gpCAM (gpcam.lbl.gov) is an API and software designed to make advanced Gaussian Process function approximation and autonomous data acquisition/Bayesian Optimization for experiments and simulations more accurate, faster, simpler, and more widely available. The tool is based on a flexible and powerful Gaussian process regression at the core. The flexibility stems from the modular design of gpCAM which allows the user to implement and import their own Python functions to customize and control almost every aspect of the software. That makes it possible to easily tune the algorithm to account for various kinds of physics and other domain knowledge and to identify and find interesting features, in Euclidean and non-Euclidean spaces. A specialized function optimizer in gpCAM can take advantage of HPC architectures for fast analysis time and reactive autonomous data acquisition. gpCAM broke a 2019 record for the largest exact GP ever run! Below you can see a simple example of how to set up an autonomous experimentation loop.
The following demonstrates a simple usage of the gpCAM API (see interactive demo).
!pip install gpcam
from gpCAM import GPOptimizer
my_gp = GPOptimizer(x_data,y_data,)
my_gp.train()
train_at = [10,20,30] #optional
for i in range(100):
new = my_gp.ask(np.array([[0.,1.]]))["x"]
my_gp.tell(new, f1(new).reshape(len(new)))
if i in train_at: my_gp.train()Main Developer: Marcus Noack ([email protected]) Many people from across the DOE national labs (especially BNL) have given insights that led to the code in it's current form. See AUTHORS for more details on that.