feat(ml,backend): implemente le service de scoring et GET /predictions (#37)
This commit is contained in:
Dorian
@@ -27,6 +27,7 @@ from app.repositories.audit_log import AuditLogRepository
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from app.repositories.login_attempt import LoginAttemptRepository
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from app.repositories.password_reset_attempt import PasswordResetAttemptRepository
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from app.repositories.password_reset_token import PasswordResetTokenRepository
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from app.repositories.prediction import PredictionRepository
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from app.repositories.reading import ReadingRepository
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from app.repositories.recommendation import RecommendationRepository
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from app.repositories.refresh_token import RefreshTokenRepository
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@@ -34,6 +35,7 @@ from app.repositories.site import SiteRepository
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from app.repositories.user import UserRepository
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from app.services.alert import AlertService
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from app.services.auth import AuthService, LoginPolicy, PasswordResetPolicy
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from app.services.prediction import PredictionService
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from app.services.reading import ReadingService
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from app.services.recommendation import RecommendationService
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from app.services.sensor import SensorService
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@@ -212,6 +214,15 @@ def get_sensor_service(session: SessionDep) -> SensorService:
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SensorServiceDep = Annotated[SensorService, Depends(get_sensor_service)]
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def get_prediction_service(session: SessionDep) -> PredictionService:
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return PredictionService(
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sites=SiteRepository(session), predictions=PredictionRepository(session)
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)
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PredictionServiceDep = Annotated[PredictionService, Depends(get_prediction_service)]
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async def get_current_principal(
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credentials: CredentialsDep,
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session: SessionDep,
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@@ -83,6 +83,13 @@ TAGS: Final[list[dict[str, Any]]] = [
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"name": "sensors",
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"description": "État de santé des capteurs par site. Réservé au rôle `admin`.",
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},
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{
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"name": "predictions",
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"description": (
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"Dernière prévision de consommation par site, calculée hors ligne par le pipeline "
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"de scoring (`ml/`) et simplement lue ici. Accessible à partir du rôle `lecteur`."
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),
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},
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]
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cookie_de_rafraichissement = APIKeyCookie(
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@@ -0,0 +1,18 @@
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from fastapi import APIRouter
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from app.api.deps import LecteurDep, PredictionServiceDep
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from app.schemas.prediction import PredictionSummaryResponse
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router = APIRouter()
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@router.get(
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"",
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response_model=PredictionSummaryResponse,
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summary="Dernière prédiction de consommation par site",
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)
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async def get_predictions(
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_: LecteurDep, service: PredictionServiceDep
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) -> PredictionSummaryResponse:
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resume = await service.summary()
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return PredictionSummaryResponse.model_validate(resume)
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@@ -5,6 +5,7 @@ from app.api.v1.endpoints import (
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alerts,
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auth,
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health,
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predictions,
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readings,
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recommendations,
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sensors,
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@@ -34,3 +35,6 @@ api_router.include_router(
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api_router.include_router(
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sensors.router, prefix="/sensors", tags=["sensors"], responses=REPONSES_ADMIN
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)
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api_router.include_router(
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predictions.router, prefix="/predictions", tags=["predictions"], responses=REPONSES_LECTEUR
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)
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@@ -0,0 +1,28 @@
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from collections.abc import Sequence
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from sqlalchemy import select
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.models.energy import Prediction
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class PredictionRepository:
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def __init__(self, session: AsyncSession) -> None:
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self._session = session
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async def latest_by_site(self) -> Sequence[Prediction]:
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# `.distinct(site_id)` compile en `DISTINCT ON (site_id)` sous PostgreSQL : une seule
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# ligne par site, la plus récente grâce à l'ordre composite qui suit. Même mécanisme que
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# `ReadingRepository.latest_by_site`. Trié sur `target_at` (couvert par
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# `ix_prediction_site_target`) plutôt que `created_at` : c'est la prévision la plus
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# récente qui compte pour un tableau de bord, pas forcément le dernier run de scoring.
