



"""simple docstring"""





from scipy.stats import spearmanr

import datasets


__lowerCAmelCase       :					Optional[Any]      					=					'''
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations imply that as data in dataset x increases, so
does data in dataset y. Negative correlations imply that as x increases,
y decreases. Correlations of -1 or +1 imply an exact monotonic relationship.

Unlike the Pearson correlation, the Spearman correlation does not
assume that both datasets are normally distributed.

The p-value roughly indicates the probability of an uncorrelated system
producing datasets that have a Spearman correlation at least as extreme
as the one computed from these datasets. The p-values are not entirely
reliable but are probably reasonable for datasets larger than 500 or so.
'''

__lowerCAmelCase       :					int      					=					'''
Args:
    predictions (`List[float]`): Predicted labels, as returned by a model.
    references (`List[float]`): Ground truth labels.
    return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns
            only the spearmanr score. Defaults to `False`.
Returns:
    spearmanr (`float`): Spearman correlation coefficient.
    p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.
Examples:
    Example 1:
        >>> spearmanr_metric = datasets.load_metric("spearmanr")
        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])
        >>> print(results)
        {\'spearmanr\': -0.7}

    Example 2:
        >>> spearmanr_metric = datasets.load_metric("spearmanr")
        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],
        ...                                     predictions=[10, 9, 2.5, 6, 4],
        ...                                     return_pvalue=True)
        >>> print(results[\'spearmanr\'])
        -0.7
        >>> print(round(results[\'spearmanr_pvalue\'], 2))
        0.19
'''

__lowerCAmelCase       :					List[Any]      					=					R'''\
@book{kokoska2000crc,
  title={CRC standard probability and statistics tables and formulae},
  author={Kokoska, Stephen and Zwillinger, Daniel},
  year={2000},
  publisher={Crc Press}
}
@article{2020SciPy-NMeth,
  author  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
            Haberland, Matt and Reddy, Tyler and Cournapeau, David and
            Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
            Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
            Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
            Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
            Kern, Robert and Larson, Eric and Carey, C J and
            Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and
            {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
            Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
            Harris, Charles R. and Archibald, Anne M. and
            Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and
            {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
  title   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
            Computing in Python}},
  journal = {Nature Methods},
  year    = {2020},
  volume  = {17},
  pages   = {261--272},
  adsurl  = {https://rdcu.be/b08Wh},
  doi     = {10.1038/s41592-019-0686-2},
}
'''



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





		"""simple docstring"""






		def    UpperCAmelCase__							(					self   ) ->						List[Any]:







								'''simple docstring'''

								return datasets.MetricInfo(
								    description=_DESCRIPTION							,    citation=_CITATION							,    inputs_description=_KWARGS_DESCRIPTION							,    features=datasets.Features(
								        {
								            """predictions""": datasets.Value("""float"""   ),
								            """references""": datasets.Value("""float"""   ),
								        }   )							,    reference_urls=["""https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.spearmanr.html"""]							,    )





		def    UpperCAmelCase__							(					self							,    _lowercase							,    _lowercase							,    _lowercase=False   ) ->						Optional[int]:







								'''simple docstring'''

								snake_case_   : Any						=			spearmanr(_lowercase							,    _lowercase   )
								if return_pvalue:
														return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
								else:
														return {"spearmanr": results[0]}






"""simple docstring"""





from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def 	__lowerCAmelCase						(		__UpperCamelCase      :			int    ):


						'''simple docstring'''






						def is_in_circle(__UpperCamelCase      :			float					, __UpperCamelCase      :			float    ) -> bool:
												snake_case_   : Dict						=			sqrt((x**2) + (y**2)    )
												# Our circle has a radius of 1, so a distance
												# greater than 1 would land outside the circle.
												return distance_from_centre <= 1

						# The proportion of guesses that landed in the circle
						snake_case_   : Tuple						=			mean(
						    int(is_in_circle(uniform(-1.0					, 1.0    )					, uniform(-1.0					, 1.0    )    )    )
						    for _ in range(__UpperCamelCase    )    )
						# The ratio of the area for circle to square is pi/4.
						snake_case_   : Union[str, Any]						=			proportion * 4
						print(F'The estimated value of pi is {pi_estimate}'    )
						print(F'The numpy value of pi is {pi}'    )
						print(F'The total error is {abs(pi - pi_estimate    )}'    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			int					, __UpperCamelCase      :			Callable[[float], float]					, __UpperCamelCase      :			float = 0.0					, __UpperCamelCase      :			float = 1.0					, ):


						'''simple docstring'''






						return mean(
						    function_to_integrate(uniform(__UpperCamelCase					, __UpperCamelCase    )    ) for _ in range(__UpperCamelCase    )    ) * (max_value - min_value)
def 	__lowerCAmelCase						(		__UpperCamelCase      :			int					, __UpperCamelCase      :			float = 0.0					, __UpperCamelCase      :			float = 1.0    ):


						'''simple docstring'''






						def identity_function(__UpperCamelCase      :			float    ) -> float:
												return x

						snake_case_   : int						=			area_under_curve_estimator(
						    __UpperCamelCase					, __UpperCamelCase					, __UpperCamelCase					, __UpperCamelCase    )
						snake_case_   : str						=			(max_value * max_value - min_value * min_value) / 2

						print("""******************"""    )
						print(F'Estimating area under y=x where x varies from {min_value} to {max_value}'    )
						print(F'Estimated value is {estimated_value}'    )
						print(F'Expected value is {expected_value}'    )
						print(F'Total error is {abs(estimated_value - expected_value    )}'    )
						print("""******************"""    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			int    ):


						'''simple docstring'''






						def function_to_integrate(__UpperCamelCase      :			float    ) -> float:
												return sqrt(4.0 - x * x    )

						snake_case_   : List[Any]						=			area_under_curve_estimator(
						    __UpperCamelCase					, __UpperCamelCase					, 0.0					, 2.0    )

						print("""******************"""    )
						print("""Estimating pi using area_under_curve_estimator"""    )
						print(F'Estimated value is {estimated_value}'    )
						print(F'Expected value is {pi}'    )
						print(F'Total error is {abs(estimated_value - pi    )}'    )
						print("""******************"""    )


if __name__ == "__main__":
					import doctest

					doctest.testmod()

