text stringlengths 1 1.02k | class_index int64 0 271 | source stringclasses 76
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|---|---|---|
num_examples_progress_update = 0
# If `update_data` is True after processing the first example/batch, initalize these resources with `init_buffer_and_writer`
buf_writer, writer, tmp_file = None, None, None
# Check if Polars is available and import it if so
if config.POLARS_AVAILABLE and... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
# Loop over single examples or batches and write to buffer/file if examples are to be updated
if not batched:
shard_iterable = enumerate(arrow_formatted_shard)
else:
num_rows = len(shard) if not drop_last_batch else len(shard) // batch_size * batch... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
if isinstance(example, pa.Table):
writer.write_row(example)
elif isinstance(example, pd.DataFrame):
writer.write_row(pa.Table.from_pandas(example))
elif (
config.POLARS... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
else:
_time = time.time()
for i, batch in shard_iterable:
num_examples_in_batch = len(batch)
indices = list(
range(*(slice(i, i + batch_size).indices(shard.num_rows)))
) # Somethi... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
"Using `.map` in batched mode on a dataset with attached indexes is allowed only if it doesn't create or remove existing examples. You can first run `.drop_index() to remove your index and then re-add it."
) from None
if update_data:
if i =... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
else:
writer.write_batch(batch)
num_examples_progress_update += num_examples_in_batch
if time.time() > _time + config.PBAR_REFRESH_TIME_INTERVAL:
_time = time.time()
yield rank, False,... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
os.remove(tmp_file.name)
raise | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
yield rank, False, num_examples_progress_update
if update_data and tmp_file is not None:
tmp_file.close()
shutil.move(tmp_file.name, cache_file_name)
umask = os.umask(0o666)
os.umask(umask)
os.chmod(cache_file_name, 0o666 & ~umask)
if update_d... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
@transmit_format
@fingerprint_transform(inplace=False)
def batch(
self,
batch_size: int,
drop_last_batch: bool = False,
num_proc: Optional[int] = None,
new_fingerprint: Optional[str] = None,
) -> "Dataset":
"""
Group samples from the dataset into batch... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
Returns:
[`Dataset`]: A new Dataset where each item is a batch of multiple samples from the original dataset.
Example:
```py
>>> from datasets import load_dataset
>>> ds = load_dataset("rotten_tomatoes", split="train")
>>> batched_ds = ds.batch(batch_size=4)
... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
@transmit_format
@fingerprint_transform(
inplace=False, ignore_kwargs=["load_from_cache_file", "cache_file_name", "desc"], version="2.0.1"
)
def filter(
self,
function: Optional[Callable] = None,
with_indices: bool = False,
with_rank: bool = False,
input_colum... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
and update the table so that the dataset only includes examples according to the filter function. | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
Args:
function (`Callable`): Callable with one of the following signatures:
- `function(example: Dict[str, Any]) -> bool` if `batched=False` and `with_indices=False` and `with_rank=False`
- `function(example: Dict[str, Any], *extra_args) -> bool` if `batched=False` and `with... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
If no function is provided, defaults to an always `True` function: `lambda x: True`.
with_indices (`bool`, defaults to `False`):
Provide example indices to `function`. Note that in this case the
signature of `function` should be `def function(example, idx[, rank]): ...`.
... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
Number of examples per batch provided to `function` if
`batched = True`. If `batched = False`, one example per batch is passed to `function`.
If `batch_size <= 0` or `batch_size == None`, provide the full dataset as a single batch to `function`.
keep_in_memory (`bool`, defaul... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
Number of rows per write operation for the cache file writer.
This value is a good trade-off between memory usage during the processing, and processing speed.
Higher value makes the processing do fewer lookups, lower value consume less temporary memory while running `map`.
fn... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
new_fingerprint (`str`, *optional*):
The new fingerprint of the dataset after transform.
