class_docstr: Dataset evaluator.
class_docstr_tok: ["Dataset","evaluator","."]
docstr: Calculate other metrics.
docstr_tok: ["Calculate","other","metrics","."]
code: def _calculate_other_metrics(self, metric: str, category: str):
      """Calculate other metrics."""
      weight = (
          len(self.data[category]["data"])
          / self.metric_total_length[metric]
      )
  
      references = [
          sample["target"]
          if isinstance(sample["target"], list)
          else [sample["target"]]
          for sample in self.data[category]["data"]
      ]
      predictions = [
          sample["output"] for sample in self.data[category]["data"]
      ]
  
      metric_method = eval("metric_helper." + metric)
  
      total_score = 0.0
      for prediction, reference in zip(predictions, references):
          score = 0.0
          for ref in reference:
              score = max(
                  score,
                  metric_method(
                      prediction,
                      ref,
                      all_classes=self.data[category][
                          "inference_kwargs"
                      ]["all_classes"],
                  ),
              )
          total_score += score
      total_score = total_score * 100 / len(predictions)
  
      self.evaluation_results[metric][category] = (
          total_score,
          len(self.data[category]["data"]),
      )
      self.evaluation_results[metric]["ALL"] += total_score * weight
  
file_path: colossalai/applications/ColossalEval/colossal_eval/evaluate/dataset_evaluator/dataset_evaluator.py
imports: import os
  from typing import Dict, List, Union
  import colossal_eval.evaluate.dataset_evaluator.metrics
  import numpy
  import tqdm
  from colossal_eval.utils import jdump
  import colossal_eval.evaluate.dataset_evaluator.gpt_judge
label_desc: Use image node_id 4 for calling the DatasetEvaluator obj's underlying member method code with example usage: obj._calculate_other_metrics(metric, category) without return types