integrate heating rule fix and quality workflow

.dockerignore

@@ -1,16 +0,0 @@
-.env
-.env.*
-!.env.example
-.venv
-.venv/*
-__pycache__
-.mypy_cache
-.pytest_cache
-.ruff_cache
-node_modules
-.idea
-.vscode
-.git
-.gitignore
-.dockerignore
-docker-compose*.yml

.env.example

@@ -1,3 +1,3 @@
+# Copy to .env for local development. Do not commit real tokens.
 SILLYHOME_HA_URL=http://homeassistant.local:8123
-SILLYHOME_HA_TOKEN=REPLACE_ME_WITH_LONG_LIVED_TOKEN
+SILLYHOME_HA_TOKEN=replace-with-a-long-lived-access-token
-SILLYHOME_MODEL_STORE=.model_store

.gitea/workflows/quality.yml

@@ -1,24 +1,31 @@
-name: quality
+name: Quality
 
 on:
   push:
-    branches: ["main", "otto/**", "feature/**"]
+    branches:
+      - "**"
   pull_request:
 
 jobs:
   test:
     runs-on: ubuntu-latest
-    strategy:
-      matrix:
-        python-version: ["3.11", "3.13"]
     steps:
-      - uses: actions/checkout@v4
+      - name: Checkout
-      - uses: actions/setup-python@v5
+        uses: actions/checkout@v4
+
+      - name: Set up Python
+        uses: actions/setup-python@v5
         with:
-          python-version: ${{ matrix.python-version }}
+          python-version: "3.11"
-          cache: pip
+
-      - run: python -m pip install --upgrade pip
+      - name: Install project
-      - run: python -m pip install -e ".[dev]"
+        run: python -m pip install --upgrade pip && python -m pip install -e ".[dev]"
-      - run: python -m pytest
+
-      - run: ruff check .
+      - name: Run tests
-      - run: mypy
+        run: pytest -q
+
+      - name: Run Ruff
+        run: ruff check .
+
+      - name: Run Mypy
+        run: mypy app tests

.gitignore

@@ -4,10 +4,10 @@
 /.vscode
 __pycache__/
 *.pyc
-*.egg-info/
 .mypy_cache/
 .pytest_cache/
 .ruff_cache/
 .env
 .env.local
 .env.*
+!.env.example

CHANGELOG.md

@@ -1,21 +1,5 @@
 # Changelog
 
 ## Unreleased
-- Deterministische, nutzerverständliche Erklärungen für jede Modellvorhersage
-
-## 0.2.0 - 2026-06-13
-- Klassifizierte Home-Assistant-Entity-Discovery mit Lernrelevanz und Filtern
-- Validierter Zugriff auf die Home-Assistant-History-API
-- Normalisierte, chronologisch sortierte numerische Zeitreihen über `/v1/history`
-- Trainierbares statistisches Baseline-Modell mit persistierten Parametern
-- Numerische Vorhersagen mit Confidence sowie MAE-/RMSE-Evaluation
-
-## 0.1.0 - 2026-06-13
 - Projektinitiierung
 - Architektur, ADRs und Roadmap
-- Einheitliche produktive FastAPI-App für HA- und ML-Routen
-- Funktionierende ENV-Konfiguration und sauberer HA-503-Zustand
-- Persistente, validierte und gegen Path Traversal gehärtete Model Registry
-- Reproduzierbares Packaging, CI-Gates und gehärteter non-root Container
-- Definierte API-Fehler und korrigierte Evaluationsmetriken
-- Scheduler-tauglicher Retraining-Service mit API und atomischem Registry-Update

Dockerfile

@@ -1,27 +0,0 @@
-FROM python:3.13-slim
-
-ENV PYTHONDONTWRITEBYTECODE=1 \
-    PYTHONUNBUFFERED=1 \
-    PIP_NO_CACHE_DIR=1 \
-    SILLYHOME_MODEL_STORE=/app/data/models
-
-WORKDIR /app
-
-RUN addgroup --system sillyhome && adduser --system --ingroup sillyhome sillyhome
-
-COPY pyproject.toml README.md ./
-COPY app ./app
-COPY backend ./backend
-RUN python -m pip install --upgrade pip && \
-    python -m pip install . && \
-    mkdir -p /app/data/models && \
-    chown -R sillyhome:sillyhome /app/data
-
-EXPOSE 8000
-
-USER sillyhome
-
-HEALTHCHECK --interval=30s --timeout=3s --start-period=10s --retries=3 \
-    CMD ["python", "-c", "import urllib.request; urllib.request.urlopen('http://127.0.0.1:8000/health', timeout=2)"]
-
-CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000"]

README.md

@@ -1,14 +1,6 @@
 # SillyHome Next
 
-Lokaler, datenschutzfreundlicher API-Prototyp für Home Assistant.
+Modern, lokal-first und datenschutzfreundliches Smart-Home-Intelligenzsystem für Home Assistant.
-
-## Reifegrad
-
-Die aktuelle Entwicklungslinie stellt eine gehärtete technische Basis bereit:
-Home-Assistant-Entities und Historie lesen, Sensoren klassifizieren,
-regelbasierte Bausteine sowie ein lokal trainierbares statistisches
-Baseline-Modell mit persistenter Registry, Confidence und echten
-Evaluationsmetriken.
 
 ## Motivation
 TheSillyHome zeigte die Idee: statt statischer Regeln das Zuhause aus Verhaltensmustern verstehen. Diese Architektur modernisiert den Ansatz in Richtung Explainable AI, hybride Intelligenzebenen und langlebige Wartbarkeit.
@@ -21,58 +13,52 @@ TheSillyHome zeigte die Idee: statt statischer Regeln das Zuhause aus Verhaltens
 - Lokal-first ohne Cloudpflicht
 - Erweiterbar, testbar, dokumentiert
 
-## Quickstart
+## Lokaler Quickstart
-1. Python-Venv anlegen und Abhängigkeiten installieren:
+
+Voraussetzung ist Python 3.11 oder neuer.
+
 ```bash
 python -m venv .venv
-source .venv/bin/activate
+. .venv/bin/activate
-pip install -e ".[dev]"
+python -m pip install --upgrade pip
-```
+python -m pip install -e ".[dev]"
-
-2. Konfiguration aus `.env.example` übernehmen und anpassen:
-```bash
 cp .env.example .env
 ```
 
-3. API starten:
+In `.env` müssen für echte Home-Assistant-Daten diese Werte gesetzt werden:
+
+```bash
+SILLYHOME_HA_URL=http://homeassistant.local:8123
+SILLYHOME_HA_TOKEN=<long-lived-access-token>
+```
+
+Alternativ werden aus Kompatibilitätsgründen auch `HA_URL` und `HA_TOKEN` gelesen.
+Tokens bleiben lokal und dürfen nicht committed, geloggt oder in Issues kopiert werden.
+
+API starten:
+
 ```bash
 uvicorn app.main:app --reload
 ```
 
-4. Erreichbar unter:
+Nützliche Checks:
-- `http://127.0.0.1:8000/health` - Health-Check
-- `http://127.0.0.1:8000/docs/` - OpenAPI-Dokumentation
-- `http://127.0.0.1:8000/v1/entities` - Home-Assistant-Entities
-- `http://127.0.0.1:8000/v1/discovery` - klassifizierte, filterbare Entities
-- `http://127.0.0.1:8000/v1/history` - normalisierte numerische Zeitreihen
-- `http://127.0.0.1:8000/ml/health` - Registry-/Serving-Health
-- `POST http://127.0.0.1:8000/ml/retrain` - Modell-Metadaten aktualisieren
-- `POST http://127.0.0.1:8000/ml/evaluate` - MAE/RMSE/Coverage berechnen
-
-Ohne vollständige HA-Konfiguration liefert `/v1/entities` bewusst `503`.
-
-### Docker Compose
 
 ```bash
-cp .env.example .env
+curl http://127.0.0.1:8000/health
-docker compose up --build -d
+curl http://127.0.0.1:8000/v1/entities
-curl --fail http://127.0.0.1:8000/health
 ```
 
-Compose veröffentlicht die API standardmäßig nur auf `127.0.0.1`. Für Zugriff aus
+Die interaktive API-Dokumentation liegt unter `http://127.0.0.1:8000/docs`.
-dem Netz muss ein authentifizierender Reverse Proxy vorgeschaltet werden.
 
-### ENV-Konfiguration (`.env.example`)
+## Qualität
-- `SILLYHOME_HA_URL` – Basis-URL deiner Home-Assistant-Instanz (z. B. `http://homeassistant.local:8123`)
-- `SILLYHOME_HA_TOKEN` – Long-Lived Access Token eines dedizierten HA-Benutzers mit minimalen Rechten
-- `SILLYHOME_MODEL_STORE` – Verzeichnis für persistierte Modell-Metadaten
 
-Niemals Administrator-Tokens oder Passwörter eintragen. `.env` gehört nicht ins
+Vor jedem Pull Request lokal laufen lassen:
-Versionskontrollsystem.
 
-### Tests
 ```bash
-pytest
+pytest -q
 ruff check .
-mypy
+mypy app tests
 ```
+
+Der Gitea-Actions-Workflow in `.gitea/workflows/quality.yml` führt dieselben Checks für
+Pushes und Pull Requests aus.

app/api/v1/entities.py

@@ -1,13 +1,10 @@
 from __future__ import annotations
 
-from datetime import datetime
+from collections.abc import Sequence
-from typing import List
 
-from fastapi import APIRouter, Depends, HTTPException, Query, status
+from fastapi import APIRouter, Depends
 
 from app.dependencies import get_ha_reader
-from app.ha.discovery import DiscoveredEntity
-from app.ha.history import EntityHistorySeries
 from app.ha.models import HaEntitySummary
 from app.ha.reader import HaReader
 
@@ -18,47 +15,7 @@ router = APIRouter(prefix="/v1", tags=["entities"])
     "/entities",
     summary="Home-Assistant-Entities auflisten",
     description="Gibt eine kompakte Zusammenfassung aller erreichbaren HA-Entitäten zurück.",
-    response_model=List[HaEntitySummary],
+    response_model=list[HaEntitySummary],
 )
-def list_entities(ha_reader: HaReader = Depends(get_ha_reader)) -> List[HaEntitySummary]:
+def list_entities(reader: HaReader = Depends(get_ha_reader)) -> Sequence[HaEntitySummary]:
-    return list(ha_reader.read_entities())
+    return reader.read_entities()
-
-
-@router.get(
-    "/discovery",
-    summary="Home-Assistant-Entities klassifizieren",
-    description="Klassifiziert Entities nach Lernrelevanz, Kontextquelle und Aktor-Rolle.",
-    response_model=List[DiscoveredEntity],
-)
-def discovery(
-    domain: List[str] | None = Query(default=None),
-    learnable: bool | None = None,
-    ha_reader: HaReader = Depends(get_ha_reader),
-) -> List[DiscoveredEntity]:
-    return list(
-        ha_reader.discover(
-            domains=set(domain) if domain else None,
-            learnable=learnable,
-        )
-    )
-
-
-@router.get(
-    "/history",
-    summary="Numerische Home-Assistant-Historie lesen",
-    description="Lädt und normalisiert numerische Zustände ausgewählter Entities.",
-    response_model=List[EntityHistorySeries],
-)
-def history(
-    entity_id: List[str] = Query(),
-    start_time: datetime = Query(),
-    end_time: datetime = Query(),
-    ha_reader: HaReader = Depends(get_ha_reader),
-) -> List[EntityHistorySeries]:
-    try:
-        return list(ha_reader.read_history(entity_id, start_time, end_time))
-    except ValueError as exc:
-        raise HTTPException(
-            status_code=status.HTTP_422_UNPROCESSABLE_CONTENT,
-            detail=str(exc),
-        ) from exc

app/config.py

@@ -8,7 +8,6 @@ from dataclasses import dataclass
 class Settings:
     ha_url: str | None = None
     ha_token: str | None = None
-    model_store: str = ".model_store"
 
     @property
     def ha_configured(self) -> bool:
@@ -19,5 +18,4 @@ def load_settings() -> Settings:
     return Settings(
         ha_url=os.getenv("SILLYHOME_HA_URL") or os.getenv("HA_URL"),
         ha_token=os.getenv("SILLYHOME_HA_TOKEN") or os.getenv("HA_TOKEN"),
-        model_store=os.getenv("SILLYHOME_MODEL_STORE", ".model_store"),
     )

app/core/exceptions.py

@@ -1,20 +0,0 @@
-from __future__ import annotations
-
-from typing import Any
-
-from fastapi import FastAPI, Request
-
-from app.ha.exceptions import HaAuthError, HaClientError, HaHttpError
-
-
-def register_exception_handlers(app: FastAPI) -> None:
-    @app.exception_handler(HaClientError)
-    async def handle_ha_client_error(request: Request, exc: HaClientError) -> Any:  # pragma: no cover - einfacher Wrapper
-        if isinstance(exc, HaAuthError):
-            return {"detail": "Ungültige Authentifizierung gegenüber Home Assistant."}
-        if isinstance(exc, HaHttpError):
-            return {
-                "detail": "Home Assistant meldet einen Fehler.",
-                "upstream_status": exc.status_code,
-            }
-        return {"detail": str(exc)}

app/ha/client.py

@@ -2,9 +2,7 @@ from __future__ import annotations
 
 import logging
 from dataclasses import dataclass
-from datetime import datetime
+from typing import Any
-import re
-from urllib.parse import quote
 
 import requests
 
@@ -17,9 +15,6 @@ from app.ha.exceptions import (
 
 logger = logging.getLogger(__name__)
 
-_ENTITY_ID_PATTERN = re.compile(r"^[a-z0-9_]+\.[a-z0-9_]+$")
-_MAX_HISTORY_SECONDS = 31 * 24 * 60 * 60
-
 
 @dataclass(frozen=True)
 class HaClientSettings:
@@ -37,91 +32,36 @@ class HaClient:
             "Content-Type": "application/json",
         })
 
