Function main

demo/guide-python/quantile_data_iterator.py:96–130

()

def main() -> None:
    rounds = 100
    it = IterForDMatrixDemo()

    # Use iterator, must be `QuantileDMatrix`.

    # In this demo, the input batches are created using cupy, and the data processing
    # (quantile sketching) will be performed on GPU. If data is loaded with CPU based
    # data structures like numpy or pandas, then the processing step will be performed
    # on CPU instead.
    m_with_it = xgboost.QuantileDMatrix(it)

    # Use regular DMatrix.
    m = xgboost.DMatrix(
        it.as_array(), it.as_array_labels(), weight=it.as_array_weights()
    )

    assert m_with_it.num_col() == m.num_col()
    assert m_with_it.num_row() == m.num_row()
    # Tree method must be `hist`.
    reg_with_it = xgboost.train(
        {"tree_method": "hist", "device": "cuda"},
        m_with_it,
        num_boost_round=rounds,
        evals=[(m_with_it, "Train")],
    )
    predict_with_it = reg_with_it.predict(m_with_it)

    reg = xgboost.train(
        {"tree_method": "hist", "device": "cuda"},
        m,
        num_boost_round=rounds,
        evals=[(m, "Train")],
    )
    predict = reg.predict(m)


if __name__ == "__main__":