def evaluate(split='valid'):
y_true = eval(split + '_data')
# Ttensor = eval(split + '_tensor')
model.eval()
y_score, _, _ = model(train_tensor)
y_score.detach_()
y_score = y_score.squeeze(0)
y_score[train_data.row, train_data.col] = 0
_, rec_items = torch.topk(y_score, args.N, dim=1)
# y_pred = torch.gather(Ttensor, 1, rec_items).cpu().numpy()
run = sort2query(rec_items[:, 0:args.N])
test = csr2test(y_true.tocsr())
evaluator = Evaluator({'recall', 'map_cut'})
evaluator.evaluate(run, test)
result = evaluator.show([
'recall_5', 'recall_10', 'recall_15', 'recall_20', 'map_cut_5',
'map_cut_10', 'map_cut_15', 'map_cut_20'
])
print(result)
for epoch in range(1, args.maxiter + 1):
train(epoch)
# test()
model.eval()
score, _, _ = model(Rtensor)
score = score.squeeze(0)
score[R.row, R.col] = 0
_, idx = torch.sort(score, 1, True)
run = sort2query(idx[:, 0:args.N])
test = csr2test(T.tocsr())
evaluator = Evaluator({'recall'})
evaluator.evaluate(run, test)
result = evaluator.show(
['recall_5', 'recall_10', 'recall_15', 'recall_20'])
print(result)
line = 'cVAE\t{}\t{}\t{}\t{}\t0'.format(args.data, args.alpha, args.beta, len(args.layer))
for _, value in result.items():
line += '\t{:.5f}'.format(value)
line += '\r\n'
file = open('result', 'a')
file.write(line)
file.close()
if args.save:
name = 'cvae' if args.rating else 'fvae'
path = directory + '/model/' + name
for l in args.layer:
path += '_' + str(l)
model.cpu()
