MarouaneSanhaji/github-issues huggingface dataset:
dataset.to_iterable_dataset() loses useful info like dataset features
State: closed

### Describe the bug

Hello,
I like the new `to_iterable_dataset` feature but I noticed something that seems to be missing.

When using `to_iterable_dataset` to transform your map style dataset into iterable dataset, you lose valuable metadata like the features. 

These metadata are useful if you want to interleave iterable datasets, cast columns etc.

### Steps to reproduce the bug

```python
dataset = load_dataset("lhoestq/demo1")["train"]
print(dataset.features)
# {'id': Value(dtype='string', id=None), 'package_name': Value(dtype='string', id=None), 'review': Value(dtype='string', id=None), 'date': Value(dtype='string', id=None), 'star': Value(dtype='int64', id=None), 'version_id': Value(dtype='int64', id=None)}
dataset = dataset.to_iterable_dataset()
print(dataset.features)
# None
```

### Expected behavior

Keep the relevant information

### Environment info

datasets==2.10.0

Hi ! Oh good catch. I think the features should be passed to `IterableDataset.from_generator()` in `to_iterable_dataset()` indeed.

Setting this as a good first issue if someone would like to contribute, otherwise we can take care of it :)

#self-assign

seems like the feature parameter is missing from `return IterableDataset.from_generator(Dataset._iter_shards, gen_kwargs={"shards": shards})`  hence it defaults to None.
Directly reading parquet files in a s3 bucket from the load_dataset method
State: open

### Feature request

Right now, we have to read the get the parquet file to the local storage. So having ability to read given the bucket directly address would be benificial 

### Motivation

In a production set up, this feature can help us a lot. So we do not need move training datafiles in between storage.  

### Your contribution

I am willing to help if there's anyway. 

Hi ! I think is in the scope of this other issue: to https://github.com/huggingface/datasets/issues/5281
Apply flake8-comprehensions to codebase
State: closed

### Feature request

Apply ruff flake8 comprehension checks to codebase. 

### Motivation

This should strictly improve the performance / readability of the codebase by removing unnecessary iteration, function calls, etc. This should generate better Python bytecode which should strictly improve performance.

I already applied this fixes to PyTorch and Sympy with little issue and have opened PRs to diffusers and transformers todo this as well.

### Your contribution

Making a PR.
Downloaded datasets do not cache at $HF_HOME
State: closed

### Describe the bug

In the huggingface course (https://huggingface.co/course/chapter3/2?fw=pt) it said that if we set HF_HOME, downloaded datasets would be cached at specified address but it does not. downloaded models from checkpoint names are downloaded and cached at HF_HOME but this is not the case for datasets, they are still cached at ~/.cache/huggingface/datasets.

### Steps to reproduce the bug

Run the following code
```
from datasets import load_dataset
raw_datasets = load_dataset("glue", "mrpc")
raw_datasets
```

it downloads and store dataset at ~/.cache/huggingface/datasets

### Expected behavior

to cache dataset at HF_HOME.

### Environment info

python 3.10.6
Kubuntu 22.04
HF_HOME located on a separate partition

Hi ! Can you make sure you set `HF_HOME` before importing `datasets` ?

Then you can print
```python
print(datasets.config.HF_CACHE_HOME)
print(datasets.config.HF_DATASETS_CACHE)
```
Added return methods for URL-references to the pushed dataset
State: open

Hi,

I was missing the ability to easily open the pushed dataset and it seemed like a quick fix.
Maybe we also want to log this info somewhere, but let me know if I need to add that too.

Cheers,
David

Hi ! Maybe we'd need to align with `transformers` and other libraries that implement `push_to_hub` to agree on what it should return.

e.g. in `transformers` the typing says it returns a string, but in practice it returns a `CommitInfo`.

Therefore I'd not add an output to `push_to_hub` here unless we had a chance to discuss more broadly.

Anyway in my opinion it should no just return the URL of the repository, but ideally the URL at the revision where the data were pushed

Perhaps a mixin or something similar could be defined on the `hfh` side to ensure the `push_to_hub` API is aligned across our projects. 

PS: this would also mean that the PRs such as https://github.com/huggingface/datasets/pull/5528 would no longer be our responsibility

cc @Wauplin 

I agree, with universability and the idea is more about returning at least something that references where to find the uploaded file/model or otherwise. 

Ideally, the referenced PR would work.

imo this would be a good use case to just use `huggingface_hub` and align to what we do there :)

@mariosasko, can you give me some pointers to where I might help implementing this for the `huggingface-hub`?

> @mariosasko: Perhaps a mixin or something similar could be defined on the hfh side to ensure the push_to_hub API is aligned across our projects.

> @julien-c: imo this would be a good use case to just use huggingface_hub and align to what we do there :)

I (finally) opened a PR to harmonize return types: https://github.com/huggingface/huggingface_hub/pull/1921. It should hopefully be shipped in next release later this week (:crossed_fingers:).
the pile datasets url seems to change back
State: closed

### Describe the bug

in #3627, the host url of the pile dataset became `https://mystic.the-eye.eu`. Now the new url is broken, but `https://the-eye.eu` seems to work again.

### Steps to reproduce the bug

```python3
from datasets import load_dataset
dataset = load_dataset("bookcorpusopen")
```
shows
```python3
ConnectionError: Couldn't reach https://mystic.the-eye.eu/public/AI/pile_preliminary_components/books1.tar.gz (ProxyError(MaxRetryError("HTTPSConnectionPool(host='mystic.the-eye.eu', port=443): Max retries exceeded with url: /public/AI/pile_pr
eliminary_components/books1.tar.gz (Caused by ProxyError('Cannot connect to proxy.', OSError('Tunnel connection failed: 504 Gateway Timeout')))")))
```

### Expected behavior

Downloading as normal.

