feat(ml,backend): implemente le service de scoring et GET /predictions (#37)

apps/backend/tests/services/test_prediction.py

@@ -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