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run_IMDB.py
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run_IMDB.py
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import time
import argparse
import torch.nn.functional as F
import torch.sparse
import numpy as np
import dgl
from utils.pytorchtools import EarlyStopping
from utils.data import load_IMDB_data
from utils.tools import evaluate_results_nc
from model import MAGNN_nc
# Params
out_dim = 3
dropout_rate = 0.5
lr = 0.005
weight_decay = 0.001
etypes_lists = [[[0, 1], [2, 3]],
[[1, 0], [1, 2, 3, 0]],
[[3, 2], [3, 0, 1, 2]]]
def run_model_IMDB(feats_type, num_layers, hidden_dim, num_heads, attn_vec_dim, rnn_type,
num_epochs, patience, repeat, save_postfix):
nx_G_lists, edge_metapath_indices_lists, features_list, adjM, type_mask, labels, train_val_test_idx = load_IMDB_data()
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
features_list = [torch.FloatTensor(features.todense()).to(device) for features in features_list]
if feats_type == 0:
in_dims = [features.shape[1] for features in features_list]
elif feats_type == 1:
in_dims = [features_list[0].shape[1]] + [10] * (len(features_list) - 1)
for i in range(1, len(features_list)):
features_list[i] = torch.zeros((features_list[i].shape[0], 10)).to(device)
elif feats_type == 2:
in_dims = [features.shape[0] for features in features_list]
in_dims[0] = features_list[0].shape[1]
for i in range(1, len(features_list)):
dim = features_list[i].shape[0]
indices = np.vstack((np.arange(dim), np.arange(dim)))
indices = torch.LongTensor(indices)
values = torch.FloatTensor(np.ones(dim))
features_list[i] = torch.sparse.FloatTensor(indices, values, torch.Size([dim, dim])).to(device)
elif feats_type == 3:
in_dims = [features.shape[0] for features in features_list]
for i in range(len(features_list)):
dim = features_list[i].shape[0]
indices = np.vstack((np.arange(dim), np.arange(dim)))
indices = torch.LongTensor(indices)
values = torch.FloatTensor(np.ones(dim))
features_list[i] = torch.sparse.FloatTensor(indices, values, torch.Size([dim, dim])).to(device)
edge_metapath_indices_lists = [[torch.LongTensor(indices).to(device) for indices in indices_list] for indices_list in
edge_metapath_indices_lists]
labels = torch.LongTensor(labels).to(device)
g_lists = []
for nx_G_list in nx_G_lists:
g_lists.append([])
for nx_G in nx_G_list:
g = dgl.DGLGraph(multigraph=True)
g.add_nodes(nx_G.number_of_nodes())
g.add_edges(*list(zip(*sorted(map(lambda tup: (int(tup[0]), int(tup[1])), nx_G.edges())))))
g_lists[-1].append(g)
train_idx = train_val_test_idx['train_idx']
val_idx = train_val_test_idx['val_idx']
test_idx = train_val_test_idx['test_idx']
svm_macro_f1_lists = []
svm_micro_f1_lists = []
nmi_mean_list = []
nmi_std_list = []
ari_mean_list = []
ari_std_list = []
for _ in range(repeat):
net = MAGNN_nc(num_layers, [2, 2, 2], 4, etypes_lists, in_dims, hidden_dim, out_dim, num_heads, attn_vec_dim,
rnn_type, dropout_rate)
net.to(device)
optimizer = torch.optim.Adam(net.parameters(), lr=lr, weight_decay=weight_decay)
target_node_indices = np.where(type_mask == 0)[0]
# training loop
net.train()
early_stopping = EarlyStopping(patience=patience, verbose=True, save_path='checkpoint/checkpoint_{}.pt'.format(save_postfix))
dur1 = []
dur2 = []
dur3 = []
for epoch in range(num_epochs):
t0 = time.time()
# training forward
net.train()
logits, embeddings = net((g_lists, features_list, type_mask, edge_metapath_indices_lists), target_node_indices)
logp = F.log_softmax(logits, 1)
train_loss = F.nll_loss(logp[train_idx], labels[train_idx])
t1 = time.time()
dur1.append(t1 - t0)
# autograd
optimizer.zero_grad()
train_loss.backward()
optimizer.step()
t2 = time.time()
dur2.append(t2 - t1)
# validation forward
net.eval()
with torch.no_grad():
logits, embeddings = net((g_lists, features_list, type_mask, edge_metapath_indices_lists), target_node_indices)
logp = F.log_softmax(logits, 1)
val_loss = F.nll_loss(logp[val_idx], labels[val_idx])
t3 = time.time()
dur3.append(t3 - t2)
# print info
print(
"Epoch {:05d} | Train_Loss {:.4f} | Val_Loss {:.4f} | Time1(s) {:.4f} | Time2(s) {:.4f} | Time3(s) {:.4f}".format(
epoch, train_loss.item(), val_loss.item(), np.mean(dur1), np.mean(dur2), np.mean(dur3)))
# early stopping
early_stopping(val_loss, net)
if early_stopping.early_stop:
print('Early stopping!')