-    def close(self) -> None:
+    def list_entities(self) -> list[dict[str, Any]]:
-        self._session.close()
-
-    def list_entities(self) -> list[dict[str, object]]:
-        payload = self._get_json("/api/states")
-        if not isinstance(payload, list):
-            raise HaUnexpectedPayloadError(
-                "Antwort von Home Assistant hat unerwartetes Format."
-            )
-        return payload
-
-    def get_history(
-        self,
-        entity_ids: list[str],
-        start_time: datetime,
-        end_time: datetime,
-    ) -> list[object]:
-        if not entity_ids:
-            raise ValueError("Mindestens eine entity_id ist erforderlich.")
-        if len(entity_ids) > 100:
-            raise ValueError("Es können höchstens 100 Entities abgefragt werden.")
-        if any(not _ENTITY_ID_PATTERN.fullmatch(entity_id) for entity_id in entity_ids):
-            raise ValueError("entity_id enthält ein ungültiges Format.")
-        if start_time.tzinfo is None or end_time.tzinfo is None:
-            raise ValueError("start_time und end_time müssen eine Zeitzone enthalten.")
-        if end_time <= start_time:
-            raise ValueError("end_time muss nach start_time liegen.")
-        if (end_time - start_time).total_seconds() > _MAX_HISTORY_SECONDS:
-            raise ValueError("History-Abfragen sind auf 31 Tage begrenzt.")
-
-        start = quote(start_time.isoformat(), safe=":+")
-        payload = self._get_json(
-            f"/api/history/period/{start}",
-            params={
-                "filter_entity_id": ",".join(entity_ids),
-                "end_time": end_time.isoformat(),
-                "minimal_response": "1",
-                "no_attributes": "1",
-            },
-        )
-        if not isinstance(payload, list):
-            raise HaUnexpectedPayloadError(
-                "History-Antwort von Home Assistant hat unerwartetes Format."
-            )
-        return payload
-
-    def _get_json(
-        self,
-        path: str,
-        *,
-        params: dict[str, str] | None = None,
-    ) -> object:
         try:
             response = self._session.get(
-                f"{self._settings.url.rstrip('/')}{path}",
+                f"{self._settings.url}/api/states",
-                params=params,
                 timeout=self._settings.timeout_seconds,
             )
         except requests.Timeout as exc:
-            raise HaTimeoutError("Zeitüberschreitung beim Zugriff auf Home Assistant.") from exc
+            raise HaTimeoutError("Home Assistant request timed out.") from exc
         except requests.RequestException as exc:
-            raise HaHttpError(
+            raise HaHttpError(status_code=502, message="Home Assistant request failed.") from exc
-                getattr(getattr(exc, "response", None), "status_code", 502),
-                "Netzwerkfehler beim Zugriff auf Home Assistant.",
-            ) from exc
 
-        if response.status_code in (401, 403):
+        if response.status_code in {401, 403}:
             raise HaAuthError(
-                response.status_code,
+                status_code=response.status_code,
-                "Authentifizierung bei Home Assistant fehlgeschlagen.",
+                message="Home Assistant authentication failed.",
             )
 
         try:
             response.raise_for_status()
         except requests.HTTPError as exc:
             raise HaHttpError(
-                response.status_code,
+                status_code=response.status_code,
-                "Home Assistant meldet einen Fehler.",
+                message="Home Assistant returned an HTTP error.",
             ) from exc
 
         try:
             payload = response.json()
         except ValueError as exc:
-            raise HaUnexpectedPayloadError(
+            raise HaUnexpectedPayloadError("Home Assistant returned invalid JSON.") from exc
-                "Antwort von Home Assistant ist kein gültiges JSON."
-            ) from exc
 
+        if not isinstance(payload, list):
+            raise HaUnexpectedPayloadError("Home Assistant states response must be a list.")
         return payload

app/ha/discovery.py

@@ -1,184 +0,0 @@
-from __future__ import annotations
-
-from enum import StrEnum
-
-from pydantic import BaseModel
-
-from app.ha.models import HaEntitySummary
-
-
-class EntityRole(StrEnum):
-    MEASUREMENT = "measurement"
-    BINARY_CONTEXT = "binary_context"
-    CONTEXT = "context"
-    ACTUATOR = "actuator"
-    UNSUPPORTED = "unsupported"
-
-
-class DiscoveredEntity(BaseModel):
-    entity_id: str
-    domain: str
-    device_class: str | None = None
-    state_class: str | None = None
-    unit_of_measurement: str | None = None
-    role: EntityRole
-    learnable: bool
-    reason: str
-
-
-_MEASUREMENT_CLASSES = frozenset({
-    "apparent_power",
-    "atmospheric_pressure",
-    "battery",
-    "carbon_dioxide",
-    "carbon_monoxide",
-    "current",
-    "distance",
-    "duration",
-    "energy",
-    "frequency",
-    "gas",
-    "humidity",
-    "illuminance",
-    "moisture",
-    "monetary",
-    "nitrogen_dioxide",
-    "nitrogen_monoxide",
-    "nitrous_oxide",
-    "ozone",
-    "pm1",
-    "pm10",
-    "pm25",
-    "power",
-    "precipitation",
-    "pressure",
-    "reactive_power",
-    "signal_strength",
-    "sound_pressure",
-    "speed",
-    "sulphur_dioxide",
-    "temperature",
-    "volatile_organic_compounds",
-    "voltage",
-    "volume",
-    "volume_flow_rate",
-    "water",
-    "weight",
-    "wind_speed",
-})
-_BINARY_CONTEXT_CLASSES = frozenset({
-    "door",
-    "garage_door",
-    "lock",
-    "motion",
-    "occupancy",
-    "opening",
-    "presence",
-    "problem",
-    "safety",
-    "smoke",
-    "sound",
-    "vibration",
-    "window",
-})
-_ACTUATOR_DOMAINS = frozenset({
-    "button",
-    "climate",
-    "cover",
-    "fan",
-    "humidifier",
-    "light",
-    "lock",
-    "scene",
-    "select",
-    "siren",
-    "switch",
-    "valve",
-})
-_CONTEXT_DOMAINS = frozenset({"device_tracker", "person", "sun", "weather", "zone"})
-_LEARNABLE_CONTEXT_DOMAINS = frozenset({"device_tracker", "person", "weather"})
-_NUMERIC_STATE_CLASSES = frozenset({"measurement", "total", "total_increasing"})
-
-
-def classify_entity(entity: HaEntitySummary) -> DiscoveredEntity:
-    if entity.domain == "sensor" and (
-        entity.state_class in _NUMERIC_STATE_CLASSES
-        or entity.device_class in _MEASUREMENT_CLASSES
-        or entity.unit_of_measurement is not None
-    ):
-        return _result(
-            entity,
-            EntityRole.MEASUREMENT,
-            learnable=True,
-            reason="Numerischer Messsensor für Zeitreihen und Training.",
-        )
-
-    if entity.domain == "binary_sensor" and entity.device_class in _BINARY_CONTEXT_CLASSES:
-        return _result(
-            entity,
-            EntityRole.BINARY_CONTEXT,
-            learnable=True,
-            reason="Binärer Kontextsensor für Zustands- und Anwesenheitsmuster.",
-        )
-
-    if entity.domain in _CONTEXT_DOMAINS:
-        learnable = entity.domain in _LEARNABLE_CONTEXT_DOMAINS
-        return _result(
-            entity,
-            EntityRole.CONTEXT,
-            learnable=learnable,
-            reason=(
-                "Kontextquelle für Training und Erklärungen."
-                if learnable
-                else "Kontextquelle ohne direkte Trainingsfreigabe."
-            ),
-        )
-
-    if entity.domain in _ACTUATOR_DOMAINS:
-        return _result(
-            entity,
-            EntityRole.ACTUATOR,
-            learnable=False,
-            reason="Aktor ist ein mögliches Automationsziel, aber kein Trainingssensor.",
-        )
-
-    return _result(
-        entity,
-        EntityRole.UNSUPPORTED,
-        learnable=False,
-        reason="Entity-Typ ist noch nicht für Lernen oder Automationen klassifiziert.",
-    )
-
-
-def discover_entities(
-    entities: list[HaEntitySummary],
-    domains: set[str] | None = None,
-    learnable: bool | None = None,
-) -> list[DiscoveredEntity]:
-    normalized_domains = {domain.strip().lower() for domain in domains or set() if domain.strip()}
-    discovered = [classify_entity(entity) for entity in entities]
-    return [
-        entity
-        for entity in discovered
-        if (not normalized_domains or entity.domain in normalized_domains)
-        and (learnable is None or entity.learnable is learnable)
-    ]
-
-
-def _result(
-    entity: HaEntitySummary,
-    role: EntityRole,
-    *,
-    learnable: bool,
-    reason: str,
-) -> DiscoveredEntity:
-    return DiscoveredEntity(
-        entity_id=entity.entity_id,
-        domain=entity.domain,
-        device_class=entity.device_class,
-        state_class=entity.state_class,
-        unit_of_measurement=entity.unit_of_measurement,
-        role=role,
-        learnable=learnable,
-        reason=reason,
-    )

app/ha/exceptions.py

@@ -2,34 +2,26 @@ from __future__ import annotations
 
 
 class HaClientError(Exception):
-    """Basisklasse für HA-Client-Fehler."""
+    """Base class for Home Assistant integration failures."""
 
-    public_detail: str | None = None
+    public_detail = "Home Assistant is currently unavailable."
 
 
 class HaTimeoutError(HaClientError):
-    """Zeitüberschreitung bei Request an Home Assistant."""
-
     public_detail = "Home Assistant request timed out."
 
 
 class HaHttpError(HaClientError):
-    """Nicht erfolgreicher HTTP-Statuscode."""
+    public_detail = "Home Assistant returned an error."
 
-    public_detail = "Home Assistant request failed."
+    def __init__(self, status_code: int, message: str | None = None) -> None:
-
+        super().__init__(message or self.public_detail)
-    def __init__(self, status_code: int, message: str = "") -> None:
-        super().__init__(message)
         self.status_code = status_code
 
 
 class HaAuthError(HaHttpError):
-    """Authentifizierung oder Berechtigung fehlgeschlagen."""
-
     public_detail = "Home Assistant authentication failed."
 
 
 class HaUnexpectedPayloadError(HaClientError):
-    """Antwort hat nicht das erwartete Format."""
+    public_detail = "Home Assistant returned an unexpected response."
-
-    public_detail = "Home Assistant returned an unexpected payload."

app/ha/history.py

@@ -1,91 +0,0 @@
-from __future__ import annotations
-
-import math
-from datetime import datetime
-
-from pydantic import BaseModel
-
-from app.ha.exceptions import HaUnexpectedPayloadError
-
-
-class NumericHistoryPoint(BaseModel):
-    timestamp: datetime
-    value: float
-
-
-class EntityHistorySeries(BaseModel):
-    entity_id: str
-    points: list[NumericHistoryPoint]
-
-
-def normalize_history_payload(payload: object) -> list[EntityHistorySeries]:
-    if not isinstance(payload, list):
-        raise HaUnexpectedPayloadError("History-Payload muss eine Liste sein.")
-
-    normalized: list[EntityHistorySeries] = []
-    for raw_series in payload:
-        if not isinstance(raw_series, list):
-            raise HaUnexpectedPayloadError("History-Serie muss eine Liste sein.")
-        series = _normalize_series(raw_series)
-        if series is not None:
-            normalized.append(series)
-
-    return sorted(normalized, key=lambda item: item.entity_id)
-
-
-def _normalize_series(raw_series: list[object]) -> EntityHistorySeries | None:
-    entity_id: str | None = None
-    points: list[NumericHistoryPoint] = []
-
-    for raw_entry in raw_series:
-        if not isinstance(raw_entry, dict):
-            raise HaUnexpectedPayloadError("History-Eintrag muss ein Objekt sein.")
-
-        raw_entity_id = raw_entry.get("entity_id")
-        if raw_entity_id is not None:
-            if not isinstance(raw_entity_id, str) or "." not in raw_entity_id:
-                raise HaUnexpectedPayloadError("History-Eintrag enthält ungültige entity_id.")
-            if entity_id is not None and entity_id != raw_entity_id:
-                raise HaUnexpectedPayloadError("History-Serie enthält mehrere Entities.")
-            entity_id = raw_entity_id
-
-        raw_state = raw_entry.get("state")
-        value = _finite_float(raw_state)
-        if value is None:
-            continue
-        if entity_id is None:
-            raise HaUnexpectedPayloadError("History-Serie enthält keine entity_id.")
-
-        raw_timestamp = raw_entry.get("last_changed") or raw_entry.get("last_updated")
-        timestamp = _parse_timestamp(raw_timestamp)
-        points.append(NumericHistoryPoint(timestamp=timestamp, value=value))
-
-    if entity_id is None or not points:
-        return None
-
-    points.sort(key=lambda point: point.timestamp)
-    return EntityHistorySeries(entity_id=entity_id, points=points)
-
-
-def _finite_float(value: object) -> float | None:
-    if isinstance(value, bool) or value is None:
-        return None
-    if not isinstance(value, (str, int, float)):
-        return None
-    try:
-        converted = float(value)
-    except (TypeError, ValueError):
-        return None
-    return converted if math.isfinite(converted) else None
-
-
-def _parse_timestamp(value: object) -> datetime:
-    if not isinstance(value, str):
-        raise HaUnexpectedPayloadError("Numerischer History-Eintrag enthält keinen Zeitstempel.")
-    try:
-        parsed = datetime.fromisoformat(value.replace("Z", "+00:00"))
-    except ValueError as exc:
-        raise HaUnexpectedPayloadError("History-Eintrag enthält ungültigen Zeitstempel.") from exc
-    if parsed.tzinfo is None:
-        raise HaUnexpectedPayloadError("History-Zeitstempel muss eine Zeitzone enthalten.")
-    return parsed

app/ha/reader.py

@@ -1,12 +1,9 @@
 from __future__ import annotations
 
 from collections.abc import Sequence
-from datetime import datetime
 from typing import Any
 
 from app.ha.client import HaClient
-from app.ha.discovery import DiscoveredEntity, discover_entities
-from app.ha.history import EntityHistorySeries, normalize_history_payload
 from app.ha.models import HaEntitySummary
 
 
@@ -18,10 +15,9 @@ class HaReader:
         entities = self._client.list_entities()
         summaries: list[HaEntitySummary] = []
         for item in entities:
-            raw_entity_id = item.get("entity_id")
+            entity_id = item.get("entity_id", "")
-            if not isinstance(raw_entity_id, str) or "." not in raw_entity_id:
+            if "." not in entity_id:
                 continue
-            entity_id = raw_entity_id
             domain = entity_id.split(".", 1)[0]
             raw_attributes = item.get("attributes") or {}
             attributes: dict[str, Any] = raw_attributes if isinstance(raw_attributes, dict) else {}
@@ -36,22 +32,6 @@ class HaReader:
             )
         return summaries
 
