#GPy parameters
kernel = 'Matern52'
lengthscale = 1.
multi_dim = True #Use multi-dimensional (16)
generate_tags = ['sgt' + str(energy[0]) + '-' + str(energy[1]), 'validation' + str(samples),
'single_lec_E_curve50/50' + str(lec_sampling) + str(lec_index)]
# Set up necessary classes)
param = Parameters(interval, samples, center_lecs=lec_center)
nsopt = NsoptCaller()
gauss = Gaussfit()
dm = Datamanager(echo=False)
if dm.num_matches(generate_tags) > 0:
answer = raw_input('Matching data for your tags already exist, add new data as well? (y for yes): ')
if answer == 'y':
continue_generate = True
def get_observable(energies, lecs):
"""Wrapper function to measure time with memory profiler."""
return nsopt.get_nsopt_observable(energies,LECM=lecs)
param.nbr_of_samples = samples
if lec_sampling == 'lhs':
print('lhs')
lecs = param.create_lhs_lecs(energy_interval=energy)
energies = lecs[:,-1]
lecs = lecs[:,0:-1]
from parameters import Parameters
from nsoptcaller import NsoptCaller
from gaussfit import Gaussfit
from datamanager import Datamanager
#from memory_profiler import profile
import numpy as np
import pickle
param = Parameters(interval, samples, center_lecs=lec_center)
nsopt = NsoptCaller()
gauss = Gaussfit()
dm = Datamanager(echo=False)
gauss.save_fig = save_fig
gauss.save_path = save_path
if dm.num_matches(training_tags) <= 0 or dm.num_matches(validation_tags) <= 0:
sys.exit('Check your tags. No matched found in database.')
else:
#Set empty arrays with zeros for training and validation data
train_obs = np.array([0])
train_energy = np.array([0])
train_lecs = np.array(np.zeros(LEC_LENGTH))
#Read database data with the specified tags
for row in dm.read(training_tags):
train_obs = np.vstack((train_obs, row.observable))
train_energy = np.vstack((train_energy, row.energy))
train_lecs = np.vstack((train_lecs, row.LECs))
# Clean up initialized zeros
train_obs = np.delete(train_obs, 0, 0)
return nsopt.get_nsopt_observable(energies,LECM=lecs)
@profile
def train_gp_model(train_obs, train_lecs):
"""Populate and optimize GP model."""
gauss.populate_gp_model(train_obs, train_lecs, rescale=rescale_data)
gauss.optimize()
@profile
def calculate_valid(val_lecs):
"""Wrapper function to measure time with memory profiler."""
mod_obs, mod_var = gauss.calculate_valid(val_lecs)
return mod_obs, mod_var
# Check if data tags is already in database
if generate_data and dm.num_matches(generate_tags) <= 0:
continue_generate = True
elif generate_data and dm.num_matches(generate_tags) > 0:
answer = raw_input('Matching data for your tags already exist, add new data as well? (y for yes): ')
if answer == 'y':
continue_generate = True
else:
continue_generate = False
# Generate
if continue_generate:
param.nbr_of_samples = samples
if lec_sampling == 'lhs':
print('lhs')
if energy_as_param: # Do we want to have energies samples as well?