本文整理汇总了Python中official.recommendation.data_preprocessing.DATASET_TO_NUM_USERS_AND_ITEMS属性的典型用法代码示例。如果您正苦于以下问题：Python data_preprocessing.DATASET_TO_NUM_USERS_AND_ITEMS属性的具体用法？Python data_preprocessing.DATASET_TO_NUM_USERS_AND_ITEMS怎么用？Python data_preprocessing.DATASET_TO_NUM_USERS_AND_ITEMS使用的例子？那么, 这里精选的属性代码示例或许可以为您提供帮助。您也可以进一步了解该属性所在类official.recommendation.data_preprocessing的用法示例。
在下文中一共展示了data_preprocessing.DATASET_TO_NUM_USERS_AND_ITEMS属性的4个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: get_inputs
# 需要导入模块: from official.recommendation import data_preprocessing [as 别名]
# 或者: from official.recommendation.data_preprocessing import DATASET_TO_NUM_USERS_AND_ITEMS [as 别名]
def get_inputs(params):
"""Returns some parameters used by the model."""
if FLAGS.download_if_missing and not FLAGS.use_synthetic_data:
movielens.download(FLAGS.dataset, FLAGS.data_dir)
if FLAGS.seed is not None:
np.random.seed(FLAGS.seed)
if FLAGS.use_synthetic_data:
producer = data_pipeline.DummyConstructor()
num_users, num_items = data_preprocessing.DATASET_TO_NUM_USERS_AND_ITEMS[
FLAGS.dataset]
num_train_steps = rconst.SYNTHETIC_BATCHES_PER_EPOCH
num_eval_steps = rconst.SYNTHETIC_BATCHES_PER_EPOCH
else:
num_users, num_items, producer = data_preprocessing.instantiate_pipeline(
dataset=FLAGS.dataset, data_dir=FLAGS.data_dir, params=params,
constructor_type=FLAGS.constructor_type,
deterministic=FLAGS.seed is not None)
num_train_steps = producer.train_batches_per_epoch
num_eval_steps = producer.eval_batches_per_epoch
return num_users, num_items, num_train_steps, num_eval_steps, producer 示例2: setUp
# 需要导入模块: from official.recommendation import data_preprocessing [as 别名]
# 或者: from official.recommendation.data_preprocessing import DATASET_TO_NUM_USERS_AND_ITEMS [as 别名]
def setUp(self):
if keras_utils.is_v2_0:
tf.compat.v1.disable_eager_execution()
self.temp_data_dir = self.get_temp_dir()
ratings_folder = os.path.join(self.temp_data_dir, DATASET)
tf.io.gfile.makedirs(ratings_folder)
np.random.seed(0)
raw_user_ids = np.arange(NUM_USERS * 3)
np.random.shuffle(raw_user_ids)
raw_user_ids = raw_user_ids[:NUM_USERS]
raw_item_ids = np.arange(NUM_ITEMS * 3)
np.random.shuffle(raw_item_ids)
raw_item_ids = raw_item_ids[:NUM_ITEMS]
users = np.random.choice(raw_user_ids, NUM_PTS)
items = np.random.choice(raw_item_ids, NUM_PTS)
scores = np.random.randint(low=0, high=5, size=NUM_PTS)
times = np.random.randint(low=1000000000, high=1200000000, size=NUM_PTS)
self.rating_file = os.path.join(ratings_folder, movielens.RATINGS_FILE)
self.seen_pairs = set()
self.holdout = {}
with tf.io.gfile.GFile(self.rating_file, "w") as f:
f.write("user_id,item_id,rating,timestamp\n")
for usr, itm, scr, ts in zip(users, items, scores, times):
pair = (usr, itm)
if pair in self.seen_pairs:
continue
self.seen_pairs.add(pair)
if usr not in self.holdout or (ts, itm) > self.holdout[usr]:
self.holdout[usr] = (ts, itm)
f.write("{},{},{},{}\n".format(usr, itm, scr, ts))
movielens.download = mock_download
movielens.NUM_RATINGS[DATASET] = NUM_PTS