-    def discover(
-        self,
-        domains: set[str] | None = None,
-        learnable: bool | None = None,
-    ) -> Sequence[DiscoveredEntity]:
-        return discover_entities(list(self.read_entities()), domains=domains, learnable=learnable)
-
-    def read_history(
-        self,
-        entity_ids: list[str],
-        start_time: datetime,
-        end_time: datetime,
-    ) -> Sequence[EntityHistorySeries]:
-        payload = self._client.get_history(entity_ids, start_time, end_time)
-        return normalize_history_payload(payload)
-
 
 def _optional_str(value: object) -> str | None:
     if value is None or value == "":

app/main.py

@@ -1,6 +1,5 @@
-from contextlib import asynccontextmanager
 from collections.abc import AsyncIterator
-from typing import cast
+from contextlib import asynccontextmanager
 
 from fastapi import FastAPI
 
@@ -9,42 +8,33 @@ from app.config import load_settings
 from app.core.exception_handlers import register_exception_handlers
 from app.ha.client import HaClient, HaClientSettings
 from app.ha.reader import HaReader
-from app.ml.registry.model_registry import ModelRegistry
-from backend.routes.ml import init_ml_routes
 
 
 @asynccontextmanager
 async def lifespan(app: FastAPI) -> AsyncIterator[None]:
-    settings = app.state.settings
+    settings = load_settings()
-    client: HaClient | None = None
+    app.state.settings = settings
-    app.state.registry = ModelRegistry(settings.model_store)
-    if hasattr(app.state, "ha_reader"):
-        del app.state.ha_reader
     if settings.ha_configured:
         client = HaClient(
             settings=HaClientSettings(
-                url=cast(str, settings.ha_url),
+                url=settings.ha_url or "",
-                token=cast(str, settings.ha_token),
+                token=settings.ha_token or "",
             )
         )
         app.state.ha_reader = HaReader(client=client)
-    try:
+    yield
-        yield
-    finally:
-        if client is not None:
-            client.close()
 
 
 app = FastAPI(
     title="SillyHome Next API",
     description="Lokales Smart-Home-Intelligenzsystem für Home Assistant.",
-    version="0.2.0",
+    version="0.1.0",
     lifespan=lifespan,
 )
-app.state.settings = load_settings()
+
 register_exception_handlers(app)
+
 app.include_router(entities_router)
-init_ml_routes(app, model_store=app.state.settings.model_store)
 
 
 @app.get("/health")

app/ml/__init__.py

@@ -1,20 +0,0 @@
-
-"""Machine-Learning-Grundbausteine für SillyHome Next."""
-__all__ = [
-    "FeatureStore",
-    "FeatureVector",
-    "FeatureModel",
-    "FeatureExplanation",
-    "PredictionResult",
-    "Predictor",
-    "RetrainingResult",
-    "RetrainingService",
-    "TrainedArtifact",
-    "TrainingPipeline",
-    "retrain_model",
-]
-from app.ml.feature_store import FeatureStore, FeatureVector
-from app.ml.explanation import FeatureExplanation
-from app.ml.predictor import PredictionResult, Predictor
-from app.ml.retraining import RetrainingResult, RetrainingService, retrain_model
-from app.ml.training import FeatureModel, TrainedArtifact, TrainingPipeline

app/ml/evaluation.py

@@ -1,89 +0,0 @@
-from __future__ import annotations
-
-import logging
-import math
-from collections.abc import Sequence
-from dataclasses import dataclass
-
-from app.ml.feature_store import FeatureVector
-from app.ml.predictor import Predictor
-from app.ml.registry.model_registry import ModelRegistry
-from app.ml.training import TrainingPipeline
-
-logger = logging.getLogger(__name__)
-
-
-@dataclass
-class Metric:
-    name: str
-    value: float
-    threshold: float | None = None
-
-
-@dataclass
-class EvalReport:
-    artifact_id: str
-    sample_size: int
-    metrics: list[Metric]
-
-
-class Evaluator:
-    def __init__(
-        self,
-        pipeline: TrainingPipeline | None = None,
-        registry: ModelRegistry | None = None,
-    ) -> None:
-        if isinstance(pipeline, ModelRegistry) and registry is None:
-            registry = pipeline
-            pipeline = None
-        if pipeline is None and registry is None:
-            raise ValueError("Evaluator erfordert TrainingPipeline oder ModelRegistry.")
-        self._pipeline = pipeline
-        self._registry = registry
-        self._predictor = Predictor(pipeline=pipeline, registry=registry)
-
-    def evaluate(self, artifact_id: str, samples: Sequence[FeatureVector]) -> EvalReport:
-        try:
-            if self._registry is not None:
-                self._registry.load_artifact(artifact_id)
-            elif self._pipeline is not None:
-                self._pipeline.export(artifact_id)
-        except KeyError as exc:
-            raise ValueError("Kein trainiertes Modell für Evaluation vorhanden.") from exc
-
-        absolute_errors: list[float] = []
-        squared_errors: list[float] = []
-        for sample in samples:
-            try:
-                prediction = self._predictor.predict(artifact_id, sample)
-            except ValueError:
-                continue
-            for feature_name, predicted in prediction.predictions.items():
-                actual = float(sample.values[feature_name])
-                error = predicted - actual
-                absolute_errors.append(abs(error))
-                squared_errors.append(error**2)
-
-        sample_size = len(absolute_errors)
-        mae = sum(absolute_errors) / sample_size if sample_size else 0.0
-        rmse = math.sqrt(sum(squared_errors) / sample_size) if sample_size else 0.0
-        expected_values = sum(len(sample.values) for sample in samples)
-        coverage = sample_size / expected_values if expected_values else 0.0
-
-        report = EvalReport(
-            artifact_id=artifact_id,
-            sample_size=sample_size,
-            metrics=[
-                Metric(name="mae", value=mae),
-                Metric(name="rmse", value=rmse),
-                Metric(name="coverage", value=coverage, threshold=0.8),
-            ],
-        )
-        logger.info(
-            "Evaluation %s -> mae=%.4f, rmse=%.4f, coverage=%.2f",
-            artifact_id,
-            mae,
-            rmse,
-            coverage,
-        )
-        return report

app/ml/explanation.py

@@ -1,57 +0,0 @@
-from __future__ import annotations
-
-from dataclasses import dataclass
-
-from app.ml.training import FeatureModel
-
-
-@dataclass(frozen=True)
-class FeatureExplanation:
-    feature: str
-    current_value: float
-    predicted_value: float
-    change: float
-    direction: str
-    sample_count: int
-    historical_mean: float
-    historical_range: tuple[float, float]
-    standard_deviation: float
-    trend_per_step: float
-    confidence: float
-    summary: str
-
-
-def explain_feature(
-    feature_name: str,
-    current_value: float,
-    predicted_value: float,
-    model: FeatureModel,
-) -> FeatureExplanation:
-    change = predicted_value - current_value
-    direction = _direction(change)
-    summary = (
-        f"{feature_name}: {direction}; Prognose {predicted_value:.3f} "
-        f"aus aktuellem Wert {current_value:.3f} und Trend {model.slope:+.3f}. "
-        f"Basis: {model.sample_count} Messwerte, Mittelwert {model.mean:.3f}, "
-        f"Confidence {model.confidence:.0%}."
-    )
-    return FeatureExplanation(
-        feature=feature_name,
-        current_value=current_value,
-        predicted_value=predicted_value,
-        change=change,
-        direction=direction,
-        sample_count=model.sample_count,
-        historical_mean=model.mean,
-        historical_range=(model.minimum, model.maximum),
-        standard_deviation=model.standard_deviation,
-        trend_per_step=model.slope,
-        confidence=model.confidence,
-        summary=summary,
-    )
-
-
-def _direction(change: float) -> str:
-    if abs(change) < 1e-12:
-        return "stabil"
-    return "steigend" if change > 0 else "fallend"

app/ml/feature_store.py

@@ -1,31 +0,0 @@
-from __future__ import annotations
-
-from collections import defaultdict
-from collections.abc import Iterable
-from dataclasses import dataclass
-
-
-@dataclass(frozen=True)
-class FeatureVector:
-    sensor_id: str
-    values: dict[str, float]
-    label: str | None = None
-
-
-class FeatureStore:
-    def __init__(self) -> None:
-        self._vectors: dict[str, list[FeatureVector]] = defaultdict(list)
-
-    def add(self, vector: FeatureVector) -> None:
-        self._vectors[vector.sensor_id].append(vector)
-
-    def add_batch(self, vectors: Iterable[FeatureVector]) -> None:
-        for vector in vectors:
-            self.add(vector)
-
-    def latest(self, sensor_id: str) -> FeatureVector | None:
-        series = self._vectors.get(sensor_id)
-        return series[-1] if series else None
-
-    def all(self) -> list[FeatureVector]:
-        return [vector for vectors in self._vectors.values() for vector in vectors]

app/ml/predictor.py

@@ -1,102 +0,0 @@
-from __future__ import annotations
-
-import logging
-import math
-from dataclasses import dataclass
-from typing import Sequence
-
-from app.ml.explanation import FeatureExplanation, explain_feature
-from app.ml.feature_store import FeatureVector
-from app.ml.registry.model_registry import ModelRegistry
-from app.ml.training import TrainedArtifact, TrainingPipeline
-
-logger = logging.getLogger(__name__)
-
-
-@dataclass(frozen=True)
-class PredictionResult:
-    artifact_id: str
-    sensor_id: str
-    predictions: dict[str, float]
-    confidence: float
-    model_type: str
-    explanations: dict[str, FeatureExplanation]
-
-
-class Predictor:
-    def __init__(
-        self,
-        pipeline: TrainingPipeline | None = None,
-        registry: ModelRegistry | None = None,
-    ) -> None:
-        if isinstance(pipeline, ModelRegistry) and registry is None:
-            registry = pipeline
-            pipeline = None
-        if pipeline is None and registry is None:
-            raise ValueError("Predictor erfordert TrainingPipeline oder ModelRegistry.")
-        self._pipeline = pipeline
-        self._registry = registry
-
-    def predict(self, artifact_id: str, entity: FeatureVector) -> PredictionResult:
-        artifact = self._get_artifact(artifact_id)
-        if entity.sensor_id not in artifact.supported_sensors:
-            raise ValueError(
-                f"Sensor '{entity.sensor_id}' wird vom Modell '{artifact_id}' nicht unterstützt."
-            )
-        sensor_models = artifact.feature_models.get(entity.sensor_id, {})
-        if not sensor_models:
-            raise ValueError(f"Modell '{artifact_id}' enthält keine statistischen Parameter.")
-
-        feature_names = sorted(set(sensor_models).intersection(entity.values))
-        if not feature_names:
-            raise ValueError(
-                f"Keine Eingabemerkmale werden vom Modell '{artifact_id}' unterstützt."
-            )
-
-        predictions: dict[str, float] = {}
-        explanations: dict[str, FeatureExplanation] = {}
-        confidences: list[float] = []
-        for feature_name in feature_names:
-            model = sensor_models[feature_name]
-            current_value = float(entity.values[feature_name])
-            if not math.isfinite(current_value):
-                raise ValueError("Vorhersagewerte müssen endlich sein.")
-            predicted_value = model.forecast(current_value)
-            predictions[feature_name] = predicted_value
-            explanations[feature_name] = explain_feature(
-                feature_name,
-                current_value,
-                predicted_value,
-                model,
-            )
-            confidences.append(model.confidence)
-
-        return PredictionResult(
-            artifact_id=artifact_id,
-            sensor_id=entity.sensor_id,
-            predictions=predictions,
-            confidence=sum(confidences) / len(confidences),
-            model_type=artifact.model_type,
-            explanations=explanations,
-        )
-
-    def predict_batch(
-        self,
-        artifact_id: str,
-        entities: Sequence[FeatureVector],
-    ) -> list[PredictionResult]:
-        return [self.predict(artifact_id, entity) for entity in entities]
-
-    @staticmethod
-    def default_artifact(pipeline: TrainingPipeline) -> TrainedArtifact:
-        artifacts = list(pipeline._artifacts)
-        if not artifacts:
-            raise ValueError("Kein trainiertes Modell gefunden.")
-        return pipeline.export(artifacts[-1])
-
-    def _get_artifact(self, artifact_id: str) -> TrainedArtifact:
-        if self._registry is not None:
-            return self._registry.load_artifact(artifact_id)
-        if self._pipeline is not None:
-            return self._pipeline.export(artifact_id)
-        raise RuntimeError("Predictor nicht initialisiert.")

app/ml/registry/__init__.py

@@ -1,3 +0,0 @@
-from .model_registry import ModelRegistry
-
-__all__ = ["ModelRegistry"]

