[Fix] docs links

.github/workflows/examples.yml

@@ -30,8 +30,9 @@ jobs:
         echo "::set-output name=BEFORE::$(git status --porcelain -b)"
     - name: Run tests
       run: |
-        python examples/tabular/20_basics/example_tabular_classification.py
-        python examples/tabular/20_basics/example_tabular_regression.py
-        python examples/tabular/40_advanced/example_custom_configuration_space.py
-        python examples/tabular/40_advanced/example_resampling_strategy.py
-        python examples/example_image_classification.py
+        python examples/20_basics/example_image_classification.py
+        python examples/20_basics/example_tabular_classification.py
+        python examples/20_basics/example_tabular_regression.py
+        python examples/40_advanced/example_custom_configuration_space.py
+        python examples/40_advanced/example_resampling_strategy.py
+        python examples/40_advanced/example_visualization.py

README.md

@@ -7,7 +7,7 @@ So far, Auto-PyTorch supports tabular data (classification, regression).
 We plan to enable image data and time-series data.
 
 
-Find the documentation [here](https://automl.github.io/Auto-PyTorch/refactor_development)
+Find the documentation [here](https://automl.github.io/Auto-PyTorch/development)
 
 
 ## Installation
@@ -31,7 +31,7 @@ python setup.py install
 If you want to contribute to Auto-PyTorch, clone the repository and checkout our current development branch
 
 ```sh
-$ git checkout refactor_development
+$ git checkout development
 ```
 
 
@@ -71,7 +71,20 @@ along with this program (see LICENSE file).
 
 ## Reference
 
+Please refer to the branch `TPAMI.2021.3067763` to reproduce the paper *Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL*.
+
+```bibtex
+  @article{zimmer-tpami21a,
+  author = {Lucas Zimmer and Marius Lindauer and Frank Hutter},
+  title = {Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL},
+  journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
+  year = {2021},
+  note = {IEEE early access; also available under https://arxiv.org/abs/2006.13799},
+  pages = {1-12}
+}
 ```
+
+```bibtex
 @incollection{mendoza-automlbook18a,
   author    = {Hector Mendoza and Aaron Klein and Matthias Feurer and Jost Tobias Springenberg and Matthias Urban and Michael Burkart and Max Dippel and Marius Lindauer and Frank Hutter},
   title     = {Towards Automatically-Tuned Deep Neural Networks},
@@ -81,14 +94,10 @@ along with this program (see LICENSE file).
   booktitle = {AutoML: Methods, Sytems, Challenges},
   publisher = {Springer},
   chapter   = {7},
-  pages     = {141--156},
-  note      = {To appear.},
+  pages     = {141--156}
 }
 ```
 
-**Note**: Previously, the name of the project was AutoNet. Since this was too generic, we changed the name to AutoPyTorch. AutoNet 2.0 in the reference mention above is indeed AutoPyTorch.
-
-
 ## Contact
 
 Auto-PyTorch is developed by the [AutoML Group of the University of Freiburg](http://www.automl.org/).

docs/conf.py

@@ -16,6 +16,7 @@
 # add these directories to sys.path here. If the directory is relative to the
 # documentation root, use os.path.abspath to make it absolute, like shown here.
 
+import datetime
 import os
 import sys
 import sphinx_bootstrap_theme
@@ -68,9 +69,9 @@
 
 sphinx_gallery_conf = {
     # path to the examples
-    'examples_dirs': ['../examples/tabular/20_basics', '../examples/tabular/40_advanced'],
+    'examples_dirs': '../examples',
     # path where to save gallery generated examples
-    'gallery_dirs': ['basics_tabular', 'advanced_tabular'],
+    'gallery_dirs': 'examples',
     #TODO: fix back/forward references for the examples.
     #'doc_module': ('autoPyTorch'),
     #'reference_url': {
@@ -107,7 +108,8 @@
 
 # General information about the project.
 project = u'AutoPyTorch'
-copyright = u'2014-2019, Machine Learning Professorship Freiburg'
+copyright = u"2014-{}, Machine Learning Professorship Freiburg".format(
+    datetime.datetime.now().year)
 
 # The version info for the project you're documenting, acts as replacement for
 # |version| and |release|, also used in various other places throughout the

docs/manual.rst

@@ -16,11 +16,11 @@ We expand the support to image processing tasks in the future.
 
