Merge pull request #423 from dvadym/fix_formatting

Fix formatting

docs/conf.py

@@ -57,9 +57,9 @@
 master_doc = "index"
 
 # General information about the project.
-project = u"PyDP"
-copyright = u"2020, OpenMined"
-author = u"OpenMined"
+project = "PyDP"
+copyright = "2020, OpenMined"
+author = "OpenMined"
 
 # The version info for the project you're documenting, acts as replacement for
 # |version| and |release|, also used in various other places throughout the
@@ -262,7 +262,7 @@
 
 # One entry per manual page. List of tuples
 # (source start file, name, description, authors, manual section).
-man_pages = [(master_doc, "pydp", u"pydp Documentation", [author], 1)]
+man_pages = [(master_doc, "pydp", "pydp Documentation", [author], 1)]
 
 # If true, show URL addresses after external links.
 # man_show_urls = False
@@ -277,7 +277,7 @@
     (
         master_doc,
         "PyDP",
-        u"PyDP Documentation",
+        "PyDP Documentation",
         author,
         "OpenMined",
         "TODO: One line description of project.",

src/pydp/ml/mechanisms/geometric.py

@@ -133,7 +133,7 @@ def get_variance(self, value):
         return (
             2
             * leading_factor
-            * (geom_series + 3 * (geom_series ** 2) + 2 * (geom_series ** 3))
+            * (geom_series + 3 * (geom_series**2) + 2 * (geom_series**3))
         )
 
     def randomise(self, value):

src/pydp/ml/mechanisms/laplace.py

@@ -195,11 +195,11 @@ def get_variance(self, value):
 
         shape = self._sensitivity / self._epsilon
 
-        variance = value ** 2 + shape * (
+        variance = value**2 + shape * (
             self._lower_bound * np.exp((self._lower_bound - value) / shape)
             - self._upper_bound * np.exp((value - self._upper_bound) / shape)
         )
-        variance += (shape ** 2) * (
+        variance += (shape**2) * (
             2
             - np.exp((self._lower_bound - value) / shape)
             - np.exp((value - self._upper_bound) / shape)
@@ -373,17 +373,17 @@ def get_variance(self, value):
         if self._scale is None:
             self._scale = self._find_scale()
 
-        variance = value ** 2
+        variance = value**2
         variance -= (
-            np.exp((self._lower_bound - value) / self._scale) * (self._lower_bound ** 2)
+            np.exp((self._lower_bound - value) / self._scale) * (self._lower_bound**2)
             + np.exp((value - self._upper_bound) / self._scale)
-            * (self._upper_bound ** 2)
+            * (self._upper_bound**2)
         ) / 2
         variance += self._scale * (
             self._lower_bound * np.exp((self._lower_bound - value) / self._scale)
             - self._upper_bound * np.exp((value - self._upper_bound) / self._scale)
         )
-        variance += (self._scale ** 2) * (
+        variance += (self._scale**2) * (
             2
             - np.exp((self._lower_bound - value) / self._scale)
             - np.exp((value - self._upper_bound) / self._scale)

src/pydp/ml/util/accountant.py

@@ -188,7 +188,7 @@ def total(self, spent_budget=None, slack=None):
         for epsilon, _ in spent_budget:
             epsilon_sum += epsilon
             epsilon_exp_sum += (1 - np.exp(-epsilon)) * epsilon / (1 + np.exp(-epsilon))
-            epsilon_sq_sum += epsilon ** 2
+            epsilon_sq_sum += epsilon**2
 
         total_epsilon_naive = epsilon_sum
         total_delta = self.__total_delta_safe(spent_budget, slack)

tests/algorithms/test_numerical_mechanisms.py

@@ -77,7 +77,7 @@ def test_gaussian_mechanism():
         priv_budg * epsilon, priv_budg * delta, l2_sensitivity
     )
     assert type(interval) is num_mech.ConfidenceInterval
-    bound = erfinv(-conf_level) * local_gaussian.std * (2 ** 0.5)
+    bound = erfinv(-conf_level) * local_gaussian.std * (2**0.5)
     lower_bound, upper_bound = value - bound, value + bound
     assert_almost_eq(lower_bound, interval.lower_bound)
     assert_almost_eq(upper_bound, interval.upper_bound)