Version 1.3.4 (#857)

* Added reference to JMLR paper.
* Typos in documentations.
* Code more readable since all typings are imported at the beginning of the file.
* Updated stale bot options.

Co-authored-by: eddiebergman <[email protected]>
Co-authored-by: Carolin Benjamins <[email protected]>
Co-authored-by: Matthias Feurer <[email protected]>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>

.github/dependabot.yml

@@ -0,0 +1,9 @@
+version: 2
+
+updates:
+  # This will check for updates to github actions every day
+  # https://docs.github.com/en/[email protected]/code-security/dependabot/working-with-dependabot/keeping-your-actions-up-to-date-with-dependabot
+  - package-ecosystem: "github-actions"
+    directory: "/"
+    schedule:
+      interval: "daily"

.github/stale.yml

@@ -6,11 +6,16 @@ daysUntilClose: 7
 
 # Issues with these labels will never be considered stale
 exemptLabels:
-  - pinned
-  - security
+  - bug
+  - dependency
+  - documentation
+  - enhancement
+  - feature
+  - test
+  - example
 
 # Label to use when marking an issue as stale
-staleLabel: wontfix
+staleLabel: stale
 
 # Comment to post when marking an issue as stale. Set to `false` to disable
 markComment: >

.github/workflows/citation.yml

@@ -11,7 +11,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Check out a copy of the repository
-        uses: actions/checkout@v2
+        uses: actions/checkout@v3
 
       - name: Check whether the citation metadata from CITATION.cff is valid
         uses: citation-file-format/[email protected]

.github/workflows/dist.yml

@@ -22,10 +22,10 @@ jobs:
 
     steps:
     - name: Checkout
-      uses: actions/checkout@v2
+      uses: actions/checkout@v3
 
     - name: Setup Python
-      uses: actions/setup-python@v2
+      uses: actions/setup-python@v4
       with:
         python-version: "3.9"
 

.github/workflows/docs.yml

@@ -26,10 +26,10 @@ jobs:
     runs-on: ubuntu-latest
     steps:
     - name: Checkout
-      uses: actions/checkout@v2
+      uses: actions/checkout@v3
 
     - name: Setup Python
-      uses: actions/setup-python@v2
+      uses: actions/setup-python@v4
       with:
         python-version: "3.9"
 

.github/workflows/examples.yml

@@ -26,9 +26,9 @@ jobs:
       fail-fast:  false
 
     steps:
-    - uses: actions/checkout@v2
+    - uses: actions/checkout@v3
     - name: Setup Python ${{ matrix.python-version }}
-      uses: actions/setup-python@v2
+      uses: actions/setup-python@v4
       with:
         python-version: ${{ matrix.python-version }}
 

.github/workflows/pre-commit.yml

@@ -21,12 +21,12 @@ jobs:
     runs-on: ubuntu-latest
 
     steps:
-    - uses: actions/checkout@v2
+    - uses: actions/checkout@v3
       with:
         submodules: recursive
 
     - name: Setup Python 3.9
-      uses: actions/setup-python@v2
+      uses: actions/setup-python@v4
       with:
         python-version: 3.9
 

.github/workflows/pytest.yml

@@ -48,10 +48,10 @@ jobs:
 
     steps:
     - name: Checkout
-      uses: actions/checkout@v2
+      uses: actions/checkout@v3
 
     - name: Setup Python
-      uses: actions/setup-python@v2
+      uses: actions/setup-python@v4
       with:
         python-version: ${{ matrix.python-version }}
 
@@ -101,7 +101,7 @@ jobs:
 
     steps:
     - name: Checkout
-      uses: actions/checkout@v2
+      uses: actions/checkout@v3
 
     - name: Conda install
       uses: conda-incubator/setup-miniconda@v2
@@ -137,10 +137,10 @@ jobs:
 
     steps:
     - name: Checkout
-      uses: actions/checkout@v2
+      uses: actions/checkout@v3
 
     - name: Setup Python
-      uses: actions/setup-python@v2
+      uses: actions/setup-python@v4
       with:
         python-version: ${{ matrix.python-version }}
 

README.md

@@ -66,20 +66,10 @@ X_val, y_val = np.random.randint(2, size=(5, 2)), np.random.randint(2, size=5)
 
 
 def train_random_forest(config):
-    """ 
-    Trains a random forest on the given hyperparameters, defined by config, and returns the accuracy
-    on the validation data.
-
-    Input:
-        config (Configuration): Configuration object derived from ConfigurationSpace.
-
-    Return:
-        cost (float): Performance measure on the validation data.
-    """
     model = RandomForestClassifier(max_depth=config["depth"])
     model.fit(X_train, y_train)
 
-    # define the evaluation metric as return
+    # Define the evaluation metric as return
     return 1 - model.score(X_val, y_val)
 
 
@@ -125,16 +115,18 @@ Freiburg](http://www.automl.org/).
 If you have found a bug, please report to [issues](https://github.com/automl/SMAC3/issues). Moreover, we are appreciating any kind of help.
 Find our guidlines for contributing to this package [here](https://github.com/automl/SMAC3/blob/master/.github/CONTRIBUTING.md).
 
-If you use SMAC in one of your research projects, please cite us:
+If you use SMAC in one of your research projects, please cite our [JMLR paper](https://jmlr.org/papers/v23/21-0888.html):
 ```
-@misc{lindauer2021smac3,
-      title={SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization}, 
-      author={Marius Lindauer and Katharina Eggensperger and Matthias Feurer and André Biedenkapp and Difan Deng and Carolin Benjamins and Tim Ruhkopf and René Sass and Frank Hutter},
-      year={2021},
-      eprint={2109.09831},
-      archivePrefix={arXiv},
-      primaryClass={cs.LG}
+@article{JMLR:v23:21-0888,
+  author  = {Marius Lindauer and Katharina Eggensperger and Matthias Feurer and André Biedenkapp and Difan Deng and Carolin Benjamins and Tim Ruhkopf and René Sass and Frank Hutter},
+  title   = {SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization},
+  journal = {Journal of Machine Learning Research},
+  year    = {2022},
+  volume  = {23},
+  number  = {54},
+  pages   = {1--9},
+  url     = {http://jmlr.org/papers/v23/21-0888.html}
 }
 ```
 
-Copyright (C) 2016-2021  [AutoML Group](http://www.automl.org/).
+Copyright (C) 2016-2022  [AutoML Group](http://www.automl.org/).

changelog.md

@@ -1,3 +1,10 @@
+# 1.3.4
+* Added reference to JMLR paper.
+* Typos in documentations.
+* Code more readable since all typings are imported at the beginning of the file.
+* Updated stale bot options.
+
+
 # 1.3.3
 * Hotfix: Since multi-objective implementation depends on normalized costs, it now is ensured that the
 cached costs are updated everytime a new entry is added.

docs/details/callbacks.rst

@@ -9,15 +9,15 @@ How to add a new callback
 ^^^^^^^^^^^^^^^^^^^^^^^^^
 
 * Implement a callback class in ``smac/callbacks.py``. There are no restrictions on how such a
-callback must look like, but it is recommended to implement the main logic inside the `__call__`
-function, such as for example in ``IncorporateRunResultCallback``.
+  callback must look like, but it is recommended to implement the main logic inside the `__call__`
+  function, such as for example in ``IncorporateRunResultCallback``.
 
 * Add your callback to ``smac.smbo.optimizer.SMBO._callbacks``, using the name of your callback
-as the key, and an empty list as the value.
+  as the key, and an empty list as the value.
 
 * Add your callback to ``smac.smbo.optimizer.SMBO._callback_to_key``, using the callback class as
-the key, and the name as value (the name used in 2.).
+  the key, and the name as value (the name used in 2.).
 
