elif args.method.lower() in ['sorecgatuser']:
dataset = SocialUser_Dataset(args)
else:
dataset = Dataset(args)
params = Parameters(args, dataset)
print(
"""Load data done [%.1f s]. #user:%d, #item:%d, #dom:%d, #train:%d, #test:%d, #valid:%d"""
% (time() - t1, params.num_users, params.num_items, params.num_doms,
params.num_train_instances, params.num_test_instances,
params.num_valid_instances))
print('Method: %s' % (params.method))
if params.method in ['sorecgatitem', 'sorecgatuser']:
model = Models(params)
model.define_model()
model.define_loss('all')
print("Model definition completed: in %.2fs" % (time() - t1))
train_step = get_optimizer(params.learn_rate,
params.optimizer).minimize(model.loss)
init = tf.global_variables_initializer()
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
print('train instances: {}'.format(params.train_matrix.nnz))
error_plot = Error_plot(save_flag=True,
res_path=params.result_path,
args_str=args_str,
args=args)
ns = NegativeSamples(params)
with tf.Session(config=config) as sess:
