Comments (2)
Hi @jmsw4bn. Thanks for pointing it out and figuring out the fix. I've opened the PR that fixes it.
As for the expected results [0.2, 0.2, 0.2, 0.94]
won't be possible since the values sum up to more than 1. An error will be raised in that case. It might be confusing what should the 0.94
come from (it would have to overlap with some other parts that are expected to be separate). Alternatively, I think you might have meant 0.02, ...
then it'd sum up to 1 and everything would work ok.
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Hi @jmsw4bn. Thanks for pointing it out and figuring out the fix. I've opened the PR that fixes it. As for the expected results
[0.2, 0.2, 0.2, 0.94]
won't be possible since the values sum up to more than 1. An error will be raised in that case. It might be confusing what should the0.94
come from (it would have to overlap with some other parts that are expected to be separate). Alternatively, I think you might have meant0.02, ...
then it'd sum up to 1 and everything would work ok.
I am sorry, I wrote the wrong values.
Actually, I test the code is with "division=[0.02, 0.02, 0.02, 0.02, 0.92]".
These values sum up to 1, and the 3rd and 4th sub_datasets have 0 samples,
you can validate the error by debuging the following codes, and the output shows the 5 sub_datasets in "partition" have 1000 1000 0 0 40000 samples respectively (the right output should be 1000 1000 1000 1000 46000):
from flwr_datasets import FederatedDataset
from flwr_datasets.utils import divide_dataset
fds = FederatedDataset(dataset="cifar10", partitioners={"train": 1})
tds = fds.load_partition(0, "train")
partition = divide_dataset(dataset=tds, division=[0.02, 0.02, 0.02, 0.02, 0.92])
print(len(partition[0]), len(partition[1]), len(partition[2]), len(partition[3]), len(partition[4]))
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