Asklearn development with smac development test (#1187)

* Changes required to test if will work with smac@development

* Changes required to test if will work with smac@development

* Fixed failing tests with new scipy 1.7 on sparse data

* flake8'd

* Use SMAC from pypi again

* undo changes

Co-authored-by: Matthias Feurer <feurerm@informatik.uni-freiburg.de>

Author: eddiebergman

doc/releases.rst

@@ -9,6 +9,38 @@
 Releases
 ========
 
+Version 0.13.0
+==============
+
+* ADD #1100: Provide access to the callbacks of SMAC.
+* ADD #1185: New leaderboard functionality to visualize models
+* FIX #1133: Refer to the correct attribute in an error message.
+* FIX #1154: Allow running Auto-sklearn on a 32-bit system.
+* MAINT #924: Instead of passing classes for the resampling strategy one has now to pass objects.
+* MAINT #1108: Limit the number of threads used by numpy and/or scikit-learn via `threadpoolctl`.
+* MAINT #1135: Simplify internal workflow of pandas handling. This results in pandas being passed
+  directly passed to scikit-learn models instead of being internally converted into a numpy array.
+  However, this should neither impact the behavior nor the performance of Auto-sklearn.
+* MAINT #1157: Drop support for Python 3.6, enable support for Python 3.9.
+* MAINT #1159: Remove the output directory argument to the classifier and regressor. Despite the
+  name, the output directory was not used and was a leftover from participating in the AutoML
+  challenges.
+* MAINT #1187: Bump requires SMAC version to at least 0.14.
+* DOC #1109: Add an FAQ.
+* DOC #1126: Add new examples on how to use scikit-learn's inspect module.
+* DOC #1136: Add a new example on how to perform multi-output regression.
+* DOC #1152: Enable link checking when buiding the documentation.
+* DOC #1158: New example on how to configure the logger for Auto-sklearn.
+* DOC #1165: Improve the readme page.
+
+Contributors v0.13.0
+********************
+
+* Matthias Feurer
+* Eddie Bergman
+* bitsbuffer
+* Francisco Rivera Valverde
+
 Version 0.12.7
 ==============
 

requirements.txt

@@ -2,7 +2,7 @@ setuptools
 typing_extensions
 
 numpy>=1.9.0
-scipy>=0.14.1,<1.7.0
+scipy>=1.7.0
 
 joblib
 scikit-learn>=0.24.0,<0.25.0
@@ -17,4 +17,4 @@ threadpoolctl
 ConfigSpace>=0.4.14,<0.5
 pynisher>=0.6.3
 pyrfr>=0.8.1,<0.9
-smac>=0.13.1,<0.14
+smac>=0.14

test/test_metalearning/pyMetaLearn/test_meta_features_sparse.py

@@ -256,28 +256,28 @@ def test_symbols_sum(sparse_data):
 
 def test_skewnesses(sparse_data_transformed):
     X_transformed, y, categorical_transformed = sparse_data_transformed
-    fixture = [0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0,
-               1.0, 0.0, -1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0,
-               0.0, 0.0, -1.0, 0.0, 0.0, 0.0,
-               -0.6969708499033568, 0.626346013011263,
-               0.3809987596624038, 1.4762248835141034,
-               0.07687661087633726, 0.36889797830360116]
+    fixture = [
+        0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
+        0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
+        -0.696970849903357, 0.626346013011262, 0.38099875966240554,
+        1.4762248835141032, 0.07687661087633788, 0.3688979783036015
+    ]
     mf = meta_features.helper_functions["Skewnesses"](X_transformed, y, logging.getLogger('Meta'))
     print(mf.value)
     print(fixture)
     np.testing.assert_allclose(mf.value, fixture)
 
 
 def test_kurtosisses(sparse_data_transformed):
-    fixture = [-3.0, -3.0, -2.0, -2.0, -3.0, -3.0, -3.0, -3.0,
-               -3.0, -2.0, -3.0, -2.0, -3.0, -3.0, -2.0, -3.0,
-               -3.0, -3.0, -3.0, -3.0, -3.0, -2.0, -3.0,
-               -3.0, -3.0, -1.1005836114255765,
-               -1.1786325509475712, -1.2387998382327912,
-               1.393438264413704, -0.9768209837948336,
-               -1.7937072296512782]
+    fixture = [
+        -3.0, -3.0, -3.0, -3.0, -3.0, -3.0, -3.0, -3.0, -3.0, -3.0, -3.0, -3.0,
+        -3.0, -3.0, -3.0, -3.0, -3.0, -3.0, -3.0, -3.0, -3.0, -3.0, -3.0, -3.0,
+        -3.0, -1.1005836114255763, -1.1786325509475744, -1.23879983823279,
+        1.3934382644137013, -0.9768209837948336, -1.7937072296512784
+    ]
     X_transformed, y, categorical_transformed = sparse_data_transformed
     mf = meta_features.helper_functions["Kurtosisses"](X_transformed, y, logging.getLogger('Meta'))
+    print(mf.value)
     np.testing.assert_allclose(mf.value, fixture)