Source code for flwr_datasets.partitioner.exponential_partitioner

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"""ExponentialPartitioner class."""


import numpy as np

from flwr_datasets.partitioner.id_to_size_fnc_partitioner import IdToSizeFncPartitioner




class ExponentialPartitioner(IdToSizeFncPartitioner):
    """Partitioner creates partitions of size that are correlated with exp(id).

    The amount of data each client gets is correlated with the exponent of partition ID.
    For instance, if the IDs range from 1 to M, client with ID 1 gets e units of
    data, client 2 gets e^2 units, and so on, up to client M which gets e^M units.
    The floor operation is applied on each of these numbers, it means floor(2.71...)
    = 2; e^2 ~ 7.39 floor(7.39) = 7. The number is rounded down = the fraction is
    always cut. The remainders of theses unassigned (fraction) samples is added to the
    biggest partition (the one with the biggest partition_id).

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

    def __init__(self, num_partitions: int) -> None:
        super().__init__(num_partitions=num_partitions, partition_id_to_size_fn=np.exp)
        if num_partitions <= 0:
            raise ValueError("The number of partitions must be greater than zero.")