class_docstr: Class that stores the results of a COCO detection evaluation.
  
  Args:
      samples: the :class:`fiftyone.core.collections.SampleCollection` used
      config: the :class:`COCOEvaluationConfig` used
      eval_key: the evaluation key
      matches: a list of
          ``(gt_label, pred_label, iou, pred_confidence, gt_id, pred_id)``
          matches. Either label can be ``None`` to indicate an unmatched
          object
      precision: an array of precision values of shape
          ``num_iou_threshs x num_classes x num_recall``
      recall: an array of recall values
      iou_threshs: an array of IoU thresholds
      classes: the list of possible classes
      thresholds (None): an optional array of decision thresholds of shape
          ``num_iou_threshs x num_classes x num_recall``
      missing (None): a missing label string. Any unmatched objects are
          given this label for evaluation purposes
      backend (None): a :class:`COCOEvaluation` backend
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docstr: Computes COCO-style mean average precision (mAP) for the specified
  classes.
  
  See `this page <https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocotools/cocoeval.py>`_
  for more details about COCO-style mAP.
  
  Args:
      classes (None): a list of classes for which to compute mAP
  
  Returns:
      the mAP in ``[0, 1]``
docstr_tok: ["Computes","COCO-style","mean","average","precision","(","mAP",")","for","the","specified","classes",".","See","`","this","page","<","https",":","\/\/github.com\/cocodataset\/cocoapi\/blob\/master\/PythonAPI\/pycocotools\/cocoeval.py",">","`","_","for","more","details","about","COCO-style","mAP",".","Args",":","classes","(","None",")",":","a","list","of","classes","for","which","to","compute","mAP","Returns",":","the","mAP","in","``","[","0",",","1","]","``"]
code: def mAP(self, classes=None):
      """Computes COCO-style mean average precision (mAP) for the specified
      classes.
  
      See `this page <https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocotools/cocoeval.py>`_
      for more details about COCO-style mAP.
  
      Args:
          classes (None): a list of classes for which to compute mAP
  
      Returns:
          the mAP in ``[0, 1]``
      """
      if classes is not None:
          class_inds = np.array(
              [self._get_class_index(c) for c in classes]
          )
          classwise_AP = self._classwise_AP[class_inds]
      else:
          classwise_AP = self._classwise_AP
  
      classwise_AP = classwise_AP[classwise_AP > -1]
      if classwise_AP.size == 0:
          return -1
  
      return np.mean(classwise_AP)
  
imports: import logging
  from collections import defaultdict
  import numpy
  import eta.core.utils
  import fiftyone.core.plots
  import fiftyone.utils.iou
  from .detection import DetectionEvaluation, DetectionEvaluationConfig, DetectionResults
label_desc: Use image node_id 3 for calling the COCODetectionResults obj's underlying member method code with example usage: obj.mAP(classes) and returns: np, unknown