smac 1.1.1

SMAC3, a Python implementation of 'Sequential Model-based Algorithm Configuration'.

Sequential Model Algorithm Configuration (SMAC)

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 and 3.9. Its Random Forest is written in C++. In further texts, SMAC is representatively mentioned for SMAC3.

---- Documention ----

Installation

Create a new environment with python 3.9 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 activate SMAC

Install swig:

conda install gxx_linux-64 gcc_linux-64 swig

Install SMAC via PyPI:

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 .

We refer to the documention for further installation options.

Minimal Example

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)


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
    return 1 - model.score(X_val, y_val)


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,
    })

    smac = SMAC4BB(scenario=scenario, tae_runner=train_random_forest)
    best_found_config = smac.optimize()

More examples can be found in the documention.

License

This program is free software: you can redistribute it and/or modify it under the terms of the 3-clause BSD license (please see the LICENSE file).

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.

You should have received a copy of the 3-clause BSD license along with this program (see LICENSE file). If not, see here.

Miscellaneous

SMAC3 is developed by the AutoML Groups of the Universities of Hannover and Freiburg.

If you have found a bug, please report to issues. Moreover, we are appreciating any kind of help. Find our guidlines for contributing to this package here.

If you use SMAC in one of your research projects, please cite us:

@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 René Sass and Frank Hutter},
      year={2021},
      eprint={2109.09831},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

Copyright (C) 2016-2021 AutoML Group. SMAC License

============

BSD 3-Clause License

Copyright (c) 2016-2018, Ml4AAD Group (http://www.ml4aad.org/) All rights reserved.

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

• Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.

• Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.

• Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

License of other files

======================

RoBO

Gaussian process files are built on code from RoBO and/or are copied from RoBO: https://github.com/automl/RoBO

smac/epm/gaussian_process.py smac/epm/gaussian_process_mcmc.py smac/epm/gp_base_prior.py smac/epm/gp_default_priors.py

License:

Copyright (c) 2015, automl All rights reserved.

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

• Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.

• Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.

• Neither the name of RoBO nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.