hyper.rst

Hyperparameter Tuning

One of the most important aspects of machine learning is hyperparameter tuning. Many machine learning models have a number of hyperparameters that control aspects of the model. These hyperparameters typically cannot be learned directly by the same learning algorithm used for the rest of learning and have to be set in an alternate fashion. The dc.hyper module contains utilities for hyperparameter tuning.

DeepChem's hyperparameter optimzation algorithms are simple and run in single-threaded fashion. They are not intended to be production grade hyperparameter utilities, but rather useful first tools as you start exploring your parameter space. As the needs of your application grow, we recommend swapping to a more heavy duty hyperparameter optimization library.

Hyperparameter Optimization API

deepchem.hyper.HyperparamOpt

Grid Hyperparameter Optimization

This is the simplest form of hyperparameter optimization that simply involves iterating over a fixed grid of possible values for hyperaparameters.

deepchem.hyper.GridHyperparamOpt

Gaussian Process Hyperparameter Optimization

deepchem.hyper.GaussianProcessHyperparamOpt