text stringlengths 1 1.02k | class_index int64 0 271 | source stringclasses 76
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|---|---|---|
class SearchResults(NamedTuple):
scores: List[float]
indices: List[int] | 59 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/search.py |
class BatchedSearchResults(NamedTuple):
total_scores: List[List[float]]
total_indices: List[List[int]] | 60 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/search.py |
class NearestExamplesResults(NamedTuple):
scores: List[float]
examples: dict | 61 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/search.py |
class BatchedNearestExamplesResults(NamedTuple):
total_scores: List[List[float]]
total_examples: List[dict] | 62 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/search.py |
class BaseIndex:
"""Base class for indexing"""
def search(self, query, k: int = 10, **kwargs) -> SearchResults:
"""
To implement.
This method has to return the scores and the indices of the retrieved examples given a certain query.
"""
raise NotImplementedError
def ... | 63 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/search.py |
Ouput:
total_scores (`List[List[float]`): The retrieval scores of the retrieved examples per query.
total_indices (`List[List[int]]`): The indices of the retrieved examples per query.
"""
total_scores, total_indices = [], []
for query in queries:
scores, indic... | 63 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/search.py |
class ElasticSearchIndex(BaseIndex):
"""
Sparse index using Elasticsearch. It is used to index text and run queries based on BM25 similarity.
An Elasticsearch server needs to be accessible, and a python client is declared with
```
es_client = Elasticsearch([{'host': 'localhost', 'port': '9200'}])
... | 64 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/search.py |
def __init__(
self,
host: Optional[str] = None,
port: Optional[int] = None,
es_client: Optional["Elasticsearch"] = None,
es_index_name: Optional[str] = None,
es_index_config: Optional[dict] = None,
):
if not _has_elasticsearch:
raise ImportError(
... | 64 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/search.py |
self.es_client = es_client if es_client is not None else Elasticsearch([{"host": host, "port": str(port)}])
self.es_index_name = (
es_index_name
if es_index_name is not None
else "huggingface_datasets_" + os.path.basename(tempfile.NamedTemporaryFile().name)
)
... | 64 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/search.py |
def add_documents(self, documents: Union[List[str], "Dataset"], column: Optional[str] = None):
"""
Add documents to the index.
If the documents are inside a certain column, you can specify it using the `column` argument.
"""
index_name = self.es_index_name
index_config = ... | 64 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/search.py |
for ok, action in es.helpers.streaming_bulk(
client=self.es_client,
index=index_name,
actions=passage_generator(),
):
progress.update(1)
successes += ok
if successes != len(documents):
logger.warning(
f"Some document... | 64 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/search.py |
Ouput:
scores (`List[List[float]`): The retrieval scores of the retrieved examples.
indices (`List[List[int]]`): The indices of the retrieved examples.
"""
response = self.es_client.search(
index=self.es_index_name,
body={"query": {"multi_match": {"query":... | 64 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/search.py |
total_scores, total_indices = [None] * len(queries), [None] * len(queries)
with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
future_to_index = {executor.submit(self.search, query, k, **kwargs): i for i, query in enumerate(queries)}
for future in concurrent.... | 64 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/search.py |
class FaissIndex(BaseIndex):
"""
Dense index using Faiss. It is used to index vectors.
Faiss is a library for efficient similarity search and clustering of dense vectors.
It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM.
You can find more ... | 65 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/search.py |
def __init__(
self,
device: Optional[Union[int, List[int]]] = None,
string_factory: Optional[str] = None,
metric_type: Optional[int] = None,
custom_index: Optional["faiss.Index"] = None,
):
"""
Create a Dense index using Faiss. You can specify `device` if you ... | 65 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/search.py |
self.string_factory = string_factory
self.metric_type = metric_type
self.faiss_index = custom_index
if not _has_faiss:
raise ImportError(
"You must install Faiss to use FaissIndex. To do so you can run `conda install -c pytorch faiss-cpu` or `conda install -c pytorch ... | 65 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/search.py |
def add_vectors(
self,
vectors: Union[np.array, "Dataset"],
column: Optional[str] = None,
batch_size: int = 1000,
train_size: Optional[int] = None,
faiss_verbose: Optional[bool] = None,
):
"""
Add vectors to the index.
If the arrays are inside ... | 65 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/search.py |
# Create index
if self.faiss_index is None:
size = len(vectors[0]) if column is None else len(vectors[0][column])
if self.string_factory is not None:
if self.metric_type is None:
index = faiss.index_factory(size, self.string_factory)
el... | 65 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/search.py |
# Set verbosity level
if faiss_verbose is not None:
self.faiss_index.verbose = faiss_verbose
if hasattr(self.faiss_index, "index") and self.faiss_index.index is not None:
self.faiss_index.index.verbose = faiss_verbose
if hasattr(self.faiss_index, "quantizer") ... | 65 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/search.py |
# Add vectors
logger.info(f"Adding {len(vectors)} vectors to the faiss index")
for i in hf_tqdm(range(0, len(vectors), batch_size)):
vecs = vectors[i : i + batch_size] if column is None else vectors[i : i + batch_size][column]
self.faiss_index.add(vecs)
@staticmethod
def... | 65 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/search.py |
# If the device id is given as an integer
if isinstance(device, int):
# Positive integers are directly mapped to GPU ids
if device > -1:
faiss_res = faiss.StandardGpuResources()
index = faiss.index_cpu_to_gpu(faiss_res, device, index)
# And neg... | 65 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/search.py |
def search(self, query: np.array, k=10, **kwargs) -> SearchResults:
"""Find the nearest examples indices to the query.
