Version 1.0.1 (#759)

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automl · Aug 6, 2021 · 407b7c9 · 407b7c9
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diff --git a/changelog.md b/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.

diff --git a/doc/conf.py b/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

diff --git a/doc/usage_recomendation.rst b/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:
 

diff --git a/examples/SMAC4BB/SMAC4BB_rosenbrock_example.py b/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

diff --git a/examples/SMAC4HPO/SMAC4HPO_acq_rosenbrock_example.py b/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

diff --git a/examples/SMAC4HPO/SMAC4HPO_gradientboosting_example.py b/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)

diff --git a/examples/SMAC4HPO/SMAC4HPO_rosenbrock_example.py b/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

diff --git a/examples/SMAC4HPO/SMAC4HPO_svm_example.py b/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()
 

diff --git a/examples/SMAC4MF/SMAC4MF_mlp_example.py b/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()
 
 

diff --git a/examples/SMAC4MF/SMAC4MF_sgd_example.py b/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)]

diff --git a/examples/function_minimization/fmin_rosenbrock_example.py b/examples/function_minimization/fmin_rosenbrock_example.py
@@ -9,6 +9,9 @@
 
 from smac.facade.func_facade import fmin_smac
 
+__copyright__ = "Copyright 2021, AutoML.org Freiburg-Hannover"
+__license__ = "3-clause BSD"
+
 
 def rosenbrock_2d(x):
     """ The 2 dimensional Rosenbrock function as a toy model

diff --git a/examples/hyperband/hyperband_mlp_example.py b/examples/hyperband/hyperband_mlp_example.py
@@ -28,6 +28,10 @@
 
 import warnings
 
+__copyright__ = "Copyright 2021, AutoML.org Freiburg-Hannover"
+__license__ = "3-clause BSD"
+
+
 digits = load_digits()
 
 

diff --git a/examples/parallel/fmin_rosenbrock_parallel_example.py b/examples/parallel/fmin_rosenbrock_parallel_example.py
@@ -22,6 +22,10 @@
 import os
 import sys
 
+__copyright__ = "Copyright 2021, AutoML.org Freiburg-Hannover"
+__license__ = "3-clause BSD"
+
+
 # https://sphinx-gallery.github.io/stable/faq.html#why-is-file-not-defined-what-can-i-use
 cwd = os.path.dirname(__file__) if '__file__' in globals() else os.getcwd()
 sys.path.append(os.path.join(cwd))

diff --git a/examples/parallel/mlp_from_cfg_func.py b/examples/parallel/mlp_from_cfg_func.py
@@ -7,6 +7,9 @@
 from sklearn.model_selection import cross_val_score, StratifiedKFold
 from sklearn.neural_network import MLPClassifier
 
+__copyright__ = "Copyright 2021, AutoML.org Freiburg-Hannover"
+__license__ = "3-clause BSD"
+
 
 # A common function to be optimized by a Real valued Intensifier
 digits = load_digits()

diff --git a/examples/parallel/parallel_sh_mlp_example.py b/examples/parallel/parallel_sh_mlp_example.py
@@ -27,6 +27,11 @@
 
 import os
 import sys
+
+__copyright__ = "Copyright 2021, AutoML.org Freiburg-Hannover"
+__license__ = "3-clause BSD"
+
+
 # https://sphinx-gallery.github.io/stable/faq.html#why-is-file-not-defined-what-can-i-use
 cwd = os.path.dirname(__file__) if '__file__' in globals() else os.getcwd()
 sys.path.append(os.path.join(cwd))

diff --git a/examples/parallel/rosenbrock_2d_delayed_func.py b/examples/parallel/rosenbrock_2d_delayed_func.py
@@ -1,5 +1,9 @@
 import time
 
+__copyright__ = "Copyright 2021, AutoML.org Freiburg-Hannover"
+__license__ = "3-clause BSD"
+
+
 # --------------------------------------------------------------
 # When working with multiprocessing, we need to provide a pickable
 # function and use __main__. Details can be found on:

diff --git a/examples/quickstart/branin/branin.py b/examples/quickstart/branin/branin.py
@@ -1,5 +1,8 @@
 import numpy as np
 