If `None`, the new fingerprint is computed using a hash of the previous fingerprint, and the transform arguments.
desc (`str`, *optional*, defaults to `None`):
Meaningful ... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
Example:
```py
>>> from datasets import load_dataset
>>> ds = load_dataset("rotten_tomatoes", split="validation")
>>> ds.filter(lambda x: x["label"] == 1)
Dataset({
features: ['text', 'label'],
num_rows: 533
})
```
"""
if l... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
indices = self.map(
function=partial(
get_indices_from_mask_function,
function,
batched,
with_indices,
with_rank,
input_columns,
self._indices,
),
with_indices=True,
... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
new_dataset._indices = indices.data
new_dataset._fingerprint = new_fingerprint
return new_dataset | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
@transmit_format
@fingerprint_transform(inplace=False, ignore_kwargs=["cache_file_name"])
def flatten_indices(
self,
keep_in_memory: bool = False,
cache_file_name: Optional[str] = None,
writer_batch_size: Optional[int] = 1000,
features: Optional[Features] = None,
... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
Args:
keep_in_memory (`bool`, defaults to `False`):
Keep the dataset in memory instead of writing it to a cache file.
cache_file_name (`str`, *optional*, default `None`):
Provide the name of a path for the cache file. It is used to store the
result... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
disable_nullable (`bool`, defaults to `False`):
Allow null values in the table.
num_proc (`int`, optional, default `None`):
Max number of processes when generating cache. Already cached shards are loaded sequentially
new_fingerprint (`str`, *optional*, defaults to... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
return self.map(
batched=True, # for speed
keep_in_memory=keep_in_memory,
cache_file_name=cache_file_name,
writer_batch_size=writer_batch_size,
features=features,
disable_nullable=disable_nullable,
new_fingerprint=new_fingerprint,
... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
if fingerprint is None:
raise ValueError("please specify a fingerprint for the dataset with indices")
if indices_cache_file_name is not None:
indices_table = MemoryMappedTable.from_file(indices_cache_file_name)
else:
indices_table = InMemoryTable.from_buffer(indices_... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
@transmit_format
@fingerprint_transform(inplace=False, ignore_kwargs=["indices_cache_file_name"])
def select(
self,
indices: Iterable,
keep_in_memory: bool = False,
indices_cache_file_name: Optional[str] = None,
writer_batch_size: Optional[int] = 1000,
new_fingerp... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
Args:
indices (`range`, `list`, `iterable`, `ndarray` or `Series`):
Range, list or 1D-array of integer indices for indexing.
If the indices correspond to a contiguous range, the Arrow table is simply sliced.
However passing a list of indices that are not conti... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
This value is a good trade-off between memory usage during the processing, and processing speed.
Higher value makes the processing do fewer lookups, lower value consume less temporary memory while running `map`.
new_fingerprint (`str`, *optional*, defaults to `None`):
The new... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
Example:
```py
>>> from datasets import load_dataset
>>> ds = load_dataset("rotten_tomatoes", split="validation")
>>> ds.select(range(4))
Dataset({
features: ['text', 'label'],
num_rows: 4
})
```
"""
if keep_in_memory and i... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
# If indices is a PyArrow array, we convert to NumPy
if isinstance(indices, (pa.Array, pa.ChunkedArray)):
indices = indices.to_numpy().astype(np.int64)
# Convert generator objects to lists
if isinstance(indices, Iterator):
indices = list(indices) | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
# If the indices are contiguous, simply slice the arrow table
if isinstance(indices, range):
if _is_range_contiguous(indices) and indices.start >= 0:
start, length = indices.start, indices.stop - indices.start
return self._select_contiguous(start, length, new_fingerpr... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
# If not contiguous, we need to create a new indices mapping
return self._select_with_indices_mapping(
indices,
keep_in_memory=keep_in_memory,
indices_cache_file_name=indices_cache_file_name,
writer_batch_size=writer_batch_size,
new_fingerprint=new_fin... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
Args:
start (`int`): start index.
length (`int`): length of the slice to select.
new_fingerprint (`str`, optional, default `None`): the new fingerprint of the dataset after transform.
