Don't exit 0 with linter errors (#26)

* Cosmetic: Make linter happy

* Fix test safe log test

Co-authored-by: Lukas Wutschitz <lukas.wutschitz@microsoft.com>

.github/workflows/test-package.yml

@@ -34,7 +34,7 @@ jobs:
         # stop the build if there are Python syntax errors or undefined names
         flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
         # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
-        flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
+        flake8 . --count --max-complexity=10 --max-line-length=127 --statistics
     - name: Test with pytest
       run: |
         pytest

prv_accountant/accountant.py

@@ -4,8 +4,6 @@
 import numpy as np
 from typing import Tuple
 
-from scipy import optimize
-
 from .other_accountants import RDP
 from . import discretisers
 from . import composers

prv_accountant/privacy_random_variables.py

@@ -18,7 +18,7 @@ def log(x):
     valid = (x > 0)
     x_is_0 = (x == 0)
     return np.where(valid, np.log(np.where(valid, x, 1)),
-        np.where(x_is_0, -np.inf, np.nan))
+                    np.where(x_is_0, -np.inf, np.nan))
 
 
 class PrivacyRandomVariable(ABC):
@@ -102,7 +102,7 @@ class PoissonSubsampledGaussianMechanism(PrivacyRandomVariable):
     def cdf(self, t):
         sigma = self.sigma
         p = self.p
-        z = np.where(t>0, log((p-1)/p + exp(t)/p), log((p-1)/p + exp(t)/p))
+        z = np.where(t > 0, log((p-1)/p + exp(t)/p), log((p-1)/p + exp(t)/p))
         return np.where(t > log(1 - p), (
                 (1.0/2.0) * p * (-erfc(np.double((1.0/4.0)*M_SQRT2*(2*pow(sigma, 2)*z - 1)/sigma))) -
                 1.0/2.0*(p - 1) * (-erfc(np.double((1.0/4.0)*M_SQRT2*(2*pow(sigma, 2)*z + 1)/sigma))) + 1
@@ -115,7 +115,7 @@ class PoissonSubsampledGaussianMechanism(PrivacyRandomVariable):
         """
         Compute RDP of this mechanism of order alpha
 
-        Based on Google's TF Privacy: https://github.com/tensorflow/privacy/blob/master/tensorflow_privacy/privacy/analysis/rdp_accountant.py 
+        Based on Google's TF Privacy: https://github.com/tensorflow/privacy/blob/master/tensorflow_privacy/privacy/analysis/rdp_accountant.py  # noqa: E501
         """
         if self.p == 0:
             return 0

tests/test_privacy_random_variables.py

@@ -1,7 +1,6 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT License.
 
-from typing import AsyncContextManager
 import scipy
 import math
 import pytest
@@ -10,9 +9,10 @@ import sys
 
 from prv_accountant.privacy_random_variables import PoissonSubsampledGaussianMechanism, log, PrivacyRandomVariableTruncated
 
+
 def test_safe_log():
-    assert np.isnan(log(-1)) == True
-    assert np.isneginf(log(0)) == True
+    assert np.isnan(log(-1)) == True  # noqa: E712
+    assert np.isneginf(log(0)) == True  # noqa: E712
     assert log(1) == pytest.approx(0)