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

2026-09-18 11:06:04 +02:00
parent 3cf9194d4c
commit e9376a98bf
21 changed files with 1369 additions and 19 deletions
@@ -0,0 +1,34 @@
+import pandas as pd
+
+from enervision_ml.data import NUMERIC_COLUMNS, OUTPUT_COLUMNS, _typer
+
+
+def make_frame_with_object_dtype_capacity() -> pd.DataFrame:
+    # Reproduit ce que `pd.read_sql` renvoie pour une colonne entierement `NULL` en base :
+    # dtype `object` rempli de `None`, pas `float64` rempli de `NaN`.
+    frame = pd.DataFrame(
+        {colonne: [1.0, 2.0] for colonne in OUTPUT_COLUMNS if colonne not in NUMERIC_COLUMNS}
+    )
+    for colonne in NUMERIC_COLUMNS:
+        frame[colonne] = pd.Series([None, None], dtype="object")
+    return frame
+
+
+def test_typer_coerces_an_all_null_object_column_to_float() -> None:
+    frame = make_frame_with_object_dtype_capacity()
+
+    typee = _typer(frame)
+
+    for colonne in NUMERIC_COLUMNS:
+        assert typee[colonne].dtype == "float64"
+        assert typee[colonne].isna().all()
+
+
+def test_typer_preserves_real_numeric_values() -> None:
+    frame = make_frame_with_object_dtype_capacity()
+    frame["capacity_kw"] = pd.Series([100.0, None], dtype="object")
+
+    typee = _typer(frame)
+
+    assert typee["capacity_kw"].tolist()[0] == 100.0
+    assert pd.isna(typee["capacity_kw"].tolist()[1])
@@ -0,0 +1,230 @@
+from datetime import UTC, datetime, timedelta
+from pathlib import Path
+from typing import Any
+
+import pandas as pd
+import pytest
+
+from enervision_ml.features import TARGET_COLUMN
+from enervision_ml.score import (
+    LAG_168H_COLUMN,
+    ScoredSite,
+    build_scoring_frame,
+    model_reference,
+    run_scoring,
+    score,
+    write_predictions,
+)
+
+
+def make_recent(
+    site_id: str, *, heures: int, depart: datetime, valeur: float = 10.0
+) -> pd.DataFrame:
+    instants = [depart + timedelta(hours=h) for h in range(heures)]
+    return pd.DataFrame(
+        {
+            "site_id": site_id,
+            "timestamp": instants,
+            TARGET_COLUMN: [valeur + h for h in range(heures)],
+            "temperature_celsius": 15.0,
+            "humidity_percent": 50.0,
+            "solar_irradiance_wm2": 0.0,
+            "is_working_hours": True,
+            "site_type": "office",
+            "capacity_kw": 100.0,
+        }
+    )
+
+
+class FakeBooster:
+    def __init__(self, valeur: float = 42.0) -> None:
+        self.valeur = valeur
+        self.appels: list[int] = []
+
+    def predict(self, X: Any) -> list[float]:
+        self.appels.append(len(X))
+        return [self.valeur] * len(X)
+
+
+class FakeConnection:
+    def __init__(self) -> None:
+        self.appels: list[tuple[Any, Any]] = []
+
+    def execute(self, statement: Any, parameters: Any = None) -> None:
+        self.appels.append((statement, parameters))
+
+
+def test_build_scoring_frame_adds_one_row_per_site_one_hour_after_the_last_reading() -> None:
+    depart = datetime(2026, 1, 1, tzinfo=UTC)
+    recent = pd.concat(
+        [
+            make_recent("site-a", heures=200, depart=depart),
+            make_recent("site-b", heures=200, depart=depart),
+        ],
+        ignore_index=True,
+    )
+
+    scoring_frame = build_scoring_frame(recent)
+
+    assert set(scoring_frame["site_id"]) == {"site-a", "site-b"}
+    derniere_lecture = depart + timedelta(hours=199)
+    assert (scoring_frame["timestamp"] == derniere_lecture + timedelta(hours=1)).all()
+
+
+def test_build_scoring_frame_computes_lags_from_real_history() -> None:
+    depart = datetime(2026, 1, 1, tzinfo=UTC)
+    recent = make_recent("site-a", heures=200, depart=depart)
+
+    scoring_frame = build_scoring_frame(recent)
+
+    ligne = scoring_frame.iloc[0]
+    # La cible future n'existe pas : le lag d'1h doit valoir la toute derniere valeur reelle.
