Version 1.0.1 (#759)

Added licenses

Author: René Sass

changelog.md

@@ -1,3 +1,9 @@
+# 1.0.1
+
+## Minor Changes
+* Added license information to every file.
+* Fixed a display bug inside usage recommendation. 
+
 # 1.0.0
 
 The main purpose of this release is to be synchronized with our upcoming paper.

doc/conf.py

@@ -22,6 +22,9 @@
 import smac
 from smac.utils.io.cmd_reader import CMDReader
 
+__copyright__ = "Copyright 2021, AutoML.org Freiburg-Hannover"
+__license__ = "3-clause BSD"
+
 
 # If extensions (or modules to document with autodoc) are in another directory,
 # add these directories to sys.path here. If the directory is relative to the

doc/usage_recomendation.rst

@@ -8,22 +8,21 @@ Usage Recommendation
 To make the usage of *SMAC* as easy as possible, we provide several facades designed for these different use cases. Here we give some general recommendations on
 when to use which facade. These recommendations are based on our experience and technical limitations and is by far not intended to be complete:
 
-+-----------------------+----------------------+-------------------------+-------------------------+
-|                       | SMAC4BB              | SMAC4HPO (Hyper-        | SMAC4AC (Algorithm-     |
-|                       | (Black-Box)          | parameter Optimization) | Configuration)          |
-+=======================+======================+=========================+=========================+
-| # parameter           | low                  | low/medium/high         | low/medium/high         |
-+-----------------------+----------------------+-------------------------+-------------------------+
-| categorical parameter | yes                  | supported               | supported               |
-+-----------------------+----------------------+-------------------------+-------------------------+
-| conditional parameter | yes                  | supported               | supported               |
-+-----------------------+----------------------+-------------------------+-------------------------+
-| instances             | no                   | None or CV-folds        | yes                     |
-+-----------------------+----------------------+-------------------------+-------------------------+
-| stochasticity         | no                   | supported               | supported               |
-+-----------------------+----------------------+-------------------------+-------------------------+
-| objective             | any (except runtime) | e.g. validation loss    | e.g. runtime or quality |
-+-----------------------+----------------------+-------------------------+-------------------------+
++-----------------------+----------------------+-----------------------------------------+-----------------------------------+
+|                       | SMAC4BB (Black-Box)  | SMAC4HPO (Hyperparameter Optimization)  | SMAC4AC (Algorithm Configuration) |
++=======================+======================+=========================================+===================================+
+| # parameter           | low                  | low/medium/high                         | low/medium/high                   |
++-----------------------+----------------------+-----------------------------------------+-----------------------------------+
+| categorical parameter | yes                  | supported                               | supported                         |
++-----------------------+----------------------+-----------------------------------------+-----------------------------------+
+| conditional parameter | yes                  | supported                               | supported                         |
++-----------------------+----------------------+-----------------------------------------+-----------------------------------+
+| instances             | no                   | None or CV-folds                        | yes                               |
++-----------------------+----------------------+-----------------------------------------+-----------------------------------+
+| stochasticity         | no                   | supported                               | supported                         |
++-----------------------+----------------------+-----------------------------------------+-----------------------------------+
+| objective             | any (except runtime) | e.g. validation loss                    | e.g. runtime or quality           |
++-----------------------+----------------------+-----------------------------------------+-----------------------------------+
 
 Some examples of typical use cases:
 

examples/SMAC4BB/SMAC4BB_rosenbrock_example.py

@@ -16,6 +16,9 @@
 # Import SMAC-utilities
 from smac.scenario.scenario import Scenario
 
+__copyright__ = "Copyright 2021, AutoML.org Freiburg-Hannover"
+__license__ = "3-clause BSD"
+
 
 def rosenbrock_2d(x):
     """ The 2 dimensional Rosenbrock function as a toy model

examples/SMAC4HPO/SMAC4HPO_acq_rosenbrock_example.py

@@ -19,6 +19,9 @@
 # Import SMAC-utilities
 from smac.scenario.scenario import Scenario
 
+__copyright__ = "Copyright 2021, AutoML.org Freiburg-Hannover"
+__license__ = "3-clause BSD"
+
 
 def rosenbrock_2d(x):
     """ The 2 dimensional Rosenbrock function as a toy model

examples/SMAC4HPO/SMAC4HPO_gradientboosting_example.py

@@ -22,6 +22,9 @@
 from smac.facade.smac_hpo_facade import SMAC4HPO
 from smac.scenario.scenario import Scenario
 
+__copyright__ = "Copyright 2021, AutoML.org Freiburg-Hannover"
+__license__ = "3-clause BSD"
+
 
 # load data and split it into training and test dataset
 X, y = make_hastie_10_2(random_state=0)

examples/SMAC4HPO/SMAC4HPO_rosenbrock_example.py

@@ -16,6 +16,9 @@
 # Import SMAC-utilities
 from smac.scenario.scenario import Scenario
 
+__copyright__ = "Copyright 2021, AutoML.org Freiburg-Hannover"
+__license__ = "3-clause BSD"
+
 
 def rosenbrock_2d(x):
     """ The 2 dimensional Rosenbrock function as a toy model

examples/SMAC4HPO/SMAC4HPO_svm_example.py

@@ -25,6 +25,10 @@
 # Import SMAC-utilities
 from smac.scenario.scenario import Scenario
 
+__copyright__ = "Copyright 2021, AutoML.org Freiburg-Hannover"
+__license__ = "3-clause BSD"
+
+
 # We load the iris-dataset (a widely used benchmark)
 iris = datasets.load_iris()
 

examples/SMAC4MF/SMAC4MF_mlp_example.py

@@ -28,6 +28,10 @@
 from smac.facade.smac_mf_facade import SMAC4MF
 from smac.scenario.scenario import Scenario
 
+__copyright__ = "Copyright 2021, AutoML.org Freiburg-Hannover"
+__license__ = "3-clause BSD"
+
+
 digits = load_digits()
 
 

examples/SMAC4MF/SMAC4MF_sgd_example.py

@@ -34,6 +34,10 @@
 # Import SMAC-utilities
 from smac.scenario.scenario import Scenario
 
+__copyright__ = "Copyright 2021, AutoML.org Freiburg-Hannover"
+__license__ = "3-clause BSD"
+
+
 # We load the MNIST-dataset (a widely used benchmark) and split it into a list of binary datasets
 digits = datasets.load_digits()
 instances = [[str(a) + str(b)] for a, b in itertools.combinations(digits.target_names, 2)]