Method embedding_agg

graphlearn/python/data/values.py:342–360

Get aggregated embedding of fixed size of neighbors of batch seed nodes. The shape of neighbors embeddings is `[batch_size, num_neighbors, float_attr_num]`, after aggregation on axis=1, the shape is `[batch_size, float_attr_num]`. Args: func ("sum" | "mean" | "min" | "max

(self, func="sum")

    return agged

  def embedding_agg(self, func="sum"):
    """
    Get aggregated embedding of fixed size of neighbors of batch seed nodes.
    The shape of neighbors embeddings is
    `[batch_size, num_neighbors, float_attr_num]`, after aggregation on axis=1,
    the shape is `[batch_size, float_attr_num]`.

    Args:
      func ("sum" | "mean" | "min" | "max" | "prod"):
        the built-in aggregate functions.
    """
    if not len(self.shape) == 2:
      raise ValueError("embedding_agg is for Nodes with 2 dimension,"
                       "and the default aggregated dimension is axis=1")
    segment_ids = \
        [i for i in range(self.shape[0]) for _ in range(self.shape[1])]
    agged =  self._agg(func, segment_ids, self.shape[0])
    return np.reshape(agged,
                      (self.shape[0], self._get_decoder().float_attr_num))

class SparseNodes(Nodes, SparseBase):
  """ SparseNodes is the returned value of full neighbor sampler which