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[ascl:2205.008] Hyperopt: Distributed asynchronous hyper-parameter optimization

The Python library Hyperopt performs serial and parallel optimization over awkward search spaces, which may include real-valued, discrete, and conditional dimensions. Three algorithms are implemented in hyperopt: Random Search, Tree of Parzen Estimators (TPE), and Adaptive TPE. Algorithms can be parallelized in two ways, using either Apache Spark or MongoDB. To use Hyperopt, the objective function to minimize and the space over which to search, and the database in which to store all the point evaluations of the search have to be described, and the search algorithm to use has to be specified.

Code site:
https://github.com/hyperopt/hyperopt https://hyperopt.github.io/hyperopt/
Used in:
https://ui.adsabs.harvard.edu/abs/2018MNRAS.475.2326P
Described in:
https://proceedings.mlr.press/v28/bergstra13.html
Bibcode:
2022ascl.soft05008B

Views: 1426

ascl:2205.008
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