Adding several plots for the slide deck.

research/differential_privacy/pate/ICLR2018/README.md

@@ -51,9 +51,11 @@ The output is written to the console.
 *   plot_partition.py — Script for producing partition.pdf, a detailed breakdown of privacy
 costs for Confident-GNMax with smooth sensitivity analysis (takes ~50 hours).
 
-*   rdp_flow.py and plot_ls_q.py are currently not used.
+*   plots_for_slides.py — Script for producing several plots for the slide deck. 
 
 *   download.py — Utility script for populating the data/ directory.
 
+*   plot_ls_q.py is not used.
+
 
 All Python files take flags. Run script_name.py --help for help on flags.

research/differential_privacy/pate/ICLR2018/rdp_flow.py → research/differential_privacy/pate/ICLR2018/plots_for_slides.py

@@ -13,9 +13,7 @@
 # limitations under the License.
 # ==============================================================================
 
-"""Plots two graphs illustrating cost of privacy per answered query.
-
-PRESENTLY NOT USED.
+"""Plots graphs for the slide deck.
 
 A script in support of the PATE2 paper. The input is a file containing a numpy
 array of votes, one query per row, one class per column. Ex:
@@ -23,7 +21,7 @@ array of votes, one query per row, one class per column. Ex:
   31, 16, ..., 0
   ...
   0, 86, ..., 438
-The output is written to a specified directory and consists of two pdf files.
+The output graphs are visualized using the TkAgg backend.
 """
 from __future__ import absolute_import
 from __future__ import division
@@ -35,10 +33,10 @@ import sys
 
 sys.path.append('..')  # Main modules reside in the parent directory.
 
-
 from absl import app
 from absl import flags
 import matplotlib
+
 matplotlib.use('TkAgg')
 import matplotlib.pyplot as plt  # pylint: disable=g-import-not-at-top
 import numpy as np
@@ -48,21 +46,30 @@ import random
 plt.style.use('ggplot')
 
 FLAGS = flags.FLAGS
-flags.DEFINE_string('counts_file', '', 'Counts file.')
+flags.DEFINE_string('counts_file', None, 'Counts file.')
 flags.DEFINE_string('figures_dir', '', 'Path where figures are written to.')
+flags.DEFINE_boolean('transparent', False, 'Set background to transparent.')
 
+flags.mark_flag_as_required('counts_file')
 
-def plot_rdp_curve_per_example(votes, sigmas):
-  orders = np.linspace(1., 100., endpoint=True, num=1000)
-  orders[0] = 1.001
 
+def setup_plot():
   fig, ax = plt.subplots()
   fig.set_figheight(4.5)
   fig.set_figwidth(4.7)
 
-  fig.patch.set_alpha(0)
+  if FLAGS.transparent:
+    fig.patch.set_alpha(0)
+
+  return fig, ax
+
 
-  for i in xrange(votes.shape[0]):
+def plot_rdp_curve_per_example(votes, sigmas):
+  orders = np.linspace(1., 100., endpoint=True, num=1000)
+  orders[0] = 1.001
+  fig, ax = setup_plot()
+
+  for i in range(votes.shape[0]):
     for sigma in sigmas:
       logq = pate.compute_logq_gaussian(votes[i,], sigma)
       rdp = pate.rdp_gaussian(logq, sigma, orders)
@@ -95,26 +102,19 @@ def plot_rdp_curve_per_example(votes, sigmas):
 
 def plot_rdp_of_sigma(v, order):
   sigmas = np.linspace(1., 1000., endpoint=True, num=1000)
-  fig, ax = plt.subplots()
-  fig.set_figheight(4.5)
-  fig.set_figwidth(4.7)
+  fig, ax = setup_plot()
 
-  fig.patch.set_alpha(0)
   y = np.zeros(len(sigmas))
 
   for i, sigma in enumerate(sigmas):
     logq = pate.compute_logq_gaussian(v, sigma)
     y[i] = pate.rdp_gaussian(logq, sigma, order)
 
-  ax.plot(
-      sigmas,
-      y,
-      alpha=.8,
-      linewidth=5)
+  ax.plot(sigmas, y, alpha=.8, linewidth=5)
 
   plt.xlim(xmin=1, xmax=1000)
   plt.ylim(ymin=0)
-  #plt.yticks([0, .0004, .0008, .0012])
+  # plt.yticks([0, .0004, .0008, .0012])
   ax.tick_params(labelleft='off')
   plt.xlabel(r'Noise $\sigma$', fontsize=16)
   plt.ylabel(r'RDP at order $\alpha={}$'.format(order), fontsize=16)
@@ -125,7 +125,7 @@ def plot_rdp_of_sigma(v, order):
 
 
 def compute_rdp_curve(votes, threshold, sigma1, sigma2, orders,
-                      target_answered):
+    target_answered):
   rdp_cum = np.zeros(len(orders))
   answered = 0
   for i, v in enumerate(votes):
@@ -147,10 +147,7 @@ def plot_rdp_total(votes, sigmas):
   orders = np.linspace(1., 100., endpoint=True, num=100)
   orders[0] = 1.1
 
