class_docstr: Open Images-style evaluation.
  
  Args:
      config: a :class:`OpenImagesEvaluationConfig`
class_docstr_tok: ["Open","Images-style","evaluation",".","Args",":","config",":","a",":","class",":","`","OpenImagesEvaluationConfig","`"]
docstr: Generates aggregate evaluation results for the samples.
  
  This method generates precision and recall curves for the configured
  IoU at ``self.config.iou``.
  
  Args:
      samples: a :class:`fiftyone.core.collections.SampleCollection`
      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
      eval_key (None): the evaluation key for this evaluation
      classes (None): the list of possible classes. If not provided, the
          observed ground truth/predicted labels are used for results
          purposes
      missing (None): a missing label string. Any unmatched objects are
          given this label for results purposes
  
  Returns:
      a :class:`OpenImagesDetectionResults`
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code: def generate_results(
      self, samples, matches, eval_key=None, classes=None, missing=None
  ):
      """Generates aggregate evaluation results for the samples.
  
      This method generates precision and recall curves for the configured
      IoU at ``self.config.iou``.
  
      Args:
          samples: a :class:`fiftyone.core.collections.SampleCollection`
          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
          eval_key (None): the evaluation key for this evaluation
          classes (None): the list of possible classes. If not provided, the
              observed ground truth/predicted labels are used for results
              purposes
          missing (None): a missing label string. Any unmatched objects are
              given this label for results purposes
  
      Returns:
          a :class:`OpenImagesDetectionResults`
      """
      precision, recall, thresholds, classes = _compute_pr_curves(
          matches, classes=classes
      )
  
      return OpenImagesDetectionResults(
          samples,
          self.config,
          eval_key,
          matches,
          precision,
          recall,
          classes,
          thresholds=thresholds,
          missing=missing,
          backend=self,
      )
  
imports: from collections import defaultdict
  from copy import deepcopy
  import numpy
  import fiftyone.core.plots
  import fiftyone.utils.iou
  from .detection import DetectionEvaluation, DetectionEvaluationConfig, DetectionResults
label_desc: Use image node_id 3 for calling the OpenImagesEvaluation obj's underlying member method code with example usage: obj.generate_results(samples, matches, eval_key, classes, missing) and returns: OpenImagesDetectionResults