 * Implement calling all registered callbacks at the correct place. This is as simple as 
-``for callback in self._callbacks['your_callback']: callback(*args, **kwargs)``, where you
-obviously need to change the callback name and signature.
+  ``for callback in self._callbacks['your_callback']: callback(*args, **kwargs)``, where you
+  obviously need to change the callback name and signature.

docs/index.rst

@@ -22,7 +22,7 @@ efficiently decide which of two configurations performs better.
 SMAC3 is written in Python3 and continuously tested with Python 3.7, 3.8 and 3.9. Its Random
 Forest is written in C++. In further texts, SMAC is representatively mentioned for SMAC3.
 
-If you use SMAC, please cite our paper:
+If you use SMAC, please cite our `JMLR paper <https://jmlr.org/papers/v23/21-0888.html>`_:
 
 .. code-block:: text
 

examples/python/plot_svm_eips.py

@@ -13,21 +13,25 @@
 logging.basicConfig(level=logging.INFO)
 
 import numpy as np
+from ConfigSpace.hyperparameters import (
+    CategoricalHyperparameter,
+    UniformFloatHyperparameter,
+)
 from sklearn import datasets, svm
 from sklearn.model_selection import cross_val_score
 
-from ConfigSpace.hyperparameters import UniformFloatHyperparameter, CategoricalHyperparameter
-
 from smac.configspace import ConfigurationSpace
+from smac.epm.uncorrelated_mo_rf_with_instances import (
+    UncorrelatedMultiObjectiveRandomForestWithInstances,
+)
 from smac.facade.smac_ac_facade import SMAC4AC
 
-# Import SMAC-utilities
-from smac.scenario.scenario import Scenario
-
 # EIPS related
 from smac.optimizer.acquisition import EIPS
 from smac.runhistory.runhistory2epm import RunHistory2EPM4EIPS
-from smac.epm.uncorrelated_mo_rf_with_instances import UncorrelatedMultiObjectiveRandomForestWithInstances
+
+# Import SMAC-utilities
+from smac.scenario.scenario import Scenario
 
 __copyright__ = "Copyright 2021, AutoML.org Freiburg-Hannover"
 __license__ = "3-clause BSD"
@@ -91,10 +95,10 @@ def svm_from_cfg(cfg):
     # It returns: Status, Cost, Runtime, Additional Infos
     def_value = svm_from_cfg(cs.get_default_configuration())
     print("Default Value: %.2f" % def_value)
-    
+
     # Optimize, using a SMAC-object
     print("Optimizing! Depending on your machine, this might take a few minutes.")
-    
+
     # Besides the kwargs used for initializing UncorrelatedMultiObjectiveRandomForestWithInstances,
     # we also need kwargs for initializing the model insides UncorrelatedMultiObjectiveModel
     model_kwargs = {"target_names": ["loss", "time"], "model_kwargs": {"seed": 1}}
@@ -105,7 +109,7 @@ def svm_from_cfg(cfg):
         model_kwargs=model_kwargs,
         tae_runner=svm_from_cfg,
         acquisition_function=EIPS,
-        runhistory2epm=RunHistory2EPM4EIPS
+        runhistory2epm=RunHistory2EPM4EIPS,
     )
 
     incumbent = smac.optimize()

setup.py

@@ -16,20 +16,6 @@
 
 
 def read_file(filepath: str) -> str:
-    """
-    Read in a files contents
-
-    Parameters
-    ----------
-    filepath : str
-        The name of the file.
-
-    Returns
-    -------
-    str
-        The contents of the file.
-    """
-
     with open(filepath, "r", encoding="utf-8") as fh:
         return fh.read()
 

smac/__init__.py

@@ -22,7 +22,7 @@
     Matthias Feurer, André Biedenkapp, Difan Deng, Carolin Benjamins, Tim Ruhkopf, René Sass
     and Frank Hutter
 """
-version = "1.3.3"
+version = "1.3.4"
 
 
 if os.name != "posix":

smac/epm/base_uncorrelated_mo_model.py

@@ -135,7 +135,7 @@ def _predict(self, X: np.ndarray, cov_return_type: Optional[str] = "diagonal_cov
         ----------
         X : np.ndarray of shape = [n_samples, n_features (config + instance
         features)]
-        cov_return_type: typing.Optional[str]
+        cov_return_type: Optional[str]
             Specifies what to return along with the mean. Refer ``predict()`` for more information.
 
