class_docstr: Evaluation class for measuring Precision, Recall and F-measure of onsets.
  
  Parameters
  ----------
  detections : str, list or numpy array
      Detected notes.
  annotations : str, list or numpy array
      Annotated ground truth notes.
  window : float, optional
      F-measure evaluation window [seconds]
  combine : float, optional
      Combine all annotated onsets within `combine` seconds.
  delay : float, optional
      Delay the detections `delay` seconds for evaluation.
class_docstr_tok: ["Evaluation","class","for","measuring","Precision",",","Recall","and","F-measure","of","onsets",".","Parameters","--","--","--","--","--","detections",":","str",",","list","or","numpy","array","Detected","notes",".","annotations",":","str",",","list","or","numpy","array","Annotated","ground","truth","notes",".","window",":","float",",","optional","F-measure","evaluation","window","[","seconds","]","combine",":","float",",","optional","Combine","all","annotated","onsets","within","`","combine","`","seconds",".","delay",":","float",",","optional","Delay","the","detections","`","delay","`","seconds","for","evaluation","."]
docstr: Format the evaluation metrics as a human readable string.
  
  Returns
  -------
  str
      Evaluation metrics formatted as a human readable string.
docstr_tok: ["Format","the","evaluation","metrics","as","a","human","readable","string",".","Returns","--","--","--","-","str","Evaluation","metrics","formatted","as","a","human","readable","string","."]
code: def tostring(self, **kwargs):
      """
      Format the evaluation metrics as a human readable string.
  
      Returns
      -------
      str
          Evaluation metrics formatted as a human readable string.
  
      """
      ret = ""
      if self.name is not None:
          ret += "%s\n  " % self.name
      ret += (
          "Onsets: %5d TP: %5d FP: %5d FN: %5d Precision: %.3f "
          "Recall: %.3f F-measure: %.3f mean: %5.1f ms std: %5.1f ms"
          % (
              self.num_annotations,
              self.num_tp,
              self.num_fp,
              self.num_fn,
              self.precision,
              self.recall,
              self.fmeasure,
              self.mean_error * 1000.0,
              self.std_error * 1000.0,
          )
      )
      return ret
  
imports: from __future__ import absolute_import, division, print_function
  import numpy
  from .None import Evaluation, MeanEvaluation, SumEvaluation, evaluation_io
  from ..io import load_onsets
  from ..utils import combine_events
label_desc: Use image node_id 4 for calling the OnsetEvaluation obj's underlying member method code with example usage: obj.tostring() and returns: ret