





'''simple docstring'''






class 	_lowercase    :


   def __init__(			self     , _UpperCAmelCase       ):
       A							:							Any				=    data
       A							:							BinaryTreeNode | None				=    None
       A							:							BinaryTreeNode | None				=    None







def 				_lowerCamelCase(	UpperCamelCase__     :			Any     ,							UpperCamelCase__     :			Optional[Any]		)		->	Any:
    if node is None:
        A							:							Dict				=    BinaryTreeNode(UpperCAmelCase_		)
        return node

    # binary search tree is not empty,
    # so we will insert it into the tree
    # if new_value is less than value of data in node,
    #  add it to left subtree and proceed recursively
    if new_value < node.data:
        A							:							Union[str, Any]				=    insert(node.left_child     ,							UpperCAmelCase_		)
    else:
        # if new_value is greater than value of data in node,
        #  add it to right subtree and proceed recursively
        A							:							Optional[int]				=    insert(node.right_child     ,							UpperCAmelCase_		)
    return node
def 				_lowerCamelCase(	UpperCamelCase__     :			Union[str, Any]		)		->	Tuple:  # if node is None,return
    if node:
        A							:							List[str]				=    inorder(node.left_child		)
        A							:							Union[str, Any]				=    [*inorder_array, node.data]
        A							:							Tuple				=    inorder_array + inorder(node.right_child		)
    else:
        A							:							Dict				=    []
    return inorder_array
def 				_lowerCamelCase(	)		->	List[str]:
    A							:							Optional[int]				=    insert(UpperCAmelCase_     ,							15		)
    insert(UpperCAmelCase_     ,							10		)
    insert(UpperCAmelCase_     ,							25		)
    insert(UpperCAmelCase_     ,							6		)
    insert(UpperCAmelCase_     ,							14		)
    insert(UpperCAmelCase_     ,							20		)
    insert(UpperCAmelCase_     ,							60		)
    return root
def 				_lowerCamelCase(	)		->	Union[str, Any]:
    # main function
    A							:							int				=    make_tree()
    print('''Printing values of binary search tree in Inorder Traversal.'''		)
    inorder(UpperCAmelCase_		)


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     main()



'''simple docstring'''

from sklearn.metrics import accuracy_score

import datasets


lowercase__            =     "\nAccuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:\nAccuracy = (TP + TN) / (TP + TN + FP + FN)\n Where:\nTP: True positive\nTN: True negative\nFP: False positive\nFN: False negative\n"


lowercase__            =     "\nArgs:\n    predictions (`list` of `int`): Predicted labels.\n    references (`list` of `int`): Ground truth labels.\n    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.\n    sample_weight (`list` of `float`): Sample weights Defaults to None.\n\nReturns:\n    accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.\n\nExamples:\n\n    Example 1-A simple example\n        >>> accuracy_metric = datasets.load_metric(\"accuracy\")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])\n        >>> print(results)\n        {'accuracy': 0.5}\n\n    Example 2-The same as Example 1, except with `normalize` set to `False`.\n        >>> accuracy_metric = datasets.load_metric(\"accuracy\")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)\n        >>> print(results)\n        {'accuracy': 3.0}\n\n    Example 3-The same as Example 1, except with `sample_weight` set.\n        >>> accuracy_metric = datasets.load_metric(\"accuracy\")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])\n        >>> print(results)\n        {'accuracy': 0.8778625954198473}\n"


lowercase__            =     "\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				):






     '''simple docstring'''



     def   UpperCAmelCase_			(						self					:	int			)		->      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.accuracy_score.html']	,     )




     def   UpperCAmelCase_			(						self					:	str	,     lowercase_					:	Union[str, Any]	,     lowercase_					:	Any	,     lowercase_					:	Tuple=True	,     lowercase_					:	Union[str, Any]=None			)		->      Dict:
      return {
          "accuracy": float(
              accuracy_score(lowercase_	,     lowercase_	,     normalize=lowercase_	,     sample_weight=lowercase_			)			)
      }




