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Merge 28ab278 into 75addb8
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glemaitre committed Dec 25, 2016
2 parents 75addb8 + 28ab278 commit 17209bc
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20 changes: 20 additions & 0 deletions doc/api.rst
Expand Up @@ -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_indexed_balanced_accuracy

.. _datasets_ref:

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1 change: 1 addition & 0 deletions doc/whats_new.rst
Expand Up @@ -28,6 +28,7 @@ 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
~~~~~~~~~~~
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7 changes: 5 additions & 2 deletions imblearn/__init__.py
Expand Up @@ -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
Expand All @@ -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__'
]
17 changes: 17 additions & 0 deletions 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_indexed_balanced_accuracy
from .classification import classification_report_imbalanced

__all__ = [
'sensitivity_specificity_support', 'sensitivity_score',
'specificity_score', 'geometric_mean_score',
'make_indexed_balanced_accuracy', 'classification_report_imbalanced'
]

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