Prepare new release (#1081)

* prepare new release

* fix unit test

Author: mfeurer

autosklearn/__version__.py

@@ -1,4 +1,4 @@
 """Version information."""
 
 # The following line *must* be the last in the module, exactly as formatted:
-__version__ = "0.12.3dev"
+__version__ = "0.12.3"

doc/releases.rst

@@ -8,6 +8,30 @@
     git log 2e29eba.. --format="%aN <%aE>" --reverse | perl -e 'my %dedupe; while (<STDIN>) { print unless $dedupe{$_}++}'
 
 
+========
+Releases
+========
+
+Version 0.12.3
+==============
+
+* FIX #1061: Fixes a bug where the model could not be printed in a jupyter notebook.
+* FIX #1075: Fixes a bug where the ensemble builder would wrongly prune good models for loss
+  functions (i.e. functions that need to be minimized such as ``logloss`` or ``mean_squared_error``.
+* FIX #1079: Fixes a bug where ``AutoMLClassifier.cv_results`` and ``AutoMLRegressor.cv_results``
+  could rank results in opposite order for loss functions (i.e. functions that need to be minimized
+  such as ``logloss`` or ``mean_squared_error``.
+* FIX: Fixes a bug in offline meta-data generation that could lead to a deadlock.
+* MAINT #1076: Uses the correct multiprocessing context for computing meta-features
+* MAINT: Cleanup readme and main directory
+
+Contributors v0.12.3
+********************
+
+* Matthias Feurer
+* ROHIT AGARWAL
+* Francisco Rivera
+
 ========
 Releases
 ========

test/test_data/test_feature_validator.py

@@ -39,7 +39,7 @@ def input_data_featuretest(request):
         array[50, 0:5] = np.nan
         return array
     elif request.param == 'numpy_numericalonly_nan':
-        array = np.random.uniform(10, size=(100, 10))
+        array = np.random.uniform(10, size=(100, 10)).astype('float')
         array[50, 0:5] = np.nan
         # Somehow array is changed to dtype object after np.nan
         return array.astype('float')