NJ_cut_test_treevals = np.load(data_dir + 'NJ_{}_cut_{}_{}_{}.npy'.format(
order, n_gluon + 2, delta_cut, test_points),
allow_pickle=True)
NJ_near_test_treevals = np.load(data_dir + 'NJ_{}_near_{}_{}_{}.npy'.format(
order, n_gluon + 2, delta_near, test_points),
allow_pickle=True)
_, pairs = D_ij(mom=test_near_momenta[0], n_gluon=n_gluon)
if cores == 0:
cores = len(pairs)
pairs, NJ_near_test_treevals_split = multiprocess_weighting(
test_near_momenta, n_gluon, NJ_near_test_treevals, cores)
NN = Model((n_gluon + 2 - 1) * 4, test_near_momenta,
NJ_near_test_treevals_split[0])
_, _, _, _, _, _, _, _ = NN.process_training_data()
model_nears = []
model_cuts = []
x_mean_nears = []
x_std_nears = []
y_mean_nears = []
y_std_nears = []
x_mean_cuts = []
x_std_cuts = []
y_mean_cuts = []
y_std_cuts = []
order, n_gluon + 2, delta, subset_points, i)
print('Looking for directory {}'.format(model_dir_new))
if os.path.exists(model_dir_new) == False:
os.mkdir(model_dir_new)
print('Directory created')
else:
print('Directory already exists')
print('Generating data subset')
indices = np.random.randint(0, points, subset_points)
subset_momenta = momenta[indices]
subset_momenta_list = subset_momenta.tolist()
subset_njet = NJ_treevals[indices]
assert len(subset_momenta) == len(subset_njet)
NN = Model((n_gluon + 2 - 1) * 4, subset_momenta_list, subset_njet)
model, x_mean, x_std, y_mean, y_std = NN.fit(layers=[20, 40, 20],
lr=lr,
epochs=1000000)
if model_dir != '':
model.save(model_dir_new + '/model')
metadata = {
'x_mean': x_mean,
'x_std': x_std,
'y_mean': y_mean,
'y_std': y_std
}
pickle_out = open(model_dir_new + "/dataset_metadata.pickle", "wb")
pickle.dump(metadata, pickle_out)
pickle_out.close()
print('Using delta_renear and delta_recut values')
cut_momenta, near_momenta, NJ_near_virt, NJ_cut_virt = cut_near_split(
momenta, virt, delta_cut=delta_recut, delta_near=delta_renear)
print('Testing on {} phase space points in total'.format(
len(cut_momenta) + len(near_momenta)))
test_points = len(cut_momenta) + len(near_momenta)
_, pairs = D_ij(mom=near_momenta[0], n_gluon=n_gluon)
if cores == 0:
cores = len(pairs)
pairs, NJ_near_virt_split = multiprocess_weighting(near_momenta, n_gluon,
NJ_near_virt, cores)
NN = Model((n_gluon + 2 - 1) * 4, near_momenta, NJ_near_virt_split[0])
_, _, _, _, _, _, _, _ = NN.process_training_data(moms=near_momenta,
labs=NJ_near_virt_split[0])
model_nears = []
model_cuts = []
x_mean_nears = []
x_std_nears = []
y_mean_nears = []
y_std_nears = []
x_mean_cuts = []
x_std_cuts = []
y_mean_cuts = []
y_std_cuts = []
data_dir +
'NJ_{}_{}_{}_{}'.format(order, n_gluon + 2, delta, test_points),
NJ_treevals)
else:
print('############### All njet files exist ###############')
NJ_treevals = np.load(data_dir + labs, allow_pickle=True)
if os.path.exists(model_dir) == False:
os.mkdir(model_dir)
print('Creating directory')
else:
print('Directory already exists')
NN = Model((n_gluon + 2 - 1) * 4, momenta, NJ_treevals)
_, _, _, _, _, _, _, _ = NN.process_training_data()
models = []
x_means = []
y_means = []
x_stds = []
y_stds = []
for i in range(training_reruns):
print('Working on model {}'.format(i))
model_dir_new = model_dir + '/{}_{}_{}_{}_{}/'.format(
order, n_gluon + 2, delta, points, i)
print('Looking for directory {}'.format(model_dir_new))
if os.path.exists(model_dir_new) == False:
os.mkdir(model_dir_new)