pnum, gnum = truth_mat.shape
dimension = args.dimensions
gene_emb = np.zeros(gnum * dimension).reshape(gnum, dimension)
phen_emb = np.zeros(pnum * dimension).reshape(pnum, dimension)
for i in range(1, gnum + 1):
pair1 = 'g' + str(i)
if pair1 not in true_index:
continue
gi = np.array(result_vector.loc['g' + str(i)])
gene_emb[i - 1, :] = gi
for i in range(1, pnum + 1):
pair2 = 'p' + str(i)
if pair2 not in true_index:
continue
pi = np.array(result_vector.loc['p' + str(i)])
phen_emb[i - 1, :] = pi
pred_mat = phen_emb.dot(gene_emb.T)
#-------------------------------new AUCn-------------------------------------
pred_mat = np.asarray(pred_mat)
#R_vers = np.ones((pnum, gnum)) - truth_mat
print(args.method)
print("基因关联表型预测：", eval.calculate_metrics_sk(pred_mat, truth_mat, mask_mat))
print("基因关联表型预测：", eval.AUC_main(pred_mat, truth_mat, mask_mat))
