deephyper.skopt.acquisition.gaussian_lcb(X, model, kappa=1.96, return_grad=False, deterministic=False)[source]#

Use the lower confidence bound to estimate the acquisition values.

The trade-off between exploitation and exploration is left to be controlled by the user through the parameter kappa.

  • X (array-like, shape (n_samples, n_features)) – Values where the acquisition function should be computed.

  • model (sklearn estimator that implements predict with return_std) – The fit estimator that approximates the function through the method predict. It should have a return_std parameter that returns the standard deviation.

  • kappa (float, default 1.96 or 'inf') – Controls how much of the variance in the predicted values should be taken into account. If set to be very high, then we are favouring exploration over exploitation and vice versa. If set to ‘inf’, the acquisition function will only use the variance which is useful in a pure exploration setting. Useless if method is not set to “LCB”.

  • return_grad (boolean, optional) – Whether or not to return the grad. Implemented only for the case where X is a single sample.


  • values (array-like, shape (X.shape[0],)) – Acquisition function values computed at X.

  • grad (array-like, shape (n_samples, n_features)) – Gradient at X.