Version 2.0.0a1

.github/workflows/dist.yml

@@ -9,12 +9,14 @@ on:
     branches:
       - main
       - development
+      - development-2.0
 
   # Trigger on a open/push to a PR targeting one of these branches
   pull_request:
     branches:
       - main
       - development
+      - development-2.0
 
 jobs:
   dist:
@@ -27,7 +29,7 @@ jobs:
     - name: Setup Python
       uses: actions/setup-python@v4
       with:
-        python-version: "3.9"
+        python-version: "3.10"
 
     - name: Build dist
       run: |

.github/workflows/docs.yml

@@ -11,12 +11,14 @@ on:
     branches:
       - main
       - development
+      - development-2.0
 
   # Trigger on a open/push to a PR targeting one of these branches
   pull_request:
     branches:
       - main
       - development
+      - development-2.0
 
 env:
   name: SMAC3
@@ -31,7 +33,7 @@ jobs:
     - name: Setup Python
       uses: actions/setup-python@v4
       with:
-        python-version: "3.9"
+        python-version: "3.10"
 
     - name: Install dependencies
       run: |

.github/workflows/pre-commit.yml

@@ -9,12 +9,14 @@ on:
     branches:
       - main
       - development
+      - development-2.0
 
   # When a push occurs on a PR that targets these branches
   pull_request:
     branches:
       - main
       - development
+      - development-2.0
 
 jobs:
   run-all-files:

.github/workflows/pytest.yml

@@ -9,12 +9,14 @@ on:
     branches:
       - main
       - development
+      - development-2.0
 
   # Triggers with push to a pr aimed at main
   pull_request:
     branches:
       - main
       - development
+      - development-2.0
 
   schedule:
     # Every day at 7AM UTC
@@ -43,7 +45,8 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.9"]
+        python-version: ["3.8", "3.9", "3.10"]
+        # python-version: ["3.8"]
         os: ["ubuntu-latest"]
 
     steps:
@@ -96,7 +99,8 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.7", "3.8", "3.9", "3.10"]
+        python-version: ["3.8", "3.9", "3.10"]
+        # python-version: ["3.8"]
         os: ["ubuntu-latest"]
 
     steps:
@@ -132,7 +136,8 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.7", "3.8", "3.9", "3.10"]
+        python-version: ["3.8", "3.9", "3.10"]
+        # python-version: ["3.8"]
         os: ["ubuntu-latest"]
 
     steps:

.gitignore

@@ -134,9 +134,13 @@ dmypy.json
 .pyre/
 
 *smac3-output_*
+*smac3_output*
 
 # macOS files
 .DS_Store
 
 # Remove docker files
-docker
+docker
+
+# Others
+src

MANIFEST.in

@@ -1,12 +1,6 @@
-# MANIFEST.in defines what files are included/excluded from a built pacakage
-# that gets installed through PyPI or through `pip install`
-# To modify, check out
-# https://packaging.python.org/en/latest/guides/using-manifest-in/
-
-# Include individual files
 include LICENSE.txt
-include requirements.txt
 include smac/py.typed  # This file is to export types
+include smac/logging.yml
 
 prune tests
 prune examples

README.md

@@ -7,27 +7,39 @@
 [![codecov
 Status](https://codecov.io/gh/automl/SMAC3/branch/master/graph/badge.svg)](https://codecov.io/gh/automl/SMAC3)
 
-SMAC is a tool for algorithm configuration to optimize the parameters of
-arbitrary algorithms, including hyperparameter optimization of Machine Learning algorithms. The main core consists of
-Bayesian Optimization in combination with an aggressive racing mechanism to
-efficiently decide which of two configurations performs better.
+SMAC is a tool for algorithm configuration to optimize the parameters of arbitrary algorithms, including hyperparameter 
+optimization of Machine Learning algorithms. The main core consists of Bayesian Optimization in combination with an 
+aggressive racing mechanism to efficiently decide which of two configurations performs better.
 
-SMAC3 is written in Python3 and continuously tested with Python 3.7, 3.8, 3.9, and 3.10. Its Random
+SMAC3 is written in Python3 and continuously tested with Python 3.8, 3.9, and 3.10. Its Random
 Forest is written in C++. In further texts, SMAC is representatively mentioned for SMAC3.
 
