fixed naming and docstrings (#52)

* bumped versions * fixed varname
neptune-ai · Sep 13, 2019 · f82e921 · f82e921
1 parent cf07a4a
commit f82e921
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Showing 3 changed files with 8 additions and 8 deletions.
diff --git a/docs/conf.py b/docs/conf.py
@@ -49,7 +49,7 @@
 # The short X.Y version
 version = '0.13'
 # The full version, including alpha/beta/rc tags
-release = '0.13.1'
+release = '0.13.2'
 
 # -- General configuration ---------------------------------------------------
 

diff --git a/neptunecontrib/monitoring/fairness.py b/neptunecontrib/monitoring/fairness.py
@@ -24,7 +24,7 @@
 from neptunecontrib.monitoring.utils import send_figure
 
 
-def log_fairness_classification_metrics(y_true, y_pred_class, sensitive,
+def log_fairness_classification_metrics(y_true, y_pred_class, sensitive_attributes,
                                         favorable_label, unfavorable_label,
                                         privileged_groups, unprivileged_groups,
                                         experiment=None, prefix=''):
@@ -47,7 +47,7 @@ def log_fairness_classification_metrics(y_true, y_pred_class, sensitive,
     Args:
         y_true (array-like, shape (n_samples)): Ground truth (correct) target values.
         y_pred_class (array-like, shape (n_samples)): Class predictions with values 0 or 1.
-        sensitive (pandas.DataFrame, shape (n_samples, k)): datafame containing only sensitive columns.
+        sensitive_attributes (pandas.DataFrame, shape (n_samples, k)): datafame containing only sensitive columns.
         favorable_label (str or int): label that is favorable, brings positive value to a person being classified.
         unfavorable_label (str or int): label that is unfavorable, brings positive value to a person being classified.
         privileged_groups (dict): dictionary with column names and list of values for those columns that
@@ -66,7 +66,7 @@ def log_fairness_classification_metrics(y_true, y_pred_class, sensitive,
 
             neptune.init()
             with neptune.create_experiment():
-                log_fairness_classification_metrics(y_test, y_test_pred_class, test['race'],
+                log_fairness_classification_metrics(y_test, y_test_pred_class, test[['race']],
                                                     favorable_label='granted_parole',
                                                     unfavorable_label='not_granted_parole',
                                                     privileged_groups={'race':['Caucasian']},
@@ -80,15 +80,15 @@ def log_fairness_classification_metrics(y_true, y_pred_class, sensitive,
 
     bias_info = {'favorable_label': favorable_label,
                  'unfavorable_label': unfavorable_label,
-                 'protected_columns': sensitive.columns.tolist()}
+                 'protected_columns': sensitive_attributes.columns.tolist()}
 
     privileged_info = _fmt_priveleged_info(privileged_groups, unprivileged_groups)
 
     data = pd.DataFrame()
     data['ground_truth'] = y_true.values
     data['prediction'] = y_pred_class.values
-    for col in sensitive.columns:
-        data[col] = sensitive[col].values
+    for col in sensitive_attributes.columns:
+        data[col] = sensitive_attributes[col].values
 
     ground_truth_test = _make_dataset(data, 'ground_truth', **bias_info, **privileged_info)
     prediction_test = _make_dataset(data, 'prediction', **bias_info, **privileged_info)

diff --git a/setup.py b/setup.py
@@ -20,7 +20,7 @@ def main():
 
     setup(
         name='neptune-contrib',
-        version='0.13.1',
+        version='0.13.2',
         description='Neptune Python library contributions',
         author='neptune.ml',
         author_email='[email protected]',