.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "python/examples/multioutput_reduced_gradient.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_python_examples_multioutput_reduced_gradient.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_python_examples_multioutput_reduced_gradient.py:


A demo for multi-output regression using reduced gradient
=========================================================

See :doc:`/tutorials/multioutput` for more information.

.. versionadded:: 3.2.0

.. note::

    The implementation is experimental and many features are missing.

.. seealso:: :ref:`sphx_glr_python_examples_multioutput_regression.py`

.. GENERATED FROM PYTHON SOURCE LINES 16-122

.. code-block:: Python


    import argparse
    from typing import Tuple

    import numpy as np
    from sklearn.base import BaseEstimator
    from sklearn.datasets import make_regression

    import xgboost as xgb
    from xgboost.objective import TreeObjective


    class LsObjMean(TreeObjective):
        """Least squared error. Reduce the size of the gradient using mean value."""

        def __init__(self, device: str) -> None:
            self.device = device

        def __call__(
            self, iteration: int, y_pred: np.ndarray, dtrain: xgb.DMatrix
        ) -> Tuple[np.ndarray, np.ndarray]:
            y_true = dtrain.get_label()
            grad = y_pred - y_true
            if self.device == "cpu":
                hess = np.ones(grad.shape)
                return grad, hess
            else:
                import cupy as cp

                hess = cp.ones(grad.shape)

                return cp.array(grad), cp.array(hess)

        def split_grad(
            self, iteration: int, grad: np.ndarray, hess: np.ndarray
        ) -> Tuple[np.ndarray, np.ndarray]:
            if self.device == "cpu":
                from numpy import mean
            else:
                from cupy import mean  # type: ignore[no-redef]

            sgrad = mean(grad, axis=1)
            shess = mean(hess, axis=1)
            return sgrad, shess


    def svd_class(device: str) -> BaseEstimator:
        """One of the methods in the sketch boost paper."""
        from sklearn.decomposition import TruncatedSVD

        svd_params = {"algorithm": "arpack", "n_components": 2, "n_iter": 8}
        svd = TruncatedSVD(**svd_params)
        return svd


    class LsObjSvd(LsObjMean):
        """Reduce the size of the gradient using SVD."""

        def __init__(self, device: str) -> None:
            super().__init__(device=device)

        def split_grad(
            self, iteration: int, grad: np.ndarray, hess: np.ndarray
        ) -> Tuple[np.ndarray, np.ndarray]:
            svd = svd_class(self.device)
            if self.device == "cuda":
                grad = grad.get()   # type: ignore
                hess = hess.get()   # type: ignore

            svd.fit(grad)
            grad = svd.transform(grad)
            hess = svd.transform(hess)
            if self.device == "cpu":
                hess = np.clip(hess, 0.01, None)
            else:
                import cupy as cp

                hess = cp.clip(hess, 0.01, None)
            return grad, hess


    def main() -> None:
        parser = argparse.ArgumentParser()
        parser.add_argument("--device", choices=["cpu", "cuda"], default="cpu")
        args = parser.parse_args()

        X, y = make_regression(
            n_samples=8192, n_features=32, n_targets=8, random_state=2026
        )
        Xy = xgb.QuantileDMatrix(X, y)

        for obj in (LsObjMean(args.device), LsObjSvd(args.device)):
            xgb.train(
                {
                    "device": args.device,
                    "multi_strategy": "multi_output_tree",
                },
                Xy,
                evals=[(Xy, "Train")],
                obj=obj,
                num_boost_round=16,
            )


    if __name__ == "__main__":
        main()


.. _sphx_glr_download_python_examples_multioutput_reduced_gradient.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: multioutput_reduced_gradient.ipynb <multioutput_reduced_gradient.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: multioutput_reduced_gradient.py <multioutput_reduced_gradient.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: multioutput_reduced_gradient.zip <multioutput_reduced_gradient.zip>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_