How to use the mriqc.logging function in mriqc

from sklearn.model_selection._search import (
    check_scoring, indexable,
    Parallel, delayed, defaultdict, rankdata
)
from sklearn.model_selection._validation import (
    _score, _num_samples, _index_param_value, _safe_split,
    logger)

from ... import logging
from builtins import object, zip
try:
    from sklearn.utils.fixes import MaskedArray
except ImportError:
    from numpy.ma import MaskedArray

LOG = logging.getLogger('mriqc.classifier')

class RobustGridSearchCV(GridSearchCV):
    def _fit(self, X, y, groups, parameter_iterable):
        """Actual fitting,  performing the search over parameters."""
        cv = check_cv(self.cv, y, classifier=is_classifier(self.estimator))
        self.scorer_ = check_scoring(self.estimator, scoring=self.scoring)

        X, y, groups = indexable(X, y, groups)
        n_splits = cv.get_n_splits(X, y, groups)

        if self.verbose > 0 and isinstance(parameter_iterable, Sized):
            n_candidates = len(parameter_iterable)
            LOG.log(19, "Fitting %d folds for each of %d candidates, totalling"
                    " %d fits", n_splits, n_candidates, n_candidates * n_splits)
        pre_dispatch = self.pre_dispatch

g_input.add_argument('--log-level', action='store', default='INFO',
                         choices=['CRITICAL', 'ERROR', 'WARN', 'INFO', 'DEBUG'])

    g_input.add_argument('-o', '--output-file', action='store', default='cv_inner_loop.csv',
                         help='the output table with cross validated scores')
    g_input.add_argument('-O', '--output-outer-cv', action='store', default='cv_outer_loop.csv',
                         help='the output table with cross validated scores')

    g_input.add_argument('--njobs', action='store', default=-1, type=int,
                         help='number of jobs')
    g_input.add_argument('--task-id', action='store')


    opts = parser.parse_args()

    logger = logging.getLogger()
    if opts.log_file is not None:
        fhl = logging.FileHandler(opts.log_file)
        fhl.setFormatter(fmt=logging.Formatter(LOG_FORMAT))
        logger.addHandler(fhl)
    logger.setLevel(opts.log_level)

    parameters = None
    if opts.parameters is not None:
        with open(opts.parameters) as paramfile:
            parameters = yaml.load(paramfile)

    cvhelper = NestedCVHelper(opts.training_data, opts.training_labels,
                              n_jobs=opts.njobs, param=parameters,
                              task_id=opts.task_id)

    cvhelper.cv_inner = read_cv(opts.cv_inner)

import pandas as pd

from .sklearn.cv_nested import nested_fit_and_score, ModelAndGridSearchCV
from .sklearn._split import RobustLeavePGroupsOut as LeavePGroupsOut

from sklearn.base import is_classifier, clone
from sklearn.metrics.scorer import check_scoring
from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection._split import check_cv

from .helper import CVHelperBase
from .. import logging

from builtins import str

LOG = logging.getLogger('mriqc.classifier')
LOG.setLevel(logging.INFO)

DEFAULT_TEST_PARAMETERS = {
    'svc_linear': [{'C': [0.1, 1]}],
}

EXCLUDE_COLUMNS = [
    'size_x', 'size_y', 'size_z',
    'spacing_x', 'spacing_y', 'spacing_z',
    'qi_1', 'qi_2',
    'tpm_overlap_csf', 'tpm_overlap_gm', 'tpm_overlap_wm',
]

FEATURE_NAMES = [
    'cjv', 'cnr', 'efc', 'fber',
    'fwhm_avg', 'fwhm_x', 'fwhm_y', 'fwhm_z',

def warn_redirect(message, category, filename, lineno, file=None, line=None):
    from mriqc import logging
    LOG = logging.getLogger('mriqc.warnings')

    if category not in cached_warnings:
        LOG.debug('captured warning (%s): %s', category, message)
        cached_warnings.append(category)

from .. import logging
from builtins import object, zip

NUMPY_MA = False
try:
    NUMPY_MA = np_version < (1, 12, 0)
except TypeError:
    pass

if NUMPY_MA:
    from sklearn.utils.fixes import MaskedArray
else:
    from numpy.ma import MaskedArray

