why the stratify option is omitted from test_train_split function?
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["Hi ! It's simply not added yet :)\r\n\r\nIf someone wants to contribute to add the `stratify` parameter I'd be happy to give some pointers.\r\n\r\nIn the meantime, I guess you can use `sklearn` or other tools to do a stratified train/test split over the **indices** of your dataset and then do\r\n```\r\ntrain_dataset = dataset.select(train_indices)\r\ntest_dataset = dataset.select(test_indices)\r\n```"
 'Hi @lhoestq I would like to add `stratify` parameter, can you give me some pointers for adding the same ?'
 'Hi ! Sure :)\r\n\r\nThe `train_test_split` method is defined here: \r\n\r\nhttps://github.com/huggingface/datasets/blob/dc62232fa1b3bcfe2fbddcb721f2d141f8908943/src/datasets/arrow_dataset.py#L3253-L3253\r\n\r\nand inside `train_test_split ` we need to create the right `train_indices` and `test_indices` that are passed here to `.select()`:\r\n\r\nhttps://github.com/huggingface/datasets/blob/dc62232fa1b3bcfe2fbddcb721f2d141f8908943/src/datasets/arrow_dataset.py#L3450-L3464\r\n\r\nFor example if your dataset is like\r\n|    |   label |\r\n|---:|--------:|\r\n|  0 |       1 |\r\n|  1 |       1 |\r\n|  2 |       0 |\r\n|  3 |       0 |\r\n\r\nand the user passes `stratify=dataset["label"]`, then you should get indices that look like this\r\n```\r\ntrain_indices = [0, 2]\r\ntest_indices = [1, 3]\r\n```\r\n\r\nthese indices will be passed to `.select` to return the stratified train and test splits :)\r\n\r\nFeel free to îng me if you have any question !'
 '@lhoestq \r\nI just added the implementation for `stratify` option here #4322 ']
why the stratify option is omitted from test_train_split function?

is there any other way implement the stratify option while splitting the dataset? as it is important point to be considered while splitting the dataset.
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