
+            ],
+            "model_version": "autopilot-light",
+        }
+    )
+    state.profiles[candidate.actuator_entity_id] = profile
+    stores.save_learning(state)
+    _append_job("autopilot_candidate", f"Kandidat {candidate_id} akzeptiert.")
+    return profile
+
+
+@app.post("/v2/autopilot/candidates/{candidate_id}/dismiss", response_model=CandidateRecommendation)
+def dismiss_candidate(candidate_id: str) -> CandidateRecommendation:
+    state = stores.learning()
+    candidate = state.candidates[candidate_id].model_copy(
+        update={"status": CandidateStatus.DISMISSED}
+    )
+    state.candidates[candidate_id] = candidate
+    stores.save_learning(state)
+    _append_job("autopilot_candidate", f"Kandidat {candidate_id} verworfen.")
+    return candidate
+
+
 @app.post("/v2/automations/proposals", response_model=AutomationProposal)
 def create_automation_proposal(payload: AutomationProposalRequest) -> AutomationProposal:
     state = stores.learning()
@@ -287,16 +386,23 @@ def automation_proposal_yaml(proposal_id: str) -> str:
 def analyze_history(request: HistoryAnalysisRequest) -> list[HistoryAnalysis]:
     runtime = stores.runtime()
     ids = request.entity_ids or list(runtime.entities)[:50]
+    history: dict[str, list[StateEvent]] = {}
+    if ha_client is not None and request.start_time is not None and request.end_time is not None:
+        history = ha_client.read_history(ids, request.start_time, request.end_time)
     result: list[HistoryAnalysis] = []
     for entity_id in ids:
         entity = runtime.entities.get(entity_id)
+        samples = history.get(entity_id, [])
         matching_audit = [item for item in runtime.audit if item.entity_id == entity_id]
+        states = [sample.new_state for sample in samples if sample.new_state is not None]
         result.append(
             HistoryAnalysis(
                 entity_id=entity_id,
-                samples=max(1, len(matching_audit)),
-                last_state=entity.state if entity else None,
-                changed_at=entity.changed_at if entity else None,
+                samples=max(1, len(samples) or len(matching_audit)),
+                last_state=states[-1] if states else (entity.state if entity else None),
+                changed_at=samples[-1].changed_at if samples else (entity.changed_at if entity else None),
+                unique_states=len(set(states)) if states else int(entity is not None),
+                transitions=_count_transitions(states),
                 recommendation=(
                     "Als Trigger geeignet."
                     if entity is not None and entity.domain in {"binary_sensor", "sensor"}
@@ -341,6 +447,31 @@ def list_models() -> list[ModelRecord]:
     return list(stores.learning().models.values())
 
 
+@app.post("/v2/models/evaluate", response_model=list[ModelEvaluation])
+def evaluate_models(request: EvaluateModelRequest) -> list[ModelEvaluation]:
+    state = stores.learning()
+    control = stores.control()
+    models = list(state.models.values())
+    if request.model_id:
+        models = [model for model in models if model.model_id == request.model_id]
+    if request.actuator_entity_id:
+        models = [model for model in models if model.actuator_entity_id == request.actuator_entity_id]
+    evaluations = [
+        _evaluate_model(model, state, control)
+        for model in models
+    ]
+    for evaluation in evaluations:
+        state.model_evaluations[evaluation.evaluation_id] = evaluation
+    stores.save_learning(state)
+    _append_job("model_evaluation", f"{len(evaluations)} Modelle bewertet.")
+    return evaluations
+
+
+@app.get("/v2/models/evaluations", response_model=list[ModelEvaluation])
+def list_model_evaluations() -> list[ModelEvaluation]:
+    return list(stores.learning().model_evaluations.values())[-100:]
+
+
 @app.get("/v2/jobs", response_model=list[JobQueueItem])
 def list_jobs() -> list[JobQueueItem]:
     return list(stores.learning().jobs.values())[-100:]
@@ -805,6 +936,158 @@ async def _ha_listener_loop(client: FutureHaClient) -> None:
             await asyncio.sleep(2)
 