LOG = logging.getLogger('mriqc.classifier')

def _len(indict):
    product = partial(reduce, operator.mul)
    return sum(product(len(v) for v in p.values()) if p else 1
               for p in indict)

class ModelParameterGrid(object):
    """
    Grid of models and parameters with a discrete number of values for each.
    Can be used to iterate over parameter value combinations with the
    Python built-in function iter.
    Read more in the :ref:`User Guide `.
    Parameters
    ----------
    param_grid : dict of string to sequence, or sequence of such
        The parameter grid to explore, as a dictionary mapping estimator

g_input.add_argument('-o', '--output-file', action='store', default='cv_inner_loop.csv',
                         help='the output table with cross validated scores')
    g_input.add_argument('-O', '--output-outer-cv', action='store', default='cv_outer_loop.csv',
                         help='the output table with cross validated scores')

    g_input.add_argument('--njobs', action='store', default=-1, type=int,
                         help='number of jobs')
    g_input.add_argument('--task-id', action='store')


    opts = parser.parse_args()

    logger = logging.getLogger()
    if opts.log_file is not None:
        fhl = logging.FileHandler(opts.log_file)
        fhl.setFormatter(fmt=logging.Formatter(LOG_FORMAT))
        logger.addHandler(fhl)
    logger.setLevel(opts.log_level)

    parameters = None
    if opts.parameters is not None:
        with open(opts.parameters) as paramfile:
            parameters = yaml.load(paramfile)

    cvhelper = NestedCVHelper(opts.training_data, opts.training_labels,
                              n_jobs=opts.njobs, param=parameters,
                              task_id=opts.task_id)

    cvhelper.cv_inner = read_cv(opts.cv_inner)
    cvhelper.cv_outer = read_cv(opts.cv_outer)

    # Run inner loop before setting held-out data, for hygene

g_input.add_argument('-o', '--output-file', action='store', default='cv_inner_loop.csv',
                         help='the output table with cross validated scores')
    g_input.add_argument('-O', '--output-outer-cv', action='store', default='cv_outer_loop.csv',
                         help='the output table with cross validated scores')

    g_input.add_argument('--njobs', action='store', default=-1, type=int,
                         help='number of jobs')
    g_input.add_argument('--task-id', action='store')


    opts = parser.parse_args()

    logger = logging.getLogger()
    if opts.log_file is not None:
        fhl = logging.FileHandler(opts.log_file)
        fhl.setFormatter(fmt=logging.Formatter(LOG_FORMAT))
        logger.addHandler(fhl)
    logger.setLevel(opts.log_level)

    parameters = None
    if opts.parameters is not None:
        with open(opts.parameters) as paramfile:
            parameters = yaml.load(paramfile)

    cvhelper = NestedCVHelper(opts.training_data, opts.training_labels,
                              n_jobs=opts.njobs, param=parameters,
                              task_id=opts.task_id)

    cvhelper.cv_inner = read_cv(opts.cv_inner)
    cvhelper.cv_outer = read_cv(opts.cv_outer)

from .sklearn.cv_nested import nested_fit_and_score, ModelAndGridSearchCV
from .sklearn._split import RobustLeavePGroupsOut as LeavePGroupsOut

from sklearn.base import is_classifier, clone
from sklearn.metrics.scorer import check_scoring
from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection._split import check_cv

from .helper import CVHelperBase
from .. import logging

from builtins import str

LOG = logging.getLogger('mriqc.classifier')
LOG.setLevel(logging.INFO)

DEFAULT_TEST_PARAMETERS = {
    'svc_linear': [{'C': [0.1, 1]}],
}

EXCLUDE_COLUMNS = [
    'size_x', 'size_y', 'size_z',
    'spacing_x', 'spacing_y', 'spacing_z',
    'qi_1', 'qi_2',
    'tpm_overlap_csf', 'tpm_overlap_gm', 'tpm_overlap_wm',
]

FEATURE_NAMES = [
    'cjv', 'cnr', 'efc', 'fber',
    'fwhm_avg', 'fwhm_x', 'fwhm_y', 'fwhm_z',
    'icvs_csf', 'icvs_gm', 'icvs_wm',