





from sklearn.metrics import precision_score

import datasets


UpperCamelCase__ :			Any       	=			"""\nPrecision 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:\nPrecision = TP / (TP + FP)\nwhere 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).\n"""


UpperCamelCase__ :			int       	=			"""\nArgs:\n    predictions (`list` of `int`): Predicted class labels.\n    references (`list` of `int`): Actual class labels.\n    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.\n    pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.\n    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\'`.\n\n        - \'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.\n        - \'micro\': Calculate metrics globally by counting the total true positives, false negatives and false positives.\n        - \'macro\': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.\n        - \'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.\n        - \'samples\': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).\n    sample_weight (`list` of `float`): Sample weights Defaults to None.\n    zero_division (`int` or `string`): Sets the value to return when there is a zero division. Defaults to \'warn\'.\n\n        - 0: Returns 0 when there is a zero division.\n        - 1: Returns 1 when there is a zero division.\n        - \'warn\': Raises warnings and then returns 0 when there is a zero division.\n\nReturns:\n    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.\n\nExamples:\n\n    Example 1-A simple binary example\n        >>> precision_metric = datasets.load_metric(\"precision\")\n        >>> results = precision_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])\n        >>> print(results)\n        {\'precision\': 0.5}\n\n    Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.\n        >>> precision_metric = datasets.load_metric(\"precision\")\n        >>> results = precision_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)\n        >>> print(round(results[\'precision\'], 2))\n        0.67\n\n    Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.\n        >>> precision_metric = datasets.load_metric(\"precision\")\n        >>> 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])\n        >>> print(results)\n        {\'precision\': 0.23529411764705882}\n\n    Example 4-A multiclass example, with different values for the `average` input.\n        >>> predictions = [0, 2, 1, 0, 0, 1]\n        >>> references = [0, 1, 2, 0, 1, 2]\n        >>> results = precision_metric.compute(predictions=predictions, references=references, average=\'macro\')\n        >>> print(results)\n        {\'precision\': 0.2222222222222222}\n        >>> results = precision_metric.compute(predictions=predictions, references=references, average=\'micro\')\n        >>> print(results)\n        {\'precision\': 0.3333333333333333}\n        >>> results = precision_metric.compute(predictions=predictions, references=references, average=\'weighted\')\n        >>> print(results)\n        {\'precision\': 0.2222222222222222}\n        >>> results = precision_metric.compute(predictions=predictions, references=references, average=None)\n        >>> print([round(res, 2) for res in results[\'precision\']])\n        [0.67, 0.0, 0.0]\n"""


UpperCamelCase__ :			Any       	=			"""\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 	_UpperCamelCase	(							datasets.Metric	):







					'''simple docstring'''

					def 							SCREAMING_SNAKE_CASE				(    self     :	int		):






						'''simple docstring'''







						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 							SCREAMING_SNAKE_CASE				(    self     :	List[str]			,       __lowercase     :	Optional[int]			,       __lowercase     :	Dict			,       __lowercase     :	List[str]=None			,       __lowercase     :	Union[str, Any]=1			,       __lowercase     :	Optional[Any]="binary"			,       __lowercase     :	Optional[int]=None			,       __lowercase     :	Union[str, Any]="warn"			,       ):






						'''simple docstring'''







						UpperCAmelCase_		    =  precision_score(
						    UpperCamelCase_			,       UpperCamelCase_			,       labels=UpperCamelCase_			,       pos_label=UpperCamelCase_			,       average=UpperCamelCase_			,       sample_weight=UpperCamelCase_			,       zero_division=UpperCamelCase_			,       )
						return {"precision": float(UpperCamelCase_		) if score.size == 1 else score}








