jamesLeeeeeee/datasets-github-issues huggingface dataset:
`ImageFolder` performs poorly with large datasets
Author: salieri | Pull request: no | State: open

### Describe the bug

While testing image dataset creation, I'm seeing significant performance bottlenecks with imagefolders when scanning a directory structure with large number of images.


## Setup
* Nested directories (5 levels deep)
* 3M+ images
* 1 `metadata.jsonl` file


## Performance Degradation Point 1

Degradation occurs because [`get_data_files_patterns`](https://github.com/huggingface/datasets/blob/main/src/datasets/data_files.py#L231-L243) runs the exact same scan for many different types of patterns, and there doesn't seem to be a way to easily limit this. It's controlled by the definition of [`ALL_DEFAULT_PATTERNS`](https://github.com/huggingface/datasets/blob/main/src/datasets/data_files.py#L82-L85). 

One scan with 3M+ files takes about 10-15 minutes to complete on my setup, so having those extra scans really slows things down – from 10 minutes to 60+. Most of the scans return no matches, but they still take a significant amount of time to complete – hence the poor performance.

As a side effect, when this scan is run on 3M+ image files, Python also consumes up to 12 GB of RAM, which is not ideal.


## Performance Degradation Point 2

The second performance bottleneck is in [`PackagedDatasetModuleFactory.get_module`](https://github.com/huggingface/datasets/blob/d7dfbc83d68e87ba002c5eb2555f7a932e59038a/src/datasets/load.py#L707-L711), which calls `DataFilesDict.from_local_or_remote`. 

It runs for a long time (60min+), consuming significant amounts of RAM – even more than the point 1 above. Based on `iostat -d 2`, it performs **zero** disk operations, which to me suggests that there is a code based bottleneck there that could be sorted out.

### Steps to reproduce the bug

```python
from datasets import load_dataset
import os
import huggingface_hub

dataset = load_dataset(
  'imagefolder',
  data_dir='/some/path',
  # just to spell it out:
  split=None,
  drop_labels=True,
  keep_in_memory=False
)

dataset.push_to_hub('account/dataset', private=True)
```

### Expected behavior

While it's certainly possible to write a custom loader to replace `ImageFolder` with, it'd be great if the off-the-shelf `ImageFolder` would by default have a setup that can scale to large datasets.

Or perhaps there could be a dedicated loader just for large datasets that trades off flexibility for performance? As in, maybe you have to define explicitly how you want it to work rather than it trying to guess your data structure like `_get_data_files_patterns()` does?

### Environment info

- `datasets` version: 2.7.1
- Platform: Linux-4.14.296-222.539.amzn2.x86_64-x86_64-with-glibc2.2.5
- Python version: 3.7.10
- PyArrow version: 10.0.1
- Pandas version: 1.3.5
Bug in sample_by="paragraph"
Author: adampauls | Pull request: no | State: closed

### Describe the bug

I think [this line](https://github.com/huggingface/datasets/blob/main/src/datasets/packaged_modules/text/text.py#L96) is wrong and should be `batch = f.read(self.config.chunksize)`. Otherwise it will never terminate because even when `f` is finished reading, `batch` will still be truthy from the last iteration.

### Steps to reproduce the bug

```
> cat test.txt
a b c

d e f
````

```python
>>> import datasets
>>> datasets.load_dataset("text", data_files={"train":"test.txt"}, sample_by="paragraph")
```

This will go on forever.

### Expected behavior

Terminates very quickly. 

### Environment info

`version = "2.6.1"` but I think the bug is still there on main.
Datasets: classification_report() got an unexpected keyword argument 'suffix'
Author: JonathanAlis | Pull request: no | State: closed

https://github.com/huggingface/datasets/blob/main/metrics/seqeval/seqeval.py


> import datasets
predictions = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
references = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
seqeval = datasets.load_metric("seqeval")
results = seqeval.compute(predictions=predictions, references=references)
print(list(results.keys()))
print(results["overall_f1"])
print(results["PER"]["f1"])

It raises the error:

> TypeError: classification_report() got an unexpected keyword argument 'suffix'

For context, versions on my pip list -v

> datasets                 1.12.1  
seqeval                  1.2.2
Fix description of streaming in the docs
Author: polinaeterna | Pull request: yes | State: closed

We say that "the data is being downloaded progressively" which is not true, it's just streamed, so I fixed it. Probably I missed some other places where it is written? 

