def print_plot_large()

in research/pate_2018/ICLR2018/rdp_cumulative.py [0:0]

• 75 lines of code
• 4 McCabe index (conditional complexity)

def print_plot_large(figures_dir, eps_lap, eps_gnmax1, answered_gnmax1,
    eps_gnmax2, partition_gnmax2, answered_gnmax2):
  """Plots a graph of LNMax vs GNMax with two parameters.

  Args:
    figures_dir: A name of the  directory where to save the plot.
    eps_lap: The cumulative privacy costs of the Laplace mechanism.
    eps_gnmax1: The cumulative privacy costs of the Gaussian mechanism (set 1).
    answered_gnmax1: The cumulative count of queries answered (set 1).
    eps_gnmax2: The cumulative privacy costs of the Gaussian mechanism (set 2).
    partition_gnmax2: Allocation of eps for set 2.
    answered_gnmax2: The cumulative count of queries answered (set 2).
  """
  xlim = 6000
  x_axis = range(0, int(xlim), 10)
  lenx = len(x_axis)
  y_lap = np.zeros(lenx)
  y_gnmax1 = np.full(lenx, np.nan, dtype=float)
  y_gnmax2 = np.full(lenx, np.nan, dtype=float)
  y1_gnmax2 = np.full(lenx, np.nan, dtype=float)

  for i in range(lenx):
    x = x_axis[i]
    y_lap[i] = eps_lap[x]
    idx1 = np.searchsorted(answered_gnmax1, x)
    if idx1 < len(eps_gnmax1):
      y_gnmax1[i] = eps_gnmax1[idx1]
    idx2 = np.searchsorted(answered_gnmax2, x)
    if idx2 < len(eps_gnmax2):
      y_gnmax2[i] = eps_gnmax2[idx2]
      fraction_step1, fraction_step2, _ = partition_gnmax2[idx2]
      y1_gnmax2[i] = eps_gnmax2[idx2] * fraction_step1 / (
          fraction_step1 + fraction_step2)

  fig, ax = plt.subplots()
  fig.set_figheight(4.5)
  fig.set_figwidth(4.7)
  ax.plot(
      x_axis,
      y_lap,
      color='r',
      ls='dashed',
      label='LNMax',
      alpha=.5,
      linewidth=5)
  ax.plot(
      x_axis,
      y_gnmax1,
      color='g',
      ls='-',
      label='Confident-GNMax (moderate)',
      alpha=.5,
      linewidth=5)
  ax.plot(
      x_axis,
      y_gnmax2,
      color='b',
      ls='-',
      label='Confident-GNMax (aggressive)',
      alpha=.5,
      linewidth=5)
  ax.fill_between(
      x_axis, [0] * lenx,
      y1_gnmax2.tolist(),
      facecolor='b',
      alpha=.3,
      hatch='\\')
  ax.plot(
      x_axis,
      y1_gnmax2,
      color='b',
      ls='-',
      label='_nolegend_',
      alpha=.5,
      linewidth=1)
  ax.fill_between(
      x_axis, y1_gnmax2.tolist(), y_gnmax2.tolist(), facecolor='b', alpha=.3)
  plt.xticks(np.arange(0, 7000, 1000))
  plt.xlim([0, xlim])
  plt.ylim([0, 1.])
  plt.xlabel('Number of queries answered', fontsize=16)
  plt.ylabel(r'Privacy cost $\varepsilon$ at $\delta=10^{-8}$', fontsize=16)
  plt.legend(loc=2, fontsize=13)  # loc=2 -- upper left
  ax.tick_params(labelsize=14)
  fout_name = os.path.join(figures_dir, 'lnmax_vs_2xgnmax_large.pdf')
  print('Saving the graph to ' + fout_name)
  fig.savefig(fout_name, bbox_inches='tight')
  plt.show()