 
+async def _autopilot_loop() -> None:
+    while True:
+        control = stores.control()
+        settings = control.autopilot
+        if settings.enabled:
+            await asyncio.to_thread(_run_autopilot_once)
+        await asyncio.sleep(stores.control().autopilot.interval_seconds)
+
+
+def _run_autopilot_once() -> AutopilotRunResult:
+    control = stores.control()
+    settings = control.autopilot
+    if not settings.enabled:
+        return AutopilotRunResult(candidates=[], trained_models=[], evaluations=[])
+    learning = stores.learning()
+    learning.candidates = {
+        candidate_id: candidate
+        for candidate_id, candidate in learning.candidates.items()
+        if _is_good_actuator(candidate.actuator_entity_id, None)
+        and _is_good_trigger_id(candidate.trigger_entity_id)
+    }
+    candidates = _generate_candidates(settings)
+    for candidate in candidates:
+        learning.candidates[candidate.candidate_id] = candidate
+    stores.save_learning(learning)
+    updated_settings = settings.model_copy(update={"last_run_at": datetime.now(timezone.utc)})
+    stores.save_control(control.model_copy(update={"autopilot": updated_settings}))
+    trained = train_models(TrainModelRequest()) if settings.auto_train else []
+    evaluations = evaluate_models(EvaluateModelRequest()) if settings.auto_evaluate else []
+    _append_job("autopilot", f"Autopilot light: {len(candidates)} Kandidaten erzeugt.")
+    return AutopilotRunResult(candidates=candidates, trained_models=trained, evaluations=evaluations)
+
+
+def _generate_candidates(settings: AutopilotSettings) -> list[CandidateRecommendation]:
+    runtime = stores.runtime()
+    existing = stores.learning().candidates
+    actuators = [
+        entity
+        for entity in runtime.entities.values()
+        if _is_good_actuator(entity.entity_id, entity.friendly_name)
+    ][:150]
+    triggers = [
+        entity
+        for entity in runtime.entities.values()
+        if entity.domain == "binary_sensor" and _is_good_trigger_id(entity.entity_id)
+    ][:500]
+    result: list[CandidateRecommendation] = []
+    for actuator in actuators:
+        best_trigger: EntityState | None = None
+        best_score = 0.0
+        best_reason = ""
+        for trigger in triggers:
+            score, reason = _candidate_score(actuator, trigger)
+            if score > best_score:
+                best_score = score
+                best_reason = reason
+                best_trigger = trigger
+        if best_trigger is None or best_score < settings.min_candidate_confidence:
+            continue
+        candidate_id = f"{actuator.entity_id}:{best_trigger.entity_id}:on"
+        if candidate_id in existing:
+            continue
+        result.append(
+            CandidateRecommendation(
+                candidate_id=candidate_id,
+                actuator_entity_id=actuator.entity_id,
+                trigger_entity_id=best_trigger.entity_id,
+                trigger_state="on" if best_trigger.domain == "binary_sensor" else None,
+                target_state="open" if actuator.domain == "cover" else "on",
+                confidence=round(best_score, 2),
+                reason=best_reason,
+            )
+        )
+        if len(result) >= 20:
+            break
+    return result
+
+
+def _candidate_score(actuator: EntityState, trigger: EntityState) -> tuple[float, str]:
+    if actuator.area_name and actuator.area_name == trigger.area_name:
+        return 0.82, f"Gleicher Raum: {actuator.area_name}"
+    actuator_tokens = _entity_tokens(actuator)
+    trigger_tokens = _entity_tokens(trigger)
+    overlap = actuator_tokens & trigger_tokens
+    if overlap:
+        return min(0.78, 0.55 + (0.08 * len(overlap))), (
+            "Aehnliche Namen: " + ", ".join(sorted(overlap)[:4])
+        )
+    if trigger.domain == "binary_sensor" and actuator.domain in {"light", "switch"}:
+        return 0.62, "Binary-Sensor passt grundsaetzlich zu Licht/Schalter."
+    return 0.0, ""
+
+
+def _is_good_trigger_id(entity_id: str) -> bool:
+    raw = entity_id.lower()
+    bad = {
+        "battery",
+        "batterie",
+        "low",
+        "update",
+        "problem",
+        "connectivity",
+        "linkquality",
+        "tamper",
+    }
+    good = {
+        "door",
+        "tuer",
+        "ture",
+        "window",
+        "fenster",
+        "motion",
+        "pir",
+        "occupancy",
+        "presence",
+        "kontakt",
+        "contact",
+    }
+    return not any(token in raw for token in bad) and any(token in raw for token in good)
+
+
+def _is_good_actuator(entity_id: str, friendly_name: str | None) -> bool:
+    domain = entity_id.split(".", 1)[0]
+    if domain not in {"light", "switch", "fan", "cover", "humidifier"}:
+        return False
+    if domain != "switch":
+        return True
+    raw = f"{entity_id} {friendly_name or ''}".lower()
+    bad = {
+        "alarm",
+        "battery",
+        "batterie",
+        "detection",
+        "linkquality",
+        "low",
+        "motion",
+        "occupancy",
+        "people",
+        "presence",
+        "problem",
+        "tamper",
+        "trigger",
+        "update",
+    }
+    return not any(token in raw for token in bad)
+
+
+def _entity_tokens(entity: EntityState) -> set[str]:
+    raw = f"{entity.entity_id} {entity.friendly_name or ''}".lower()
+    return {part for part in raw.replace(".", "_").split("_") if len(part) >= 4}
+
+
 def _set_websocket_status(status: str) -> None:
     runtime = stores.runtime()
     if runtime.websocket_status == status:
@@ -884,6 +1167,64 @@ def _decide_automation_proposal(
     return updated
 
