Using a registry instead of calling globals for fetching feature types
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The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6727). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.
2024-03-10T17:52:48Z
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CONTRIBUTOR
looks like some files are missing in your google storage
2024-03-11T17:34:24Z
https://github.com/huggingface/datasets/pull/6727#issuecomment-1989038476
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cc @mariosasko is it related to https://github.com/huggingface/datasets/pull/6474 ? The files should ideally not move for backward compatibility anyway
2024-03-12T11:46:37Z
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COLLABORATOR
@lhoestq All the files are still there.

The problem is that the `natural_questions` is now a no-code dataset, so the test's paths are no longer correct (unless the revision is pinned to the previous version). 

@psmyth94 This has been fixed on `main`, so you can make the CI tests green with the following:
```python
git remote add upstream https://github.com/huggingface/datasets.git
git pull upstream main
git push
```
2024-03-12T15:02:30Z
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Thank you @mariosasko ! I'm updating this branch if you don't mind @psmyth94 
2024-03-12T16:59:32Z
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<details>
<summary>Show benchmarks</summary>

PyArrow==8.0.0

<details>
<summary>Show updated benchmarks!</summary>

### Benchmark: benchmark_array_xd.json

| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.004903 / 0.011353 (-0.006450) | 0.003105 / 0.011008 (-0.007903) | 0.061980 / 0.038508 (0.023471) | 0.029726 / 0.023109 (0.006617) | 0.243406 / 0.275898 (-0.032492) | 0.262530 / 0.323480 (-0.060950) | 0.003905 / 0.007986 (-0.004081) | 0.002617 / 0.004328 (-0.001712) | 0.047851 / 0.004250 (0.043601) | 0.040397 / 0.037052 (0.003345) | 0.259461 / 0.258489 (0.000972) | 0.285059 / 0.293841 (-0.008782) | 0.027321 / 0.128546 (-0.101225) | 0.009876 / 0.075646 (-0.065770) | 0.206999 / 0.419271 (-0.212273) | 0.034906 / 0.043533 (-0.008626) | 0.245120 / 0.255139 (-0.010019) | 0.270490 / 0.283200 (-0.012710) | 0.017341 / 0.141683 (-0.124342) | 1.128182 / 1.452155 (-0.323973) | 1.173024 / 1.492716 (-0.319693) |

### Benchmark: benchmark_getitem\_100B.json

| metric | get_batch_of\_1024\_random_rows | get_batch_of\_1024\_rows | get_first_row | get_last_row |
|--------|---|---|---|---|
| new / old (diff) | 0.089337 / 0.018006 (0.071331) | 0.298256 / 0.000490 (0.297767) | 0.000216 / 0.000200 (0.000016) | 0.000047 / 0.000054 (-0.000007) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.018179 / 0.037411 (-0.019233) | 0.061275 / 0.014526 (0.046749) | 0.073137 / 0.176557 (-0.103419) | 0.119603 / 0.737135 (-0.617532) | 0.073969 / 0.296338 (-0.222370) |

### Benchmark: benchmark_iterating.json

| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.283109 / 0.215209 (0.067900) | 2.765441 / 2.077655 (0.687787) | 1.471276 / 1.504120 (-0.032844) | 1.346365 / 1.541195 (-0.194830) | 1.360668 / 1.468490 (-0.107822) | 0.549947 / 4.584777 (-4.034830) | 2.344213 / 3.745712 (-1.401499) | 2.700905 / 5.269862 (-2.568956) | 1.689936 / 4.565676 (-2.875741) | 0.061985 / 0.424275 (-0.362290) | 0.004923 / 0.007607 (-0.002684) | 0.329833 / 0.226044 (0.103788) | 3.277580 / 2.268929 (1.008652) | 1.833987 / 55.444624 (-53.610638) | 1.571023 / 6.876477 (-5.305454) | 1.573259 / 2.142072 (-0.568813) | 0.627504 / 4.805227 (-4.177723) | 0.114106 / 6.500664 (-6.386558) | 0.041197 / 0.075469 (-0.034272) |

### Benchmark: benchmark_map_filter.json

| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |
|--------|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.967400 / 1.841788 (-0.874388) | 11.046527 / 8.074308 (2.972219) | 9.542214 / 10.191392 (-0.649178) | 0.140745 / 0.680424 (-0.539679) | 0.013627 / 0.534201 (-0.520574) | 0.288429 / 0.579283 (-0.290855) | 0.260509 / 0.434364 (-0.173855) | 0.324704 / 0.540337 (-0.215633) | 0.419366 / 1.386936 (-0.967570) |

</details>
PyArrow==latest

<details>
<summary>Show updated benchmarks!</summary>

### Benchmark: benchmark_array_xd.json

| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.005123 / 0.011353 (-0.006230) | 0.003119 / 0.011008 (-0.007890) | 0.048931 / 0.038508 (0.010423) | 0.032067 / 0.023109 (0.008958) | 0.276825 / 0.275898 (0.000927) | 0.297589 / 0.323480 (-0.025890) | 0.004075 / 0.007986 (-0.003911) | 0.002579 / 0.004328 (-0.001750) | 0.047862 / 0.004250 (0.043612) | 0.044032 / 0.037052 (0.006980) | 0.289469 / 0.258489 (0.030980) | 0.327269 / 0.293841 (0.033428) | 0.029369 / 0.128546 (-0.099177) | 0.010180 / 0.075646 (-0.065466) | 0.057111 / 0.419271 (-0.362161) | 0.051046 / 0.043533 (0.007513) | 0.276758 / 0.255139 (0.021619) | 0.296084 / 0.283200 (0.012884) | 0.017376 / 0.141683 (-0.124306) | 1.154486 / 1.452155 (-0.297669) | 1.192699 / 1.492716 (-0.300018) |

