Run a larger benchmark¶
[1]:
# import olympus
from olympus import Olympus
[2]:
# create olympus
olymp = Olympus()
[3]:
from olympus import list_planners
list_planners()
[3]:
['BasinHopping',
'Cma',
'ConjugateGradient',
'DifferentialEvolution',
'Genetic',
'Gpyopt',
'Grid',
'Hyperopt',
'LatinHypercube',
'Lbfgs',
'ParticleSwarms',
'Phoenics',
'RandomSearch',
'Simplex',
'Slsqp',
'Snobfit',
'Sobol',
'SteepestDescent']
[4]:
planners=['Gpyopt', 'Hyperopt', 'ConjugateGradient']
[5]:
olymp.benchmark(dataset='alkox', planners=planners, num_iter=20)
[INFO] Loading emulator using a BayesNeuralNet model for the dataset alkox...
[INFO] Loading emulator using a BayesNeuralNet model for the dataset alkox...
[INFO] Loading emulator using a BayesNeuralNet model for the dataset alkox...
[INFO] Loading emulator using a BayesNeuralNet model for the dataset alkox...
[INFO] Loading emulator using a BayesNeuralNet model for the dataset alkox...
[INFO] Loading emulator using a BayesNeuralNet model for the dataset alkox...
[INFO] Loading emulator using a BayesNeuralNet model for the dataset alkox...
[INFO] Loading emulator using a BayesNeuralNet model for the dataset alkox...
[INFO] Loading emulator using a BayesNeuralNet model for the dataset alkox...
[INFO] Loading emulator using a BayesNeuralNet model for the dataset alkox...
[INFO] Loading emulator using a BayesNeuralNet model for the dataset alkox...
[INFO] Loading emulator using a BayesNeuralNet model for the dataset alkox...
[INFO] Loading emulator using a BayesNeuralNet model for the dataset alkox...
[INFO] Loading emulator using a BayesNeuralNet model for the dataset alkox...
[INFO] Loading emulator using a BayesNeuralNet model for the dataset alkox...
Plot results¶
[6]:
from olympus import Plotter
[7]:
plotter = Plotter()
plotter.plot_from_db(olymp.evaluator.database)
[ ]: