
"""simple docstring"""

from sklearn.metrics import matthews_corrcoef

import datasets


SCREAMING_SNAKE_CASE_    					=						'''
Compute the Matthews correlation coefficient (MCC)

The Matthews correlation coefficient is used in machine learning as a
measure of the quality of binary and multiclass classifications. It takes
into account true and false positives and negatives and is generally
regarded as a balanced measure which can be used even if the classes are of
very different sizes. The MCC is in essence a correlation coefficient value
between -1 and +1. A coefficient of +1 represents a perfect prediction, 0
an average random prediction and -1 an inverse prediction.  The statistic
is also known as the phi coefficient. [source: Wikipedia]
'''

SCREAMING_SNAKE_CASE_    					=						'''
Args:
    predictions (list of int): Predicted labels, as returned by a model.
    references (list of int): Ground truth labels.
    sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.
Returns:
    matthews_correlation (dict containing float): Matthews correlation.
Examples:
    Example 1, a basic example with only predictions and references as inputs:
        >>> matthews_metric = datasets.load_metric("matthews_correlation")
        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
        ...                                     predictions=[1, 2, 2, 0, 3, 3])
        >>> print(round(results[\'matthews_correlation\'], 2))
        0.54

    Example 2, the same example as above, but also including sample weights:
        >>> matthews_metric = datasets.load_metric("matthews_correlation")
        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
        ...                                     predictions=[1, 2, 2, 0, 3, 3],
        ...                                     sample_weight=[0.5, 3, 1, 1, 1, 2])
        >>> print(round(results[\'matthews_correlation\'], 2))
        0.1

    Example 3, the same example as above, but with sample weights that cause a negative correlation:
        >>> matthews_metric = datasets.load_metric("matthews_correlation")
        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
        ...                                     predictions=[1, 2, 2, 0, 3, 3],
        ...                                     sample_weight=[0.5, 1, 0, 0, 0, 1])
        >>> print(round(results[\'matthews_correlation\'], 2))
        -0.25
'''

SCREAMING_SNAKE_CASE_    					=						'''\
@article{scikit-learn,
  title={Scikit-learn: Machine Learning in {P}ython},
  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
  journal={Journal of Machine Learning Research},
  volume={12},
  pages={2825--2830},
  year={2011}
}
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION     ,			_KWARGS_DESCRIPTION							)
class      _UpperCAmelCase							(		datasets.Metric							):




      def     a_      (							self							)						->					Dict:
          return datasets.MetricInfo(
              description=_DESCRIPTION   ,       citation=_CITATION   ,       inputs_description=_KWARGS_DESCRIPTION   ,       features=datasets.Features(
                  {
                      'predictions': datasets.Value('int32'							),
                      'references': datasets.Value('int32'							),
                  }							)   ,       reference_urls=[
                  'https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html'
              ]   ,       )




      def     a_      (							self   ,       lowercase_   ,       lowercase_   ,       lowercase_=None							)						->					Union[str, Any]:
          return {
              "matthews_correlation": float(matthews_corrcoef(lowercase_   ,       lowercase_   ,       sample_weight=lowercase_							)							),
          }





"""simple docstring"""

import os
from collections.abc import Iterator




def     lowercase__						(					lowerCAmelCase				:					str = "." ) ->      Iterator[str]:






    """simple docstring"""




    for dir_path, dir_names, filenames in os.walk(lowerCAmelCase ):
        UpperCAmelCase									=					[d for d in dir_names if d != 'scripts' and d[0] not in '._']
        for filename in filenames:
            if filename == "__init__.py":
                continue
            if os.path.splitext(lowerCAmelCase )[1] in (".py", ".ipynb"):
                yield os.path.join(lowerCAmelCase ,				lowerCAmelCase ).lstrip('./' )




def     lowercase__						(					lowerCAmelCase				:					Union[str, Any] ) ->      Any:






    """simple docstring"""




    return F"{i * '  '}*" if i else "\n##"




def     lowercase__						(					lowerCAmelCase				:					str ,				lowerCAmelCase				:					str ) ->      str:






    """simple docstring"""




    UpperCAmelCase									=					old_path.split(os.sep )
    for i, new_part in enumerate(new_path.split(os.sep ) ):
        if (i + 1 > len(lowerCAmelCase ) or old_parts[i] != new_part) and new_part:
            print(F"{md_prefix(lowerCAmelCase )} {new_part.replace('_' ,				' ' ).title()}" )
    return new_path




def     lowercase__						(					lowerCAmelCase				:					str = "." ) ->      None:






    """simple docstring"""




    UpperCAmelCase									=					''
    for filepath in sorted(good_file_paths(lowerCAmelCase ) ):
        UpperCAmelCase			,							UpperCAmelCase									=					os.path.split(lowerCAmelCase )
        if filepath != old_path:
            UpperCAmelCase									=					print_path(lowerCAmelCase ,				lowerCAmelCase )
        UpperCAmelCase									=					(filepath.count(os.sep ) + 1) if filepath else 0
        UpperCAmelCase									=					F"{filepath}/{filename}".replace(' ' ,				'%20' )
        UpperCAmelCase									=					os.path.splitext(filename.replace('_' ,				' ' ).title() )[0]
        print(F"{md_prefix(lowerCAmelCase )} [{filename}]({url})" )


if __name__ == "__main__":
   print_directory_md('''.''')



