



'''simple docstring'''





from sklearn.metrics import fa_score

import datasets


_lowerCAmelCase										=     '''
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
'''


_lowerCAmelCase										=     '''
Args:
    predictions (`list` of `int`): Predicted labels.
    references (`list` of `int`): Ground truth labels.
    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.
    pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.
    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\'`.

        - \'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.
        - \'micro\': Calculate metrics globally by counting the total true positives, false negatives and false positives.
        - \'macro\': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
        - \'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.
        - \'samples\': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).
    sample_weight (`list` of `float`): Sample weights Defaults to None.

Returns:
    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.

Examples:

    Example 1-A simple binary example
        >>> f1_metric = datasets.load_metric("f1")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])
        >>> print(results)
        {\'f1\': 0.5}

    Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.
        >>> f1_metric = datasets.load_metric("f1")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)
        >>> print(round(results[\'f1\'], 2))
        0.67

    Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.
        >>> f1_metric = datasets.load_metric("f1")
        >>> 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])
        >>> print(round(results[\'f1\'], 2))
        0.35

    Example 4-A multiclass example, with different values for the `average` input.
        >>> predictions = [0, 2, 1, 0, 0, 1]
        >>> references = [0, 1, 2, 0, 1, 2]
        >>> results = f1_metric.compute(predictions=predictions, references=references, average="macro")
        >>> print(round(results[\'f1\'], 2))
        0.27
        >>> results = f1_metric.compute(predictions=predictions, references=references, average="micro")
        >>> print(round(results[\'f1\'], 2))
        0.33
        >>> results = f1_metric.compute(predictions=predictions, references=references, average="weighted")
        >>> print(round(results[\'f1\'], 2))
        0.27
        >>> results = f1_metric.compute(predictions=predictions, references=references, average=None)
        >>> print(results)
        {\'f1\': array([0.8, 0. , 0. ])}
'''


_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}
}
'''






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,			_KWARGS_DESCRIPTION					)
class 	lowerCAmelCase_(    datasets.Metric					):
  '''simple docstring'''




  def 							UpperCAmelCase_     (   self				)   ->		str:
      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  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase=None  ,__UpperCAmelCase=1  ,__UpperCAmelCase="binary"  ,__UpperCAmelCase=None				)   ->		List[str]:
      lowerCAmelCase__			:  Union[str, Any]											= fa_score(
          __UpperCAmelCase  ,__UpperCAmelCase  ,labels=__UpperCAmelCase  ,pos_label=__UpperCAmelCase  ,average=__UpperCAmelCase  ,sample_weight=__UpperCAmelCase				)
      return {"f1": float(__UpperCAmelCase				) if score.size == 1 else score}







'''simple docstring'''





class 	lowerCAmelCase_:
  '''simple docstring'''




  def __init__(   self  ,__UpperCAmelCase				)   ->		Tuple:
      lowerCAmelCase__			:  Union[str, Any]											= n
      lowerCAmelCase__			:  int											= [None] * self.n
      lowerCAmelCase__			:  Union[str, Any]											= 0  # index of the first element
      lowerCAmelCase__			:  Union[str, Any]											= 0
      lowerCAmelCase__			:  Union[str, Any]											= 0




  def __len__(   self				)   ->		int:
      return self.size




  def 							UpperCAmelCase_     (   self				)   ->		bool:
      return self.size == 0




  def 							UpperCAmelCase_     (   self				)   ->		Optional[Any]:
      return False if self.is_empty() else self.array[self.front]




  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase				)   ->		Union[str, Any]:
      if self.size >= self.n:
          raise Exception("""QUEUE IS FULL"""				)

      lowerCAmelCase__			:  str											= data
      lowerCAmelCase__			:  List[str]											= (self.rear + 1) % self.n
      self.size += 1
      return self






  def 							UpperCAmelCase_     (   self				)   ->		int:
      if self.size == 0:
          raise Exception("""UNDERFLOW"""				)

      lowerCAmelCase__			:  int											= self.array[self.front]
      lowerCAmelCase__			:  Optional[Any]											= None
      lowerCAmelCase__			:  int											= (self.front + 1) % self.n
      self.size -= 1
      return temp


