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.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "python/examples/cross_validation.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_cross_validation.py>`
        to download the full example code.

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

.. _sphx_glr_python_examples_cross_validation.py:


Demo for using cross validation
===============================

.. GENERATED FROM PYTHON SOURCE LINES 5-92

.. code-block:: Python

    import os

    import numpy as np

    import xgboost as xgb

    # load data in do training
    CURRENT_DIR = os.path.dirname(__file__)
    dtrain = xgb.DMatrix(
        os.path.join(CURRENT_DIR, "../data/agaricus.txt.train?format=libsvm")
    )
    param = {"max_depth": 2, "eta": 1, "objective": "binary:logistic"}
    num_round = 2

    print("running cross validation")
    # do cross validation, this will print result out as
    # [iteration]  metric_name:mean_value+std_value
    # std_value is standard deviation of the metric
    xgb.cv(
        param,
        dtrain,
        num_round,
        nfold=5,
        metrics={"error"},
        seed=0,
        callbacks=[xgb.callback.EvaluationMonitor(show_stdv=True)],
    )

    print("running cross validation, disable standard deviation display")
    # do cross validation, this will print result out as
    # [iteration]  metric_name:mean_value
    res = xgb.cv(
        param,
        dtrain,
        num_boost_round=10,
        nfold=5,
        metrics={"error"},
        seed=0,
        callbacks=[
            xgb.callback.EvaluationMonitor(show_stdv=False),
            xgb.callback.EarlyStopping(3),
        ],
    )
    print(res)
    print("running cross validation, with preprocessing function")


    # define the preprocessing function
    # used to return the preprocessed training, test data, and parameter
    # we can use this to do weight rescale, etc.
    # as a example, we try to set scale_pos_weight
    def fpreproc(dtrain, dtest, param):
        label = dtrain.get_label()
        ratio = float(np.sum(label == 0)) / np.sum(label == 1)
        param["scale_pos_weight"] = ratio
        return (dtrain, dtest, param)


    # do cross validation, for each fold
    # the dtrain, dtest, param will be passed into fpreproc
    # then the return value of fpreproc will be used to generate
    # results of that fold
    xgb.cv(param, dtrain, num_round, nfold=5, metrics={"auc"}, seed=0, fpreproc=fpreproc)

    ###
    # you can also do cross validation with customized loss function
    # See custom_objective.py
    ##
    print("running cross validation, with customized loss function")


    def logregobj(preds, dtrain):
        labels = dtrain.get_label()
        preds = 1.0 / (1.0 + np.exp(-preds))
        grad = preds - labels
        hess = preds * (1.0 - preds)
        return grad, hess


    def evalerror(preds, dtrain):
        labels = dtrain.get_label()
        return "error", float(sum(labels != (preds > 0.0))) / len(labels)


    param = {"max_depth": 2, "eta": 1}
    # train with customized objective
    xgb.cv(param, dtrain, num_round, nfold=5, seed=0, obj=logregobj, feval=evalerror)


.. _sphx_glr_download_python_examples_cross_validation.py:

.. only:: html

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

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

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

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

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

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

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


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