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).