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5e6aba8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 | import numpy as np
import pandas as pd
from .model_registry import load_model
DEFAULT_MODEL = "v1_rf_te"
def make_features(payload: dict, te_data: dict):
cp = str(payload["code_postal"]).zfill(5)
departement = cp[:2]
global_mean = te_data["global_mean"]
mapping = te_data["mapping"]
cp_te = mapping.get(cp, global_mean)
return pd.DataFrame([{
"departement": departement,
"surface_reelle_bati": payload["surface_reelle_bati"],
"nombre_pieces_principales": payload["nombre_pieces_principales"],
"type_local": payload["type_local"],
"cp_te": cp_te,
}])
def _score_from_width(pi_width: float, p5: float | None, p95: float | None) -> float:
if p5 is None or p95 is None:
return 0.5 # fallback if metadata missing
denom = (p95 - p5) if (p95 - p5) != 0 else 1e-9
score = 1 - (pi_width - p5) / denom
return float(np.clip(score, 0, 1))
def predict(payload: dict, model_version: str = DEFAULT_MODEL):
bundle = load_model(model_version)
X = make_features(payload, bundle.te_mapping)
pipe = bundle.model # Pipeline(preprocess + model)
prix_m2 = float(pipe.predict(X)[0])
pre = pipe.named_steps["preprocess"]
core_model = pipe.named_steps["model"]
# ----------------------------
# Confidence: RF vs HGB
# ----------------------------
pi_width = None
q10 = None
q90 = None
# Case 1: RandomForestRegressor (has estimators_)
if hasattr(core_model, "estimators_"):
Xt = pre.transform(X)
all_tree_preds = np.array([tree.predict(Xt)[0]
for tree in core_model.estimators_])
q10 = float(np.quantile(all_tree_preds, 0.10))
q90 = float(np.quantile(all_tree_preds, 0.90))
pi_width = q90 - q10
# Case 2: HistGradientBoostingRegressor -> use bootstrap models
else:
# bundle.bootstrap_models is loaded in model_registry.py (from metadata.confidence.bootstrap_dir)
if bundle.bootstrap_models:
boot_preds = np.array([float(m.predict(X)[0])
for m in bundle.bootstrap_models])
q10 = float(np.quantile(boot_preds, 0.10))
q90 = float(np.quantile(boot_preds, 0.90))
pi_width = q90 - q10
else:
# no bootstrap models available => cannot compute uncertainty properly
pi_width = None
score = _score_from_width(
pi_width=float(pi_width) if pi_width is not None else 0.0,
p5=bundle.pi_p5,
p95=bundle.pi_p95,
) if pi_width is not None else 0.5
surface = float(payload["surface_reelle_bati"])
prix_total = prix_m2 * surface
return {
"model_version": model_version,
"prix_m2": round(prix_m2, 2),
"prix_total_estime": round(prix_total, 2),
"score_confiance": round(score, 3),
# optional debug
"intervalle_largeur": None if pi_width is None else round(float(pi_width), 2),
"q10": None if q10 is None else round(q10, 2),
"q90": None if q90 is None else round(q90, 2),
"confidence_method": bundle.metadata.get("confidence", {}).get("method"),
}
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