If `None`, the new fingerprint is computed using a hash of the previous fingerprint,... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
# If the array is empty we do nothing
if len(self) == 0:
return self
_check_valid_indices_value(start, len(self))
_check_valid_indices_value(start + length - 1, len(self))
if self._indices is None or length == 0:
return Dataset(
self.data.slice(st... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
@transmit_format
@fingerprint_transform(inplace=False, ignore_kwargs=["indices_cache_file_name"])
def _select_with_indices_mapping(
self,
indices: Iterable,
keep_in_memory: bool = False,
indices_cache_file_name: Optional[str] = None,
writer_batch_size: Optional[int] = 100... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
Args:
indices (sequence, iterable, range, ndarray or Series): List or 1D-array of integer indices for indexing.
keep_in_memory (`bool`, default `False`): Keep the indices mapping in memory instead of writing it to a cache file.
indices_cache_file_name (`str`, optional, default `None`... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
If `None`, the new fingerprint is computed using a hash of the previous fingerprint, and the transform arguments | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
Example:
```py
>>> from datasets import load_dataset
>>> ds = load_dataset("rotten_tomatoes", split="validation")
>>> ds._select_with_indices_mapping(range(4))
Dataset({
features: ['text', 'label'],
num_rows: 4
})
```
"""
i... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
# Prepare the writer for our indices arrow table
if keep_in_memory or indices_cache_file_name is None:
buf_writer = pa.BufferOutputStream()
tmp_file = None
writer = ArrowWriter(
stream=buf_writer, writer_batch_size=writer_batch_size, fingerprint=new_fingerprin... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
size = len(self)
if indices:
_check_valid_indices_value(int(max(indices)), size=size)
_check_valid_indices_value(int(min(indices)), size=size)
else:
return self._select_contiguous(0, 0, new_fingerprint=new_fingerprint)
indices_array = pa.array(indices, type=p... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
with writer:
try:
writer.write_table(indices_table)
writer.finalize() # close_stream=bool(buf_writer is None)) We only close if we are writing in a file
except (Exception, KeyboardInterrupt):
if tmp_file is not None:
tmp_file.... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
# Return new Dataset object
if buf_writer is None:
return self._new_dataset_with_indices(
indices_cache_file_name=indices_cache_file_name, fingerprint=new_fingerprint
)
else:
return self._new_dataset_with_indices(indices_buffer=buf_writer.getvalue(), f... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
```py
>>> from datasets import load_dataset
>>> ds = load_dataset("rotten_tomatoes", split="train")
>>> list(ds.take(3))
[{'label': 1,
'text': 'the rock is destined to be the 21st century\'s new " conan " and that he\'s going to make a splash even greater than arnold schwarzeneg... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
'text': 'the gorgeously elaborate continuation of " the lord of the rings " trilogy is so huge that a column of words cannot adequately describe co-writer/director peter jackson\'s expanded vision of j . r . r . tolkien\'s middle-earth .'},
{'label': 1, 'text': 'effective but too-tepid biopic'},
{'lab... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
def take(self, n: int) -> "Dataset":
"""
Create a new [`Dataset`] with only the first `n` elements.
Args:
n (`int`):
Number of elements to take.
Example:
```py
>>> from datasets import load_dataset
>>> ds = load_dataset("rotten_tomat... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
@transmit_format
@fingerprint_transform(inplace=False, ignore_kwargs=["load_from_cache_file", "indices_cache_file_name"])
def sort(
self,
column_names: Union[str, Sequence_[str]],
reverse: Union[bool, Sequence_[bool]] = False,
null_placement: str = "at_end",
keep_in_memor... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
Args:
column_names (`Union[str, Sequence[str]]`):
Column name(s) to sort by.
reverse (`Union[bool, Sequence[bool]]`, defaults to `False`):
If `True`, sort by descending order rather than ascending. If a single bool is provided,
the value is applied... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
<Added version="1.14.2"/>
keep_in_memory (`bool`, defaults to `False`):
Keep the sorted indices in memory instead of writing it to a cache file.
load_from_cache_file (`Optional[bool]`, defaults to `True` if caching is enabled):
If a cache file storing the sorted i... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
The new fingerprint of the dataset after transform.
