HA-008 HA-009: add discovery and history pipeline

Closes #17

Closes #18

.gitignore

@@ -4,6 +4,7 @@
 /.vscode
 __pycache__/
 *.pyc
+*.egg-info/
 .mypy_cache/
 .pytest_cache/
 .ruff_cache/

CHANGELOG.md

@@ -1,6 +1,11 @@
 # Changelog
 
 ## Unreleased
+- 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`
+
+## 0.1.0 - 2026-06-13
 - Projektinitiierung
 - Architektur, ADRs und Roadmap
 - Einheitliche produktive FastAPI-App für HA- und ML-Routen
@@ -8,3 +13,4 @@
 - 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

README.md

@@ -42,7 +42,10 @@ uvicorn app.main:app --reload
 - `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
 
 Ohne vollständige HA-Konfiguration liefert `/v1/entities` bewusst `503`.
 

app/api/v1/entities.py

@@ -1,10 +1,13 @@
 from __future__ import annotations
 
+from datetime import datetime
 from typing import List
 
-from fastapi import APIRouter, Depends
+from fastapi import APIRouter, Depends, HTTPException, Query, status
 
 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
 
@@ -19,3 +22,43 @@ router = APIRouter(prefix="/v1", tags=["entities"])
 )
 def list_entities(ha_reader: HaReader = Depends(get_ha_reader)) -> List[HaEntitySummary]:
     return list(ha_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/ha/client.py

@@ -2,6 +2,9 @@ from __future__ import annotations
 
 import logging
 from dataclasses import dataclass
+from datetime import datetime
+import re
+from urllib.parse import quote
 
 import requests
 
@@ -14,6 +17,9 @@ 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:
@@ -35,9 +41,58 @@ class HaClient:
         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('/')}/api/states",
+                f"{self._settings.url.rstrip('/')}{path}",
+                params=params,
                 timeout=self._settings.timeout_seconds,
             )
         except requests.Timeout as exc:
@@ -69,9 +124,4 @@ class HaClient:
                 "Antwort von Home Assistant ist kein gültiges JSON."
             ) from exc
 
-        if not isinstance(payload, list):
-            raise HaUnexpectedPayloadError(
-                "Antwort von Home Assistant hat unerwartetes Format."
-            )
-
         return payload

app/ha/discovery.py

@@ -0,0 +1,184 @@
+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/history.py

@@ -0,0 +1,91 @@
+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

@@ -1,9 +1,12 @@
 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
 
 
@@ -33,6 +36,22 @@ 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/ml/__init__.py

@@ -1,5 +1,14 @@
 
 """Machine-Learning-Grundbausteine für SillyHome Next."""
-__all__ = ["FeatureStore", "FeatureVector", "TrainedArtifact", "TrainingPipeline"]
+__all__ = [
+    "FeatureStore",
+    "FeatureVector",
+    "RetrainingResult",
+    "RetrainingService",
+    "TrainedArtifact",
+    "TrainingPipeline",
+    "retrain_model",
+]
 from app.ml.feature_store import FeatureStore, FeatureVector
+from app.ml.retraining import RetrainingResult, RetrainingService, retrain_model
 from app.ml.training import TrainedArtifact, TrainingPipeline

app/ml/registry/model_registry.py

@@ -5,6 +5,7 @@ import logging
 import os
 from pathlib import Path
 import re
+from threading import RLock
 from collections.abc import Iterable
 
 from app.ml.training import TrainedArtifact
@@ -19,22 +20,31 @@ class ModelRegistry:
         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)
-        self._persist(artifact)
-        self._artifacts[artifact.artifact_id] = artifact
-        return artifact
+        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)
-        if artifact_id not in self._artifacts:
-            raise KeyError(f"Artifact '{artifact_id}' nicht registriert.")
-        return self._artifacts[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]:
-        return list(self._artifacts.values())
+        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")):

app/ml/retraining.py

@@ -0,0 +1,43 @@
+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)

backend/routes/ml.py

@@ -10,6 +10,7 @@ from pydantic import BaseModel, Field
 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__)
 
@@ -45,6 +46,23 @@ 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]
+    replaced: bool
+
+
 @router.get("/health", response_model=HealthResponse, status_code=200)
 def health() -> HealthResponse:
     return HealthResponse(status="ok")
@@ -57,6 +75,31 @@ def list_models(request: Request) -> ModelsResponse:
     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),
+        replaced=result.replaced,
+    )
+
+
 @router.post("/predict", response_model=PredictResponse, status_code=200)
 def predict(payload: PredictRequest, request: Request) -> PredictResponse:
     registry = _require_registry(request)

docs/ha_data.md

@@ -0,0 +1,42 @@
+# 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

@@ -12,6 +12,7 @@ Modell-Artefakt- und Vorhersage-Schnittstelle.
 - Standard: `http://127.0.0.1:8000/ml`
 - Health: `/health`
 - Modelle: `/models`
+- Retraining: `/retrain`
 - Einzelvorhersage: `/predict`
 - Batchvorhersage: `/batch`
 
@@ -65,6 +66,35 @@ Einzelne Vorhersage für einen Sensor.
 }
 ```
 
+### `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"],
+  "replaced": false
+}
+```
+
 ### `POST /ml/batch`
 
 Batch-Vorhersage für mehrere Sensorwerte.
@@ -114,11 +144,15 @@ Batch-Vorhersage für mehrere Sensorwerte.
 ## Betrieb
 
 Die produktive App lädt Artefakte aus `SILLYHOME_MODEL_STORE`. Neue Artefakte
-werden derzeit intern über `ModelRegistry.register(...)` registriert. Die
-Registry speichert validiertes JSON atomisch und lädt es beim Neustart.
+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

@@ -38,6 +38,21 @@ Der Report enthält:
 
 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.

tests/api/test_entities.py

@@ -1,8 +1,11 @@
 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
@@ -15,6 +18,38 @@ 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:
@@ -59,3 +94,44 @@ 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

@@ -50,3 +50,60 @@ def test_unsupported_sensor_returns_422(tmp_path: Path) -> None:
         )
 
     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"],
+        "replaced": False,
+    }
+    assert replaced.status_code == 200
+    assert replaced.json() == {
+        "model_id": "home-model",
+        "supported_sensors": ["sensor.bedroom"],
+        "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

tests/ha/test_discovery.py

@@ -0,0 +1,76 @@
+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

@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+from datetime import datetime, timezone
 from unittest.mock import Mock
 
 import pytest
@@ -68,4 +69,60 @@ def test_list_entities_rejects_invalid_json() -> None:
 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()
+        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

@@ -1,5 +1,7 @@
 from __future__ import annotations
 
+from datetime import datetime, timezone
+
 from app.ha.client import HaClient, HaClientSettings
 from app.ha.reader import HaReader
 
@@ -26,6 +28,22 @@ 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())
@@ -35,3 +53,25 @@ 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

@@ -0,0 +1,92 @@
+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_model_registry.py

@@ -19,6 +19,17 @@ def test_registry_loads_persisted_artifacts_after_restart(tmp_path: Path) -> Non
     assert restarted.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)

tests/ml/test_retraining.py

@@ -0,0 +1,39 @@
+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", [])