### Environment info

- `datasets` version: 2.9.0
- Platform: Linux-5.4.143.bsk.7-amd64-x86_64-with-glibc2.31
- Python version: 3.9.2
- PyArrow version: 6.0.1
- Pandas version: 1.5.3

Thanks for reporting, @wjfwzzc.

I am transferring this issue to the corresponding dataset on the Hub: https://huggingface.co/datasets/bookcorpusopen/discussions/1

Thank you. All fixes are done:
- [x] https://huggingface.co/datasets/bookcorpusopen/discussions/2
- [x] https://huggingface.co/datasets/the_pile/discussions/1
- [x] https://huggingface.co/datasets/the_pile_books3/discussions/1
- [x] https://huggingface.co/datasets/the_pile_openwebtext2/discussions/2
- [x] https://huggingface.co/datasets/the_pile_stack_exchange/discussions/2
IndexError: invalid index of a 0-dim tensor. Use `tensor.item()` in Python or `tensor.item<T>()` in C++ to convert a 0-dim tensor to a number
State: closed

### Describe the bug

When dataset contains a 0-dim tensor, formatting.py raises a following error and fails.
```bash
Traceback (most recent call last):
  File "<path>/lib/python3.8/site-packages/datasets/formatting/formatting.py", line 501, in format_row
    return _unnest(formatted_batch)
  File "<path>/lib/python3.8/site-packages/datasets/formatting/formatting.py", line 137, in _unnest
    return {key: array[0] for key, array in py_dict.items()}
  File "<path>/lib/python3.8/site-packages/datasets/formatting/formatting.py", line 137, in <dictcomp>
    return {key: array[0] for key, array in py_dict.items()}
IndexError: invalid index of a 0-dim tensor. Use `tensor.item()` in Python or `tensor.item<T>()` in C++ to convert a 0-dim tensor to a number
```

### Steps to reproduce the bug

Load whichever dataset and add transform method to add 0-dim tensor. Or create/find a dataset containing 0-dim tensor. E.g.

```python
from datasets import load_dataset
import torch

dataset = load_dataset("lambdalabs/pokemon-blip-captions", split='train')
def t(batch):
    return {"test": torch.tensor(1)}
    
dataset.set_transform(t)
d_0 = dataset[0]
```

### Expected behavior

Extractor will correctly get a row from the dataset, even if it contains 0-dim tensor.

### Environment info

`datasets==2.8.0`, but it looks like it is also applicable to main branch version (as of 16th February)

Hi! The `set_transform` does not apply a custom formatting transform on a single example but the entire batch, so the fixed version of your transform would look as follows:
```python
from datasets import load_dataset
import torch

dataset = load_dataset("lambdalabs/pokemon-blip-captions", split='train')
def t(batch):
    return {"test": torch.tensor([1] * len(batch[next(iter(batch))]))}
    
dataset.set_transform(t)
d_0 = dataset[0]
```

Still, the formatter's error message should mention that a dict of **sequences** is expected as the returned value (not just a dict) to make debugging easier.

I can take this

Fixed in #5553 

> Hi! The `set_transform` does not apply a custom formatting transform on a single example but the entire batch, so the fixed version of your transform would look as follows:
> 
> ```python
> from datasets import load_dataset
> import torch
> 
> dataset = load_dataset("lambdalabs/pokemon-blip-captions", split='train')
> def t(batch):
>     return {"test": torch.tensor([1] * len(batch[next(iter(batch))]))}
>     
> dataset.set_transform(t)
> d_0 = dataset[0]
> ```
> 
> Still, the formatter's error message should mention that a dict of **sequences** is expected as the returned value (not just a dict) to make debugging easier.

ok, will change it according to suggestion. Thanks for the reply!
Increase speed of data files resolution
State: closed

Certain datasets like `bigcode/the-stack-dedup` have so many files that loading them takes forever right from the data files resolution step.

`datasets` uses file patterns to check the structure of the repository but it takes too much time to iterate over and over again on all the data files.

This comes from `resolve_patterns_in_dataset_repository` which calls `_resolve_single_pattern_in_dataset_repository`, which iterates on all the files at
```python
glob_iter = [PurePath(filepath) for filepath in fs.glob(PurePath(pattern).as_posix()) if fs.isfile(filepath)]
```
but calling `glob` on such a dataset is too expensive. Indeed it calls `ls()` in `hffilesystem.py` too many times.

Maybe `glob` can be more optimized in `hffilesystem.py`, or the data files resolution can directly be implemented in the filesystem by checking its `dir_cache` ?

#self-assign

You were right, if `self.dir_cache` is not None in glob, it is exactly the same as what is returned by find, at least for all the tests we have, and some extended evaluation I did across a random sample of about 1000 datasets. 

Thanks for the nice hints, and let me know if this is not exactly what we want here!

see PR: https://github.com/huggingface/datasets/pull/5704



I think we can make the data files resolution (significantly) faster in 2 steps:

1. `glob` calls `find` (which in turn calls `ls`), so we need `find` to be fast, and this can be achieved by fetching all the entries in a single API call and avoiding calls to `ls`. Implementing this for `HfFileSystem.find` (the one in `huggingface_hub`) is on my TO-DO list.
2. caching the repeated `find` calls in `_get_data_files_patterns` when the `data_files` patterns are not provided in `load_dataset`. To address this, we can introduce a `_resolve_single_pattern` function that would accept a filesystem object and a list of regex patterns to resolve. Then we can wrap this filesystem object in `_get_data_files_patterns` with an object that would cache the find calls before resolving the patterns with `_resolve_single_pattern`. (Feel free to suggest a cleaner implementation)

WDYT?

