I am using Gaussian Process Regressor to fit data for a Bayesian Optimiser. This is a relevant part of my Python code.
from sklearn.gaussian_process import GaussianProcessRegressor from sklearn.gaussian_process.kernels import (RBF, Matern, RationalQuadratic, ExpSineSquared, DotProduct, ConstantKernel) kernel = Matern(length_scale=lenSc,nu=2.5) gp = GaussianProcessRegressor(kernel=kernel) l2=10 Xi=np.concatenate((nprand.uniform(10.0,30.0,[l2,6]),nprand.uniform(-0.3,0.3,[l2,42]),nprand.uniform(0.0,30.0,[l2,1]),nprand.uniform(-30.0,30.0,[l2,1]),nprand.uniform(0.0,0.3,[l2,6]),nprand.uniform(5.0,20.0,[l2,6])),axis=1) yi=map(lambda x: f1(x), Xi) gp.fit(Xi, yi) while(1): #Some code to get the new values- cc and nv Xi=numpy.row_stack((Xi,cc)) yi=np.concatenate((yi,[nv])) gp.fit(Xi, yi)
Each iteration of the Bayesian Optimiser, I add a new element to $Xi$ and $yi$ and fit $gp$, the Gaussian Process, again. As the size of $Xi$ and $yi$ reach over 200, the time taken to fit the data becomes noticeable, this decreases the overall efficiency of my Bayesian Optimiser.
The Bayesian Optimiser has to make around 1000 iterations, each iteration needs to fit the data all over again. So, I was wondering if there's a way to incrementally fit the data? If that's not already being done by sklearn's GaussianProcessRegressor.