.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "python/examples/individual_trees.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_individual_trees.py>`
        to download the full example code.

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

.. _sphx_glr_python_examples_individual_trees.py:


Demo for prediction using individual trees and model slices
===========================================================

.. GENERATED FROM PYTHON SOURCE LINES 5-101

.. code-block:: Python


    import os

    import numpy as np
    from scipy.special import logit
    from sklearn.datasets import load_svmlight_file

    import xgboost as xgb

    CURRENT_DIR = os.path.dirname(__file__)
    train = os.path.join(CURRENT_DIR, "../data/agaricus.txt.train")
    test = os.path.join(CURRENT_DIR, "../data/agaricus.txt.test")


    def individual_tree() -> None:
        """Get prediction from each individual tree and combine them together."""
        X_train, y_train = load_svmlight_file(train)
        X_test, y_test = load_svmlight_file(test)
        Xy_train = xgb.QuantileDMatrix(X_train, y_train)

        n_rounds = 4
        # Specify the base score, otherwise xgboost will estimate one from the training
        # data.
        base_score = 0.5
        params = {
            "max_depth": 2,
            "eta": 1,
            "objective": "reg:logistic",
            "tree_method": "hist",
            "base_score": base_score,
        }
        booster = xgb.train(params, Xy_train, num_boost_round=n_rounds)

        # Use logit to inverse the base score back to raw leaf value (margin)
        scores = np.full((X_test.shape[0],), logit(base_score))
        for i in range(n_rounds):
            # - Use output_margin to get raw leaf values
            # - Use iteration_range to get prediction for only one tree
            # - Use previous prediction as base marign for the model
            Xy_test = xgb.DMatrix(X_test, base_margin=scores)

            if i == n_rounds - 1:
                # last round, get the transformed prediction
                scores = booster.predict(
                    Xy_test, iteration_range=(i, i + 1), output_margin=False
                )
            else:
                # get raw leaf value for accumulation
                scores = booster.predict(
                    Xy_test, iteration_range=(i, i + 1), output_margin=True
                )

        full = booster.predict(xgb.DMatrix(X_test), output_margin=False)
        np.testing.assert_allclose(scores, full)


    def model_slices() -> None:
        """Inference with each individual tree using model slices."""
        X_train, y_train = load_svmlight_file(train)
        X_test, y_test = load_svmlight_file(test)
        Xy_train = xgb.QuantileDMatrix(X_train, y_train)

        n_rounds = 4
        # Specify the base score, otherwise xgboost will estimate one from the training
        # data.
        base_score = 0.5
        params = {
            "max_depth": 2,
            "eta": 1,
            "objective": "reg:logistic",
            "tree_method": "hist",
            "base_score": base_score,
        }
        booster = xgb.train(params, Xy_train, num_boost_round=n_rounds)
        trees = [booster[t] for t in range(n_rounds)]

        # Use logit to inverse the base score back to raw leaf value (margin)
        scores = np.full((X_test.shape[0],), logit(base_score))
        for i, t in enumerate(trees):
            # Feed previous scores into base margin.
            Xy_test = xgb.DMatrix(X_test, base_margin=scores)

            if i == n_rounds - 1:
                # last round, get the transformed prediction
                scores = t.predict(Xy_test, output_margin=False)
            else:
                # get raw leaf value for accumulation
                scores = t.predict(Xy_test, output_margin=True)

        full = booster.predict(xgb.DMatrix(X_test), output_margin=False)
        np.testing.assert_allclose(scores, full)


    if __name__ == "__main__":
        individual_tree()
        model_slices()


.. _sphx_glr_download_python_examples_individual_trees.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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