Also changed docstrings for `StreamingDownloadManager`'s `download` and `extract` to reflect the same, as these docstrings are displayed in the documentation cc @lhoestq
Add DatasetDict.to_pandas
Author: lhoestq | Pull request: yes | State: closed

From discussions in https://github.com/huggingface/datasets/issues/5189, for tabular data it doesn't really make sense to have to do
```python
df = load_dataset(...)["train"].to_pandas()
```
because many datasets are not split.

In this PR I added `to_pandas` to `DatasetDict` which returns the DataFrame:

If there's only one split, you don't need to specify the split name:
```python
df = load_dataset(...).to_pandas()
```
EDIT: and if a dataset has multiple splits:
```python
df = load_dataset(...).to_pandas(splits=["train", "test"])
# or
df = load_dataset(...).to_pandas(splits="all")

# raises an error because you need to select the split(s) to convert
load_dataset(...).to_pandas()
```

I do have one question though @merveenoyan @adrinjalali @mariosasko:

Should we raise an error if there are multiple splits and ask the user to choose one explicitly ?
Add `features` param to `IterableDataset.map`
Author: alvarobartt | Pull request: yes | State: closed

## Description

As suggested by @lhoestq in #3888, we should be adding the param `features` to `IterableDataset.map` so that the features can be preserved (not turned into `None` as that's the default behavior) whenever the user passes those as param, so as to be consistent with `Dataset.map`, as it provides the `features` param so that those are not inferred by default, but specified by the user, and later validated by `ArrowWriter`.

This is internally handled already by the functions relying on `IterableDataset.map` such as `rename_column`, `rename_columns`, and `remove_columns` as described in #5287.

## Usage Example

```python
from datasets import load_dataset, Features

ds = load_dataset("rotten_tomatoes", split="validation", streaming=True)
print(ds.info.features)
ds = ds.map(
    lambda x: {"target": x["label"]},
    features=Features(
        {"target": ds.info.features["label"], "label": ds.info.features["label"], "text": ds.info.features["text"]}
    ),
)
print(ds.info.features)
```
Support xPath for Windows pathnames
Author: albertvillanova | Pull request: yes | State: closed

This PR implements a string representation of `xPath`, which is valid for local paths (also windows) and remote URLs.

Additionally, some `os.path` methods are fixed for remote URLs on Windows machines.

Now, on Windows machines:
```python
In [2]: str(xPath("C:\\dir\\file.txt"))
Out[2]: 'C:\\dir\\file.txt'
In [3]: str(xPath("http://domain.com/file.txt"))
Out[3]: 'http://domain.com/file.txt'
```
timit_asr doesn't load the test split.
Author: seyong92 | Pull request: no | State: closed

### Describe the bug

When I use the function ```timit = load_dataset('timit_asr', data_dir=data_dir)```, it only loads train split, not test split.

I tried to change the directory and filename to lower case to upper case for the test split, but it does not work at all.

```python
DatasetDict({
    train: Dataset({
        features: ['file', 'audio', 'text', 'phonetic_detail', 'word_detail', 'dialect_region', 'sentence_type', 'speaker_id', 'id'],
        num_rows: 4620
    })
    test: Dataset({
        features: ['file', 'audio', 'text', 'phonetic_detail', 'word_detail', 'dialect_region', 'sentence_type', 'speaker_id', 'id'],
        num_rows: 0
    })
})
```

The directory structure of both splits are same. (DIALECT_REGION / SPEAKER_CODE / DATA_FILES)

### Steps to reproduce the bug

1. just use ```timit = load_dataset('timit_asr', data_dir=data_dir)```

### Expected behavior

```python
DatasetDict({
    train: Dataset({
        features: ['file', 'audio', 'text', 'phonetic_detail', 'word_detail', 'dialect_region', 'sentence_type', 'speaker_id', 'id'],
        num_rows: 4620
    })
    test: Dataset({
        features: ['file', 'audio', 'text', 'phonetic_detail', 'word_detail', 'dialect_region', 'sentence_type', 'speaker_id', 'id'],
        num_rows: 1680
    })
})
```

### Environment info

- ubuntu 20.04
- python 3.9.13
- datasets 2.7.1
Skip dataset verifications by default
Author: mariosasko | Pull request: yes | State: closed

Skip the dataset verifications (split and checksum verifications, duplicate keys check) by default unless a dataset is being tested (`datasets-cli test/run_beam`). The main goal is to avoid running the checksum check in the default case due to how expensive it can be for large datasets.

