




from sklearn.metrics import matthews_corrcoef

import datasets


_snake_case            = "\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"

_snake_case            = "\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"

_snake_case            = "\\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 		lowercase							(     datasets.Metric	):

     def       a__			(						self       )		->							int:
           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	,  _a	,  _a	,  _a=None       )		->							int:
           return {
               "matthews_correlation": float(matthews_corrcoef(SCREAMING_SNAKE_CASE_	,  SCREAMING_SNAKE_CASE_	,  sample_weight=SCREAMING_SNAKE_CASE_       )       ),
           }










from unittest import TestCase

from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset







class 		lowercase							(     UpperCamelCase__	):

     def       a__			(						self       )		->							Optional[int]:
           return [
               {"col_1": 3, "col_2": "a"},
               {"col_1": 2, "col_2": "b"},
               {"col_1": 1, "col_2": "c"},
               {"col_1": 0, "col_2": "d"},
           ]

     def       a__			(						self       )		->							Optional[int]:
           _A							:  Union[str, Any]    =		{"""col_1""": [3, 2, 1, 0], """col_2""": ["""a""", """b""", """c""", """d"""]}
           return Dataset.from_dict(_a       )

     def       a__			(						self       )		->							Any:
           _A							:  str    =		self._create_example_records()
           _A							:  List[Any]    =		Dataset.from_list(_a       )
           self.assertListEqual(dset.column_names	,  ["""col_1""", """col_2"""]       )
           for i, r in enumerate(_a       ):
                 self.assertDictEqual(_a	,  example_records[i]       )

     def       a__			(						self       )		->							List[str]:
           _A							:  Dict    =		self._create_example_records()
           _A							:  List[str]    =		Dataset.from_list(_a       )
           _A							:  str    =		Dataset.from_dict({k: [r[k] for r in example_records] for k in example_records[0]}       )
           self.assertEqual(dset.info	,  dset_from_dict.info       )

     def       a__			(						self       )		->							str:  # checks what happens with missing columns
           _A							:  List[str]    =		[{"""col_1""": 1}, {"""col_2""": """x"""}]
           _A							:  List[str]    =		Dataset.from_list(_a       )
           self.assertDictEqual(dset[0]	,  {"""col_1""": 1}       )
           self.assertDictEqual(dset[1]	,  {"""col_1""": None}       )  # NB: first record is used for columns

     def       a__			(						self       )		->							Dict:  # checks if the type can be inferred from the second record
           _A							:  List[Any]    =		[{"""col_1""": []}, {"""col_1""": [1, 2]}]
           _A							:  str    =		Dataset.from_list(_a       )
           self.assertEqual(dset.info.features["""col_1"""]	,  Sequence(Value("""int64"""       )       )       )



     def       a__			(						self       )		->							Dict:
           _A							:  List[str]    =		Dataset.from_list([]       )
           self.assertEqual(len(_a       )	,  0       )
           self.assertListEqual(dset.column_names	,  []       )