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requete = (
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select(Prediction)
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.distinct(Prediction.site_id)
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.order_by(
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Prediction.site_id,
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Prediction.target_at.desc(),
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Prediction.prediction_id.desc(),
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)
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)
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return (await self._session.scalars(requete)).all()
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@@ -0,0 +1,43 @@
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from datetime import datetime
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from enum import StrEnum
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from pydantic import BaseModel, ConfigDict
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class PredictionTargetMetric(StrEnum):
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CONSUMPTION_KWH = "consumption_kwh"
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CONSUMPTION_KW = "consumption_kw"
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class PredictionStatus(StrEnum):
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AVAILABLE = "available"
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INSUFFICIENT_DATA = "insufficient_data"
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ERROR = "error"
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class SitePredictionResponse(BaseModel):
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model_config = ConfigDict(from_attributes=True)
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target_at: datetime
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target_metric: PredictionTargetMetric
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period_minutes: int | None
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predicted_value: float | None
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status: PredictionStatus
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failure_reason: str | None
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model_reference: str
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created_at: datetime
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class SitePredictionSummaryResponse(BaseModel):
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model_config = ConfigDict(from_attributes=True)
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site_id: str
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site_name: str
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prediction: SitePredictionResponse | None
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class PredictionSummaryResponse(BaseModel):
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model_config = ConfigDict(from_attributes=True)
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timestamp: datetime
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sites: list[SitePredictionSummaryResponse]
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@@ -0,0 +1,68 @@
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from dataclasses import dataclass
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from datetime import UTC, datetime
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from app.models.energy import Prediction, Site
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from app.repositories.prediction import PredictionRepository
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from app.repositories.site import SiteRepository
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@dataclass(frozen=True, slots=True)
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class SitePrediction:
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target_at: datetime
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target_metric: str
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period_minutes: int | None
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predicted_value: float | None
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status: str
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failure_reason: str | None
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model_reference: str
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created_at: datetime
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@dataclass(frozen=True, slots=True)
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class SitePredictionSummary:
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site_id: str
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site_name: str
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prediction: SitePrediction | None
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@dataclass(frozen=True, slots=True)
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class PredictionSummary:
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timestamp: datetime
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sites: list[SitePredictionSummary]
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class PredictionService:
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def __init__(self, sites: SiteRepository, predictions: PredictionRepository) -> None:
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self._sites = sites
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self._predictions = predictions
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async def summary(self) -> PredictionSummary:
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sites = await self._sites.list_all()
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dernieres = {p.site_id: p for p in await self._predictions.latest_by_site()}
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return PredictionSummary(
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timestamp=datetime.now(UTC),
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sites=[_resume_site(site, dernieres.get(site.site_id)) for site in sites],
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)
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def _resume_site(site: Site, derniere: Prediction | None) -> SitePredictionSummary:
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# Piège : l'absence de ligne signifie « jamais scoré », pas une valeur pseudo-statut, qui
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# n'existe pas dans la contrainte de la table. `prediction` reste `None` plutôt que de
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# fabriquer un statut absent du domaine `available`/`insufficient_data`/`error`.
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prediction = None
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if derniere is not None:
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prediction = SitePrediction(
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target_at=derniere.target_at,
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target_metric=derniere.target_metric,
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period_minutes=derniere.period_minutes,
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predicted_value=derniere.predicted_value,
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status=derniere.status,
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failure_reason=derniere.failure_reason,
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model_reference=derniere.model_reference,
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created_at=derniere.created_at,
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)
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return SitePredictionSummary(
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site_id=site.site_id, site_name=site.site_name, prediction=prediction
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)
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@@ -1632,6 +1632,62 @@
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}
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]
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}
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},
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"/api/v1/predictions": {
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"get": {
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"tags": [
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"predictions"
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],
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"summary": "Dernière prédiction de consommation par site",
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"operationId": "get_predictions_api_v1_predictions_get",
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"responses": {
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"200": {
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"description": "Successful Response",
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"content": {
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"application/json": {
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"schema": {
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"$ref": "#/components/schemas/PredictionSummaryResponse"
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}
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}
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}
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},
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"500": {
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"description": "Erreur interne. `correlation` identifie la trace côté serveur, qui n'est pas renvoyée au client.",
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"content": {
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"application/json": {
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"schema": {
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"$ref": "#/components/schemas/InternalErrorResponse"
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}
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}
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}
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},
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"401": {
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"description": "Jeton absent, illisible, périmé, ou rendu caduc par un changement de rôle ou une désactivation. L'en-tête `WWW-Authenticate` porte la cause dans `error=`.",
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"content": {
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"application/json": {
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"schema": {
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"$ref": "#/components/schemas/ErrorResponse"
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}
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}
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}
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},
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"403": {
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"description": "Mot de passe provisoire à changer (`detail` vaut `password_change_required`).",
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"content": {
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"application/json": {
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"schema": {
