Method __init__

graphlearn/python/data/decoder.py:29–98

Initialize a data source decoder. Args: weighted (boolean, Optional): Whether the data source has weights. Default is False. labeled (boolean, Optional): Whether the data source has labels. Default is False. attr_types (list, Optional): Attribute type list if

(self,
               weighted=False,
               labeled=False,
               attr_types=[],
               attr_delimiter=":",
               attr_dims=[])

  """

  def __init__(self,
               weighted=False,
               labeled=False,
               attr_types=[],
               attr_delimiter=":",
               attr_dims=[]):
    """ Initialize a data source decoder.

    Args:
      weighted (boolean, Optional): Whether the data source has weights.
        Default is False.
      labeled (boolean, Optional): Whether the data source has labels.
        Default is False.
      attr_types (list, Optional): Attribute type list if attributes exist.
        Default is None, which means no attribute exists. Each element must 
        be string, tuple or list.

        Valid types like below:
        attr_types = ['int', 'float', 'string']
        attr_types = ['int', ('string', 10), 'string'] # 10 means bucket size
        For ('string', 10), we will get the string and hash it into 10 buckets
        directly. The raw attribute can by any string.

        # True means multi-val splited by ',', only for string attribute
        attr_types = ['int', ('string', 10, True)]

        # 10 means bucket size
        attr_types = ['int', ('int', 10), 'string']
        For ('int', 10), we will cast the string to int first and then hash it
        into 10 buckets. In this way, the raw attribute must be an integer.

        When `attr_dims` is assigned, be sure that the string attribute must
        be configured with a bucket size. Bucket size for int attribute is
        optional, and it will be considered as a continuous attribute if bucket
        size is not assigned.

      attr_delimiter (string, Optional): The delimiter to seperate attributes.
        Default is ':'. If attributes exist, all of them are concatenated
        together with a delimiter in the raw storage. We need to know how to
        parse them.
      attr_dims (list, Optional): An integer list, the element of which
        represents the dimension of the corresponding attribute that will be
        encodeding to. Default is None, which means no attribute exists.

        All valid configurations of attr_type and attr_dim are as shown below.
        |    attr_type     |attr_dim|             encoded into                 |
        |    ---------     | -- |                  --------                    |
        |     "string"     | 8  |   Dynamic bucket embedding variable, dim=8   |
        |   ("string",10)  | 8  |   Embedding variable, bucketsize=10, dim=8   |
        |("string",10,True)| 8  |Sparse embedding variable, bucketsize=10,dim=8|
        |("string",None,True)| 8|   Sparse dynamic embedding variable, dim=8   |
        |       "int"      |None|           Continues numeric tensor           |
        |       "int"      | 8  |   Dynamic bucket embedding variable, dim=8   |
        |    ("int",10)    | 8  |   Embedding variable, bucket size=10, dim=8  |
        |      "float"     |None|           Continues numeric tensor           |
        Note that dynamic bucket embedding variable is only supported in PAI-TF.
        For continues numeric attribute, attr_dim should be either None or 0.
    """