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.. "python/examples/basic_walkthrough.py"
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.. only:: html

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

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

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

.. _sphx_glr_python_examples_basic_walkthrough.py:


Getting started with XGBoost
============================

This is a simple example of using the native XGBoost interface, there are other
interfaces in the Python package like scikit-learn interface and Dask interface.


See :doc:`/python/python_intro` and :doc:`/tutorials/index` for other references.

.. GENERATED FROM PYTHON SOURCE LINES 12-75

.. code-block:: Python

    import os
    import pickle

    import numpy as np
    from sklearn.datasets import load_svmlight_file

    import xgboost as xgb

    # Make sure the demo knows where to load the data.
    CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
    XGBOOST_ROOT_DIR = os.path.dirname(os.path.dirname(CURRENT_DIR))
    DEMO_DIR = os.path.join(XGBOOST_ROOT_DIR, "demo")

    # X is a scipy csr matrix, XGBoost supports many other input types,
    X, y = load_svmlight_file(os.path.join(DEMO_DIR, "data", "agaricus.txt.train"))
    dtrain = xgb.DMatrix(X, y)
    # validation set
    X_test, y_test = load_svmlight_file(os.path.join(DEMO_DIR, "data", "agaricus.txt.test"))
    dtest = xgb.DMatrix(X_test, y_test)

    # specify parameters via map, definition are same as c++ version
    param = {"max_depth": 2, "eta": 1, "objective": "binary:logistic"}

    # specify validations set to watch performance
    watchlist = [(dtest, "eval"), (dtrain, "train")]
    # number of boosting rounds
    num_round = 2
    bst = xgb.train(param, dtrain, num_boost_round=num_round, evals=watchlist)

    # run prediction
    preds = bst.predict(dtest)
    labels = dtest.get_label()
    print(
        "error=%f"
        % (
            sum(1 for i in range(len(preds)) if int(preds[i] > 0.5) != labels[i])
            / float(len(preds))
        )
    )
    bst.save_model("model-0.json")
    # dump model
    bst.dump_model("dump.raw.txt")
    # dump model with feature map
    bst.dump_model("dump.nice.txt", os.path.join(DEMO_DIR, "data/featmap.txt"))

    # save dmatrix into binary buffer
    dtest.save_binary("dtest.dmatrix")
    # save model
    bst.save_model("model-1.json")
    # load model and data in
    bst2 = xgb.Booster(model_file="model-1.json")
    dtest2 = xgb.DMatrix("dtest.dmatrix")
    preds2 = bst2.predict(dtest2)
    # assert they are the same
    assert np.sum(np.abs(preds2 - preds)) == 0

    # alternatively, you can pickle the booster
    pks = pickle.dumps(bst2)
    # load model and data in
    bst3 = pickle.loads(pks)
    preds3 = bst3.predict(dtest2)
    # assert they are the same
    assert np.sum(np.abs(preds3 - preds)) == 0


.. _sphx_glr_download_python_examples_basic_walkthrough.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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