





from sklearn.metrics import matthews_corrcoef

import datasets


__magic_name__   :						int    =			"""\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass classifications. It takes\ninto account true and false positives and negatives and is generally\nregarded as a balanced measure which can be used even if the classes are of\nvery different sizes. The MCC is in essence a correlation coefficient value\nbetween -1 and +1. A coefficient of +1 represents a perfect prediction, 0\nan average random prediction and -1 an inverse prediction.  The statistic\nis also known as the phi coefficient. [source: Wikipedia]\n"""

__magic_name__   :						Dict    =			"""\nArgs:\n    predictions (list of int): Predicted labels, as returned by a model.\n    references (list of int): Ground truth labels.\n    sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.\nReturns:\n    matthews_correlation (dict containing float): Matthews correlation.\nExamples:\n    Example 1, a basic example with only predictions and references as inputs:\n        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n        ...                                     predictions=[1, 2, 2, 0, 3, 3])\n        >>> print(round(results[\'matthews_correlation\'], 2))\n        0.54\n\n    Example 2, the same example as above, but also including sample weights:\n        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n        ...                                     predictions=[1, 2, 2, 0, 3, 3],\n        ...                                     sample_weight=[0.5, 3, 1, 1, 1, 2])\n        >>> print(round(results[\'matthews_correlation\'], 2))\n        0.1\n\n    Example 3, the same example as above, but with sample weights that cause a negative correlation:\n        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n        ...                                     predictions=[1, 2, 2, 0, 3, 3],\n        ...                                     sample_weight=[0.5, 1, 0, 0, 0, 1])\n        >>> print(round(results[\'matthews_correlation\'], 2))\n        -0.25\n"""

__magic_name__   :						Dict    =			"""\\n@article{scikit-learn,\n  title={Scikit-learn: Machine Learning in {P}ython},\n  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n  journal={Journal of Machine Learning Research},\n  volume={12},\n  pages={2825--2830},\n  year={2011}\n}\n"""


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION					,    _KWARGS_DESCRIPTION			)
class       __SCREAMING_SNAKE_CASE	(						datasets.Metric			):



     '''simple docstring'''






     def 	UpperCamelCase(					self	):
       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 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase=None	):
       return {
           "matthews_correlation": float(matthews_corrcoef(UpperCamelCase_						,   UpperCamelCase_						,   sample_weight=UpperCamelCase_	)	),
       }









'''simple docstring'''



import functools




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				len(SCREAMING_SNAKE_CASE__       )
  _snake_case     		=				len(SCREAMING_SNAKE_CASE__       )

  @functools.cache
  def min_distance(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ) -> int:
    # if first word index is overflow - delete all from the second word
    if indexa >= len_worda:
      return len_worda - indexa
    # if second word index is overflow - delete all from the first word
    if indexa >= len_worda:
      return len_worda - indexa
    _snake_case     		=				int(worda[indexa] != worda[indexa]       )  # current letters not identical
    return min(
        1 + min_distance(indexa + 1					,	SCREAMING_SNAKE_CASE__       )					,	1 + min_distance(SCREAMING_SNAKE_CASE__					,	indexa + 1       )					,	diff + min_distance(indexa + 1					,	indexa + 1       )					,	)

  return min_distance(0					,	0       )


if __name__ == "__main__":
 import doctest

 doctest.testmod()