Good idea :) 

For 2:

That would work ! It's also possible to have a FileSystem with a cache on `.find` and use it inside the resolver passed to `_get_data_files_patterns`. Right now they're pretty simple:

```python
# for remote repositories
resolver = partial(_resolve_single_pattern_in_dataset_repository, dataset_info, base_path=base_path)
# for local
resolver = partial(_resolve_single_pattern_locally, base_path)
```

something like this maybe (with Quentin's reimplementation of `HfFilesystem.find`)?

 ```
 @lru_cache(max_size=None)
 def _find(self, path, maxdepth=None, withdirs=False, detail=False, **kwargs):
```

In any case please let me know if I can help in any way!
train_test_split in arrow_dataset does not ensure to keep single classes in test set
State: closed

### Describe the bug

When I have a dataset with very few (e.g. 1) examples per class and I call the train_test_split function on it, sometimes the single class will be in the test set. thus will never be considered for training.

### Steps to reproduce the bug

```
import numpy as np
from datasets import Dataset

data = [
    {'label': 0, 'text': "example1"},
    {'label': 1, 'text': "example2"},
    {'label': 1, 'text': "example3"},
    {'label': 1, 'text': "example4"},
    {'label': 0, 'text': "example5"},
    {'label': 1, 'text': "example6"},
    {'label': 2, 'text': "example7"},
    {'label': 2, 'text': "example8"}
]

for _ in range(10):
    data_set = Dataset.from_list(data)
    data_set = data_set.train_test_split(test_size=0.5)
    data_set["train"]

    unique_labels_train = np.unique(data_set["train"][:]["label"])
    unique_labels_test = np.unique(data_set["test"][:]["label"])
    assert len(unique_labels_train) >= len(unique_labels_test) 
```

### Expected behavior

I expect to have every available class at least once in my training set.

### Environment info

- `datasets` version: 2.9.0
- Platform: Linux-5.15.65+-x86_64-with-debian-bullseye-sid
- Python version: 3.7.12
- PyArrow version: 11.0.0
- Pandas version: 1.3.5


Hi! You can get this behavior by specifying `stratify_by_column="label"` in `train_test_split`.

This is the full example:
```python
import numpy as np
from datasets import Dataset, ClassLabel

data = [
    {'label': 0, 'text': "example1"},
    {'label': 1, 'text': "example2"},
    {'label': 1, 'text': "example3"},
    {'label': 1, 'text': "example4"},
    {'label': 0, 'text': "example5"},
    {'label': 1, 'text': "example6"},
    {'label': 2, 'text': "example7"},
    {'label': 2, 'text': "example8"}
]

for _ in range(10):
    data_set = Dataset.from_list(data)
    data_set = data_set.cast_column("label", ClassLabel(num_classes=3))
    data_set = data_set.train_test_split(test_size=0.5, stratify_by_column="label")
    unique_labels_train = np.unique(data_set["train"][:]["label"])
    unique_labels_test = np.unique(data_set["test"][:]["label"])
    assert len(unique_labels_train) >= len(unique_labels_test) 
```
Invalid Arrow data from JSONL
State: open

This code fails:
```python
from datasets import Dataset

ds = Dataset.from_json(path_to_file)
ds.data.validate()
```
raises

```python
ArrowInvalid: Column 2: In chunk 1: Invalid: Struct child array #3 invalid: Invalid: Length spanned by list offsets (4064) larger than values array (length 4063)
```

This causes many issues for @TevenLeScao:

- `map` fails because it fails to rewrite invalid arrow arrays

    ```python
    ~/Desktop/hf/datasets/src/datasets/arrow_writer.py in write_examples_on_file(self)
        438             if all(isinstance(row[0][col], (pa.Array, pa.ChunkedArray)) for row in self.current_examples):
        439                 arrays = [row[0][col] for row in self.current_examples]
    --> 440                 batch_examples[col] = array_concat(arrays)
        441             else:
        442                 batch_examples[col] = [

    ~/Desktop/hf/datasets/src/datasets/table.py in array_concat(arrays)
    1885 
    1886     if not _is_extension_type(array_type):
    -> 1887         return pa.concat_arrays(arrays)
    1888 
    1889     def _offsets_concat(offsets):

    ~/.virtualenvs/hf-datasets/lib/python3.7/site-packages/pyarrow/array.pxi in pyarrow.lib.concat_arrays()

    ~/.virtualenvs/hf-datasets/lib/python3.7/site-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status()

    ~/.virtualenvs/hf-datasets/lib/python3.7/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status()

    ArrowIndexError: array slice would exceed array length
    ```


- `to_dict()` **segfaults** ⚠️

    ```python
    /Users/runner/work/crossbow/crossbow/arrow/cpp/src/arrow/array/data.cc:99:  Check failed: (off) <= (length) Slice offset greater 
    than array length
    ```

To reproduce: unzip the archive and run the above code using `sanity_oscar_en.jsonl`
[sanity_oscar_en.jsonl.zip](https://github.com/huggingface/datasets/files/10734124/sanity_oscar_en.jsonl.zip)

PS: reading using pandas and converting to Arrow works though (note that the dataset lives in RAM in this case):
```python
ds = Dataset.from_pandas(pd.read_json(path_to_file, lines=True))
ds.data.validate()
```
[INVALID PR]
State: closed

Hi to whoever is reading this! 🤗

## What's in this PR?

~~Basically, I've removed the 🤗`datasets` installation as `python -m pip install ".[quality]" in the `check_code_quality` job in `.github/workflows/ci.yaml`, as we don't need to install the whole package to run the CI, unless that's done on purpose e.g. to check that the Python package installation succeeds before running the tests over the matrix of os?~~