app/ml/registry/model_registry.py

@@ -1,162 +0,0 @@
-from __future__ import annotations
-
-import json
-import logging
-import math
-import os
-from pathlib import Path
-import re
-from threading import RLock
-from collections.abc import Iterable
-
-from app.ml.training import FeatureModel, TrainedArtifact
-
-logger = logging.getLogger(__name__)
-
-_ARTIFACT_ID_PATTERN = re.compile(r"^[A-Za-z0-9][A-Za-z0-9._-]{0,127}$")
-
-
-class ModelRegistry:
-    def __init__(self, root: str | Path) -> None:
-        self._root = Path(root).resolve()
-        self._root.mkdir(parents=True, exist_ok=True)
-        self._artifacts: dict[str, TrainedArtifact] = {}
-        self._lock = RLock()
-        self._load_existing()
-
-    def register(self, artifact: TrainedArtifact) -> TrainedArtifact:
-        registered, _ = self.register_with_status(artifact)
-        return registered
-
-    def register_with_status(self, artifact: TrainedArtifact) -> tuple[TrainedArtifact, bool]:
-        self._validate_artifact_id(artifact.artifact_id)
-        with self._lock:
-            replaced = artifact.artifact_id in self._artifacts
-            self._persist(artifact)
-            self._artifacts[artifact.artifact_id] = artifact
-        return artifact, replaced
-
-    def load_artifact(self, artifact_id: str) -> TrainedArtifact:
-        self._validate_artifact_id(artifact_id)
-        with self._lock:
-            if artifact_id not in self._artifacts:
-                raise KeyError(f"Artifact '{artifact_id}' nicht registriert.")
-            return self._artifacts[artifact_id]
-
-    def list_models(self) -> Iterable[TrainedArtifact]:
-        with self._lock:
-            return [self._artifacts[key] for key in sorted(self._artifacts)]
-
-    def _load_existing(self) -> None:
-        for source in sorted(self._root.glob("*.json")):
-            try:
-                raw = json.loads(source.read_text(encoding="utf-8"))
-                artifact_id = raw["artifact_id"]
-                supported_sensors = raw["supported_sensors"]
-                model_type = raw.get("model_type", "metadata")
-                raw_feature_models = raw.get("feature_models", {})
-                if not isinstance(artifact_id, str) or not isinstance(supported_sensors, list):
-                    raise ValueError("invalid artifact structure")
-                if not isinstance(model_type, str):
-                    raise ValueError("model_type must be a string")
-                self._validate_artifact_id(artifact_id)
-                if source.name != f"{artifact_id}.json":
-                    raise ValueError("artifact id does not match filename")
-                if not all(isinstance(sensor, str) for sensor in supported_sensors):
-                    raise ValueError("supported_sensors must contain strings")
-                feature_models = _deserialize_feature_models(raw_feature_models)
-            except (KeyError, TypeError, ValueError, json.JSONDecodeError) as exc:
-                raise ValueError(f"Ungültiges Modell-Artefakt: {source.name}") from exc
-
-            self._artifacts[artifact_id] = TrainedArtifact(
-                artifact_id=artifact_id,
-                supported_sensors=tuple(supported_sensors),
-                feature_models=feature_models,
-                model_type=model_type,
-            )
-
-    def _persist(self, artifact: TrainedArtifact) -> None:
-        target = self._root / f"{artifact.artifact_id}.json"
-        temporary = target.with_suffix(".json.tmp")
-        payload = {
-            "artifact_id": artifact.artifact_id,
-            "supported_sensors": list(artifact.supported_sensors),
-            "model_type": artifact.model_type,
-            "feature_models": {
-                sensor_id: {
-                    feature_name: {
-                        "sample_count": model.sample_count,
-                        "mean": model.mean,
-                        "standard_deviation": model.standard_deviation,
-                        "minimum": model.minimum,
-                        "maximum": model.maximum,
-                        "slope": model.slope,
-                        "intercept": model.intercept,
-                    }
-                    for feature_name, model in sorted(models.items())
-                }
-                for sensor_id, models in sorted(artifact.feature_models.items())
-            },
-        }
-        temporary.write_text(
-            json.dumps(payload, ensure_ascii=True, sort_keys=True) + "\n",
-            encoding="utf-8",
-        )
-        os.replace(temporary, target)
-        logger.info("Modell gespeichert: %s", target)
-
-    @staticmethod
-    def _validate_artifact_id(artifact_id: str) -> None:
-        if not _ARTIFACT_ID_PATTERN.fullmatch(artifact_id) or ".." in artifact_id:
-            raise ValueError(
-                "artifact_id darf nur Buchstaben, Ziffern, Punkt, Unterstrich "
-                "und Bindestrich enthalten."
-            )
-
-
-def _deserialize_feature_models(raw: object) -> dict[str, dict[str, FeatureModel]]:
-    if not isinstance(raw, dict):
-        raise ValueError("feature_models must be an object")
-
-    result: dict[str, dict[str, FeatureModel]] = {}
-    for sensor_id, raw_features in raw.items():
-        if not isinstance(sensor_id, str) or not isinstance(raw_features, dict):
-            raise ValueError("invalid sensor feature models")
-        features: dict[str, FeatureModel] = {}
-        for feature_name, raw_model in raw_features.items():
-            if not isinstance(feature_name, str) or not isinstance(raw_model, dict):
-                raise ValueError("invalid feature model")
-            sample_count = raw_model.get("sample_count")
-            if not isinstance(sample_count, int) or isinstance(sample_count, bool) or sample_count < 1:
-                raise ValueError("sample_count must be a positive integer")
-            values = {
-                key: _finite_number(raw_model.get(key))
-                for key in (
-                    "mean",
-                    "standard_deviation",
-                    "minimum",
-                    "maximum",
-                    "slope",
-                    "intercept",
-                )
-            }
-            features[feature_name] = FeatureModel(
-                sample_count=sample_count,
-                mean=values["mean"],
-                standard_deviation=values["standard_deviation"],
-                minimum=values["minimum"],
-                maximum=values["maximum"],
-                slope=values["slope"],
-                intercept=values["intercept"],
-            )
-        result[sensor_id] = features
-    return result
-
-
-def _finite_number(value: object) -> float:
-    if not isinstance(value, (int, float)) or isinstance(value, bool):
-        raise ValueError("feature model values must be finite numbers")
-    converted = float(value)
-    if not math.isfinite(converted):
-        raise ValueError("feature model values must be finite numbers")
-    return converted

app/ml/retraining.py

@@ -1,43 +0,0 @@
-from __future__ import annotations
-
-from collections.abc import Iterable
-from dataclasses import dataclass
-
-from app.ml.feature_store import FeatureStore, FeatureVector
-from app.ml.registry.model_registry import ModelRegistry
-from app.ml.training import TrainedArtifact, TrainingPipeline
-
-
-@dataclass(frozen=True)
-class RetrainingResult:
-    artifact: TrainedArtifact
-    replaced: bool
-
-
-class RetrainingService:
-    """Runs one retraining cycle without owning scheduling or background threads."""
-
-    def __init__(self, registry: ModelRegistry) -> None:
-        self._registry = registry
-
-    def retrain(
-        self,
-        artifact_id: str,
-        vectors: Iterable[FeatureVector],
-    ) -> RetrainingResult:
-        store = FeatureStore()
-        store.add_batch(vectors)
-        pipeline = TrainingPipeline(store)
-        artifact = pipeline.run(artifact_id)
-        _, replaced = self._registry.register_with_status(artifact)
-        return RetrainingResult(artifact=artifact, replaced=replaced)
-
-
-def retrain_model(
-    registry: ModelRegistry,
-    artifact_id: str,
-    vectors: Iterable[FeatureVector],
-) -> RetrainingResult:
-    """Scheduler-compatible entry point for exactly one retraining run."""
-
-    return RetrainingService(registry).retrain(artifact_id, vectors)

app/ml/training.py

@@ -1,117 +0,0 @@
-from __future__ import annotations
-
-import logging
-import math
-from collections import defaultdict
-from dataclasses import dataclass, field
-
-from app.ml.feature_store import FeatureStore
-
-logger = logging.getLogger(__name__)
-
-
-@dataclass(frozen=True)
-class FeatureModel:
-    sample_count: int
-    mean: float
-    standard_deviation: float
-    minimum: float
-    maximum: float
-    slope: float
-    intercept: float
-
-    def forecast(self, current_value: float | None = None) -> float:
-        if current_value is not None:
-            return current_value + self.slope
-        return self.intercept + self.slope * self.sample_count
-
-    @property
-    def confidence(self) -> float:
-        sample_score = self.sample_count / (self.sample_count + 2)
-        scale = abs(self.mean) if abs(self.mean) > 1e-9 else 1.0
-        stability_score = 1.0 / (1.0 + self.standard_deviation / scale)
-        return min(0.99, max(0.05, sample_score * stability_score))
-
-
-@dataclass(frozen=True)
-class TrainedArtifact:
-    artifact_id: str
-    supported_sensors: tuple[str, ...]
-    feature_models: dict[str, dict[str, FeatureModel]] = field(default_factory=dict)
-    model_type: str = "statistical_baseline"
-
-
-class TrainingPipeline:
-    def __init__(self, store: FeatureStore) -> None:
-        self._store = store
-        self._artifacts: dict[str, TrainedArtifact] = {}
-
-    def run(self, artifact_id: str) -> TrainedArtifact:
-        vectors = self._store.all()
-        if not vectors:
-            raise ValueError("FeatureStore enthält keine Trainingsdaten.")
-
-        samples: dict[str, dict[str, list[float]]] = defaultdict(lambda: defaultdict(list))
-        for vector in vectors:
-            for feature_name, raw_value in vector.values.items():
-                value = float(raw_value)
-                if math.isfinite(value):
-                    samples[vector.sensor_id][feature_name].append(value)
-
-        feature_models = {
-            sensor_id: {
-                feature_name: _fit_feature(values)
-                for feature_name, values in sorted(features.items())
-                if values
-            }
-            for sensor_id, features in sorted(samples.items())
-        }
-        feature_models = {
-            sensor_id: models for sensor_id, models in feature_models.items() if models
-        }
-        if not feature_models:
-            raise ValueError("Trainingsdaten enthalten keine endlichen numerischen Werte.")
-
-        sensors = tuple(feature_models)
-        artifact = TrainedArtifact(
-            artifact_id=artifact_id,
-            supported_sensors=sensors,
-            feature_models=feature_models,
-        )
-        self._artifacts[artifact_id] = artifact
-        logger.info("Training abgeschlossen für %s mit %d Sensoren", artifact_id, len(sensors))
-        return artifact
-
-    def export(self, artifact_id: str) -> TrainedArtifact:
-        if artifact_id not in self._artifacts:
-            raise KeyError(f"Artifact '{artifact_id}' nicht gefunden.")
-        return self._artifacts[artifact_id]
-
-
-def _fit_feature(values: list[float]) -> FeatureModel:
-    sample_count = len(values)
-    mean = sum(values) / sample_count
-    variance = sum((value - mean) ** 2 for value in values) / sample_count
-    standard_deviation = math.sqrt(variance)
-
-    if sample_count == 1:
-        slope = 0.0
-        intercept = mean
-    else:
-        x_mean = (sample_count - 1) / 2
-        denominator = sum((index - x_mean) ** 2 for index in range(sample_count))
-        numerator = sum(
-            (index - x_mean) * (value - mean) for index, value in enumerate(values)
-        )
-        slope = numerator / denominator
-        intercept = mean - slope * x_mean
-
-    return FeatureModel(
-        sample_count=sample_count,
-        mean=mean,
-        standard_deviation=standard_deviation,
-        minimum=min(values),
-        maximum=max(values),
-        slope=slope,
-        intercept=intercept,
-    )

app/rules/heating.py

@@ -6,29 +6,26 @@ from app.ha.models import HaEntitySummary
 from app.rules.recommender import Rule
 
 
+HEATING_SENSOR_DEVICE_CLASSES = frozenset({"temperature", "humidity"})
+HEATING_BINARY_SENSOR_DEVICE_CLASSES = frozenset({"occupancy", "presence"})
+
+
 class HeatingRule(Rule):
-    """Heizungsregel: Nur auf heizungsrelevante Entitäten reagieren.
-
-    Triggert bei:
-    - `climate`-Entitäten direkt
-    - `sensor` mit `device_class` in {temperature, humidity}
-    - `binary_sensor` mit `device_class` in {occupancy, presence}
-
-    Alle anderen Domains/Device-Klassen bleiben ohne Effekt.
-    """
-
-    HEATING_SENSOR_CLASSES: frozenset[str] = frozenset({"temperature", "humidity"})
-    HEATING_PRESENCE_CLASSES: frozenset[str] = frozenset({"occupancy", "presence"})
-
     def matches(self, entities: Sequence[HaEntitySummary]) -> bool:
-        for item in entities:
+        for entity in entities:
-            if item.domain == "climate":
+            if entity.domain == "climate":
                 return True
-            if item.domain == "sensor" and item.device_class in self.HEATING_SENSOR_CLASSES:
+            if (
+                entity.domain == "sensor"
+                and entity.device_class in HEATING_SENSOR_DEVICE_CLASSES
+            ):
                 return True
-            if item.domain == "binary_sensor" and item.device_class in self.HEATING_PRESENCE_CLASSES:
+            if (
+                entity.domain == "binary_sensor"
+                and entity.device_class in HEATING_BINARY_SENSOR_DEVICE_CLASSES
+            ):
                 return True
         return False
 
     def recommendation(self, entities: Sequence[HaEntitySummary]) -> str:
-        return "Prüfe Heizungsregelung: Aktiviere energiesparenden Modus bei Abwesenheit."
+        return "Prüfe Heizungsregelung: Aktiviere energiesparenden Modus bei Abwesenheit."

backend/__init__.py

@@ -1 +0,0 @@
-"""Secondary application entry points for SillyHome Next."""

backend/app.py

@@ -1,43 +0,0 @@
-from collections.abc import AsyncIterator
-from contextlib import asynccontextmanager
-
-from fastapi import FastAPI
-from starlette.datastructures import State
-
-from backend.routes.ml import init_ml_routes
-from app.ml.registry.model_registry import ModelRegistry
-from app.ml.training import TrainingPipeline
-from app.ml.feature_store import FeatureStore, FeatureVector
-
-
-@asynccontextmanager
-async def lifespan(application: FastAPI) -> AsyncIterator[None]:
-    application.state.registry = ModelRegistry(application.state.model_store)
-    _seed_default_model(application.state)
-    yield
-
-
-def create_app() -> FastAPI:
-    application = FastAPI(title="SillyHome Next ML", lifespan=lifespan)
-    init_ml_routes(application)
-    return application
-
-
-def _seed_default_model(state: State) -> None:
-    registry = getattr(state, "registry", None)
-    if registry is None:
-        registry = ModelRegistry(".model_store")
-        state.registry = registry
-
-    if list(registry.list_models()):
-        return
-
-    store = FeatureStore()
-    store.add(FeatureVector(sensor_id="sensor.front_door", values={"contact": 1.0}))
-    store.add(FeatureVector(sensor_id="sensor.living_room", values={"temperature": 21.0}))
-    pipeline = TrainingPipeline(store)
-    artifact = pipeline.run("default")
-    registry.register(artifact)
-
-
-app = create_app()

backend/routes/__init__.py

@@ -1 +0,0 @@
-"""API route modules."""

