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IDEA_T_Drive_demo.py
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IDEA_T_Drive_demo.py
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import torch
import torch.optim as optim
from modules.IDEA import GenNet_exp
from modules.IDEA import DiscNet
from modules.loss import get_pre_gen_loss
from modules.loss import get_gen_loss
from modules.loss import get_disc_loss
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(0)
# ====================
data_name = 'T-Drive'
num_nodes = 1279 # Number of nodes
num_snaps = 300 # Number of snapshots
max_thres = 5000 # Threshold for maximum edge weight
noise_dim = 512 # Dimensionality of noise input
pos_dim = 256 # Dimensionality of positional embedding
GNN_feat_dim = pos_dim
FEM_dims = [GNN_feat_dim, 128, 128] # Layer configuration of feature extraction module (FEM)
EDM_dims = [(FEM_dims[-1]+noise_dim), 512, 256] # Layer configuration of embedding derivation module (EDM)
EAM_dims = [(EDM_dims[-1]+FEM_dims[-1]), 256, 128] # Layer configuration of embedding aggregation module (EAM)
disc_dims = [FEM_dims[-1], 128, 64, 32] # Layer configuration of discriminator
save_flag = False # Flag whether to save the trained model (w.r.t. each epoch)
# ====================
alpha = 20 # Parameter to balance the ER loss
beta = 0.1 # Parameter to balance the SDM loss
lambd = 0.0 # Parameter of attentive aligning unit
theta = 0.1 # Decaying factor
# ====================
edge_seq = np.load('data/%s_edge_seq.npy' % (data_name), allow_pickle=True)
rand_seq = np.load('data/%s_rand_feat_seq.npy' % (data_name))
# ==========
# Get positional embedding
pos_emb = None
for p in range(num_nodes):
if p==0:
pos_emb = get_pos_emb(p, pos_dim)
else:
pos_emb = np.concatenate((pos_emb, get_pos_emb(p, pos_dim)), axis=0)
feat_tnr = torch.FloatTensor(pos_emb).to(device)
# ====================
dropout_rate = 0.0 # Dropout rate
win_size = 5 # Window size of historical snapshots
epsilon = 0.01 # Threshold of the zero-refining
num_pre_epochs = 20 # Number of pre-training epochs
num_epochs = 200 # Number of training epochs
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
# ====================
# Get align matrices (i.e., identity matrices for level-1)
align_mat = torch.eye(num_nodes).to(device)
align_list = []
for i in range(win_size):
align_list.append(align_mat)
# ==========
feat_list = []
for i in range(win_size+1):
feat_list.append(feat_tnr)
# ==========
num_nodes_list = []
for i in range(win_size+1):
num_nodes_list.append(num_nodes)
# ====================
# Define the model
gen_net = GenNet_exp(FEM_dims, EDM_dims, EAM_dims, dropout_rate).to(device) # Generator
disc_net = DiscNet(FEM_dims, disc_dims, dropout_rate).to(device) # Discriminator
# ==========
# Define the optimizer
pre_gen_opt = optim.Adam(gen_net.parameters(), lr=1e-4, weight_decay=1e-5)
gen_opt = optim.Adam(gen_net.parameters(), lr=1e-4, weight_decay=1e-5)
disc_opt = optim.Adam(disc_net.parameters(), lr=1e-4, weight_decay=1e-5)
# ====================
# Pre-training of generator
for epoch in range(num_pre_epochs):
# ====================
# Pre-train the model
gen_net.train()
disc_net.train()
# ==========
train_cnt = 0
gen_loss_list = []
# ==========
for tau in range(win_size, num_train_snaps):
# ====================
sup_list = [] # List of GNN support (tensor)
noise_list = [] # List of random noise inputs
for t in range(tau-win_size, tau):
# ==========
edges = edge_seq[t]
adj = get_adj_wei(edges, 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
noise_tnr = torch.FloatTensor(rand_seq[t][0]).to(device)
noise_list.append(noise_tnr)
# ==========
gnd_list = []
real_sup_list = []
for t in range(tau-win_size+1, tau+1):
# ==========
edges = edge_seq[t]
gnd = get_adj_wei(edges, num_nodes, max_thres)
gnd_norm = gnd/max_thres # Normalize the edge weights to [0, 1]
