class PairwiseSampler(object):
def __init__(self, batch_size=512, data_name="ml_100k", num_neg=1):
self.batch_size = batch_size
self.data_name = data_name
self.data = Data()
self.num_neg = num_neg
self.dataset = Dataset(data_name=self.data_name)
def get_train_data(self):
user_movie = self.dataset.get_user_movie()
num_item = self.dataset.get_max_movie_id()
data_value = self.data.get_train_data(data_name=self.data_name)
for idx in range(len(data_value)):
j = np.random.choice(num_item) + 1
while j in user_movie[data_value[idx, 0]]:
j = np.random.choice(num_item) + 1
data_value[idx, 2] = j
return data_value
def get_train_batch(self):
data_value = self.get_train_data()
for start in range(0, len(data_value), self.batch_size):
end = min(start + self.batch_size, len(data_value))
yield data_value[start:end]
def get_test_batch(self):
data_value = self.data.get_test_data(data_name=self.data_name)
for start in range(0, len(data_value), self.batch_size):
end = min(start + self.batch_size, len(data_value))
yield data_value[start:end]
def get_batch_number(self):
data_value = self.data.get_train_data(data_name=self.data_name)
return (len(data_value) + self.batch_size - 1) // self.batch_size
class PointSampler(object):
def __init__(self, batch_size=512, data_name="ml_100k", num_neg=1):
self.batch_size = batch_size
self.data = Data()
self.data_name = data_name
self.num_neg = num_neg
self.dataset = Dataset(data_name=self.data_name)
def get_train_data(self, keep_label=False, value_for_negative=0.):
self.data_value = self.data.get_train_data(data_name=self.data_name)
user_movie_train = self.dataset.get_user_movie_for_train()
num_item = self.dataset.get_max_movie_id()
new_data_value = []
for user_item in self.data_value:
user, item, label = user_item[0], user_item[1], user_item[2]
if keep_label:
new_data_value.append([user, item, label])
else:
new_data_value.append([user, item, 1])
for i in range(self.num_neg):
j = np.random.choice(num_item)
while j in user_movie_train[user]:
j = np.random.choice(num_item)
new_data_value.append([user, j, value_for_negative])
new_data_value = np.array(new_data_value)
new_data_value[:, 2].astype(np.float32)
return new_data_value
def get_train_batch(self,
shuffle=False,
keep_label=False,
value_for_negative=0.):
self.data_value_batch = self.get_train_data(
keep_label=keep_label, value_for_negative=value_for_negative)
if shuffle:
index = [i for i in range(len(self.data_value_batch))]
random.shuffle(index)
self.data_value_batch = self.data_value_batch[index]
for start in range(0, len(self.data_value_batch), self.batch_size):
end = min(start + self.batch_size, len(self.data_value_batch))
yield self.data_value_batch[start:end]
def get_test_batch(self):
test_data = self.data.get_test_data()
len_test_data = len(test_data)
batch_index = np.random.choice(len_test_data, size=self.batch_size)
batch_data = test_data[batch_index, :]
return batch_data
def get_batch_number(self):
i = 1
data_value = self.data.get_train_data(data_name=self.data_name)
return (len(data_value)) // self.batch_size * (i + self.num_neg)
