


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 						UpperCAmelCase_					( datasets.Metric):







							def      snake_case__   (     self):

														'''simple docstring'''

														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      snake_case__   (     self,    __a,    __a,    __a=None):

														'''simple docstring'''

														return {
														    "matthews_correlation": float(matthews_corrcoef(__a,    __a,    sample_weight=__a)),
														}





from __future__ import annotations

from collections.abc import MutableSequence



class 						UpperCAmelCase_					:







							def __init__(     self,    __a,    __a):

														'''simple docstring'''

														if len(__a) != degree + 1:
																					raise ValueError(
																					    "The number of coefficients should be equal to the degree + 1.")

														_lowerCAmelCase  :							list[float]				     =					list(__a)
														_lowerCAmelCase  :							Any				     =					degree







							def __add__(     self,    __a):

														'''simple docstring'''

														if self.degree > polynomial_a.degree:
																					_lowerCAmelCase  :							Dict				     =					self.coefficients[:]
																					for i in range(polynomial_a.degree + 1):
																												coefficients[i] += polynomial_a.coefficients[i]
																					return Polynomial(self.degree,    __a)
														else:
																					_lowerCAmelCase  :							Union[str, Any]				     =					polynomial_a.coefficients[:]
																					for i in range(self.degree + 1):
																												coefficients[i] += self.coefficients[i]
																					return Polynomial(polynomial_a.degree,    __a)







							def __sub__(     self,    __a):

														'''simple docstring'''

														return self + polynomial_a * Polynomial(0,    [-1])







							def __neg__(     self):

														'''simple docstring'''

														return Polynomial(self.degree,    [-c for c in self.coefficients])







							def __mul__(     self,    __a):

														'''simple docstring'''

														_lowerCAmelCase  :							list[float]				     =					[0] * (self.degree + polynomial_a.degree + 1)
														for i in range(self.degree + 1):
																					for j in range(polynomial_a.degree + 1):
																												coefficients[i + j] += (
																												    self.coefficients[i] * polynomial_a.coefficients[j]
																												)

														return Polynomial(self.degree + polynomial_a.degree,    __a)







							def      snake_case__   (     self,    __a):

														'''simple docstring'''

														_lowerCAmelCase  :							int | float				     =					0
														for i in range(self.degree + 1):
																					result += self.coefficients[i] * (substitution**i)
														return result







							def __str__(     self):

														'''simple docstring'''

														_lowerCAmelCase  :							List[str]				     =					""
														for i in range(self.degree,    -1,    -1):
																					if self.coefficients[i] == 0:
																												continue
																					elif self.coefficients[i] > 0:
																												if polynomial:
																																			polynomial += " + "
																					else:
																												polynomial += " - "

																					if i == 0:
																												polynomial += str(abs(self.coefficients[i]))
																					elif i == 1:
																												polynomial += str(abs(self.coefficients[i])) + "x"
																					else:
																												polynomial += str(abs(self.coefficients[i])) + "x^" + str(__a)

														return polynomial







							def __repr__(     self):

														'''simple docstring'''

														return self.__str__()







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							list[float]				     =					[0] * self.degree
														for i in range(self.degree):
																					_lowerCAmelCase  :							List[Any]				     =					self.coefficients[i + 1] * (i + 1)
														return Polynomial(self.degree - 1,    __a)







							def      snake_case__   (     self,    __a = 0):

														'''simple docstring'''

														_lowerCAmelCase  :							list[float]				     =					[0] * (self.degree + 2)
														_lowerCAmelCase  :							Optional[Any]				     =					constant
														for i in range(self.degree + 1):
																					_lowerCAmelCase  :							Dict				     =					self.coefficients[i] / (i + 1)
														return Polynomial(self.degree + 1,    __a)







							def __eq__(     self,    __a):

														'''simple docstring'''

														if not isinstance(__a,    __a):
																					return False

														if self.degree != polynomial_a.degree:
																					return False

														for i in range(self.degree + 1):
																					if self.coefficients[i] != polynomial_a.coefficients[i]:
																												return False

														return True







							def __ne__(     self,    __a):

														'''simple docstring'''

														return not self.__eq__(__a)
