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

ml/tests/test_data.py

@@ -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])