Function generate_classification_model

tests/python/generate_models.py:107–141

()

def generate_classification_model() -> None:
    print("Classification")
    X, y = make_classification(
        n_samples=kRows,
        n_features=kCols,
        random_state=2025,
        n_classes=kClasses,
        n_informative=4,
        n_redundant=0,
    )
    w = np.random.default_rng(2025).uniform(size=X.shape[0])

    data = xgboost.DMatrix(X, label=y, weight=w)
    booster = xgboost.train(
        {
            "num_class": kClasses,
            "tree_method": "hist",
            "num_parallel_tree": kForests,
            "max_depth": kMaxDepth,
        },
        num_boost_round=kRounds,
        dtrain=data,
    )
    booster.save_model(booster_ubj("cls"))
    booster.save_model(booster_json("cls"))

    cls = xgboost.XGBClassifier(
        tree_method="hist",
        num_parallel_tree=kForests,
        max_depth=kMaxDepth,
        n_estimators=kRounds,
    )
    cls.fit(X, y, sample_weight=w)
    cls.save_model(skl_ubj("cls"))
    cls.save_model(skl_json("cls"))


def generate_ranking_model() -> None: