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
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).