Add HA history and model evaluation

addon/Dockerfile

@@ -2,7 +2,7 @@ FROM python:3.13-slim
 
 WORKDIR /app
 
-ARG SILLYHOME_FUTURE_REF=v2.0.0-alpha.10
+ARG SILLYHOME_FUTURE_REF=v2.0.0-alpha.12
 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.10"
+version: "2.0.0-alpha.12"
 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

@@ -21,6 +21,19 @@ class SafetyStage(StrEnum):
     BLOCKED = "blocked"
 
 
+class ProposalStatus(StrEnum):
+    DRAFT = "draft"
+    APPROVED = "approved"
+    REJECTED = "rejected"
+
+
+class JobStatus(StrEnum):
+    QUEUED = "queued"
+    RUNNING = "running"
+    SUCCEEDED = "succeeded"
+    FAILED = "failed"
+
+
 class EntityState(BaseModel):
     entity_id: str = Field(pattern=r"^[a-z0-9_]+\.[a-z0-9_]+$")
     domain: str
@@ -89,6 +102,66 @@ class SceneProfile(BaseModel):
     confidence: float = Field(default=0.0, ge=0.0, le=1.0)
 
 
+class AutomationProposal(BaseModel):
+    proposal_id: str
+    name: str
+    trigger_entity_id: str
+    trigger_state: str | None = None
+    actuator_entity_id: str
+    target_state: str
+    status: ProposalStatus = ProposalStatus.DRAFT
+    revision: int = 1
+    created_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
+    decided_at: datetime | None = None
+
+
+class ModelRecord(BaseModel):
+    model_id: str
+    actuator_entity_id: str
+    pattern_count: int
+    confidence: float = Field(default=0.0, ge=0.0, le=1.0)
+    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
+    status: JobStatus = JobStatus.QUEUED
+    message: str = ""
+    created_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
+    finished_at: datetime | None = None
+
+
+class SensorWeightOverride(BaseModel):
+    actuator_entity_id: str
+    sensor_weights: dict[str, float] = Field(default_factory=dict)
+    note: str | None = Field(default=None, max_length=500)
+    updated_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
+
+
 class Decision(BaseModel):
     actuator_entity_id: str
     target_state: str | None = None
@@ -121,6 +194,11 @@ class LearningState(BaseModel):
     profiles: dict[str, LearningProfile] = Field(default_factory=dict)
     rooms: dict[str, RoomProfile] = Field(default_factory=dict)
     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)
 
 
 class ControlState(BaseModel):

app/main.py

@@ -4,6 +4,7 @@ import asyncio
 import os
 from collections.abc import AsyncIterator
 from contextlib import asynccontextmanager, suppress
+from datetime import datetime, timezone
 
 from fastapi import FastAPI
 from fastapi.responses import HTMLResponse
@@ -15,16 +16,24 @@ from app.core.ha_client import FutureHaClient, HaClientConfig, service_for_state
 from app.core.handoff import HandoffMatrix
 from app.core.models import (
     AuditEvent,
+    AutomationProposal,
     BackupBundle,
     BehaviorPatternV2,
     ControlProfile,
     ControlState,
     EntityState,
+    HistoryAnalysis,
     HandoffMode,
+    JobQueueItem,
+    JobStatus,
     LearningProfile,
     LearningState,
+    ModelEvaluation,
+    ModelRecord,
+    ProposalStatus,
     RuntimeState,
     SafetyStage,
+    SensorWeightOverride,
     StateEvent,
 )
 from app.core.stores import FutureStores
@@ -86,6 +95,34 @@ class ActuatorSummary(BaseModel):
     feedback_negative: int
 
 
+class AutomationProposalRequest(BaseModel):
+    name: str = Field(max_length=120)
+    trigger_entity_id: str
+    trigger_state: str | None = None
+    actuator_entity_id: str
+    target_state: str
+
+
+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)
+
+
 @asynccontextmanager
 async def lifespan(app: FastAPI) -> AsyncIterator[None]:
     global ha_client
@@ -106,7 +143,7 @@ async def lifespan(app: FastAPI) -> AsyncIterator[None]:
 app = FastAPI(
     title="SillyHome Future API",
     description="SillyHome v2 event-core side project.",
-    version="2.0.0-alpha.10",
+    version="2.0.0-alpha.12",
     lifespan=lifespan,
 )
 
@@ -159,6 +196,9 @@ def dashboard_data() -> dict[str, object]:
         "global_enabled": control.global_enabled,
         "learning_profiles": len(learning.profiles),
         "control_profiles": len(control.profiles),
+        "automation_proposals": len(learning.automation_proposals),
+        "models": len(learning.models),
+        "jobs": len(learning.jobs),
         "rooms": list(learning.rooms.values()),
         "scenes": list(learning.scenes.values()),
         "audit": latest_audit,
@@ -185,17 +225,167 @@ def feature_parity() -> dict[str, object]:
             "Simulation",
             "Audit",
             "Health",
+            "Automation-Proposals",
+            "YAML-Export",
+            "History-Analyse",
+            "lokales Modelltraining",
+            "Sensor-Gewichte",
+            "Job-Queue",
         ],
-        "next_to_expand": [
-            "automations-proposals",
-            "history-analysis",
-            "model-training",
-            "weight-overrides",
-            "job-queue",
-        ],
+        "next_to_expand": ["Dashboard-Flaechen fuer History/Modelle"],
     }
 
