class_docstr: Simple Precision, Recall, F-measure and Accuracy evaluation based on the
  numbers of true/false positive/negative detections.
  
  Parameters
  ----------
  num_tp : int
      Number of true positive detections.
  num_fp : int
      Number of false positive detections.
  num_tn : int
      Number of true negative detections.
  num_fn : int
      Number of false negative detections.
  name : str
      Name to be displayed.
  
  Notes
  -----
  This class is only suitable for a 1-class evaluation problem.
class_docstr_tok: ["Simple","Precision",",","Recall",",","F-measure","and","Accuracy","evaluation","based","on","the","numbers","of","true\/false","positive\/negative","detections",".","Parameters","--","--","--","--","--","num_tp",":","int","Number","of","true","positive","detections",".","num_fp",":","int","Number","of","false","positive","detections",".","num_tn",":","int","Number","of","true","negative","detections",".","num_fn",":","int","Number","of","false","negative","detections",".","name",":","str","Name","to","be","displayed",".","Notes","--","--","-","This","class","is","only","suitable","for","a","1-class","evaluation","problem","."]
code: def __str__(self):
      return self.tostring()
  
imports: from __future__ import absolute_import, division, print_function
  import numpy
  from .None import chords, beats, notes, onsets, tempo
  from .beats import BeatEvaluation, BeatMeanEvaluation
  from .chords import ChordEvaluation, ChordMeanEvaluation, ChordSumEvaluation
  from .key import KeyEvaluation, KeyMeanEvaluation
  from .notes import NoteEvaluation, NoteMeanEvaluation, NoteSumEvaluation
  from .onsets import OnsetEvaluation, OnsetMeanEvaluation, OnsetSumEvaluation
  from .tempo import TempoEvaluation, TempoMeanEvaluation
label_desc: Use image node_id 13 for calling the SimpleEvaluation obj's underlying member method code with example usage: obj.__str__() and returns: self