class_docstr: Evaluation class for measuring Precision, Recall and F-measure based on
  numpy arrays or lists with true/false positive/negative detections.
  
  Parameters
  ----------
  tp : list or numpy array
      True positive detections.
  fp : list or numpy array
      False positive detections.
  tn : list or numpy array
      True negative detections.
  fn : list or numpy array
      False negative detections.
  name : str
      Name to be displayed.
class_docstr_tok: ["Evaluation","class","for","measuring","Precision",",","Recall","and","F-measure","based","on","numpy","arrays","or","lists","with","true\/false","positive\/negative","detections",".","Parameters","--","--","--","--","--","tp",":","list","or","numpy","array","True","positive","detections",".","fp",":","list","or","numpy","array","False","positive","detections",".","tn",":","list","or","numpy","array","True","negative","detections",".","fn",":","list","or","numpy","array","False","negative","detections",".","name",":","str","Name","to","be","displayed","."]
docstr: Number of false negative detections.
docstr_tok: ["Number","of","false","negative","detections","."]
code: def num_fn(self):
      """Number of false negative detections."""
      return len(self.fn)
  
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 5 for calling the Evaluation obj's underlying member method code with example usage: obj.num_fn() and returns: len