bi* D d dl mZmZ ddlmZmZmZ ddlmZm Z ddl
m Z ddl m
Z
mZmZ ddlmZ ddlmZ dd lmZ ej, e Z ed
ee
Z dd ee d
eee dee dee dee ded defdZ ddee dee dee de def
dZ y ) )OptionalTypeVar )Dataset _concatenate_map_style_datasets _interleave_map_style_datasets) DatasetDictIterableDatasetDict) DatasetInfo)IterableDataset _concatenate_iterable_datasets _interleave_iterable_datasets)
NamedSplit)logging)Literal DatasetTypeNdatasets
probabilitiesseedinfosplitstopping_strategyfirst_exhausted
all_exhaustedreturnc
ddl m} ddlm} | s t d t | D ] \ }} t
| ||f s|t
| t t f rA| st d| d t d| dt | d t t | d
t d| d t | j d |d
k( rt
| | r||fn||f\ }
} t
|
rt d|
j d j d| d |dvrt | d
|u rt | ||||| S t | ||||| S )u
Interleave several datasets (sources) into a single dataset.
The new dataset is constructed by alternating between the sources to get the examples.
You can use this function on a list of [`Dataset`] objects, or on a list of [`IterableDataset`] objects.
- If `probabilities` is `None` (default) the new dataset is constructed by cycling between each source to get the examples.
- If `probabilities` is not `None`, the new dataset is constructed by getting examples from a random source at a time according to the provided probabilities.
The resulting dataset ends when one of the source datasets runs out of examples except when `oversampling` is `True`,
in which case, the resulting dataset ends when all datasets have ran out of examples at least one time.
Note for iterable datasets:
In a distributed setup or in PyTorch DataLoader workers, the stopping strategy is applied per process.
Therefore the "first_exhausted" strategy on an sharded iterable dataset can generate less samples in total (up to 1 missing sample per subdataset per worker).
Args:
datasets (`List[Dataset]` or `List[IterableDataset]`):
List of datasets to interleave.
probabilities (`List[float]`, *optional*, defaults to `None`):
If specified, the new dataset is constructed by sampling
examples from one source at a time according to these probabilities.
seed (`int`, *optional*, defaults to `None`):
The random seed used to choose a source for each example.
info ([`DatasetInfo`], *optional*):
Dataset information, like description, citation, etc.
split ([`NamedSplit`], *optional*):
Name of the dataset split.
stopping_strategy (`str`, defaults to `first_exhausted`):
Two strategies are proposed right now, `first_exhausted` and `all_exhausted`.
By default, `first_exhausted` is an undersampling strategy, i.e the dataset construction is stopped as soon as one dataset has ran out of samples.
If the strategy is `all_exhausted`, we use an oversampling strategy, i.e the dataset construction is stopped as soon as every samples of every dataset has been added at least once.
Note that if the strategy is `all_exhausted`, the interleaved dataset size can get enormous:
- with no probabilities, the resulting dataset will have `max_length_datasets*nb_dataset` samples.
- with given probabilities, the resulting dataset will have more samples if some datasets have really low probability of visiting.
Returns:
[`Dataset`] or [`IterableDataset`]: Return type depends on the input `datasets`
parameter. `Dataset` if the input is a list of `Dataset`, `IterableDataset` if the input is a list of
`IterableDataset`.
