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xgboost/python-package/xgboost/callback.py  view on Meta::CPAN

# coding: utf-8
# pylint: disable= invalid-name
"""Training Library containing training routines."""
from __future__ import absolute_import

from . import rabit
from .core import EarlyStopException


def _get_callback_context(env):
    """return whether the current callback context is cv or train"""
    if env.model is not None and env.cvfolds is None:
        context = 'train'
    elif env.model is None and env.cvfolds is not None:
        context = 'cv'
    return context


def _fmt_metric(value, show_stdv=True):
    """format metric string"""
    if len(value) == 2:
        return '%s:%g' % (value[0], value[1])
    elif len(value) == 3:
        if show_stdv:
            return '%s:%g+%g' % (value[0], value[1], value[2])
        else:
            return '%s:%g' % (value[0], value[1])
    else:
        raise ValueError("wrong metric value")


def print_evaluation(period=1, show_stdv=True):
    """Create a callback that print evaluation result.

    We print the evaluation results every ``period`` iterations
    and on the first and the last iterations.

    Parameters
    ----------
    period : int
        The period to log the evaluation results

    show_stdv : bool, optional
         Whether show stdv if provided

    Returns
    -------
    callback : function
        A callback that print evaluation every period iterations.
    """
    def callback(env):
        """internal function"""
        if env.rank != 0 or len(env.evaluation_result_list) == 0 or period is False:
            return
        i = env.iteration
        if (i % period == 0 or i + 1 == env.begin_iteration or i + 1 == env.end_iteration):
            msg = '\t'.join([_fmt_metric(x, show_stdv) for x in env.evaluation_result_list])
            rabit.tracker_print('[%d]\t%s\n' % (i, msg))
    return callback