pylint complaints fixed.

research/differential_privacy/privacy_accountant/python/rdp_accountant.py

@@ -37,13 +37,12 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-from absl import app
-from absl import flags
-
 import math
-import numpy as np
 import sys
 
+from absl import app
+from absl import flags
+import numpy as np
 from scipy import special
 
 FLAGS = flags.FLAGS
@@ -373,6 +372,9 @@ def get_privacy_spent(orders, rdp, target_eps=None, target_delta=None):
               epsilon. Exactly one of target_eps and target_delta must be None.
   Returns:
     eps, delta, opt_order.
+
+  Raises:
+    ValueError: If target_eps and target_delta are messed up.
   """
   if target_eps is None and target_delta is None:
     raise ValueError(
@@ -390,7 +392,8 @@ def get_privacy_spent(orders, rdp, target_eps=None, target_delta=None):
     return eps, target_delta, opt_order
 
 
-def main(_): pass
+def main(_):
+  pass
 
 
 if __name__ == "__main__":

research/differential_privacy/privacy_accountant/python/rdp_accountant_test.py

@@ -20,8 +20,8 @@ from __future__ import division
 from __future__ import print_function
 
 from absl.testing import absltest
-import numpy as np
 import mpmath as mp
+import numpy as np
 
 import rdp_accountant
 
@@ -32,9 +32,8 @@ class TestGaussianMoments(absltest.TestCase):
   ##############################
 
   def _pdf_gauss_mp(self, x, sigma, mean):
-    return 1. / mp.sqrt(2. * sigma ** 2 * mp.pi) * mp.exp(-(x - mean)
-                                                           ** 2 / (
-                                                              2. * sigma ** 2))
+    return 1. / mp.sqrt(2. * sigma ** 2 * mp.pi) * mp.exp(
+        -(x - mean) ** 2 / (2. * sigma ** 2))
 
   def _integral_inf_mp(self, fn):
     integral, _ = mp.quad(
@@ -78,7 +77,7 @@ class TestGaussianMoments(absltest.TestCase):
     b_numeric = self._integral_inf_mp(b_lambda_fn)
 
     if verbose:
-      z0, z1 = rdp_accountant._compute_zs(sigma, q)
+      _, z1 = rdp_accountant._compute_zs(sigma, q)
       print("z1 = ", z1)
       print("x in the Taylor series = ", q / (1 - q) * np.exp(
           (2 * z1 - 1) / (2 * sigma ** 2)))