notebook_manager.py

# ╔══════════════════════════════════════════════════════════════════════════════╗
# ║  NOTEBOOK MANAGER — Impact Vacances Scolaires sur Prédictions XGB            ║
# ║  Version simple : avant/après correction à destination du manager            ║
# ╚══════════════════════════════════════════════════════════════════════════════╝

# ════════════════════════════════════════════════════════════════════════════════
# CELLULE 1 — Imports
# ════════════════════════════════════════════════════════════════════════════════
import pandas as pd
import numpy as np
from datetime import date
import warnings
warnings.filterwarnings("ignore")

# ════════════════════════════════════════════════════════════════════════════════
# CELLULE 2 — Calendrier vacances scolaires + mapping DR→zone
# ════════════════════════════════════════════════════════════════════════════════
VACANCES = {
    "2023-2024": {
        "A": [(date(2023,10,21),date(2023,11,5)), (date(2023,12,23),date(2024,1,7)),
              (date(2024,2,17),date(2024,3,3)), (date(2024,4,13),date(2024,4,28)),
              (date(2024,7,6),date(2024,9,1))],
        "B": [(date(2023,10,21),date(2023,11,5)), (date(2023,12,23),date(2024,1,7)),
              (date(2024,2,24),date(2024,3,10)), (date(2024,4,20),date(2024,5,5)),
              (date(2024,7,6),date(2024,9,1))],
        "C": [(date(2023,10,21),date(2023,11,5)), (date(2023,12,23),date(2024,1,7)),
              (date(2024,2,10),date(2024,2,25)), (date(2024,4,6),date(2024,4,21)),
              (date(2024,7,6),date(2024,9,1))],
    },
    "2024-2025": {
        "A": [(date(2024,10,19),date(2024,11,3)), (date(2024,12,21),date(2025,1,5)),
              (date(2025,2,8),date(2025,2,23)), (date(2025,4,5),date(2025,4,20)),
              (date(2025,7,5),date(2025,8,31))],
        "B": [(date(2024,10,19),date(2024,11,3)), (date(2024,12,21),date(2025,1,5)),
              (date(2025,2,22),date(2025,3,9)), (date(2025,4,19),date(2025,5,4)),
              (date(2025,7,5),date(2025,8,31))],
        "C": [(date(2024,10,19),date(2024,11,3)), (date(2024,12,21),date(2025,1,5)),
              (date(2025,2,15),date(2025,3,2)), (date(2025,4,12),date(2025,4,27)),
              (date(2025,7,5),date(2025,8,31))],
    },
    "2025-2026": {
        "A": [(date(2025,10,18),date(2025,11,2)), (date(2025,12,20),date(2026,1,4)),
              (date(2026,2,14),date(2026,3,1)), (date(2026,4,4),date(2026,4,19)),
              (date(2026,7,4),date(2026,8,31))],
        "B": [(date(2025,10,18),date(2025,11,2)), (date(2025,12,20),date(2026,1,4)),
              (date(2026,2,21),date(2026,3,8)), (date(2026,4,11),date(2026,4,26)),
              (date(2026,7,4),date(2026,8,31))],
        "C": [(date(2025,10,18),date(2025,11,2)), (date(2025,12,20),date(2026,1,4)),
              (date(2026,2,7),date(2026,2,22)), (date(2026,3,28),date(2026,4,12)),
              (date(2026,7,4),date(2026,8,31))],
    },
    "2026-2027": {
        "A": [(date(2026,10,17),date(2026,11,1)), (date(2026,12,19),date(2027,1,3)),
              (date(2027,2,13),date(2027,2,28)), (date(2027,4,3),date(2027,4,18)),
              (date(2027,7,3),date(2027,8,31))],
        "B": [(date(2026,10,17),date(2026,11,1)), (date(2026,12,19),date(2027,1,3)),
              (date(2027,2,20),date(2027,3,7)), (date(2027,4,10),date(2027,4,25)),
              (date(2027,7,3),date(2027,8,31))],
        "C": [(date(2026,10,17),date(2026,11,1)), (date(2026,12,19),date(2027,1,3)),
              (date(2027,2,6),date(2027,2,21)), (date(2027,3,27),date(2027,4,11)),
              (date(2027,7,3),date(2027,8,31))],
    },
}

DR_TO_ZONE = {
    "SIR": "A", "AUV": "A", "ALP": "A", "PCH": "A", "LIM": "A",
    "AQN": "A", "PYL": "A", "BRG": "A", "AFC": "A",
    "PIC": "B", "NPC": "B", "PAS": "B", "CAZ": "B", "CAR": "B",
    "NOR": "B", "BRE": "B", "CEN": "B", "PDL": "B",
    "LOR": "C", "MPS": "C", "LRO": "C", "NMP": "C",
    "PAR": "C", "IFE": "C", "IFO": "C",
}

def get_zone(dr): return DR_TO_ZONE.get(dr, "C")
def get_annee_scolaire(d): return f"{d.year}-{d.year+1}" if d.month >= 9 else f"{d.year-1}-{d.year}"

def is_vacances(d, zone, vac):
    for debut, fin in vac.get(zone, []):
        if debut <= d <= fin: return True
    return False

def get_periode_vacances(d, vac):
    for zone in ["A","B","C"]:
        for debut, fin in vac.get(zone, []):
            if debut <= d <= fin:
                m = d.month
                if m in [10,11]: return "Toussaint"
                elif m in [12,1]: return "Noel"
                elif m in [2,3]: return "Hiver"
                elif m in [4,5]: return "Printemps"
                elif m in [7,8]: return "Ete"
    return "Hors_vacances"

def add_vacances(df):
    df = df.copy()
    df["Date"] = pd.to_datetime(df["Date"]).dt.tz_localize(None)
    df["zone_vacances"] = df["DR"].apply(get_zone)
    df["annee_scolaire"] = df["Date"].apply(lambda d: get_annee_scolaire(d.date()))
    def _vac(row):
        return is_vacances(row["Date"].date(), row["zone_vacances"], VACANCES.get(row["annee_scolaire"], {}))
    def _per(row):
        return get_periode_vacances(row["Date"].date(), VACANCES.get(row["annee_scolaire"], {}))
    df["is_vacances_zone"] = df.apply(_vac, axis=1)
    df["periode_vacances"] = df.apply(_per, axis=1)
    return df

