test(ml,backend): couvre ML vers DB, puis la chaine complete jusqu'a l'API
Le pipeline ML n'avait aucun test touchant PostgreSQL : `ml/README.md` le disait, faute de base joignable en CI. Le marqueur `integration` de `ml/pyproject.toml` etait declare et porte par zero test. - `ml/tests/conftest.py` : deux fixtures d'acces a la base, jamais interchangeables. `connexion_ml` annule sa transaction, `parc` valide ses ecritures parce que `run_scoring` ouvre sa propre connexion et ne verrait rien d'autre. Garde sur le nom de base, marque uuid sur chaque site, nettoyage dans l'ordre des cles etrangeres. - `test_data_integration.py` : les neuf colonnes du contrat confrontees au schema Alembic reel, la borne `since`, l'ordre de tri dont dependent des lags positionnels, et le typage des colonnes entierement nulles. - `test_score_integration.py` : les contraintes de `prediction` vues depuis le code qui ecrit, l'empilement volontaire de deux runs, et `run_scoring` de bout en bout sur un booster reel. - `apps/backend/tests/test_chaine_ml_api.py` : lance les vrais binaires `enervision_ml.train` et `.score` en sous-processus, comme les DAGs, puis relit par `GET /api/v1/predictions`. Marqueur `chaine` distinct : le job `integration` du backend n'a pas l'environnement de ml/. - `ml.yml` : job `integration`, seul du depot a reunir les deux environnements uv et une base. Ses `paths` incluent les migrations du backend, sans quoi le schema deriverait du SQL du pipeline sans que rien ne casse. - Makefile : `migrate-test`, qui manquait (`enervision_test` n'a jamais recu de table), `ml-test-integration` et `test-chaine`.
This commit is contained in:
Johan LEROY
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"""Piege : ce fichier porte le marqueur `chaine`, pas `integration` - test_the_ml_binaries...()
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Il lance les vrais binaires `enervision_ml.train` et `enervision_ml.score` dans l'environnement
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uv de `ml/`, que le job `integration` de `backend.yml` n'installe pas. Un marqueur distinct evite
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que ce job, et `make test`, ne le selectionnent et n'echouent faute de `ml/.venv`.
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"""
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import math
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import os
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import subprocess
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from collections.abc import AsyncIterator, Iterator
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from dataclasses import dataclass, field
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from datetime import UTC, datetime, timedelta
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from functools import partial
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from pathlib import Path
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from typing import Any
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from uuid import uuid4
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import anyio
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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 sqlalchemy import delete, insert, make_url
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from app.api.deps import get_current_principal
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from app.core.config import get_settings
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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.db.session import get_session_factory
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from app.models.energy import Prediction, Reading, Site
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pytestmark = pytest.mark.chaine
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RACINE = Path(__file__).resolve().parents[3]
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ML = RACINE / "ml"
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PYTHON_ML = Path(os.environ.get("ML_PYTHON", ML / ".venv" / "bin" / "python"))
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HEURES_COMPLETES = 400
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HEURES_INSUFFISANTES = 100
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def lecteur() -> Principal:
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return Principal(
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id=uuid4(),
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email="lecteur@enervision.fr",
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role=Role.LECTEUR,
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kind=AccountKind.HUMAIN,
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must_change_password=False,
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)
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def url_ml() -> str:
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"""Derive la chaine du pipeline de celle du backend plutot que de la recopier : les deux
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cotes visent ainsi la meme base, dans leur dialecte respectif."""
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return (
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make_url(get_settings().database_url)
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.set(drivername="postgresql+psycopg")
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.render_as_string(hide_password=False)
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)
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def lance_ml(module: str, *arguments: str, journal: Path) -> subprocess.CompletedProcess[str]:
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if not PYTHON_ML.exists():
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pytest.fail(
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f"Environnement ml/ absent ({PYTHON_ML}). Lancer `cd ml && uv sync --all-groups`."
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)
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return subprocess.run( # noqa: S603 -- argv en liste, sans shell, binaire resolu dans le depot
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[str(PYTHON_ML), "-m", module, *arguments],
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cwd=ML,
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text=True,
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capture_output=True,
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timeout=600,
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check=False,
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env={
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**os.environ,
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"ML_DATABASE_URL": url_ml(),
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"MLFLOW_TRACKING_URI": f"sqlite:///{journal}/mlflow.db",
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},
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)
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async def executer(module: str, *arguments: str, journal: Path) -> subprocess.CompletedProcess[str]:
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resultat = await anyio.to_thread.run_sync(
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partial(lance_ml, module, *arguments, journal=journal)
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)
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assert resultat.returncode == 0, resultat.stderr
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return resultat
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@dataclass
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class Parc:
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sites: list[str] = field(default_factory=list)
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def lignes_horaires(site_id: str, *, heures: int, fin: datetime) -> list[dict[str, Any]]:
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return [
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{
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"site_id": site_id,
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"timestamp": fin - timedelta(hours=decalage),
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"source": "api_history",
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"consumption_kwh": 50.0 + math.sin(decalage / 12.0) * 10.0,
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"temperature_celsius": 15.0,
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"humidity_percent": 50.0,
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"solar_irradiance_wm2": 0.0,
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"is_working_hours": True,
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"raw_data": {},
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}
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for decalage in reversed(range(heures))
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]
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@pytest.fixture
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async def parc() -> AsyncIterator[Parc]:
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"""Deux sites dotes d'un historique complet, un troisieme qui n'atteint pas le lag de 168 h.
