IidPartitioner#

class IidPartitioner(num_partitions: int)[source]#

Bases: Partitioner

Partitioner creates each partition sampled randomly from the dataset.

Parameters:: num_partitions (int) – The total number of partitions that the data will be divided into.

Examples

>>> from flwr_datasets import FederatedDataset
>>> from flwr_datasets.partitioner import IidPartitioner
>>>
>>> partitioner = IidPartitioner(num_partitions=10)
>>> fds = FederatedDataset(dataset="mnist", partitioners={"train": partitioner})
>>> partition = fds.load_partition(0)

Methods

`is_dataset_assigned`()	Check if a dataset has been assigned to the partitioner.
`load_partition`(partition_id)	Load a single IID partition based on the partition index.

Attributes

`dataset`	Dataset property.
`num_partitions`	Total number of partitions.

property dataset: Dataset#: Dataset property.

is_dataset_assigned() → bool#

Check if a dataset has been assigned to the partitioner.

This method returns True if a dataset is already set for the partitioner, otherwise, it returns False.

Returns:: dataset_assigned – True if a dataset is assigned, otherwise False.
Return type:: bool

load_partition(partition_id: int) → Dataset[source]#

Load a single IID partition based on the partition index.

Parameters:: partition_id (int) – the index that corresponds to the requested partition
Returns:: dataset_partition – single dataset partition
Return type:: Dataset

property num_partitions: int#: Total number of partitions.