Module core.steps.split.base_split_step

Classes

BaseSplit(statistics: tensorflow_metadata.proto.v0.statistics_pb2.DatasetFeatureStatisticsList = None, schema: tensorflow_metadata.proto.v0.schema_pb2.Schema = None, **kwargs) : Base split class. Each custom data split should derive from this. In order to define a custom split, override the base split’s partition_fn method.

Base Split constructor.

Args:
    statistics: Parsed statistics output of a preceding StatisticsGen.
    schema: Parsed schema output of a preceding SchemaGen.

### Ancestors (in MRO)

* zenml.core.steps.base_step.BaseStep

### Class variables

`STEP_TYPE`
:

### Methods

`get_num_splits(self)`
:   Returns the total number of splits.
    
    Returns:
        A positive integer, the number of splits.

`get_split_names(self) ‑> List[str]`
:   Returns the names of the splits associated with this split step.
    
    Returns:
        A list of strings, which are the split names.

`partition_fn(self)`
:   Returns the partition function associated with the current split type,
    along with keyword arguments used in the signature of the partition
    function.
    
    To be eligible in use in a Split Step, the partition_fn has to adhere
    to the following design contract:
    
    1. The signature is of the following type:
    
        >>> def partition_fn(element, n, **kwargs) -> int,
    
        where n is the number of splits;
    2. The partition_fn only returns signed integers i less than n, i.e. ::
    
            0 ≤ i ≤ n - 1.
    
    Returns:
        A tuple (partition_fn, kwargs) of the partition function and its
         additional keyword arguments (see above).