+    assert ligne[f"{TARGET_COLUMN}_lag_1h"] == recent[TARGET_COLUMN].iloc[-1]
+
+
+def test_build_scoring_frame_flags_insufficient_history_under_168_hours() -> None:
+    depart = datetime(2026, 1, 1, tzinfo=UTC)
+    recent = make_recent("site-a", heures=100, depart=depart)
+
+    scoring_frame = build_scoring_frame(recent)
+
+    assert pd.isna(scoring_frame.iloc[0][LAG_168H_COLUMN])
+
+
+def test_build_scoring_frame_accepts_a_full_week_of_history() -> None:
+    depart = datetime(2026, 1, 1, tzinfo=UTC)
+    recent = make_recent("site-a", heures=169, depart=depart)
+
+    scoring_frame = build_scoring_frame(recent)
+
+    assert not pd.isna(scoring_frame.iloc[0][LAG_168H_COLUMN])
+
+
+def test_build_scoring_frame_filters_to_a_single_site() -> None:
+    depart = datetime(2026, 1, 1, tzinfo=UTC)
+    recent = pd.concat(
+        [
+            make_recent("site-a", heures=200, depart=depart),
+            make_recent("site-b", heures=200, depart=depart),
+        ],
+        ignore_index=True,
+    )
+
+    scoring_frame = build_scoring_frame(recent, site_id="site-a")
+
+    assert scoring_frame["site_id"].tolist() == ["site-a"]
+
+
+def test_build_scoring_frame_returns_empty_when_there_is_no_recent_reading() -> None:
+    recent = make_recent("site-a", heures=0, depart=datetime(2026, 1, 1, tzinfo=UTC))
+
+    scoring_frame = build_scoring_frame(recent)
+
+    assert scoring_frame.empty
+
+
+def test_score_marks_insufficient_history_without_calling_the_model() -> None:
+    depart = datetime(2026, 1, 1, tzinfo=UTC)
+    scoring_frame = build_scoring_frame(make_recent("site-a", heures=100, depart=depart))
+    booster = FakeBooster()
+
+    resultats = score(booster, scoring_frame)  # type: ignore[arg-type]
+
+    assert resultats == [
+        ScoredSite(
+            site_id="site-a",
+            target_at=resultats[0].target_at,
+            status="insufficient_data",
+            predicted_value=None,
+            failure_reason=resultats[0].failure_reason,
+        )
+    ]
+    assert booster.appels == []
+
+
+def test_score_predicts_when_history_is_sufficient() -> None:
+    depart = datetime(2026, 1, 1, tzinfo=UTC)
+    scoring_frame = build_scoring_frame(make_recent("site-a", heures=200, depart=depart))
+    booster = FakeBooster(valeur=99.5)
+
+    resultats = score(booster, scoring_frame)  # type: ignore[arg-type]
+
+    assert len(resultats) == 1
+    assert resultats[0].status == "available"
+    assert resultats[0].predicted_value == 99.5
+    assert resultats[0].failure_reason is None
+    assert booster.appels == [1]
+
+
+def test_write_predictions_does_nothing_when_there_is_nothing_to_write() -> None:
+    connection = FakeConnection()
+
+    write_predictions(connection, [], reference="lightgbm-test")  # type: ignore[arg-type]
+
+    assert connection.appels == []
+
+
+def test_write_predictions_sends_one_row_per_result() -> None:
+    connection = FakeConnection()
+    resultats = [
+        ScoredSite("site-a", datetime(2026, 1, 1, tzinfo=UTC), "available", 42.0, None),
+        ScoredSite(
+            "site-b",
+            datetime(2026, 1, 1, tzinfo=UTC),
+            "insufficient_data",
+            None,
+            "pas assez d'historique",
+        ),
+    ]
+
+    write_predictions(connection, resultats, reference="lightgbm-test")  # type: ignore[arg-type]
+
+    assert len(connection.appels) == 1
+    _, lignes = connection.appels[0]
+    assert len(lignes) == 2
+    assert lignes[0]["model_reference"] == "lightgbm-test"
+    assert lignes[0]["target_metric"] == "consumption_kwh"
+    assert lignes[0]["period_minutes"] == 60
+
+
+def test_model_reference_is_stable_for_the_same_file_content(tmp_path: Path) -> None:
+    model_path = tmp_path / "model.txt"
+    model_path.write_bytes(b"contenu-du-modele")
+
+    assert model_reference(model_path) == model_reference(model_path)
+
+
+def test_model_reference_changes_with_the_file_content(tmp_path: Path) -> None:
+    premier = tmp_path / "model-a.txt"
+    premier.write_bytes(b"version-1")
+    second = tmp_path / "model-b.txt"
+    second.write_bytes(b"version-2")
+
+    assert model_reference(premier) != model_reference(second)
+
+
+def test_run_scoring_in_csv_mode_scores_without_touching_a_database(tmp_path: Path) -> None:
+    depart = datetime(2026, 1, 1, tzinfo=UTC)
+    frame = pd.concat(
+        [
+            make_recent("site-a", heures=400, depart=depart),
+            make_recent("site-b", heures=400, depart=depart),
+        ],
+        ignore_index=True,
+    )
+    csv_path = tmp_path / "recent.csv"
+    frame.to_csv(csv_path, index=False)
+
+    model_path = tmp_path / "model.txt"
+    model_path.write_bytes(b"peu importe le contenu pour ce test")
+
+    with pytest.MonkeyPatch.context() as monkeypatch:
+        monkeypatch.setattr(
+            "enervision_ml.score.lgb.Booster", lambda model_file: FakeBooster(valeur=7.0)
+        )
+
+        resultats = run_scoring(model_path=model_path, csv_path=csv_path)
+
+    assert {r.site_id for r in resultats} == {"site-a", "site-b"}
+    assert all(r.status == "available" for r in resultats)
+    assert all(r.predicted_value == 7.0 for r in resultats)