# 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.
# ==============================================================================
"""InnerDirichlet partitioner."""
import warnings
from typing import Optional, Union
import numpy as np
import datasets
from flwr_datasets.common.typing import NDArrayFloat, NDArrayInt
from flwr_datasets.partitioner.partitioner import Partitioner
[docs]
class InnerDirichletPartitioner(Partitioner): # pylint: disable=R0902
"""Partitioner based on Dirichlet distribution.
Each partition is created based on the Dirichlet distribution, where the
probability corresponds to the fractions of samples of specific classes.
This process is iterative (sample by sample assignment), where first, the
partition ID to which the class will be assigned is chosen (at random, uniformly),
and then the class is decided based on the Dirichlet probabilities (note that when
a class gets exhausted - no more samples exists to sample from - the probability of
sampling this class is set as zero and the remaining probabilities renormalized).
Implementation based on: Federated Learning Based on Dynamic Regularization
(https://arxiv.org/abs/2111.04263).
Parameters
----------
partition_sizes : Union[List[int], NDArrayInt]
The sizes of all partitions.
partition_by : str
Column name of the labels (targets) based on which Dirichlet sampling works.
alpha : Union[int, float, List[float], NDArrayFloat]
Concentration parameter to the Dirichlet distribution (a single value for
symmetric Dirichlet distribution, or a list/NDArray of length equal to the
number of unique classes)
shuffle: bool
Whether to randomize the order of samples. Shuffling applied after the
samples assignment to partitions.
seed: int
Seed used for dataset shuffling. It has no effect if `shuffle` is False.
Examples
--------
>>> from flwr_datasets import FederatedDataset
>>> from flwr_datasets.partitioner import InnerDirichletPartitioner
>>>
>>> partitioner = InnerDirichletPartitioner(
>>> partition_sizes=[6_000] * 10, partition_by="label", alpha=0.5
>>> )
>>> fds = FederatedDataset(dataset="mnist", partitioners={"train": partitioner})
>>> partition = fds.load_partition(0)
>>> print(partition[0]) # Print the first example
"""
def __init__( # pylint: disable=R0913
self,
partition_sizes: Union[list[int], NDArrayInt],
partition_by: str,
alpha: Union[int, float, list[float], NDArrayFloat],
shuffle: bool = True,
seed: Optional[int] = 42,
) -> None:
super().__init__()
# Attributes based on the constructor
self._partition_sizes = _instantiate_partition_sizes(partition_sizes)
self._initial_alpha = alpha
self._alpha: Optional[NDArrayFloat] = None
self._partition_by = partition_by
self._shuffle = shuffle
self._seed = seed
# Utility attributes
self._initialized_alpha = False
self._rng = np.random.default_rng(seed=self._seed) # NumPy random generator
# The attributes below are determined during the first call to load_partition
self._unique_classes: Optional[Union[list[int], list[str]]] = None
self._num_unique_classes: Optional[int] = None
self._num_partitions = len(self._partition_sizes)
self._partition_id_to_indices: dict[int, list[int]] = {}
self._partition_id_to_indices_determined = False
[docs]
def load_partition(self, partition_id: int) -> datasets.Dataset:
"""Load a partition based on the partition index.
Parameters
----------
partition_id : int
the index that corresponds to the requested partition
Returns
-------
dataset_partition : Dataset
single partition of a dataset
"""
# The partitioning is done lazily - only when the first partition is
# requested. Only the first call creates the indices assignments for all the
# partition indices.
self._check_num_partitions_correctness_if_needed()
self._check_partition_sizes_correctness_if_needed()
self._check_the_sum_of_partition_sizes()
self._determine_num_unique_classes_if_needed()
self._alpha = self._initialize_alpha_if_needed(self._initial_alpha)
self._determine_partition_id_to_indices_if_needed()
return self.dataset.select(self._partition_id_to_indices[partition_id])
@property
def num_partitions(self) -> int:
"""Total number of partitions."""
self._check_num_partitions_correctness_if_needed()
self._check_partition_sizes_correctness_if_needed()
self._check_the_sum_of_partition_sizes()
self._determine_num_unique_classes_if_needed()
self._alpha = self._initialize_alpha_if_needed(self._initial_alpha)
self._determine_partition_id_to_indices_if_needed()
return self._num_partitions
def _initialize_alpha_if_needed(
self, alpha: Union[int, float, list[float], NDArrayFloat]
) -> NDArrayFloat:
"""Convert alpha to the used format in the code a NDArrayFloat.
The alpha can be provided in constructor can be in different format for user
convenience. The format into which it's transformed here is used throughout the
code for computation.
Parameters
----------
alpha : Union[int, float, List[float], NDArrayFloat]
Concentration parameter to the Dirichlet distribution
Returns
-------
alpha : NDArrayFloat
Concentration parameter in a format ready to used in computation.
"""
if self._initialized_alpha:
assert self._alpha is not None
return self._alpha
if isinstance(alpha, int):
assert self._num_unique_classes is not None
alpha = np.array([float(alpha)], dtype=float).repeat(
self._num_unique_classes
)
elif isinstance(alpha, float):
assert self._num_unique_classes is not None
alpha = np.array([alpha], dtype=float).repeat(self._num_unique_classes)
elif isinstance(alpha, list):
if len(alpha) != self._num_unique_classes:
raise ValueError(
"When passing alpha as a List, its length needs needs to be "
"of length equal to the number of unique classes."