+__copyright__ = "Copyright 2021, AutoML.org Freiburg-Hannover"
+__license__ = "3-clause BSD"
+
 
 def branin(x):
     x1 = x[0]

diff --git a/examples/quickstart/branin/branin_fmin_example.py b/examples/quickstart/branin/branin_fmin_example.py
@@ -4,6 +4,10 @@
 from branin import branin
 from smac.facade.func_facade import fmin_smac
 
+__copyright__ = "Copyright 2021, AutoML.org Freiburg-Hannover"
+__license__ = "3-clause BSD"
+
+
 """
 Example for the use of fmin_smac, a basic SMAC-wrapper to optimize a
 function.

diff --git a/examples/quickstart/branin/cmdline_wrapper.py b/examples/quickstart/branin/cmdline_wrapper.py
@@ -2,6 +2,10 @@
 
 from branin import branin
 
+__copyright__ = "Copyright 2021, AutoML.org Freiburg-Hannover"
+__license__ = "3-clause BSD"
+
+
 """
 This is a wrapper used by SMAC to optimize parameters on the branin-function.
 

diff --git a/examples/quickstart/branin/restore_state.py b/examples/quickstart/branin/restore_state.py
@@ -9,6 +9,10 @@
 from smac.stats.stats import Stats
 from smac.utils.io.traj_logging import TrajLogger
 
+__copyright__ = "Copyright 2021, AutoML.org Freiburg-Hannover"
+__license__ = "3-clause BSD"
+
+
 """
 This file runs SMAC and then restores the run with an extended computation
 budget. This will also work for SMAC runs that have crashed and are continued.

diff --git a/examples/quickstart/spear_qcp/SMAC4AC_SH_spear_qcp.py b/examples/quickstart/spear_qcp/SMAC4AC_SH_spear_qcp.py
@@ -14,6 +14,9 @@
 from smac.intensification.successive_halving import SuccessiveHalving
 from smac.scenario.scenario import Scenario
 
+__copyright__ = "Copyright 2021, AutoML.org Freiburg-Hannover"
+__license__ = "3-clause BSD"
+
 
 if __name__ == '__main__':
     scenario = Scenario('scenario.txt')

diff --git a/scripts/smac-validate.py b/scripts/smac-validate.py
@@ -20,6 +20,10 @@
 from smac.utils.io.traj_logging import TrajLogger
 from smac.utils.validate import Validator
 
+__copyright__ = "Copyright 2021, AutoML.org Freiburg-Hannover"
+__license__ = "3-clause BSD"
+
+
 if __name__ == "__main__":
     parser = ArgumentParser(formatter_class=ArgumentDefaultsHelpFormatter)
     req_opts = parser.add_argument_group("Required Options")

diff --git a/setup.py b/setup.py
@@ -3,6 +3,9 @@
 import os
 from setuptools import setup
 
+__copyright__ = "Copyright 2021, AutoML.org Freiburg-Hannover"
+__license__ = "3-clause BSD"
+
 
 with open('requirements.txt') as fh:
     requirements = [line.strip() for line in fh.readlines()]

diff --git a/smac/__init__.py b/smac/__init__.py
@@ -4,6 +4,8 @@
 
 from smac.utils import dependencies
 
+__copyright__ = "Copyright 2021, AutoML.org Freiburg-Hannover"
+__license__ = "3-clause BSD"
 __version__ = '1.0.0'
 __author__ = 'Marius Lindauer, Katharina Eggensperger, Matthias Feurer, André Biedenkapp, ' \
              'Difan Deng, Carolin Benjamins, René Sass ' \

diff --git a/smac/callbacks.py b/smac/callbacks.py
@@ -4,6 +4,10 @@
     from smac.optimizer.smbo import SMBO
 from smac.runhistory.runhistory import RunInfo, RunValue
 
+__copyright__ = "Copyright 2021, AutoML.org Freiburg-Hannover"
+__license__ = "3-clause BSD"
+
+
 """Callbacks for SMAC.
 
