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IDEA_IoT_chpt.py
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IDEA_IoT_chpt.py
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import torch
from utils import *
import scipy.sparse
import random
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
def setup_seed(seed):
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
random.seed(seed)
torch.backends.cudnn.deterministic = True
setup_seed(100)
# ====================
data_name = 'IoT'
num_nodes_gbl = 668 # Number of accumulated nodes
num_snaps = 144 # Number of snapshots
max_thres = 1024 # Threshold for maximum edge weight
noise_dim = 48 # Dimensionality of noise input
feat_dim = 32 # Dimensionality of node feature
pos_dim = 32 # Dimensionality of position embedding
# ====================
lambd = 0.4 # Hyper-parameter of attentive aligning unit
# ====================
edge_seq = np.load('data/%s_edge_seq.npy' % (data_name), allow_pickle=True)
node_set_seq = np.load('data/%s_node_seq.npy' % (data_name), allow_pickle=True)
mod_seq = np.load('data/%s_mod_seq.npy' % (data_name), allow_pickle=True)
align_seq_gbl = np.load('data/%s_align_seq.npy' % (data_name), allow_pickle=True)
# =========
node_map_seq_gbl = []
num_nodes_seq_gbl = []
for t in range(num_snaps):
node_set = node_set_seq[t]
node_map = get_node_map(node_set)
node_map_seq_gbl.append(node_map)
num_nodes_seq_gbl.append(len(node_set))
# ==========
# Get global node features
feat_gbl = np.load('data/%s_feat.npy' % (data_name), allow_pickle=True)
feat_lcl_seq = []
for t in range(num_snaps):
node_set = node_set_seq[t]
node_idxs = sorted(list(node_set))
feat_lcl = feat_gbl[node_idxs, :]
feat_lcl_seq.append(feat_lcl)
# ====================
win_size = 10 # Window size of historical snapshots
epsilon = 1e-5 # Threshold of the zero-refining
num_test_snaps = 50 # Number of test snapshots
num_val_snaps = 10 # Number of validation snapshots
num_train_snaps = num_snaps-num_test_snaps-num_val_snaps # Number of training snapshots
# ====================
# Load check point
gen_net = torch.load('chpt/IDEA_%s.pkl' % (data_name)).to(device)
# ====================
# Evaluate the model on the test set
gen_net.eval()
# ==========
RMSE_list_L2 = []
MAE_list_L2 = []
MLSD_list_L2 = []
MR_list_L2 = []
# ==========
RMSE_list_L3 = []
MAE_list_L3 = []
MLSD_list_L3 = []
MR_list_L3 = []
for tau in range(num_snaps-num_test_snaps, num_snaps):
# ====================
sup_list = [] # List of GNN support (tensor)
noise_list = [] # List of random noise inputs
align_list = [] # List of align matrices
feat_list = [] # List of feature input
num_nodes_list = []
pre_node_map_list = []
for t in range(tau-win_size, tau):
# ==========
edges = edge_seq[t]
num_nodes = num_nodes_seq_gbl[t]
node_map = node_map_seq_gbl[t]
adj = get_adj_wei_map(edges, node_map, num_nodes, max_thres)
adj_norm = adj/max_thres # Normalize the edge weights to [0, 1]
# ==========
# Transfer the GNN support to a sparse tensor
sup = get_gnn_sup(adj_norm)
sup_sp = sp.sparse.coo_matrix(sup)
sup_sp = sparse_to_tuple(sup_sp)
idxs = torch.LongTensor(sup_sp[0].astype(float)).to(device)
vals = torch.FloatTensor(sup_sp[1]).to(device)
sup_tnr = torch.sparse.FloatTensor(idxs.t(), vals, sup_sp[2]).float().to(device)
sup_list.append(sup_tnr)
# =========
# Generate the random noise via random projection
mod_tnr = torch.FloatTensor(mod_seq[t]).to(device)
rand_mat = rand_proj(num_nodes, noise_dim)
rand_tnr = torch.FloatTensor(rand_mat).to(device)
noise_tnr = torch.mm(mod_tnr, rand_tnr)