gnd_norm += np.eye(num_nodes)
gnd_tnr = torch.FloatTensor(gnd_norm).to(device)
gnd_list.append(gnd_tnr)
# ==========
# Transfer the GNN support to a sparse tensor
sup = get_gnn_sup_woSE(gnd_norm)
sup_tnr = torch.FloatTensor(sup).to(device)
real_sup_list.append(sup_tnr)
# ====================
# Train the generator
adj_est_list = gen_net(sup_list, feat_list, noise_list, align_list, num_nodes_list, lambd, pred_flag=False)
pre_gen_loss = get_pre_gen_loss(adj_est_list, gnd_list, theta)
pre_gen_opt.zero_grad()
pre_gen_loss.backward()
pre_gen_opt.step()
# ====================
gen_loss_list.append(pre_gen_loss.item())
train_cnt += 1
if train_cnt % 100 == 0:
print('-Train %d / %d' % (train_cnt, num_train_snaps))
gen_loss_mean = np.mean(gen_loss_list)
print('#%d Pre-Train G-Loss %f' % (epoch, gen_loss_mean))
# ====================
# Validate the model
gen_net.eval()
disc_net.eval()
# ==========
RMSE_list = []
MAE_list = []
MLSD_list = []
MR_list = []
for tau in range(num_snaps-num_test_snaps-num_val_snaps, num_snaps-num_test_snaps):
# ====================
sup_list = [] # List of GNN support (tensor)
noise_list = [] # List of random noise inputs
for t in range(tau-win_size, tau):
# ==========
edges = edge_seq[t]
adj = get_adj_wei(edges, 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
noise_tnr = torch.FloatTensor(rand_seq[t][0]).to(device)
noise_list.append(noise_tnr)
# ==========
# 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 = adj_est_list[-1]
if torch.cuda.is_available():
adj_est = adj_est.cpu().data.numpy()
else:
adj_est = adj_est.data.numpy()
adj_est *= max_thres # Rescale the edge weights to the original value range
# ==========
# Refine the prediction result
for r in range(num_nodes):
adj_est[r, r] = 0
for r in range(num_nodes):
for c in range(num_nodes):
if adj_est[r, c]<=epsilon:
adj_est[r, c] = 0
# ====================
# Get the ground-truth
edges = edge_seq[tau]
gnd = get_adj_wei(edges, num_nodes, max_thres)
# ====================
# Evaluate the prediction result
RMSE = get_RMSE(adj_est, gnd, num_nodes)
MAE = get_MAE(adj_est, gnd, num_nodes)
MLSD = get_MLSD(adj_est, gnd, num_nodes)
MR = get_MR(adj_est, gnd, num_nodes)
# ==========
RMSE_list.append(RMSE)
MAE_list.append(MAE)
MLSD_list.append(MLSD)
MR_list.append(MR)
# ====================
RMSE_mean = np.mean(RMSE_list)
RMSE_std = np.std(RMSE_list, ddof=1)
MAE_mean = np.mean(MAE_list)
MAE_std = np.std(MAE_list, ddof=1)
MLSD_mean = np.mean(MLSD_list)
MLSD_std = np.std(MLSD_list, ddof=1)
MR_mean = np.mean(MR_list)
MR_std = np.std(MR_list, ddof=1)
print('Val Pre-#%d RMSE %f %f MAE %f %f MLSD %f %f MR %f %f'
% (epoch, RMSE_mean, RMSE_std, MAE_mean, MAE_std, MLSD_mean, MLSD_std, MR_mean, MR_std))
# ==========
f_input = open('res/%s_IDEA_rec.txt' % (data_name), 'a+')
f_input.write('Val Pre #%d RMSE %f %f MAE %f %f MLSD %f %f MR %f %f\n'
% (epoch, RMSE_mean, RMSE_std, MAE_mean, MAE_std, MLSD_mean, MLSD_std, MR_mean, MR_std))
f_input.close()
# ====================
# Test the model
gen_net.eval()
disc_net.eval()
# ==========
RMSE_list = []
MAE_list = []
MLSD_list = []
MR_list = []
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
for t in range(tau-win_size, tau):
# ==========
edges = edge_seq[t]
adj = get_adj_wei(edges, 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
noise_tnr = torch.FloatTensor(rand_seq[t][0]).to(device)
noise_list.append(noise_tnr)
# ==========
# 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 = adj_est_list[-1]
if torch.cuda.is_available():
adj_est = adj_est.cpu().data.numpy()
else:
adj_est = adj_est.data.numpy()
adj_est *= max_thres # Rescale the edge weights to the original value range
# ==========
# Refine the prediction result
for r in range(num_nodes):
adj_est[r, r] = 0
for r in range(num_nodes):
for c in range(num_nodes):
if adj_est[r, c] <= epsilon:
adj_est[r, c] = 0
