updated docstrings

docs/conf.py

@@ -33,6 +33,8 @@
                         'sacred.observers',
                         'scipy',
                         'seaborn',
+                        'sklearn',
+                        'sklearn.metrics',
                         'skopt',
                         'scikitplot',
                         'scikitplot.metrics']
@@ -46,7 +48,7 @@
 # The short X.Y version
 version = '0.12'
 # The full version, including alpha/beta/rc tags
-release = '0.12.0'
+release = '0.12.1'
 
 # -- General configuration ---------------------------------------------------
 

docs/index.rst

@@ -1,5 +1,5 @@
 neptune-contrib: open-source contributions to Neptune.ml
-===========================================
+========================================================
 
 This library is a collection of helpers and extensions that make working
 with `Neptune app`_ more effective and better. It is build on top of neptune-client
@@ -65,15 +65,15 @@ And the best thing is you can extend it yourself or... tell us to do it for you
 
 
 Bug Reports and Questions
------------------------
+-------------------------
 
 neptune-contrib is MIT-licensed and the source code is available on `GitHub`_. If you
 find yourself in any trouble drop an isse on `Git Issues`_, fire a feature request on
 `Git Feature Request`_ or ask us on the `Neptune community forum`_ or `Neptune community spectrum`_.
 
 
 Contribute
------------------------
+----------
 
 We keep an updated list of open issues/feature ideas on github project page `Github projects`_.
 If you feel like taking a shot at one of those do go for it!

neptunecontrib/monitoring/metrics.py

@@ -28,13 +28,16 @@ def log_binary_classification_metrics(y_true, y_pred, threshold=0.5, experiment=
     """Creates metric chartsa and calculates classification metrics and logs them to Neptune.
 
     Class-based metrics that are logged: 'accuracy', 'precision', 'recall', 'f1_score', 'f2_score',
-       'matthews_corrcoef', 'cohen_kappa', 'true_positive_rate', 'true_negative_rate', 'positive_predictive_value',
-       'negative_predictive_value', 'false_positive_rate', 'false_negative_rate', 'false_discovery_rate'
+    'matthews_corrcoef', 'cohen_kappa', 'true_positive_rate', 'true_negative_rate', 'positive_predictive_value',
+    'negative_predictive_value', 'false_positive_rate', 'false_negative_rate', 'false_discovery_rate'
     For each class-based metric, a curve with metric/threshold is logged to 'metrics_by_threshold' channel.
+
     Losses that are logged: 'brier_loss', 'log_loss'
+
     Other metrics that are logged: 'roc_auc', 'ks_statistic', 'avg_precision'
+
     Curves that are logged: 'roc_auc', 'precision_recall_curve', 'ks_statistic_curve', 'cumulative_gain_curve',
-       'lift_curve',
+    'lift_curve',
 
     Args:
         y_true (array-like, shape (n_samples)): Ground truth (correct) target values.

setup.py

@@ -5,7 +5,8 @@ def main():
     extras = {
         'bots': ['python-telegram-bot'],
         'hpo': ['scikit-optimize==0.5.2', 'scipy'],
-        'monitoring': ['scikit-optimize==0.5.2', 'sacred==0.7.5', 'scikit-plot==0.3.7', 'seaborn==0.8.1'],
+        'monitoring': ['scikit-optimize==0.5.2', 'sacred==0.7.5', 'scikit-learn==0.21.3',
+                       'scikit-plot==0.3.7', 'seaborn==0.8.1'],
         'versioning': ['boto3', 'numpy'],
         'viz': ['altair==2.3.0'],
     }
@@ -19,7 +20,7 @@ def main():
 
     setup(
         name='neptune-contrib',
-        version='0.12.0',
+        version='0.12.1',
         description='Neptune Python library contributions',
         author='neptune.ml',
         author_email='[email protected]',