### Benchmark: benchmark_getitem\_100B.json

| metric | get_batch_of\_1024\_random_rows | get_batch_of\_1024\_rows | get_first_row | get_last_row |
|--------|---|---|---|---|
| new / old (diff) | 0.085981 / 0.018006 (0.067974) | 0.296956 / 0.000490 (0.296466) | 0.000211 / 0.000200 (0.000011) | 0.000050 / 0.000054 (-0.000004) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.021239 / 0.037411 (-0.016172) | 0.074851 / 0.014526 (0.060326) | 0.085676 / 0.176557 (-0.090881) | 0.125876 / 0.737135 (-0.611259) | 0.087573 / 0.296338 (-0.208765) |

### Benchmark: benchmark_iterating.json

| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.289220 / 0.215209 (0.074011) | 2.812342 / 2.077655 (0.734688) | 1.572886 / 1.504120 (0.068766) | 1.446442 / 1.541195 (-0.094752) | 1.458737 / 1.468490 (-0.009753) | 0.562010 / 4.584777 (-4.022767) | 2.422896 / 3.745712 (-1.322816) | 2.578408 / 5.269862 (-2.691454) | 1.689998 / 4.565676 (-2.875678) | 0.064782 / 0.424275 (-0.359493) | 0.005051 / 0.007607 (-0.002556) | 0.339982 / 0.226044 (0.113938) | 3.309882 / 2.268929 (1.040953) | 1.910273 / 55.444624 (-53.534351) | 1.649723 / 6.876477 (-5.226753) | 1.744073 / 2.142072 (-0.397999) | 0.651905 / 4.805227 (-4.153323) | 0.114606 / 6.500664 (-6.386058) | 0.040030 / 0.075469 (-0.035439) |

### Benchmark: benchmark_map_filter.json

| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |
|--------|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 1.008374 / 1.841788 (-0.833414) | 11.547300 / 8.074308 (3.472992) | 9.966061 / 10.191392 (-0.225331) | 0.144874 / 0.680424 (-0.535550) | 0.014400 / 0.534201 (-0.519801) | 0.285435 / 0.579283 (-0.293848) | 0.274755 / 0.434364 (-0.159609) | 0.323105 / 0.540337 (-0.217232) | 0.439172 / 1.386936 (-0.947764) |

</details>
</details>

![](https://cml.dev/watermark.png#4591ac120e9d6c082b2479d2005c04b9c36f539c "CML watermark")

2024-03-13T10:51:59Z
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Hello,

When working with bio-data, each feature often has metadata associated with it (e.g. species, lineage, snp position, etc). To store this, I like to use the feature classes with the added `metadata` attribute. However, when saving or loading with custom features, you get an error since that class doesn't exist in the global namespace in `datasets.features.features`. Take for example,



```python
from dataclasses import dataclass, field
from datasets import Dataset
from datasets.features.features import Value, Features

@dataclass
class FeatureA(Value):
    metadata: dict = field(default=dict)
    _type: str = field(default="FeatureA", init=False, repr=False)

@dataclass
class FeatureB(Value):
    metadata: dict = field(default_factory=dict)
    _type: str = field(default="FeatureB", init=False, repr=False)

test_data = {
    "a": [1, 2, 3],
    "b": [4, 5, 6],
}
test_data = Dataset.from_dict(
    test_data, 
    features=Features({
        "a": FeatureA("int32", metadata={"species": "lactobacillus acetotolerans"}),
        "b": FeatureB("int32", metadata={"species": "lactobacillus iners"}),
    })
)

# returns an error since FeatureA and FeatureB are not in the global namespace
test_data.save_to_disk('./test_data')
```


    Saving the dataset (0/1 shards):   0%|          | 0/3 [00:00<?, ? examples/s]



    ---------------------------------------------------------------------------

    KeyError                                  Traceback (most recent call last)

    Cell In[2], line 28
         19 test_data = Dataset.from_dict(
         20     test_data, 
         21     features=Features({
       (...)
         24     })
         25 )
         27 # returns an error since FeatureA and FeatureB are not in the global namespace
    ---> 28 test_data.save_to_disk('./test_data')
    ...
    File ~\Documents\datasets\src\datasets\features\features.py:1361, in generate_from_dict(obj)
       1359     return {key: generate_from_dict(value) for key, value in obj.items()}
       1360 obj = dict(obj)
    -> 1361 class_type = globals()[obj.pop("_type")]
       1363 if class_type == Sequence:
       1364     return Sequence(feature=generate_from_dict(obj["feature"]), length=obj.get("length", -1))
    

    KeyError: 'FeatureA'


We can avoid this by having a registry (like formatters) and doing


```python
from datasets.features.features import register_feature
register_feature(FeatureA, "FeatureA")
register_feature(FeatureB, "FeatureB")
test_data.save_to_disk('./test_data')
```


    Saving the dataset (1/1 shards): 100%|------| 3/3 [00:00<00:00, 211.13 examples/s]


and loading from disk returns with all metadata information


```python
from datasets import load_from_disk
test_data = load_from_disk('./test_data')
test_data.features
```




    {'a': FeatureA(dtype='int32', id=None, metadata={'species': 'lactobacillus acetotolerans'}),
     'b': FeatureB(dtype='int32', id=None, metadata={'species': 'lactobacillus iners'})}


