parser.add_argument(
'--concat_comp',
default=False,
type=bool,
help=
'option to re-use vector of elemental composition after global summation of crystal feature.(default: False)'
)
args = parser.parse_args(sys.argv[1:])
# GATGNN --- parameters
crystal_property = args.property
data_src = args.data_src
material_name = args.to_predict
_, _, RSM = use_property(crystal_property, data_src, True)
norm_action, classification = set_model_properties(crystal_property)
number_layers = args.num_layers
number_neurons = args.num_neurons
n_heads = args.num_heads
xtra_l = args.use_hidden_layers
global_att = args.global_attention
attention_technique = args.cluster_option
concat_comp = args.concat_comp
# SETTING UP CODE TO RUN ON GPU
gpu_id = 0
device = torch.device(f'cuda:{gpu_id}' if torch.cuda.is_available() else 'cpu')
# MODEL HYPER-PARAMETERS
'--concat_comp',
default=False,
type=bool,
help=
'option to re-use vector of elemental composition after global summation of crystal feature.(default: False)'
)
parser.add_argument('--train_size',
default=0.8,
type=float,
help='ratio size of the training-set (default:0.8)')
args = parser.parse_args(sys.argv[1:])
# GATGNN --- parameters
crystal_property = args.property
data_src = args.data_src
source_comparison, training_num, RSM = use_property(crystal_property, data_src)
norm_action, classification = set_model_properties(crystal_property)
if training_num == None: training_num = args.train_size
number_layers = args.num_layers
number_neurons = args.num_neurons
n_heads = args.num_heads
xtra_l = args.use_hidden_layers
global_att = args.global_attention
attention_technique = args.cluster_option
concat_comp = args.concat_comp
# SETTING UP CODE TO RUN ON GPU
gpu_id = 0
device = torch.device(f'cuda:{gpu_id}' if torch.cuda.is_available() else 'cpu')