# ════════════════════════════════════════════════════════════════════════════════
# CELLULE 3 — Rapport simple pour le manager
# ════════════════════════════════════════════════════════════════════════════════

def rapport_manager(df):
    """
    Rapport simple : montre la baisse des prédictions après post-processing.
    """
    df = add_vacances(df)
    dfp = df[(df["count"] > 0) & (df["prediction_XGB"].notna())].copy()
    
    if len(dfp) == 0:
        print("❌ Aucune donnée passée avec prédiction valide.")
        return df
    
    m_v = dfp["is_vacances_zone"]
    if m_v.sum() == 0:
        print("❌ Aucun jour de vacances trouvé.")
        return df
    
    facteur_global = dfp.loc[m_v, "count"].mean() / max(dfp.loc[m_v, "prediction_XGB"].mean(), 1)
    
    df["prediction_XGB_corrige"] = df["prediction_XGB"].astype(float)
    m_v_all = df["is_vacances_zone"] & df["prediction_XGB"].notna()
    df.loc[m_v_all, "prediction_XGB_corrige"] = df.loc[m_v_all, "prediction_XGB"] * facteur_global
    
    print("=" * 65)
    print("📊 IMPACT POST-PROCESSING VACANCES SCOLAIRES")
    print("=" * 65)
    print(f"\n📅 Données analysées : {len(dfp):,} jours passés")
    print(f"🏖️  Jours en vacances  : {m_v.sum():,}")
    print(f"📚 Jours hors vacances : {(~m_v).sum():,}")
    
    print(f"\n{'─'*65}")
    print("📉 VOLUMES MOYENS PRÉDITS — AVANT vs APRÈS CORRECTION")
    print(f"{'─'*65}")
    
    res = []
    for label, mask in [
        ("Vacances scolaires", dfp["is_vacances_zone"]),
        ("Hors vacances", ~dfp["is_vacances_zone"]),
    ]:
        if mask.sum() < 2: continue
        sub = dfp[mask]
        avant = sub["prediction_XGB"].mean()
        apres = sub["prediction_XGB_corrige"].mean()
        baisse = ((apres - avant) / max(avant, 1)) * 100
        res.append({
            "Période": label,
            "n jours": int(mask.sum()),
            "Avant correction": round(avant, 1),
            "Après correction": round(apres, 1),
            "Différence": f"{baisse:+.1f}%"
        })
    
    print(pd.DataFrame(res).to_string(index=False))
    
    if m_v.sum() > 0 and (~m_v).sum() > 0:
        baisse_reelle = ((dfp.loc[m_v, "count"].mean() - dfp.loc[~m_v, "count"].mean())
                         / max(dfp.loc[~m_v, "count"].mean(), 1)) * 100
        print(f"\n🔴 Baisse RÉELLE des volumes en vacances : {baisse_reelle:.1f}%")
        print(f"💡 Correction appliquée : facteur ×{facteur_global:.4f} (baisse de {(1-facteur_global)*100:.1f}%)")
    
    def mae(y_true, y_pred):
        return np.mean(np.abs(np.asarray(y_true) - np.asarray(y_pred)))
    
    print(f"\n{'─'*65}")
    print("📈 PRÉCISION (MAE) — AVANT vs APRÈS")
    print(f"{'─'*65}")
    
    res_mae = []
    for label, mask in [
        ("Toutes périodes", pd.Series([True]*len(dfp), index=dfp.index)),
        ("Vacances scolaires", dfp["is_vacances_zone"]),
        ("Hors vacances", ~dfp["is_vacances_zone"]),
    ]:
        if mask.sum() < 2: continue
        yt = dfp.loc[mask, "count"].values
        mae_avant = mae(yt, dfp.loc[mask, "prediction_XGB"].values)
        mae_apres = mae(yt, dfp.loc[mask, "prediction_XGB_corrige"].values)
        gain = ((mae_avant - mae_apres) / max(mae_avant, 1)) * 100
        res_mae.append({
            "Période": label,
            "MAE avant": round(mae_avant, 2),
            "MAE après": round(mae_apres, 2),
            "Gain": f"{gain:+.1f}%"
        })
    
    print(pd.DataFrame(res_mae).to_string(index=False))
    
    print(f"\n{'='*65}")
    print("✅ RÉSUMÉ")
    print(f"{'='*65}")
    print(f"   • Facteur correcteur : ×{facteur_global:.4f}")
    print(f"   • Appliqué sur : {m_v_all.sum():,} jours en vacances (passés + futurs)")
    print(f"   • Hors vacances : inchangé")
    print(f"   • Impact : les prédictions en vacances sont corrigées à la baisse")
    print(f"     pour refléter la baisse réelle observée sur le passé.")
    
    return df

# ════════════════════════════════════════════════════════════════════════════════
# CELLULE 4 — Exécution
# ════════════════════════════════════════════════════════════════════════════════
# df = rapport_manager(df)