Metadata-Version: 2.1
Name: smac
Version: 0.12.1
Summary: SMAC3, a Python implementation of 'Sequential Model-based Algorithm Configuration'.
Home-page: UNKNOWN
Author: Marius Lindauer, Matthias Feurer, Katharina Eggensperger, Joshua Marben, André Biedenkapp, Aaron Klein,\
Author-email: "fh@cs.uni-freiburg.de"
License: 3-clause BSD
Description: # SMAC v3 Project
        
        Copyright (C) 2016-2018  [AutoML Group](http://www.automl.org/)
        
        __Attention__: This package is a re-implementation of the original SMAC tool
        (see reference below).
        However, the reimplementation slightly differs from the original SMAC.
        For comparisons against the original SMAC, we refer to a stable release of SMAC (v2) in Java
        which can be found [here](http://www.cs.ubc.ca/labs/beta/Projects/SMAC/).
        
        The documentation can be found [here](https://automl.github.io/SMAC3/).
        
        Status for master branch:
        
        [![Build Status](https://travis-ci.org/automl/SMAC3.svg?branch=master)](https://travis-ci.org/automl/SMAC3)
        [![Codacy Badge](https://api.codacy.com/project/badge/Grade/58f47a4bd25e45c9a4901ebca68118ff?branch=master)](https://www.codacy.com/app/automl/SMAC3?utm_source=github.com&amp;utm_medium=referral&amp;utm_content=automl/SMAC3&amp;utm_campaign=Badge_Grade)
        [![codecov Status](https://codecov.io/gh/automl/SMAC3/branch/master/graph/badge.svg)](https://codecov.io/gh/automl/SMAC3)
        
        Status for development branch
        
        [![Build Status](https://travis-ci.org/automl/SMAC3.svg?branch=development)](https://travis-ci.org/automl/SMAC3)
        [![Codacy Badge](https://api.codacy.com/project/badge/Grade/58f47a4bd25e45c9a4901ebca68118ff?branch=development)](https://www.codacy.com/app/automl/SMAC3?utm_source=github.com&amp;utm_medium=referral&amp;utm_content=automl/SMAC3&amp;utm_campaign=Badge_Grade)
        [![codecov](https://codecov.io/gh/automl/SMAC3/branch/development/graph/badge.svg)](https://codecov.io/gh/automl/SMAC3)
        
        # OVERVIEW
        
        SMAC is a tool for algorithm configuration to optimize the parameters of
        arbitrary algorithms across a set of instances. This also includes
        hyperparameter optimization of ML algorithms. The main core consists of
        Bayesian Optimization in combination with a aggressive racing mechanism to
        efficiently decide which of two configuration performs better.
        
        For a detailed description of its main idea,
        we refer to
        
            Hutter, F. and Hoos, H. H. and Leyton-Brown, K.
            Sequential Model-Based Optimization for General Algorithm Configuration
            In: Proceedings of the conference on Learning and Intelligent OptimizatioN (LION 5)
        
        
        SMAC v3 is written in Python3 and continuously tested with python3.5 and
        python3.6. Its [Random Forest](https://github.com/automl/random_forest_run)
        is written in C++.
        
        # Installation
        
        ## Requirements
        
        Besides the listed requirements (see `requirements.txt`), the random forest
        used in SMAC3 requires SWIG (>= 3.0, <4.0) as a build dependency:
        
        ```apt-get install swig```
        
        On Arch Linux (or any distribution with swig4 as default implementation):
        
        ```
        pacman -Syu swig3
        ln -s /usr/bin/swig-3 /usr/bin/swig
        ```
        
        ## Installation via pip
        
        SMAC3 is available on PyPI.
        
        ```pip install smac```
        
        ## Manual Installation
        
        ```
        git clone https://github.com/automl/SMAC3.git && cd SMAC3
        cat requirements.txt | xargs -n 1 -L 1 pip install
        pip install .
        ```
        
        ## Installation in Anaconda
        
        If you use Anaconda as your Python environment, you have to install three
        packages **before** you can install SMAC:
        
        ```conda install gxx_linux-64 gcc_linux-64 swig```
        
        ## Optional dependencies
        
        SMAC3 comes with a set of optional dependencies that can be installed using
        [setuptools extras](https://setuptools.readthedocs.io/en/latest/setuptools.html#declaring-extras-optional-features-with-their-own-dependencies):
        
        - `lhd`: Latin hypercube design
        - `gp`: Gaussian process models
        
        These can be installed from PyPI or manually:
        
        ```
        # from PyPI
        pip install smac[gp]
        
        # manually
        pip install .[gp,lhd]
        ```
        
        For convenience there is also an `all` meta-dependency that installs all optional dependencies:
        ```
        pip install smac[all]
        ```
        
        # 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 <https://opensource.org/licenses/BSD-3-Clause>.
        
        # USAGE
        
        The usage of SMAC v3 is mainly the same as provided with [SMAC v2.08](http://www.cs.ubc.ca/labs/beta/Projects/SMAC/v2.08.00/manual.pdf).
        It supports the same parameter configuration space syntax
        (except for extended forbidden constraints) and interface to
        target algorithms.
        
        # Examples
        
        See examples/
        
          * examples/rosenbrock.py - example on how to optimize a Python function
          * examples/spear_qcp/run.sh - example on how to optimize the SAT solver Spear
            on a set of SAT formulas
        
        # Contact
        
        SMAC3 is developed by the [AutoML Group of the University of Freiburg](http://www.automl.org/).
        
        If you found a bug, please report to <https://github.com/automl/SMAC3/issues>.
        
        Our guidelines for contributing to this package can be found [here](https://github.com/automl/SMAC3/blob/master/.github/CONTRIBUTING.md)
        
        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.
        
        
        
        
Keywords: machine learning,algorithm configuration,hyperparameter optimization,tuning
Platform: Linux
Classifier: Development Status :: 3 - Alpha
Classifier: Topic :: Utilities
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: License :: OSI Approved :: BSD License
Requires-Python: >=3.5.2
Description-Content-Type: text/markdown
Provides-Extra: gp
Provides-Extra: lhd
Provides-Extra: documentation
Provides-Extra: all