PS: Maybe we should deprecate `ignore_verifications`, which is `True` now by default, and give it a different name?
Return a split Dataset in load_dataset
Author: lhoestq | Pull request: yes | State: closed

...instead of a DatasetDict.

```python
# now supported
ds = load_dataset("squad")
ds[0]  
for example in ds:
    pass

# still works
ds["train"]
ds["validation"]

# new
ds.splits  # Dict[str, Dataset] | None

# soon to be supported (not in this PR)
ds = load_dataset("dataset_with_no_splits")
ds[0]
for example in ds:
    pass
```

I implemented `Dataset.__getitem__` and `IterableDataset.__getitem__` to be able to get a split from a dataset.
The splits are defined by the `ds.info.splits` dictionary.

Therefore a dataset is a table that optionally has some splits defined in the dataset info. And a split dataset is the concatenation of all its splits.

I made as little breaking changes as possible. Notable breaking changes:
- `load_dataset("potato").keys() / .items() / .values() /` don't work anymore, since we don't return a dict
- same for `for split_name in load_dataset("potato")`, since we now iterate on the examples
- ..

TODO:
- [x] Update push_to_hub
- [x] Update save_to_disk/load_from_disk
- [ ] check for other breaking changes
- [ ] fix existing tests
- [ ] add new tests
- [ ] docs

This is related to https://github.com/huggingface/datasets/issues/5189, to extend `load_dataset` to return datasets without splits
Bug in xopen with Windows pathnames
Author: albertvillanova | Pull request: no | State: closed

Currently, `xopen` function has a bug with local Windows pathnames:

From its implementation:
```python
def xopen(file: str, mode="r", *args, **kwargs):
    file = _as_posix(PurePath(file))
    main_hop, *rest_hops = file.split("::")
    if is_local_path(main_hop):
        return open(file, mode, *args, **kwargs)
```

On a Windows machine, if we pass the argument:
```python
xopen("C:\\Users\\USERNAME\\filename.txt")
```
it returns
```python
open("C:/Users/USERNAME/filename.txt")
```
Bug in xjoin with Windows pathnames
Author: albertvillanova | Pull request: no | State: closed

Currently, `xjoin` function has a bug with local Windows pathnames: instead of returning the OS-dependent join pathname, it always returns it in POSIX format.

```python
from datasets.download.streaming_download_manager import xjoin

path = xjoin("C:\\Users\\USERNAME", "filename.txt")
```

Join path should be:
```python
"C:\\Users\\USERNAME\\filename.txt"
```
However it is:
```python
"C:/Users/USERNAME/filename.txt"
```
Missing documentation build for versions 2.7.1 and 2.6.2 
Author: albertvillanova | Pull request: no | State: closed

After the patch releases [2.7.1](https://github.com/huggingface/datasets/releases/tag/2.7.1) and [2.6.2](https://github.com/huggingface/datasets/releases/tag/2.6.2), the online docs were not properly built (the build_documentation workflow was not triggered).

There was a fix by:
- #5291

However, both documentations were built from main branch, instead of their corresponding version branch.

We are rebuilding them.
[build doc] for v2.7.1 & v2.6.2
Author: mishig25 | Pull request: yes | State: closed

Do NOT merge. Using this PR to build docs for [v2.7.1](https://github.com/huggingface/datasets/pull/5291/commits/f4914af20700f611b9331a9e3ba34743bbeff934) & [v2.6.2](https://github.com/huggingface/datasets/pull/5291/commits/025f85300a0874eeb90a20393c62f25ac0accaa0)
Added support for JXL images.
Author: alexjc | Pull request: yes | State: open

JPEG-XL is the most advanced of the next-generation of image codecs, supporting both lossless and lossy files — with better compression and quality than PNG and JPG respectively.  It has reduced the disk sizes and bandwidth required for many of the datasets I use.

Pillow does not yet support JXL, but there's a plugin as a separate Python library that does (`pip install jxlpy`), and I've tested that this change works as expected when the plugin is imported.

Dataset used for testing, you must `git pull` as loading it from Python won't work until `datasets-server` is also changed to support JXL files:
https://huggingface.co/datasets/texturedesign/td01_natural-ground-textures

The case where the plugin is not imported first raises an error:
```
PIL.UnidentifiedImageError: cannot identify image file 'td01/train/set01/01_145523.jxl'
```

In order to enable support for JXL even before pillow supports this, should this exception be handled with a better error message?  I'd expect/hope JXL support to follow in one of the pillow quarterly releases in the next 6-9 months.
Lossy json serialization - deserialization of dataset info 
Author: anuragprat1k | Pull request: no | State: open

### Describe the bug

Saving a dataset to disk as json (using `to_json`) and then loading it again (using `load_dataset`) results in features whose labels are not type-cast correctly. In the code snippet below, `features.label` should have a label of type `ClassLabel` but has type `Value` instead. 

### Steps to reproduce the bug

```
from datasets import load_dataset

def test_serdes_from_json(d):
    dataset = load_dataset(d, split="train")
    dataset.to_json('_test')
    dataset_loaded = load_dataset("json", data_files='_test', split='train')
    try:
        assert dataset_loaded.info.features == dataset.info.features, "features unequal!"
    except Exception as ex:
        print(f'{ex}')
        print(f'expected {dataset.info.features}, \nactual { dataset_loaded.info.features }')
    
test_serdes_from_json('rotten_tomatoes')
```

Output
```
features unequal!
expected {'text': Value(dtype='string', id=None), 'label': ClassLabel(names=['neg', 'pos'], id=None)}, 
actual {'text': Value(dtype='string', id=None), 'label': Value(dtype='int64', id=None)}
```


### Expected behavior

The deserialized `features.label` should have type `ClassLabel`.

### Environment info

- `datasets` version: 2.6.1
- Platform: Linux-5.10.144-127.601.amzn2.x86_64-x86_64-with-glibc2.17
- Python version: 3.7.13
- PyArrow version: 7.0.0
- Pandas version: 1.2.3
Fix methods using `IterableDataset.map` that lead to `features=None`
Author: alvarobartt | Pull request: yes | State: closed

As currently `IterableDataset.map` is setting the `info.features` to `None` every time as we don't know the output of the dataset in advance, `IterableDataset` methods such as `rename_column`, `rename_columns`, and `remove_columns`. that internally use `map` lead to the features being `None`.

This PR is related to #3888, #5245, and #5284 

## ✅ Current solution

The code in this PR is basically making sure that if the features were there since the beginning and a `rename_column`/`rename_columns` happens, those are kept and the rename is applied to the `Features` too. Also, if the features were not there before applying `rename_column`, `rename_columns` or `remove_columns`, a batch is prefetched and the features are being inferred (that could potentially be part of `IterableDataset.__init__` in case the `info.features` value is `None`).

## 💡 Ideas

Some ideas were proposed in https://github.com/huggingface/datasets/issues/3888, but probably the most consistent solution even though it may take some time is to actually do the type inferencing during the `IterableDataset.__init__` in case the provided `info.features` is `None`, otherwise, we can just use the provided features.

Additionally, as mentioned at https://github.com/huggingface/datasets/issues/3888, we could also include a `features` parameter to the `map` function, but that's probably more tedious.

Also thanks to @lhoestq for sharing some ideas in both https://github.com/huggingface/datasets/issues/3888 and https://github.com/huggingface/datasets/issues/5245 :hugs:
Features of IterableDataset set to None by remove column
Author: sanchit-gandhi | Pull request: no | State: closed

### Describe the bug

The `remove_column` method of the IterableDataset sets the dataset features to None.

### Steps to reproduce the bug

```python
from datasets import Audio, load_dataset

# load LS in streaming mode
dataset = load_dataset("librispeech_asr", "clean", split="validation", streaming=True)

# check original features
print("Original features: ", dataset.features.keys())

# define features to remove: we KEEP audio and text
COLUMNS_TO_REMOVE = ['chapter_id', 'speaker_id', 'file', 'id']

dataset = dataset.remove_columns(COLUMNS_TO_REMOVE)

# check processed features, uh-oh!
print("Processed features: ", dataset.features)

# streaming the first audio sample still works
print("First sample:", next(iter(ds)))
```

**Print Output:**

```
Original features:  dict_keys(['file', 'audio', 'text', 'speaker_id', 'chapter_id', 'id'])
Processed features:  None
First sample: {'audio': {'path': '2277-149896-0000.flac', 'array': array([ 0.00186157,  0.0005188 ,  0.00024414, ..., -0.00097656,
       -0.00109863, -0.00146484]), 'sampling_rate': 16000}, 'text': "HE WAS IN A FEVERED STATE OF MIND OWING TO THE BLIGHT HIS WIFE'S ACTION THREATENED TO CAST UPON HIS ENTIRE FUTURE"}
```

### Expected behavior

The features should be those **not** removed by the `remove_column` method, i.e. audio and text.

### Environment info

- `datasets` version: 2.7.1
- Platform: Linux-5.10.133+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.15
- PyArrow version: 9.0.0
- Pandas version: 1.3.5

(Running on Google Colab for a blog post: https://colab.research.google.com/drive/1ySCQREPZEl4msLfxb79pYYOWjUZhkr9y#scrollTo=8pRDGiVmH2ml)

cc @polinaeterna @lhoestq
Support cloud storage in load_dataset
Author: lhoestq | Pull request: no | State: open

Would be nice to be able to do

```python
data_files=["s3://..."]  # or gs:// or any cloud storage path
storage_options = {...}
load_dataset(..., data_files=data_files, storage_options=storage_options)
```

The idea would be to use `fsspec` as in `download_and_prepare` and `save_to_disk`.

