EHN: New metrics (#204)

Author: glemaitre

doc/api.rst

@@ -119,6 +119,26 @@ Functions
 
    pipeline.make_pipeline
 
+.. _metrics_ref:
+
+Metrics
+=======
+
+.. automodule:: imblearn.metrics
+   :no-members:
+   :no-inherited-members:
+
+.. currentmodule:: imblearn
+
+Functions
+---------
+.. autosummary::
+:toctree: generated/
+   metrics.sensitivity_specificity_support
+   metrics.sensitivity_score
+   metrics.specificity_score
+   metrics.geometric_mean_score
+   metrics.make_index_balanced_accuracy
 
 .. _datasets_ref:
 

doc/whats_new.rst

@@ -29,6 +29,7 @@ New features
 ~~~~~~~~~~~~
 
 - Added AllKNN under sampling technique. By `Dayvid Oliveira`_.
+- Added a module `metrics` implementing some specific scoring function for the problem of balancing. By `Guillaume Lemaitre`_ and `Christos Aridas`_.
 
 Enhancement
 ~~~~~~~~~~~

imblearn/__init__.py

@@ -10,6 +10,9 @@
 ensemble
     Module which provides methods generating an ensemble of
     under-sampled subsets.
+metrics
+    Module which provides metrics to quantified the classification performance
+    with imbalanced dataset.
 over_sampling
     Module which provides methods to under-sample a dataset.
 under-sampling
@@ -31,6 +34,6 @@
 
 # list all submodules available in imblearn and version
 __all__ = [
-    'combine', 'ensemble', 'over_sampling', 'under_sampling', 'pipeline',
-    '__version__'
+    'combine', 'ensemble', 'metrics', 'over_sampling', 'under_sampling',
+    'pipeline', '__version__'
 ]

imblearn/metrics/__init__.py

@@ -0,0 +1,17 @@
+"""
+The :mod:`imblearn.metrics` module includes score functions, performance
+metrics and pairwise metrics and distance computations.
+"""
+
+from .classification import sensitivity_specificity_support
+from .classification import sensitivity_score
+from .classification import specificity_score
+from .classification import geometric_mean_score
+from .classification import make_index_balanced_accuracy
+from .classification import classification_report_imbalanced
+
+__all__ = [
+    'sensitivity_specificity_support', 'sensitivity_score',
+    'specificity_score', 'geometric_mean_score',
+    'make_index_balanced_accuracy', 'classification_report_imbalanced'
+]