
from sklearn.metrics import mean_absolute_error

import datasets


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

__lowerCamelCase							= """\
Mean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual
values.
"""


__lowerCamelCase							= """
Args:
    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Estimated target values.
    references: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Ground truth (correct) target values.
    sample_weight: array-like of shape (n_samples,), default=None
        Sample weights.
    multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"
        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.

                 \"raw_values\" : Returns a full set of errors in case of multioutput input.

                 \"uniform_average\" : Errors of all outputs are averaged with uniform weight.

Returns:
    mae : mean absolute error.
        If multioutput is \"raw_values\", then mean absolute error is returned for each output separately. If multioutput is \"uniform_average\" or an ndarray of weights, then the weighted average of all output errors is returned.
        MAE output is non-negative floating point. The best value is 0.0.
Examples:

    >>> mae_metric = datasets.load_metric(\"mae\")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mae': 0.5}

    If you're using multi-dimensional lists, then set the config as follows :

    >>> mae_metric = datasets.load_metric(\"mae\", \"multilist\")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mae': 0.75}
    >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput='raw_values')
    >>> print(results)
    {'mae': array([0.5, 1. ])}
"""






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



   def        _SCREAMING_SNAKE_CASE     (self	:      Any				)	->			Union[str, Any]:


          '''simple docstring'''


          return datasets.MetricInfo(
              description=_DESCRIPTION					,		citation=_CITATION					,		inputs_description=_KWARGS_DESCRIPTION					,		features=datasets.Features(self._get_feature_types()				)					,		reference_urls=[
                  "https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html"
              ]					,		)



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


          '''simple docstring'''


          if self.config_name == "multilist":
                 return {
                     "predictions": datasets.Sequence(datasets.Value("float"				)				),
                     "references": datasets.Sequence(datasets.Value("float"				)				),
                 }
          else:
                 return {
                     "predictions": datasets.Value("float"				),
                     "references": datasets.Value("float"				),
                 }



   def        _SCREAMING_SNAKE_CASE     (self	:      int					,		snake_case__	:      Union[str, Any]					,		snake_case__	:      List[Any]					,		snake_case__	:      Dict=None					,		snake_case__	:      List[Any]="uniform_average"				)	->			str:


          '''simple docstring'''


          snake_case :     int           =   mean_absolute_error(snake_case__					,		snake_case__					,		sample_weight=snake_case__					,		multioutput=snake_case__				)

          return {"mae": mae_score}







def     UpperCamelCase      (					__lowerCamelCase  :				list       ,      __lowerCamelCase  :				list       ,      __lowerCamelCase  :				list       ,      __lowerCamelCase  :				int       ,      __lowerCamelCase  :				int					):
       if target == 0:
              answer.append(path.copy()					)
       else:
              for index in range(__lowerCamelCase       ,      len(__lowerCamelCase					)					):
                     if target >= candidates[index]:
                            path.append(candidates[index]					)
                            backtrack(__lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase       ,      target - candidates[index]       ,      __lowerCamelCase					)
                            path.pop(len(__lowerCamelCase					) - 1					)


def     UpperCamelCase      (					__lowerCamelCase  :				list       ,      __lowerCamelCase  :				int					):
       snake_case :     Optional[Any]           =   []  # type: list[int]
       snake_case :     Tuple           =   []  # type: list[int]
       backtrack(__lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase       ,      0					)
       return answer


def     UpperCamelCase      (					):
       print(combination_sum([-8, 2.3, 0]       ,      1					)					)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   main()