)
alpha = np.asarray(alpha)
elif isinstance(alpha, np.ndarray):
# pylint: disable=R1720
if alpha.ndim == 1 and alpha.shape[0] != self._num_unique_classes:
raise ValueError(
"When passing alpha as an NDArray, its length needs needs to be "
"of length equal to the number of unique classes."
)
elif alpha.ndim == 2:
alpha = alpha.flatten()
if alpha.shape[0] != self._num_unique_classes:
raise ValueError(
"When passing alpha as an NDArray, its length needs needs to be"
" of length equal to the number of unique classes."
)
else:
raise ValueError("The given alpha format is not supported.")
if not (alpha > 0).all():
raise ValueError(
f"Alpha values should be strictly greater than zero. "
f"Instead it'd be converted to {alpha}"
)
return alpha
def _determine_partition_id_to_indices_if_needed(
self,
) -> None: # pylint: disable=R0914
"""Create an assignment of indices to the partition indices."""
if self._partition_id_to_indices_determined:
return
# Create class priors for the whole partitioning process
assert self._alpha is not None
class_priors = self._rng.dirichlet(alpha=self._alpha, size=self._num_partitions)
targets = np.asarray(self.dataset[self._partition_by])
# List representing indices of each class
assert self._num_unique_classes is not None
idx_list = [np.where(targets == i)[0] for i in range(self._num_unique_classes)]
class_sizes = [len(idx_list[i]) for i in range(self._num_unique_classes)]
client_indices = [
np.zeros(self._partition_sizes[cid]).astype(np.int64)
for cid in range(self._num_partitions)
]
# Node id to number of sample left for allocation for that partition id
partition_id_to_left_to_allocate = dict(
zip(range(self._num_partitions), self._partition_sizes)
)
not_full_partition_ids = list(range(self._num_partitions))
while np.sum(list(partition_id_to_left_to_allocate.values())) != 0:
# Choose a partition
current_partition_id = self._rng.choice(not_full_partition_ids)
# If current partition is full resample a client
if partition_id_to_left_to_allocate[current_partition_id] == 0:
# When the partition is full, exclude it from the sampling list
not_full_partition_ids.pop(
not_full_partition_ids.index(current_partition_id)
)
continue
partition_id_to_left_to_allocate[current_partition_id] -= 1
# Access the label distribution of the chosen client
current_probabilities = class_priors[current_partition_id]
while True:
# curr_class = np.argmax(np.random.uniform() <= curr_prior)
curr_class = self._rng.choice(
list(range(self._num_unique_classes)), p=current_probabilities
)
# Redraw class label if there are no samples left to be allocated from
# that class
if class_sizes[curr_class] == 0:
# Class got exhausted, set probabilities to 0
class_priors[:, curr_class] = 0
# Renormalize such that the probability sums to 1
row_sums = class_priors.sum(axis=1, keepdims=True)
class_priors = class_priors / row_sums
# Adjust the current_probabilities (it won't sum up to 1 otherwise)
current_probabilities = class_priors[current_partition_id]
continue
class_sizes[curr_class] -= 1
# Store sample index at the empty array cell
index = partition_id_to_left_to_allocate[current_partition_id]
client_indices[current_partition_id][index] = idx_list[curr_class][
class_sizes[curr_class]
]
break
partition_id_to_indices = {
cid: client_indices[cid].tolist() for cid in range(self._num_partitions)
}
# Shuffle the indices if the shuffle is True.
# Note that the samples from this partitioning do not necessarily require
# shuffling, the order should exhibit consecutive samples.
if self._shuffle:
for indices in partition_id_to_indices.values():
# In place shuffling
self._rng.shuffle(indices)
self._partition_id_to_indices = partition_id_to_indices
self._partition_id_to_indices_determined = True
def _check_num_partitions_correctness_if_needed(self) -> None:
"""Test num_partitions when the dataset is given (in load_partition)."""
if not self._partition_id_to_indices_determined:
if self._num_partitions > self.dataset.num_rows:
raise ValueError(
"The number of partitions needs to be smaller or equal to "
" the number of samples in the dataset."
)
def _check_partition_sizes_correctness_if_needed(self) -> None:
"""Test partition_sizes when the dataset is given (in load_partition)."""
if not self._partition_id_to_indices_determined:
if sum(self._partition_sizes) > self.dataset.num_rows:
raise ValueError(
"The sum of the `partition_sizes` needs to be smaller or equal to "
"the number of samples in the dataset."
)
def _check_num_partitions_greater_than_zero(self) -> None:
"""Test num_partition left sides correctness."""
if not self._num_partitions > 0:
raise ValueError("The number of partitions needs to be greater than zero.")
def _determine_num_unique_classes_if_needed(self) -> None:
self._unique_classes = self.dataset.unique(self._partition_by)
assert self._unique_classes is not None
self._num_unique_classes = len(self._unique_classes)
def _check_the_sum_of_partition_sizes(self) -> None:
if np.sum(self._partition_sizes) != len(self.dataset):
warnings.warn(
"The sum of the partition_sizes does not sum to the whole "
"dataset size. Make sure that is the desired behavior.",
stacklevel=1,
)
def _instantiate_partition_sizes(
partition_sizes: Union[list[int], NDArrayInt]
) -> NDArrayInt:
"""Transform list to the ndarray of ints if needed."""
if isinstance(partition_sizes, list):
partition_sizes = np.asarray(partition_sizes)
elif isinstance(partition_sizes, np.ndarray):
pass
else:
raise ValueError(
f"The type of partition_sizes is incorrect. Given: "
f"{type(partition_sizes)}"
)
if not all(partition_sizes >= 0):
raise ValueError("The samples numbers must be greater or equal to zero.")
return partition_sizes