def __init__(self, args):
self.args = args
self.log_str_path = './train_log/' + args.model + str(
args.number) + args.mod + args.dataset + 'GL' + str(
args.global_lr) + 'LE' + str(args.local_epoch) + 'LL' + str(
args.local_lr) + '.log'
self.mod_str_path = './saved_models/' + args.model + str(
args.number) + args.mod + args.dataset + 'GL' + str(
args.global_lr) + 'LE' + str(args.local_epoch) + 'LL' + str(
args.local_lr)
self.log = Logging(self.log_str_path)
self.user_num, self.item_num, self.train_data, self.train_support_mat, self.val_negative_dict, self.val_negative, self.train_mat, self.sup_max, self.query_max = data_prepare.load_all(
self.args)
self.Train_data = data_prepare.Train_data(self.train_data,
self.item_num,
self.train_support_mat,
self.sup_max, self.query_max,
self.args)
self.Train_data.ng_train_sample()
eval_ = args.model + "(self.item_num,args).cuda()"
self.model = eval(eval_)
self.maml_train_batch_sampler = data.BatchSampler(
data.RandomSampler(range(self.Train_data.idx)),
batch_size=self.args.batch_size,
drop_last=False)
def __init__(self):
self.logger = Logging.get_logger(self.__class__.__name__)
self.analytic_solver = EquationSolver()
self.implicit_solver = ImplicitNumericalEquationSolver()
self.explicit_solver = ExplicitNumericalEquationSolver()
self.max_clip_value = 10e+30
self.min_clip_value = -10e+30
def __init__(self):
self.logger = Logging.get_logger(self.__class__.__name__)
self.logger.debug('EquationSolver is created')
class meta_learner():
def __init__(self, args):
self.args = args
self.log_str_path = './train_log/' + args.model + str(
args.number) + args.mod + args.dataset + 'GL' + str(
args.global_lr) + 'LE' + str(args.local_epoch) + 'LL' + str(
args.local_lr) + '.log'
self.mod_str_path = './saved_models/' + args.model + str(
args.number) + args.mod + args.dataset + 'GL' + str(
args.global_lr) + 'LE' + str(args.local_epoch) + 'LL' + str(
args.local_lr)
self.log = Logging(self.log_str_path)
self.user_num, self.item_num, self.train_data, self.train_support_mat, self.val_negative_dict, self.val_negative, self.train_mat, self.sup_max, self.query_max = data_prepare.load_all(
self.args)
self.Train_data = data_prepare.Train_data(self.train_data,
self.item_num,
self.train_support_mat,
self.sup_max, self.query_max,
self.args)
self.Train_data.ng_train_sample()
eval_ = args.model + "(self.item_num,args).cuda()"
self.model = eval(eval_)
self.maml_train_batch_sampler = data.BatchSampler(
data.RandomSampler(range(self.Train_data.idx)),
batch_size=self.args.batch_size,
drop_last=False)
def train(self):
print("data loaded")
max_hr, max_ndcg = 0.0, 0.0
val_hr, val_ndcg = self.model.evaluate(self.Train_data,
self.val_negative_dict,
self.val_negative)
str_to_log = '---epoch:{},train_query_loss:{},val_hr:{},val_ndcg:{}'.format(
0, 0, val_hr, val_ndcg)
self.log.record(str_to_log)
for epoch in range(self.args.train_epoch):
total_loss = 0.0
for batch_idx_list in self.maml_train_batch_sampler:
positive_itm, sup_itm, sup_label, sup_mdsk, positive_mdsk, query_itm, query_label, query_mdsk, len_pos = self.Train_data.get_batch(
batch_idx_list)
t1 = time.time()
step_loss = self.model.global_forward(positive_itm, sup_itm,
sup_label, sup_mdsk,
positive_mdsk, query_itm,
query_label, query_mdsk,
len_pos)
total_loss += step_loss
t2 = time.time()
val_hr, val_ndcg = self.model.evaluate(self.Train_data,
self.val_negative_dict,
self.val_negative)
str_to_log = '---epoch:{},train_query_loss:{},val_hr:{},val_ndcg:{}'.format(
epoch, total_loss, val_hr, val_ndcg)
self.log.record(str_to_log)
if val_ndcg > max_ndcg:
torch.save(self.model.state_dict(), self.mod_str_path + '.mod')
max_hr = max(max_hr, val_hr)
max_ndcg = max(max_ndcg, val_ndcg)