Code Documentation: Hyperparameter Optimization¶
mastml.hyper_opt Module¶
This module contains methods for optimizing hyperparameters of models
- HyperOptUtils:
- This class contains various helper utilities for setting up and running hyperparameter optimization
- GridSearch:
- This class performs a basic grid search over the parameters and value ranges of interest to find the best set of model hyperparameters in the provided grid of values
- RandomizedSearch:
- This class performs a randomized search over the parameters and value ranges of interest to find the best set of model hyperparameters in the provided grid of values. Often faster than GridSearch. Instead of a grid of values, it takes a probability distribution name as input (e.g. “norm”)
- BayesianSearch:
- This class performs a Bayesian search over the parameters and value ranges of interest to find the best set of model hyperparameters in the provided grid of values. Often faster than GridSearch.
Classes¶
BayesSearchCV (estimator, search_spaces[, …]) |
Bayesian optimization over hyper parameters. |
BayesianSearch (param_names, param_values[, …]) |
Class to conduct a Bayesian search to find optimized model hyperparameter values |
Categorical (categories[, prior, transform, name]) |
Search space dimension that can take on categorical values. |
GridSearch (param_names, param_values[, …]) |
Class to conduct a grid search to find optimized model hyperparameter values |
GridSearchCV (estimator, param_grid, *[, …]) |
Exhaustive search over specified parameter values for an estimator. |
HyperOptUtils (param_names, param_values) |
Helper class providing useful methods for other hyperparameter optimization classes. |
Integer (low, high[, prior, base, transform, …]) |
Search space dimension that can take on integer values. |
Metrics (metrics_list[, metrics_type]) |
Class containing access to a wide range of metrics from scikit-learn and a number of MAST-ML custom-written metrics |
RandomizedSearch (param_names, param_values) |
Class to conduct a randomized search to find optimized model hyperparameter values |
RandomizedSearchCV (estimator, …[, n_iter, …]) |
Randomized search on hyper parameters. |
Real (low, high[, prior, base, transform, …]) |
Search space dimension that can take on any real value. |
SklearnModel (model, **kwargs) |
Class to wrap any sklearn estimator, and provide some new dataframe functionality |