def plot_partition()

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

• 51 lines of code
• 5 McCabe index (conditional complexity)

def plot_partition(figures_dir, gnmax_conf, print_order):
  """Plots an expert version of the privacy-per-answered-query graph.

  Args:
    figures_dir: A name of the directory where to save the plot.
    eps: The cumulative privacy cost.
    partition: Allocation of the privacy cost.
    answered: Cumulative number of queries answered.
    order_opt: The list of optimal orders.
  """
  eps_partitioned, answered, ss_std_opt, order_opt = gnmax_conf

  xlim = 10000
  x = range(0, int(xlim), 10)
  lenx = len(x)
  y0 = np.full(lenx, np.nan, dtype=float)  # delta
  y1 = np.full(lenx, np.nan, dtype=float)  # delta + step1
  y2 = np.full(lenx, np.nan, dtype=float)  # delta + step1 + step2
  y3 = np.full(lenx, np.nan, dtype=float)  # delta + step1 + step2 + ss
  noise_std = np.full(lenx, np.nan, dtype=float)

  y_right = np.full(lenx, np.nan, dtype=float)

  for i in range(lenx):
    idx = np.searchsorted(answered, x[i])
    if idx < len(eps_partitioned):
      y0[i] = eps_partitioned[idx].delta
      y1[i] = y0[i] + eps_partitioned[idx].step1
      y2[i] = y1[i] + eps_partitioned[idx].step2
      y3[i] = y2[i] + eps_partitioned[idx].ss

      noise_std[i] = ss_std_opt[idx]
      y_right[i] = order_opt[idx]

  # plt.close('all')
  fig, ax = plt.subplots()
  fig.set_figheight(4.5)
  fig.set_figwidth(4.7)
  fig.patch.set_alpha(0)

  l1 = ax.plot(
      x, y3, color='b', ls='-', label=r'Total privacy cost', linewidth=1).pop()

  for y in (y0, y1, y2):
    ax.plot(x, y, color='b', ls='-', label=r'_nolegend_', alpha=.5, linewidth=1)

  ax.fill_between(x, [0] * lenx, y0.tolist(), facecolor='b', alpha=.5)
  ax.fill_between(x, y0.tolist(), y1.tolist(), facecolor='b', alpha=.4)
  ax.fill_between(x, y1.tolist(), y2.tolist(), facecolor='b', alpha=.3)
  ax.fill_between(x, y2.tolist(), y3.tolist(), facecolor='b', alpha=.2)

  ax.fill_between(x, (y3 - noise_std).tolist(), (y3 + noise_std).tolist(),
                  facecolor='r', alpha=.5)


  plt.xticks(np.arange(0, xlim + 1000, 2000))
  plt.xlim([0, xlim])
  ax.set_ylim([0, 3.])

  ax.set_xlabel('Number of queries answered', fontsize=16)
  ax.set_ylabel(r'Privacy cost $\varepsilon$ at $\delta=10^{-8}$', fontsize=16)

  # Merging legends.
  if print_order:
    ax2 = ax.twinx()
    l2 = ax2.plot(
        x, y_right, 'r', ls='-', label=r'Optimal order', linewidth=5,
        alpha=.5).pop()
    ax2.grid(False)
    # ax2.set_ylabel(r'Optimal Renyi order', fontsize=16)
    ax2.set_ylim([0, 200.])
    # ax.legend((l1, l2), (l1.get_label(), l2.get_label()), loc=0, fontsize=13)

  ax.tick_params(labelsize=14)
  plot_filename = os.path.join(figures_dir, 'partition.pdf')
  print('Saving the graph to ' + plot_filename)
  fig.savefig(plot_filename, bbox_inches='tight', dpi=800)
  plt.show()