This repository contains Python code for Bayesian optimization using Gaussian processes. It contains two directories:
python: Contains two python scriptsgp.pyandplotters.py, that contain the optimization code, and utility functions to plot iterations of the algorithm, respectively.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.
The signature of the optimization function is:
bayesian_optimisation(n_iters, sample_loss, bounds, x0=None, n_pre_samples=5,
gp_params=None, random_search=False, alpha=1e-5, epsilon=1e-7)and its docstring is:
bayesian_optimisation
Uses Gaussian Processes to optimise the loss function `sample_loss`.
Arguments:
----------
n_iters: integer.
Number of iterations to run the search algorithm.
sample_loss: function.
Function to be optimised.
bounds: array-like, shape = [n_params, 2].
Lower and upper bounds on the parameters of the function `sample_loss`.
x0: array-like, shape = [n_pre_samples, n_params].
Array of initial points to sample the loss function for. If None, randomly
samples from the loss function.
n_pre_samples: integer.
If x0 is None, samples `n_pre_samples` initial points from the loss function.
gp_params: dictionary.
Dictionary of parameters to pass on to the underlying Gaussian Process.
random_search: integer.
Flag that indicates whether to perform random search or L-BFGS-B optimisation
over the acquisition function.
alpha: double.
Variance of the error term of the GP.
epsilon: double.
Precision tolerance for floats.
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