class_docstr: An evaluation instance provides metrics per time series as well as overall
  metrics.
class_docstr_tok: ["An","evaluation","instance","provides","metrics","per","time","series","as","well","as","overall","metrics","."]
docstr: Aggregates the provided evaluations into a single performance object.
  
  Args:
      evaluations: The evaluations.
  
  Returns:
      The performance object. Since this is not part of the evaluation, it has the
          `num_model_parameters` attribute unset (set to zero).
docstr_tok: ["Aggregates","the","provided","evaluations","into","a","single","performance","object",".","Args",":","evaluations",":","The","evaluations",".","Returns",":","The","performance","object",".","Since","this","is","not","part","of","the","evaluation",",","it","has","the","`","num_model_parameters","`","attribute","unset","(","set","to","zero",")","."]
code: def performance(cls, evaluations: list[Evaluation]) -> Performance:
      """
      Aggregates the provided evaluations into a single performance object.
  
      Args:
          evaluations: The evaluations.
  
      Returns:
          The performance object. Since this is not part of the evaluation, it has the
              `num_model_parameters` attribute unset (set to zero).
      """
      metrics = [e.summary for e in evaluations]
      kwargs = {
          m: (
              Metric(0, 0)
              if m == "num_model_parameters"
              else Metric(
                  np.mean(
                      [
                          metric[m] if m in metric else np.nan
                          for metric in metrics
                      ]
                  ),
                  np.std(
                      [
                          metric[m] if m in metric else np.nan
                          for metric in metrics
                      ]
                  ),
              )
          )
          for m in Performance.metrics()
      }
      return Performance(**kwargs)
  
file_path: gluonts/src/gluonts/nursery/tsbench/src/tsbench/forecasts/evaluation.py
imports: from __future__ import annotations
  from dataclasses import dataclass
  from typing import Dict, List
  import numpy
  from tsbench.config.dataset import EvaluationDataset
  from tsbench.evaluations.metrics import Metric, Performance
  from .metrics import abs_error_sum, abs_target_mean, abs_target_sum, mase, naive_error, ncrps, rmse, smape
  from .quantile import QuantileForecasts
label_desc: Use image node_id 1 for calling the Evaluation obj's underlying member method code with example usage: obj.performance(cls, evaluations) and returns: Performance