(fps, fns, save_path)
This might be a good metric when you are doing a binary classification problem
      with two species and you care how it is incorrectly classifying both species.
    Given false positive and false negative rates,
      produce a DET(detection error rate) Curve.
    The false positive rate is assumed to be increasing while the false
    negative rate is assumed to be decreasing.
This might be a good metric when you are doing a binary classification problem
      with two species and you care how it is incorrectly classifying both species.
    Given false positive and false negative rates,
      produce a DET(detection error rate) Curve.
    The false positive rate is assumed to be increasing while the false
    negative rate is assumed to be decreasing.
This might be a good metric when you are doing a binary classification problem with two species and you care how it is incorrectly classifying both species . Given false positive and false negative rates produce a DET ( detection error rate ) Curve . The false positive rate is assumed to be increasing while the false negative rate is assumed to be decreasing .
def plot_det(fps, fns, save_path):
  """
    This might be a good metric when you are doing a binary classification problem
      with two species and you care how it is incorrectly classifying both species.
    Given false positive and false negative rates,
      produce a DET(detection error rate) Curve.
    The false positive rate is assumed to be increasing while the false
    negative rate is assumed to be decreasing.
  """
  # pylint: disable=unused-argument, unused-variable, unused-variable, invalid-name
  axis_min = min(fps[0], fns[-1])
  fig, ax = plt.subplots()
  ax.plot(fps, fns)
  ax.yscale('log')
  ax.xscale('log')
  ticks_to_use = [
      0.001, 0.002, 0.005, 0.01, 0.02, 0.05, 0.1, 0.2, 0.5, 1, 2, 5, 10, 20, 50
  ]
  ax.get_xaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())
  ax.get_yaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())
  ax.set_xticks(ticks_to_use)
  ax.set_yticks(ticks_to_use)
  ax.axis([0.001, 50, 0.001, 50])
  ax.savefig(save_path)
def plot_det ( fps , fns , save_path ) : # pylint: disable=unused-argument, unused-variable, unused-variable, invalid-name axis_min = min ( fps [ 0 ] , fns [ - 1 ] ) fig , ax = plt . subplots ( ) ax . plot ( fps , fns ) ax . yscale ( 'log' ) ax . xscale ( 'log' ) ticks_to_use = [ 0.001 , 0.002 , 0.005 , 0.01 , 0.02 , 0.05 , 0.1 , 0.2 , 0.5 , 1 , 2 , 5 , 10 , 20 , 50 ] ax . get_xaxis ( ) . set_major_formatter ( matplotlib . ticker . ScalarFormatter ( ) ) ax . get_yaxis ( ) . set_major_formatter ( matplotlib . ticker . ScalarFormatter ( ) ) ax . set_xticks ( ticks_to_use ) ax . set_yticks ( ticks_to_use ) ax . axis ( [ 0.001 , 50 , 0.001 , 50 ] ) ax . savefig ( save_path )