Python data_preprocessing.DATASET_TO_NUM_USERS_AND_ITEMS屬性代碼示例

本文整理匯總了Python中official.recommendation.data_preprocessing.DATASET_TO_NUM_USERS_AND_ITEMS屬性的典型用法代碼示例。如果您正苦於以下問題：Python data_preprocessing.DATASET_TO_NUM_USERS_AND_ITEMS屬性的具體用法？Python data_preprocessing.DATASET_TO_NUM_USERS_AND_ITEMS怎麽用？Python data_preprocessing.DATASET_TO_NUM_USERS_AND_ITEMS使用的例子？那麽, 這裏精選的屬性代碼示例或許可以為您提供幫助。您也可以進一步了解該屬性所在類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屬性的4個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。

示例1: get_inputs

# 需要導入模塊: from official.recommendation import data_preprocessing [as 別名]
# 或者: from official.recommendation.data_preprocessing import DATASET_TO_NUM_USERS_AND_ITEMS [as 別名]
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

開發者ID:IntelAI，項目名稱:models，代碼行數:25，代碼來源:ncf_common.py

示例2: setUp

# 需要導入模塊: from official.recommendation import data_preprocessing [as 別名]
# 或者: from official.recommendation.data_preprocessing import DATASET_TO_NUM_USERS_AND_ITEMS [as 別名]
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)

開發者ID:ShivangShekhar，項目名稱:Live-feed-object-device-identification-using-Tensorflow-and-OpenCV，代碼行數:41，代碼來源:data_test.py

示例3: setUp

# 需要導入模塊: from official.recommendation import data_preprocessing [as 別名]
# 或者: from official.recommendation.data_preprocessing import DATASET_TO_NUM_USERS_AND_ITEMS [as 別名]
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)

開發者ID:generalized-iou，項目名稱:g-tensorflow-models，代碼行數:39，代碼來源:data_test.py

示例4: setUp

# 需要導入模塊: from official.recommendation import data_preprocessing [as 別名]
# 或者: from official.recommendation.data_preprocessing import DATASET_TO_NUM_USERS_AND_ITEMS [as 別名]
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)

開發者ID:isobar-us，項目名稱:multilabel-image-classification-tensorflow，代碼行數:39，代碼來源:data_test.py

注：本文中的official.recommendation.data_preprocessing.DATASET_TO_NUM_USERS_AND_ITEMS屬性示例由純淨天空整理自Github/MSDocs等開源代碼及文檔管理平台，相關代碼片段篩選自各路編程大神貢獻的開源項目，源碼版權歸原作者所有，傳播和使用請參考對應項目的License；未經允許，請勿轉載。