File size: 9,591 Bytes
46f9144 c12ec1a 46f9144 c12ec1a 46f9144 |
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 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 |
from __future__ import annotations
import argparse
import json
import logging
from pathlib import Path
from typing import Dict, List
import joblib
import numpy as np
import pandas as pd
from src.constants import CANDIDATE_CATEGORIES
from src.features.build_features import (
aggregate_by_event,
compute_national_reference,
expand_by_category,
load_elections_long,
load_mapping,
)
LOGGER = logging.getLogger(__name__)
TYPE_HISTORY_BLEND = {
"presidentielles": 0.4,
"legislatives": 0.35,
"europeennes": 0.3,
"regionales": 0.3,
"departementales": 0.3,
"municipales": 0.2,
}
def blend_with_type_history(
preds: np.ndarray,
feature_df: pd.DataFrame,
target_type: str,
) -> np.ndarray:
base_weight = TYPE_HISTORY_BLEND.get(str(target_type).lower(), 0.0)
if base_weight <= 0 or preds.size == 0:
return preds
hist_cols = [f"prev_share_type_lag1_{cat}" for cat in CANDIDATE_CATEGORIES]
if not all(col in feature_df.columns for col in hist_cols):
return preds
hist_vals = feature_df[hist_cols].to_numpy(dtype=float)
mask = np.isnan(hist_vals)
available = (~mask).sum(axis=1).astype(float)
if np.nanmax(available) == 0:
return preds
ratio = (available / len(CANDIDATE_CATEGORIES)).reshape(-1, 1)
weights = base_weight * ratio
hist_vals = np.where(mask, preds, hist_vals)
blended = (1 - weights) * preds + weights * hist_vals
blended = np.clip(blended, 0, None)
sums = blended.sum(axis=1, keepdims=True)
sums[sums == 0] = 1
return blended / sums
def filter_history(df: pd.DataFrame, target_year: int, commune_code: str | None) -> pd.DataFrame:
df = df[df["annee"] < target_year]
if commune_code:
df = df[df["code_commune"] == commune_code]
return df
def build_feature_matrix(
elections_long: pd.DataFrame,
mapping: pd.DataFrame,
target_type: str,
target_year: int,
) -> pd.DataFrame:
expanded = expand_by_category(elections_long, mapping)
local = aggregate_by_event(expanded)
nat = compute_national_reference(local)
local = local.merge(nat, on=["election_type", "election_year", "round", "category"], how="left")
local["dev_to_nat"] = local["share"] - local["share_nat"]
local = local.sort_values("date_scrutin")
last_any_share = (
local.sort_values("date_scrutin").groupby(["code_bv", "category"])["share"].last()
)
last_any_dev = (
local.sort_values("date_scrutin").groupby(["code_bv", "category"])["dev_to_nat"].last()
)
last_type_share = (
local[local["election_type"] == target_type]
.sort_values("date_scrutin")
.groupby(["code_bv", "category"])["share"]
.last()
)
last_type_dev = (
local[local["election_type"] == target_type]
.sort_values("date_scrutin")
.groupby(["code_bv", "category"])["dev_to_nat"]
.last()
)
# Swing entre les deux derniers scrutins tous types
swing_any = (
local.groupby(["code_bv", "category"])["share"]
.apply(lambda s: s.iloc[-1] - s.iloc[-2] if len(s) >= 2 else np.nan)
.rename("swing_any")
)
turnout_any = local.groupby("code_bv")["turnout_pct"].last()
turnout_type = (
local[local["election_type"] == target_type]
.sort_values("date_scrutin")
.groupby("code_bv")["turnout_pct"]
.last()
)
bureaux = sorted(local["code_bv"].dropna().unique())
records: List[dict] = []
for code_bv in bureaux:
record = {
"commune_code": str(code_bv).split("-")[0],
"code_bv": code_bv,
"election_type": target_type,
"election_year": target_year,
"round": 1,
"date_scrutin": f"{target_year}-01-01",
"prev_turnout_any_lag1": turnout_any.get(code_bv, np.nan),
"prev_turnout_same_type_lag1": turnout_type.get(code_bv, np.nan),
}
for cat in CANDIDATE_CATEGORIES:
record[f"prev_share_any_lag1_{cat}"] = last_any_share.get((code_bv, cat), np.nan)
record[f"prev_share_type_lag1_{cat}"] = last_type_share.get((code_bv, cat), np.nan)
record[f"prev_dev_to_national_any_lag1_{cat}"] = last_any_dev.get((code_bv, cat), np.nan)
record[f"prev_dev_to_national_type_lag1_{cat}"] = last_type_dev.get((code_bv, cat), np.nan)
record[f"swing_any_{cat}"] = swing_any.get((code_bv, cat), np.nan)
records.append(record)
return pd.DataFrame.from_records(records)
def compute_references(local: pd.DataFrame, target_year: int) -> Dict[str, Dict[str, float]]:
refs: Dict[str, Dict[str, float]] = {}
leg = (
local[(local["election_type"] == "legislatives") & (local["election_year"] < target_year)]