~~So I just wanted to check whether the time was reduced doing this (which I assume it will), plus whether this is something that can be improved, or just discarded in case you're also using that step to make sure that the package can be installed.~~

## What's missing?

~~I was just wondering whether you consider replacing `isort` and `flake8` with `ruff` (if possible), since it's way faster, more information at [`ruff`](https://github.com/charliermarsh/ruff). Before creating this PR the average time of the `check_code_quality` job was around 40s.~~

## Edit

Sorry for the inconvenience this may have caused, didn't realise that the config is defined in `setup.cfg` and `pyproject.toml`, so running those without installing the Python package leads to failure, my bad 😞 


_The documentation is not available anymore as the PR was closed or merged._
Checking that split name is correct happens only after the data is downloaded
State: open

### Describe the bug

Verification of split names (=indexing data by split) happens after downloading the data. So when the split name is incorrect, users learn about that only after the data is fully downloaded, for large datasets it might take a lot of time.

### Steps to reproduce the bug

Load any dataset with random split name, for example:
```python
from datasets import load_dataset

load_dataset("mozilla-foundation/common_voice_11_0", "en", split="blabla")
```
and the download will start smoothly, despite there is no split named "blabla".

### Expected behavior

Raise error when split name is incorrect. 

### Environment info

`datasets==2.9.1.dev0`
Reload features from Parquet metadata
State: closed

Resolves #5482. 

Attaches feature metadata to parquet files serialised using `Dataset.to_parquet`. 

This allows retrieving data with "rich" feature types (e.g., `datasets.features.image.Image` or `datasets.features.audio.Audio`) from parquet files without cumbersome casting (for an example, see #5482).

@lhoestq It seems that it is sufficient to attach metadata to the schema prior to serialising and features are loaded back with correct types afterwards automatically. 

I used the following script to test the implementation:

```python
from pathlib import Path

import datasets

dataset_name = "Maysee/tiny-imagenet"
ds = datasets.load_dataset(dataset_name, split=datasets.Split.TRAIN)
output_directory_path = Path(__file__).parent.joinpath("example_test_outputs", dataset_name.replace("/", "_"))
output_directory_path.mkdir(exist_ok=True, parents=True)
output_filepath = output_directory_path.joinpath("ds.parquet")
ds.to_parquet(str(output_filepath))

reloaded_ds = datasets.load_dataset(str(output_directory_path), split=datasets.Split.TRAIN)

assert ds.features == reloaded_ds.features
```

Prior to the change in this PR this script raises an `AssertionError` and the `Image` features lose their type after serialisation. After the change in this PR, the assertion does not raise an error and manual inspection of the features shows type `Image` for the respective columns of `reloaded_ds `.

Some open questions:
* How/where can I best add new unit tests for this implementation?
* What dataset would I best use in the tests? I chose `Maysee/tiny-imagenet` mainly because it is small and contains an ?Image` feature that can be used to test, but I'd be happy for suggestions on a suitable data source to use.
* Currently I'm calling `datasets.arrow_writer.ArrowWriter._build_metadata` as I need the same logic. However, I'm not happy with the coupling between `datasets.io.parquet` and `datasets.arrow_writer` it leaves me with. Suggest to factor this common logic out into a helper function and reuse it from both of these. Do you agree and if yes, could you please guide me where I would best place this function?

Many thanks in advance and kind regards,
MFreidank


  

Thanks a lot for your help @lhoestq. I've simplified what turned out to be a simple fix and added the unit test.

Does this look ready to be merged or is there anything I'm still missing?

Cool ! I think you just need to remove the unused import in `io/parquet.py`
```
src/datasets/io/parquet.py:4:1: F401 'pyarrow as pa' imported but unused
```
and we're good to merge :)

_The documentation is not available anymore as the PR was closed or merged._

> Cool ! I think you just need to remove the unused import in `io/parquet.py`
> 
> ```
> src/datasets/io/parquet.py:4:1: F401 'pyarrow as pa' imported but unused
> ```
> 
> and we're good to merge :)

Done! Thanks a lot, this was fun :)
Improve inconsistency of `Dataset.map` interface for `load_from_cache_file`
State: closed

### Feature request


1. Replace the `load_from_cache_file` default value to `True`. 
2. Remove or alter checks from `is_caching_enabled` logic.

### Motivation

I stumbled over an inconsistency in the `Dataset.map` interface. The documentation (and source) states for the parameter `load_from_cache_file`:
```
load_from_cache_file (`bool`, defaults to `True` if caching is enabled):
    If a cache file storing the current computation from `function`
    can be identified, use it instead of recomputing.
```

1. `load_from_cache_file` default value is `None`, while being annotated as `bool`
2. It is inconsistent with other method signatures like `filter`, that have the default value `True`
3. The logic is inconsistent, as the `map` method checks if caching is enabled through `is_caching_enabled`. This logic is not used for other similar methods.

### Your contribution

I am not fully aware of the logic behind caching checks. If this is just a inconsistency that historically grew, I would suggest to remove the `is_caching_enabled` logic as the "default" logic. Maybe someone can give insights, if environment variables have a higher priority than local variables or vice versa. 

If this is clarified, I could adjust the source  according to the "Feature request" section of this issue.

Hi, thanks for noticing this! We can't just remove the cache control as this allows us to control where the arrow files generated by the ops are written (cached on disk if enabled or a temporary directory if disabled). The right way to address this inconsistency would be by having `load_from_cache_file=None` by default everywhere.

Hi! Yes, this seems more plausible. I can implement that. One last thing is the type annotation `load_from_cache_file: bool = None`. Which I then would change to `load_from_cache_file: Optional[bool] = None`.