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"$ref": "#/components/schemas/ErrorResponse"
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}
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}
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}
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}
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},
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"security": [
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{
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"Jeton d'accès": []
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}
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]
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}
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}
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},
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"components": {
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@@ -1885,6 +1941,45 @@
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],
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"title": "PasswordChangeRequest"
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},
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"PredictionStatus": {
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"type": "string",
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"enum": [
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"available",
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"insufficient_data",
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"error"
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],
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"title": "PredictionStatus"
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},
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"PredictionSummaryResponse": {
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"properties": {
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"timestamp": {
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"type": "string",
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"format": "date-time",
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"title": "Timestamp"
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},
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"sites": {
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"items": {
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"$ref": "#/components/schemas/SitePredictionSummaryResponse"
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},
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"type": "array",
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"title": "Sites"
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}
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},
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"type": "object",
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"required": [
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"timestamp",
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"sites"
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],
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"title": "PredictionSummaryResponse"
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},
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"PredictionTargetMetric": {
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"type": "string",
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"enum": [
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"consumption_kwh",
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"consumption_kw"
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],
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"title": "PredictionTargetMetric"
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},
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"PrincipalResponse": {
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"properties": {
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"id": {
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@@ -2297,6 +2392,104 @@
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],
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"title": "SensorStatusResponse"
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},
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"SitePredictionResponse": {
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"properties": {
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"target_at": {
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"type": "string",
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"format": "date-time",
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"title": "Target At"
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},
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"target_metric": {
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"$ref": "#/components/schemas/PredictionTargetMetric"
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},
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"period_minutes": {
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"anyOf": [
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{
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"type": "integer"
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},
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{
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"type": "null"
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}
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],
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"title": "Period Minutes"
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},
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"predicted_value": {
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"anyOf": [
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{
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"type": "number"
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},
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{
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"type": "null"
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}
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],
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"title": "Predicted Value"
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},
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"status": {
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"$ref": "#/components/schemas/PredictionStatus"
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},
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"failure_reason": {
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"anyOf": [
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{
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"type": "string"
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},
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{
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"type": "null"
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}
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],
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"title": "Failure Reason"
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},
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"model_reference": {
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"type": "string",
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"title": "Model Reference"
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},
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"created_at": {
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"type": "string",
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"format": "date-time",
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"title": "Created At"
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}
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},
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"type": "object",
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"required": [
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"target_at",
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"target_metric",
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"period_minutes",
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"predicted_value",
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"status",
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"failure_reason",
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"model_reference",
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"created_at"
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],
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"title": "SitePredictionResponse"
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},
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"SitePredictionSummaryResponse": {
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"properties": {
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"site_id": {
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"type": "string",
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"title": "Site Id"
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},
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"site_name": {
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"type": "string",
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"title": "Site Name"
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},
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"prediction": {
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"anyOf": [
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{
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"$ref": "#/components/schemas/SitePredictionResponse"
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},
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{
|
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"type": "null"
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}
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]
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}
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},
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"type": "object",
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"required": [
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"site_id",
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"site_name",
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"prediction"
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],
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"title": "SitePredictionSummaryResponse"
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},
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"SiteResponse": {
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"properties": {
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"site_id": {
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@@ -2752,6 +2945,10 @@
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{
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"name": "sensors",
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"description": "État de santé des capteurs par site. Réservé au rôle `admin`."
|
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},
|
||||
{
|
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"name": "predictions",
|
||||
"description": "Dernière prévision de consommation par site, calculée hors ligne par le pipeline de scoring (`ml/`) et simplement lue ici. Accessible à partir du rôle `lecteur`."