If `None`, the new fingerprint is computed using a hash of the previous fingerprint, and the transform arguments | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
Example:
```py
>>> from datasets import load_dataset
>>> ds = load_dataset('rotten_tomatoes', split='validation')
>>> ds['label'][:10]
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
>>> sorted_ds = ds.sort('label')
>>> sorted_ds['label'][:10]
[0, 0, 0, 0, 0, 0, 0, 0, 0, ... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
# Check proper format of and for duplicates in column_names
if isinstance(column_names, str):
column_names = [column_names]
# Check proper format and length of reverse
if not isinstance(reverse, bool):
if len(reverse) != len(column_names):
raise ValueErro... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
# Change null_placement to conform to pyarrow's sort_indices() while ensuring backwards compatability
if null_placement not in ["at_start", "at_end"]:
if null_placement == "first":
null_placement = "at_start"
elif null_placement == "last":
null_placement =... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
# Check if we've already cached this computation (indexed by a hash)
if self.cache_files:
if indices_cache_file_name is None:
# we create a unique hash from the function, current dataset file and the mapping args
indices_cache_file_name = self._get_cache_file_path(new... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
indices = pc.sort_indices(sort_table, sort_keys=sort_keys, null_placement=null_placement)
return self.select(
indices=indices,
keep_in_memory=keep_in_memory,
indices_cache_file_name=indices_cache_file_name,
writer_batch_size=writer_batch_size,
new_fin... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
Currently shuffling uses numpy random generators.
You can either supply a NumPy BitGenerator to use, or a seed to initiate NumPy's default random generator (PCG64).
Shuffling takes the list of indices `[0:len(my_dataset)]` and shuffles it to create an indices mapping.
However as soon as your [`... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
```python
my_dataset[0] # fast
my_dataset = my_dataset.shuffle(seed=42)
my_dataset[0] # up to 10x slower
my_dataset = my_dataset.flatten_indices() # rewrite the shuffled dataset on disk as contiguous chunks of data
my_dataset[0] # fast again
```
In this case,... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
Args:
seed (`int`, *optional*):
A seed to initialize the default BitGenerator if `generator=None`.
If `None`, then fresh, unpredictable entropy will be pulled from the OS.
If an `int` or `array_like[ints]` is passed, then it will be passed to SeedSequence to d... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
indices_cache_file_name (`str`, *optional*):
Provide the name of a path for the cache file. It is used to store the
shuffled indices instead of the automatically generated cache file name.
writer_batch_size (`int`, defaults to `1000`):
Number of rows per write... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
Example:
```py
>>> from datasets import load_dataset
>>> ds = load_dataset("rotten_tomatoes", split="validation")
>>> ds['label'][:10]
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
# set a seed
>>> shuffled_ds = ds.shuffle(seed=42)
>>> shuffled_ds['label'][:10]
... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
if seed is not None and generator is not None:
raise ValueError("Both `seed` and `generator` were provided. Please specify just one of them.")
if generator is not None and not isinstance(generator, np.random.Generator):
raise ValueError("The provided generator must be an instance of num... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
# Check if we've already cached this computation (indexed by a hash)
if self.cache_files:
if indices_cache_file_name is None:
# we create a unique hash from the function, current dataset file and the mapping args
indices_cache_file_name = self._get_cache_file_path(new... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
return self.select(
indices=permutation,
keep_in_memory=keep_in_memory,
indices_cache_file_name=indices_cache_file_name if not keep_in_memory else None,
writer_batch_size=writer_batch_size,
new_fingerprint=new_fingerprint,
) | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
@transmit_format
@fingerprint_transform(
inplace=False,
randomized_function=True,
fingerprint_names=["train_new_fingerprint", "test_new_fingerprint"],
ignore_kwargs=["load_from_cache_file", "train_indices_cache_file_name", "test_indices_cache_file_name"],
)
def train_test_spl... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
"""Return a dictionary ([`datasets.DatasetDict`]) with two random train and test subsets (`train` and `test` `Dataset` splits).
Splits are created from the dataset according to `test_size`, `train_size` and `shuffle`. | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
This method is similar to scikit-learn `train_test_split`. | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
Args:
test_size (`numpy.random.Generator`, *optional*):
Size of the test split
If `float`, should be between `0.0` and `1.0` and represent the proportion of the dataset to include in the test split.
If `int`, represents the absolute number of test samples.
... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
stratify_by_column (`str`, *optional*, defaults to `None`):
The column name of labels to be used to perform stratified split of data.
seed (`int`, *optional*):
A seed to initialize the default BitGenerator if `generator=None`.
