def from_yaml(self, yaml_file, training=True):
"Initiate from YAML file"
print(yaml_file)
file_exists(yaml_file)
with open(yaml_file) as f:
y = yaml.load(f, Loader=yaml.FullLoader)
if training:
mom_file = y["training"]["mom_file"]
nj_file = y["training"]["nj_file"]
else:
mom_file = y["testing"]["mom_file"]
nj_file = y["testing"]["nj_file"]
delta_cut = y["delta_cut"]
delta_near = y["delta_near"]
model_base_dir = y["model_base_dir"]
model_dir = y["model_dir"]
training_reruns = y["training"]["training_reruns"]
all_legs = bool_convert(y["all_legs"])
all_pairs = bool_convert(y["all_pairs"])
scaling = y.get("scaling", "standardise")
layers = y["training"].get("layers", [20, 40, 20])
lr = y["training"].get("lr", 0.01)
activation = y["training"].get("activation", "tanh")
loss = y["training"].get("loss", "mean_squared_error")
epochs = y["training"].get("epochs", 1000000)
high_precision = bool_convert(y["training"].get(
"high_precision", "False"))
model_dataset = bool_convert(y["training"].get("model_dataset",
"False"))
return FKSModelRun(
mom_file=mom_file,
nj_file=nj_file,
delta_cut=delta_cut,
delta_near=delta_near,
model_base_dir=model_base_dir,
model_dir=model_dir,
training_reruns=training_reruns,
all_legs=all_legs,
all_pairs=all_pairs,
scaling=scaling,
layers=layers,
lr=lr,
activation=activation,
loss=loss,
epochs=epochs,
high_precision=high_precision,
model_dataset=model_dataset,
)
def load_data(self):
file_exists(self.mom_file)
file_exists(self.nj_file)
momenta = np.load(self.mom_file, allow_pickle=True)
print('############### Momenta loaded ###############')
nj = np.load(self.nj_file, allow_pickle=True)
print('############### NJet loaded ###############')
momenta = momenta.tolist()
print('Training on {} PS points'.format(len(momenta)))
self.nlegs = len(momenta[0]) - 2
return momenta, nj
parser.add_argument('-v',
'--verbose',
help="Verbose",
type=bool,
required=False)
args = parser.parse_args()
return args
if __name__ == "__main__":
args = parse()
if args.yaml_file != "False":
file_exists(args.yaml_file)
with open(args.yaml_file) as f:
yaml = yaml.load(f, Loader=yaml.FullLoader)
args.model_base_dir = yaml["model_base_dir"]
args.model_dir = yaml["model_dir"]
args.training_reruns = yaml["training"]["training_reruns"]
args.out_dir = yaml["model_dir"]
for i in range(args.training_reruns):
print('Working on training run {}'.format(i))
mod_dir = args.model_base_dir + args.model_dir + '_{}/'.format(i)
output = args.out_base_dir + '/' + args.out_dir
if not os.path.exists(output):
os.mkdir(output)