


from sklearn.metrics import fa_score

import datasets


lowerCAmelCase__  :Any             =							"\nThe F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:\nF1 = 2 * (precision * recall) / (precision + recall)\n"


lowerCAmelCase__  :Optional[Any]             =							"\nArgs:\n    predictions (`list` of `int`): Predicted labels.\n    references (`list` of `int`): Ground truth labels.\n    labels (`list` of `int`): The set of labels to include when `average` is not set to `'binary'`, and the order of the labels if `average` is `None`. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class. Labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in `predictions` and `references` are used in sorted order. Defaults to None.\n    pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.\n    average (`string`): This parameter is required for multiclass/multilabel targets. If set to `None`, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `'binary'`.\n\n        - 'binary': Only report results for the class specified by `pos_label`. This is applicable only if the classes found in `predictions` and `references` are binary.\n        - 'micro': Calculate metrics globally by counting the total true positives, false negatives and false positives.\n        - 'macro': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.\n        - 'weighted': Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `'macro'` to account for label imbalance. This option can result in an F-score that is not between precision and recall.\n        - 'samples': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).\n    sample_weight (`list` of `float`): Sample weights Defaults to None.\n\nReturns:\n    f1 (`float` or `array` of `float`): F1 score or list of f1 scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher f1 scores are better.\n\nExamples:\n\n    Example 1-A simple binary example\n        >>> f1_metric = datasets.load_metric(\"f1\")\n        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])\n        >>> print(results)\n        {'f1': 0.5}\n\n    Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.\n        >>> f1_metric = datasets.load_metric(\"f1\")\n        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)\n        >>> print(round(results['f1'], 2))\n        0.67\n\n    Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.\n        >>> f1_metric = datasets.load_metric(\"f1\")\n        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], sample_weight=[0.9, 0.5, 3.9, 1.2, 0.3])\n        >>> print(round(results['f1'], 2))\n        0.35\n\n    Example 4-A multiclass example, with different values for the `average` input.\n        >>> predictions = [0, 2, 1, 0, 0, 1]\n        >>> references = [0, 1, 2, 0, 1, 2]\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"macro\")\n        >>> print(round(results['f1'], 2))\n        0.27\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"micro\")\n        >>> print(round(results['f1'], 2))\n        0.33\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"weighted\")\n        >>> print(round(results['f1'], 2))\n        0.27\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average=None)\n        >>> print(results)\n        {'f1': array([0.8, 0. , 0. ])}\n"


lowerCAmelCase__  :List[Any]             =							"\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 		__a				(    datasets.Metric      ):
		def      UpperCAmelCase__    (      self						)			->	Optional[Any]:




									"""simple docstring"""





									return datasets.MetricInfo(
									    description=_DESCRIPTION      ,   citation=_CITATION      ,   inputs_description=_KWARGS_DESCRIPTION      ,   features=datasets.Features(
									        {
									            'predictions': datasets.Sequence(datasets.Value('int32'						)						),
									            'references': datasets.Sequence(datasets.Value('int32'						)						),
									        }
									        if self.config_name == 'multilabel'
									        else {
									            'predictions': datasets.Value('int32'						),
									            'references': datasets.Value('int32'						),
									        }						)      ,   reference_urls=['https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html']      ,   )






		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE=None      ,   _SCREAMING_SNAKE_CASE=1      ,   _SCREAMING_SNAKE_CASE="binary"      ,   _SCREAMING_SNAKE_CASE=None						)			->	List[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      fa_score(
									    lowercase_      ,   lowercase_      ,   labels=lowercase_      ,   pos_label=lowercase_      ,   average=lowercase_      ,   sample_weight=lowercase_						)
									return {"f1": float(lowercase_						) if score.size == 1 else score}




from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray

def 	lowerCAmelCase__			(      a__:		NDArray[floataa]      ,  a__:		NDArray[floataa]      ,  a__:		list[int]      ,  a__:		int      ,  )  ->		list[float]:


							'''simple docstring'''





							_UpperCAmelCase		,	_UpperCAmelCase					=      coefficient_matrix.shape
							_UpperCAmelCase		,	_UpperCAmelCase					=      constant_matrix.shape

							if rowsa != colsa:
														_UpperCAmelCase					=      F'''Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}'''
														raise ValueError(a__	)

							if colsa != 1:
														_UpperCAmelCase					=      F'''Constant matrix must be nx1 but received {rowsa}x{colsa}'''
														raise ValueError(a__	)

							if rowsa != rowsa:
														_UpperCAmelCase					=      (
														    'Coefficient and constant matrices dimensions must be nxn and nx1 but '
														    F'''received {rowsa}x{colsa} and {rowsa}x{colsa}'''
														)
														raise ValueError(a__	)

							if len(a__	) != rowsa:
														_UpperCAmelCase					=      (
														    'Number of initial values must be equal to number of rows in coefficient '
														    F'''matrix but received {len(a__	)} and {rowsa}'''
														)
														raise ValueError(a__	)

							if iterations <= 0:
														raise ValueError('Iterations must be at least 1'	)

							_UpperCAmelCase					=      np.concatenate(
							    (coefficient_matrix, constant_matrix)      ,  axis=1	)

							_UpperCAmelCase		,	_UpperCAmelCase					=      table.shape

							strictly_diagonally_dominant(a__	)

							# Iterates the whole matrix for given number of times
							for _ in range(a__	):
														_UpperCAmelCase					=      []
														for row in range(a__	):
																					_UpperCAmelCase					=      0
																					for col in range(a__	):
																												if col == row:
																																			_UpperCAmelCase					=      table[row][col]
																												elif col == cols - 1:
																																			_UpperCAmelCase					=      table[row][col]
																												else:
																																			temp += (-1) * table[row][col] * init_val[col]
																					_UpperCAmelCase					=      (temp + val) / denom
																					new_val.append(a__	)
														_UpperCAmelCase					=      new_val

							return [float(a__	) for i in new_val]

def 	lowerCAmelCase__			(      a__:		NDArray[floataa]	)  ->		bool:


							'''simple docstring'''





							_UpperCAmelCase		,	_UpperCAmelCase					=      table.shape

							_UpperCAmelCase					=      True

							for i in range(0      ,  a__	):
														_UpperCAmelCase					=      0
														for j in range(0      ,  cols - 1	):
																					if i == j:
																												continue
																					else:
																												total += table[i][j]

														if table[i][i] <= total:
																					raise ValueError('Coefficient matrix is not strictly diagonally dominant'	)

							return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
		import doctest

		doctest.testmod()