# ====================
# Get the ground-truth
edges = edge_seq[tau]
gnd = get_adj_wei(edges, num_nodes, max_thres)
# ====================
# Evaluate the prediction result
RMSE = get_RMSE(adj_est, gnd, num_nodes)
MAE = get_MAE(adj_est, gnd, num_nodes)
MLSD = get_MLSD(adj_est, gnd, num_nodes)
MR = get_MR(adj_est, gnd, num_nodes)
# ==========
RMSE_list.append(RMSE)
MAE_list.append(MAE)
MLSD_list.append(MLSD)
MR_list.append(MR)
# ====================
RMSE_mean = np.mean(RMSE_list)
RMSE_std = np.std(RMSE_list, ddof=1)
MAE_mean = np.mean(MAE_list)
MAE_std = np.std(MAE_list, ddof=1)
MLSD_mean = np.mean(MLSD_list)
MLSD_std = np.std(MLSD_list, ddof=1)
MR_mean = np.mean(MR_list)
MR_std = np.std(MR_list, ddof=1)
print('Test Pre-#%d RMSE %f %f MAE %f %f MLSD %f %f MR %f %f\n'
% (epoch, RMSE_mean, RMSE_std, MAE_mean, MAE_std, MLSD_mean, MLSD_std, MR_mean, MR_std))
# ==========
f_input = open('res/%s_IDEA_rec.txt' % (data_name), 'a+')
f_input.write('Test Pre #%d RMSE %f %f MAE %f %f MLSD %f %f MR %f %f\n'
% (epoch, RMSE_mean, RMSE_std, MAE_mean, MAE_std, MLSD_mean, MLSD_std, MR_mean, MR_std))
f_input.write('\n')
f_input.close()
# ====================
# Joint optimization of the generator & discriminator
for epoch in range(num_epochs):
# ====================
# Train the model
gen_net.train()
disc_net.train()
# ==========
train_cnt = 0
disc_loss_list = []
gen_loss_list = []
for tau in range(win_size, num_train_snaps):
# ====================
sup_list = [] # List of GNN support (tensor)
noise_list = [] # List of random noise inputs
for t in range(tau-win_size, tau):
# ==========
edges = edge_seq[t]
adj = get_adj_wei(edges, 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
noise_tnr = torch.FloatTensor(rand_seq[t][0]).to(device)
noise_list.append(noise_tnr)
# ==========
gnd_list = []
real_sup_list = []
for t in range(tau-win_size+1, tau+1):
# ==========
edges = edge_seq[t]
gnd = get_adj_wei(edges, num_nodes, max_thres)
gnd_norm = gnd/max_thres # Normalize the edge weights to [0, 1]
gnd_norm += np.eye(num_nodes)
gnd_tnr = torch.FloatTensor(gnd_norm).to(device)
gnd_list.append(gnd_tnr)
# ==========
# Transfer the GNN support to a sparse tensor
sup = get_gnn_sup_woSE(gnd_norm)
sup_tnr = torch.FloatTensor(sup).to(device)
real_sup_list.append(sup_tnr)
# ====================
# Train the discriminator
adj_est_list = gen_net(sup_list, feat_list, noise_list, align_list, num_nodes_list, lambd, pred_flag=False)
disc_real_list, disc_fake_list = disc_net(real_sup_list, feat_list, adj_est_list, feat_list)
disc_loss = get_disc_loss(disc_real_list, disc_fake_list, theta)
disc_opt.zero_grad()
disc_loss.backward()
disc_opt.step()
# ==========
# Train the generator
adj_est_list = gen_net(sup_list, feat_list, noise_list, align_list, num_nodes_list, lambd, pred_flag=False)
_, disc_fake_list = disc_net(real_sup_list, feat_list, adj_est_list, feat_list)
gen_loss = get_gen_loss(adj_est_list, gnd_list, disc_fake_list, max_thres, alpha, beta, theta)
gen_opt.zero_grad()
gen_loss.backward()
gen_opt.step()
# ====================
gen_loss_list.append(gen_loss.item())
disc_loss_list.append(disc_loss.item())
train_cnt += 1
if train_cnt % 100 == 0:
print('-Train %d / %d' % (train_cnt, num_train_snaps))
gen_loss_mean = np.mean(gen_loss_list)
disc_loss_mean = np.mean(disc_loss_list)
print('#%d Train G-Loss %f D-Loss %f' % (epoch, gen_loss_mean, disc_loss_mean))
# ====================
# Save the trained model (w.r.t. current epoch)
if save_flag:
torch.save(gen_net, 'pt/%s_IDEA_gen_%d.pkl' % (data_name, epoch))
torch.save(disc_net, 'pt/%s_IDEA_disc_%d.pkl' % (data_name, epoch))
# ====================
# Validate the model
gen_net.eval()
disc_net.eval()
# ==========
RMSE_list = []
MAE_list = []
MLSD_list = []
MR_list = []
for tau in range(num_snaps-num_test_snaps-num_val_snaps, num_snaps-num_test_snaps):
# ====================
sup_list = [] # List of GNN support (tensor)