from __future__ import annotations

from collections.abc import Iterable





class        lowerCamelCase__:
      def __init__(							self:					Dict       ,			UpperCamelCase_:					int       ,			UpperCamelCase_:					str  ):
             __lowerCamelCase,  __lowerCamelCase  =		float(UpperCamelCase_  ), float(UpperCamelCase_  )
      def __eq__(							self:					List[Any]       ,			UpperCamelCase_:					List[str]  ):
             return self.x == other.x and self.y == other.y
      def __ne__(							self:					Tuple       ,			UpperCamelCase_:					Dict  ):
             return not self == other
      def __gt__(							self:					List[Any]       ,			UpperCamelCase_:					List[Any]  ):
             if self.x > other.x:
                    return True
             elif self.x == other.x:
                    return self.y > other.y
             return False
      def __lt__(							self:					str       ,			UpperCamelCase_:					int  ):
             return not self > other
      def __ge__(							self:					Dict       ,			UpperCamelCase_:					Any  ):
             if self.x > other.x:
                    return True
             elif self.x == other.x:
                    return self.y >= other.y
             return False
      def __le__(							self:					Union[str, Any]       ,			UpperCamelCase_:					Optional[Any]  ):
             if self.x < other.x:
                    return True
             elif self.x == other.x:
                    return self.y <= other.y
             return False
      def __repr__(							self:					Union[str, Any]  ):
             return F'({self.x}, {self.y})'




      def __hash__(							self:					List[Any]  ):
             return hash(self.x  )




def    lowerCamelCase__			(		A__			:   list[Point] | list[list[float]] | Iterable[list[float]]    ,     ):





       '''simple docstring'''



       __lowerCamelCase  =		[]
       if list_of_tuples:
              for p in list_of_tuples:
                     if isinstance(A__    ,     A__      ):
                            points.append(A__      )
                     else:
                            try:
                                   points.append(Point(p[0]    ,     p[1]      )      )
                            except (IndexError, TypeError):
                                   print(
                                       f'Ignoring deformed point {p}. All points'
                                       """ must have at least 2 coordinates."""      )
       return points




def    lowerCamelCase__			(		A__			:   list[Point] | list[list[float]]      ):





       '''simple docstring'''



       if not hasattr(A__    ,     """__iter__"""      ):
              __lowerCamelCase  =		f'Expecting an iterable object but got an non-iterable type {points}'
              raise ValueError(A__      )

       if not points:
              __lowerCamelCase  =		f'Expecting a list of points but got {points}'
              raise ValueError(A__      )

       return _construct_points(A__      )




def    lowerCamelCase__			(		A__			:   Point    ,     A__			:   Point    ,     A__			:   Point      ):





       '''simple docstring'''



       __lowerCamelCase  =		(a.x * b.y + b.x * c.y + c.x * a.y) - (a.y * b.x + b.y * c.x + c.y * a.x)
       return det




def    lowerCamelCase__			(		A__			:   list[Point]      ):





       '''simple docstring'''



       __lowerCamelCase  =		sorted(_validate_input(A__      )      )
       __lowerCamelCase  =		len(A__      )
       __lowerCamelCase  =		set()

       for i in range(n - 1      ):
              for j in range(i + 1    ,     A__      ):
                     __lowerCamelCase  =		__lowerCamelCase  =		False
                     __lowerCamelCase  =		True
                     for k in range(A__      ):
                            if k != i and k != j:
                                   __lowerCamelCase  =		_det(points[i]    ,     points[j]    ,     points[k]      )

                                   if det_k > 0:
                                          __lowerCamelCase  =		True
                                   elif det_k < 0:
                                          __lowerCamelCase  =		True
                                   else:
                                          # point[i], point[j], point[k] all lie on a straight line
                                          # if point[k] is to the left of point[i] or it's to the
                                          # right of point[j], then point[i], point[j] cannot be
                                          # part of the convex hull of A
                                          if points[k] < points[i] or points[k] > points[j]:
                                                 __lowerCamelCase  =		False
                                                 break

                            if points_left_of_ij and points_right_of_ij:
                                   __lowerCamelCase  =		False
                                   break

                     if ij_part_of_convex_hull:
                            convex_set.update([points[i], points[j]]      )

       return sorted(A__      )




def    lowerCamelCase__			(		A__			:   list[Point]      ):





       '''simple docstring'''



       __lowerCamelCase  =		sorted(_validate_input(A__      )      )
       __lowerCamelCase  =		len(A__      )

       # divide all the points into an upper hull and a lower hull
       # the left most point and the right most point are definitely
       # members of the convex hull by definition.
       # use these two anchors to divide all the points into two hulls,
       # an upper hull and a lower hull.