data_preprocessing.DATASET_TO_NUM_USERS_AND_ITEMS[DATASET] = (NUM_USERS,
NUM_ITEMS) 开发者ID:ShivangShekhar，项目名称:Live-feed-object-device-identification-using-Tensorflow-and-OpenCV，代码行数:41，代码来源:data_test.py
示例3: setUp
# 需要导入模块: from official.recommendation import data_preprocessing [as 别名]
# 或者: from official.recommendation.data_preprocessing import DATASET_TO_NUM_USERS_AND_ITEMS [as 别名]
def setUp(self):
self.temp_data_dir = self.get_temp_dir()
ratings_folder = os.path.join(self.temp_data_dir, DATASET)
tf.gfile.MakeDirs(ratings_folder)
np.random.seed(0)
raw_user_ids = np.arange(NUM_USERS * 3)
np.random.shuffle(raw_user_ids)
raw_user_ids = raw_user_ids[:NUM_USERS]
raw_item_ids = np.arange(NUM_ITEMS * 3)
np.random.shuffle(raw_item_ids)
raw_item_ids = raw_item_ids[:NUM_ITEMS]
users = np.random.choice(raw_user_ids, NUM_PTS)
items = np.random.choice(raw_item_ids, NUM_PTS)
scores = np.random.randint(low=0, high=5, size=NUM_PTS)
times = np.random.randint(low=1000000000, high=1200000000, size=NUM_PTS)
self.rating_file = os.path.join(ratings_folder, movielens.RATINGS_FILE)
self.seen_pairs = set()
self.holdout = {}
with tf.gfile.Open(self.rating_file, "w") as f:
f.write("user_id,item_id,rating,timestamp\n")
for usr, itm, scr, ts in zip(users, items, scores, times):
pair = (usr, itm)
if pair in self.seen_pairs:
continue
self.seen_pairs.add(pair)
if usr not in self.holdout or (ts, itm) > self.holdout[usr]:
self.holdout[usr] = (ts, itm)
f.write("{},{},{},{}\n".format(usr, itm, scr, ts))
movielens.download = mock_download
movielens.NUM_RATINGS[DATASET] = NUM_PTS
data_preprocessing.DATASET_TO_NUM_USERS_AND_ITEMS[DATASET] = (NUM_USERS,
NUM_ITEMS) 示例4: setUp
# 需要导入模块: from official.recommendation import data_preprocessing [as 别名]
# 或者: from official.recommendation.data_preprocessing import DATASET_TO_NUM_USERS_AND_ITEMS [as 别名]
def setUp(self):
self.temp_data_dir = self.get_temp_dir()
ratings_folder = os.path.join(self.temp_data_dir, DATASET)
tf.gfile.MakeDirs(ratings_folder)
np.random.seed(0)
raw_user_ids = np.arange(NUM_USERS * 3)
np.random.shuffle(raw_user_ids)
raw_user_ids = raw_user_ids[:NUM_USERS]
raw_item_ids = np.arange(NUM_ITEMS * 3)
np.random.shuffle(raw_item_ids)
raw_item_ids = raw_item_ids[:NUM_ITEMS]
users = np.random.choice(raw_user_ids, NUM_PTS)
items = np.random.choice(raw_item_ids, NUM_PTS)
scores = np.random.randint(low=0, high=5, size=NUM_PTS)
times = np.random.randint(low=1000000000, high=1200000000, size=NUM_PTS)
rating_file = os.path.join(ratings_folder, movielens.RATINGS_FILE)
self.seen_pairs = set()
self.holdout = {}
with tf.gfile.Open(rating_file, "w") as f:
f.write("user_id,item_id,rating,timestamp\n")
for usr, itm, scr, ts in zip(users, items, scores, times):
pair = (usr, itm)
if pair in self.seen_pairs:
continue
self.seen_pairs.add(pair)
if usr not in self.holdout or (ts, itm) > self.holdout[usr]:
self.holdout[usr] = (ts, itm)
f.write("{},{},{},{}\n".format(usr, itm, scr, ts))
movielens.download = mock_download
movielens.NUM_RATINGS[DATASET] = NUM_PTS
data_preprocessing.DATASET_TO_NUM_USERS_AND_ITEMS[DATASET] = (NUM_USERS,
NUM_ITEMS)