# How to run:
# 1) sudo apt-get install python-pip
# 2) pip install gpyopt
import high_dimensional_sampling as hds
from high_dimensional_sampling.optimisation import GPyOpt
procedure = GPyOpt(initial_design_numdata=5,
aquisition_type='EI',
exact_feval=True,
de_duplication=True,
num_cores=2,
max_iter=1000,
max_time=60,
eps=1e-6)
experiment = hds.OptimisationExperiment(procedure, './hds')
feeder = hds.functions.FunctionFeeder()
feeder.load_function('Rastrigin', {'dimensionality': 5})
for function in feeder:
experiment.run(function, finish_line=1000)
bestfit_logL_values = np.zeros(len(convthresh_values))
count = 0
for convthresh in convthresh_values:
RESULTS_FOLDER = "/home/user/high-dimensional-sampling/" + numdim + "D_diver_" + str(
convthresh) + "_" + str(numpoints)
procedure = optimisation.PyScannerBit(scanner="diver",
multinest_tol=0.5,
multinest_nlive=100,
polychord_tol=1.0,
polychord_nlive=20,
diver_convthresh=convthresh,
diver_np=numpoints,
twalk_sqrtr=1.05,
random_point_number=10000,
toy_mcmc_point_number=10,
badass_points=1000,
badass_jumps=10,
pso_np=400,
output_path=RESULTS_FOLDER)
experiment = hds.OptimisationExperiment(procedure, RESULTS_FOLDER)
# Perform experiment with function
for function in feeder:
#if MPI.COMM_WORLD.Get_rank() == 0:
experiment.run(function,
finish_line=int(MAXIMUM_NUMBER_OF_SAMPLES))
# Maximum number of samples to take
MAX_N_SAMPLES = int(1e7)
for function in functions:
# Create ParticleFilter instance
procedure = optimisation.ParticleFilter(seed_size=N_SEED,
iteration_size=N_ITERATION_STEP,
boundaries=np.array(function.ranges),
initial_width=1,
wc_decay_rate=input_width_decay,
wc_apply_every_n_iterations=1,
sc_scales_with_boundary=False,
sc_logarithmic=input_stdev_configs,
kc_survival_rate=input_survival_rates)
# Sample seed and get function values at that seed
seed_x = np.random.rand(N_SEED, input_dimensionality)
for i, d in enumerate(function.ranges):
width = d[1] - d[0]
seed_x[:, i] = seed_x[:, i] * width + d[0]
seed_y = function(seed_x)
procedure.set_seed(seed_x, seed_y)
# Format results folder
results_folder = RESULTS_FOLDER.format(input_dimensionality, input_width_decay, input_stdev_configs, input_survival_rates)
# Run experiment
experiment = hds.OptimisationExperiment(procedure, results_folder)
experiment.run(function, finish_line=int(MAX_N_SAMPLES))