regs_ui = list(np.float32(eval(regs)))
# original_matrix, train_matrix, test_matrix, num_users, num_items \
# = mtl.load_as_matrix(datafile='Data/books_and_elecs_merged.csv')
original_matrix \
= mtl.load_original_matrix(datafile='Data/ml-1m/ratings.dat', header=['uid', 'iid', 'ratings', 'time'], sep='::')
# original_matrix = mtl.matrix_theshold(original_matrix,threshold=2)
original_matrix = mtl.matrix_to_binary(original_matrix, 0)
# train_matrix, test_matrix = mtl.matrix_split(original_matrix,n_item_per_user=num_test)
train_matrix, test_matrix = mtl.matrix_split(original_matrix,
opt='prediction',
mode='user',
test_size=0.2,
random_state=10)
gpu_options = tf.GPUOptions(allow_growth=True)
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True,
intra_op_parallelism_threads=8,
inter_op_parallelism_threads=8,
gpu_options=gpu_options)) as sess:
model = BPRMF(sess,
top_K=ndcgk,
num_ranking_neg=num_ranking_list - num_test,
num_factors=num_factors,
regs_ui=regs_ui,
lr=lr,
epochs=num_epochs,
args = parseArgs()
num_epochs, batch_size, \
reg_embs, num_neg, lr, ndcgk, num_factors, num_ranking_list, num_test = \
args.epochs, args.batch_size,\
args.ebregs, args.num_neg, args.lr, args.ndcgk, args.nfactors, args.num_rk, args.ntest
reg_embs = list(np.float32(eval(reg_embs)))
# original_matrix, train_matrix, test_matrix, num_users, num_items \
# = mtl.load_as_matrix(datafile='Data/books_and_elecs_merged.csv')
original_matrix \
= mtl.load_original_matrix(datafile='Data/ml-100k/u.data',header=['uid','iid','ratings','time'],sep='\t')
original_matrix = mtl.matrix_to_binary(original_matrix, 0)
train_matrix, test_matrix = mtl.matrix_split(original_matrix,
opt='ranking',
n_item_per_user=num_test)
num_users, num_items = original_matrix.shape
print("Number of users is {0}".format(num_users))
print("Number of items is {0}".format(num_items))
print("Number of ratings for all is {0}".format(original_matrix.nnz))
print("Number of ratings for training is {0}".format(train_matrix.nnz))
print("Ratings density for training is {0}".format(
train_matrix.nnz / (num_users * num_items)))
gpu_options = tf.GPUOptions(allow_growth=True)
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True,
intra_op_parallelism_threads=8,
inter_op_parallelism_threads=8,
gpu_options=gpu_options)) as sess:
num_epochs, batch_size, lr, topk, num_rk, num_test,\
regs_ui, num_factors, alpha = \
args.epochs, args.batch_size, args.lr, args.ndcgk, args.num_rk, args.ntest,\
args.regs_ui,args.nfactors,args.alpha
regs_ui = list(np.float32(eval(regs_ui)))
# original_matrix, train_matrix, test_matrix,num_users, num_items \
# = mtl.load_as_matrix(datafile='Data/ml-100k/u.data', header=['uid', 'iid', 'ratings', 'time'], sep='\t')
original_matrix \
= mtl.load_original_matrix(datafile='Data/ml-100k/u.data', header=['uid', 'iid', 'ratings', 'time'], sep='\t')
original_matrix = mtl.matrix_theshold(original_matrix, threshold=2)
train_matrix, test_matrix = mtl.matrix_split(original_matrix,
n_item_per_user=num_test)
test_matrix = mtl.matrix_to_binary(test_matrix, 0)
gpu_options = tf.GPUOptions(allow_growth=True)
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True,
intra_op_parallelism_threads=8,
inter_op_parallelism_threads=8,
gpu_options=gpu_options)) as sess:
model = WRMF(sess,
num_factors=num_factors,
regs_ui=regs_ui,
alpha=alpha,
lr=lr,
topk=topk,
num_ranking_neg=num_rk - num_test,