shuffle as boolean while converting dataset to dataclass #1213
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Hey @mrastgoo, thank you for this PR!
Your tests fail because of network issues with OpenML. This is unrelated to your PR, so I reran them.
| @@ -702,6 +703,8 @@ def _fetch_dataset_as_dataclass( | |||
| df = pd.read_parquet(info["path"]) | |||
| else: | |||
| df = pd.read_csv(info["path"], **read_csv_kwargs) | |||
| if shuffling: | |||
| df = shuffle(df, random_state=42).reset_index(drop=True) | |||
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I think the random state should be a function parameter, as it would allow for control for multiple seeds during testing and more fine-grained control on reproducibility.
I'm not 100% sure we should reset indices, because it could be surprising for users. WDYT?
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Thanks @mrastgoo! I'm not sure we need to add a parameter to the fetcher, at least it is not required for addressing #1178 . we could shuffle it in the the example code (which by default is folded for the tabular_learner example, and which is not shown for the tablereport) rather than in the fetcher itself. WDYT? |
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I'm not sure we need to add a parameter to the fetcher, at least it is not required for addressing [1]#1178 . we could shuffle it in the the example code (which by default is folded for the tabular_learner example, and which is not shown for the tablereport) rather than in the fetcher itself
I'd rather keep the code examples as simple as possible (every character counts), and shuffle in the fetcher, by default, in a reproducible way. And for this, an easy way of doing things would be to cut and reorder the dataset at a fix index, like "cutting" a deck of cards.
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I can see why you prefer that, in this way the fetcher will return the original data as it is, with the same index. However it will make the example longer. |
I am not sure to understand the purpose of "cutting" @GaelVaroquaux, how many cuts do we do ? Do we want to shuffle as well ? and should we keep the orginal index or reorder them ? |
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I am not sure to understand the purpose of "cutting" @GaelVaroquaux,
The whole goal of the modification is to have the first few lines not as nasty.
how many cuts do we do ?
Let's try one.
Do we want to shuffle as well ?
No, that way it's stable and reproducible (random number generators are not always reproducible across hardware)
and should we keep the orginal index or reorder them ?
Not reorder, but reset to avoid something looking strange
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and IIUC the reordering is not optional and no new parameter is exposed to the user right? |
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and IIUC the reordering is not optional and no new parameter is exposed to the user right?
As you wish
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> and IIUC the reordering is not optional and no new parameter is exposed to the user right?
As you wish
Ok in that case I'd rather not add a parameter, just do the transformation every time
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Then what about editing the dataset, putting the reordered version on Figshare and fetching that directly? |
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Then what about editing the dataset, putting the reordered version on Figshare and fetching that directly?
I always like having a form of traceability of the data, so I think that I would prefer not changing the upstream data
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closes #1178
adding
shufflingas boolean in_fetch_dataset_as_dataclassand setting it to True infetch_employee_salaries