|
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}
|
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]
|
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}
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@@ -37,6 +37,7 @@ ROUTES_A_ROLE = {
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("GET", "/api/v1/stats/summary"),
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("GET", "/api/v1/readings"),
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("GET", "/api/v1/sensors/status"),
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("GET", "/api/v1/predictions"),
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}
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@@ -0,0 +1,82 @@
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from collections.abc import Callable, Iterator
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from datetime import UTC, datetime
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from uuid import uuid4
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import pytest
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from fastapi import FastAPI
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from httpx import AsyncClient
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from app.api.deps import get_current_principal, get_prediction_service
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from app.core.principal import Principal
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from app.core.roles import AccountKind, Role
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from app.services.prediction import PredictionSummary, SitePrediction, SitePredictionSummary
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TARGET_AT = datetime(2026, 9, 16, 13, 0, tzinfo=UTC)
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CREATED_AT = datetime(2026, 9, 16, 12, 0, tzinfo=UTC)
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def principal(role: Role = Role.LECTEUR) -> Principal:
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return Principal(
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id=uuid4(),
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email=f"{role.value}@enervision.fr",
|
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role=role,
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kind=AccountKind.HUMAIN,
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must_change_password=False,
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)
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|
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|
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class FauxService:
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def __init__(self) -> None:
|
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self.resume = PredictionSummary(
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timestamp=datetime.now(UTC),
|
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sites=[
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SitePredictionSummary(
|
||||
site_id="SITE001",
|
||||
site_name="Bureau Paris La Défense",
|
||||
prediction=SitePrediction(
|
||||
target_at=TARGET_AT,
|
||||
target_metric="consumption_kwh",
|
||||
period_minutes=60,
|
||||
predicted_value=812.5,
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status="available",
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failure_reason=None,
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model_reference="lightgbm-abc123",
|
||||
created_at=CREATED_AT,
|
||||
),
|
||||
),
|
||||
SitePredictionSummary(site_id="SITE002", site_name="Usine Lyon", prediction=None),
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||||
],
|
||||
)
|
||||
|
||||
async def summary(self) -> PredictionSummary:
|
||||
return self.resume
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def servi(app: FastAPI) -> Iterator[Callable[[], FauxService]]:
|
||||
def installe() -> FauxService:
|
||||
service = FauxService()
|
||||
app.dependency_overrides[get_prediction_service] = lambda: service
|
||||
app.dependency_overrides[get_current_principal] = lambda: principal()
|
||||
return service
|
||||
|
||||
yield installe
|
||||
app.dependency_overrides.pop(get_prediction_service, None)
|
||||
app.dependency_overrides.pop(get_current_principal, None)
|
||||
|
||||
|
||||
async def test_get_predictions_returns_the_service_result(
|
||||
servi: Callable[[], FauxService], client: AsyncClient
|
||||
) -> None:
|
||||
servi()
|
||||
|
||||
response = await client.get("/api/v1/predictions")
|
||||
|
||||
assert response.status_code == 200
|
||||
corps = response.json()
|
||||
premier, second = corps["sites"]
|
||||
assert premier["site_id"] == "SITE001"
|
||||
assert premier["prediction"]["predicted_value"] == 812.5
|
||||
assert premier["prediction"]["status"] == "available"
|
||||
assert second["site_id"] == "SITE002"
|
||||
assert second["prediction"] is None
|
||||
@@ -0,0 +1,92 @@
|
||||
from datetime import UTC, datetime
|
||||
|
||||
import pytest
|
||||
from sqlalchemy.ext.asyncio import AsyncSession
|
||||
|
||||
from app.models.energy import Prediction
|
||||
from app.repositories.prediction import PredictionRepository
|
||||
from tests.repositories.test_site import creer as creer_site
|
||||
from tests.repositories.test_site import identifiant as identifiant_site
|
||||
|
||||
pytestmark = pytest.mark.integration
|
||||
|
||||
|
||||
async def creer_prediction(
|
||||
session: AsyncSession, *, site_id: str, **overrides: object
|
||||
) -> Prediction:
|
||||
prediction = Prediction(
|
||||
site_id=site_id,
|
||||
target_at=overrides.get("target_at", datetime(2026, 9, 16, tzinfo=UTC)),
|
||||
target_metric=overrides.get("target_metric", "consumption_kwh"),
|
||||
period_minutes=overrides.get("period_minutes", 60),
|
||||
predicted_value=overrides.get("predicted_value", 42.0),
|
||||
model_reference=overrides.get("model_reference", "lightgbm-test"),
|
||||
status=overrides.get("status", "available"),
|
||||
failure_reason=overrides.get("failure_reason"),
|
||||
)
|
||||
session.add(prediction)
|
||||
await session.flush()
|
||||
return prediction
|
||||
|
||||
|
||||
async def test_latest_by_site_keeps_only_the_most_recent_target(session: AsyncSession) -> None:
|
||||
site = await creer_site(session)
|
||||
depot = PredictionRepository(session)
|
||||
ancienne = await creer_prediction(
|
||||
session, site_id=site.site_id, target_at=datetime(2026, 9, 1, tzinfo=UTC)
|
||||
)
|
||||
recente = await creer_prediction(
|
||||
session, site_id=site.site_id, target_at=datetime(2026, 9, 15, tzinfo=UTC)
|
||||
)
|
||||
|
||||
resultats = await depot.latest_by_site()
|
||||
identifiants = [
|
||||
p.prediction_id
|
||||
for p in resultats
|
||||
if p.prediction_id in (ancienne.prediction_id, recente.prediction_id)
|
||||
]
|
||||
await session.rollback()
|
||||
|
||||
assert identifiants == [recente.prediction_id]
|
||||
|
||||
|
||||
async def test_latest_by_site_returns_one_row_per_site(session: AsyncSession) -> None:
|
||||
premier = await creer_site(session)
|
||||
second = await creer_site(session)
|
||||
depot = PredictionRepository(session)
|
||||
voulue_premier = await creer_prediction(session, site_id=premier.site_id)
|
||||
voulue_second = await creer_prediction(session, site_id=second.site_id)
|
||||
|
||||
resultats = await depot.latest_by_site()
|
||||
identifiants = {p.site_id for p in resultats if p.site_id in (premier.site_id, second.site_id)}
|
||||
await session.rollback()
|
||||
|
||||
assert identifiants == {voulue_premier.site_id, voulue_second.site_id}
|
||||
|
||||
|
||||
async def test_latest_by_site_keeps_an_insufficient_data_prediction(session: AsyncSession) -> None:
|
||||
site = await creer_site(session)
|
||||
depot = PredictionRepository(session)
|
||||
voulue = await creer_prediction(
|
||||
session,
|
||||
site_id=site.site_id,
|
||||
status="insufficient_data",
|
||||
predicted_value=None,
|
||||
failure_reason="pas assez d'historique",
|
||||
)
|
||||
|
||||
resultats = await depot.latest_by_site()
|
||||
identifiants = [p.prediction_id for p in resultats if p.site_id == site.site_id]
|
||||
await session.rollback()
|
||||
|
||||
assert identifiants == [voulue.prediction_id]
|
||||
|
||||
|
||||
async def test_latest_by_site_returns_an_empty_list_when_there_is_nothing(
|
||||
session: AsyncSession,
|
||||
) -> None:
|
||||
depot = PredictionRepository(session)
|
||||
|
||||