PR #5515 

Yes, `Optional[bool]` is the correct type annotation and thanks for the PR.
Some functions use a param named `type` shouldn't that be avoided since it's a Python reserved name?
State: closed

Hi @mariosasko, @lhoestq, or whoever reads this! :)

After going through `ArrowDataset.set_format` I found out that the `type` param is actually named `type` which is a Python reserved name as you may already know, shouldn't that be renamed to `format_type` before the 3.0.0 is released?

Just wanted to get your input, and if applicable, tackle this issue myself! Thanks 🤗

Hi! Let's not do this - renaming it would be a breaking change, and going through the deprecation cycle is only worth it if it improves user experience.

Hi @mariosasko, ok it makes sense. Anyway, don't you think it's worth it at some point to start a deprecation cycle e.g. `fs` in `load_from_disk`? It doesn't affect user experience but it's for sure a bad practice IMO, but's up to you 😄 Feel free to close this issue otherwise!

I don't think deprecating a param name in this particular instance is worth the hassle, so I'm closing the issue 🙂.

Sure, makes sense @mariosasko thanks!
Milvus integration for search
State: open

Signed-off-by: Filip Haltmayer <filip.haltmayer@zilliz.com>

The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5510). All of your documentation changes will be reflected on that endpoint.

To the maintainer, sorry about the repeated run requests for formatting. Missed the `make style` outlined in contributing guidelines. 

Anything I can do to get the workflow to run? @lhoestq 

cc @mariosasko 

> Anything I can do to get the workflow to run?

You can merge `main` into your branch to fix code formatting (we switched from isort+flake8 to ruff this week), and then run `make style`

I believe that should be good. @mariosasko
Add a static `__all__` to `__init__.py` for typecheckers
State: open

This adds a static `__all__` field to `__init__.py`, allowing typecheckers to know which symbols are accessible from `datasets` at runtime. In particular [Pyright](https://github.com/microsoft/pylance-release/issues/2328#issuecomment-1029381258) seems to rely on this. At this point I have added all (modulo oversight) the symbols mentioned in the Reference part of [the docs](https://huggingface.co/docs/datasets), but that could be adjusted. As a side effect, only these symbols will be imported by `from datasets import *`, which may or may not be a good thing (and if it isn't, that's easy to fix).

Another option would be to add a pyi stub, but I think `__all__` should be the most pythonic solution.

This should fix #3841.

The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5509). All of your documentation changes will be reflected on that endpoint.

Hi! I've commented on the original issue to provide some context. Feel free to share your opinion there.
Saving a dataset after setting format to torch doesn't work, but only if filtering
State: closed

### Describe the bug

Saving a dataset after setting format to torch doesn't work, but only if filtering

