num_epochs, batch_size, lr,\
regs_ui, regs_bias, num_factors = \
args.epochs, args.batch_size, args.lr,\
args.regs_ui, args.regs_bias,args.nfactors
regs_ui = list(np.float32(eval(regs_ui)))
regs_bias = list(np.float32(eval(regs_bias)))
# original_matrix = mtl.load_original_matrix('Data/ml-1m/ratings.dat', header=['uid', 'iid', 'ratings', 'time'], sep='::')
# train_matrix, test_matrix = mtl.matrix_split(original_matrix, opt='prediction', mode='user', test_size=0.1, seed=42)
# gtl.matrix_to_mat('SVD_ML1M_90.mat',opt='coo', original=original_matrix, train=train_matrix, test=test_matrix)
# gtl.matrix_to_excel('svd_all.xlsx',opt='coo',train_all=train_matrix, test_all=test_matrix)
# print("Saved!")
data = gtl.load_mat_as_matrix('Data/SVD_ML1M_90.mat', opt='coo')
original_matrix, train_matrix, test_matrix = data['original'], data[
'train'], data['test']
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 = SVD(sess,
num_factors=num_factors,
regs_ui=regs_ui,
regs_bias=regs_bias,
lr=lr,
epochs=num_epochs,
'flixster-5': 'Data/flixster-hr-5.mat',
'ymov-full': 'Data/ymov-hr-full.mat',
'ymov-345': 'Data/ymov-hr-345.mat',
'ymov-45': 'Data/ymov-hr-45.mat',
'ymov-5': 'Data/ymov-hr-5.mat',
'ymus-full': 'Data/ymus-hr-full.mat',
'ymus-345': 'Data/ymus-hr-345.mat',
'ymus-45': 'Data/ymus-hr-45.mat',
'ymus-5': 'Data/ymus-hr-5.mat',
}
dataset = 'filmtrust-34'
path = path_dict[dataset]
print('Loading Data From {0}'.format(path))
data = gtl.load_mat_as_matrix(path, opt='coo')
original_matrix, train_matrix, test_matrix = data['original'], data['train'], data['test']
print('Users:{0}, Items:{1}, Ratings:{2}'.format(original_matrix.shape[0], original_matrix.shape[1], original_matrix.nnz))
gpu_options = tf.GPUOptions(allow_growth=True)
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True,
intra_op_parallelism_threads=24,
inter_op_parallelism_threads=24,
gpu_options=gpu_options)) as sess:
model = AMF(sess,
top_K=[5,10],
num_factors=40,
reg=0.015,
reg_adv=100,
noise_type='grad',
args.topk, args.ranking_ratio, args.nfactors, args.out_neg_ratio, args.epsilon,\
args.ae_regs
ae_regs = list(np.float32(eval(ae_regs)))
# original_matrix \
# = mtl.load_original_matrix(datafile='Data/ml-1m/ratings.dat', header=['uid', 'iid', 'ratings', 'time'], sep='::')
# train_matrix, test_matrix = mtl.matrix_split(original_matrix, opt='prediction', mode='user', test_size=0.1, seed=10)
# original_matrix = mtl.matrix_to_binary(original_matrix, 0)
# train_matrix, test_matrix = mtl.matrix_to_binary(train_matrix,0), mtl.matrix_to_binary(test_matrix,0)
# gtl.matrix_to_mat('Data/ML1M_90_Data.mat', opt='coo', original=original_matrix, train=train_matrix, test=test_matrix)
#
data = gtl.load_mat_as_matrix('Data/ML1M_90_Data.mat', opt='coo')
original_matrix, train_matrix, test_matrix = data['original'], data['train'], data['test']
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 = GAN_AE(sess,
top_K=topK, ranking_list_ratio=ranking_list_ratio, neg_ratio=neg_ratio,
num_factors=num_factors, ae_regs=ae_regs, eps=epsilon,
lr=lr,
epochs=num_epochs, batch_size=batch_size, T=1000, verbose=True)
model.prepare_data(original_matrix=original_matrix, train_matrix=train_matrix, test_matrix=test_matrix)
model.build_model()
# original_matrix \
# = mtl.load_original_matrix(datafile='Data/ml-1m/ratings.dat', header=['uid', 'iid', 'ratings', 'time'], sep='::')
# train_matrix, test_matrix \
# = mtl.matrix_split(original_matrix, opt='prediction', mode='user', test_size=0.2, random_state=10)
# train_matrix, test_matrix \
# = mtl.matrix_split(original_matrix, opt='ranking', mode='mat', n_item_per_user=1, random_state=10)
# original_matrix = mtl.matrix_to_binary(original_matrix, 0)
# train_matrix = mtl.matrix_to_binary(train_matrix, 0)
# test_matrix = mtl.matrix_to_binary(test_matrix, 0)
# gtl.matrix_to_mat('Data/ML1M_Rank_200_1_Data.mat', opt='coo', original=original_matrix, train=train_matrix, test=test_matrix)
data = gtl.load_mat_as_matrix('Data/ML1M_Rank_200_1_Data.mat', opt='coo')
original_matrix, train_matrix, test_matrix = data['original'], data[
'train'], data['test']
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 = GAN_AE(sess,
top_K=topK,
neg_ratio=neg_ratio,
num_factors=num_factors,
ae_regs=ae_regs,
eps=40,
lr=lr,