break
# testing with evaluate_results_nc
net.load_state_dict(torch.load('checkpoint/checkpoint_{}.pt'.format(save_postfix)))
net.eval()
with torch.no_grad():
logits, embeddings = net((g_lists, features_list, type_mask, edge_metapath_indices_lists), target_node_indices)
svm_macro_f1_list, svm_micro_f1_list, nmi_mean, nmi_std, ari_mean, ari_std = evaluate_results_nc(
embeddings[test_idx].cpu().numpy(), labels[test_idx].cpu().numpy(), num_classes=out_dim)
svm_macro_f1_lists.append(svm_macro_f1_list)
svm_micro_f1_lists.append(svm_micro_f1_list)
nmi_mean_list.append(nmi_mean)
nmi_std_list.append(nmi_std)
ari_mean_list.append(ari_mean)
ari_std_list.append(ari_std)
# print out a summary of the evaluations
svm_macro_f1_lists = np.transpose(np.array(svm_macro_f1_lists), (1, 0, 2))
svm_micro_f1_lists = np.transpose(np.array(svm_micro_f1_lists), (1, 0, 2))
nmi_mean_list = np.array(nmi_mean_list)
nmi_std_list = np.array(nmi_std_list)
ari_mean_list = np.array(ari_mean_list)
ari_std_list = np.array(ari_std_list)
print('----------------------------------------------------------------')
print('SVM tests summary')
print('Macro-F1: ' + ', '.join(['{:.6f}~{:.6f} ({:.1f})'.format(
macro_f1[:, 0].mean(), macro_f1[:, 1].mean(), train_size) for macro_f1, train_size in
zip(svm_macro_f1_lists, [0.8, 0.6, 0.4, 0.2])]))
print('Micro-F1: ' + ', '.join(['{:.6f}~{:.6f} ({:.1f})'.format(
micro_f1[:, 0].mean(), micro_f1[:, 1].mean(), train_size) for micro_f1, train_size in
zip(svm_micro_f1_lists, [0.8, 0.6, 0.4, 0.2])]))
print('K-means tests summary')
print('NMI: {:.6f}~{:.6f}'.format(nmi_mean_list.mean(), nmi_std_list.mean()))
print('ARI: {:.6f}~{:.6f}'.format(ari_mean_list.mean(), ari_std_list.mean()))
if __name__ == '__main__':
ap = argparse.ArgumentParser(description='MRGNN testing for the IMDB dataset')
ap.add_argument('--feats-type', type=int, default=2,
help='Type of the node features used. ' +
'0 - loaded features; ' +
'1 - only target node features (zero vec for others); ' +
'2 - only target node features (id vec for others); ' +
'3 - all id vec. Default is 2.')
ap.add_argument('--layers', type=int, default=2, help='Number of layers. Default is 2.')
ap.add_argument('--hidden-dim', type=int, default=64, help='Dimension of the node hidden state. Default is 64.')
ap.add_argument('--num-heads', type=int, default=8, help='Number of the attention heads. Default is 8.')
ap.add_argument('--attn-vec-dim', type=int, default=128, help='Dimension of the attention vector. Default is 128.')
ap.add_argument('--rnn-type', default='RotatE0', help='Type of the aggregator. Default is RotatE0.')
ap.add_argument('--epoch', type=int, default=100, help='Number of epochs. Default is 100.')
ap.add_argument('--patience', type=int, default=10, help='Patience. Default is 10.')
ap.add_argument('--repeat', type=int, default=1, help='Repeat the training and testing for N times. Default is 1.')
ap.add_argument('--save-postfix', default='IMDB', help='Postfix for the saved model and result. Default is IMDB.')
args = ap.parse_args()
run_model_IMDB(args.feats_type, args.layers, args.hidden_dim, args.num_heads, args.attn_vec_dim, args.rnn_type,
args.epoch, args.patience, args.repeat, args.save_postfix)