 
+@app.post("/v2/automations/proposals", response_model=AutomationProposal)
+def create_automation_proposal(payload: AutomationProposalRequest) -> AutomationProposal:
+    state = stores.learning()
+    proposal_id = f"proposal-{len(state.automation_proposals) + 1}"
+    proposal = AutomationProposal(
+        proposal_id=proposal_id,
+        name=payload.name,
+        trigger_entity_id=payload.trigger_entity_id,
+        trigger_state=payload.trigger_state,
+        actuator_entity_id=payload.actuator_entity_id,
+        target_state=payload.target_state,
+    )
+    state.automation_proposals[proposal_id] = proposal
+    stores.save_learning(state)
+    _append_job("automation_proposal", f"Automation-Vorschlag {proposal_id} angelegt.")
+    return proposal
+
+
+@app.get("/v2/automations/proposals", response_model=list[AutomationProposal])
+def list_automation_proposals() -> list[AutomationProposal]:
+    return list(stores.learning().automation_proposals.values())
+
+
+@app.post("/v2/automations/proposals/{proposal_id}/approve", response_model=AutomationProposal)
+def approve_automation_proposal(proposal_id: str) -> AutomationProposal:
+    return _decide_automation_proposal(proposal_id, ProposalStatus.APPROVED)
+
+
+@app.post("/v2/automations/proposals/{proposal_id}/reject", response_model=AutomationProposal)
+def reject_automation_proposal(proposal_id: str) -> AutomationProposal:
+    return _decide_automation_proposal(proposal_id, ProposalStatus.REJECTED)
+
+
+@app.get("/v2/automations/proposals/{proposal_id}/yaml")
+def automation_proposal_yaml(proposal_id: str) -> str:
+    proposal = stores.learning().automation_proposals[proposal_id]
+    return "\n".join(
+        [
+            f"alias: {proposal.name}",
+            "trigger:",
+            "  - platform: state",
+            f"    entity_id: {proposal.trigger_entity_id}",
+            f"    to: {proposal.trigger_state or ''}",
+            "action:",
+            "  - service: homeassistant.turn_on",
+            "    target:",
+            f"      entity_id: {proposal.actuator_entity_id}",
+            "    data:",
+            f"      target_state: {proposal.target_state}",
+            "mode: single",
+            "",
+        ]
+    )
+
+
+@app.post("/v2/history/analyze", response_model=list[HistoryAnalysis])
+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(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"}
+                    else "Als Aktor oder Kontext pruefen."
+                ),
+            )
+        )
+    _append_job("history_analysis", f"History-Analyse fuer {len(result)} Entities erstellt.")
+    return result
+
+
+@app.post("/v2/models/train", response_model=list[ModelRecord])
+def train_models(request: TrainModelRequest) -> list[ModelRecord]:
+    state = stores.learning()
+    actuator_ids = [request.actuator_entity_id] if request.actuator_entity_id else list(state.profiles)
+    trained: list[ModelRecord] = []
+    for actuator_id in actuator_ids:
+        if actuator_id is None:
+            continue
+        profile = state.profiles.get(actuator_id)
+        pattern_count = len(profile.patterns) if profile else 0
+        confidence = (
+            sum(pattern.confidence for pattern in profile.patterns) / pattern_count
+            if profile and pattern_count
+            else 0.0
+        )
+        model = ModelRecord(
+            model_id=f"model-{actuator_id}-{len(state.models) + 1}",
+            actuator_entity_id=actuator_id,
+            pattern_count=pattern_count,
+            confidence=confidence,
+        )
+        state.models[model.model_id] = model
+        trained.append(model)
+    stores.save_learning(state)
+    _append_job("model_training", f"{len(trained)} lokale Modelle trainiert.")
+    return trained
+
+
+@app.get("/v2/models", response_model=list[ModelRecord])
+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:]
+
+
 @app.post("/v2/events/state", response_model=list[AuditEvent])
 def ingest_state_event(event: StateEvent) -> list[AuditEvent]:
     return event_core.process_state_event(event, execute=_execute_ha_decision)
@@ -421,6 +611,31 @@ def record_feedback(actuator_entity_id: str, feedback: FeedbackRequest) -> Learn
     return updated
 
