code: 
  
  
  
  
  
  from sklearn.metrics import roc_auc_score
  
  import datasets
  
  
  _UpperCamelCase       = '''
  This metric computes the area under the curve (AUC) for the Receiver Operating Characteristic Curve (ROC). The return values represent how well the model used is predicting the correct classes, based on the input data. A score of `0.5` means that the model is predicting exactly at chance, i.e. the model\'s predictions are correct at the same rate as if the predictions were being decided by the flip of a fair coin or the roll of a fair die. A score above `0.5` indicates that the model is doing better than chance, while a score below `0.5` indicates that the model is doing worse than chance.
  
  This metric has three separate use cases:
      - binary: The case in which there are only two different label classes, and each example gets only one label. This is the default implementation.
      - multiclass: The case in which there can be more than two different label classes, but each example still gets only one label.
      - multilabel: The case in which there can be more than two different label classes, and each example can have more than one label.
  '''
  
  _UpperCamelCase       = '''
  Args:
  - references (array-like of shape (n_samples,) or (n_samples, n_classes)): Ground truth labels. Expects different input based on use case:
      - binary: expects an array-like of shape (n_samples,)
      - multiclass: expects an array-like of shape (n_samples,)
      - multilabel: expects an array-like of shape (n_samples, n_classes)
  - prediction_scores (array-like of shape (n_samples,) or (n_samples, n_classes)): Model predictions. Expects different inputs based on use case:
      - binary: expects an array-like of shape (n_samples,)
      - multiclass: expects an array-like of shape (n_samples, n_classes)
      - multilabel: expects an array-like of shape (n_samples, n_classes)
  - average (`str`): Type of average, and is ignored in the binary use case. Defaults to \'macro\'. Options are:
      - `\'micro\'`: Calculates metrics globally by considering each element of the label indicator matrix as a label. Only works with the multilabel use case.
      - `\'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 (i.e. the number of true instances for each label).
      - `\'samples\'`: Calculate metrics for each instance, and find their average. Only works with the multilabel use case.
      - `None`:  No average is calculated, and scores for each class are returned. Only works with the multilabels use case.
  - sample_weight (array-like of shape (n_samples,)): Sample weights. Defaults to None.
  - max_fpr (`float`): If not None, the standardized partial AUC over the range [0, `max_fpr`] is returned. Must be greater than `0` and less than or equal to `1`. Defaults to `None`. Note: For the multiclass use case, `max_fpr` should be either `None` or `1.0` as ROC AUC partial computation is not currently supported for `multiclass`.
  - multi_class (`str`): Only used for multiclass targets, where it is required. Determines the type of configuration to use. Options are:
      - `\'ovr\'`: Stands for One-vs-rest. Computes the AUC of each class against the rest. This treats the multiclass case in the same way as the multilabel case. Sensitive to class imbalance even when `average == \'macro\'`, because class imbalance affects the composition of each of the \'rest\' groupings.
      - `\'ovo\'`: Stands for One-vs-one. Computes the average AUC of all possible pairwise combinations of classes. Insensitive to class imbalance when `average == \'macro\'`.
  - labels (array-like of shape (n_classes,)): Only used for multiclass targets. List of labels that index the classes in
      `prediction_scores`. If `None`, the numerical or lexicographical order of the labels in
      `prediction_scores` is used. Defaults to `None`.
  Returns:
      roc_auc (`float` or array-like of shape (n_classes,)): Returns array if in multilabel use case and `average=\'None\'`. Otherwise, returns `float`.
  Examples:
      Example 1:
          >>> roc_auc_score = datasets.load_metric("roc_auc")
          >>> refs = [1, 0, 1, 1, 0, 0]
          >>> pred_scores = [0.5, 0.2, 0.99, 0.3, 0.1, 0.7]
          >>> results = roc_auc_score.compute(references=refs, prediction_scores=pred_scores)
          >>> print(round(results[\'roc_auc\'], 2))
          0.78
  
      Example 2:
          >>> roc_auc_score = datasets.load_metric("roc_auc", "multiclass")
          >>> refs = [1, 0, 1, 2, 2, 0]
          >>> pred_scores = [[0.3, 0.5, 0.2],
          ...                 [0.7, 0.2, 0.1],
          ...                 [0.005, 0.99, 0.005],
          ...                 [0.2, 0.3, 0.5],
          ...                 [0.1, 0.1, 0.8],
          ...                 [0.1, 0.7, 0.2]]
          >>> results = roc_auc_score.compute(references=refs, prediction_scores=pred_scores, multi_class=\'ovr\')
          >>> print(round(results[\'roc_auc\'], 2))
          0.85
  
      Example 3:
          >>> roc_auc_score = datasets.load_metric("roc_auc", "multilabel")
          >>> refs = [[1, 1, 0],
          ...         [1, 1, 0],
          ...         [0, 1, 0],
          ...         [0, 0, 1],
          ...         [0, 1, 1],
          ...         [1, 0, 1]]
          >>> pred_scores = [[0.3, 0.5, 0.2],
          ...                 [0.7, 0.2, 0.1],
          ...                 [0.005, 0.99, 0.005],
          ...                 [0.2, 0.3, 0.5],
          ...                 [0.1, 0.1, 0.8],
          ...                 [0.1, 0.7, 0.2]]
          >>> results = roc_auc_score.compute(references=refs, prediction_scores=pred_scores, average=None)
          >>> print([round(res, 2) for res in results[\'roc_auc\']])
          [0.83, 0.38, 0.94]
  '''
  
  _UpperCamelCase       = '''\
  @article{doi:10.1177/0272989X8900900307,
  author = {Donna Katzman McClish},
  title ={Analyzing a Portion of the ROC Curve},
  journal = {Medical Decision Making},
  volume = {9},
  number = {3},
  pages = {190-195},
  year = {1989},
  doi = {10.1177/0272989X8900900307},
      note ={PMID: 2668680},
  URL = {https://doi.org/10.1177/0272989X8900900307},
  eprint = {https://doi.org/10.1177/0272989X8900900307}
  }
  
  
  @article{10.1023/A:1010920819831,
  author = {Hand, David J. and Till, Robert J.},
  title = {A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems},
  year = {2001},
  issue_date = {November 2001},
  publisher = {Kluwer Academic Publishers},
  address = {USA},
  volume = {45},
  number = {2},
  issn = {0885-6125},
  url = {https://doi.org/10.1023/A:1010920819831},
  doi = {10.1023/A:1010920819831},
  journal = {Mach. Learn.},
  month = {oct},
  pages = {171–186},
  numpages = {16},
  keywords = {Gini index, AUC, error rate, ROC curve, receiver operating characteristic}
  }
  
  
  @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   )       ->   Tuple:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          return datasets.MetricInfo(
              description=_DESCRIPTION				,						citation=_CITATION				,						inputs_description=_KWARGS_DESCRIPTION				,						features=datasets.Features(
                  {
                      "prediction_scores": datasets.Sequence(datasets.Value("float"   )   ),
                      "references": datasets.Value("int32"   ),
                  }
                  if self.config_name == "multiclass"
                  else {
                      "references": datasets.Sequence(datasets.Value("int32"   )   ),
                      "prediction_scores": datasets.Sequence(datasets.Value("float"   )   ),
                  }
                  if self.config_name == "multilabel"
                  else {
                      "references": datasets.Value("int32"   ),
                      "prediction_scores": datasets.Value("float"   ),
                  }   )				,						reference_urls=["https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html"]				,						)
  
  
  
  
  
