Added mypy support

.github/workflows/tests.yml

@@ -77,6 +77,12 @@ jobs:
         make check-coverage-python
       env:
         MIN_COVERAGE: 80
+    - name: Mypy test
+      run: |
+        pipenv install mypy
+        pipenv run mypy tests/ pydp/
+        pipenv run mypy examples/carrots_demo examples/restaurant_demo
+
     # Need to see how to run the c++ code coverage
     # - name: Check C++ code coverage
     #   if: runner.os == 'Linux' # Coverage will be the same on all systems so only running the check on Linux

Pipfile

@@ -13,6 +13,7 @@ sphinx = "*"
 sphinx-rtd-theme = "*"
 gcovr = "*"
 coverage = "*"
+mypy = "*"
 
 [packages]
 jupyter = "*"

examples/carrots_demo/carrots.py

@@ -11,13 +11,13 @@
 import statistics as s
 from pathlib import Path
 
-import pandas as pd
-import pydp as dp  # our privacy library
+import pandas as pd  # type: ignore
+import pydp as dp  # this library
 from pydp.algorithms.laplacian import BoundedSum, BoundedMean, Count, Max
 
-# Creating a class ClassReporter
-
+from typing import Union
 
+# Creating a class ClassReporter
 class CarrotReporter:
 
     # Function to read the csv file and creating a dataframe
@@ -62,14 +62,16 @@ def private_mean(self, privacy_budget: float) -> float:
         return x.quick_result(list(self._df["carrots_eaten"]))
 
     # Function to return the DP count of the number of animals who ate more than "limit" carrots.
-    def private_count_above(self, privacy_budget: float, limit: int) -> int:
+    def private_count_above(
+        self, privacy_budget: float, limit: int
+    ) -> Union[int, float]:
         x = Count(privacy_budget, dtype="int")
         return x.quick_result(
             list(self._df[self._df.carrots_eaten > limit]["carrots_eaten"])
         )
 
     # Function to return the DP maximum of the number of carrots eaten by any one animal.
-    def private_max(self, privacy_budget: float) -> int:
+    def private_max(self, privacy_budget: float) -> Union[int, float]:
         # 0 and 150 are the upper and lower limits for the search bound.
         x = Max(privacy_budget, 0, 150, dtype="int")
         return x.quick_result(list(self._df["carrots_eaten"]))

examples/restaurant_demo/restaurant.py

@@ -3,7 +3,7 @@
 
 import math
 import statistics as s
-import pandas as pd
+import pandas as pd  # type: ignore
 from collections import defaultdict
 
 # Assumptions:

pydp/algorithms/_algorithm.py

@@ -1,6 +1,6 @@
 import math
 
-from .._pydp import _algorithms
+from .._pydp import _algorithms  # type: ignore
 
 from typing import Union, List
 

pydp/algorithms/laplacian/_percentile.py

@@ -28,4 +28,4 @@ def percentile(self) -> float:
         """
         percentile Gets the value that was set in the constructor.
         """
-        return self._MetaAlgorithm__algorithm.percentile
+        return self._MetaAlgorithm__algorithm.percentile  # type: ignore

pydp/distributions/__init__.py

@@ -1 +1 @@
-from .._pydp._distributions import *
+from .._pydp._distributions import *  # type: ignore

pydp/util/__init__.py

@@ -1 +1 @@
-from .._pydp._util import *
+from .._pydp._util import *  # type: ignore

tests/base/test_logging.py

@@ -1,5 +1,5 @@
-import pytest
-import pydp as dp
+import pytest  # type: ignore
+import pydp as dp  # type: ignore
 
 # TODO: Check whether we should delete logging public binding or allow it
 pytestmark = pytest.mark.skip(reason="we do not return allow user to set up logging.")

tests/base/test_percentile.py

@@ -1,5 +1,5 @@
-import pytest
-import pydp as dp
+import pytest  # type: ignore
+import pydp as dp  # type: ignore
 
 # TODO: check whether to delete this test suit or update it
 pytestmark = pytest.mark.skip(

tests/base/test_status.py

@@ -1,6 +1,6 @@
-import pytest
+import pytest  # type: ignore
 
-import pydp as dp
+import pydp as dp  # type: ignore
 
 # TODO: Check whether we should delete status public bindings or return status to the user
 pytestmark = pytest.mark.skip(reason="we do not return status to the user.")