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import pandas as pd
from pathlib import Path
from .log_utils import log_prediction
from .preprocess_utils import maybe_apply_feature_scaler, maybe_inverse_target
from ..model_loader import pipeline_has_scaler
from typing import Dict, List, Tuple
import numpy as np
def ui_to_internal_row(
ui_dict: Dict[str, object],
expected_cols: List[str],
encoder,
) -> pd.DataFrame:
row = {}
# === A) GENDER_1.0 ==========================================
if "Gender_1.0" in expected_cols:
g_str = ui_dict["Gender"]
g_df = pd.DataFrame([[g_str]], columns=["Gender"])
g_encoded = float(encoder.transform(g_df)[0, 0])
row["Gender_1.0"] = 1.0 if g_encoded == 1.0 else 0.0
# === B) WORKOUT_TYPE_* (HIIT / Strength / Yoga / Cardio) ===
workout_types = ["Cardio", "Strength", "HIIT", "Yoga"]
selected_wt = ui_dict["Workout_Type"]
for wt in workout_types:
col = f"Workout_Type_{wt}"
if col in expected_cols:
row[col] = 1.0 if selected_wt == wt else 0.0
# # === C) BODY_PART_* (One-Hot) ===============================
# body_parts = ["Abs", "Arms", "Back", "Chest", "Forearms", "Legs", "Shoulders"]
# selected_bp = ui_dict["Body Part"]
# for bp in body_parts:
# col = f"Body Part_{bp}"
# if col in expected_cols:
# row[col] = 1.0 if selected_bp == bp else 0.0
# === E) COPIE DIRECTE DES AUTRES COLONNES ===================
for col in expected_cols:
if col in row:
continue
if col in ui_dict:
row[col] = ui_dict[col]
return pd.DataFrame([row], columns=expected_cols)
def predict_single(
payload: Dict[str, object],
internal_expected: List[str],
model,
feature_scaler,
target_scaler,
log_dir: Path,
model_path: Path,
schema: dict,
target_name: str,
encoder,
) -> Tuple[float, str]:
"""
Implémentation officielle :
UI → encodage Gender → scaling features → prédiction → inverse_transform cible.
"""
# 0) Règle métier : si Workout_Type == "None" → prédiction forcée à 1 XP
workout_type_raw = payload.get("Workout_Type")
if isinstance(workout_type_raw, str) and workout_type_raw.strip().lower() == "none":
y_xp = 1.0
# Logging même si règle métier
log_prediction(
log_dir=log_dir,
row_in=payload,
y_hat=y_xp,
latency_ms=0,
model_filename=model_path.name,
model_version=schema.get("model_version", "unknown"),
target_name=target_name,
)
meta = (
f"Model: {model_path.name} | "
f"Version: {schema.get('model_version','?')} | "
f"Features: {', '.join(internal_expected)} | "
f"Rule applied: Workout_Type=None → y_xp=1"
)
return y_xp, meta
# 1) Construire le DF interne
X_raw = ui_to_internal_row(payload, internal_expected, encoder)
# 2) Scaling des features
X_scaled = pd.DataFrame(
feature_scaler.transform(X_raw),
columns=internal_expected,
index=X_raw.index,
)
# 3) Prédiction standardisée
y_std = float(model.predict(X_scaled)[0])
# 4) Remise en unités réelles
y_xp = float(target_scaler.inverse_transform(np.array([[y_std]]))[0, 0])
y_xp = round(y_xp, 2)
# 5) Logging
log_prediction(
log_dir=log_dir,
row_in=payload,
y_hat=y_xp,
latency_ms=0,
model_filename=model_path.name,
model_version=schema.get("model_version", "unknown"),
target_name=target_name,
)
meta = (
f"Model: {model_path.name} | "
f"Version: {schema.get('model_version','?')} | "
f"Features: {', '.join(internal_expected)}"
)
return y_xp, meta
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