  
  
        def 						UpperCAmelCase		( self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase="macro"				,						UpperCAmelCase=None				,						UpperCAmelCase=None				,						UpperCAmelCase="raise"				,						UpperCAmelCase=None				,						)       ->   List[Any]:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          return {
              "roc_auc": roc_auc_score(
                  UpperCAmelCase				,						UpperCAmelCase				,						average=UpperCAmelCase				,						sample_weight=UpperCAmelCase				,						max_fpr=UpperCAmelCase				,						multi_class=UpperCAmelCase				,						labels=UpperCAmelCase				,						)
          }
  
  
style_context: 
  
  
  
  
  
  from __future__ import annotations
  
  from collections.abc import Callable
  from typing import Generic, TypeVar
  
  _UpperCamelCase       = TypeVar('''T''')
  _UpperCamelCase       = TypeVar('''U''')
  
  
  
  
  class 				_lowerCamelCase (    Generic[T, U]    ):
  
  
  
        """simple docstring"""
  
  
  
  
  
  
        def __init__( self				,						UpperCAmelCase				,						UpperCAmelCase   )       ->   Dict:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				Any        =       key
          __snake_case   :				Tuple        =       val
          __snake_case   :				int        =       0
          __snake_case   :				DoubleLinkedListNode[T, U] | None        =       None
          __snake_case   :				DoubleLinkedListNode[T, U] | None        =       None
  
  
  
  
  
  
  
        def __repr__( self   )       ->   str:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          return "Node: key: {}, val: {}, freq: {}, has next: {}, has prev: {}".format(
              self.key				,						self.val				,						self.freq				,						self.next is not None				,						self.prev is not None   )
  
  
  
  
  class 				_lowerCamelCase (    Generic[T, U]    ):
  
  
  
        """simple docstring"""
  
  
  
  
  
  
        def __init__( self   )       ->   None:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				DoubleLinkedListNode[T, U]        =       DoubleLinkedListNode(UpperCAmelCase				,						UpperCAmelCase   )
          __snake_case   :				DoubleLinkedListNode[T, U]        =       DoubleLinkedListNode(UpperCAmelCase				,						UpperCAmelCase   )
          __snake_case    ,   __snake_case   :				Any        =       self.rear, self.head
  
  
  
  
  
  
  
        def __repr__( self   )       ->   str:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				Union[str, Any]        =       ["DoubleLinkedList"]
          __snake_case   :				List[Any]        =       self.head
          while node.next is not None:
            rep.append(str(UpperCAmelCase   )   )
            __snake_case   :				List[Any]        =       node.next
          rep.append(str(self.rear   )   )
          return ",\n    ".join(UpperCAmelCase   )
  
  
  
  
  
  
  
        def 						UpperCAmelCase		( self				,						UpperCAmelCase   )       ->   None:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				Dict        =       self.rear.prev
  
          # All nodes other than self.head are guaranteed to have non-None previous
          assert previous is not None
  
          __snake_case   :				int        =       node
          __snake_case   :				List[str]        =       previous
          __snake_case   :				Optional[int]        =       node
          __snake_case   :				int        =       self.rear
          node.freq += 1
          self._position_node(UpperCAmelCase   )
  
  
  
  
  
  
  
        def 						UpperCAmelCase		( self				,						UpperCAmelCase   )       ->   None:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          while node.prev is not None and node.prev.freq > node.freq:
            # swap node with previous node
            __snake_case   :				List[Any]        =       node.prev
  
            __snake_case   :				Dict        =       previous_node.prev
            __snake_case   :				Union[str, Any]        =       node.prev
            __snake_case   :				Union[str, Any]        =       previous_node
            __snake_case   :				int        =       node
  
  
  
  
  
  
  
        def 						UpperCAmelCase		( self				,						UpperCAmelCase   )       ->   DoubleLinkedListNode[T, U] | None:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          if node.prev is None or node.next is None:
            return None
  
          __snake_case   :				Optional[int]        =       node.next
          __snake_case   :				Optional[Any]        =       node.prev
          __snake_case   :				Optional[int]        =       None
          __snake_case   :				Any        =       None
          return node
  
  
  
  
  class 				_lowerCamelCase (    Generic[T, U]    ):
  