Example:
For regular datasets (map-style):
```python
>>> from datasets import Dataset, interleave_datasets
>>> d1 = Dataset.from_dict({"a": [0, 1, 2]})
>>> d2 = Dataset.from_dict({"a": [10, 11, 12]})
>>> d3 = Dataset.from_dict({"a": [20, 21, 22]})
>>> dataset = interleave_datasets([d1, d2, d3], probabilities=[0.7, 0.2, 0.1], seed=42, stopping_strategy="all_exhausted")
>>> dataset["a"]
[10, 0, 11, 1, 2, 20, 12, 10, 0, 1, 2, 21, 0, 11, 1, 2, 0, 1, 12, 2, 10, 0, 22]
>>> dataset = interleave_datasets([d1, d2, d3], probabilities=[0.7, 0.2, 0.1], seed=42)
>>> dataset["a"]
[10, 0, 11, 1, 2]
>>> dataset = interleave_datasets([d1, d2, d3])
>>> dataset["a"]
[0, 10, 20, 1, 11, 21, 2, 12, 22]
>>> dataset = interleave_datasets([d1, d2, d3], stopping_strategy="all_exhausted")
>>> dataset["a"]
[0, 10, 20, 1, 11, 21, 2, 12, 22]
>>> d1 = Dataset.from_dict({"a": [0, 1, 2]})
>>> d2 = Dataset.from_dict({"a": [10, 11, 12, 13]})
>>> d3 = Dataset.from_dict({"a": [20, 21, 22, 23, 24]})
>>> dataset = interleave_datasets([d1, d2, d3])
>>> dataset["a"]
[0, 10, 20, 1, 11, 21, 2, 12, 22]
>>> dataset = interleave_datasets([d1, d2, d3], stopping_strategy="all_exhausted")
>>> dataset["a"]
[0, 10, 20, 1, 11, 21, 2, 12, 22, 0, 13, 23, 1, 10, 24]
>>> dataset = interleave_datasets([d1, d2, d3], probabilities=[0.7, 0.2, 0.1], seed=42)
>>> dataset["a"]
[10, 0, 11, 1, 2]
>>> dataset = interleave_datasets([d1, d2, d3], probabilities=[0.7, 0.2, 0.1], seed=42, stopping_strategy="all_exhausted")
>>> dataset["a"]
[10, 0, 11, 1, 2, 20, 12, 13, ..., 0, 1, 2, 0, 24]
For datasets in streaming mode (iterable):
>>> from datasets import interleave_datasets
>>> d1 = load_dataset('allenai/c4', 'es', split='train', streaming=True)
>>> d2 = load_dataset('allenai/c4', 'fr', split='train', streaming=True)
>>> dataset = interleave_datasets([d1, d2])
>>> iterator = iter(dataset)
>>> next(iterator)
{'text': 'Comprar Zapatillas para niña en chancla con goma por...'}
>>> next(iterator)
{'text': 'Le sacre de philippe ier, 23 mai 1059 - Compte Rendu...'
```
r )r )r z/Unable to interleave an empty list of datasets.aExpected a list of Dataset objects or a list of IterableDataset objects, but element at position is an empty dataset dictionary.Dataset at position has at least one split: N
Please pick one to interleave with the other datasets, for example: dataset[''] is a .r Unable to interleave a (at position 0) with a (at position K). Expected a list of Dataset objects or a list of IterableDataset objects.r z: is not supported. Please enter a valid stopping_strategy.)r r r )
arrow_datasetr iterable_datasetr
ValueError enumerate
isinstancer r
listnextitertype__name__r r ) r r r r r r r r idataset dataset_type
other_types K/home/cdr/jupyterlab/.venv/lib/python3.12/site-packages/datasets/combine.pyinterleave_datasetsr9 s H '1 JKK )
7'G_#=>'K1D#EF ${|}{~ : : !*1#-FtG}o Vddhimnuivdwcxxz| stusvv| ~B CJ ~K ~T ~T }U UV W
6.8'.J/*Q`biPj
%L* G\2 ),*?*?)@@XYcYlYlXmm{|}{~ J K
). DD -..hijjw - mTEUf
- mTEUf
dsetsaxisc
~ | s t d t | D ] \ }}t |t t f s|t |t
t f rA|st d| d t d| dt | dt t | d t d| dt | j d |d
k( r,t |t r t t fn t t f\ }}t | rt d |j d j d
| d t u rt | ||| S t | ||| S )a
Converts a list of [`Dataset`] with the same schema into a single [`Dataset`].
Args:
dsets (`List[datasets.Dataset]`):
List of Datasets to concatenate.
info (`DatasetInfo`, *optional*):
Dataset information, like description, citation, etc.
split (`NamedSplit`, *optional*):
Name of the dataset split.
axis (`{0, 1}`, defaults to `0`):
Axis to concatenate over, where `0` means over rows (vertically) and `1` means over columns
(horizontally).
Example:
```py
>>> ds3 = concatenate_datasets([ds1, ds2])
```
z0Unable to concatenate an empty list of datasets.r r r r! r" r# r$ r% r r& r' r( r) )r r r< )r, r- r. r r r r
r/ r0 r1 r2 r3 r r
)r; r r r< r4 r5 r6 r7 s r8 concatenate_datasetsr> s : KLL &
7'G_#=>'K1D#EF ${|}{~ : : !*1#-FtG}o Vddhimnuivdwcxxz| stusvv| ~B CJ ~K ~T ~T }U UV W
6.8'.J/*Q`biPj
%L* G\2 ),*?*?)@@XYcYlYlXmm{|}{~ J K
). w .u4uSWXX-e$eRVWWr: )NNNNr )NNr ) typingr r r* r r r dataset_dictr r
r r r+ r r
r splitsr utilsr utils.py_utilsr
get_loggerr3 loggerr r/ floatintr9 r> r: r8 rI s $ c c : l l #
H % mWo>
,0 "&"&EV
I
; I
DK(I
3-I
;
I
J I
AB
I
I
\ #'"& 9X 9X
;
9X J 9X 9X
9Xr: