Hacktoberfest 2026: the issues maintainers tagged for October, open and beginner-friendly. Browse Hacktoberfest issues

partition_dataset

Open Beginner friendly
  • Is something wrong?
#9,220 0 comments 0 reactions 0 assignees View on GitHub

Maintainers usually reply within 3 days

Nobody has claimed this yet.

Assessment

Difficulty
2/5
Estimated time
1-3 hours
Newbie friendliness
72/100
Issue type
Bug
Clarity
Clearly specified
Activity status
Active
Tech stack
python, pytorch
Domain
data

Research direction

Read the rounding logic in monai/data/utils.py around lines 1217-1224 where partition_dataset computes each partition's size, and reproduce the bug with the snippet in the issue (e.g. partition_dataset(list(range(10)), ratios=[1,1,1]) drops items). The fix should distribute leftover items to partitions rounded down the most, and the reporter says they have a fix plus tests ready — comment quickly to coordinate before starting, since a PR may appear. Done: every item lands in exactly one partition for the reported ratios/lengths, existing tests in tests for partition_dataset still pass, and new tests cover the leftover-item cases.

Written by the indexing model from the issue text.

Description

When partition_dataset(data, ratios=...) splits a list, it computes every partition's size by rounding independently (see monai/data/utils.py, lines 1217–1224 at commit 6920610). If those rounded sizes add up to less than len(data), the extra items end up in no partition, and no warning or error is raised.

With three ratios this affects roughly one length in five: for 0.8/0.1/0.1 it happens for 200 of the lengths from 1 to 1000, and for 1/1/1 it happens for 334 of them. With five equal ratios, as many as two items can disappear. Because partition_dataset_classes applies the same function to each class, it can lose up to one item per class. Splits into two parts are not affected.

I found this by reading the code and then testing it.

from monai.data import partition_dataset

print(partition_dataset(list(range(10)), ratios=[1, 1, 1]))

data = list(range(63)) # e.g. the 63 training cases of MSD Task06_Lung
train, val, test = partition_dataset(data, ratios=[0.8, 0.1, 0.1], shuffle=True, seed=0)
print(len(train), len(val), len(test), "in no split:", sorted(set(data) - set(train + val + test)))

Output on dev (6920610):

[[0, 1, 2], [3, 4, 5], [6, 7, 8]]
50 6 6 in no split: [44]

The same thing occurs for other Decathlon training-set sizes. Examples: 303 cases (Task08_HepaticVessel) and 484 cases (Task01_BrainTumour) with 0.8/0.1/0.1; 63 and 303 cases with 0.7/0.15/0.15; and 32 cases (Task05_Prostate) with five equal ratios, where two cases are lost. In a Decathlon-style pipeline (load_decathlon_datalist → partition_dataset → CacheDataset → DataLoader) with 63 cases, one case is never loaded by the train, validation or test split.

Expected behavior

Each item should land in exactly one partition, as the docstring example shows ([0.6, 0.2, 0.2] on 5 items gives [[1, 2, 3], [4], [5]]). The documentation says drop_last does not apply when ratios are used.

Environment

MONAI 1.6.1rc0+50.g6920610c (dev), PyTorch 2.14.1+cpu, NumPy 2.5.4, Python 3.12.13, Linux.

Additional context

I have a small fix ready. It hands the leftover items to the partitions that were rounded down the most, and splits that already cover every item stay unchanged. It comes with tests, and I can open a PR.

Dominant language
Python
Stars
8.8k
Forks
1.7k
Avg merge
5d 3h
Merged PRs (30d)
13

Getting set up

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

More from Project-MONAI/MONAI

All issues in Project-MONAI/MONAI

Similar issues

More Python issues

Get new issues in your inbox

A short digest of beginner-friendly GitHub issues.