Function generate_ranking_model

tests/python/generate_models.py:144–175

()

def generate_ranking_model() -> None:
    print("Learning to Rank")
    X, y, qid, w = make_ltr(
        n_samples=kRows, n_features=kCols, n_query_groups=7, max_rel=3
    )

    data = xgboost.DMatrix(X, y, weight=w, qid=qid)
    booster = xgboost.train(
        {
            "objective": "rank:ndcg",
            "num_parallel_tree": kForests,
            "tree_method": "hist",
            "max_depth": kMaxDepth,
            "base_score": 0.5,
        },
        num_boost_round=kRounds,
        dtrain=data,
    )
    booster.save_model(booster_ubj("ltr"))
    booster.save_model(booster_json("ltr"))

    ranker = xgboost.sklearn.XGBRanker(
        n_estimators=kRounds,
        tree_method="hist",
        objective="rank:ndcg",
        max_depth=kMaxDepth,
        num_parallel_tree=kForests,
        base_score=0.5,
    )
    ranker.fit(X, y, qid=qid, sample_weight=w)
    ranker.save_model(skl_ubj("ltr"))
    ranker.save_model(skl_json("ltr"))


def generate_aft_survival_models() -> None: