VerticalEvenPartitioner

class VerticalEvenPartitioner(num_partitions: int, active_party_columns: str | list[str] | None = None, active_party_columns_mode: Literal['add_to_first', 'add_to_last', 'create_as_first', 'create_as_last', 'add_to_all'] | int = 'add_to_last', drop_columns: str | list[str] | None = None, shared_columns: str | list[str] | None = None, shuffle: bool = True, seed: int | None = 42)

Bases: Partitioner

Partitioner that splits features (columns) evenly into vertical partitions.

Enables selection of “active party” column(s) and placement into a specific partition or creation of a new partition just for it. Also enables droping columns and sharing specified columns across all partitions.

Parameters:

• num_partitions (int) – Number of partitions to create.

• active_party_column (Optional[Union[str, list[str]]]) – Column(s) (typically representing labels) associated with the “active party” (which can be the server).

• active_party_columns_mode (Union[Literal[["add_to_first", "add_to_last", "create_as_first", "create_as_last", "add_to_all"], int]) –

  Determines how to assign the active party columns:

  • ”add_to_first”: Append active party columns to the first partition.

  • ”add_to_last”: Append active party columns to the last partition.

  • ”create_as_first”: Create a new partition at the start containing only these columns.

  • ”create_as_last”: Create a new partition at the end containing only these columns.

  • ”add_to_all”: Append active party columns to all partitions.

  • int: Append active party columns to the specified partition index.

• drop_columns (Optional[Union[str, list[str]]]) – Columns to remove entirely from the dataset before partitioning.

• shared_columns (Optional[Union[str, list[str]]]) – Columns to duplicate into every partition after initial partitioning.

• shuffle (bool) – Whether to shuffle the order of columns before partitioning.

• seed (Optional[int]) – Random seed for shuffling columns. Has no effect if shuffle=False.

Examples

>>> from flwr_datasets import FederatedDataset
>>> from flwr_datasets.partitioner import VerticalEvenPartitioner
>>>
>>> partitioner = VerticalEvenPartitioner(
...     num_partitions=3,
...     active_party_columns="income",
...     active_party_columns_mode="add_to_last",
...     shuffle=True,
...     seed=42
... )
>>> fds = FederatedDataset(
...     dataset="scikit-learn/adult-census-income",
...     partitioners={"train": partitioner}
... )
>>> partitions = [fds.load_partition(i) for i in range(fds.partitioners["train"].num_partitions)]
>>> print([partition.column_names for partition in partitions])

Methods

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

Attributes

dataset	Dataset property.
num_partitions	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

Load a partition based on the partition index.

Parameters:

partition_id (int) – The index that corresponds to the requested partition.

Returns:

dataset_partition – Single partition of a dataset.

Return type:

Dataset

property num_partitions: int

Number of partitions.