



"""simple docstring"""
from sklearn.metrics import fa_score

import datasets


A:   Dict								=		"\nThe F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:\nF1 = 2 * (precision * recall) / (precision + recall)\n"


A:   Tuple								=		"\nArgs:\n    predictions (`list` of `int`): Predicted labels.\n    references (`list` of `int`): Ground truth labels.\n    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.\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\nReturns:\n    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.\n\nExamples:\n\n    Example 1-A simple binary example\n        >>> f1_metric = datasets.load_metric(\"f1\")\n        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])\n        >>> print(results)\n        {'f1': 0.5}\n\n    Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.\n        >>> f1_metric = datasets.load_metric(\"f1\")\n        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)\n        >>> print(round(results['f1'], 2))\n        0.67\n\n    Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.\n        >>> f1_metric = datasets.load_metric(\"f1\")\n        >>> 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])\n        >>> print(round(results['f1'], 2))\n        0.35\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 = f1_metric.compute(predictions=predictions, references=references, average=\"macro\")\n        >>> print(round(results['f1'], 2))\n        0.27\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"micro\")\n        >>> print(round(results['f1'], 2))\n        0.33\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"weighted\")\n        >>> print(round(results['f1'], 2))\n        0.27\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average=None)\n        >>> print(results)\n        {'f1': array([0.8, 0. , 0. ])}\n"


A:   List[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       SCREAMING_SNAKE_CASE__  (					datasets.Metric ):

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Optional[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.f1_score.html"""]      ,     )




   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE=None      ,     _SCREAMING_SNAKE_CASE=1      ,     _SCREAMING_SNAKE_CASE="binary"      ,     _SCREAMING_SNAKE_CASE=None						)  ->      Any:





    '''simple docstring'''




    UpperCAmelCase	:					Tuple	=	fa_score(
        _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     labels=_SCREAMING_SNAKE_CASE      ,     pos_label=_SCREAMING_SNAKE_CASE      ,     average=_SCREAMING_SNAKE_CASE      ,     sample_weight=_SCREAMING_SNAKE_CASE						)
    return {"f1": float(_SCREAMING_SNAKE_CASE						) if score.size == 1 else score}





"""simple docstring"""
from math import pi, sqrt, tan

def       _snake_case						(						UpperCamelCase						:		float						):
 if side_length < 0:
  raise ValueError("""surface_area_cube() only accepts non-negative values"""						)
 return 6 * side_length**2

def       _snake_case						(						UpperCamelCase						:		float			,		UpperCamelCase						:		float			,		UpperCamelCase						:		float						):
 if length < 0 or breadth < 0 or height < 0:
  raise ValueError("""surface_area_cuboid() only accepts non-negative values"""						)
 return 2 * ((length * breadth) + (breadth * height) + (length * height))

def       _snake_case						(						UpperCamelCase						:		float						):
 if radius < 0:
  raise ValueError("""surface_area_sphere() only accepts non-negative values"""						)
 return 4 * pi * radius**2

def       _snake_case						(						UpperCamelCase						:		float						):
 if radius < 0:
  raise ValueError("""surface_area_hemisphere() only accepts non-negative values"""						)
 return 3 * pi * radius**2

def       _snake_case						(						UpperCamelCase						:		float			,		UpperCamelCase						:		float						):
 if radius < 0 or height < 0:
  raise ValueError("""surface_area_cone() only accepts non-negative values"""						)
 return pi * radius * (radius + (height**2 + radius**2) ** 0.5)

def       _snake_case						(						UpperCamelCase						:		float			,		UpperCamelCase						:		float			,		UpperCamelCase						:		float						):
 if radius_a < 0 or radius_a < 0 or height < 0:
  raise ValueError(
      """surface_area_conical_frustum() only accepts non-negative values"""						)
 UpperCAmelCase	:					Tuple	=	(height**2 + (radius_a - radius_a) ** 2) ** 0.5
 return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)

def       _snake_case						(						UpperCamelCase						:		float			,		UpperCamelCase						:		float						):
 if radius < 0 or height < 0:
  raise ValueError("""surface_area_cylinder() only accepts non-negative values"""						)
 return 2 * pi * radius * (height + radius)

def       _snake_case						(						UpperCamelCase						:		float			,		UpperCamelCase						:		float						):
 if torus_radius < 0 or tube_radius < 0:
  raise ValueError("""surface_area_torus() only accepts non-negative values"""						)
 if torus_radius < tube_radius:
  raise ValueError(
      """surface_area_torus() does not support spindle or self intersecting tori"""						)
 return 4 * pow(UpperCamelCase			,		2						) * torus_radius * tube_radius

def       _snake_case						(						UpperCamelCase						:		float			,		UpperCamelCase						:		float						):
 if length < 0 or width < 0:
  raise ValueError("""area_rectangle() only accepts non-negative values"""						)
 return length * width

