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from random import shuffle,randint,choice
# training_data:[[user_id, item_id, float(weight)]...]
def next_batch_pairwise(data,batch_size,n_negs=1):
"""
args:
data
batch_sieze
n_negs = 64
return:
u_idx : [batch_size]
i_idx : [batch_size]
j_idx : [batch_size,64]
"""
training_data = data.training_data
# 传进来的参数data是data.ui_graph下的Interaction类
shuffle(training_data)
batch_id = 0
data_size = len(training_data)
while batch_id < data_size:
if batch_id + batch_size <= data_size:
users = [training_data[idx][0] for idx in range(batch_id, batch_size + batch_id)]
items = [training_data[idx][1] for idx in range(batch_id, batch_size + batch_id)]
batch_id += batch_size
else:
users = [training_data[idx][0] for idx in range(batch_id, data_size)]
items = [training_data[idx][1] for idx in range(batch_id, data_size)]
batch_id = data_size
u_idx, i_idx, j_idx = [], [], []
item_list = list(data.item.keys()) # 以列表的方式来获取所有item_id
for i, user in enumerate(users):
i_idx.append(data.item[items[i]])
u_idx.append(data.user[user])
neg_idx=[]
for m in range(64):
neg_item = choice(item_list)
while neg_item in data.training_set_u[user]:
neg_item = choice(item_list)
neg_idx.append(data.item[neg_item])
# 这里的nef_item_id的shpe应该是[batch,64]
j_idx.append(neg_idx)
yield u_idx, i_idx, j_idx
def next_batch_pointwise(data,batch_size):
training_data = data.training_data
data_size = len(training_data)
batch_id = 0
while batch_id < data_size:
if batch_id + batch_size <= data_size:
users = [training_data[idx][0] for idx in range(batch_id, batch_size + batch_id)]
items = [training_data[idx][1] for idx in range(batch_id, batch_size + batch_id)]
batch_id += batch_size
else:
users = [training_data[idx][0] for idx in range(batch_id, data_size)]
items = [training_data[idx][1] for idx in range(batch_id, data_size)]
batch_id = data_size
u_idx, i_idx, y = [], [], []
for i, user in enumerate(users):
i_idx.append(data.item[items[i]])
u_idx.append(data.user[user])
y.append(1)
for instance in range(4):
item_j = randint(0, data.item_num - 1)
while data.id2item[item_j] in data.training_set_u[user]:
item_j = randint(0, data.item_num - 1)
u_idx.append(data.user[user])
i_idx.append(item_j)
y.append(0)
yield u_idx, i_idx, y
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