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jblitzar
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github-python-corpus

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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)')
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')
# DATA PARAMETERS
random_num = 456;random.seed(random_num)
# MODEL HYPER-PARAMETERS
num_epochs = 500
learning_rate = 5e-3
batch_size = 256
stop_patience = 150
best_epoch = 1
adj_epochs = 50
milestones = [150,250]
test_param = {'batch_size':256, 'shuffle': False}
# DATALOADER/ TARGET NORMALIZATION
src_CIF = 'CIF-DATA_NEW' if data_src == 'NEW' else 'CIF-DATA'
dataset = pd.read_csv(f'DATA/{src_CIF}/id_prop.csv',names=['material_ids','label']).sample(frac=1,random_state=random_num)
NORMALIZER = DATA_normalizer(dataset.label.values)
CRYSTAL_DATA = CIF_Dataset(dataset, root_dir = f'DATA/{src_CIF}/',**RSM)
idx_list = list(range(len(dataset)))
random.shuffle(idx_list)
train_idx,test_val = train_test_split(idx_list,train_size=training_num,random_state=random_num)
test_idx,_ = train_test_split(test_val,test_size=0.5,random_state=random_num)
testing_set = CIF_Lister(test_idx,CRYSTAL_DATA,NORMALIZER,norm_action, df=dataset,src=data_src)
# NEURAL-NETWORK
the_network = GATGNN(n_heads,classification,neurons=number_neurons,nl=number_layers,xtra_layers=xtra_l,global_attention=global_att,
unpooling_technique=attention_technique,concat_comp=concat_comp,edge_format=data_src)
net = the_network.to(device)
# LOSS & OPTMIZER & SCHEDULER
if classification == 1: criterion = nn.CrossEntropyLoss().cuda(); funct = torch_accuracy
else : criterion = nn.SmoothL1Loss().cuda() ; funct = torch_MAE
optimizer = optim.AdamW(net.parameters(), lr = learning_rate, weight_decay = 1e-1)
# LOADING MODEL
net = the_network.to(device)
net.interpretation = True
net.load_state_dict(torch.load(f'TRAINED/{crystal_property}.pt',map_location=device))
# METRICS-OBJECT INITIALIZATION
metrics = METRICS(crystal_property,num_epochs,criterion,funct,device)
print(f'> EVALUATING MODEL ...')
# TESTING PHASE
test_loader = torch_DataLoader(dataset=testing_set, **test_param)
true_label, pred_label = torch.tensor([]).to(device),torch.tensor([]).to(device)
testset_idx = torch.tensor([]).to(device)
num_elements = torch.tensor([]).to(device)
net.eval()
for data in test_loader:
data = data.to(device)
with torch.no_grad():
predictions = net(data)
print(f'(batch --- :{data.y.shape[0]:4})','---',metrics.eval_func(predictions,data.y).item())
true_label = torch.cat([true_label,data.y.float()],dim=0)
pred_label = torch.cat([pred_label,predictions.float()],dim=0)
testset_idx = torch.cat([testset_idx,data.the_idx],dim=0)
num_elements = torch.cat([num_elements,data.num_atoms],dim=0)
test_result = metrics.eval_func(pred_label,true_label)
print(f'RESULT ---> {test_result:.5f}')
true_label = true_label.cpu().numpy()
pred_label = pred_label.cpu().numpy()
testset_idx = testset_idx.cpu().numpy()
num_elements = num_elements.cpu().numpy()
csv_file = pd.DataFrame(zip(dataset.iloc[testset_idx].material_ids.values,true_label,pred_label,num_elements,testset_idx),
columns=['material_ids',f'Measured {crystal_property}',f'Predicted {crystal_property}','Num_nodes','General_id'])