This has been requested several times already. Some users want to use their data from private cloud storage to train models

related:

https://github.com/huggingface/datasets/issues/3490
https://github.com/huggingface/datasets/issues/5244
[forum](https://discuss.huggingface.co/t/how-to-use-s3-path-with-load-dataset-with-streaming-true/25739/2)
Import error
Author: feketedavid1012 | Pull request: no | State: closed

https://github.com/huggingface/datasets/blob/cd3d8e637cfab62d352a3f4e5e60e96597b5f0e9/src/datasets/__init__.py#L28

Hy, 
I have error at the above line. I have python version 3.8.13, the message says I need python>=3.7, which is True, but I think the if statement not working properly (or the message wrong)
load_dataset does not read jsonl metadata file properly
Author: 065294847 | Pull request: no | State: closed

### Describe the bug

Hi, I'm following [this page](https://huggingface.co/docs/datasets/image_dataset) to create a dataset of images and captions via an image folder and a metadata.json file, but I can't seem to get the dataloader to recognize the "text" column. It just spits out "image" and "label" as features. 

Below is code to reproduce my exact example/problem.

### Steps to reproduce the bug

```ruby 
dataset_link="19Unu89Ih_kP6zsE7f9Mkw8dy3NwHopRF"
id = dataset_link
output = 'Godardv01.zip'
gdown.download(id=id, output=output, quiet=False)

ds = load_dataset("imagefolder", data_dir="/kaggle/working/Volumes/TOSHIBA/Godard_imgs/Volumes/TOSHIBA/Godard_imgs/Full/train", split="train", drop_labels=False)
print(ds)
```

### Expected behavior

I would expect that it returned "image" and "text" columns from the code above.

### Environment info

- `datasets` version: 2.1.0
- Platform: Linux-5.15.65+-x86_64-with-debian-bullseye-sid
- Python version: 3.7.12
- PyArrow version: 5.0.0
- Pandas version: 1.3.5
Bug in downloading common_voice data and snall chunk of it to one's own hub
Author: capsabogdan | Pull request: no | State: closed

### Describe the bug

I'm trying to load the common voice dataset. Currently there is no implementation to download just par tof the data, and I need just one part of it, without downloading the entire dataset



Help please?


![image](https://user-images.githubusercontent.com/48530104/203260511-26df766f-6013-4eaf-be26-8aa13794def2.png)


### Steps to reproduce the bug

So here is what I have done:
1. Download common_voice data 
2. Trim part of it and publish it to my own repo.
3. Download data from my own repo, but am getting this error.

### Expected behavior

There shouldn't be an error in downloading part of the data and publishing it to one's own repo

### Environment info

common_voice 11
load_dataset possibly broken for gated datasets?
Author: TristanThrush | Pull request: no | State: closed

### Describe the bug

When trying to download the [winoground dataset](https://huggingface.co/datasets/facebook/winoground), I get this error unless I roll back the version of huggingface-hub:

```
[/usr/local/lib/python3.7/dist-packages/huggingface_hub/utils/_validators.py](https://localhost:8080/#) in validate_repo_id(repo_id)
    165     if repo_id.count("/") > 1:
    166         raise HFValidationError(
--> 167             "Repo id must be in the form 'repo_name' or 'namespace/repo_name':"
    168             f" '{repo_id}'. Use `repo_type` argument if needed."
    169         )

HFValidationError: Repo id must be in the form 'repo_name' or 'namespace/repo_name': 'datasets/facebook/winoground'. Use `repo_type` argument if needed
```

### Steps to reproduce the bug

Install requirements:

```
pip install transformers
pip install datasets
# It works if you uncomment the following line, rolling back huggingface hub:
# pip install huggingface-hub==0.10.1
```

Then:

```
from datasets import load_dataset
auth_token = ""  # Replace with an auth token, which you can get from your huggingface account: Profile -> Settings -> Access Tokens -> New Token
winoground = load_dataset("facebook/winoground", use_auth_token=auth_token)["test"]
```





### Expected behavior

Downloading of the datset

### Environment info

Just a google colab; see here: https://colab.research.google.com/drive/15wwOSte2CjTazdnCWYUm2VPlFbk2NGc0?usp=sharing