Add pep8-naming

sdmetrics/multi_table/detection/parent_child.py

@@ -4,11 +4,11 @@
 
 from sdmetrics.multi_table.detection.base import DetectionMetric
 from sdmetrics.single_table.detection import LogisticDetection, SVCDetection
-from sdmetrics.utils import NestedAttrsMeta
+from sdmetrics.utils import nested_attrs_meta
 
 
 class ParentChildDetectionMetric(DetectionMetric,
-                                 metaclass=NestedAttrsMeta('single_table_metric')):
+                                 metaclass=nested_attrs_meta('single_table_metric')):
     """Base class for Multi-table Detection metrics based on parent-child relationships.
 
     These metrics denormalize the parent-child relationships from the dataset and then

sdmetrics/multi_table/multi_single_table.py

@@ -6,10 +6,10 @@
 
 from sdmetrics import single_table
 from sdmetrics.multi_table.base import MultiTableMetric
-from sdmetrics.utils import NestedAttrsMeta
+from sdmetrics.utils import nested_attrs_meta
 
 
-class MultiSingleTableMetric(MultiTableMetric, metaclass=NestedAttrsMeta('single_table_metric')):
+class MultiSingleTableMetric(MultiTableMetric, metaclass=nested_attrs_meta('single_table_metric')):
     """MultiTableMetric subclass that applies a SingleTableMetric on each table.
 
     This class can either be used by creating a subclass that inherits from it and

sdmetrics/single_table/multi_column_pairs.py

@@ -6,10 +6,13 @@
 
 from sdmetrics import column_pairs
 from sdmetrics.single_table.base import SingleTableMetric
-from sdmetrics.utils import NestedAttrsMeta
+from sdmetrics.utils import nested_attrs_meta
 
 
-class MultiColumnPairsMetric(SingleTableMetric, metaclass=NestedAttrsMeta('column_pairs_metric')):
+class MultiColumnPairsMetric(
+    SingleTableMetric,
+    metaclass=nested_attrs_meta('column_pairs_metric')
+):
     """SingleTableMetric subclass that applies a ColumnPairsMetric on each possible column pair.
 
     Attributes:

sdmetrics/single_table/multi_single_column.py

@@ -5,11 +5,11 @@
 
 from sdmetrics import single_column
 from sdmetrics.single_table.base import SingleTableMetric
-from sdmetrics.utils import NestedAttrsMeta
+from sdmetrics.utils import nested_attrs_meta
 
 
 class MultiSingleColumnMetric(SingleTableMetric,
-                              metaclass=NestedAttrsMeta('single_column_metric')):
+                              metaclass=nested_attrs_meta('single_column_metric')):
     """SingleTableMetric subclass that applies a SingleColumnMetric on each column.
 
     This class can either be used by creating a subclass that inherits from it and

sdmetrics/single_table/privacy/numerical_sklearn.py

@@ -1,7 +1,7 @@
 import numpy as np
 from sklearn.linear_model import LinearRegression
 from sklearn.neural_network import MLPRegressor
-from sklearn.svm import SVR as svr
+from sklearn.svm import SVR
 
 from sdmetrics.single_table.privacy.base import NumericalPrivacyMetric, PrivacyAttackerModel
 
@@ -75,7 +75,7 @@ def fit(self, X, Y):
         n_labels = Y.shape[1]
         for idx in range(n_labels):
             Y_col = Y[:, idx]
-            predictor = svr()
+            predictor = SVR()
             predictor.fit(X, Y_col)
             self.predictors.append(predictor)
 

sdmetrics/utils.py

@@ -4,7 +4,7 @@
 from collections import Counter
 
 
-def NestedAttrsMeta(nested):
+def nested_attrs_meta(nested):
     """Metaclass factory that defines a Metaclass with a dynamic attribute name."""
 
     class Metaclass(type):

setup.cfg

@@ -34,6 +34,7 @@ universal = 1
 [flake8]
 max-line-length = 99
 exclude = docs, .tox, .git, __pycache__, .ipynb_checkpoints
+ignore-names = X, Y, X_train, X_test, Y_col
 
 [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',
+    'pep8-naming>=0.12.1,<0.13',
 
     # fix style issues
     'autoflake>=1.1,<2',