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from __future__ import annotations
import argparse
import logging
from pathlib import Path
from typing import Dict, Iterable, Tuple
import numpy as np
import pandas as pd
import sqlalchemy as sa
from sqlalchemy.dialects.postgresql import insert
from src.constants import CANDIDATE_CATEGORIES
from src.data import preprocess as preprocess_module
from src.db.schema import (
bureaux,
categories,
communes,
create_schema,
elections,
get_engine,
results_local,
results_national,
)
from src.features import build_features
LOGGER = logging.getLogger(__name__)
TARGET_COLS = [f"target_share_{c}" for c in CANDIDATE_CATEGORIES]
ID_COLS = ["commune_code", "code_bv", "election_type", "election_year", "round", "date_scrutin"]
def load_panel(input_path: Path) -> pd.DataFrame:
if not input_path.exists():
raise FileNotFoundError(f"Dataset panel introuvable : {input_path}")
if input_path.suffix == ".parquet":
return pd.read_parquet(input_path)
return pd.read_csv(input_path, sep=";")
def ensure_panel_exists(panel_path: Path, elections_long_path: Path, mapping_path: Path) -> pd.DataFrame:
if panel_path.exists():
return load_panel(panel_path)
LOGGER.info("Panel manquant, tentative de reconstruction via preprocess + build_features.")
if not elections_long_path.exists():
preprocess_module.preprocess_all(Path("data/raw"), elections_long_path.parent, preprocess_module.DEFAULT_META_CONFIG)
build_features.build_panel(elections_long_path, mapping_path, panel_path, csv_output=None)
return load_panel(panel_path)
def check_mass(panel: pd.DataFrame, tolerance: float = 0.05) -> None:
sums = panel[TARGET_COLS].sum(axis=1)
bad = panel[(sums < (1 - tolerance)) | (sums > (1 + tolerance))]
if not bad.empty:
LOGGER.warning("Somme des parts hors intervalle attendu pour %s lignes (tol=%s).", len(bad), tolerance)
def melt_panel(panel: pd.DataFrame) -> pd.DataFrame:
long_df = panel.melt(id_vars=ID_COLS + ["turnout_pct"], value_vars=TARGET_COLS, var_name="category", value_name="share")
long_df["category"] = long_df["category"].str.replace("target_share_", "", regex=False)
return long_df
def _upsert_simple(conn, table, rows: Iterable[dict], index_elements: Iterable[str]) -> None:
stmt = insert(table).values(list(rows))
stmt = stmt.on_conflict_do_nothing(index_elements=list(index_elements))
if rows:
conn.execute(stmt)
def ingest(panel: pd.DataFrame, engine) -> None:
check_mass(panel)
panel = panel.copy()
panel["round"] = panel["round"].fillna(1).astype(int)
panel["date_scrutin"] = pd.to_datetime(panel["date_scrutin"]).dt.date
long_df = melt_panel(panel)
long_df = long_df[long_df["category"].isin(CANDIDATE_CATEGORIES)]
long_df["share_pct"] = (long_df["share"].astype(float) * 100).round(6)
with engine.begin() as conn:
create_schema(conn)
LOGGER.info("Schéma vérifié.")
