class_docstr: Summarize mean average recall metrics on a dataset and model per detections and area range.
  
  Parameters
  ----------
  area_range: tuple, default: (32**2, 96**2)
      Slices for small/medium/large buckets.
  {additional_check_init_params:2*indent}
class_docstr_tok: ["Summarize","mean","average","recall","metrics","on","a","dataset","and","model","per","detections","and","area","range",".","Parameters","--","--","--","--","--","area_range",":","tuple",",","default",":","(","32","*","*","2",",","96","*","*","2",")","Slices","for","small\/medium\/large","buckets",".","{","additional_check_init_params:2","*","indent","}"]
docstr: Add condition - AR score is greater than given score.
  
  Parameters
  ----------
  min_score : float
      Minimum score to pass the check.
docstr_tok: ["Add","condition","-","AR","score","is","greater","than","given","score",".","Parameters","--","--","--","--","--","min_score",":","float","Minimum","score","to","pass","the","check","."]
code: def add_condition_test_average_recall_greater_than(
      self: MPR, min_score: float
  ) -> MPR:
      """Add condition - AR score is greater than given score.
  
      Parameters
      ----------
      min_score : float
          Minimum score to pass the check.
      """
  
      def condition(df: pd.DataFrame):
          min_col_per_row = df.idxmin(axis=1)
          min_score_per_row = [
              df.loc[r, c] for r, c in min_col_per_row.items()
          ]
          loc_min_row = np.argmin(min_score_per_row)
          score = min_score_per_row[loc_min_row]
          area = min_col_per_row.index[loc_min_row]
          iou = min_col_per_row[loc_min_row]
          category = (
              ConditionCategory.FAIL
              if score < min_score
              else ConditionCategory.PASS
          )
  
          details = f"Found lowest score of {format_number(score)} for area {area} and IoU {iou}"
          return ConditionResult(category, details)
  
      return self.add_condition(
          f"Scores are greater than {min_score}", condition
      )
  
file_path: deepchecks/deepchecks/vision/checks/model_evaluation/mean_average_recall_report.py
imports: import math
  from typing import Optional, Tuple, TypeVar
  import numpy
  import pandas
  from deepchecks.core import CheckResult, ConditionResult, DatasetKind
  from deepchecks.core.condition import ConditionCategory
  from deepchecks.utils.strings import format_number
  from deepchecks.vision._shared_docs import docstrings
  from deepchecks.vision.base_checks import SingleDatasetCheck
  from deepchecks.vision.context import Context
  from deepchecks.vision.metrics_utils.detection_precision_recall import ObjectDetectionAveragePrecision
  from deepchecks.vision.vision_data import TaskType
  from deepchecks.vision.vision_data.batch_wrapper import BatchWrapper
label_desc: Use image node_id 5 for calling the MeanAverageRecallReport obj's underlying member method code with example usage: obj.add_condition_test_average_recall_greater_than(min_score) and returns: self, ConditionResult