SKSurrogate is a suite of tools which implements surrogate optimization for expensive
functions based on scikit-learn. The main purpose of SKSurrogate is to facilitate
hyperparameter optimization for machine learning models and optimized pipeline design (AutoML).
The version of the surrogate optimization implemented here heavily relies on regressors.
A custom regressor based on Hilbert Space techniques is implemented, but all scikit-learn
regressors are accepted for optimization.
Finding an optimized pipeline -based on a given list of transformers and estimators is a time-consuming task. A version of evolutionary optimization has been implemented to reduce its time in lieu of global optimality.
SKSurrogate heavily depends on NumPy,
scipy, and scikit-learn,
for its main functionalities. Other dependencies include pandas,
matplotlib, SALib,
peewee, and
tqdm.
The documentation is produced by Sphinx and is intended to cover code usage as well as a bit of theory to explain each method briefly. For more details refer to the documentation at sksurrogate.rtfd.io.
This code is distributed under MIT license:
Copyright (c) 2019 Mehdi Ghasemi
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
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