If `None`, then fresh, unpredicta... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
load_from_cache_file (`Optional[bool]`, defaults to `True` if caching is enabled):
If a cache file storing the splits indices
can be identified, use it instead of recomputing.
train_cache_file_name (`str`, *optional*):
Provide the name of a path for the cache ... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
Higher value makes the processing do fewer lookups, lower value consume less temporary memory while running `map`.
train_new_fingerprint (`str`, *optional*, defaults to `None`):
The new fingerprint of the train set after transform.
If `None`, the new fingerprint is computed u... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
Example:
```py
>>> from datasets import load_dataset
>>> ds = load_dataset("rotten_tomatoes", split="validation")
>>> ds = ds.train_test_split(test_size=0.2, shuffle=True)
DatasetDict({
train: Dataset({
features: ['text', 'label'],
num... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
# stratified split
>>> ds = load_dataset("imdb",split="train")
Dataset({
features: ['text', 'label'],
num_rows: 25000
})
>>> ds = ds.train_test_split(test_size=0.2, stratify_by_column="label")
DatasetDict({
train: Dataset({
feat... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
if len(self.list_indexes()) > 0:
raise DatasetTransformationNotAllowedError(
"Using `.train_test_split` on a dataset with attached indexes is not allowed. You can first run `.drop_index() to remove your index and then re-add it."
)
# If the array is empty we do nothing
... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
# Safety checks similar to scikit-learn's ones.
# (adapted from https://github.com/scikit-learn/scikit-learn/blob/fd237278e895b42abe8d8d09105cbb82dc2cbba7/sklearn/model_selection/_split.py#L1750)
n_samples = len(self)
if (
isinstance(test_size, int)
and (test_size >= n_sa... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
if (
isinstance(train_size, int)
and (train_size >= n_samples or train_size <= 0)
or isinstance(train_size, float)
and (train_size <= 0 or train_size >= 1)
):
raise ValueError(
f"train_size={train_size} should be either positive and sma... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
if isinstance(train_size, float) and isinstance(test_size, float) and train_size + test_size > 1:
raise ValueError(
f"The sum of test_size and train_size = {train_size + test_size}, should be in the (0, 1)"
" range. Reduce test_size and/or train_size."
)
... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
if n_train + n_test > n_samples:
raise ValueError(
f"The sum of train_size and test_size = {n_train + n_test}, "
"should be smaller than the number of "
f"samples {n_samples}. Reduce test_size and/or "
"train_size."
)
n_tra... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
if generator is None and shuffle is True:
if seed is None:
_, seed, pos, *_ = np.random.get_state()
seed = seed[pos] if pos < 624 else seed[0]
_ = np.random.random() # do 1 step of rng
generator = np.random.default_rng(seed)
# Check if we... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
if train_indices_cache_file_name is None:
train_indices_cache_file_name = self._get_cache_file_path(train_new_fingerprint)
if test_indices_cache_file_name is None:
test_indices_cache_file_name = self._get_cache_file_path(test_new_fingerprint)
if (
... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
"test": self._new_dataset_with_indices(
fingerprint=test_new_fingerprint, indices_cache_file_name=test_indices_cache_file_name
),
}
)
if not shuffle:
if stratify_by_column is not None:
raise Value... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
f"Stratifying by column is only supported for {ClassLabel.__name__} column, and column {stratify_by_column} is {type(self._info.features[stratify_by_column]).__name__}."
)
try:
train_indices, test_indices = next(
stratified_shuffle_split_ge... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
raise error | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
# random partition
else:
permutation = generator.permutation(len(self))
test_indices = permutation[:n_test]
train_indices = permutation[n_test : (n_test + n_train)]
train_split = self.select(
indices=train_indices,
keep_in_memo... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
def shard(
self,
num_shards: int,
index: int,
contiguous: bool = True,
keep_in_memory: bool = False,
indices_cache_file_name: Optional[str] = None,
writer_batch_size: Optional[int] = 1000,
) -> "Dataset":
"""Return the `index`-nth shard from dataset sp... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
On the other hand, `dataset.shard(n, i, contiguous=False)` contains all elements of the dataset whose index mod `n = i`.