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Les ecritures sont validees : les binaires ML ouvrent leur propre connexion et ne verraient
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pas une transaction en cours.
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"""
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fin = datetime.now(UTC).replace(minute=0, second=0, microsecond=0) - timedelta(hours=1)
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marque = uuid4().hex[:12]
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complets = [f"TEST-{marque}-A", f"TEST-{marque}-B"]
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partiel = f"TEST-{marque}-C"
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parc = Parc(sites=[*complets, partiel])
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async with get_session_factory()() as session:
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await session.execute(
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insert(Site),
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[
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{
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"site_id": site_id,
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"site_name": f"Site {site_id}",
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"site_type": "office",
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"capacity_kw": 100.0,
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}
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for site_id in parc.sites
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],
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)
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for site_id in complets:
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await session.execute(
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insert(Reading), lignes_horaires(site_id, heures=HEURES_COMPLETES, fin=fin)
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)
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await session.execute(
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insert(Reading), lignes_horaires(partiel, heures=HEURES_INSUFFISANTES, fin=fin)
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)
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await session.commit()
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try:
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yield parc
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finally:
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async with get_session_factory()() as session:
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await session.execute(delete(Prediction).where(Prediction.site_id.in_(parc.sites)))
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await session.execute(delete(Reading).where(Reading.site_id.in_(parc.sites)))
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await session.execute(delete(Site).where(Site.site_id.in_(parc.sites)))
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await session.commit()
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@pytest.fixture
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def principal_lecteur(app: FastAPI) -> Iterator[None]:
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app.dependency_overrides[get_current_principal] = lecteur
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yield
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app.dependency_overrides.pop(get_current_principal, None)
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async def resume_du_site(client: AsyncClient, site_id: str) -> dict[str, Any]:
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reponse = await client.get("/api/v1/predictions")
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assert reponse.status_code == 200
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sites = reponse.json()["sites"]
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return next(site for site in sites if site["site_id"] == site_id)
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async def entraine_et_score(parc: Parc, tmp_path: Path, *arguments: str) -> Path:
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modele = tmp_path / "lightgbm-consumption.txt"
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await executer(
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"enervision_ml.train",
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"--model-output",
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str(modele),
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"--mlflow-tracking-uri",
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f"sqlite:///{tmp_path}/mlflow.db",
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journal=tmp_path,
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)
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await executer("enervision_ml.score", "--model", str(modele), *arguments, journal=tmp_path)
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return modele
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async def test_the_ml_binaries_produce_a_prediction_that_the_api_serves(
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parc: Parc, tmp_path: Path, client: AsyncClient, principal_lecteur: None
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) -> None:
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await entraine_et_score(parc, tmp_path)
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servi = await resume_du_site(client, parc.sites[0])
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assert servi["prediction"]["status"] == "available"
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assert servi["prediction"]["predicted_value"] is not None
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assert servi["prediction"]["target_metric"] == "consumption_kwh"
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async def test_the_api_exposes_the_failure_reason_of_a_site_without_enough_history(
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parc: Parc, tmp_path: Path, client: AsyncClient, principal_lecteur: None
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) -> None:
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await entraine_et_score(parc, tmp_path)
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servi = await resume_du_site(client, parc.sites[-1])
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assert servi["prediction"]["status"] == "insufficient_data"
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assert servi["prediction"]["predicted_value"] is None
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assert servi["prediction"]["failure_reason"] is not None
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async def test_the_api_serves_the_latest_run_when_the_score_cli_runs_twice(
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parc: Parc, tmp_path: Path, client: AsyncClient, principal_lecteur: None
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) -> None:
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modele = await entraine_et_score(parc, tmp_path)
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premier = await resume_du_site(client, parc.sites[0])
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await executer("enervision_ml.score", "--model", str(modele), journal=tmp_path)
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second = await resume_du_site(client, parc.sites[0])
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assert second["prediction"]["created_at"] >= premier["prediction"]["created_at"]
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assert second["prediction"]["model_reference"] == premier["prediction"]["model_reference"]
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