-  fig, ax = plt.subplots()
-  fig.set_figheight(4.5)
-  fig.set_figwidth(4.7)
-  fig.patch.set_alpha(0)
+  fig, ax = setup_plot()
 
   target_answered = 2000
 
@@ -171,7 +168,6 @@ def plot_rdp_total(votes, sigmas):
   #       label=r'Data-independent bound, $\sigma$={}'.format(int(sigma)),
   #       linewidth=10)
 
-
   plt.xlim(xmin=1, xmax=100)
   plt.ylim(ymin=0)
   plt.xticks([1, 20, 40, 60, 80, 100])
@@ -185,41 +181,32 @@ def plot_rdp_total(votes, sigmas):
   plt.show()
 
 
-def plot_one_curve():
-  fig, ax = plt.subplots()
-  fig.set_figheight(4.5)
-  fig.set_figwidth(4.7)
-
-  fig.patch.set_alpha(0)
+def plot_data_ind_curve():
+  fig, ax = setup_plot()
 
   orders = np.linspace(1., 10., endpoint=True, num=1000)
   orders[0] = 1.01
 
   ax.plot(
-    orders,
-    pate.rdp_data_independent_gaussian(1., orders),
-    alpha=.5,
-    color='gray',
-    linewidth=10)
+      orders,
+      pate.rdp_data_independent_gaussian(1., orders),
+      alpha=.5,
+      color='gray',
+      linewidth=10)
 
-  #plt.yticks([])
+  # plt.yticks([])
   plt.xlim(xmin=1, xmax=10)
   plt.ylim(ymin=0)
-
+  plt.xticks([1, 3, 5, 7, 9])
+  ax.tick_params(labelsize=14)
   plt.show()
 
 
-def plot_two_curves():
+def plot_two_data_ind_curves():
   orders = np.linspace(1., 100., endpoint=True, num=1000)
   orders[0] = 1.001
 
-  fig, ax = plt.subplots()
-  fig.set_figheight(4.5)
-  fig.set_figwidth(4.7)
-  fig.patch.set_alpha(0)
-
-  ax.plot([], [])
-  ax.plot([], [])
+  fig, ax = setup_plot()
 
   for sigma in [100, 150]:
     ax.plot(
@@ -242,10 +229,7 @@ def plot_two_curves():
 
 
 def scatter_plot(votes, threshold, sigma1, sigma2, order):
-  fig, ax = plt.subplots()
-  fig.set_figheight(4.5)
-  fig.set_figwidth(4.7)
-  fig.patch.set_alpha(0)
+  fig, ax = setup_plot()
   x = []
   y = []
   for i, v in enumerate(votes):
@@ -259,13 +243,14 @@ def scatter_plot(votes, threshold, sigma1, sigma2, order):
       y.append(pate.rdp_gaussian(logq_step2, sigma2, order))
 
   print('Selected {} queries.'.format(len(x)))
-  #data_ind = pate.rdp_data_independent_gaussian(sigma, order)
-  #plt.plot([0, 5000], [data_ind, data_ind], color='tab:blue', linestyle='-', linewidth=2)
+  # Plot the data-independent curve:
+  # data_ind = pate.rdp_data_independent_gaussian(sigma, order)
+  # plt.plot([0, 5000], [data_ind, data_ind], color='tab:blue', linestyle='-', linewidth=2)
   ax.set_yscale('log')
   plt.xlim(xmin=0, xmax=5000)
   plt.ylim(ymin=1e-300, ymax=1)
   plt.yticks([1, 1e-100, 1e-200, 1e-300])
-  plt.scatter(x, y, s = 1, alpha=0.5)
+  plt.scatter(x, y, s=1, alpha=0.5)
   plt.ylabel(r'RDP at $\alpha={}$'.format(order), fontsize=16)
   plt.xlabel(r'max count', fontsize=16)
   ax.tick_params(labelsize=14)
@@ -278,21 +263,20 @@ def main(argv):
   print('Reading raw votes from ' + fin_name)
   sys.stdout.flush()
 
-  #plot_one_curve()
-  #plot_two_curves()
-
-  votes = np.load(fin_name)
-  votes = votes[:12000,]  # truncate to 4000 samples
+  plot_data_ind_curve()
+  plot_two_data_ind_curves()
 
   v1 = [2550, 2200, 250]  # based on votes[2,]
-  #v2 = [2600, 2200, 200]  # based on votes[381,]
-  #plot_rdp_curve_per_example(np.array([v1]), (100., 150.))
+  # v2 = [2600, 2200, 200]  # based on votes[381,]
+  plot_rdp_curve_per_example(np.array([v1]), (100., 150.))
 
-  #plot_rdp_of_sigma(np.array(v1), 20.)
+  plot_rdp_of_sigma(np.array(v1), 20.)
+
+  votes = np.load(fin_name)
 
-  #plot_rdp_total(votes, (100., 150.))
-  scatter_plot(votes[:6000, ], None, None, 100, 20)
-  scatter_plot(votes[:6000, ], 3500, 1500, 100, 20)
+  plot_rdp_total(votes[:12000, ], (100., 150.))
+  scatter_plot(votes[:6000, ], None, None, 100, 20)  # w/o thresholding
+  scatter_plot(votes[:6000, ], 3500, 1500, 100, 20)  # with thresholding
 
 
 if __name__ == '__main__':