### Steps to reproduce the bug

```
a = Dataset.from_dict({"b": [1, 2]})
a.set_format('torch')
a.save_to_disk("test_save") # saves successfully
a.filter(None).save_to_disk("test_save_filter") # does not
>> [...] TypeError: Provided `function` which is applied to all elements of table returns a `dict` of types [<class 'torch.Tensor'>]. When using `batched=True`, make sure provided `function` returns a `dict` of types like `(<class 'list'>, <class 'numpy.ndarray'>)`.

# note: skipping the format change to torch lets this work.



### Expected behavior

Saving to work

### Environment info

- `datasets` version: 2.4.0
- Platform: Linux-6.1.9-arch1-1-x86_64-with-glibc2.36
- Python version: 3.10.9
- PyArrow version: 9.0.0
- Pandas version: 1.4.4

Hey, I'm a research engineer working on language modelling wanting to contribute to open source. I was wondering if I could give it a shot?

Hi! This issue was fixed in https://github.com/huggingface/datasets/pull/4972, so please install `datasets>=2.5.0` to avoid it.
Optimise behaviour in respect to indices mapping
State: open

_Originally [posted](https://huggingface.slack.com/archives/C02V51Q3800/p1675443873878489?thread_ts=1675418893.373479&cid=C02V51Q3800) on Slack_

Considering all this, perhaps for Datasets 3.0, we can do the following:
* [ ] have `continuous=True` by default in `.shard`  (requested in the survey and makes more sense for us since it doesn't create an indices mapping)
* [x] allow calling `save_to_disk` on "unflattened" datasets
* [ ] remove "hidden" expensive calls in `save_to_disk`, `unique`, `concatenate_datasets`, etc. For instance, instead of silently calling `flatten_indices`  where it's needed, it's probably better to be explicit (considering how expensive these ops can be) and raise an error instead
PyTorch BatchSampler still loads from Dataset one-by-one
State: closed

### Describe the bug

In [the docs here](https://huggingface.co/docs/datasets/use_with_pytorch#use-a-batchsampler), it mentions the issue of the Dataset being read one-by-one, then states that using a BatchSampler resolves the issue.

I'm not sure if this is a mistake in the docs or the code, but it seems that the only way for a Dataset to be passed a list of indexes  by PyTorch (instead of one index at a time) is to define a `__getitems__` method (note the plural) on the Dataset object, and since the HF Dataset doesn't have this, PyTorch executes [this line of code](https://github.com/pytorch/pytorch/blob/master/torch/utils/data/_utils/fetch.py#L58), reverting to fetching one-by-one.

### Steps to reproduce the bug

You can put a breakpoint in `Dataset.__getitem__()` or just print the args from there and see that it's called multiple times for a single `next(iter(dataloader))`, even when using the code from the docs:
```py
from torch.utils.data.sampler import BatchSampler, RandomSampler
batch_sampler = BatchSampler(RandomSampler(ds), batch_size=32, drop_last=False)
dataloader = DataLoader(ds, batch_sampler=batch_sampler)
```



### Expected behavior

The expected behaviour would be for it to fetch batches from the dataset, rather than one-by-one.

To demonstrate that there is room for improvement: once I have a HF dataset `ds`, if I just add this line:
```py
ds.__getitems__ = ds.__getitem__
```
...then the time taken to loop over the dataset improves considerably (for wikitext-103, from one minute to 13 seconds with batch size 32). Probably not a big deal in the grand scheme of things, but seems like an easy win.

### Environment info

- `datasets` version: 2.9.0
- Platform: Linux-5.10.102.1-microsoft-standard-WSL2-x86_64-with-glibc2.31
- Python version: 3.10.8
- PyArrow version: 10.0.1
- Pandas version: 1.5.3

This change seems to come from a few months ago in the PyTorch side. That's good news and it means we may not need to pass a batch_sampler as soon as we add `Dataset.__getitems__` to get the optimal speed :)

Thanks for reporting ! Would you like to open a PR to add `__getitems__` and remove this outdated documentation ?

Yeah I figured this was the sort of thing that probably once worked. I can confirm that you no longer need the batch sampler, just `batch_size=n` in the `DataLoader`.

I'll pass on the PR, I'm flat out right now, sorry.
`load_dataset` has ~4 seconds of overhead for cached data
State: open

### Feature request

When loading a dataset that has been cached locally, the `load_dataset` function takes a lot longer than it should take to fetch the dataset from disk (or memory).
This is particularly noticeable for smaller datasets. For example, wikitext-2, comparing `load_data` (once cached) and `load_from_disk`, the `load_dataset` method takes 40 times longer.

⏱    4.84s ⮜ load_dataset
⏱    119ms ⮜ load_from_disk

### Motivation

I assume this is doing something like checking for a newer version.

If so, that's an age old problem: do you make the user wait _every single time they load from cache_ or do you do something like load from cache always, _then_ check for a newer version and alert if they have stale data. The decision usually revolves around what percentage of the time the data will have been updated, and how dangerous old data is.

For most datasets it's extremely unlikely that there will be a newer version on any given run, so 99% of the time this is just wasted time.

Maybe you don't want to make that decision for all users, but at least having the _option_ to not wait for checks would be an improvement.

### Your contribution

.

Hi ! To skip the verification step that checks if newer data exist, you can enable offline mode with `HF_DATASETS_OFFLINE=1`.

Although I agree this step should be much faster for datasets hosted on the HF Hub - we could just compare the commit hash from the local data and the remote git repository. We're not been leveraging the git commit hashes, since the library was built before we even had git repositories for each dataset on HF.

Thanks @lhoestq, for memory when I recorded those times I had `HF_DATASETS_OFFLINE` set.
TypeError: 'bool' object is not iterable when filtering a datasets.arrow_dataset.Dataset
State: closed

### Describe the bug

Hi, 

Thanks for the amazing work on the library!

**Describe the bug**

I think I might have noticed a small bug in the filter method. 
Having loaded a dataset using `load_dataset`,  when I try to filter out empty entries with `batched=True`, I get a TypeError.

### Steps to reproduce the bug

```
train_dataset = train_dataset.filter(
            function=lambda example: example["image"] is not None, 
            batched=True,
            batch_size=10)
```
Error message:

```
File .../lib/python3.9/site-packages/datasets/fingerprint.py:480, in fingerprint_transform.<locals>._fingerprint.<locals>.wrapper(*args, **kwargs)
    476             validate_fingerprint(kwargs[fingerprint_name])
    478 # Call actual function
--> 480 out = func(self, *args, **kwargs)
...
-> 5666 indices_array = [i for i, to_keep in zip(indices, mask) if to_keep]
   5667 if indices_mapping is not None:
   5668     indices_array = pa.array(indices_array, type=pa.uint64())