       # all points to the left (above) the line joining the extreme points belong to the
       # upper hull
       # all points to the right (below) the line joining the extreme points below to the
       # lower hull
       # ignore all points on the line joining the extreme points since they cannot be
       # part of the convex hull

       __lowerCamelCase  =		points[0]
       __lowerCamelCase  =		points[n - 1]

       __lowerCamelCase  =		{left_most_point, right_most_point}
       __lowerCamelCase  =		[]
       __lowerCamelCase  =		[]

       for i in range(1    ,     n - 1      ):
              __lowerCamelCase  =		_det(A__    ,     A__    ,     points[i]      )

              if det > 0:
                     upper_hull.append(points[i]      )
              elif det < 0:
                     lower_hull.append(points[i]      )

       _construct_hull(A__    ,     A__    ,     A__    ,     A__      )
       _construct_hull(A__    ,     A__    ,     A__    ,     A__      )

       return sorted(A__      )




def    lowerCamelCase__			(		A__			:   list[Point]    ,     A__			:   Point    ,     A__			:   Point    ,     A__			:   set[Point]      ):





       '''simple docstring'''



       if points:
              __lowerCamelCase  =		None
              __lowerCamelCase  =		float("""-inf"""      )
              __lowerCamelCase  =		[]

              for p in points:
                     __lowerCamelCase  =		_det(A__    ,     A__    ,     A__      )

                     if det > 0:
                            candidate_points.append(A__      )

                            if det > extreme_point_distance:
                                   __lowerCamelCase  =		det
                                   __lowerCamelCase  =		p

              if extreme_point:
                     _construct_hull(A__    ,     A__    ,     A__    ,     A__      )
                     convex_set.add(A__      )
                     _construct_hull(A__    ,     A__    ,     A__    ,     A__      )




def    lowerCamelCase__			(		A__			:   list[Point]      ):





       '''simple docstring'''



       __lowerCamelCase  =		sorted(_validate_input(A__      )      )
       __lowerCamelCase  =		len(A__      )

       __lowerCamelCase  =		points[:2]
       for i in range(2    ,     A__      ):
              __lowerCamelCase  =		_det(convex_hull[1]    ,     convex_hull[0]    ,     points[i]      )
              if det > 0:
                     convex_hull.insert(0    ,     points[i]      )
                     break
              elif det < 0:
                     convex_hull.append(points[i]      )
                     break
              else:
                     __lowerCamelCase  =		points[i]
       i += 1

       for j in range(A__    ,     A__      ):
              if (
                  _det(convex_hull[0]    ,     convex_hull[-1]    ,     points[j]      ) > 0
                  and _det(convex_hull[-1]    ,     convex_hull[0]    ,     points[1]      ) < 0
              ):
                     # The point lies within the convex hull
                     continue

              convex_hull.insert(0    ,     points[j]      )
              convex_hull.append(points[j]      )
              while _det(convex_hull[0]    ,     convex_hull[1]    ,     convex_hull[2]      ) >= 0:
                     del convex_hull[1]
              while _det(convex_hull[-1]    ,     convex_hull[-2]    ,     convex_hull[-3]      ) <= 0:
                     del convex_hull[-2]

    # `convex_hull` is contains the convex hull in circular order
       return sorted(convex_hull[1:] if len(A__      ) > 3 else convex_hull      )




def    lowerCamelCase__			(		):





       '''simple docstring'''



       __lowerCamelCase  =		[
           (0, 3),
           (2, 2),
           (1, 1),
           (2, 1),
           (3, 0),
           (0, 0),
           (3, 3),
           (2, -1),
           (2, -4),
           (1, -3),
       ]
       # the convex set of points is
       # [(0, 0), (0, 3), (1, -3), (2, -4), (3, 0), (3, 3)]
       __lowerCamelCase  =		convex_hull_bf(A__      )

       __lowerCamelCase  =		convex_hull_recursive(A__      )
       assert results_bf == results_recursive

       __lowerCamelCase  =		convex_hull_melkman(A__      )
       assert results_bf == results_melkman

       print(A__      )


if __name__ == "__main__":
       main()

