Source code for flwr_datasets.partitioner.natural_id_partitioner

# Copyright 2023 Flower Labs GmbH. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Natural id partitioner class that works with Hugging Face Datasets."""


import numpy as np
from tqdm import tqdm

import datasets
from flwr_datasets.common.typing import NDArrayInt
from flwr_datasets.partitioner.partitioner import Partitioner




class NaturalIdPartitioner(Partitioner):
    """Partitioner for a dataset that can be divided by a column with partition ids.

    Parameters
    ----------
    partition_by: str
        The name of the column that contains the unique values of partitions.


    Examples
    --------
    "flwrlabs/shakespeare" dataset

    >>> from flwr_datasets import FederatedDataset
    >>> from flwr_datasets.partitioner import NaturalIdPartitioner
    >>>
    >>> partitioner = NaturalIdPartitioner(partition_by="character_id")
    >>> fds = FederatedDataset(dataset="flwrlabs/shakespeare",
    >>>                        partitioners={"train": partitioner})
    >>> partition = fds.load_partition(0)

    "sentiment140" (aka Twitter) dataset

    >>> from flwr_datasets import FederatedDataset
    >>> from flwr_datasets.partitioner import NaturalIdPartitioner
    >>>
    >>> partitioner = NaturalIdPartitioner(partition_by="user")
    >>> fds = FederatedDataset(dataset="sentiment140",
    >>>                        partitioners={"train": partitioner})
    >>> partition = fds.load_partition(0)
    """

    def __init__(
        self,
        partition_by: str,
    ):
        super().__init__()
        self._partition_id_to_natural_id: dict[int, str] = {}
        self._natural_id_to_partition_id: dict[str, int] = {}
        self._partition_id_to_indices: dict[int, NDArrayInt] = {}
        self._partition_by = partition_by

    def _create_int_partition_id_to_natural_id(self) -> None:
        """Create a mapping from int indices to unique client ids from dataset.

        Natural ids come from the column specified in `partition_by`.
        """
        unique_natural_ids = self.dataset.unique(self._partition_by)
        self._partition_id_to_natural_id = dict(
            zip(range(len(unique_natural_ids)), unique_natural_ids)
        )

    def _create_natural_id_to_int_partition_id(self) -> None:
        """Create a mapping from unique client ids from dataset to int indices.

        Natural ids come from the column specified in `partition_by`. This object is
        inverse of the `self._partition_id_to_natural_id`. This method assumes that
        `self._partition_id_to_natural_id` already exist.
        """
        self._natural_id_to_partition_id = {
            value: key for key, value in self._partition_id_to_natural_id.items()
        }

    def _create_partition_id_to_indices(self) -> None:
        natural_id_to_indices = {}  # type: ignore
        natural_ids = np.array(self.dataset[self._partition_by])

        for index, natural_id in tqdm(
            enumerate(natural_ids), desc="Generating partition_id_to_indices"
        ):
            if natural_id not in natural_id_to_indices:
                natural_id_to_indices[natural_id] = []
            natural_id_to_indices[natural_id].append(index)

        self._partition_id_to_indices = {
            self._natural_id_to_partition_id[natural_id]: indices
            for natural_id, indices in natural_id_to_indices.items()
        }



    def load_partition(self, partition_id: int) -> datasets.Dataset:
        """Load a single partition corresponding to a single `partition_id`.

        The choice of the partition is based on unique integers assigned to each
        natural id present in the dataset in the `partition_by` column.

        Parameters
        ----------
        partition_id : int
            the index that corresponds to the requested partition

        Returns
        -------
        dataset_partition : Dataset
            single dataset partition
        """
        if len(self._partition_id_to_natural_id) == 0:
            self._create_int_partition_id_to_natural_id()
            self._create_natural_id_to_int_partition_id()

        if len(self._partition_id_to_indices) == 0:
            self._create_partition_id_to_indices()

        return self.dataset.select(self._partition_id_to_indices[partition_id])


    @property
    def num_partitions(self) -> int:
        """Total number of partitions."""
        if len(self._partition_id_to_natural_id) == 0:
            self._create_int_partition_id_to_natural_id()
            self._create_natural_id_to_int_partition_id()
        return len(self._partition_id_to_natural_id)

    @property
    def partition_id_to_natural_id(self) -> dict[int, str]:
        """Node id to corresponding natural id present.

        Natural ids are the unique values in `partition_by` column in dataset.
        """
        return self._partition_id_to_natural_id

    @partition_id_to_natural_id.setter
    def partition_id_to_natural_id(self, value: dict[int, str]) -> None:
        raise AttributeError(
            "Setting the partition_id_to_natural_id dictionary is not allowed."
        )