TypeError: 'bool' object is not iterable
```

**Removing batched=True allows to bypass the issue.** 

### Expected behavior

According to the doc, "[batch_size corresponds to the] number of examples per batch provided to function if batched = True", so we shouldn't need to remove the batchd=True arg?

source: https://huggingface.co/docs/datasets/v2.9.0/en/package_reference/main_classes#datasets.Dataset.filter

### Environment info

- `datasets` version: 2.9.0
- Platform: Linux-5.4.0-122-generic-x86_64-with-glibc2.31
- Python version: 3.9.10
- PyArrow version: 10.0.1
- Pandas version: 1.5.3

Hi! Instead of a single boolean, your filter function should return an iterable (of booleans) in the batched mode like so:
```python
train_dataset = train_dataset.filter(
            function=lambda batch: [image is not None for image in batch["image"]], 
            batched=True,
            batch_size=10)
```

PS: You can make this operation much faster by operating directly on the arrow data to skip the decoding part:
```python
train_dataset = train_dataset.with_format("arrow")
train_dataset = train_dataset.filter(
            function=lambda table: table["image"].is_valid().to_pylist(), 
            batched=True,
            batch_size=100)
train_dataset = train_dataset.with_format(None)
```

Thank a lot!

I hit the same issue and the error message isn't really clear on what's going wrong. It might be helpful to update the docs with a batched example.
Add a `reduce` method
State: closed

### Feature request

Right now the `Dataset` class implements `map()` and `filter()`, but leaves out the third functional idiom popular among Python users: `reduce`. 

### Motivation

A `reduce` method is often useful when calculating dataset statistics, for example, the occurrence of a particular n-gram or the average line length of a code dataset.  

### Your contribution

I haven't contributed to `datasets` before, but I don't expect this will be too difficult, since the implementation will closely follow that of `map` and `filter`. I could have a crack over the weekend. 

Hi! Sure, feel free to open a PR, so we can see the API you have in mind.

I would like to give it a go! #self-assign

Closing as `Dataset.map` can be used instead (see https://github.com/huggingface/datasets/pull/5533#issuecomment-1440571658 and https://github.com/huggingface/datasets/pull/5533#issuecomment-1446403263)

Hello, is it possible for this issue/PR to be revisited? The problem with the alternatives presented (besides multiple map stages) is that they don't use the cache. A reduce operation is just as expensive as a map operation because it also goes over the entire dataset. It's equally worth caching.

Personally, I have a situation where I would need this and map is far from ideal. I'm working on updating a project of mine to use Huggingface Datasets, and I need to port the loop at https://github.com/colonelwatch/abstracts-search/blob/b90f31ee4cc6e394f829d3a6d9d0311ca390ada9/train.py#L112-L138. Please forgive the code style, here's what it does in English. I have a dataset of about 95 million embeddings, out of which 16384 is taken as a "query" set. For each embedding in the query set, I need to find the ten closest neighbors. These nearest neighbors are used to tune the parameters of a faiss index. The solution is to set up an "accumulator" comprising of the ten closest so far and their distances, then do a single scan over the 95 million (memmapped), then save the results of the "accumulator" for when I want to prototype another index.

The closest approximation to this is multiple map stages, but with such a large "accumulator" having the RAM to do a big batch size becomes critical. At a batch size of 1000, the intermediate accumulators would in theory be about 120 GB! That can be more if I want higher precision than float32. It would already be about the same size as the original embeddings. Using larger batch sizes puts strain on the RAM because I'd be dealing with batch_size x 16384 distances. The best I'd gotten with my RAM, single-threaded, was 65536, and for speed I had to use that thread to feed a GPU. It'd be better if I could use multiple threads to get high throughput instead, or even do all the work in CPU, but to fit the threads I'd need the batch size to be smaller.

All of this intermediate memory could be eliminated if there was a reduce operation.
Update audio installation doc page
State: closed

Our [installation documentation page](https://huggingface.co/docs/datasets/installation#audio) says that one can use Datasets for mp3 only with `torchaudio<0.12`. `torchaudio>0.12` is actually supported too but requires a specific version of ffmpeg which is not easily installed on all linux versions but there is a custom ubuntu repo for it, we have insctructions in the code: https://github.com/huggingface/datasets/blob/main/src/datasets/features/audio.py#L327 
So we should update the doc page. But first investigate [this issue](5488). 

Totally agree, the docs should be in sync with our code.

Indeed to avoid confusing users, I think we should have updated the docs at the same time as this PR:
- #5167

@albertvillanova yeah sure I should have, but I forgot back then, sorry for that 😶

No, @polinaeterna, nothing to be sorry about.

My comment was for all of us datasets team, as a reminder: when making a PR, but also when reviewing some other's PR, we should not forget to update the corresponding docstring and doc pages. It is something we can improve if we help each other in reminding about it... :hugs: 

@polinaeterna I think we can close this issue now as we no longer use `torchaudio` for decoding.
Push_to_hub in a pull request
State: closed

Right now `ds.push_to_hub()` can push a dataset on `main` or on a new branch with `branch=`, but there is no way to open a pull request. Even passing `branch=refs/pr/x` doesn't seem to work: it tries to create a branch with that name

cc @nateraw 

It should be possible to tweak the use of `huggingface_hub` in `push_to_hub` to make it open a PR or push to an existing PR

Assigned to myself and will get to it in the next week, but if someone finds this issue annoying and wants to submit a PR before I do, just ping me here and I'll reassign :). 

I would like to be assigned to this issue, @nateraw . #self-assign
Adding `sep` to TextConfig 
State: open

I have a local a `.txt` file that follows the `CONLL2003` format which I need to load using `load_script`. However, by using `sample_by='line'`, one can only split the dataset into lines without splitting each line into columns. Would it be reasonable to add a `sep` argument in combination with `sample_by='paragraph'` to parse a paragraph into an array for each column ? If so, I am happy to contribute!

## Environment
* `python 3.8.10`
* `datasets 2.9.0`

## Snippet of `train.txt`

```txt
Distribution	NN	O	O
and	NN	O	O
dynamics	NN	O	O
of	NN	O	O
electron	NN	O	B-RP
complexes	NN	O	I-RP
in	NN	O	O
cyanobacterial	NN	O	B-R
membranes	NN	O	I-R

The	NN	O	O
occurrence	NN	O	O
of	NN	O	O
prostaglandin	NN	O	B-R
F2α	NN	O	I-R
in	NN	O	O
Pharbitis	NN	O	B-R
seedlings	NN	O	I-R
grown	NN	O	O
under	NN	O	O
short	NN	O	B-P
days	NN	O	I-P
or	NN	O	I-P
days	NN	O	I-P

```

## Current Behaviour 
```python
# defining 4 features ['tokens', 'pos_tags', 'chunk_tags', 'ner_tags'] here would fail with `ValueError: Length of names (4) does not match length of arrays (1)`
dataset = datasets.load_dataset(path='text', features=features, data_files={'train': 'train.txt'}, sample_by='line')
dataset['train']['tokens'][0]
>>> 'Distribution\tNN\tO\tO'
```

## Expected Behaviour / Suggestion
```python
# suppose we defined 4 features ['tokens', 'pos_tags', 'chunk_tags', 'ner_tags']
dataset = datasets.load_dataset(path='text', features=features, data_files={'train': 'train.txt'}, sample_by='paragraph', sep='\t')
dataset['train']['tokens'][0]
>>> ['Distribution', 'and', 'dynamics', ... ]

dataset['train']['ner_tags'][0]
>>> ['O', 'O', 'O', ... ]
```

Hi @omar-araboghli, thanks for your proposal.

Have you tried to use "csv" loader instead of "text"? That already has a `sep` argument.

Hi @albertvillanova, thanks for the quick response!

Indeed, I have been trying to use `csv` instead of `text`. However I am still not able to define range of rows as one sequence, that is achievable with passing `sample_by='paragraph'` to the `TextConfig`

For instance, the below code

