reduce sphinx warnings (#933)

Author: mfeurer

doc/api.rst

@@ -17,7 +17,7 @@ Classification
     :members:
     :inherited-members: show_models, fit_ensemble, refit, sprint_statistics
 
-.. autoclass:: autosklearn.experimental.AutoSklearn2Classifier
+.. autoclass:: autosklearn.experimental.askl2.AutoSklearn2Classifier
     :inherited-members: show_models, fit_ensemble, refit, sprint_statistics, fit, predict, predict_proba
 
 

doc/manual.rst

@@ -121,7 +121,7 @@ to build an ensemble based on the models’ prediction for the validation set. T
 
 * ``ensemble_size`` determines the maximal size of the ensemble. If it is set to zero, no ensemble will be constructed.
 * ``ensemble_nbest`` allows the user to directly specify the number of models considered for the ensemble.  This hyperparameter can be an integer *n*, such that only the best *n* models are used in the final ensemble. If a float between 0.0 and 1.0 is provided, ``ensemble_nbest`` would be interpreted as a fraction suggesting the percentage of models to use in the ensemble building process (namely, if ensemble_nbest is a float, library pruning is implemented as described in `Caruana et al. (2006) <https://dl.acm.org/doi/10.1109/ICDM.2006.76>`_).
- * ``max_models_on_disc`` defines the maximum number of models that are kept on the disc, as a mechanism to control the amount of disc space consumed by *auto-sklearn*. Throughout the automl process, different individual models are optimized, and their predictions (and other metadata) is stored on disc. The user can set the upper bound on how many models are acceptable to keep on disc, yet this variable takes priority in the definition of the number of models used by the ensemble builder (that is, the minimum of ``ensemble_size``, ``ensemble_nbest`` and ``max_models_on_disc`` determines the maximal amount of models used in the ensemble). If set to None, this feature is disabled.
+* ``max_models_on_disc`` defines the maximum number of models that are kept on the disc, as a mechanism to control the amount of disc space consumed by *auto-sklearn*. Throughout the automl process, different individual models are optimized, and their predictions (and other metadata) is stored on disc. The user can set the upper bound on how many models are acceptable to keep on disc, yet this variable takes priority in the definition of the number of models used by the ensemble builder (that is, the minimum of ``ensemble_size``, ``ensemble_nbest`` and ``max_models_on_disc`` determines the maximal amount of models used in the ensemble). If set to None, this feature is disabled.
 
 Inspecting the results
 ======================

doc/releases.rst

@@ -17,8 +17,8 @@ Version 0.8
 * ADD #803: multi-output regression
 * ADD #893: new Auto-sklearn mode Auto-sklearn 2.0
 
-Contributors
-************
+Contributors v0.8.0
+*******************
 
 * Chu-Cheng Fu
 * Matthias Feurer
@@ -43,8 +43,8 @@ Version 0.7.1
 * FIX #876: allow Auto-sklearn model to be cloned
 * FIX #879: allow 1-D binary predictions
 
-Contributors
-************
+Contributors v0.7.1
+*******************
 
 * Matthias Feurer
 * Xiaodong DENG
@@ -67,8 +67,8 @@ Version 0.7.0
 * FIX #778: support for python 3.8
 * FIX #781: support for pandas 1.x
 
-Contributors
-************
+Contributors v0.7.0
+*******************
 
 * Andrew Nader
 * Gui Miotto
@@ -88,8 +88,8 @@ Version 0.6.0
 * FIX #680: Remove unnecessary print statement
 * FIX #600: Remove unnecessary warning
 
-Contributors
-************
+Contributors v0.6.0
+*******************
 
 * Guilherme Miotto
 * Matthias Feurer
@@ -103,8 +103,8 @@ Version 0.5.2
 * ADD #676: Allow brackets [ ] inside the temporary and output directory paths.
 * ADD #424: (Experimental) scripts to reproduce the results from the original Auto-sklearn paper.
 
-Contributors
-************
+Contributors v0.5.2
+*******************
 
 * Jin Woo Ahn
 * Herilalaina Rakotoarison
@@ -123,8 +123,8 @@ Version 0.5.1
 * FIX #626: Fixes an issue where losses were not minimized, but maximized.
 * MAINT #646: Do no longer restrict the numpy version to be less than 1.14.5.
 
-Contributors
-************
+Contributors v0.5.1
+*******************
 
 * Jin Woo Ahn
 * Taneli Mielikäinen
@@ -141,8 +141,8 @@ Version 0.5.0
 * TEST #614: Test installation with clean Ubuntu on travis-ci.
 * MAINT: Fixed broken link and typo in the documentation.
 
-Contributors
-************
+Contributors v0.5.0
+*******************
 
 * Mohd Shahril
 * Adrian
@@ -168,8 +168,8 @@ Version 0.4.2
 * MAINT: remove dependency on the six package
 * MAINT: upgrade to XGBoost 0.80
 
-Contributors
-************
+Contributors v0.4.2
+*******************
 
 * Taneli Mielikäinen
 * Matthias Feurer
@@ -201,8 +201,8 @@ Version 0.4.1
 * Travis-ci now runs flake8 to enforce pep8 style guide, and uses travis-ci instead of circle-ci
   for deployment.
 
-Contributors
-************
+Contributors v0.4.1
+*******************
 
 * Matthias Feurer
 * Manuel Streuhofer
@@ -249,8 +249,8 @@ Version 0.4.0
 * Safeguard Auto-sklearn against deleting directories it did not create (Issue
   `#317 <https://github.com/automl/auto-sklearn/issues/317>`_.
 
-Contributors
-************
+Contributors v0.4.0
+*******************
 
 * Matthias Feurer
 * kaa
@@ -278,8 +278,8 @@ Version 0.3.0
 * Allow passing an SMBO object into the ``AutoSklearnClassifier`` and
   ``AutoSklearnRegressor``.
 
-Contributors
-************
+Contributors v0.3.0
+*******************
 
 * Matthias Feurer
 * Jesper van Engelen
@@ -297,8 +297,8 @@ Version 0.2.1
 * Typo fixes in print statements.
 * New method to retrieve the models used in the final ensemble.
 
-Contributors
-************
+Contributors v0.2.1
+*******************
 
 * Matthias Feurer
 * Katharina Eggensperger
@@ -321,8 +321,8 @@ Version 0.2.0
 * *auto-sklearn* can use multiprocessing in calls to ``predict()`` and
   ``predict_proba``. By `Laurent Sorber <https://github.com/lsorber>`_.
 
-Contributors
-************
+Contributors v0.2.0
+*******************
 
 * Matthias Feurer
 * Katharina Eggensperger
@@ -334,8 +334,8 @@ Version 0.1.x
 
 There are no release notes for auto-sklearn prior to version 0.2.0.
 
-Contributors
-************
+Contributors v0.1.x
+*******************
 
 * Matthias Feurer
 * Katharina Eggensperger