 
+def _count_transitions(states: list[str]) -> int:
+    if not states:
+        return 0
+    transitions = 0
+    previous = states[0]
+    for state in states[1:]:
+        transitions += int(state != previous)
+        previous = state
+    return transitions
+
+
+def _evaluate_model(
+    model: ModelRecord,
+    learning: LearningState,
+    control: ControlState,
+) -> ModelEvaluation:
+    profile = learning.profiles.get(
+        model.actuator_entity_id,
+        LearningProfile(actuator_entity_id=model.actuator_entity_id),
+    )
+    control_profile = control.profiles.get(
+        model.actuator_entity_id,
+        ControlProfile(actuator_entity_id=model.actuator_entity_id),
+    )
+    coverage = min(1.0, model.pattern_count / 5)
+    dry_run_events = max(1, control_profile.dry_run_events)
+    dry_run_success_rate = control_profile.dry_run_successes / dry_run_events
+    feedback_total = profile.feedback_positive + profile.feedback_negative
+    feedback_score = (
+        profile.feedback_positive / feedback_total if feedback_total else 0.5
+    )
+    score = round(
+        (model.confidence * 0.35)
+        + (coverage * 0.2)
+        + (dry_run_success_rate * 0.3)
+        + (feedback_score * 0.15),
+        4,
+    )
+    reasons = [
+        f"Modell-Confidence {model.confidence:.0%}",
+        f"Pattern-Abdeckung {coverage:.0%}",
+        f"Dry-run-Erfolg {dry_run_success_rate:.0%}",
+        f"Feedback-Score {feedback_score:.0%}",
+    ]
+    verdict = "bereit" if score >= 0.82 and control_profile.active_ready else "weiter testen"
+    return ModelEvaluation(
+        evaluation_id=f"eval-{model.model_id}-{len(learning.model_evaluations) + 1}",
+        model_id=model.model_id,
+        actuator_entity_id=model.actuator_entity_id,
+        score=score,
+        coverage=coverage,
+        dry_run_success_rate=dry_run_success_rate,
+        feedback_score=feedback_score,
+        verdict=verdict,
+        reasons=reasons,
+    )
+
+
 def _execute_ha_decision(decision: object) -> bool:
     if ha_client is None or not hasattr(decision, "actuator_entity_id"):
         return False
@@ -1069,6 +1410,8 @@ def _dashboard_html() -> str:
         ['Vorschlaege', data.automation_proposals],
         ['Modelle', data.models],
         ['Jobs', data.jobs],
+        ['Kandidaten', data.candidates],
+        ['Autopilot', data.autopilot_enabled ? 'aktiv' : 'aus'],
         ['Räume', data.rooms.length],
         ['Szenen', data.scenes.length],
       ];