def       _snake_case						(						UpperCamelCase						:		float						):
 if side_length < 0:
  raise ValueError("""area_square() only accepts non-negative values"""						)
 return side_length**2

def       _snake_case						(						UpperCamelCase						:		float			,		UpperCamelCase						:		float						):
 if base < 0 or height < 0:
  raise ValueError("""area_triangle() only accepts non-negative values"""						)
 return (base * height) / 2

def       _snake_case						(						UpperCamelCase						:		float			,		UpperCamelCase						:		float			,		UpperCamelCase						:		float						):
 if sidea < 0 or sidea < 0 or sidea < 0:
  raise ValueError("""area_triangle_three_sides() only accepts non-negative values"""						)
 elif sidea + sidea < sidea or sidea + sidea < sidea or sidea + sidea < sidea:
  raise ValueError("""Given three sides do not form a triangle"""						)
 UpperCAmelCase	:					Union[str, Any]	=	(sidea + sidea + sidea) / 2
 UpperCAmelCase	:					Union[str, Any]	=	sqrt(
     semi_perimeter
     * (semi_perimeter - sidea)
     * (semi_perimeter - sidea)
     * (semi_perimeter - sidea)						)
 return area

def       _snake_case						(						UpperCamelCase						:		float			,		UpperCamelCase						:		float						):
 if base < 0 or height < 0:
  raise ValueError("""area_parallelogram() only accepts non-negative values"""						)
 return base * height

def       _snake_case						(						UpperCamelCase						:		float			,		UpperCamelCase						:		float			,		UpperCamelCase						:		float						):
 if basea < 0 or basea < 0 or height < 0:
  raise ValueError("""area_trapezium() only accepts non-negative values"""						)
 return 1 / 2 * (basea + basea) * height

def       _snake_case						(						UpperCamelCase						:		float						):
 if radius < 0:
  raise ValueError("""area_circle() only accepts non-negative values"""						)
 return pi * radius**2

def       _snake_case						(						UpperCamelCase						:		float			,		UpperCamelCase						:		float						):
 if radius_x < 0 or radius_y < 0:
  raise ValueError("""area_ellipse() only accepts non-negative values"""						)
 return pi * radius_x * radius_y

def       _snake_case						(						UpperCamelCase						:		float			,		UpperCamelCase						:		float						):
 if diagonal_a < 0 or diagonal_a < 0:
  raise ValueError("""area_rhombus() only accepts non-negative values"""						)
 return 1 / 2 * diagonal_a * diagonal_a

def       _snake_case						(						UpperCamelCase						:		int			,		UpperCamelCase						:		float						):
 if not isinstance(UpperCamelCase			,		UpperCamelCase						) or sides < 3:
  raise ValueError(
      """area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides"""						)
 elif length < 0:
  raise ValueError(
      """area_reg_polygon() only accepts non-negative values as \
length of a side"""						)
 return (sides * length**2) / (4 * tan(pi / sides						))
 return (sides * length**2) / (4 * tan(pi / sides						))


if __name__ == "__main__":
      import doctest

      doctest.testmod(verbose=True)  # verbose so we can see methods missing tests

      print("[DEMO] Areas of various geometric shapes: \n")
      print(f"""Rectangle: {area_rectangle(1_0, 2_0) = }""")
      print(f"""Square: {area_square(1_0) = }""")
      print(f"""Triangle: {area_triangle(1_0, 1_0) = }""")
      print(f"""Triangle: {area_triangle_three_sides(5, 1_2, 1_3) = }""")
      print(f"""Parallelogram: {area_parallelogram(1_0, 2_0) = }""")
      print(f"""Rhombus: {area_rhombus(1_0, 2_0) = }""")
      print(f"""Trapezium: {area_trapezium(1_0, 2_0, 3_0) = }""")
      print(f"""Circle: {area_circle(2_0) = }""")
      print(f"""Ellipse: {area_ellipse(1_0, 2_0) = }""")
      print("\nSurface Areas of various geometric shapes: \n")
      print(f"""Cube: {surface_area_cube(2_0) = }""")
      print(f"""Cuboid: {surface_area_cuboid(1_0, 2_0, 3_0) = }""")
      print(f"""Sphere: {surface_area_sphere(2_0) = }""")
      print(f"""Hemisphere: {surface_area_hemisphere(2_0) = }""")
      print(f"""Cone: {surface_area_cone(1_0, 2_0) = }""")
      print(f"""Conical Frustum: {surface_area_conical_frustum(1_0, 2_0, 3_0) = }""")
      print(f"""Cylinder: {surface_area_cylinder(1_0, 2_0) = }""")
      print(f"""Torus: {surface_area_torus(2_0, 1_0) = }""")
      print(f"""Equilateral Triangle: {area_reg_polygon(3, 1_0) = }""")
      print(f"""Square: {area_reg_polygon(4, 1_0) = }""")
      print(f"""Reqular Pentagon: {area_reg_polygon(5, 1_0) = }""")