  
  
        """simple docstring"""
  
  
  
  
  
  
        UpperCAmelCase_						:      dict[Callable[[T], U], LFUCache[T, U]]       ={}
  
  
  
  
  
  
  
        def __init__( self				,						UpperCAmelCase   )       ->   Tuple:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				DoubleLinkedList[T, U]        =       DoubleLinkedList()
          __snake_case   :				str        =       capacity
          __snake_case   :				str        =       0
          __snake_case   :				Optional[int]        =       0
          __snake_case   :				Any        =       0
          __snake_case   :				dict[T, DoubleLinkedListNode[T, U]]        =       {}
  
  
  
  
  
  
  
        def __repr__( self   )       ->   str:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          return (
              F"""CacheInfo(hits={self.hits}, misses={self.miss}, """
              F"""capacity={self.capacity}, current_size={self.num_keys})"""
          )
  
  
  
  
  
  
  
        def __contains__( self				,						UpperCAmelCase   )       ->   bool:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          return key in self.cache
  
  
  
  
  
  
  
        def 						UpperCAmelCase		( self				,						UpperCAmelCase   )       ->   U | None:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          if key in self.cache:
            self.hits += 1
            __snake_case   :				DoubleLinkedListNode[T, U]        =       self.cache[key]
            __snake_case   :				Union[str, Any]        =       self.list.remove(self.cache[key]   )
            assert node == value_node
  
            # node is guaranteed not None because it is in self.cache
            assert node is not None
            self.list.add(UpperCAmelCase   )
            return node.val
          self.miss += 1
          return None
  
  
  
  
  
  
  
        def 						UpperCAmelCase		( self				,						UpperCAmelCase				,						UpperCAmelCase   )       ->   None:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          if key not in self.cache:
            if self.num_keys >= self.capacity:
              # delete first node when over capacity
              __snake_case   :				Optional[Any]        =       self.list.head.next
  
              # guaranteed to have a non-None first node when num_keys > 0
              # explain to type checker via assertions
              assert first_node is not None
              assert first_node.key is not None
              assert self.list.remove(UpperCAmelCase   ) is not None
              # first_node guaranteed to be in list
  
              del self.cache[first_node.key]
              self.num_keys -= 1
            __snake_case   :				Union[str, Any]        =       DoubleLinkedListNode(UpperCAmelCase				,						UpperCAmelCase   )
            self.list.add(self.cache[key]   )
            self.num_keys += 1
  
          else:
            __snake_case   :				Union[str, Any]        =       self.list.remove(self.cache[key]   )
            assert node is not None  # node guaranteed to be in list
            __snake_case   :				Optional[Any]        =       value
            self.list.add(UpperCAmelCase   )
  
  
  
  
  
  
  
        @classmethod
        def 						UpperCAmelCase		( cls				,						UpperCAmelCase = 128   )       ->   Callable[[Callable[[T], U]], Callable[..., U]]:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          def cache_decorator_inner(UpperCAmelCase   ) -> Callable[..., U]:
            def cache_decorator_wrapper(*UpperCAmelCase   ) -> U:
              if func not in cls.decorator_function_to_instance_map:
                __snake_case   :				str        =       LFUCache(UpperCAmelCase   )
  
              __snake_case   :				Dict        =       cls.decorator_function_to_instance_map[func].get(args[0]   )
              if result is None:
                __snake_case   :				Optional[int]        =       func(*UpperCAmelCase   )
                cls.decorator_function_to_instance_map[func].put(args[0]				,						UpperCAmelCase   )
              return result
  
            def cache_info() -> LFUCache[T, U]:
              return cls.decorator_function_to_instance_map[func]
  
            setattr(UpperCAmelCase				,						"cache_info"				,						UpperCAmelCase   )  # noqa: B010
  
            return cache_decorator_wrapper
  
          return cache_decorator_inner
  
  
  if __name__ == "__main__":
       import doctest
  
       doctest.testmod()
  
  