backend/routes/ml.py

@@ -1,253 +0,0 @@
-from __future__ import annotations
-
-import logging
-from datetime import datetime, timezone
-from collections.abc import Sequence
-
-from fastapi import APIRouter, FastAPI, HTTPException, Request, status
-from pydantic import BaseModel, Field
-
-from app.ml.evaluation import Evaluator
-from app.ml.feature_store import FeatureVector
-from app.ml.predictor import Predictor
-from app.ml.registry.model_registry import ModelRegistry
-from app.ml.retraining import retrain_model
-
-logger = logging.getLogger(__name__)
-
-router = APIRouter(prefix="/ml", tags=["ml"])
-
-
-class HealthResponse(BaseModel):
-    status: str
-    updated_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
-
-
-class PredictRequest(BaseModel):
-    model_id: str = Field(..., alias="modelId")
-    sensor_id: str
-    values: dict[str, float]
-
-
-class PredictResponse(BaseModel):
-    model_id: str
-    sensor_id: str
-    predictions: dict[str, float]
-    confidence: float
-    model_type: str
-    explanations: dict[str, "FeatureExplanationResponse"]
-
-
-class FeatureExplanationResponse(BaseModel):
-    feature: str
-    current_value: float
-    predicted_value: float
-    change: float
-    direction: str
-    sample_count: int
-    historical_mean: float
-    historical_range: tuple[float, float]
-    standard_deviation: float
-    trend_per_step: float
-    confidence: float
-    summary: str
-
-
-class BatchRequest(BaseModel):
-    requests: Sequence[PredictRequest]
-
-
-class BatchResponse(BaseModel):
-    predictions: Sequence[PredictResponse]
-
-
-class ModelsResponse(BaseModel):
-    models: list[str]
-
-
-class TrainingSample(BaseModel):
-    sensor_id: str = Field(min_length=1)
-    values: dict[str, float]
-    label: str | None = None
-
-
-class RetrainRequest(BaseModel):
-    model_id: str = Field(..., alias="modelId", min_length=1, max_length=128)
-    samples: list[TrainingSample] = Field(min_length=1)
-
-
-class RetrainResponse(BaseModel):
-    model_id: str
-    supported_sensors: list[str]
-    trained_features: int
-    model_type: str
-    replaced: bool
-
-
-class EvaluateRequest(BaseModel):
-    model_id: str = Field(..., alias="modelId", min_length=1, max_length=128)
-    samples: list[TrainingSample] = Field(min_length=1)
-
-
-class MetricResponse(BaseModel):
-    name: str
-    value: float
-    threshold: float | None = None
-
-
-class EvaluateResponse(BaseModel):
-    model_id: str
-    sample_size: int
-    metrics: list[MetricResponse]
-
-
-@router.get("/health", response_model=HealthResponse, status_code=200)
-def health() -> HealthResponse:
-    return HealthResponse(status="ok")
-
-
-@router.get("/models", response_model=ModelsResponse, status_code=200)
-def list_models(request: Request) -> ModelsResponse:
-    registry = _require_registry(request)
-    models = [artifact.artifact_id for artifact in registry.list_models()]
-    return ModelsResponse(models=models)
-
-
-@router.post("/retrain", response_model=RetrainResponse, status_code=200)
-def retrain(payload: RetrainRequest, request: Request) -> RetrainResponse:
-    registry = _require_registry(request)
-    vectors = [
-        FeatureVector(
-            sensor_id=sample.sensor_id,
-            values=sample.values,
-            label=sample.label,
-        )
-        for sample in payload.samples
-    ]
-    try:
-        result = retrain_model(registry, payload.model_id, vectors)
-    except ValueError as exc:
-        raise HTTPException(
-            status_code=status.HTTP_422_UNPROCESSABLE_CONTENT,
-            detail=str(exc),
-        ) from exc
-    return RetrainResponse(
-        model_id=result.artifact.artifact_id,
-        supported_sensors=list(result.artifact.supported_sensors),
-        trained_features=sum(
-            len(feature_models)
-            for feature_models in result.artifact.feature_models.values()
-        ),
-        model_type=result.artifact.model_type,
-        replaced=result.replaced,
-    )
-
-
-@router.post("/evaluate", response_model=EvaluateResponse, status_code=200)
-def evaluate(payload: EvaluateRequest, request: Request) -> EvaluateResponse:
-    registry = _require_registry(request)
-    vectors = [
-        FeatureVector(
-            sensor_id=sample.sensor_id,
-            values=sample.values,
-            label=sample.label,
-        )
-        for sample in payload.samples
-    ]
-    try:
-        report = Evaluator(registry=registry).evaluate(payload.model_id, vectors)
-    except ValueError as exc:
-        try:
-            registry.load_artifact(payload.model_id)
-        except KeyError:
-            raise HTTPException(
-                status_code=status.HTTP_404_NOT_FOUND,
-                detail=str(exc),
-            ) from exc
-        raise HTTPException(
-            status_code=status.HTTP_422_UNPROCESSABLE_CONTENT,
-            detail=str(exc),
-        ) from exc
-    return EvaluateResponse(
-        model_id=report.artifact_id,
-        sample_size=report.sample_size,
-        metrics=[
-            MetricResponse(name=metric.name, value=metric.value, threshold=metric.threshold)
-            for metric in report.metrics
-        ],
-    )
-
-
-@router.post("/predict", response_model=PredictResponse, status_code=200)
-def predict(payload: PredictRequest, request: Request) -> PredictResponse:
-    registry = _require_registry(request)
-    predictor = Predictor(registry=registry)
-    vector = FeatureVector(sensor_id=payload.sensor_id, values=payload.values)
-    try:
-        prediction = predictor.predict(payload.model_id, vector)
-    except KeyError as exc:
-        raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail=str(exc)) from exc
-    except ValueError as exc:
-        raise HTTPException(
-            status_code=status.HTTP_422_UNPROCESSABLE_CONTENT,
-            detail=str(exc),
-        ) from exc
-    return PredictResponse(
-        model_id=payload.model_id,
-        sensor_id=payload.sensor_id,
-        predictions=prediction.predictions,
-        confidence=prediction.confidence,
-        model_type=prediction.model_type,
-        explanations={
-            name: FeatureExplanationResponse(**explanation.__dict__)
-            for name, explanation in prediction.explanations.items()
-        },
-    )
-
-
-@router.post("/batch", response_model=BatchResponse, status_code=200)
-def predict_batch(payload: BatchRequest, request: Request) -> BatchResponse:
-    registry = _require_registry(request)
-    predictor = Predictor(registry=registry)
-    responses: list[PredictResponse] = []
-    for item in payload.requests:
-        vector = FeatureVector(sensor_id=item.sensor_id, values=item.values)
-        try:
-            prediction = predictor.predict(item.model_id, vector)
-        except KeyError as exc:
-            raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail=str(exc)) from exc
-        except ValueError as exc:
-            raise HTTPException(
-                status_code=status.HTTP_422_UNPROCESSABLE_CONTENT,
-                detail=str(exc),
-            ) from exc
-        responses.append(
-            PredictResponse(
-                model_id=item.model_id,
-                sensor_id=item.sensor_id,
-                predictions=prediction.predictions,
-                confidence=prediction.confidence,
-                model_type=prediction.model_type,
-                explanations={
-                    name: FeatureExplanationResponse(**explanation.__dict__)
-                    for name, explanation in prediction.explanations.items()
-                },
-            )
-        )
-    return BatchResponse(predictions=responses)
-
-
-def _require_registry(request: Request) -> ModelRegistry:
-    registry = getattr(request.app.state, "registry", None)
-    if not isinstance(registry, ModelRegistry):
-        raise HTTPException(
-            status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
-            detail="ML registry nicht initialisiert.",
-        )
-    return registry
-
-
-def init_ml_routes(app: FastAPI, model_store: str = ".model_store") -> None:
-    app.state.model_store = model_store
-    app.include_router(router)
-    logger.info("ML routes registered")

docker-compose.yml

@@ -1,23 +0,0 @@
-services:
-  api:
-    build: .
-    ports:
-      - "127.0.0.1:8000:8000"
-    env_file:
-      - path: .env
-        required: false
-    environment:
-      SILLYHOME_MODEL_STORE: /app/data/models
-    volumes:
-      - model-data:/app/data/models
-    read_only: true
-    tmpfs:
-      - /tmp
-    security_opt:
-      - no-new-privileges:true
-    cap_drop:
-      - ALL
-    restart: unless-stopped
-
-volumes:
-  model-data:

docs/ha_data.md

@@ -1,42 +0,0 @@
-# Home-Assistant-Datenpipeline
-
-SillyHome Next trennt aktuelle Entity-Metadaten, Discovery und historische
-Messwerte. Dadurch gelangen nur klassifizierte, geeignete Daten in spätere
-Trainings- und Erklärungsprozesse.
-
-## Entity Discovery
-
-`GET /v1/discovery` klassifiziert Home-Assistant-Entities in:
-
-- `measurement`: numerische Messsensoren, für Training geeignet
-- `binary_context`: binäre Kontextsensoren wie Bewegung oder Anwesenheit
-- `context`: Personen-, Wetter- und Standortkontext
-- `actuator`: mögliche Automationsziele, nicht als Trainingssensor verwendet
-- `unsupported`: noch nicht klassifizierte Entity-Typen
-
-Optionale Query-Parameter:
-
-- `domain=sensor` kann mehrfach angegeben werden
-- `learnable=true|false` filtert nach Trainingsrelevanz
-
-## Historische Daten
-
-Historische Zustände werden über Home Assistants
-`/api/history/period/<start>`-Schnittstelle geladen. Abfragen verlangen:
-
-- mindestens eine Entity-ID, maximal 100
-- zeitzonenbehaftete Start- und Endzeit
-- ein Enddatum nach dem Startdatum
-- maximal 31 Tage pro Abfrage
-
-Die Normalisierung übernimmt nur endliche numerische Zustände. `unknown`,
-`unavailable`, nichtnumerische Werte, `NaN` und unendliche Werte werden nicht
-als Trainingsdaten verwendet. Ergebnisse werden je Entity chronologisch
-sortiert.
-
-## Datenschutz und Betrieb
-
-Die Daten bleiben lokal. Home-Assistant-Tokens gehören ausschließlich in die
-Umgebungskonfiguration und dürfen nicht protokolliert oder versioniert werden.
-Die API sollte nur lokal oder hinter einem authentifizierenden Reverse Proxy
-erreichbar sein.

docs/ml_api.md

@@ -1,185 +0,0 @@
-# ML-Serving-API
-
-Diese Dokumentation beschreibt die REST-Endpunkte der aktuellen
-Modell-Artefakt- und Vorhersage-Schnittstelle.
-
-Das Serving verwendet ein lokal trainiertes statistisches Baseline-Modell.
-
-## Basis-URL
-
-- Standard: `http://127.0.0.1:8000/ml`
-- Health: `/health`
-- Modelle: `/models`
-- Retraining: `/retrain`
-- Evaluation: `/evaluate`
-- Einzelvorhersage: `/predict`
-- Batchvorhersage: `/batch`
-
-Der Standard-Start erfolgt über `uvicorn app.main:app`, danach stehen HA- und
-ML-Routen in derselben Anwendung bereit.
-
-## Endpoints
-
-### `GET /ml/health`
-
-Health-Check der ML-Services.
-
-**Beispielantwort**
-```json
-{
-  "status": "ok",
-  "updated_at": "2026-06-11T12:00:00Z"
-}
-```
-
-### `GET /ml/models`
-
-Listet alle registrierten Modell-Artefakte auf.
-
-**Beispielantwort**
-```json
-{
-  "models": ["default"]
-}
-```
-
-### `POST /ml/predict`
-
-Einzelne Vorhersage für einen Sensor.
-
-**Request**
-```json
-{
-  "modelId": "default",
-  "sensor_id": "sensor.kitchen",
-  "values": {"temperature": 21.0}
-}
-```
-
-**Antwort**
-```json
-{
-  "model_id": "default",
-  "sensor_id": "sensor.kitchen",
-  "predictions": {"temperature": 21.4},
-  "confidence": 0.78,
-  "model_type": "statistical_baseline",
-  "explanations": {
-    "temperature": {
-      "direction": "steigend",
-      "change": 0.4,
-      "sample_count": 24,
-      "historical_mean": 20.7,
-      "trend_per_step": 0.4,
-      "summary": "temperature: steigend; Prognose ..."
-    }
-  }
-}
-```
-
-Die Erklärung nennt pro Merkmal den aktuellen und prognostizierten Wert,
-Richtung, Veränderung, Datenbasis, historischen Bereich, Streuung, Trend und
-Confidence. Sie wird deterministisch aus den gespeicherten Modellparametern
-erzeugt.
-
-### `POST /ml/retrain`
-
-Trainiert die Artefakt-Metadaten aus neuen Sensordaten. Existiert `modelId`
-bereits, wird das Artefakt atomisch ersetzt und beim nächsten Prozessstart aus
-dem Modellverzeichnis geladen.
-
-**Request**
-```json
-{
-  "modelId": "home-model",
-  "samples": [
-    {
-      "sensor_id": "sensor.kitchen",
-      "values": {"temperature": 21.0},
-      "label": "occupied"
-    }
-  ]
-}
-```
-
-**Antwort**
-```json
-{
-  "model_id": "home-model",
-  "supported_sensors": ["sensor.kitchen"],
-  "trained_features": 1,
-  "model_type": "statistical_baseline",
-  "replaced": false
-}
-```
-
-### `POST /ml/evaluate`
-
-Vergleicht Modellvorhersagen mit Validierungsdaten und liefert MAE, RMSE und
-Coverage. Der Request verwendet dasselbe Sample-Format wie `/ml/retrain`.
-
-### `POST /ml/batch`
-
-Batch-Vorhersage für mehrere Sensorwerte.
-
-**Request**
-```json
-{
-  "requests": [
-    {
-      "modelId": "default",
-      "sensor_id": "sensor.kitchen",
-      "values": {"temperature": 21.0}
-    },
-    {
-      "modelId": "default",
-      "sensor_id": "sensor.bedroom",
-      "values": {"temperature": 18.5}
-    }
-  ]
-}
-```
-
-**Antwort**
-```json
-{
-  "predictions": [
-    {
-      "model_id": "default",
-      "sensor_id": "sensor.kitchen",
-      "predictions": {"temperature": 21.4},
-      "confidence": 0.78,
-      "model_type": "statistical_baseline"
-    },
-    {
-      "model_id": "default",
-      "sensor_id": "sensor.bedroom",
-      "predictions": {"temperature": 18.3},
-      "confidence": 0.74,
-      "model_type": "statistical_baseline"
-    }
-  ]
-}
-```
-
-## Fehlerfälle
-
-- `404 Not Found`: Modell nicht registriert.
-- `422 Unprocessable Content`: Sensor wird vom Modell nicht unterstützt oder Eingabe ist ungültig.
-- `503 Service Unavailable`: Registry ist nicht initialisiert.
-
-## Betrieb
-
-Die produktive App lädt Artefakte aus `SILLYHOME_MODEL_STORE`. Neue Artefakte
-werden über `/ml/retrain`, `RetrainingService` oder direkt über
-`ModelRegistry.register(...)` registriert. Die Registry speichert validiertes
-JSON atomisch und lädt es beim Neustart. Die API sollte nur in einem
-vertrauenswürdigen Netz oder hinter einem authentifizierenden Reverse Proxy
-erreichbar sein.
-
-## Verweise
-
-- `app/ml/predictor.py`
-- `app/ml/retraining.py`
-- `app/ml/registry/model_registry.py`
-- `backend/routes/ml.py`

docs/ml_training.md

@@ -1,68 +0,0 @@
-# ML Training- und Evaluations-Workflow
-
-SillyHome Next trainiert ein lokales statistisches Baseline-Modell pro Sensor
-und Merkmal. Es benötigt keine Cloud und keine externe ML-Laufzeit.
-
-## 1. Daten sammeln
-
-Alle Trainingsvektoren werden über `FeatureStore.add(...)` oder `add_batch(...)` eingepflegt. Jeder Vektor enthält eine Sensor-ID sowie ein Dictionary mit Merkmalen.
-
-## 2. Statistisches Artefakt erzeugen
-
-```python
-store = FeatureStore()
-store.add(FeatureVector(sensor_id="sensor.kitchen", values={"temperature": 21.0}))
-pipeline = TrainingPipeline(store)
-artifact = pipeline.run("my_artifact")
-pipeline.export("my_artifact")
-```
-
-`TrainingPipeline.run(...)` berechnet für jedes numerische Merkmal:
-
-- Stichprobenzahl
-- Mittelwert und Standardabweichung
-- Minimum und Maximum
-- linearen Trend mit Steigung und Achsenabschnitt
-
-Die nächste Vorhersage kombiniert den letzten beobachteten Wert mit der
-trainierten Trendsteigung. Die Confidence berücksichtigt Datenmenge und
-Stabilität.
-
-## 3. Modell evaluieren
-
-```python
-evaluator = Evaluator(pipeline)
-report = evaluator.evaluate(artifact.artifact_id, validation_samples)
-```
-
-Der Report enthält echte numerische Vergleichsmetriken:
-- `artifact_id`
-- `sample_size`
-- `mae` (Mean Absolute Error)
-- `rmse` (Root Mean Squared Error)
-- `coverage` für den Anteil auswertbarer Merkmale
-
-## 4. Modell registrieren
-
-Das trainierte Artefakt kann anschließend über `ModelRegistry.register(artifact)` bereitgestellt werden. Die ML-Serving-API stellt es unter `/ml/predict` und `/ml/batch` zur Verfügung.
-
-## 5. Retraining ausführen
-
-`RetrainingService.retrain(...)` führt genau einen Trainingslauf aus und ersetzt
-ein vorhandenes Artefakt mit derselben ID atomisch in der Registry:
-
-```python
-service = RetrainingService(registry)
-result = service.retrain("home-model", vectors)
-```
-
-Scheduler, Cronjobs oder Home-Assistant-Automationen können alternativ die
-zustandslose Funktion `retrain_model(registry, artifact_id, vectors)` aufrufen.
-Der Service startet bewusst keinen eigenen Hintergrundprozess. Über
-`POST /ml/retrain` kann derselbe Ablauf per API angestoßen werden.
-
-## Hinweise
-- Für reproduzierbare Sensor-Reihenfolgen wird in `TrainingPipeline.run(...)` eine sortierte Sensor-Liste verwendet.
-- Fehlende Trainingsdaten lösen `ValueError` aus; nicht registrierte Artefakte lösen `KeyError` aus.
-- Nur endliche numerische Werte werden trainiert.
-- `coverage` bleibt im Bereich 0 bis 1.