 > [Documention](https://automl.github.io/SMAC3)
 
 > [Roadmap](https://github.com/orgs/automl/projects/5/views/2)
 
 
+## Important: Changes in v2.0
+
+With the next big major release of SMAC, we drastically boosted the user experience by improving the APIs and how the 
+pipelining is done (see [changelog](CHANGELOG.md)). However, cleaning the code base, we removed the command-line 
+interface (calling a target function from a script is still supported), runtime optimization, and pSMAC. Also,
+ python 3.7 is not supported anymore. If you depend on those functionalities, we kindly ask you to keep using v1.4.
+
+We are excited to introduce the new major release and look forward to developing new features on the new code base. 
+We hope you enjoy this new user experience as much as we do. 🚀
+
+
 ## Installation
 
-Create a new environment with python 3.9 and make sure swig is installed either on your system or
+This instruction is for the installation on a Linux system, for Windows and Mac and further information see the [documention](https://automl.github.io/SMAC3/main/installation.html).
+
+Create a new environment with python 3.10 and make sure swig is installed either on your system or
 inside the environment. We demonstrate the installation via anaconda in the following:
 
 Create and activate environment:
 ```
-conda create -n SMAC python=3.9
+conda create -n SMAC python=3.10
 conda activate SMAC
 ```
 
@@ -44,56 +56,41 @@ pip install smac
 Or alternatively, clone the environment:
 ```
 git clone https://github.com/automl/SMAC3.git && cd SMAC3
-pip install -r requirements.txt
-pip install .
+pip install -e .[dev]
 ```
 
-We refer to the [documention](https://automl.github.io/SMAC3) for further installation options.
-
 
 ## Minimal Example
 
 ```py
-import numpy as np
-
-from sklearn.ensemble import RandomForestClassifier
-from ConfigSpace import ConfigurationSpace
-from ConfigSpace.hyperparameters import UniformIntegerHyperparameter
-from smac.facade.smac_bb_facade import SMAC4BB
-from smac.scenario.scenario import Scenario
-
-
-X_train, y_train = np.random.randint(2, size=(20, 2)), np.random.randint(2, size=20)
-X_val, y_val = np.random.randint(2, size=(5, 2)), np.random.randint(2, size=5)
+from ConfigSpace import Configuration, ConfigurationSpace
 
+import numpy as np
+from smac import HyperparameterOptimizationFacade, Scenario
+from sklearn import datasets
+from sklearn.svm import SVC
+from sklearn.model_selection import cross_val_score
 
-def train_random_forest(config):
-    model = RandomForestClassifier(max_depth=config["depth"])
-    model.fit(X_train, y_train)
+iris = datasets.load_iris()
 
-    # Define the evaluation metric as return
-    return 1 - model.score(X_val, y_val)
 
+def train(config: Configuration, seed: int = 0) -> float:
+    classifier = SVC(C=config["C"], random_state=seed)
+    scores = cross_val_score(classifier, iris.data, iris.target, cv=5)
+    return 1 - np.mean(scores)
 
-if __name__ == "__main__":
-    # Define your hyperparameters
-    configspace = ConfigurationSpace()
-    configspace.add_hyperparameter(UniformIntegerHyperparameter("depth", 2, 100))
 
-    # Provide meta data for the optimization
-    scenario = Scenario({
-        "run_obj": "quality",  # Optimize quality (alternatively runtime)
-        "runcount-limit": 10,  # Max number of function evaluations (the more the better)
-        "cs": configspace,
-    })
+configspace = ConfigurationSpace({"C": (0.100, 1000.0)})
 
-    smac = SMAC4BB(scenario=scenario, tae_runner=train_random_forest)
-    best_found_config = smac.optimize()
+# Scenario object specifying the optimization environment
+scenario = Scenario(configspace, deterministic=True, n_trials=200)
 
+# Use SMAC to find the best configuration/hyperparameters
+smac = HyperparameterOptimizationFacade(scenario, train)
+incumbent = smac.optimize()
 ```
 
-More examples can be found in the [documention](https://automl.github.io/SMAC3).
-
+More examples can be found in the [documention](https://automl.github.io/SMAC3/main/examples/).
 
 
 ## License
@@ -109,15 +106,18 @@ You should have received a copy of the 3-clause BSD license
 along with this program (see LICENSE file).
 If not, see [here](https://opensource.org/licenses/BSD-3-Clause).
 
+
 ## Miscellaneous
 
 SMAC3 is developed by the [AutoML Groups of the Universities of Hannover and
 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 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 our [JMLR paper](https://jmlr.org/papers/v23/21-0888.html):
+If you use SMAC in one of your research projects, please cite our 
+[JMLR paper](https://jmlr.org/papers/v23/21-0888.html):
 ```
 @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},
@@ -131,4 +131,4 @@ If you use SMAC in one of your research projects, please cite our [JMLR paper](h
 }
 ```
 
-Copyright (C) 2016-2022  [AutoML Group](http://www.automl.org/).
+Copyright (C) 2016-2022  [AutoML Group](http://www.automl.org).