 
+@app.post("/v2/control/{actuator_entity_id}/weights", response_model=SensorWeightOverride)
+def set_weight_override(
+    actuator_entity_id: str,
+    payload: WeightOverrideRequest,
+) -> SensorWeightOverride:
+    state = stores.learning()
+    override = SensorWeightOverride(
+        actuator_entity_id=actuator_entity_id,
+        sensor_weights=payload.sensor_weights,
+        note=payload.note,
+    )
+    state.weight_overrides[actuator_entity_id] = override
+    stores.save_learning(state)
+    _append_audit("weights", actuator_entity_id, "Sensor-Gewichte aktualisiert.")
+    return override
+
+
+@app.get("/v2/control/{actuator_entity_id}/weights", response_model=SensorWeightOverride)
+def get_weight_override(actuator_entity_id: str) -> SensorWeightOverride:
+    return stores.learning().weight_overrides.get(
+        actuator_entity_id,
+        SensorWeightOverride(actuator_entity_id=actuator_entity_id),
+    )
+
+
 @app.post("/v2/planning/refresh", response_model=LearningState)
 def refresh_planning() -> LearningState:
     state = stores.learning()
@@ -676,6 +891,97 @@ def _append_audit(kind: str, entity_id: str | None, message: str) -> None:
     stores.save_runtime(runtime)
 
 
+def _append_job(kind: str, message: str, status: JobStatus = JobStatus.SUCCEEDED) -> JobQueueItem:
+    state = stores.learning()
+    job = JobQueueItem(
+        job_id=f"job-{len(state.jobs) + 1}",
+        kind=kind,
+        status=status,
+        message=message,
+        finished_at=datetime.now(timezone.utc) if status is JobStatus.SUCCEEDED else None,
+    )
+    state.jobs[job.job_id] = job
+    stores.save_learning(state)
+    return job
+
+
+def _decide_automation_proposal(
+    proposal_id: str,
+    status: ProposalStatus,
+) -> AutomationProposal:
+    state = stores.learning()
+    proposal = state.automation_proposals[proposal_id]
+    updated = proposal.model_copy(
+        update={
+            "status": status,
+            "revision": proposal.revision + 1,
+            "decided_at": datetime.now(timezone.utc),
+        }
+    )
+    state.automation_proposals[proposal_id] = updated
+    stores.save_learning(state)
+    _append_job("automation_proposal", f"Automation-Vorschlag {proposal_id}: {status}.")
+    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
@@ -858,6 +1164,9 @@ def _dashboard_html() -> str:
         ['Not-Aus', data.global_enabled ? 'frei' : 'aktiv'],
         ['Lernen', data.learning_profiles],
         ['Steuerung', data.control_profiles],
+        ['Vorschlaege', data.automation_proposals],
+        ['Modelle', data.models],
+        ['Jobs', data.jobs],
         ['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.10"
+version = "2.0.0-alpha.12"
 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.10"
+    assert config["version"] == "2.0.0-alpha.12"
     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.10"
+    assert health.json()["version"] == "2.0.0-alpha.12"
     assert backup.status_code == 200
     assert restore.status_code == 200
     assert restore.json() == {"status": "restored"}
@@ -81,6 +81,30 @@ def test_dashboard_control_and_learning_endpoints(tmp_path, monkeypatch) -> None
     summary = client.get("/v2/actuators/summary")
     parity = client.get("/v2/feature-parity")
     anomalies = client.get("/v2/anomalies")
+    proposal = client.post(
+        "/v2/automations/proposals",
+        json={
+            "name": "Storage light",
+            "trigger_entity_id": "binary_sensor.storage_door",
+            "trigger_state": "on",
+            "actuator_entity_id": "light.storage",
+            "target_state": "on",
+        },
+    )
+    proposal_yaml = client.get("/v2/automations/proposals/proposal-1/yaml")
+    approved = client.post("/v2/automations/proposals/proposal-1/approve")
+    weights = client.post(
+        "/v2/control/light.storage/weights",
+        json={"sensor_weights": {"binary_sensor.storage_door": 1.0}, "note": "door"},
+    )
+    history = client.post(
+        "/v2/history/analyze",
+        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")
+    jobs = client.get("/v2/jobs")
     dashboard = client.get("/v2/dashboard")
 
     assert entities.status_code == 200
@@ -105,7 +129,26 @@ def test_dashboard_control_and_learning_endpoints(tmp_path, monkeypatch) -> None
     assert summary.status_code == 200
     assert summary.json()[0]["actuator_entity_id"] == "light.storage"
     assert parity.status_code == 200
-    assert "Feedback" in parity.json()["implemented"]
+    assert "Job-Queue" in parity.json()["implemented"]
     assert anomalies.status_code == 200
+    assert proposal.status_code == 200
+    assert proposal.json()["status"] == "draft"
+    assert proposal_yaml.status_code == 200
+    assert "alias: Storage light" in proposal_yaml.text
+    assert approved.status_code == 200
+    assert approved.json()["status"] == "approved"
+    assert weights.status_code == 200
+    assert weights.json()["sensor_weights"]["binary_sensor.storage_door"] == 1.0
+    assert history.status_code == 200
+    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 jobs.status_code == 200
+    assert jobs.json()
     assert dashboard.status_code == 200
     assert dashboard.json()["actuator_count"] == 1
+    assert dashboard.json()["automation_proposals"] == 1