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Python code for bayesian optimization using Gaussian processes
How effective is the bayesian_optimization for finding constrained minima of functions compared to other methods, such as for example TNC and fmin_L_bfgs from scipy.optimize?
Are there examples of such applications?
In gp.py (when finding the next sample):
next_sample = sample_next_hyperparameter(expected_improvement, model, yp, greater_is_better=True, bounds=bounds, n_restarts=100)
Here greater_is_better
is True means that its trying to maximize the loss function instead of minimizing it.
In line 48:
expected_improvement[sigma == 0.0] == 0.0
It should be:
expected_improvement[sigma == 0.0] = 0.0
Hey love the implementation it is very clean.
I am having some issues making it work for integer-valued hyperparameters and am wondering if you have any suggestions?
Can you please specify under which license has the code been published?
Thanks,
Daniel
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