def _next_batch_data(self):
if self.neg_sample_args['strategy'] == 'by':
uid_list = self.uid_list[self.pr:self.pr + self.step]
data_list = []
idx_list = []
positive_u = []
positive_i = torch.tensor([], dtype=torch.int64)
for idx, uid in enumerate(uid_list):
index = self.uid2index[uid]
data_list.append(self._neg_sampling(self.dataset[index]))
idx_list += [
idx for i in range(self.uid2items_num[uid] * self.times)
]
positive_u += [idx for i in range(self.uid2items_num[uid])]
positive_i = torch.cat(
(positive_i, self.dataset[index][self.iid_field]), 0)
cur_data = cat_interactions(data_list)
idx_list = torch.from_numpy(np.array(idx_list))
positive_u = torch.from_numpy(np.array(positive_u))
self.pr += self.step
return cur_data, idx_list, positive_u, positive_i
else:
cur_data = self._neg_sampling(self.dataset[self.pr:self.pr +
self.step])
self.pr += self.step
return cur_data, None, None, None
def _next_batch_data(self):
uid_list = self.uid_list[self.pr:self.pr + self.step]
data_list = []
for uid in uid_list:
index = self.uid2index[uid]
data_list.append(self.dataset[index])
cur_data = cat_interactions(data_list)
pos_len_list = self.uid2items_num_pos[uid_list]
user_len_list = self.uid2items_num[uid_list]
cur_data.set_additional_info(list(pos_len_list), list(user_len_list))
self.pr += self.step
return cur_data
def _neg_sampling(self, data):
if self.user_inter_in_one_batch:
data_len = len(data[self.uid_field])
data_list = []
for i in range(data_len):
uids = data[self.uid_field][i: i + 1]
neg_iids = self.sampler.sample_by_user_ids(uids, self.neg_sample_by)
cur_data = data[i: i + 1]
data_list.append(self.sampling_func(cur_data, neg_iids))
return cat_interactions(data_list)
else:
uids = data[self.uid_field]
neg_iids = self.sampler.sample_by_user_ids(uids, self.neg_sample_by)
return self.sampling_func(data, neg_iids)
def _neg_sample_by_point_wise_sampling(self, inter_feat, pos_idx, neg_iids,
neg_idx, pos_iids):
pos_inter_num = len(pos_idx[0])
neg_inter_num = len(neg_idx[0])
new_data_pos = inter_feat[pos_idx].repeat(self.neg_sample_by + 1)
new_data_pos[self.iid_field][pos_inter_num:] = neg_iids
new_data_pos = self.dataset.join(new_data_pos)
labels_pos = torch.zeros(pos_inter_num * (self.neg_sample_by + 1))
labels_pos[:pos_inter_num] = 1
new_data_pos.update(Interaction({self.label_field: labels_pos}))
if neg_inter_num > 0:
new_data_neg = inter_feat[neg_idx].repeat(
round(self.neg_sample_by / 3) + 1)
new_data_neg[self.iid_field][neg_inter_num:] = pos_iids
new_data_neg = self.dataset.join(new_data_neg)
labels_neg = torch.ones(neg_inter_num *
(round(self.neg_sample_by / 3) + 1))
labels_neg[:neg_inter_num] = 0
new_data_neg.update(Interaction({self.label_field: labels_neg}))
new_data = cat_interactions([new_data_pos, new_data_neg])
else:
new_data = cat_interactions([new_data_pos])
return new_data
def _next_batch_data(self):
if self.user_inter_in_one_batch:
uid_list = self.uid_list[self.pr:self.pr + self.step]
data_list = []
for uid in uid_list:
index = self.uid2index[uid]
data_list.append(self._neg_sampling(self.dataset[index]))
cur_data = cat_interactions(data_list)
pos_len_list = self.uid2items_num[uid_list]
user_len_list = pos_len_list * self.times
cur_data.set_additional_info(list(pos_len_list),
list(user_len_list))
self.pr += self.step
return cur_data
else:
cur_data = self._neg_sampling(self.dataset[self.pr:self.pr +
self.step])
self.pr += self.step
return cur_data