pyproject.toml

@@ -1,10 +1,6 @@
-[build-system]
-requires = ["setuptools>=69"]
-build-backend = "setuptools.build_meta"
-
 [project]
 name = "sillyhome-next"
-version = "0.2.0"
+version = "0.1.0"
 description = "Lokales Smart-Home-Intelligenzsystem für Home Assistant"
 requires-python = ">=3.11"
 dependencies = [
@@ -28,10 +24,6 @@ addopts = "-q"
 
 [tool.mypy]
 strict = true
-files = ["app", "backend", "tests"]
-
-[tool.setuptools.packages.find]
-include = ["app*", "backend*"]
 
 [tool.ruff]
 line-length = 100

tests/api/test_entities.py

@@ -1,11 +1,8 @@
 from collections.abc import Sequence
-from datetime import datetime
 
 from fastapi.testclient import TestClient
 
 from app.ha.exceptions import HaTimeoutError
-from app.ha.discovery import DiscoveredEntity, EntityRole
-from app.ha.history import EntityHistorySeries, NumericHistoryPoint
 from app.ha.models import HaEntitySummary
 from app.ha.reader import HaReader
 from app.main import app
@@ -18,38 +15,6 @@ class FakeHaReader(HaReader):
     def read_entities(self) -> Sequence[HaEntitySummary]:
         return [HaEntitySummary(entity_id="sensor.temperature", domain="sensor")]
 
-    def discover(
-        self,
-        domains: set[str] | None = None,
-        learnable: bool | None = None,
-    ) -> Sequence[DiscoveredEntity]:
-        result = DiscoveredEntity(
-            entity_id="sensor.temperature",
-            domain="sensor",
-            device_class="temperature",
-            role=EntityRole.MEASUREMENT,
-            learnable=True,
-            reason="Numerischer Messsensor für Zeitreihen und Training.",
-        )
-        if domains and result.domain not in domains:
-            return []
-        if learnable is not None and result.learnable is not learnable:
-            return []
-        return [result]
-
-    def read_history(
-        self,
-        entity_ids: list[str],
-        start_time: datetime,
-        end_time: datetime,
-    ) -> Sequence[EntityHistorySeries]:
-        return [
-            EntityHistorySeries(
-                entity_id=entity_ids[0],
-                points=[NumericHistoryPoint(timestamp=start_time, value=21.5)],
-            )
-        ]
-
 
 class TimeoutHaReader(HaReader):
     def __init__(self) -> None:
@@ -84,6 +49,8 @@ def test_entities_returns_reader_data() -> None:
 
 def test_entities_returns_503_without_home_assistant_config() -> None:
     with TestClient(app) as client:
+        if hasattr(app.state, "ha_reader"):
+            delattr(app.state, "ha_reader")
         response = client.get("/v1/entities")
     assert response.status_code == 503
 
@@ -94,44 +61,3 @@ def test_entities_maps_ha_errors_without_leaking_details() -> None:
         response = client.get("/v1/entities")
     assert response.status_code == 504
     assert response.json() == {"detail": "Home Assistant request timed out."}
-
-
-def test_discovery_filters_entities() -> None:
-    with TestClient(app) as client:
-        app.state.ha_reader = FakeHaReader()
-        response = client.get("/v1/discovery?domain=sensor&learnable=true")
-
-    assert response.status_code == 200
-    assert response.json() == [
-        {
-            "entity_id": "sensor.temperature",
-            "domain": "sensor",
-            "device_class": "temperature",
-            "state_class": None,
-            "unit_of_measurement": None,
-            "role": "measurement",
-            "learnable": True,
-            "reason": "Numerischer Messsensor für Zeitreihen und Training.",
-        }
-    ]
-
-
-def test_history_returns_normalized_series() -> None:
-    with TestClient(app) as client:
-        app.state.ha_reader = FakeHaReader()
-        response = client.get(
-            "/v1/history",
-            params=[
-                ("entity_id", "sensor.temperature"),
-                ("start_time", "2026-06-01T00:00:00Z"),
-                ("end_time", "2026-06-02T00:00:00Z"),
-            ],
-        )
-
-    assert response.status_code == 200
-    assert response.json() == [
-        {
-            "entity_id": "sensor.temperature",
-            "points": [{"timestamp": "2026-06-01T00:00:00Z", "value": 21.5}],
-        }
-    ]

tests/api/test_ml_routes.py

@@ -1,184 +0,0 @@
-from __future__ import annotations
-
-from pathlib import Path
-
-from fastapi.testclient import TestClient
-
-from app.main import app
-
-
-def test_ml_routes_are_exposed_by_production_app() -> None:
-    with TestClient(app) as client:
-        health = client.get("/ml/health")
-        models = client.get("/ml/models")
-
-    assert health.status_code == 200
-    assert models.status_code == 200
-    assert isinstance(models.json()["models"], list)
-
-
-def test_unknown_model_returns_404() -> None:
-    with TestClient(app) as client:
-        response = client.post(
-            "/ml/predict",
-            json={
-                "modelId": "missing",
-                "sensor_id": "sensor.kitchen",
-                "values": {"temperature": 21.0},
-            },
-        )
-
-    assert response.status_code == 404
-
-
-def test_unsupported_sensor_returns_422(tmp_path: Path) -> None:
-    from app.ml.registry.model_registry import ModelRegistry
-    from app.ml.training import TrainedArtifact
-
-    registry = ModelRegistry(tmp_path)
-    registry.register(TrainedArtifact("default", ("sensor.kitchen",)))
-
-    with TestClient(app) as client:
-        app.state.registry = registry
-        response = client.post(
-            "/ml/predict",
-            json={
-                "modelId": "default",
-                "sensor_id": "sensor.unknown",
-                "values": {"temperature": 21.0},
-            },
-        )
-
-    assert response.status_code == 422
-
-
-def test_retrain_creates_and_replaces_persisted_model(tmp_path: Path) -> None:
-    from app.ml.registry.model_registry import ModelRegistry
-
-    registry = ModelRegistry(tmp_path)
-    with TestClient(app) as client:
-        app.state.registry = registry
-        created = client.post(
-            "/ml/retrain",
-            json={
-                "modelId": "home-model",
-                "samples": [
-                    {
-                        "sensor_id": "sensor.kitchen",
-                        "values": {"temperature": 21.0},
-                    }
-                ],
-            },
-        )
-        replaced = client.post(
-            "/ml/retrain",
-            json={
-                "modelId": "home-model",
-                "samples": [
-                    {
-                        "sensor_id": "sensor.bedroom",
-                        "values": {"temperature": 18.0},
-                    }
-                ],
-            },
-        )
-
-    assert created.status_code == 200
-    assert created.json() == {
-        "model_id": "home-model",
-        "supported_sensors": ["sensor.kitchen"],
-        "trained_features": 1,
-        "model_type": "statistical_baseline",
-        "replaced": False,
-    }
-    assert replaced.status_code == 200
-    assert replaced.json() == {
-        "model_id": "home-model",
-        "supported_sensors": ["sensor.bedroom"],
-        "trained_features": 1,
-        "model_type": "statistical_baseline",
-        "replaced": True,
-    }
-    restarted = ModelRegistry(tmp_path)
-    assert restarted.load_artifact("home-model").supported_sensors == ("sensor.bedroom",)
-
-
-def test_retrain_rejects_empty_samples() -> None:
-    with TestClient(app) as client:
-        response = client.post(
-            "/ml/retrain",
-            json={"modelId": "home-model", "samples": []},
-        )
-
-    assert response.status_code == 422
-
-
-def test_predict_returns_numeric_forecast_and_confidence(tmp_path: Path) -> None:
-    from app.ml.feature_store import FeatureStore, FeatureVector
-    from app.ml.registry.model_registry import ModelRegistry
-    from app.ml.training import TrainingPipeline
-
-    store = FeatureStore()
-    store.add_batch(
-        [
-            FeatureVector("sensor.kitchen", {"temperature": 19.0}),
-            FeatureVector("sensor.kitchen", {"temperature": 20.0}),
-        ]
-    )
-    registry = ModelRegistry(tmp_path)
-    registry.register(TrainingPipeline(store).run("home-model"))
-
-    with TestClient(app) as client:
-        app.state.registry = registry
-        response = client.post(
-            "/ml/predict",
-            json={
-                "modelId": "home-model",
-                "sensor_id": "sensor.kitchen",
-                "values": {"temperature": 21.0},
-            },
-        )
-
-    assert response.status_code == 200
-    assert response.json()["predictions"] == {"temperature": 22.0}
-    assert 0.0 < response.json()["confidence"] <= 1.0
-    assert response.json()["model_type"] == "statistical_baseline"
-    explanation = response.json()["explanations"]["temperature"]
-    assert explanation["direction"] == "steigend"
-    assert explanation["change"] == 1.0
-    assert explanation["sample_count"] == 2
-
-
-def test_evaluate_returns_real_error_metrics(tmp_path: Path) -> None:
-    from app.ml.feature_store import FeatureStore, FeatureVector
-    from app.ml.registry.model_registry import ModelRegistry
-    from app.ml.training import TrainingPipeline
-
-    store = FeatureStore()
-    store.add_batch(
-        [
-            FeatureVector("sensor.kitchen", {"temperature": 19.0}),
-            FeatureVector("sensor.kitchen", {"temperature": 20.0}),
-        ]
-    )
-    registry = ModelRegistry(tmp_path)
-    registry.register(TrainingPipeline(store).run("home-model"))
-
-    with TestClient(app) as client:
-        app.state.registry = registry
-        response = client.post(
-            "/ml/evaluate",
-            json={
-                "modelId": "home-model",
-                "samples": [
-                    {
-                        "sensor_id": "sensor.kitchen",
-                        "values": {"temperature": 21.0},
-                    }
-                ],
-            },
-        )
-
-    assert response.status_code == 200
-    metrics = {metric["name"]: metric["value"] for metric in response.json()["metrics"]}
-    assert metrics == {"mae": 1.0, "rmse": 1.0, "coverage": 1.0}

tests/ha/test_discovery.py

@@ -1,76 +0,0 @@
-from __future__ import annotations
-
-import pytest
-
-from app.ha.discovery import EntityRole, classify_entity, discover_entities
-from app.ha.models import HaEntitySummary
-
-
-@pytest.mark.parametrize(
-    ("entity", "role", "learnable"),
-    [
-        (
-            HaEntitySummary(
-                entity_id="sensor.temperature",
-                domain="sensor",
-                device_class="temperature",
-                state_class="measurement",
-                unit_of_measurement="°C",
-            ),
-            EntityRole.MEASUREMENT,
-            True,
-        ),
-        (
-            HaEntitySummary(
-                entity_id="binary_sensor.motion",
-                domain="binary_sensor",
-                device_class="motion",
-            ),
-            EntityRole.BINARY_CONTEXT,
-            True,
-        ),
-        (
-            HaEntitySummary(entity_id="person.simon", domain="person"),
-            EntityRole.CONTEXT,
-            True,
-        ),
-        (
-            HaEntitySummary(entity_id="light.living_room", domain="light"),
-            EntityRole.ACTUATOR,
-            False,
-        ),
-        (
-            HaEntitySummary(entity_id="camera.driveway", domain="camera"),
-            EntityRole.UNSUPPORTED,
-            False,
-        ),
-    ],
-)
-def test_classify_entity(
-    entity: HaEntitySummary,
-    role: EntityRole,
-    learnable: bool,
-) -> None:
-    result = classify_entity(entity)
-    assert result.role is role
-    assert result.learnable is learnable
-
-
-def test_discovery_filters_domain_and_learnable() -> None:
-    entities = [
-        HaEntitySummary(
-            entity_id="sensor.temperature",
-            domain="sensor",
-            device_class="temperature",
-        ),
-        HaEntitySummary(entity_id="sensor.status", domain="sensor"),
-        HaEntitySummary(
-            entity_id="binary_sensor.motion",
-            domain="binary_sensor",
-            device_class="motion",
-        ),
-    ]
-
-    result = discover_entities(entities, domains={" SENSOR "}, learnable=True)
-
-    assert [item.entity_id for item in result] == ["sensor.temperature"]

tests/ha/test_ha_client.py

@@ -1,6 +1,5 @@
 from __future__ import annotations
 
-from datetime import datetime, timezone
 from unittest.mock import Mock
 
 import pytest
@@ -16,7 +15,7 @@ from app.ha.exceptions import (
 
 
 def _client_with_response(response: Mock) -> HaClient:
-    client = HaClient(HaClientSettings(url="http://ha.local", token="test-token"))
+    client = HaClient(HaClientSettings(url="http://ha.local", token="secret-token"))
     client._session.get = Mock(return_value=response)  # type: ignore[method-assign]
     return client
 
