Function generate_regression_model

tests/python/generate_models.py:37–66

()

def generate_regression_model() -> None:
    print("Regression")
    X, y = make_categorical(
        n_samples=kRows, n_features=kCols, n_categories=16, onehot=False, cat_ratio=0.5
    )
    w = np.random.default_rng(2025).uniform(size=X.shape[0])
    data = xgboost.DMatrix(X, label=y, weight=w)
    booster = xgboost.train(
        {
            "tree_method": "hist",
            "num_parallel_tree": kForests,
            "max_depth": kMaxDepth,
            "base_score": 0.5,
        },
        num_boost_round=kRounds,
        dtrain=data,
    )
    booster.save_model(booster_ubj("reg"))
    booster.save_model(booster_json("reg"))

    reg = xgboost.XGBRegressor(
        tree_method="hist",
        num_parallel_tree=kForests,
        max_depth=kMaxDepth,
        n_estimators=kRounds,
        base_score=0.5,
    )
    reg.fit(X, y, sample_weight=w)
    reg.save_model(skl_ubj("reg"))
    reg.save_model(skl_json("reg"))


def generate_logistic_model() -> None: