



from sklearn.metrics import precision_score

import datasets


__UpperCAmelCase						  =     '''
Precision is the fraction of correctly labeled positive examples out of all of the examples that were labeled as positive. It is computed via the equation:
Precision = TP / (TP + FP)
where TP is the True positives (i.e. the examples correctly labeled as positive) and FP is the False positive examples (i.e. the examples incorrectly labeled as positive).
'''


__UpperCAmelCase						  =     '''
Args:
    predictions (`list` of `int`): Predicted class labels.
    references (`list` of `int`): Actual class labels.
    labels (`list` of `int`): The set of labels to include when `average` is not set to `\'binary\'`. If `average` is `None`, it should be the label order. 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.
    zero_division (`int` or `string`): Sets the value to return when there is a zero division. Defaults to \'warn\'.

        - 0: Returns 0 when there is a zero division.
        - 1: Returns 1 when there is a zero division.
        - \'warn\': Raises warnings and then returns 0 when there is a zero division.

Returns:
    precision (`float` or `array` of `float`): Precision score or list of precision scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher values indicate that fewer negative examples were incorrectly labeled as positive, which means that, generally, higher scores are better.

Examples:

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

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

    Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.
        >>> precision_metric = datasets.load_metric("precision")
        >>> results = precision_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(results)
        {\'precision\': 0.23529411764705882}

    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 = precision_metric.compute(predictions=predictions, references=references, average=\'macro\')
        >>> print(results)
        {\'precision\': 0.2222222222222222}
        >>> results = precision_metric.compute(predictions=predictions, references=references, average=\'micro\')
        >>> print(results)
        {\'precision\': 0.3333333333333333}
        >>> results = precision_metric.compute(predictions=predictions, references=references, average=\'weighted\')
        >>> print(results)
        {\'precision\': 0.2222222222222222}
        >>> results = precision_metric.compute(predictions=predictions, references=references, average=None)
        >>> print([round(res, 2) for res in results[\'precision\']])
        [0.67, 0.0, 0.0]
'''


__UpperCAmelCase						  =     '''
@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   ):


      def   _lowerCamelCase			( 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.precision_score.html"]      ,       )







      def   _lowerCamelCase			( self						:  List[str]      ,       _a						:  List[Any]      ,       _a						:  Union[str, Any]      ,       _a						:  int=None      ,       _a						:  List[Any]=1      ,       _a						:  Optional[int]="binary"      ,       _a						:  Union[str, Any]=None      ,       _a						:  Tuple="warn"      ,       ):
             a__:       Optional[int]											=precision_score(
                 _a      ,       _a      ,       labels=_a      ,       pos_label=_a      ,       average=_a      ,       sample_weight=_a      ,       zero_division=_a      ,       )
             return {"precision": float(_a			) if score.size == 1 else score}










from __future__ import annotations

from typing import Generic, TypeVar

__UpperCAmelCase						  =     TypeVar('''T''')


class 	lowerCamelCase__ (		Generic[T]   ):


						def __init__( self						:  int      ,       _a						:  T			):
													a__:       int											=data  # Assign data
													a__:       Node[T] | None											=None  # Initialize next as null
													a__:       Node[T] | None											=None  # Initialize prev as null


class 	lowerCamelCase__ (		Generic[T]   ):


						def __init__( self						:  Optional[Any]			):
													a__:       Node[T] | None											=None


						def   _lowerCamelCase			( self						:  List[Any]      ,       _a						:  T			):
													if self.head is None:
																				a__:       Dict											=Node(_a			)
													else:
																				a__:       int											=Node(_a			)
																				a__:       Optional[Any]											=new_node
																				a__:       int											=self.head
																				a__:       int											=None
																				a__:       Any											=new_node


						def   _lowerCamelCase			( self						:  Tuple			):
													if self.head is None:
																				return None
													else:
																				assert self.head is not None
																				a__:       Optional[int]											=self.head.data
																				a__:       Union[str, Any]											=self.head.next
																				if self.head is not None:
																											a__:       str											=None
																				return temp


						def   _lowerCamelCase			( self						:  int			):
													return self.head.data if self.head is not None else None


						def __len__( self						:  Union[str, Any]			):
													a__:       Tuple											=self.head
													a__:       int											=0
													while temp is not None:
																				count += 1
																				a__:       List[str]											=temp.next
													return count


						def   _lowerCamelCase			( self						:  int			):
													return self.head is None







						def   _lowerCamelCase			( self						:  Union[str, Any]			):
													print("stack elements are:"			)
													a__:       Optional[int]											=self.head
													while temp is not None:
																				print(temp.data      ,       end="->"			)
																				a__:       Union[str, Any]											=temp.next


# Code execution starts here
if __name__ == "__main__":
		# Start with the empty stack
		__UpperCAmelCase						  =     Stack()

		# Insert 4 at the beginning. So stack becomes 4->None
		print('''Stack operations using Doubly LinkedList''')
		stack.push(4)

		# Insert 5 at the beginning. So stack becomes 4->5->None
		stack.push(5)

		# Insert 6 at the beginning. So stack becomes 4->5->6->None
		stack.push(6)

		# Insert 7 at the beginning. So stack becomes 4->5->6->7->None
		stack.push(7)

		# Print the stack
		stack.print_stack()

		# Print the top element
		print('''\nTop element is ''', stack.top())

		# Print the stack size
		print('''Size of the stack is ''', len(stack))

		# pop the top element
		stack.pop()

		# pop the top element
		stack.pop()

		# two elements have now been popped off
		stack.print_stack()

		# Print True if the stack is empty else False
		print('''\nstack is empty:''', stack.is_empty())





