Metadata-Version: 2.1
Name: BOHB_HPO
Version: 0.0.6
Summary: Bayesian Optimization Hyperband Hyperparameter Optimization
Home-page: https://github.com/goktug97/bohb_hpo
Author: Göktuğ Karakaşlı
Author-email: karakasligk@gmail.com
License: MIT
Description: Bayesian Optimization Hyperband Hyperparameter Optimization
        ===========================================================
        
        Implementation for [BOHB](http://proceedings.mlr.press/v80/falkner18a.html)
        
        ## Requirements
            - numpy
            - scipy
            - statsmodels
            - torch (example)
        
        ## Installation
        ```bash
        pip3 install bohb-hpo
        ```
        
        ## Usage
        
        ``` Python
        from bohb import BOHB
        import bohb.configspace as cs
        
        
        def objective(step, alpha, beta):
            return 1 / (alpha * step + 0.1) + beta
        
        
        def evaluate(params, n_iterations):
            loss = 0.0
            for i in range(int(n_iterations)):
                loss += objective(**params, step=i)
            return loss/n_iterations
        
        
        alpha = cs.CategoricalHyperparameter('alpha', [0.001, 0.01, 0.1])
        beta = cs.CategoricalHyperparameter('beta', [1, 2, 3])
        configspace = cs.ConfigurationSpace([alpha, beta], seed=123)
        
        opt = BOHB(configspace, evaluate, max_budget=30, min_budget=1)
        best = opt.optimize()
        ```
        
        See [examples](https://github.com/goktug97/bohb-hpo/tree/master/examples)
        
        ## TODO
            - Conditional Parameters
            - Parallel Optimization (Implemented but not working properly)
            - Better Logging
            - More Hyperparameters
        
        ## License
        bohb-hpo is licensed under the MIT License.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Requires-Python: >=3.7
Description-Content-Type: text/markdown