noise_list = [] # List of random noise inputs
for t in range(tau-win_size, tau):
# ==========
edges = edge_seq[t]
adj = get_adj_wei(edges, 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
noise_tnr = torch.FloatTensor(rand_seq[t][0]).to(device)
noise_list.append(noise_tnr)
# ==========
# 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 = adj_est_list[-1]
if torch.cuda.is_available():
adj_est = adj_est.cpu().data.numpy()
else:
adj_est = adj_est.data.numpy()
adj_est *= max_thres # Rescale the edge weights to the original value range
# ==========
# Refine the prediction result
for r in range(num_nodes):
adj_est[r, r] = 0
for r in range(num_nodes):
for c in range(num_nodes):
if adj_est[r, c] <= epsilon:
adj_est[r, c] = 0
# ====================
# Get the ground-truth
edges = edge_seq[tau]
gnd = get_adj_wei(edges, num_nodes, max_thres)
# ====================
# Evaluate the prediction result
RMSE = get_RMSE(adj_est, gnd, num_nodes)
MAE = get_MAE(adj_est, gnd, num_nodes)
MLSD = get_MLSD(adj_est, gnd, num_nodes)
MR = get_MR(adj_est, gnd, num_nodes)
# ==========
RMSE_list.append(RMSE)
MAE_list.append(MAE)
MLSD_list.append(MLSD)
MR_list.append(MR)
# ====================
RMSE_mean = np.mean(RMSE_list)
RMSE_std = np.std(RMSE_list, ddof=1)
MAE_mean = np.mean(MAE_list)
MAE_std = np.std(MAE_list, ddof=1)
MLSD_mean = np.mean(MLSD_list)
MLSD_std = np.std(MLSD_list, ddof=1)
MR_mean = np.mean(MR_list)
MR_std = np.std(MR_list, ddof=1)
print('Val #%d RMSE %f %f MAE %f %f MLSD %f %f MR %f %f'
% (epoch, RMSE_mean, RMSE_std, MAE_mean, MAE_std, MLSD_mean, MLSD_std, MR_mean, MR_std))
# ==========
f_input = open('res/%s_IDEA_rec.txt' % (data_name), 'a+')
f_input.write('Val #%d RMSE %f %f MAE %f %f MLSD %f %f MR %f %f\n'
% (epoch, RMSE_mean, RMSE_std, MAE_mean, MAE_std, MLSD_mean, MLSD_std, MR_mean, MR_std))
f_input.close()
# ====================
# Test the model
gen_net.eval()
disc_net.eval()
# ==========
RMSE_list = []
MAE_list = []
MLSD_list = []
MR_list = []
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
for t in range(tau-win_size, tau):
# ==========
edges = edge_seq[t]
adj = get_adj_wei(edges, 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
noise_tnr = torch.FloatTensor(rand_seq[t][0]).to(device)
noise_list.append(noise_tnr)
# ==========
# 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 = adj_est_list[-1]
if torch.cuda.is_available():
adj_est = adj_est.cpu().data.numpy()
else:
adj_est = adj_est.data.numpy()
adj_est *= max_thres # Rescale the edge weights to the original value range
# ==========
# Refine the prediction result
for r in range(num_nodes):
adj_est[r, r] = 0
for r in range(num_nodes):
for c in range(num_nodes):
if adj_est[r, c]<=epsilon:
adj_est[r, c] = 0
# ====================
# Get the ground-truth
edges = edge_seq[tau]
gnd = get_adj_wei(edges, num_nodes, max_thres)
# ====================
# Evaluate the prediction result
RMSE = get_RMSE(adj_est, gnd, num_nodes)
MAE = get_MAE(adj_est, gnd, num_nodes)
MLSD = get_MLSD(adj_est, gnd, num_nodes)
MR = get_MR(adj_est, gnd, num_nodes)
# ==========
RMSE_list.append(RMSE)
MAE_list.append(MAE)
MLSD_list.append(MLSD)
MR_list.append(MR)
# ====================
RMSE_mean = np.mean(RMSE_list)
RMSE_std = np.std(RMSE_list, ddof=1)
MAE_mean = np.mean(MAE_list)
MAE_std = np.std(MAE_list, ddof=1)
MLSD_mean = np.mean(MLSD_list)
MLSD_std = np.std(MLSD_list, ddof=1)
MR_mean = np.mean(MR_list)
MR_std = np.std(MR_list, ddof=1)
print('Test #%d RMSE %f %f MAE %f %f MLSD %f %f MR %f %f\n'
% (epoch, RMSE_mean, RMSE_std, MAE_mean, MAE_std, MLSD_mean, MLSD_std, MR_mean, MR_std))
# ==========
f_input = open('res/%s_IDEA_rec.txt' % (data_name), 'a+')
f_input.write('Test #%d RMSE %f %f MAE %f %f MLSD %f %f MR %f %f\n'
% (epoch, RMSE_mean, RMSE_std, MAE_mean, MAE_std, MLSD_mean, MLSD_std, MR_mean, MR_std))
f_input.write('\n')
f_input.close()