```python
import datasets

dataset = datasets.load_dataset(
    path='csv',
    data_files={'train': TRAINING_SET_PATH},
    sep='\t',
    header=None,
    column_names=['tokens', 'pos_tags', 'chunk_tags', 'ner_tags']
)
```

leads to 

```python
dataset
>>> DatasetDict({
    train: Dataset({
        features: ['tokens', 'pos_tags', 'chunk_tags', 'ner_tags'],
        num_rows: 62543
    })
})

dataset['train'][0]
>>> {'tokens': 'Distribution',
 'pos_tags': 'NN',
 'chunk_tags': 'O',
 'ner_tags': 'O'
}
```
Is there a way to deal with multiple csv rows as one dataset instance, where each column is a sequence of those rows ?
Reload features from Parquet metadata
State: closed

The idea would be to allow this :
```python 
ds.to_parquet("my_dataset/ds.parquet")
reloaded = load_dataset("my_dataset")
assert ds.features == reloaded.features 
```
And it should also work with Image and Audio types (right now they're reloaded as a dict type)

This can be implemented by storing and reading the feature types in the parquet metadata, as we do for arrow files.

I'd be happy to have a look, if nobody else has started working on this yet @lhoestq. 

It seems to me that for the `arrow` format features are currently attached as metadata [in `datasets.arrow_writer`](https://github.com/huggingface/datasets/blob/5f810b7011a8a4ab077a1847c024d2d9e267b065/src/datasets/arrow_writer.py#L412)  and retrieved from the metadata at `load_dataset` time using [`datasets.features.features.from_arrow_schema`](https://github.com/huggingface/datasets/blob/5f810b7011a8a4ab077a1847c024d2d9e267b065/src/datasets/features/features.py#L1602). 

This will need to be replicated for `parquet` via calls to [this api](https://arrow.apache.org/docs/python/generated/pyarrow.parquet.write_metadata.html) from `io.parquet.ParquetWriter` and `io.parquet.ParquetReader` [respectively](https://github.com/huggingface/datasets/blob/5f810b7011a8a4ab077a1847c024d2d9e267b065/src/datasets/io/parquet.py#L104).

Any other important considerations?


Thanks @MFreidank ! That's correct :)

Reading the metadata to infer the features can be ideally done in the `parquet.py` file in `packaged_builder` when a parquet file is read. You can cast the arrow table to the schema you get from the features.arrow_schema

#self-assign
Unpin sqlalchemy once issue is fixed
State: closed

Once the source issue is fixed:
- pandas-dev/pandas#51015

we should revert the pin introduced in:
- #5476

@albertvillanova It looks like that issue has been fixed so I made a PR to unpin sqlalchemy! 

The source issue:
- https://github.com/pandas-dev/pandas/issues/40686

has been fixed:
- https://github.com/pandas-dev/pandas/pull/48576

The fix was released yesterday (2023-04-03) only in `pandas-2.0.0`:
- https://github.com/pandas-dev/pandas/releases/tag/v2.0.0

but it will not be back-ported to `pandas-1`:
- https://github.com/pandas-dev/pandas/pull/48576#issuecomment-1466467159

Also note that `pandas-2.0.0` dropped support for Python 3.7:
- https://github.com/pandas-dev/pandas/issues/41678
- https://github.com/pandas-dev/pandas/pull/41989

Therefore, we cannot unpin `sqlalchemy` until we drop support for Python 3.7 (these Python users cannot use `pandas-2`).
Dataset scan time is much slower than using native arrow
State: closed

### Describe the bug

I'm basically running the same scanning experiment from the tutorials https://huggingface.co/course/chapter5/4?fw=pt except now I'm comparing to a native pyarrow version.

I'm finding that the native pyarrow approach is much faster (2 orders of magnitude). Is there something I'm missing that explains this phenomenon?

### Steps to reproduce the bug

https://colab.research.google.com/drive/11EtHDaGAf1DKCpvYnAPJUW-LFfAcDzHY?usp=sharing

### Expected behavior

I expect scan times to be on par with using pyarrow directly.

### Environment info

standard colab environment

Hi ! In your code you only iterate on the Arrow buffers - you don't actually load the data as python objects. For a fair comparison, you can modify your code using:
```diff
-     for _ in range(0, len(table), bsz):
-         _ = {k:table[k][_ : _ + bsz] for k in cols}
+     for _ in range(0, len(table), bsz):
+         _ = {k:table[k][_ : _ + bsz].to_pylist() for k in cols}
```

I re-ran your code and got a speed ratio of 1.00x and 1.02x

Ah I see, datasets is implicitly making this conversion. Thanks for pointing that out!

If it's not too much, I would also suggest updating some of your docs with the same `.to_pylist()` conversion in the code snippet that follows [here](https://huggingface.co/course/chapter5/4?fw=pt#:~:text=let%E2%80%99s%20run%20a%20little%20speed%20test%20by%20iterating%20over%20all%20the%20elements%20in%20the%20PubMed%20Abstracts%20dataset%3A).

This code snippet shows `datasets` code that reads the Arrow data as python objects already, there is no need to add to_pylist. Or were you thinking about something else ?