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "sillyhome-future"
-version = "2.0.0-alpha.11"
+version = "2.0.0-alpha.15"
 description = "SillyHome v2 event-core prototype"
 requires-python = ">=3.11"
 dependencies = [

tests/test_addon_config.py

@@ -9,7 +9,7 @@ def test_addon_config_declares_future_addon() -> None:
     config = yaml.safe_load(Path("addon/config.yaml").read_text(encoding="utf-8"))
 
     assert config["slug"] == "sillyhome_future"
-    assert config["version"] == "2.0.0-alpha.11"
+    assert config["version"] == "2.0.0-alpha.15"
     assert config["ingress"] is True
     assert config["ingress_port"] == 8099
     assert config["homeassistant_api"] is True

tests/test_future_api.py

@@ -19,7 +19,7 @@ def test_health_and_backup_roundtrip(tmp_path, monkeypatch) -> None:  # type: ig
     restore = client.post("/v2/backup/restore", json=backup.json())
 
     assert health.status_code == 200
-    assert health.json()["version"] == "2.0.0-alpha.11"
+    assert health.json()["version"] == "2.0.0-alpha.15"
     assert backup.status_code == 200
     assert restore.status_code == 200
     assert restore.json() == {"status": "restored"}
@@ -39,6 +39,19 @@ def test_dashboard_control_and_learning_endpoints(tmp_path, monkeypatch) -> None
                     entity_id="binary_sensor.storage_door",
                     domain="binary_sensor",
                     state="off",
+                    area_name="Storage",
+                ),
+                "light.storage_door": EntityState(
+                    entity_id="light.storage_door",
+                    domain="light",
+                    state="off",
+                    area_name="Storage",
+                ),
+                "switch.storage_motion": EntityState(
+                    entity_id="switch.storage_motion",
+                    domain="switch",
+                    state="off",
+                    area_name="Storage",
                 ),
             }
         )
@@ -102,6 +115,13 @@ def test_dashboard_control_and_learning_endpoints(tmp_path, monkeypatch) -> None
         json={"entity_ids": ["binary_sensor.storage_door"]},
     )
     models = client.post("/v2/models/train", json={"actuator_entity_id": "light.storage"})
+    evaluation = client.post("/v2/models/evaluate", json={"actuator_entity_id": "light.storage"})
+    evaluations = client.get("/v2/models/evaluations")
+    autopilot = client.post("/v2/autopilot/run")
+    candidates = client.get("/v2/autopilot/candidates")
+    accepted = client.post(
+        f"/v2/autopilot/candidates/{autopilot.json()['candidates'][0]['candidate_id']}/accept"
+    )
     jobs = client.get("/v2/jobs")
     dashboard = client.get("/v2/dashboard")
 
@@ -109,6 +129,7 @@ def test_dashboard_control_and_learning_endpoints(tmp_path, monkeypatch) -> None
     assert [item["entity_id"] for item in entities.json()] == [
         "binary_sensor.storage_door",
         "light.storage",
+        "light.storage_door",
     ]
     assert control.status_code == 200
     assert control.json()["stage"] == "dry_run"
@@ -141,8 +162,20 @@ def test_dashboard_control_and_learning_endpoints(tmp_path, monkeypatch) -> None
     assert history.json()[0]["entity_id"] == "binary_sensor.storage_door"
     assert models.status_code == 200
     assert models.json()[0]["actuator_entity_id"] == "light.storage"
+    assert evaluation.status_code == 200
+    assert evaluation.json()[0]["verdict"] in {"bereit", "weiter testen"}
+    assert evaluations.status_code == 200
+    assert evaluations.json()
+    assert autopilot.status_code == 200
+    assert autopilot.json()["candidates"]
+    assert all(
+        candidate["actuator_entity_id"] != "switch.storage_motion"
+        for candidate in autopilot.json()["candidates"]
+    )
+    assert candidates.status_code == 200
+    assert accepted.status_code == 200
     assert jobs.status_code == 200
     assert jobs.json()
     assert dashboard.status_code == 200
-    assert dashboard.json()["actuator_count"] == 1
+    assert dashboard.json()["actuator_count"] == 2
     assert dashboard.json()["automation_proposals"] == 1