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millesima-ai-engine/main.py
2026-03-03 04:18:30 +01:00

64 lines
2.0 KiB
Python
Executable File

#!/usr/bin/env python3
from os import getcwd
from os.path import normpath, join
from sys import argv
from pandas import read_csv, DataFrame
from cleaning import (display_info,
drop_empty_appellation,
mean_robert,
mean_robinson,
mean_suckling,
fill_missing_scores,
encode_appellation)
def load_csv(filename: str) -> DataFrame:
path: str = normpath(join(getcwd(), filename))
return read_csv(path)
def save_csv(df: DataFrame, out_filename: str) -> None:
df.to_csv(out_filename, index=False)
def main() -> None:
if len(argv) != 2:
raise ValueError(f"Usage: {argv[0]} <filename.csv>")
df = load_csv(argv[1])
display_info(df, "Avant le nettoyage")
df = drop_empty_appellation(df)
save_csv(df, "donnee_clean.csv")
display_info(df, "Après nettoyage d'appellations manquantes")
#la moyenne des notes des vins pour chaque appellation
robert_means = mean_robert(df)
save_csv(robert_means, "mean_robert_by_appellation.csv")
display_info(robert_means, "Moyennes Robert par appellation")
robinson_means = mean_robinson(df)
save_csv(robinson_means, "mean_robinson_by_appellation.csv")
display_info(robinson_means, "Moyennes Robinson par appellation")
suckling_means = mean_suckling(df)
save_csv(suckling_means, "mean_suckling_by_appellation.csv")
display_info(suckling_means, "Moyennes Suckling par appellation")
df_missing_scores = fill_missing_scores(df)
save_csv(df_missing_scores, "donnee_filled.csv")
display_info(df_missing_scores, "Après remplissage des notes manquantes par la moyenne de l'appellation")
df_ready = encode_appellation(df_missing_scores)
save_csv(df_ready, "donnee_ready.csv")
display_info(df_ready, "Après remplacer la colonne 'Appellation' par des colonnes indicatrices")
if __name__ == "__main__":
try:
main()
except Exception as e:
print(f"ERREUR: {e}")