.sort_values("date_scrutin")
.groupby(["code_bv", "category"])
.last()
)
mun2020 = (
local[(local["election_type"] == "municipales") & (local["election_year"] == 2020)]
.sort_values("date_scrutin")
.groupby(["code_bv", "category"])
.last()
)
refs["leg"] = {(code_bv, cat): row["share"] for (code_bv, cat), row in leg.iterrows()}
refs["mun2020"] = {(code_bv, cat): row["share"] for (code_bv, cat), row in mun2020.iterrows()}
return refs
def load_feature_columns(path: Path, df: pd.DataFrame) -> List[str]:
if path.exists():
return json.loads(path.read_text())
# fallback: use all non-target columns except identifiers
exclude = {"commune_code", "code_bv", "election_type", "election_year", "round", "date_scrutin"}
return [c for c in df.columns if c not in exclude]
def predict(
model_path: Path,
feature_df: pd.DataFrame,
feature_cols: List[str],
refs: Dict[str, Dict[str, float]],
) -> pd.DataFrame:
model = joblib.load(model_path)
# Align feature set with trained columns (add missing as NaN)
missing_cols = [c for c in feature_cols if c not in feature_df.columns]
for col in missing_cols:
feature_df[col] = np.nan
preds = model.predict(feature_df[feature_cols])
preds = np.clip(preds, 0, 1)
sums = preds.sum(axis=1, keepdims=True)
sums[sums == 0] = 1
preds = preds / sums
target_type = None
if "election_type" in feature_df.columns and not feature_df.empty:
target_type = str(feature_df["election_type"].iloc[0])
if target_type:
preds = blend_with_type_history(preds, feature_df, target_type)
preds_pct = preds * 100
rows = []
for idx, row in feature_df.iterrows():
code_bv = row["code_bv"]
record = {
"commune_code": row["commune_code"],
"code_bv": code_bv,
}
for cat_idx, cat in enumerate(CANDIDATE_CATEGORIES):
pred_val = preds_pct[idx, cat_idx]
record[f"predicted_share_{cat}"] = round(float(pred_val), 2)
leg_ref = refs["leg"].get((code_bv, cat))
mun_ref = refs["mun2020"].get((code_bv, cat))
record[f"delta_leg_{cat}"] = "N/A" if leg_ref is None else round(float(pred_val - leg_ref * 100), 2)
record[f"delta_mun2020_{cat}"] = "N/A" if mun_ref is None else round(float(pred_val - mun_ref * 100), 2)
rows.append(record)
return pd.DataFrame(rows)
def main() -> None:
parser = argparse.ArgumentParser(description="Prédictions bureau par bureau pour une échéance cible.")
parser.add_argument("--model-path", type=Path, default=Path("models/hist_gradient_boosting.joblib"), help="Modèle entraîné.")
parser.add_argument("--feature-columns", type=Path, default=Path("models/feature_columns.json"), help="Colonnes de features attendues.")
parser.add_argument("--elections-long", type=Path, default=Path("data/interim/elections_long.parquet"), help="Historique long.")
parser.add_argument("--mapping", type=Path, default=Path("config/nuances.yaml"), help="Mapping nuances->catégories.")
parser.add_argument("--target-election-type", type=str, default="municipales", help="Type d'élection cible.")
parser.add_argument("--target-year", type=int, default=2026, help="Année cible.")
parser.add_argument("--commune-code", type=str, default="34301", help="Code commune à filtrer (Sete=34301).")
parser.add_argument("--output-dir", type=Path, default=Path("predictions"), help="Répertoire de sortie.")
args = parser.parse_args()
logging.basicConfig(level=logging.INFO, format="%(levelname)s %(message)s")
elections_long = load_elections_long(args.elections_long)
elections_long = filter_history(elections_long, args.target_year, args.commune_code)
mapping = load_mapping(args.mapping)
feature_df = build_feature_matrix(elections_long, mapping, args.target_election_type, args.target_year)
if feature_df.empty:
raise RuntimeError("Aucune donnée historique disponible pour construire les features.")
feature_cols = load_feature_columns(args.feature_columns, feature_df)
refs = compute_references(
aggregate_by_event(expand_by_category(elections_long, mapping)).assign(
election_type=lambda d: d["election_type"]
),
args.target_year,
)
preds_df = predict(args.model_path, feature_df, feature_cols, refs)
args.output_dir.mkdir(parents=True, exist_ok=True)
output_path = args.output_dir / f"pred_{args.target_election_type}_{args.target_year}_sete.csv"
preds_df.to_csv(output_path, index=False)
LOGGER.info("Prédictions écrites dans %s", output_path)
if __name__ == "__main__":
main()
|