test data generated from training data?

[diskun00]

Hi Rianne,

I have a question regarding the support matrix data. From the code, it seems you are using train rating matrix as a full dataset to generate also test support matrix.

1. rating support matrix rating_mx_train is generated from training rating data. (in testing, it contains training and validation data).
   

   gc-mc/gcmc/preprocessing.py

   Lines 191 to 193 in 722f37d

   	rating_mx_train = np.zeros(num_users * num_items, dtype=np.float32)
   	rating_mx_train[train_idx] = labels[train_idx].astype(np.float32) + 1.
   	rating_mx_train = sp.csr_matrix(rating_mx_train.reshape(num_users, num_items))

2. support matrix is generated from the adj_train which is the rating_mx_train
   

   gc-mc/gcmc/train.py

   Lines 203 to 216 in 722f37d

   	adj_train_int = sp.csr_matrix(adj_train, dtype=np.int32)
   	for i in range(NUMCLASSES):
   	# build individual binary rating matrices (supports) for each rating
   	support_unnormalized = sp.csr_matrix(adj_train_int == i + 1, dtype=np.float32)
   	if support_unnormalized.nnz == 0 and DATASET != 'yahoo_music':
   	# yahoo music has dataset split with not all ratings types present in training set.
   	# this produces empty adjacency matrices for these ratings.
   	sys.exit('ERROR: normalized bipartite adjacency matrix has only zero entries!!!!!')
   	support_unnormalized_transpose = support_unnormalized.T
   	support.append(support_unnormalized)
   	support_t.append(support_unnormalized_transpose)

3. But then 'test_support' is extracted from 'support'.
   

   gc-mc/gcmc/train.py

   Line 246 in 722f37d

   test_support = support[np.array(test_u)]

Shouldn't we change line 192 to
rating_mx_train[idx_nonzero] = labels[idx_nonzero].astype(np.float32) + 1.0
such that all rating_mx_train contains all rating data.

gc-mc/gcmc/preprocessing.py

Lines 191 to 193 in 722f37d

	rating_mx_train = np.zeros(num_users * num_items, dtype=np.float32)
	rating_mx_train[train_idx] = labels[train_idx].astype(np.float32) + 1.
	rating_mx_train = sp.csr_matrix(rating_mx_train.reshape(num_users, num_items))