Données parcours-sup 2021-2025

loading '2021'
loading '2022'
loading '2023'
loading '2024'
loading '2025'

import pandas
from teachpyx.tools.pandas import read_csv_cached
from sklearn.metrics import mean_absolute_error
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from sklearn.ensemble import HistGradientBoostingRegressor

# from skrub import TableReport


def get_data():
    urls = {
        "2021": "https://data.enseignementsup-recherche.gouv.fr/api/explore/v2.1/catalog/datasets/fr-esr-parcoursup_2021/exports/csv?lang=fr&timezone=Europe%2FBerlin&use_labels=true&delimiter=%3B",
        "2022": "https://data.enseignementsup-recherche.gouv.fr/api/explore/v2.1/catalog/datasets/fr-esr-parcoursup_2022/exports/csv?lang=fr&timezone=Europe%2FBerlin&use_labels=true&delimiter=%3B",
        "2023": "https://data.enseignementsup-recherche.gouv.fr/api/explore/v2.1/catalog/datasets/fr-esr-parcoursup_2023/exports/csv?lang=fr&timezone=Europe%2FBerlin&use_labels=true&delimiter=%3B",
        "2024": "https://data.enseignementsup-recherche.gouv.fr/api/explore/v2.1/catalog/datasets/fr-esr-parcoursup_2024/exports/csv?lang=fr&timezone=Europe%2FBerlin&use_labels=true&delimiter=%3B",
        "2025": "https://data.enseignementsup-recherche.gouv.fr/api/explore/v2.1/catalog/datasets/fr-esr-parcoursup/exports/csv?lang=fr&timezone=Europe%2FBerlin&use_labels=true&delimiter=%3B",
    }

    dfs = {}
    for k, url in urls.items():
        print(f"loading {k!r}")
        dfs[k] = read_csv_cached(url, sep=";")

    return pandas.concat(dfs.values(), axis=0)


def select_variables_and_clean(df):
    keys = [
        "Région de l’établissement",
        "Session",
        "Statut de l’établissement de la filière de formation (public, privé…)",
        "Sélectivité",
        "Code UAI de l'établissement",
        "Établissement",
        "Filière de formation détaillée bis",
        "Filière de formation très agrégée",
        "Filière de formation.1",
        "Académie de l’établissement",
        "Code départemental de l’établissement",
        "Commune de l’établissement",
        "Concours communs et banque d'épreuves",
    ]
    cible = "Effectif total des candidats pour une formation"
    columns = set(df.columns)
    assert set(keys) & set(columns) == set(
        keys
    ), f"Missing columns {set(keys) - set(keys) & set(columns)} in {sorted(df.columns)}"
    subset = df[[*keys, cible]]
    mask = subset.duplicated(subset=keys, keep=False)
    return subset[~mask].reset_index(drop=True), cible


def compute_oracle(table, cible):
    vars = [c for c in table.columns if c != cible]
    f2025 = table["Session"] == 2025
    f2024 = table["Session"] == 2024
    ftwo = table[f2025 | f2024]
    piv = (
        pandas.pivot_table(
            ftwo,
            index=[c for c in vars if c != "Session"],
            columns="Session",
            values=cible,
        )
        .dropna(axis=0)
        .sort_index()
    )
    # Keep only rows where both 2024 and 2025 have non-missing values
    piv = piv.dropna(axis=0, how="any")
    if piv.empty:
        raise ValueError(
            "Not enough overlapping data between 2024 and 2025 to compute oracle."
        )
    return mean_absolute_error(piv[2025], piv[2024])


def split_train_test(table, cible):
    X, y = table.drop(cible, axis=1), table[cible]

    train_test = X["Session"] < 2025

    drop = ["Session", "Code UAI de l'établissement", "Établissement"]

    train_X = X[train_test].drop(drop, axis=1)
    train_y = y[train_test]
    test_X = X[~train_test].drop(drop, axis=1)
    test_y = y[~train_test]
    return train_X, test_X, train_y, test_y


def make_pipeline(table, cible):
    vars = [c for c in table.columns if c != cible]
    num_cols = ["Capacité de l’établissement par formation"]
    cat_cols = [c for c in vars if c not in num_cols]

    transformers = []
    if num_cols:
        transformers.append(("num", StandardScaler(), num_cols))
    if cat_cols:
        transformers.append(("cats", OneHotEncoder(handle_unknown="ignore"), cat_cols))

    model = Pipeline(
        [
            (
                "preprocessing",
                ColumnTransformer(transformers),
            ),
            ("regressor", HistGradientBoostingRegressor()),
        ]
    )
    return model


data = get_data()
table, cible = select_variables_and_clean(data)
# oracle = compute_oracle(table, cible)
# print(f"oracle : {oracle}")

# train_X, test_X, train_y, test_y = split_train_test(table, cible)
# model = make_pipeline(table, cible)
# model.fit(train_X, train_y)