def evaluate_test(model, g, inputs, labels, test_mask, lp_dict, coeffs, meta):
model.eval()
with torch.no_grad():
pred = model(g, inputs).squeeze()
output = pred.cuda()
labels = labels.cuda()
idx_test = lp_dict['idx_test']
idx_train = lp_dict['idx_train']
adj = sparse_mx_to_torch_sparse_tensor(normalize(lp_dict['sp_adj']))
#print(adj.to_dense()[np.arange(100), np.arange(100)+1])
labels, output, adj = labels.cpu(), output.cpu(), adj.cpu()
loss = F.mse_loss(output[idx_test].squeeze(), labels[idx_test].squeeze())
r2_test = compute_r2(output[idx_test], labels[idx_test])
lp_output = lp_refine(idx_test, idx_train, labels, output, adj,
torch.tanh(coeffs[0]).item(),
torch.exp(coeffs[1]).item())
lp_r2_test = compute_r2(lp_output, labels[idx_test])
lp_output_raw_cov = lp_refine(idx_test, idx_train, labels, output, adj)
lp_r2_test_raw_cov = compute_r2(lp_output_raw_cov, labels[idx_test])
print("------------")
print("election year {}".format(meta))
print("loss:", loss.item())
print("raw_r2:", r2_test)
print("refined_r2:", lp_r2_test)
print("refined_r2_raw_cov:", lp_r2_test_raw_cov)
print("------------")
def evaluate_test(model, features, labels, test_mask, lp_dict, coeffs, meta="2012"):
model.eval()
with torch.no_grad():
output = model(features).squeeze()
output = output.cuda()
labels = labels.cuda()
idx_test = lp_dict['idx_test']
idx_train = lp_dict['idx_train']
adj = sparse_mx_to_torch_sparse_tensor(normalize(lp_dict['sp_adj']))
labels, output, adj = labels.cpu(), output.cpu(), adj.cpu()
loss = F.mse_loss(output[idx_test].squeeze(), labels[idx_test].squeeze())
r2_test = compute_r2(output[idx_test], labels[idx_test])
lp_output = lp_refine(idx_test, idx_train, labels, output, adj, torch.tanh(coeffs[0]).item(), torch.exp(coeffs[1]).item())
lp_r2_test = compute_r2(lp_output, labels[idx_test])
lp_output_raw_conv = lp_refine(idx_test, idx_train, labels, output, adj)
lp_r2_test_raw_conv = compute_r2(lp_output_raw_conv, labels[idx_test])
print("------------")
print("election year {}".format(meta))
print("loss:", loss.item())
print("raw_r2:", r2_test)
print("refined_r2:", lp_r2_test)
print("refined_r2_raw_conv:", lp_r2_test_raw_conv)
print("------------")
def evaluate_test(model, g, inputs, labels, test_mask, batch_size, device,
lp_dict, coeffs, meta):
model.eval()
with th.no_grad():
pred = model.inference(g, inputs, batch_size, device).view(-1)
output = pred.cuda()
labels = labels.cuda()
idx_test = lp_dict['idx_test']
idx_train = lp_dict['idx_train']
adj = lp_dict['adj']
labels, output, adj = labels.cpu(), output.cpu(), adj.cpu()
loss = F.mse_loss(output[idx_test].squeeze(), labels[idx_test].squeeze())
r2_test = compute_r2(output[test_mask], labels[test_mask])
lp_output = lp_refine(idx_test, idx_train, labels, output, adj,
torch.tanh(coeffs[0]).item(),
torch.exp(coeffs[1]).item())
lp_r2_test = compute_r2(lp_output, labels[idx_test])
lp_output_raw_conv = lp_refine(idx_test, idx_train, labels, output, adj)
lp_r2_test_raw_conv = R2(lp_output_raw_conv, labels[idx_test])
print("------------")
print("election year {}".format(meta))
print("loss:", loss.item())
print("raw_r2:", r2_test)
print("refined_r2:", lp_r2_test)
print("refined_r2_raw_conv:", lp_r2_test_raw_conv)
print("------------")
model.train()
return lp_r2_test
def evaluate_test(model, g, inputs, labels, test_mask, batch_size, device, lp_dict, meta):
model.eval()
with th.no_grad():
pred = model.inference(g, inputs, batch_size, device).view(-1)
output = pred.to(device)
labels = labels.to(device)
idx_test = lp_dict['idx_test']
idx_train = lp_dict['idx_train']
adj = sparse_mx_to_torch_sparse_tensor(normalize(lp_dict['sp_adj']))
labels, output, adj = labels.cpu(), output.cpu(), adj.cpu()
loss = F.mse_loss(output[idx_test].squeeze(), labels[idx_test].squeeze())
r2_test = compute_r2(output[test_mask], labels[test_mask])
lp_output = lp_refine(idx_test, idx_train, labels, output, adj)
lp_r2_test = compute_r2(lp_output, labels[idx_test])
print("------------")
print("election year {}".format(meta))
print("loss:", loss.item())
print("raw_r2:", r2_test)
print("refined_r2:", lp_r2_test)
print("------------")
model.train()
def test(adj, features, labels, test_meta):
model.eval()
output = model(features, adj).view(-1)
loss_test = loss(output, labels, idx_test, adj, coeffs, True)
r2_test = R2(output[idx_test], labels[idx_test])
labels, output, adj = labels.cpu(), output.cpu(), adj.cpu()
lp_output = lp_refine(idx_test, idx_train, labels, output, adj,
torch.tanh(coeffs[0]).item(),
torch.exp(coeffs[1]).item())
lp_r2_test = R2(lp_output, labels[idx_test])
lp_output_raw_conv = lp_refine(idx_test, idx_train, labels, output, adj)
lp_r2_test_raw_conv = R2(lp_output_raw_conv, labels[idx_test])
print("Test set ({}) results:".format(test_meta),
"loss= {:.4f}".format(loss_test.item()),
"R2= {:.4f}".format(r2_test.item()),
"LP_R2= {:.4f}".format(lp_r2_test.item()),
"LP_R2_raw_conv= {:.4f}\n".format(lp_r2_test_raw_conv.item()))
def test(adj, features, labels, test_meta):
model.eval()
output = model(features, adj).view(-1)
'''
np.save("test_lp/output_all.npy", output.cpu().detach().numpy())
np.save("test_lp/labels_all.npy", labels.cpu().detach().numpy())
np.save("test_lp/idx_train.npy", idx_train.cpu().detach().numpy())
np.save("test_lp/idx_val.npy", idx_val.cpu().detach().numpy())
np.save("test_lp/idx_test.npy", idx_test.cpu().detach().numpy())
'''
loss_test = loss(output, labels, idx_test)
r2_test = R2(output[idx_test], labels[idx_test])
labels, output, adj = labels.cpu(), output.cpu(), adj.cpu()
#adj = torch.FloatTensor(np.load("test_lp/raw_S.npy"))
lp_output = lp_refine(idx_test, idx_train, labels, output, adj)
lp_r2_test = R2(lp_output, labels[idx_test])
print("Test set ({}) results:".format(test_meta),
"loss= {:.4f}".format(loss_test.item()),
"R2= {:.4f}".format(r2_test.item()),
"LP_R2= {:.4f}\n".format(lp_r2_test.item()))