class_docstr: Class for averaging chord evaluation scores, averaging piecewise (i.e.
  ignoring the lengths of the pieces). For a detailed description of the
  available metrics, refer to ChordEvaluation.
  
  Parameters
  ----------
  eval_objects : list
      Evaluation objects.
  name : str, optional
      Name to be displayed.
class_docstr_tok: ["Class","for","averaging","chord","evaluation","scores",",","averaging","piecewise","(","i.e",".","ignoring","the","lengths","of","the","pieces",")",".","For","a","detailed","description","of","the","available","metrics",",","refer","to","ChordEvaluation",".","Parameters","--","--","--","--","--","eval_objects",":","list","Evaluation","objects",".","name",":","str",",","optional","Name","to","be","displayed","."]
code: def __init__(self, eval_objects, name=None):
      self.name = name or "piecewise mean for %d files" % len(
          eval_objects
      )
      self.eval_objects = eval_objects
  
imports: import numpy
  from .None import evaluation_io, EvaluationMixin
  from ..io import load_chords
label_desc: Use image node_id 1 to create a new ChordMeanEvaluation object from inherited base classes: ChordEvaluation with example: obj = ChordMeanEvaluation(eval_objects, name)