 Examples
 ========
-* `Classification <examples/tabular/20_basics/example_tabular_classification.html>`_
-* `Regression <examples/tabular/20_basics/example_tabular_regression.html>`_
-* `Customizing the search space <examples/tabular/40_advanced/example_custom_configuration_space.html>`_
-* `Changing the resampling strategy <examples/tabular/40_advanced/example_resampling_strategy.html>`_
-* `Visualizing the results <examples/tabular/40_advanced/example_visualization.html>`_
+* `Classification <examples/20_basics/example_tabular_classification.html>`_
+* `Regression <examples/20_basics/example_tabular_regression.html>`_
+* `Customizing the search space <examples/40_advanced/example_custom_configuration_space.html>`_
+* `Changing the resampling strategy <examples/40_advanced/example_resampling_strategy.html>`_
+* `Visualizing the results <examples/40_advanced/example_visualization.html>`_
 
 Resource Allocation
 ===================

examples/tabular/20_basics/example_tabular_classification.py → examples/20_basics/example_tabular_classification.py

@@ -40,11 +40,12 @@
     # Build and fit a classifier
     # ==========================
     api = TabularClassificationTask(
-        temporary_directory='./tmp/autoPyTorch_example_tmp_01',
-        output_directory='./tmp/autoPyTorch_example_out_01',
-        # To maintain logs of the run, set the next two as False
-        delete_tmp_folder_after_terminate=True,
-        delete_output_folder_after_terminate=True,
+        # To maintain logs of the run, you can uncomment the
+        # Following lines
+        # temporary_directory='./tmp/autoPyTorch_example_tmp_01',
+        # output_directory='./tmp/autoPyTorch_example_out_01',
+        # delete_tmp_folder_after_terminate=False,
+        # delete_output_folder_after_terminate=False,
         seed=42,
     )
 

examples/tabular/20_basics/example_tabular_regression.py → examples/20_basics/example_tabular_regression.py

@@ -48,13 +48,7 @@
     ############################################################################
     # Build and fit a regressor
     # ==========================
-    api = TabularRegressionTask(
-        temporary_directory='./tmp/autoPyTorch_example_tmp_02',
-        output_directory='./tmp/autoPyTorch_example_out_02',
-        # To maintain logs of the run, set the next two as False
-        delete_tmp_folder_after_terminate=True,
-        delete_output_folder_after_terminate=True
-    )
+    api = TabularRegressionTask()
 
     ############################################################################
     # Search for an ensemble of machine learning algorithms

examples/tabular/40_advanced/example_resampling_strategy.py → examples/40_advanced/example_resampling_strategy.py

@@ -43,11 +43,6 @@
     # Build and fit a classifier with default resampling strategy
     # ===========================================================
     api = TabularClassificationTask(
-        temporary_directory='./tmp/autoPyTorch_example_tmp_03',
-        output_directory='./tmp/autoPyTorch_example_out_03',
-        # To maintain logs of the run, set the next two as False
-        delete_tmp_folder_after_terminate=True,
-        delete_output_folder_after_terminate=True,
         # 'HoldoutValTypes.holdout_validation' with 'val_share': 0.33
         # is the default argument setting for TabularClassificationTask.
         # It is explicitly specified in this example for demonstrational
@@ -85,11 +80,6 @@
     # Build and fit a classifier with Cross validation resampling strategy
     # ====================================================================
     api = TabularClassificationTask(
-        temporary_directory='./tmp/autoPyTorch_example_tmp_04',
-        output_directory='./tmp/autoPyTorch_example_out_04',
-        # To maintain logs of the run, set the next two as False
-        delete_tmp_folder_after_terminate=True,
-        delete_output_folder_after_terminate=True,
         resampling_strategy=CrossValTypes.k_fold_cross_validation,
         resampling_strategy_args={'num_splits': 3}
     )
@@ -123,11 +113,6 @@
     # Build and fit a classifier with Stratified resampling strategy
     # ==============================================================
     api = TabularClassificationTask(
-        temporary_directory='./tmp/autoPyTorch_example_tmp_05',
-        output_directory='./tmp/autoPyTorch_example_out_05',
-        # To maintain logs of the run, set the next two as False
-        delete_tmp_folder_after_terminate=True,
-        delete_output_folder_after_terminate=True,
         # For demonstration purposes, we use
         # Stratified hold out validation. However,
         # one can also use CrossValTypes.stratified_k_fold_cross_validation.