tp['rating'])
data = sparse.csr_matrix((vals, (rows, cols)),
dtype=np.float32,
shape=shape)
return data
train_data = load_data(os.path.join(DATA_DIR, 'train.csv'))
test_data = load_data(os.path.join(DATA_DIR, 'test_full.csv'))
vad_data = load_data(os.path.join(DATA_DIR, 'validation.csv'))
alpha = args.alpha
print("alpha", alpha)
if binary > 0:
train_data = binarize_rating(train_data)
model_name = 'wg_pf_cau_user_add'
dat_name = DATA_DIR.split('/')[-1]
out_filename = model_name+ \
'_datadir'+str(dat_name) + \
'_bin'+str(binary)+ \
'_cauk0_'+str(caudim)+ \
'outK'+str(outdim)+ \
"_nitr"+str(n_iter)+ \
"_batch"+str(M)+ \
"_thold"+str(int(thold+1))+ \
"_pU"+str(args.priorU)+ \
"_pV"+str(args.priorV)+ \
shape=(end_idx - start_idx + 1, n_items))
data_te = sparse.csr_matrix((ratings_te, (rows_te, cols_te)),
dtype='float64',
shape=(end_idx - start_idx + 1, n_items))
return data_tr, data_te
train_data = load_train_data(os.path.join(DATA_DIR, 'train.csv')).tocsr()
vad_data_tr, vad_data_te = load_tr_te_data(
os.path.join(DATA_DIR, 'validation_tr.csv'),
os.path.join(DATA_DIR, 'validation_te.csv'))
test_data_tr, test_data_te = load_tr_te_data(
os.path.join(DATA_DIR, 'test_tr.csv'),
os.path.join(DATA_DIR, 'test_te.csv'))
if binary > 0:
train_data = binarize_rating(train_data)
vad_data_tr, vad_data_te = binarize_rating(
vad_data_tr), binarize_rating(vad_data_te)
test_data_tr, test_data_te = binarize_rating(
test_data_tr), binarize_rating(test_data_te)
model_name = 'sg_pmf_obs'
dat_name = DATA_DIR.split('/')[-1]
out_filename = model_name+ \
'_datadir'+str(dat_name) + \
'_bin'+str(binary)+ \
'_cauk0_'+str(caudim)+ \
'outK'+str(outdim)+ \
"_nitr"+str(n_iter)+ \