_upsert_simple(conn, categories, [{"name": cat} for cat in CANDIDATE_CATEGORIES], ["name"])
cat_map = dict(conn.execute(sa.select(categories.c.name, categories.c.id)))
commune_rows = [
{"name_normalized": code, "insee_code": code}
for code in sorted(long_df["commune_code"].dropna().unique())
]
_upsert_simple(conn, communes, commune_rows, ["insee_code"])
commune_map = dict(conn.execute(sa.select(communes.c.insee_code, communes.c.id)))
def bureau_code_only(code_bv: str) -> str:
if "-" in str(code_bv):
parts = str(code_bv).split("-", 1)
return parts[1]
return str(code_bv)
bureau_rows = []
for _, row in long_df.drop_duplicates(subset=["commune_code", "code_bv"]).iterrows():
commune_id = commune_map.get(row["commune_code"])
if commune_id is None:
continue
bureau_rows.append(
{
"commune_id": commune_id,
"bureau_code": bureau_code_only(row["code_bv"]),
"bureau_label": None,
}
)
_upsert_simple(conn, bureaux, bureau_rows, ["commune_id", "bureau_code"])
bureau_map = {
(commune_id, bureau_code): bureau_id
for bureau_id, commune_id, bureau_code in conn.execute(
sa.select(bureaux.c.id, bureaux.c.commune_id, bureaux.c.bureau_code)
)
}
election_rows = []
for _, row in panel.drop_duplicates(subset=["election_type", "election_year", "round"]).iterrows():
election_rows.append(
{
"election_type": row["election_type"],
"election_year": int(row["election_year"]),
"round": int(row["round"]) if not pd.isna(row["round"]) else None,
"date": row["date_scrutin"],
}
)
_upsert_simple(conn, elections, election_rows, ["election_type", "election_year", "round"])
election_map: Dict[Tuple[str, int, int], int] = {
(etype, year, int(round_) if round_ is not None else 1): eid
for eid, etype, year, round_ in conn.execute(
sa.select(elections.c.id, elections.c.election_type, elections.c.election_year, elections.c.round)
)
}
local_rows = []
for row in long_df.itertuples(index=False):
commune_id = commune_map.get(row.commune_code)
if commune_id is None:
continue
bureau_id = bureau_map.get((commune_id, bureau_code_only(row.code_bv)))
election_id = election_map.get((row.election_type, int(row.election_year), int(row.round)))
category_id = cat_map.get(row.category)
if None in (bureau_id, election_id, category_id):
continue
turnout_pct = None if pd.isna(row.turnout_pct) else float(row.turnout_pct) * 100
local_rows.append(
{
"bureau_id": bureau_id,
"election_id": election_id,
"category_id": category_id,
"share_pct": None if pd.isna(row.share_pct) else float(row.share_pct),
"votes": None,
"expressed": None,
"turnout_pct": turnout_pct,
}
)
if local_rows:
stmt = insert(results_local).values(local_rows)
stmt = stmt.on_conflict_do_update(
index_elements=["bureau_id", "election_id", "category_id"],
set_={
"share_pct": stmt.excluded.share_pct,
"votes": stmt.excluded.votes,
"expressed": stmt.excluded.expressed,
"turnout_pct": stmt.excluded.turnout_pct,
},
)
conn.execute(stmt)
LOGGER.info("Résultats locaux insérés/mis à jour : %s lignes", len(local_rows))
nat_rows = []
nat = (
long_df.groupby(["election_type", "election_year", "round", "category"], as_index=False)
.agg(share=("share_pct", "mean"))
.rename(columns={"share": "share_pct"})
)
# Participation moyenne par scrutin
turnout_nat = panel.groupby(["election_type", "election_year", "round"], as_index=False)["turnout_pct"].mean()
nat = nat.merge(turnout_nat, on=["election_type", "election_year", "round"], how="left")
for row in nat.itertuples(index=False):
election_id = election_map.get((row.election_type, int(row.election_year), int(row.round)))
category_id = cat_map.get(row.category)
if None in (election_id, category_id):
continue
nat_rows.append(
{
"election_id": election_id,
"category_id": category_id,
"share_pct": None if pd.isna(row.share_pct) else float(row.share_pct),
"votes": None,
"expressed": None,
"turnout_pct": None if pd.isna(row.turnout_pct) else float(row.turnout_pct * 100),
}
)
if nat_rows:
stmt = insert(results_national).values(nat_rows)
stmt = stmt.on_conflict_do_update(
index_elements=["election_id", "category_id"],
set_={
"share_pct": stmt.excluded.share_pct,
"votes": stmt.excluded.votes,
"expressed": stmt.excluded.expressed,
"turnout_pct": stmt.excluded.turnout_pct,
},
)
conn.execute(stmt)
LOGGER.info("Référentiels nationaux insérés/mis à jour : %s lignes", len(nat_rows))
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description="Ingestion du panel harmonisé dans PostgreSQL.")
parser.add_argument("--input", type=Path, default=Path("data/processed/panel.parquet"), help="Chemin vers le panel parquet.")
parser.add_argument(
"--elections-long",
type=Path,
default=Path("data/interim/elections_long.parquet"),
help="Format long (fallback pour reconstruire le panel).",
)
parser.add_argument(
"--mapping",
type=Path,
default=Path("data/mapping_candidats_blocs.csv"),
help="Mapping nuance -> catégorie (fallback).",
)
return parser.parse_args()
def main() -> None:
logging.basicConfig(level=logging.INFO, format="%(levelname)s %(message)s")
args = parse_args()
panel = ensure_panel_exists(args.input, args.elections_long, args.mapping)
engine = get_engine()
ingest(panel, engine)
if __name__ == "__main__":
main()
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