Be sure to shard before using any randomizing operator (such as `shuffle`).
It is best if the shard operator is used early in the dataset pipeline. | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
Args:
num_shards (`int`):
How many shards to split the dataset into.
index (`int`):
Which shard to select and return.
contiguous: (`bool`, defaults to `True`):
Whether to select contiguous blocks of indices for shards.
keep_... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
Higher value makes the processing do fewer lookups, lower value consume less temporary memory while running `map`. | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
Example:
```py
>>> from datasets import load_dataset
>>> ds = load_dataset("rotten_tomatoes", split="validation")
>>> ds
Dataset({
features: ['text', 'label'],
num_rows: 1066
})
>>> ds.shard(num_shards=2, index=0)
Dataset({
... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
return self.select(
indices=indices,
keep_in_memory=keep_in_memory,
indices_cache_file_name=indices_cache_file_name,
writer_batch_size=writer_batch_size,
)
def to_csv(
self,
path_or_buf: Union[PathLike, BinaryIO],
batch_size: Optional[... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
Args:
path_or_buf (`PathLike` or `FileOrBuffer`):
Either a path to a file (e.g. `file.csv`), a remote URI (e.g. `hf://datasets/username/my_dataset_name/data.csv`),
or a BinaryIO, where the dataset will be saved to in the specified format.
batch_size (`int`, *optio... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
<Added version="2.19.0"/>
**to_csv_kwargs (additional keyword arguments):
Parameters to pass to pandas's [`pandas.DataFrame.to_csv`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_csv.html).
<Changed version="2.10.0">
Now, `index` defaults ... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
return CsvDatasetWriter(
self,
path_or_buf,
batch_size=batch_size,
num_proc=num_proc,
storage_options=storage_options,
**to_csv_kwargs,
).write()
def to_dict(self, batch_size: Optional[int] = None) -> Union[dict, Iterator[dict]]:
... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
Example:
```py
>>> ds.to_list()
```
"""
return query_table(
table=self._data,
key=slice(0, len(self)),
indices=self._indices,
).to_pylist()
def to_json(
self,
path_or_buf: Union[PathLike, BinaryIO],
batch_s... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
Args:
path_or_buf (`PathLike` or `FileOrBuffer`):
Either a path to a file (e.g. `file.json`), a remote URI (e.g. `hf://datasets/username/my_dataset_name/data.json`),
or a BinaryIO, where the dataset will be saved to in the specified format.
batch_size (`int`, *opt... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
<Added version="2.19.0"/>
**to_json_kwargs (additional keyword arguments):
Parameters to pass to pandas's [`pandas.DataFrame.to_json`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_json.html).
Default arguments are `lines=True` and `orient="records".
... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
return JsonDatasetWriter(
self,
path_or_buf,
batch_size=batch_size,
num_proc=num_proc,
storage_options=storage_options,
**to_json_kwargs,
).write()
def to_pandas(
self, batch_size: Optional[int] = None, batched: bool = False
... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
```py
>>> ds.to_pandas()
```
"""
if not batched:
return query_table(
table=self._data,
key=slice(0, len(self)),
indices=self._indices,
).to_pandas(types_mapper=pandas_types_mapper)
else:
batch_siz... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
def to_polars(
self,
batch_size: Optional[int] = None,
batched: bool = False,
schema_overrides: Optional[dict] = None,
rechunk: bool = True,
) -> Union["pl.DataFrame", Iterator["pl.DataFrame"]]:
"""Returns the dataset as a `polars.DataFrame`. Can also return a generat... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
Args:
batched (`bool`):
Set to `True` to return a generator that yields the dataset as batches
of `batch_size` rows. Defaults to `False` (returns the whole datasets once).
batch_size (`int`, *optional*):
The size (number of rows) of the batches if ... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
if not batched:
return pl.from_arrow(
query_table(
table=self._data,
key=slice(0, len(self)),
indices=self._indices if self._indices is not None else None,
),
schema_overri... | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
for offset in range(0, len(self), batch_size)
)
else:
raise ValueError("Polars needs to be installed to be able to return Polars dataframes.") | 4 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/arrow_dataset.py |
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