noise_list.append(noise_tnr)
# ==========
align_tnr = torch.FloatTensor(align_seq_gbl[t]).to(device)
align_list.append(align_tnr)
feat_lcl = feat_lcl_seq[t]
feat_tnr = torch.FloatTensor(feat_lcl).to(device)
feat_list.append(feat_tnr)
num_nodes_list.append(num_nodes)
pre_node_map_list.append(node_map)
# ==========
# Get ground-truth
edges = edge_seq[tau]
# ==========
# For L3
num_nodes_L3 = num_nodes_seq_gbl[tau] # Number of nodes for L3
node_map_L3 = node_map_seq_gbl[tau]
gnd_L3 = get_adj_wei_map(edges, node_map_L3, num_nodes_L3, max_thres) # Ground-truth
feat_lcl = feat_lcl_seq[tau]
feat_tnr = torch.FloatTensor(feat_lcl).to(device)
feat_list.append(feat_tnr)
num_nodes_list.append(num_nodes_L3)
# ==========
# For L2
node_idxs_L2 = get_node_idxs_L2(pre_node_map_list, node_map_L3)
num_nodes_L2 = len(node_idxs_L2) # Number of nodes for L2
gnd_L2 = gnd_L3[node_idxs_L2, :]
gnd_L2 = gnd_L2[:, node_idxs_L2]
# ==========
# Get the prediction result
adj_est_list = gen_net(sup_list, feat_list, noise_list, align_list, num_nodes_list, lambd, pred_flag=True)
adj_est_L3 = adj_est_list[-1]
adj_est_L2 = adj_est_L3[node_idxs_L2, :]
adj_est_L2 = adj_est_L2[:, node_idxs_L2]
if torch.cuda.is_available():
adj_est_L2 = adj_est_L2.cpu().data.numpy()
adj_est_L3 = adj_est_L3.cpu().data.numpy()
else:
adj_est_L2 = adj_est_L2.data.numpy()
adj_est_L3 = adj_est_L3.data.numpy()
# Rescale the edge weights to the original value range
adj_est_L2 *= max_thres
adj_est_L3 *= max_thres
# ==========
# Refine the prediction result
for r in range(num_nodes_L3):
if r<num_nodes_L2:
adj_est_L2[r, r] = 0
adj_est_L3[r, r] = 0
for r in range(num_nodes_L3):
for c in range(num_nodes_L3):
if r<num_nodes_L2 and c<num_nodes_L2:
if adj_est_L2[r, c]<=epsilon:
adj_est_L2[r, c] = 0
if adj_est_L3[r, c]<=epsilon:
adj_est_L3[r, c] = 0
# ====================
# Evaluate the prediction result
RMSE_L2 = get_RMSE(adj_est_L2, gnd_L2, num_nodes_L2)
MAE_L2 = get_MAE(adj_est_L2, gnd_L2, num_nodes_L2)
MLSD_L2 = get_MLSD(adj_est_L2, gnd_L2, num_nodes_L2)
MR_L2 = get_MR(adj_est_L2, gnd_L2, num_nodes_L2)
# ==========
RMSE_list_L2.append(RMSE_L2)
MAE_list_L2.append(MAE_L2)
MLSD_list_L2.append(MLSD_L2)
MR_list_L2.append(MR_L2)
# ==========
RMSE_L3 = get_RMSE(adj_est_L3, gnd_L3, num_nodes_L3)
MAE_L3 = get_MAE(adj_est_L3, gnd_L3, num_nodes_L3)
MLSD_L3 = get_MLSD(adj_est_L3, gnd_L3, num_nodes_L3)
MR_L3 = get_MR(adj_est_L3, gnd_L3, num_nodes_L3)
# ==========
RMSE_list_L3.append(RMSE_L3)
MAE_list_L3.append(MAE_L3)
MLSD_list_L3.append(MLSD_L3)
MR_list_L3.append(MR_L3)
# ====================
RMSE_mean_L2 = np.mean(RMSE_list_L2)
RMSE_std_L2 = np.std(RMSE_list_L2, ddof=1)
MAE_mean_L2 = np.mean(MAE_list_L2)
MAE_std_L2 = np.std(MAE_list_L2, ddof=1)
MLSD_mean_L2 = np.mean(MLSD_list_L2)
MLSD_std_L2 = np.std(MLSD_list_L2, ddof=1)
MR_mean_L2 = np.mean(MR_list_L2)
MR_std_L2 = np.std(MR_list_L2, ddof=1)
print('(L2) Test RMSE %f %f MAE %f %f MLSD %f %f MR %f %f'
% (RMSE_mean_L2, RMSE_std_L2, MAE_mean_L2, MAE_std_L2,
MLSD_mean_L2, MLSD_std_L2, MR_mean_L2, MR_std_L2))
# ==========
RMSE_mean_L3 = np.mean(RMSE_list_L3)
RMSE_std_L3 = np.std(RMSE_list_L3, ddof=1)
MAE_mean_L3 = np.mean(MAE_list_L3)
MAE_std_L3 = np.std(MAE_list_L3, ddof=1)
MLSD_mean_L3 = np.mean(MLSD_list_L3)
MLSD_std_L3 = np.std(MLSD_list_L3, ddof=1)
MR_mean_L3 = np.mean(MR_list_L3)
MR_std_L3 = np.std(MR_list_L3, ddof=1)
print('(L3) Test RMSE %f %f MAE %f %f MLSD %f %f MR %f %f\n'
% (RMSE_mean_L3, RMSE_std_L3, MAE_mean_L3, MAE_std_L3,
MLSD_mean_L3, MLSD_std_L3, MR_mean_L3, MR_std_L3))