@@ -33,12 +32,14 @@ def _response(status_code: int = 200, payload: object | None = None) -> Mock:
 def test_list_entities_returns_home_assistant_payload() -> None:
     payload = [{"entity_id": "sensor.temperature", "state": "21"}]
     client = _client_with_response(_response(payload=payload))
+
     assert client.list_entities() == payload
 
 
 def test_list_entities_maps_timeout() -> None:
-    client = HaClient(HaClientSettings(url="http://ha.local", token="test-token"))
+    client = HaClient(HaClientSettings(url="http://ha.local", token="secret-token"))
-    client._session.get = Mock(side_effect=requests.Timeout("timed out"))  # type: ignore[method-assign]
+    client._session.get = Mock(side_effect=requests.Timeout("secret-token"))  # type: ignore[method-assign]
+
     with pytest.raises(HaTimeoutError):
         client.list_entities()
 
@@ -46,15 +47,19 @@ def test_list_entities_maps_timeout() -> None:
 @pytest.mark.parametrize("status_code", [401, 403])
 def test_list_entities_maps_auth_errors(status_code: int) -> None:
     client = _client_with_response(_response(status_code=status_code))
+
     with pytest.raises(HaAuthError) as exc_info:
         client.list_entities()
+
     assert exc_info.value.status_code == status_code
 
 
 def test_list_entities_maps_http_errors() -> None:
     client = _client_with_response(_response(status_code=500))
+
     with pytest.raises(HaHttpError) as exc_info:
         client.list_entities()
+
     assert exc_info.value.status_code == 500
 
 
@@ -62,67 +67,13 @@ def test_list_entities_rejects_invalid_json() -> None:
     response = _response()
     response.json.side_effect = ValueError("not json")
     client = _client_with_response(response)
+
     with pytest.raises(HaUnexpectedPayloadError):
         client.list_entities()
 
 
 def test_list_entities_rejects_non_list_payload() -> None:
     client = _client_with_response(_response(payload={"entity_id": "sensor.temperature"}))
+
     with pytest.raises(HaUnexpectedPayloadError):
         client.list_entities()
-
-
-def test_get_history_calls_home_assistant_history_api() -> None:
-    response = _response(payload=[[{"entity_id": "sensor.temperature", "state": "21.0"}]])
-    client = _client_with_response(response)
-    start = datetime(2026, 6, 1, tzinfo=timezone.utc)
-    end = datetime(2026, 6, 2, tzinfo=timezone.utc)
-
-    payload = client.get_history(["sensor.temperature"], start, end)
-
-    assert payload == [[{"entity_id": "sensor.temperature", "state": "21.0"}]]
-    client._session.get.assert_called_once()  # type: ignore[attr-defined]
-    call = client._session.get.call_args  # type: ignore[attr-defined]
-    assert "/api/history/period/2026-06-01T00:00:00+00:00" in call.args[0]
-    assert call.kwargs["params"]["filter_entity_id"] == "sensor.temperature"
-    assert call.kwargs["params"]["end_time"] == "2026-06-02T00:00:00+00:00"
-
-
-@pytest.mark.parametrize(
-    ("entity_ids", "start", "end"),
-    [
-        (
-            [],
-            datetime(2026, 6, 1, tzinfo=timezone.utc),
-            datetime(2026, 6, 2, tzinfo=timezone.utc),
-        ),
-        (
-            ["sensor.temperature"],
-            datetime(2026, 6, 1),
-            datetime(2026, 6, 2, tzinfo=timezone.utc),
-        ),
-        (
-            ["sensor.temperature"],
-            datetime(2026, 6, 2, tzinfo=timezone.utc),
-            datetime(2026, 6, 1, tzinfo=timezone.utc),
-        ),
-        (
-            ["invalid entity"],
-            datetime(2026, 6, 1, tzinfo=timezone.utc),
-            datetime(2026, 6, 2, tzinfo=timezone.utc),
-        ),
-        (
-            ["sensor.temperature"],
-            datetime(2026, 5, 1, tzinfo=timezone.utc),
-            datetime(2026, 6, 2, tzinfo=timezone.utc),
-        ),
-    ],
-)
-def test_get_history_validates_request(
-    entity_ids: list[str],
-    start: datetime,
-    end: datetime,
-) -> None:
-    client = HaClient(HaClientSettings(url="http://ha.local", token="test-token"))
-    with pytest.raises(ValueError):
-        client.get_history(entity_ids, start, end)

tests/ha/test_ha_reader.py

@@ -1,7 +1,5 @@
 from __future__ import annotations
 
-from datetime import datetime, timezone
-
 from app.ha.client import HaClient, HaClientSettings
 from app.ha.reader import HaReader
 
@@ -28,22 +26,6 @@ class FakeHaClient(HaClient):
             },
         ]
 
-    def get_history(
-        self,
-        entity_ids: list[str],
-        start_time: datetime,
-        end_time: datetime,
-    ) -> list[object]:
-        return [
-            [
-                {
-                    "entity_id": entity_ids[0],
-                    "state": "21.5",
-                    "last_changed": start_time.isoformat(),
-                }
-            ]
-        ]
-
 
 def test_ha_reader_returns_summaries() -> None:
     reader = HaReader(FakeHaClient())
@@ -53,25 +35,3 @@ def test_ha_reader_returns_summaries() -> None:
     assert domains == {"sensor", "light"}
     sensor = next(item for item in summaries if item.entity_id == "sensor.temperature")
     assert sensor.unit_of_measurement == "°C"
-
-
-def test_ha_reader_discovers_learnable_sensors() -> None:
-    reader = HaReader(FakeHaClient())
-
-    discovered = reader.discover(learnable=True)
-
-    assert [entity.entity_id for entity in discovered] == ["sensor.temperature"]
-
-
-def test_ha_reader_normalizes_history() -> None:
-    reader = HaReader(FakeHaClient())
-    start = datetime(2026, 6, 1, tzinfo=timezone.utc)
-
-    history = reader.read_history(
-        ["sensor.temperature"],
-        start,
-        datetime(2026, 6, 2, tzinfo=timezone.utc),
-    )
-
-    assert history[0].entity_id == "sensor.temperature"
-    assert history[0].points[0].value == 21.5

tests/ha/test_history.py

@@ -1,92 +0,0 @@
-from __future__ import annotations
-
-from datetime import datetime, timezone
-
-import pytest
-
-from app.ha.exceptions import HaUnexpectedPayloadError
-from app.ha.history import normalize_history_payload
-
-
-def test_normalize_history_payload_groups_and_sorts_numeric_states() -> None:
-    payload = [
-        [
-            {
-                "entity_id": "sensor.temperature",
-                "state": "22.5",
-                "last_changed": "2026-06-01T12:15:00+00:00",
-            },
-            {
-                "state": "21.0",
-                "last_changed": "2026-06-01T12:00:00Z",
-            },
-        ],
-        [
-            {
-                "entity_id": "sensor.humidity",
-                "state": 45,
-                "last_updated": "2026-06-01T12:00:00+00:00",
-            }
-        ],
-    ]
-
-    result = normalize_history_payload(payload)
-
-    assert [series.entity_id for series in result] == [
-        "sensor.humidity",
-        "sensor.temperature",
-    ]
-    temperature = result[1]
-    assert [point.value for point in temperature.points] == [21.0, 22.5]
-    assert temperature.points[0].timestamp == datetime(
-        2026, 6, 1, 12, 0, tzinfo=timezone.utc
-    )
-
-
-def test_normalize_history_payload_skips_non_numeric_and_non_finite_states() -> None:
-    payload = [
-        [
-            {
-                "entity_id": "sensor.temperature",
-                "state": state,
-                "last_changed": "2026-06-01T12:00:00+00:00",
-            }
-            for state in ("unknown", "unavailable", "nan", "inf", "-inf", True, None)
-        ]
-    ]
-
-    assert normalize_history_payload(payload) == []
-
-
-@pytest.mark.parametrize(
-    "payload",
-    [
-        {},
-        [{}],
-        [["invalid"]],
-        [[{"entity_id": "invalid", "state": "21", "last_changed": "2026-06-01"}]],
-        [[{"entity_id": "sensor.a", "state": "21", "last_changed": "invalid"}]],
-        [[{"state": "21", "last_changed": "2026-06-01T12:00:00+00:00"}]],
-        [
-            [
-                {
-                    "entity_id": "sensor.a",
-                    "state": "21",
-                    "last_changed": "2026-06-01T12:00:00+00:00",
-                },
-                {
-                    "entity_id": "sensor.b",
-                    "state": "22",
-                    "last_changed": "2026-06-01T12:01:00+00:00",
-                },
-            ]
-        ],
-    ],
-)
-def test_normalize_history_payload_rejects_malformed_structure(payload: object) -> None:
-    with pytest.raises(HaUnexpectedPayloadError):
-        normalize_history_payload(payload)
-
-
-def test_normalize_history_payload_accepts_empty_series() -> None:
-    assert normalize_history_payload([[]]) == []

tests/ml/test_evaluation.py

@@ -1,56 +0,0 @@
-from __future__ import annotations
-
-import pytest
-
-from app.ml.evaluation import Evaluator
-from app.ml.feature_store import FeatureStore, FeatureVector
-from app.ml.training import TrainingPipeline
-
-
-def _vector(sensor_id: str, temperature: float, label: str | None = None) -> FeatureVector:
-    return FeatureVector(sensor_id=sensor_id, values={"temperature": temperature}, label=label)
-
-
-def evaluator_factory() -> Evaluator:
-    store = FeatureStore()
-    store.add_batch([_vector("sensor.kitchen", 19.0), _vector("sensor.bedroom", 18.5)])
-    pipeline = TrainingPipeline(store)
-    pipeline.run("artifact_v1")
-    return Evaluator(pipeline)
-
-
-def test_evaluate_returns_report_with_metrics() -> None:
-    evaluator = evaluator_factory()
-    report = evaluator.evaluate(
-        "artifact_v1",
-        [
-            _vector("sensor.kitchen", 21.0),
-            _vector("sensor.bedroom", 18.5),
-        ],
-    )
-    assert report.artifact_id == "artifact_v1"
-    assert report.sample_size == 2
-    assert {metric.name for metric in report.metrics} == {"mae", "rmse", "coverage"}
-    assert next(metric.value for metric in report.metrics if metric.name == "coverage") == 1.0
-    assert next(metric.value for metric in report.metrics if metric.name == "mae") == 0.0
-
-
-def test_evaluate_without_training_raises_value_error() -> None:
-    evaluator = Evaluator(TrainingPipeline(FeatureStore()))
-    with pytest.raises(ValueError):
-        evaluator.evaluate("artifact_v1", [])
-
-
-def test_coverage_counts_only_supported_sensor_features() -> None:
-    evaluator = evaluator_factory()
-    report = evaluator.evaluate(
-        "artifact_v1",
-        [
-            _vector("sensor.kitchen", 21.0),
-            FeatureVector(sensor_id="sensor.kitchen", values={"humidity": 50.0}),
-            _vector("sensor.kitchen_extra", 20.0),
-        ],
-    )
-
-    metrics = {metric.name: metric.value for metric in report.metrics}
-    assert metrics["coverage"] == pytest.approx(1 / 3)

tests/ml/test_explanation.py

@@ -1,34 +0,0 @@
-from __future__ import annotations
-
-from app.ml.explanation import explain_feature
-from app.ml.training import FeatureModel
-
-
-def _model(slope: float) -> FeatureModel:
-    return FeatureModel(
-        sample_count=4,
-        mean=20.0,
-        standard_deviation=1.0,
-        minimum=18.0,
-        maximum=22.0,
-        slope=slope,
-        intercept=18.5,
-    )
-
-
-def test_explain_feature_describes_rising_forecast() -> None:
-    explanation = explain_feature("temperature", 21.0, 21.5, _model(0.5))
-
-    assert explanation.direction == "steigend"
-    assert explanation.change == 0.5
-    assert explanation.historical_range == (18.0, 22.0)
-    assert "4 Messwerte" in explanation.summary
-    assert "Trend +0.500" in explanation.summary
-
-
-def test_explain_feature_describes_stable_and_falling_forecasts() -> None:
-    stable = explain_feature("humidity", 50.0, 50.0, _model(0.0))
-    falling = explain_feature("temperature", 21.0, 20.5, _model(-0.5))
-
-    assert stable.direction == "stabil"
-    assert falling.direction == "fallend"

tests/ml/test_feature_store.py

@@ -1,46 +0,0 @@
-from __future__ import annotations
-
-from app.ml.feature_store import FeatureStore, FeatureVector
-
-
-def _vector(sensor_id: str, temperature: float, label: str | None = None) -> FeatureVector:
-    return FeatureVector(sensor_id=sensor_id, values={"temperature": temperature}, label=label)
-
-
-def test_append_and_latest_returns_last_vector() -> None:
-    store = FeatureStore()
-    vectors = [_vector("sensor.living_room", 20.0), _vector("sensor.living_room", 21.5)]
-    for item in vectors:
-        store.add(item)
-    assert store.latest("sensor.living_room") == vectors[-1]
-
-
-def test_latest_returns_none_when_empty() -> None:
-    store = FeatureStore()
-    assert store.latest("sensor.living_room") is None
-
-
-def test_add_batch_appends_all_vectors() -> None:
-    store = FeatureStore()
-    vectors = [
-        _vector("sensor.kitchen", 19.0),
-        _vector("sensor.kitchen", 20.0),
-        _vector("sensor.bathroom", 23.5),
-    ]
-    store.add_batch(vectors)
-    assert len(store.all()) == 3
-    latest = store.latest("sensor.kitchen")
-    assert latest is not None
-    assert latest.values["temperature"] == 20.0
-
-
-def test_different_sensors_are_stored_independently() -> None:
-    store = FeatureStore()
-    store.add(_vector("sensor.living_room", 21.0))
-    store.add(_vector("sensor.bedroom", 18.5))
-    living_room = store.latest("sensor.living_room")
-    bedroom = store.latest("sensor.bedroom")
-    assert living_room is not None
-    assert bedroom is not None
-    assert living_room.values["temperature"] == 21.0
-    assert bedroom.values["temperature"] == 18.5