         Returns

smac/epm/gaussian_process_mcmc.py

@@ -1,4 +1,4 @@
-import typing
+from typing import List, Optional, Tuple, Union, cast
 
 import logging
 import warnings
@@ -74,8 +74,8 @@ class GaussianProcessMCMC(BaseModel):
     def __init__(
         self,
         configspace: ConfigurationSpace,
-        types: typing.List[int],
-        bounds: typing.List[typing.Tuple[float, float]],
+        types: List[int],
+        bounds: List[Tuple[float, float]],
         seed: int,
         kernel: Kernel,
         n_mcmc_walkers: int = 20,
@@ -84,8 +84,8 @@ def __init__(
         normalize_y: bool = True,
         mcmc_sampler: str = "emcee",
         average_samples: bool = False,
-        instance_features: typing.Optional[np.ndarray] = None,
-        pca_components: typing.Optional[int] = None,
+        instance_features: Optional[np.ndarray] = None,
+        pca_components: Optional[int] = None,
     ):
         super().__init__(
             configspace=configspace,
@@ -101,7 +101,7 @@ def __init__(
         self.chain_length = chain_length
         self.burned = False
         self.burnin_steps = burnin_steps
-        self.models = []  # type: typing.List[GaussianProcess]
+        self.models = []  # type: List[GaussianProcess]
         self.normalize_y = normalize_y
         self.mcmc_sampler = mcmc_sampler
         self.average_samples = average_samples
@@ -155,15 +155,15 @@ def _train(self, X: np.ndarray, y: np.ndarray, do_optimize: bool = True) -> "Gau
                     # Initialize the walkers by sampling from the prior
                     dim_samples = []
 
-                    prior = None  # type: typing.Optional[typing.Union[typing.List[Prior], Prior]]
+                    prior = None  # type: Optional[Union[List[Prior], Prior]]
                     for dim, prior in enumerate(self._all_priors):
                         # Always sample from the first prior
                         if isinstance(prior, list):
                             if len(prior) == 0:
                                 prior = None
                             else:
                                 prior = prior[0]
-                        prior = typing.cast(typing.Optional[Prior], prior)
+                        prior = cast(Optional[Prior], prior)
                         if prior is None:
                             raise NotImplementedError()
                         else:
@@ -325,7 +325,7 @@ def _ll(self, theta: np.ndarray) -> float:
         else:
             return lml
 
-    def _ll_w_grad(self, theta: np.ndarray) -> typing.Tuple[float, np.ndarray]:
+    def _ll_w_grad(self, theta: np.ndarray) -> Tuple[float, np.ndarray]:
         """Returns the marginal log likelihood (+ the prior) for a hyperparameter configuration
         theta.
 
@@ -375,8 +375,8 @@ def _ll_w_grad(self, theta: np.ndarray) -> typing.Tuple[float, np.ndarray]:
             return lml, grad
 
     def _predict(
-        self, X_test: np.ndarray, cov_return_type: typing.Optional[str] = "diagonal_cov"
-    ) -> typing.Tuple[np.ndarray, np.ndarray]:
+        self, X_test: np.ndarray, cov_return_type: Optional[str] = "diagonal_cov"
+    ) -> Tuple[np.ndarray, np.ndarray]:
         r"""
         Returns the predictive mean and variance of the objective function
         at X average over all hyperparameter samples.
@@ -389,7 +389,7 @@ def _predict(
         ----------
         X_test: np.ndarray (N, D)
             Input test points
-        cov_return_type: typing.Optional[str]
+        cov_return_type: Optional[str]
             Specifies what to return along with the mean. Refer ``predict()`` for more information.
 
         Returns