{"id":13992719,"url":"https://github.com/thuijskens/bayesian-optimization","last_synced_at":"2025-07-22T16:31:29.664Z","repository":{"id":55638855,"uuid":"75725827","full_name":"thuijskens/bayesian-optimization","owner":"thuijskens","description":"Python code for bayesian optimization using Gaussian processes","archived":false,"fork":false,"pushed_at":"2016-12-06T14:53:44.000Z","size":695,"stargazers_count":306,"open_issues_count":4,"forks_count":127,"subscribers_count":13,"default_branch":"master","last_synced_at":"2024-08-10T14:11:28.720Z","etag":null,"topics":["gaussian-processes","hyperparameter-optimization","optimization"],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/thuijskens.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2016-12-06T11:39:47.000Z","updated_at":"2024-07-12T09:28:37.000Z","dependencies_parsed_at":"2022-08-15T05:10:36.182Z","dependency_job_id":null,"html_url":"https://github.com/thuijskens/bayesian-optimization","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/thuijskens%2Fbayesian-optimization","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/thuijskens%2Fbayesian-optimization/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/thuijskens%2Fbayesian-optimization/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/thuijskens%2Fbayesian-optimization/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/thuijskens","download_url":"https://codeload.github.com/thuijskens/bayesian-optimization/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":227133874,"owners_count":17735817,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["gaussian-processes","hyperparameter-optimization","optimization"],"created_at":"2024-08-09T14:02:06.283Z","updated_at":"2024-11-29T13:31:08.615Z","avatar_url":"https://github.com/thuijskens.png","language":"Jupyter Notebook","funding_links":[],"categories":["Jupyter Notebook"],"sub_categories":[],"readme":"# Bayesian optimization with Gaussian processes\n\nThis repository contains Python code for Bayesian optimization using Gaussian processes. It contains two directories:\n\n* `python`: Contains two python scripts `gp.py` and `plotters.py`, that contain the optimization code, and utility functions to plot iterations of the algorithm, respectively.\n* `ipython-notebooks`: Contains an IPython notebook that uses the Bayesian algorithm to tune the hyperparameters of a support vector machine on a dummy classification task.\n\nThe signature of the optimization function is:\n\n```python\nbayesian_optimisation(n_iters, sample_loss, bounds, x0=None, n_pre_samples=5,\n                      gp_params=None, random_search=False, alpha=1e-5, epsilon=1e-7)\n```\n\nand its docstring is:\n\n```\nbayesian_optimisation\n\n  Uses Gaussian Processes to optimise the loss function `sample_loss`.\n\n  Arguments:\n  ----------\n      n_iters: integer.\n          Number of iterations to run the search algorithm.\n      sample_loss: function.\n          Function to be optimised.\n      bounds: array-like, shape = [n_params, 2].\n          Lower and upper bounds on the parameters of the function `sample_loss`.\n      x0: array-like, shape = [n_pre_samples, n_params].\n          Array of initial points to sample the loss function for. If None, randomly\n          samples from the loss function.\n      n_pre_samples: integer.\n          If x0 is None, samples `n_pre_samples` initial points from the loss function.\n      gp_params: dictionary.\n          Dictionary of parameters to pass on to the underlying Gaussian Process.\n      random_search: integer.\n          Flag that indicates whether to perform random search or L-BFGS-B optimisation\n          over the acquisition function.\n      alpha: double.\n          Variance of the error term of the GP.\n      epsilon: double.\n          Precision tolerance for floats.\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fthuijskens%2Fbayesian-optimization","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fthuijskens%2Fbayesian-optimization","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fthuijskens%2Fbayesian-optimization/lists"}