fix(get_dataset): dataset sampling for large datasets #71
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oyangz
previously approved these changes
Oct 23, 2023
nathanng17
previously approved these changes
Oct 23, 2023
malhotra18
previously approved these changes
Oct 23, 2023
| data = ray.data.from_pandas(data.to_pandas().sample(num_records, random_state=SEED)) | ||
| if count > 100000: | ||
| # If count is larger than 100000, we take the first 100000 row, and then sample from that to | ||
| # maintain deterministic behaviour |
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nit, Could you please also add reason here, in comments. For using take_batch instead?
I would say also add a TODO to revisit this, this logic is adding an inherent bias towards first 100000 rows of the dataset.
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I agree we should come back to this. Added a TODO
keerthanvasist
dismissed stale reviews from malhotra18, nathanng17, and oyangz
via
991a611
|
Merging since I had previous approvals |
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Description of changes:
dataset sampling for large datasets: For large datasets like
cnn_dailymailandxsum, we were unable to sample the dataset using theto_pandas()method - in order to solve for that use case, we are now usingtake_batchas a workaround when there are large datasets.By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice.