def get_date_adj_list(self, origin_base_path, start_idx, duration, sep='\t', normalize=False, row_norm=False, add_eye=False, data_type='tensor'):
assert data_type in ['tensor', 'matrix']
date_dir_list = sorted(os.listdir(origin_base_path))
# print('adj list: ', date_dir_list)
date_adj_list = []
for i in range(start_idx, min(start_idx + duration, self.max_time_num)):
original_graph_path = os.path.join(origin_base_path, date_dir_list[i])
spmat = get_sp_adj_mat(original_graph_path, self.full_node_list, sep=sep)
# spmat = sp.coo_matrix((np.exp(alpha * spmat.data), (spmat.row, spmat.col)), shape=(self.node_num, self.node_num))
if add_eye:
spmat = spmat + sp.eye(spmat.shape[0])
if normalize:
spmat = get_normalized_adj(spmat, row_norm=row_norm)
# data type
if data_type == 'tensor':
sptensor = sparse_mx_to_torch_sparse_tensor(spmat)
date_adj_list.append(sptensor.cuda() if self.has_cuda else sptensor)
else: # data_type == matrix
date_adj_list.append(spmat)
# print(len(date_adj_list))
return date_adj_list
means=means,
stds=stds)
val_input, val_target, val_mean_t, val_std_t = generate_dataset(
val_original_data,
num_timesteps_input=num_timesteps_input,
num_timesteps_output=num_timesteps_output,
means=means,
stds=stds)
test_input, test_target, test_mean_t, test_std_t = generate_dataset(
test_original_data,
num_timesteps_input=num_timesteps_input,
num_timesteps_output=num_timesteps_output,
means=means,
stds=stds)
A_wave = get_normalized_adj(A)
A_wave = torch.from_numpy(A_wave)
# A_wave = torch.memory_format(padding)
A_wave = A_wave.to(device=args.device)
net = STGCN(A_wave.shape[0], training_input.shape[3], num_timesteps_input,
num_timesteps_output).to(device=args.device)
optimizer = torch.optim.Adam(net.parameters(), lr=1e-3)
loss_criterion = nn.MSELoss()
training_losses = []
validation_losses = []
validation_maes = []
for epoch in range(epochs):
loss = train_epoch(training_input,
pin_memory=True)
val_loader = Data.DataLoader(
dataset=val_dataset,
batch_size=batch_size,
drop_last=True,
num_workers=32,
pin_memory=True)
logger.info(f"finish loading dataset, dataset length:{len(dataset)}")
#############################ele distance matrix###################
ele_A = np.load('Ybus_inv.npy')
for i in range(0,ele_A.shape[0]):
for j in range(0,ele_A.shape[1]):
ele_A[i,j] = ele_A[i,i]-2*ele_A[i,j]-ele_A[j,j]
# print(ele_A)
ele_A_wave = get_normalized_adj(ele_A)
ele_A_wave = torch.from_numpy(ele_A_wave)
ele_A_wave = ele_A_wave.to(device=args.device)
###################################################################
#############################adjacency matrix###################
A = pd.read_excel('AdjacencyMatrix.xls').values.astype(np.float32)
A_wave = get_normalized_adj(A)
A_wave = torch.from_numpy(A_wave)
A_wave = A_wave.to(device=args.device)
###################################################################
print("starting a new model")
net = STGCN(A_wave.shape[0],
feature_num,
num_timesteps_input,
