A question about the 'adj_mats_orig'

[bbjy]

@marinkaz Thank you so much for your work! Would you please explain to me why the adj_mats_orig contains both gene_adj and gene_adj.transpose (the same to drug_drug_adj_list)? I think that a 'gene_adj' is enough, as the code below.
Looking forward to your reply. Thank you!
` #data representation

adj_mats_orig = {
(0, 0): [gene_adj, gene_adj.transpose(copy=True)],
(0, 1): [gene_drug_adj],
(1, 0): [drug_gene_adj],
(1, 1): drug_drug_adj_list + [x.transpose(copy=True) for x in drug_drug_adj_list],
}`

`#In my view, the data representation should be as follows.

adj_mats_orig = {
(0, 0): [gene_adj],
(0, 1): [gene_drug_adj],
(1, 0): [drug_gene_adj],
(1, 1): drug_drug_adj_list,
}`

[hurleyLi]

I have the same question. I think this will cause a huge data leakage problem in your training, because your validation and test set is created independently for gene_adj and gene_adj.transpose(copy=True), and therefore the edges from the validation / test set in gene_adj is actually included in the training set of gene_adj.transpose(copy=True).

Same problem goes for the train / validate set between gene_drug_adj and drug_gene_adj. The validation edges from gene_drug_adj are actually used for training in drug_gene_adj, and vise versa.

Could you please clarify?
Thanks!

[zch42]

@bbjy I guess the author would like to use adj and adj.transpose to represent undirect graphs for PPI network and drug-drug network. For the interactions between proteins and drugs, the information flow is represented with bipartite graphs.

[zch42]

@hurleyLi I am also confused on the problem you mentioned.
Here are the source codes for spliting training/val/testing edges:

    def mask_test_edges(self, edge_type, type_idx):
        edges_all, _, _ = preprocessing.sparse_to_tuple(self.adj_mats[edge_type][type_idx])
        num_test = max(50, int(np.floor(edges_all.shape[0] * self.val_test_size)))
        num_val = max(50, int(np.floor(edges_all.shape[0] * self.val_test_size)))

        all_edge_idx = list(range(edges_all.shape[0]))
        np.random.shuffle(all_edge_idx)

        val_edge_idx = all_edge_idx[:num_val]
        val_edges = edges_all[val_edge_idx]

        test_edge_idx = all_edge_idx[num_val:(num_val + num_test)]
        test_edges = edges_all[test_edge_idx]

        train_edges = np.delete(edges_all, np.hstack([test_edge_idx, val_edge_idx]), axis=0)

It seems that the author splits the edges independently for different type_idx, which will cause training and cross validation overlap.