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Questions tagged [gpyopt]

Questions about GPyOpt. A Python package for Gaussian process optimization using GPy. Performs global optimization with different acquisition functions. You can use GPyOpt to optimize physical experiments and tune the parameters of Machine Learning algorithms. It is able to handle large data sets via sparse Gaussian process models.

Questions about GPyOpt. A Python package for Gaussian process optimization using GPy. Performs global optimization with different acquisition functions. You can use GPyOpt to optimize physical experiments and tune the parameters of Machine Learning algorithms. It is able to handle large data sets via sparse Gaussian process models.

Read more about the package here

12 questions
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How do I add limiting conditions when using GpyOpt?

Currently I try to minimize the function and get optimized parameters using GPyOpt. import GPy import GPyOpt from math import log def f(x): x0,x1,x2,x3,x4,x5 = x[:,0],x[:,1],x[:,2],x[:,3],x[:,4],x[:,5], f0 = 0.2 * log(x0) f1 = 0.3 *…
takak
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GPyOpt - how to run a physical experiment?

I'm trying to do some physical experiments to find a formulation that optimizes some parameters. By physical experiments I mean I have a chemistry bench, I'm mixing stuff together, then measuring the properties of that formulation. Historically I've…
Mark
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How to combine plot of two GPy models?

I have calculated two GP regression models and would like to have them plotted in the same figure. Model 1 kernel = GPy.kern.RBF(input_dim=1, variance=.1, lengthscale=1.) m1 = GPy.models.GPRegression(xa, ya,kernel) m1.optimize_restarts(num_restarts…
hH1sG0n3
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Should "First Step" example of GPyOpt find minimum?

"First Step" page from GPyOpt shows pretty image, which looks like a minimum, found by code above Unfortunately, when I run the very same code, I get or i.e. vertical line very rarely goes to minimum. Is this my misunderstanding, or the library…
Dims
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GpyOpt ignores constraints. What is the correct way to specify them?

I want to run a constrained optimization in GpyOpt. Say, I want to minimize where s.t. at least one is non-zero and, and not more than 3 can be equal to 1. So I specify constraints: Based on the reference manual here, it looks like we can…
Nick To
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Multitask/multioutput GPy Coregionalized Regression with non-Gaussian Likelihood and Laplace inference function

I want to perform coregionalized regression in GPy, however I am using a Bernoulli likelihood and then to estimate that as a Gaussian, I use Laplace inference. The code below shows how I would usually run a single-output GP with this set up (with my…
gregory
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Python / GPyOpt: Optimizing only one argument

I´m currently trying to find the minimum of some function f(arg1, arg2, arg3, ...) via Gaussian optimization using the GPyOpt module. While f(...) takes many input arguments, I only want to optimize a single one of them. How do you do that? My…
Leander Moesinger
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GPy and GPflow mathematical background - references

Does GPy and GPflow share a common mathematical background? I'm asking this because I'm using GPy but I cannot see the references. However, GPflow provides references in its examples. Is it Ok using keep using GPy or would you suggest the use GPflow…
Nip
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Why the using optimized parameters (MSE is the minimize objective) in the XGBRegressor gives me different RMSE than the optimized RMSE?

I am dealing with a regressor problem. I am at the step to tune the parameters of XGBRegressor model so I use the library GPyOpt to get the optimized parameters. The functions returns an array of 5 elements and the minimized MSE which is 1813. Then…
Hang Nguyen
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GP Regression Model Predicting very similar numbers for all test inputs

I'm using doc2vec embeddings of amino acid sequences to try and predict kinetic rate. I've tried both standardising and not standardising my input vectors (X) but unless I standardise my output variable (kinetic rates), my GP model predicts very…
Hayley Smith
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GPyOpt get cost variance for optimum X

I've used GPyOpt to optimise a many-dimensional model opt = BayesianOptimization(f=my_eval_func, domain=domain, constraints=constraints) opt.run_optimization(max_iter=20) After doing so I get retrieve the optimal co-ordinates with opt.x_opt, and…
beldaz
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GPyOpt iteratively finding the maximum target function value; retrieve suggested next location

I just started to use GPy and GPyOpt. I aim to design an iterative process to find the position of x where the y is the maximum. The dummy x-array spans from 0 to 100 with a 0.5 step. The dummy y-array is the function of x-array. The true function…
Chenyang
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