Add flake8-quotes

sdmetrics/goal.py

@@ -10,6 +10,6 @@ class Goal(Enum):
     minimized, or maximized.
     """
 
-    IGNORE = "ignore"
-    MAXIMIZE = "maximize"
-    MINIMIZE = "minimize"
+    IGNORE = 'ignore'
+    MAXIMIZE = 'maximize'
+    MINIMIZE = 'minimize'

sdmetrics/single_table/bayesian_network.py

@@ -40,7 +40,7 @@ def _likelihoods(cls, real_data, synthetic_data, metadata=None, structure=None):
         try:
             from pomegranate import BayesianNetwork
         except ImportError:
-            raise ImportError("Please install pomegranate with `pip install pomegranate`")
+            raise ImportError('Please install pomegranate with `pip install pomegranate`')
 
         metadata = cls._validate_inputs(real_data, synthetic_data, metadata)
         structure = metadata.get('structure', structure)

sdmetrics/single_table/detection/sklearn.py

@@ -46,11 +46,11 @@ class LogisticDetection(ScikitLearnClassifierDetectionMetric):
     The output of the metric is one minus the average ROC AUC score obtained.
     """
 
-    name = "LogisticRegression Detection"
+    name = 'LogisticRegression Detection'
 
     @staticmethod
     def _get_classifier():
-        return LogisticRegression(solver="lbfgs")
+        return LogisticRegression(solver='lbfgs')
 
 
 class SVCDetection(ScikitLearnClassifierDetectionMetric):
@@ -62,7 +62,7 @@ class SVCDetection(ScikitLearnClassifierDetectionMetric):
     The output of the metric is one minus the average ROC AUC score obtained.
     """
 
-    name = "SVC Detection"
+    name = 'SVC Detection'
 
     @staticmethod
     def _get_classifier():

sdmetrics/single_table/privacy/base.py

@@ -14,8 +14,8 @@ class CategoricalType(Enum):
     The value can be one-hot-encoded, or coded as class number.
     """
 
-    CLASS_NUM = "Class_num"
-    ONE_HOT = "One_hot"
+    CLASS_NUM = 'Class_num'
+    ONE_HOT = 'One_hot'
 
 
 class CategoricalPrivacyMetric(SingleTableMetric):
@@ -320,7 +320,7 @@ def fit(self, synthetic_data, key_fields, sensitive_fields):
             sensitive_fields(list[str]):
                 The names of the sensitive columns.
         """
-        raise NotImplementedError("Please implement fit method of attackers")
+        raise NotImplementedError('Please implement fit method of attackers')
 
     def predict(self, key_data):
         """Make a prediction of the sensitive data given keys.
@@ -333,7 +333,7 @@ def predict(self, key_data):
             tuple:
                 The predicted sensitive data.
         """
-        raise NotImplementedError("Please implement predict method of attackers")
+        raise NotImplementedError('Please implement predict method of attackers')
 
     def score(self, key_data, sensitive_data):
         """Score based on the belief of the attacker, in the form P(sensitive_data|key|data).

setup.cfg

@@ -34,6 +34,7 @@ universal = 1
 [flake8]
 max-line-length = 99
 exclude = docs, .tox, .git, __pycache__, .ipynb_checkpoints
+inline-quotes = single
 
 [isort]
 include_trailing_comment = True

setup.py

@@ -51,6 +51,7 @@
     'flake8>=3.7.7,<4',
     'flake8-absolute-import>=1.0,<2',
     'isort>=4.3.4,<5',
+    'flake8-quotes>=3.3.0,<4',
 
     # fix style issues
     'autoflake>=1.1,<2',

tests/integration/single_column/statistical/test_cstest.py

@@ -5,7 +5,7 @@
 from sdmetrics.single_column.statistical.cstest import CSTest
 
 
-@pytest.mark.parametrize("array_like", [np.array, pd.Series])
+@pytest.mark.parametrize('array_like', [np.array, pd.Series])
 def test_max(array_like):
     data = array_like(['a', 'b', 'b', 'c', 'c', 'c'] * 100)
     output = CSTest.compute(data, data)
@@ -15,7 +15,7 @@ def test_max(array_like):
     assert normalized == 1
 
 
-@pytest.mark.parametrize("array_like", [np.array, pd.Series])
+@pytest.mark.parametrize('array_like', [np.array, pd.Series])
 def test_min(array_like):
     real = array_like(['a', 'b', 'b', 'c', 'c', 'c'] * 100)
     synth = array_like(['d', 'e', 'e', 'f', 'f', 'f'] * 100)
@@ -26,7 +26,7 @@ def test_min(array_like):
     assert normalized == 0
 
 
-@pytest.mark.parametrize("array_like", [np.array, pd.Series])
+@pytest.mark.parametrize('array_like', [np.array, pd.Series])
 def test_good(array_like):
     real = array_like(['a', 'b', 'b', 'c', 'c', 'c'] * 100)
     synth = array_like(['a', 'b', 'b', 'b', 'c', 'c'] * 100)
@@ -37,7 +37,7 @@ def test_good(array_like):
     assert 0.5 < normalized <= 1.0
 
 
-@pytest.mark.parametrize("array_like", [np.array, pd.Series])
+@pytest.mark.parametrize('array_like', [np.array, pd.Series])
 def test_bad(array_like):
     real = array_like(['a', 'b', 'b', 'c', 'c', 'c'] * 100)
     synth = array_like(['a', 'a', 'a', 'a', 'b', 'c'] * 100)

tests/integration/single_column/statistical/test_kstest.py

@@ -5,7 +5,7 @@
 from sdmetrics.single_column.statistical.kstest import KSTest
 
 
-@pytest.mark.parametrize("array_like", [np.array, pd.Series])
+@pytest.mark.parametrize('array_like', [np.array, pd.Series])
 def test_max(array_like):
     data = array_like(np.random.normal(size=1000))
     output = KSTest.compute(data, data)
@@ -15,7 +15,7 @@ def test_max(array_like):
     assert normalized == 1
 
 
-@pytest.mark.parametrize("array_like", [np.array, pd.Series])
+@pytest.mark.parametrize('array_like', [np.array, pd.Series])
 def test_min(array_like):
     real = array_like(np.random.normal(size=1000))
     synth = array_like(np.random.normal(loc=1000, scale=10, size=1000))
@@ -26,7 +26,7 @@ def test_min(array_like):
     assert normalized == 0
 
 
-@pytest.mark.parametrize("array_like", [np.array, pd.Series])
+@pytest.mark.parametrize('array_like', [np.array, pd.Series])
 def test_good(array_like):
     real = array_like(np.random.normal(size=1000))
     synth = array_like(np.random.normal(loc=0.1, size=1000))
@@ -37,7 +37,7 @@ def test_good(array_like):
     assert 0.5 < normalized <= 1.0
 
 
-@pytest.mark.parametrize("array_like", [np.array, pd.Series])
+@pytest.mark.parametrize('array_like', [np.array, pd.Series])
 def test_bad(array_like):
     real = array_like(np.random.normal(size=1000))
     synth = array_like(np.random.normal(loc=3, scale=3, size=1000))