resultats = [p for p in await depot.latest_by_site() if p.site_id == identifiant_site()]
|
||||
|
||||
assert resultats == []
|
||||
@@ -0,0 +1,121 @@
|
||||
from dataclasses import dataclass
|
||||
from datetime import UTC, datetime
|
||||
|
||||
from app.services.prediction import PredictionService
|
||||
|
||||
TARGET_AT = datetime(2026, 9, 16, 13, 0, tzinfo=UTC)
|
||||
CREATED_AT = datetime(2026, 9, 16, 12, 0, tzinfo=UTC)
|
||||
|
||||
|
||||
@dataclass
|
||||
class FauxSite:
|
||||
site_id: str
|
||||
site_name: str
|
||||
|
||||
|
||||
@dataclass
|
||||
class FauxPrediction:
|
||||
site_id: str
|
||||
target_at: datetime
|
||||
target_metric: str
|
||||
period_minutes: int | None
|
||||
predicted_value: float | None
|
||||
status: str
|
||||
failure_reason: str | None
|
||||
model_reference: str
|
||||
created_at: datetime
|
||||
|
||||
|
||||
class FauxDepotSites:
|
||||
def __init__(self, sites: list[FauxSite]) -> None:
|
||||
self._sites = sites
|
||||
|
||||
async def list_all(self) -> list[FauxSite]:
|
||||
return self._sites
|
||||
|
||||
|
||||
class FauxDepotPredictions:
|
||||
def __init__(self, predictions: list[FauxPrediction]) -> None:
|
||||
self._predictions = predictions
|
||||
|
||||
async def latest_by_site(self) -> list[FauxPrediction]:
|
||||
return self._predictions
|
||||
|
||||
|
||||
def prediction_disponible(site_id: str = "A") -> FauxPrediction:
|
||||
return FauxPrediction(
|
||||
site_id=site_id,
|
||||
target_at=TARGET_AT,
|
||||
target_metric="consumption_kwh",
|
||||
period_minutes=60,
|
||||
predicted_value=812.5,
|
||||
status="available",
|
||||
failure_reason=None,
|
||||
model_reference="lightgbm-abc123",
|
||||
created_at=CREATED_AT,
|
||||
)
|
||||
|
||||
|
||||
async def test_summary_attaches_the_latest_prediction_to_its_site() -> None:
|
||||
service = PredictionService(
|
||||
sites=FauxDepotSites([FauxSite("A", "Site A")]), # type: ignore[arg-type]
|
||||
predictions=FauxDepotPredictions([prediction_disponible("A")]), # type: ignore[arg-type]
|
||||
)
|
||||
|
||||
resume = await service.summary()
|
||||
|
||||
site = resume.sites[0]
|
||||
assert site.site_id == "A"
|
||||
assert site.prediction is not None
|
||||
assert site.prediction.predicted_value == 812.5
|
||||
assert site.prediction.status == "available"
|
||||
|
||||
|
||||
async def test_summary_leaves_prediction_none_for_a_site_never_scored() -> None:
|
||||
service = PredictionService(
|
||||
sites=FauxDepotSites([FauxSite("A", "Site A")]), # type: ignore[arg-type]
|
||||
predictions=FauxDepotPredictions([]), # type: ignore[arg-type]
|
||||
)
|
||||
|
||||
resume = await service.summary()
|
||||
|
||||
assert resume.sites[0].prediction is None
|
||||
|
||||
|
||||
async def test_summary_carries_an_insufficient_data_prediction_without_a_value() -> None:
|
||||
insuffisante = FauxPrediction(
|
||||
site_id="A",
|
||||
target_at=TARGET_AT,
|
||||
target_metric="consumption_kwh",
|
||||
period_minutes=60,
|
||||
predicted_value=None,
|
||||
status="insufficient_data",
|
||||
failure_reason="pas assez d'historique",
|
||||
model_reference="lightgbm-abc123",
|
||||
created_at=CREATED_AT,
|
||||
)
|
||||
service = PredictionService(
|
||||
sites=FauxDepotSites([FauxSite("A", "Site A")]), # type: ignore[arg-type]
|
||||
predictions=FauxDepotPredictions([insuffisante]), # type: ignore[arg-type]
|
||||
)
|
||||
|
||||
resume = await service.summary()
|
||||
|
||||
site = resume.sites[0]
|
||||
assert site.prediction is not None
|
||||
assert site.prediction.status == "insufficient_data"
|
||||
assert site.prediction.predicted_value is None
|
||||
assert site.prediction.failure_reason == "pas assez d'historique"
|
||||
|
||||
|
||||
async def test_summary_covers_every_site_even_with_a_single_prediction_in_the_repository() -> None:
|
||||
service = PredictionService(
|
||||
sites=FauxDepotSites([FauxSite("A", "Site A"), FauxSite("B", "Site B")]), # type: ignore[arg-type]
|
||||
predictions=FauxDepotPredictions([prediction_disponible("A")]), # type: ignore[arg-type]
|
||||
)
|
||||
|
||||
resume = await service.summary()
|
||||
|
||||
par_site = {site.site_id: site for site in resume.sites}
|
||||
assert par_site["A"].prediction is not None
|
||||
assert par_site["B"].prediction is None
|
||||
Reference in New Issue
Block a user