tests/ml/test_model_registry.py

@@ -1,68 +0,0 @@
-from __future__ import annotations
-
-import json
-from pathlib import Path
-
-import pytest
-
-from app.ml.registry.model_registry import ModelRegistry
-from app.ml.training import TrainedArtifact
-
-
-def test_registry_loads_persisted_artifacts_after_restart(tmp_path: Path) -> None:
-    registry = ModelRegistry(tmp_path)
-    artifact = TrainedArtifact("model-v1", ("sensor.kitchen", "sensor.bedroom"))
-    registry.register(artifact)
-
-    restarted = ModelRegistry(tmp_path)
-
-    assert restarted.load_artifact("model-v1") == artifact
-
-
-def test_registry_persists_statistical_parameters(tmp_path: Path) -> None:
-    from app.ml.feature_store import FeatureStore, FeatureVector
-    from app.ml.training import TrainingPipeline
-
-    store = FeatureStore()
-    store.add_batch(
-        [
-            FeatureVector("sensor.kitchen", {"temperature": 19.0}),
-            FeatureVector("sensor.kitchen", {"temperature": 20.0}),
-        ]
-    )
-    artifact = TrainingPipeline(store).run("model-v1")
-
-    ModelRegistry(tmp_path).register(artifact)
-
-    assert ModelRegistry(tmp_path).load_artifact("model-v1") == artifact
-
-
-def test_registry_replaces_persisted_artifact_after_restart(tmp_path: Path) -> None:
-    registry = ModelRegistry(tmp_path)
-    registry.register(TrainedArtifact("model-v1", ("sensor.kitchen",)))
-    replacement = TrainedArtifact("model-v1", ("sensor.bedroom",))
-
-    registry.register(replacement)
-
-    assert registry.load_artifact("model-v1") == replacement
-    assert ModelRegistry(tmp_path).load_artifact("model-v1") == replacement
-
-
-@pytest.mark.parametrize("artifact_id", ["../escape", "nested/model", "..", ""])
-def test_registry_rejects_unsafe_artifact_ids(tmp_path: Path, artifact_id: str) -> None:
-    registry = ModelRegistry(tmp_path)
-
-    with pytest.raises(ValueError):
-        registry.register(TrainedArtifact(artifact_id, ("sensor.kitchen",)))
-
-    assert list(tmp_path.parent.glob("escape.json")) == []
-
-
-def test_registry_rejects_corrupt_persisted_artifact(tmp_path: Path) -> None:
-    (tmp_path / "broken.json").write_text(
-        json.dumps({"artifact_id": "../broken", "supported_sensors": []}),
-        encoding="utf-8",
-    )
-
-    with pytest.raises(ValueError, match="broken.json"):
-        ModelRegistry(tmp_path)

tests/ml/test_predictor.py

@@ -1,63 +0,0 @@
-from __future__ import annotations
-
-import pytest
-
-from app.ml.feature_store import FeatureStore, FeatureVector
-from app.ml.predictor import Predictor
-from app.ml.training import TrainingPipeline
-
-
-def _vector(sensor_id: str, temperature: float, label: str | None = None) -> FeatureVector:
-    return FeatureVector(sensor_id=sensor_id, values={"temperature": temperature}, label=label)
-
-
-def predictor() -> Predictor:
-    store = FeatureStore()
-    store.add_batch(
-        [
-            _vector("sensor.kitchen", 19.0),
-            _vector("sensor.kitchen", 20.0),
-            _vector("sensor.bedroom", 18.5),
-        ]
-    )
-    pipeline = TrainingPipeline(store)
-    pipeline.run("artifact_v1")
-    return Predictor(pipeline)
-
-
-def test_predict_returns_statistical_forecast() -> None:
-    p = predictor()
-    result = p.predict("artifact_v1", _vector("sensor.kitchen", 21.0))
-    assert result.artifact_id == "artifact_v1"
-    assert result.sensor_id == "sensor.kitchen"
-    assert result.predictions == {"temperature": 22.0}
-    assert 0.0 < result.confidence <= 1.0
-    assert result.model_type == "statistical_baseline"
-    explanation = result.explanations["temperature"]
-    assert explanation.direction == "steigend"
-    assert explanation.current_value == 21.0
-    assert explanation.predicted_value == 22.0
-    assert explanation.sample_count == 2
-
-
-def test_predict_rejects_unknown_sensor() -> None:
-    p = predictor()
-    with pytest.raises(ValueError):
-        p.predict("artifact_v1", _vector("sensor.unknown", 10.0))
-
-
-def test_predict_batch_matches_single_calls() -> None:
-    p = predictor()
-    entities = [_vector("sensor.kitchen", 21.0), _vector("sensor.bedroom", 19.0)]
-    assert p.predict_batch("artifact_v1", entities) == [
-        p.predict("artifact_v1", item) for item in entities
-    ]
-
-
-def test_default_artifact_returns_last_registered() -> None:
-    store = FeatureStore()
-    store.add_batch([_vector("sensor.kitchen", 19.0), _vector("sensor.bedroom", 18.5)])
-    pipeline = TrainingPipeline(store)
-    pipeline.run("first")
-    pipeline.run("second")
-    assert Predictor.default_artifact(pipeline).artifact_id == "second"

tests/ml/test_retraining.py

@@ -1,39 +0,0 @@
-from __future__ import annotations
-
-from pathlib import Path
-
-import pytest
-
-from app.ml.feature_store import FeatureVector
-from app.ml.registry.model_registry import ModelRegistry
-from app.ml.retraining import RetrainingService, retrain_model
-
-
-def _vector(sensor_id: str) -> FeatureVector:
-    return FeatureVector(sensor_id=sensor_id, values={"temperature": 21.0})
-
-
-def test_retraining_registers_new_artifact(tmp_path: Path) -> None:
-    registry = ModelRegistry(tmp_path)
-
-    result = retrain_model(registry, "home-model", [_vector("sensor.kitchen")])
-
-    assert result.replaced is False
-    assert registry.load_artifact("home-model") == result.artifact
-
-
-def test_retraining_replaces_existing_artifact(tmp_path: Path) -> None:
-    registry = ModelRegistry(tmp_path)
-    service = RetrainingService(registry)
-    service.retrain("home-model", [_vector("sensor.kitchen")])
-
-    result = service.retrain("home-model", [_vector("sensor.bedroom")])
-
-    assert result.replaced is True
-    assert result.artifact.supported_sensors == ("sensor.bedroom",)
-    assert ModelRegistry(tmp_path).load_artifact("home-model") == result.artifact
-
-
-def test_retraining_rejects_empty_training_data(tmp_path: Path) -> None:
-    with pytest.raises(ValueError, match="keine Trainingsdaten"):
-        retrain_model(ModelRegistry(tmp_path), "home-model", [])

tests/ml/test_training.py

@@ -1,53 +0,0 @@
-from __future__ import annotations
-
-import pytest
-
-from app.ml.feature_store import FeatureStore, FeatureVector
-from app.ml.training import TrainingPipeline
-
-
-def _vector(sensor_id: str, temperature: float, label: str | None = None) -> FeatureVector:
-    return FeatureVector(sensor_id=sensor_id, values={"temperature": temperature}, label=label)
-
-
-def store_with_data() -> TrainingPipeline:
-    store = FeatureStore()
-    store.add_batch(
-        [
-            _vector("sensor.kitchen", 19.0),
-            _vector("sensor.kitchen", 20.0),
-            _vector("sensor.bedroom", 18.5),
-        ]
-    )
-    return TrainingPipeline(store)
-
-
-def test_run_returns_trained_artifact() -> None:
-    pipeline = store_with_data()
-    artifact = pipeline.run("artifact_v1")
-    assert artifact.artifact_id == "artifact_v1"
-    assert artifact.supported_sensors == ("sensor.bedroom", "sensor.kitchen")
-    kitchen = artifact.feature_models["sensor.kitchen"]["temperature"]
-    assert kitchen.sample_count == 2
-    assert kitchen.mean == 19.5
-    assert kitchen.slope == 1.0
-    assert kitchen.forecast() == 21.0
-
-
-def test_run_without_data_raises_value_error() -> None:
-    pipeline = TrainingPipeline(FeatureStore())
-    with pytest.raises(ValueError):
-        pipeline.run("artifact_v1")
-
-
-def test_export_returns_registered_artifact() -> None:
-    pipeline = store_with_data()
-    pipeline.run("artifact_v1")
-    exported = pipeline.export("artifact_v1")
-    assert exported == pipeline.export("artifact_v1")
-
-
-def test_export_missing_artifact_raises_key_error() -> None:
-    pipeline = store_with_data()
-    with pytest.raises(KeyError):
-        pipeline.export("artifact_v1")

tests/ml/test_training_evaluation.py

@@ -1,33 +0,0 @@
-from __future__ import annotations
-
-from app.ml.evaluation import Evaluator, EvalReport, Metric
-from app.ml.feature_store import FeatureStore, FeatureVector
-from app.ml.training import TrainingPipeline
-
-
-def _vector(sensor_id: str, temperature: float, label: str | None = None) -> FeatureVector:
-    return FeatureVector(sensor_id=sensor_id, values={"temperature": temperature}, label=label)
-
-
-def test_end_to_end_training_then_evaluation() -> None:
-    store = FeatureStore()
-    store.add_batch([_vector("sensor.kitchen", 19.0), _vector("sensor.bedroom", 18.5)])
-    pipeline = TrainingPipeline(store)
-    artifact = pipeline.run("artifact_v1")
-
-    evaluator = Evaluator(pipeline)
-    samples = [
-        _vector("sensor.kitchen", 21.0),
-        _vector("sensor.bedroom", 18.5),
-    ]
-    report = evaluator.evaluate(artifact.artifact_id, samples)
-    assert isinstance(report, EvalReport)
-    assert report.sample_size == len(samples)
-    assert any(metric.name == "coverage" for metric in report.metrics)
-
-
-def test_metric_helpers_are_serializable() -> None:
-    metric = Metric(name="mae", value=0.85, threshold=1.0)
-    assert metric.name == "mae"
-    assert metric.value == 0.85
-    assert metric.threshold == 1.0

tests/rules/test_heating.py

@@ -1,64 +1,48 @@
 from __future__ import annotations
 
-import pytest
-
 from app.ha.models import HaEntitySummary
 from app.rules.heating import HeatingRule
+from app.rules.recommender import Recommender
 
 
-def _entity(entity_id: str, domain: str, device_class: str | None = None) -> HaEntitySummary:
+def _sensor(entity_id: str, device_class: str | None = None) -> HaEntitySummary:
-    return HaEntitySummary(entity_id=entity_id, domain=domain, device_class=device_class)
+    return HaEntitySummary(entity_id=entity_id, domain="sensor", device_class=device_class)
 
 
-# --- positive cases --------------------------------------------------------
+def _binary_sensor(entity_id: str, device_class: str | None = None) -> HaEntitySummary:
-@pytest.mark.parametrize(
+    return HaEntitySummary(
-    "entity",
+        entity_id=entity_id,
-    [
+        domain="binary_sensor",
-        _entity("climate.living_room", "climate"),
+        device_class=device_class,
-        _entity("sensor.temperature_living", "sensor", "temperature"),
+    )
-        _entity("sensor.humidity_bathroom", "sensor", "humidity"),
+
-        _entity("binary_sensor.living_room_occupancy", "binary_sensor", "occupancy"),
+
-        _entity("binary_sensor.entrance_presence", "binary_sensor", "presence"),
+def _climate(entity_id: str) -> HaEntitySummary:
-    ],
+    return HaEntitySummary(entity_id=entity_id, domain="climate")
-    ids=lambda e: e.entity_id,
+
-)
+
-def test_heating_rule_triggers_for_relevant_entities(entity: HaEntitySummary) -> None:
+def test_heating_rule_triggers() -> None:
     rule = HeatingRule()
-    assert rule.matches([entity]) is True
+    assert rule.matches([_climate("climate.living_room")])
+    assert rule.matches([_sensor("sensor.temperature_living", device_class="temperature")])
+    assert rule.matches([_sensor("sensor.humidity_bath", device_class="humidity")])
+    assert rule.matches([_binary_sensor("binary_sensor.occupancy_living", "occupancy")])
+    assert rule.matches([_binary_sensor("binary_sensor.presence_entry", "presence")])
 
 
-# --- negative cases -------------------------------------------------------
+def test_heating_rule_ignores_non_relevant_sensors() -> None:
-@pytest.mark.parametrize(
-    "entity",
-    [
-        _entity("sensor.power_consumption", "sensor", "power"),
-        _entity("sensor.door", "sensor", "door"),
-        _entity("sensor.energy", "sensor", "energy"),
-        _entity("binary_sensor.door_window", "binary_sensor", "door"),
-        _entity("binary_sensor.motion", "binary_sensor", "motion"),
-        _entity("light.living_room", "light"),
-        _entity("switch.plug", "switch"),
-        _entity("sensor.some_random", "sensor"),
-        _entity("binary_sensor.some_binary", "binary_sensor"),
-    ],
-    ids=lambda e: e.entity_id,
-)
-def test_heating_rule_ignores_non_heating_entities(entity: HaEntitySummary) -> None:
     rule = HeatingRule()
-    assert rule.matches([entity]) is False
+    assert not rule.matches([_sensor("sensor.temperature_living")])
+    assert not rule.matches([_sensor("sensor.power", device_class="power")])
+    assert not rule.matches([_sensor("sensor.voltage", device_class="voltage")])
+    assert not rule.matches([_sensor("sensor.door", device_class="door")])
+    assert not rule.matches([_sensor("sensor.window", device_class="window")])
+    assert not rule.matches([_sensor("sensor.light", device_class="illuminance")])
+    assert not rule.matches([_binary_sensor("binary_sensor.window", device_class="window")])
 
 
-def test_heating_rule_mixed_list_returns_true() -> None:
+def test_recommender_uses_rule() -> None:
-    rule = HeatingRule()
+    recommender = Recommender(rules=[HeatingRule()])
-    entities = [
+    assert recommender.run([_climate("climate.living_room")]) == [
-        _entity("sensor.power", "sensor", "power"),
+        "Prüfe Heizungsregelung: Aktiviere energiesparenden Modus bei Abwesenheit."
-        _entity("climate.living_room", "climate"),
-        _entity("light.ceiling", "light"),
     ]
-    assert rule.matches(entities) is True
-
-
-def test_heating_rule_recommendation_is_stable() -> None:
-    rule = HeatingRule()
-    expected = "Prüfe Heizungsregelung: Aktiviere energiesparenden Modus bei Abwesenheit."
-    assert rule.recommendation([_entity("climate.living_room", "climate")]) == expected

tests/test_config.py

@@ -1,18 +0,0 @@
-from __future__ import annotations
-
-from pytest import MonkeyPatch
-
-from app.config import load_settings
-
-
-def test_load_settings_reads_documented_environment(monkeypatch: MonkeyPatch) -> None:
-    monkeypatch.setenv("SILLYHOME_HA_URL", "http://ha.local:8123")
-    monkeypatch.setenv("SILLYHOME_HA_TOKEN", "secret")
-    monkeypatch.setenv("SILLYHOME_MODEL_STORE", "/tmp/models")
-
-    settings = load_settings()
-
-    assert settings.ha_url == "http://ha.local:8123"
-    assert settings.ha_token == "secret"
-    assert settings.model_store == "/tmp/models"
-    assert settings.ha_configured