Hyperopt Library

References

(Komer et al., 2014) ⇒ Brent Komer, James Bergstra, and Chris Eliasmith. “Hyperopt-sklearn: Automatic hyperparameter configuration for scikit-learn." In ICML workshop on AutoML. 2014.
- ABSTRACT: Hyperopt-sklearn is a new software project that provides automatic algorithm configuration of the Scikit-learn machine learning library. Following Auto-Weka, we take the view that the choice of classifier and even the choice of preprocessing module can be taken together to represent a single large hyper-parameter optimization problem. We use Hyperopt to define a search space that encompasses many standard components (e.g. SVM, RF, KNN, PCA, TFIDF) and common patterns of composing them together. We demonstrate, using search algorithms in Hyperopt and standard benchmarking data sets (MNIST, 20-Newsgroups, Convex Shapes), that searching this space is practical and effective. In particular, we improve on best-known scores for the model space for both MNIST and Convex Shapes.
- Index Terms: bayesian optimization, model selection, hyperparameter optimization, scikit-learn

(Bergstra et al., 2013) ⇒ James Bergstra, Dan Yamins, and David D. Cox. (2013). “Hyperopt: A python library for optimizing the hyperparameters of machine learning algorithms."
- ABSTRACT: Sequential model-based optimization (also known as Bayesian optimization) is one of the most efficient methods (per function evaluation) of function minimization. This efficiency makes it appropriate for optimizing the hyperparameters of machine learning algorithms that are slow to train. The Hyperopt library provides algorithms and parallelization infrastructure for performing hyperparameter optimization (model selection) in Python. This paper presents an introductory tutorial on the usage of the Hyperopt library, including the description of search spaces, minimization (in serial and parallel), and the analysis of the results collected in the course of minimization. The paper closes with some discussion of ongoing and future work.
- Index Terms: Bayesian optimization, hyperparameter optimization, model selection.