 Callbacks allow customizing the behavior of SMAC to ones needs. Currently, the list of implemented callbacks is

diff --git a/smac/configspace/__init__.py b/smac/configspace/__init__.py
@@ -7,6 +7,10 @@
 from ConfigSpace.util import get_one_exchange_neighbourhood
 from smac.configspace.util import convert_configurations_to_array
 
+__copyright__ = "Copyright 2021, AutoML.org Freiburg-Hannover"
+__license__ = "3-clause BSD"
+
+
 __all__ = ["ConfigurationSpace",
            "Configuration",
            "Constant",

diff --git a/smac/configspace/util.py b/smac/configspace/util.py
@@ -1,9 +1,11 @@
 from typing import List
-
 import numpy as np
 
 from smac.configspace import Configuration
 
+__copyright__ = "Copyright 2021, AutoML.org Freiburg-Hannover"
+__license__ = "3-clause BSD"
+
 
 def convert_configurations_to_array(configs: List[Configuration]) -> np.ndarray:
     """Impute inactive hyperparameters in configurations with their default.

diff --git a/smac/epm/base_gp.py b/smac/epm/base_gp.py
@@ -10,6 +10,9 @@
 from sklearn.gaussian_process.kernels import Kernel, KernelOperator
 from sklearn.gaussian_process import GaussianProcessRegressor
 
+__copyright__ = "Copyright 2021, AutoML.org Freiburg-Hannover"
+__license__ = "3-clause BSD"
+
 
 class BaseModel(AbstractEPM):
 

diff --git a/smac/epm/base_imputor.py b/smac/epm/base_imputor.py
@@ -2,7 +2,6 @@
 
 import numpy as np
 
-
 __author__ = "Katharina Eggensperger"
 __copyright__ = "Copyright 2015, ML4AAD"
 __license__ = "3-clause BSD"

diff --git a/smac/epm/base_rf.py b/smac/epm/base_rf.py
@@ -11,6 +11,9 @@
     ConfigurationSpace,
 )
 
+__copyright__ = "Copyright 2021, AutoML.org Freiburg-Hannover"
+__license__ = "3-clause BSD"
+
 
 class BaseModel(AbstractEPM):
 

diff --git a/smac/epm/gaussian_process.py b/smac/epm/gaussian_process.py
@@ -12,6 +12,10 @@
 from sklearn.gaussian_process.kernels import Kernel
 from sklearn.gaussian_process import GaussianProcessRegressor
 
+__copyright__ = "Copyright 2021, AutoML.org Freiburg-Hannover"
+__license__ = "3-clause BSD"
+
+
 logger = logging.getLogger(__name__)
 
 

diff --git a/smac/epm/gaussian_process_mcmc.py b/smac/epm/gaussian_process_mcmc.py
@@ -20,6 +20,10 @@
 from sklearn.gaussian_process.kernels import Kernel
 from sklearn.gaussian_process import GaussianProcessRegressor
 
+__copyright__ = "Copyright 2021, AutoML.org Freiburg-Hannover"
+__license__ = "3-clause BSD"
+
+
 logger = logging.getLogger(__name__)
 
 

diff --git a/smac/epm/gp_base_prior.py b/smac/epm/gp_base_prior.py
@@ -6,6 +6,9 @@
 
 from smac.utils.constants import VERY_SMALL_NUMBER
 
+__copyright__ = "Copyright 2021, AutoML.org Freiburg-Hannover"
+__license__ = "3-clause BSD"
+
 
 class Prior(object):
 

diff --git a/smac/epm/gp_kernels.py b/smac/epm/gp_kernels.py
@@ -10,6 +10,10 @@
 
 from smac.epm.gp_base_prior import Prior
 
+__copyright__ = "Copyright 2021, AutoML.org Freiburg-Hannover"
+__license__ = "3-clause BSD"
+
+
 # This file contains almost no type annotations to simplify comparing it to the original scikit-learn version!
 
 
@@ -519,7 +523,6 @@ def _call(
         eval_gradient: bool = False,
         active: Optional[np.ndarray] = None,
     ) -> Union[np.ndarray, Tuple[np.ndarray, np.ndarray]]:
-
         """Return the kernel k(X, Y) and optionally its gradient.
 
         Parameters

diff --git a/smac/epm/rf_with_instances.py b/smac/epm/rf_with_instances.py
@@ -7,7 +7,6 @@
 from smac.epm.base_rf import BaseModel
 from smac.utils.constants import N_TREES, VERY_SMALL_NUMBER
 
-
 __author__ = "Aaron Klein"
 __copyright__ = "Copyright 2015, ML4AAD"
 __license__ = "3-clause BSD"