Module core.steps.split.base_split_step


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.

    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


### Methods

:   Returns the total number of splits.
        A positive integer, the number of splits.

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

:   Returns the partition function associated with the current split type,
    along with keyword arguments used in the signature of the partition
    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.
        A tuple (partition_fn, kwargs) of the partition function and its
         additional keyword arguments (see above).