Release/0.3.0 (#518)

* Bump to 0.3.0rc1

* Fix setup.py

* Bump to rc2

* Add accuracy version of default metrics

* Bump version number

CHANGELOG.md

@@ -3,7 +3,7 @@ All notable changes to this project will be documented in this file.
 
 The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
-## [Unreleased]
+## [0.3.0] - 2019-02-28
 ### Added
 - Added torchbearer.variational, a sub-package for implementations of state of the art variational auto-encoders
 - Added SimpleUniform and SimpleExponential distributions
@@ -20,7 +20,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Added documentation about serialization.
 - Added support for indefinite data loading. Iterators can now be run until complete independent of epochs or iterators can be refreshed during an epoch if complete. 
 - Added support for batch intervals in interval checkpointer
-- Added line magic ``%torchbearer notebook`` 
+- Added line magic ``%torchbearer notebook``
+- Added 'accuracy' variants of 'acc' default metrics
 ### Changed
 - Changed the default behaviour of the std metric to compute the sample std, in line with torch.std
 - Tqdm precision argument now rounds to decimal places rather than significant figures

setup.py

@@ -15,7 +15,7 @@
 setup(
     name='torchbearer',
     version=version_dict['__version__'],
-    packages=['torchbearer', 'torchbearer.metrics', 'torchbearer.callbacks', 'tests', 'tests.metrics', 'tests.callbacks'],
+    packages=['torchbearer', 'torchbearer.metrics', 'torchbearer.callbacks', 'torchbearer.variational', 'tests', 'tests.metrics', 'tests.callbacks', 'tests.variational'],
     url='https://github.com/ecs-vlc/torchbearer',
     download_url='https://github.com/ecs-vlc/torchbearer/archive/' + version_dict['__version__'] + '.tar.gz',
     license='GPL-3.0',

torchbearer/metrics/default.py

@@ -4,10 +4,10 @@
 following strings:
 
 - '`acc`' or '`accuracy`': The :class:`.DefaultAccuracy` metric
-- '`binary_acc`': The :class:`.BinaryAccuracy` metric
-- '`cat_acc`': The :class:`.CategoricalAccuracy` metric
-- '`top_5_acc`': The :class:`.TopKCategoricalAccuracy` metric
-- '`top_10_acc`': The :class:`.TopKCategoricalAccuracy` metric with k=10
+- '`binary_acc`' or '`binary_accuracy`': The :class:`.BinaryAccuracy` metric
+- '`cat_acc`' or '`cat_accuracy`': The :class:`.CategoricalAccuracy` metric
+- '`top_5_acc`' or '`top_5_accuracy`': The :class:`.TopKCategoricalAccuracy` metric
+- '`top_10_acc`' or '`top_10_accuracy`': The :class:`.TopKCategoricalAccuracy` metric with k=10
 - '`mse`': The :class:`.MeanSquaredError` metric
 - '`loss`': The :class:`.Loss` metric
 - '`epoch`': The :class:`.Epoch` metric

torchbearer/metrics/primitives.py

@@ -19,6 +19,7 @@ def super(_, obj):
     return old_super(obj.__class__, obj)
 
 
+@default_for_key('binary_accuracy')
 @default_for_key('binary_acc')
 @running_mean
 @mean
@@ -47,6 +48,7 @@ def process(self, *args):
         return torch.eq(y_pred, y_true).view(-1).float()
 
 
+@default_for_key('cat_accuracy')
 @default_for_key('cat_acc')
 @running_mean
 @mean
@@ -79,6 +81,8 @@ def process(self, *args):
         return (y_pred == y_true).float()
 
 
+@default_for_key('top_10_accuracy', k=10)
+@default_for_key('top_5_accuracy')
 @default_for_key('top_10_acc', k=10)
 @default_for_key('top_5_acc')
 @running_mean

torchbearer/version.py

@@ -1 +1 @@
-__version__ = '0.2.7.dev'
+__version__ = '0.3.0'