Args:
query (`np.array`): The query as a numpy array.
k (`int`): The number of examples to retrieve.
Ouput:
scores (`List[List[float]`): Th... | 65 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/search.py |
def search_batch(self, queries: np.array, k=10, **kwargs) -> BatchedSearchResults:
"""Find the nearest examples indices to the queries.
Args:
queries (`np.array`): The queries as a numpy array.
k (`int`): The number of examples to retrieve.
Ouput:
total_scor... | 65 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/search.py |
if self.device is not None and isinstance(self.device, (int, list, tuple)):
index = faiss.index_gpu_to_cpu(self.faiss_index)
else:
index = self.faiss_index
with fsspec.open(str(file), "wb", **(storage_options or {})) as f:
faiss.write_index(index, faiss.BufferedIOWri... | 65 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/search.py |
# Instances of FaissIndex is essentially just a wrapper for faiss indices.
faiss_index = cls(device=device)
with fsspec.open(str(file), "rb", **(storage_options or {})) as f:
index = faiss.read_index(faiss.BufferedIOReader(faiss.PyCallbackIOReader(f.read)))
faiss_index.faiss_index = ... | 65 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/search.py |
class IndexableMixin:
"""Add indexing features to `datasets.Dataset`"""
def __init__(self):
self._indexes: Dict[str, BaseIndex] = {}
def __len__(self):
raise NotImplementedError
def __getitem__(self, key):
raise NotImplementedError
def is_index_initialized(self, index_nam... | 66 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/search.py |
Args:
index_name (`str`): Index name.
Returns:
[`BaseIndex`]
"""
self._check_index_is_initialized(index_name)
return self._indexes[index_name] | 66 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/search.py |
def add_faiss_index(
self,
column: str,
index_name: Optional[str] = None,
device: Optional[Union[int, List[int]]] = None,
string_factory: Optional[str] = None,
metric_type: Optional[int] = None,
custom_index: Optional["faiss.Index"] = None,
batch_size: int... | 66 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/search.py |
Args:
column (`str`): The column of the vectors to add to the index.
index_name (Optional `str`): The index_name/identifier of the index. This is the index_name that is used to call `.get_nearest` or `.search`.
By default it corresponds to `column`.
device (Optional `... | 66 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/search.py |
batch_size (Optional `int`): Size of the batch to use while adding vectors to the FaissIndex. Default value is 1000.
<Added version="2.4.0"/>
train_size (Optional `int`): If the index needs a training step, specifies how many vectors will be used to train the index.
faiss_verbose... | 66 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/search.py |
def add_faiss_index_from_external_arrays(
self,
external_arrays: np.array,
index_name: str,
device: Optional[Union[int, List[int]]] = None,
string_factory: Optional[str] = None,
metric_type: Optional[int] = None,
custom_index: Optional["faiss.Index"] = None,
... | 66 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/search.py |
Args:
external_arrays (`np.array`): If you want to use arrays from outside the lib for the index, you can set `external_arrays`.
It will use `external_arrays` to create the Faiss index instead of the arrays in the given `column`.
index_name (`str`): The index_name/identifier of t... | 66 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/search.py |
custom_index (Optional `faiss.Index`): Custom Faiss index that you already have instantiated and configured for your needs.
batch_size (Optional `int`): Size of the batch to use while adding vectors to the FaissIndex. Default value is 1000.
<Added version="2.4.0"/>
train_size (Op... | 66 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/search.py |
def save_faiss_index(self, index_name: str, file: Union[str, PurePath], storage_options: Optional[Dict] = None):
"""Save a FaissIndex on disk.
Args:
index_name (`str`): The index_name/identifier of the index. This is the index_name that is used to call `.get_nearest` or `.search`.
... | 66 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/search.py |
def load_faiss_index(
self,
index_name: str,
file: Union[str, PurePath],
device: Optional[Union[int, List[int]]] = None,
storage_options: Optional[Dict] = None,
):
"""Load a FaissIndex from disk.
If you want to do additional configurations, you can have acces... | 66 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/search.py |
Args:
index_name (`str`): The index_name/identifier of the index. This is the index_name that is used to
call `.get_nearest` or `.search`.
file (`str`): The path to the serialized faiss index on disk or remote URI (e.g. `"s3://my-bucket/index.faiss"`).
device (Optiona... | 66 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/search.py |
"""
index = FaissIndex.load(file, device=device, storage_options=storage_options)
if index.faiss_index.ntotal != len(self):
raise ValueError(
f"Index size should match Dataset size, but Index '{index_name}' at {file} has {index.faiss_index.ntotal} elements while the dataset h... | 66 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/search.py |
Args:
column (`str`): The column of the documents to add to the index.
index_name (Optional `str`): The index_name/identifier of the index. This is the index name that is used to call `.get_nearest` or `.search`.
By default it corresponds to `column`.
host (Optional `... | 66 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/search.py |
{
"settings": {
"number_of_shards": 1,
"analysis": {"analyzer": {"stop_standard": {"type": "standard", " stopwords": "_english_"}}},
},
"mappings": {
"properties": {
"text": {
... | 66 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/search.py |
def load_elasticsearch_index(
self,
index_name: str,
es_index_name: str,
host: Optional[str] = None,
port: Optional[int] = None,
es_client: Optional["Elasticsearch"] = None,
es_index_config: Optional[dict] = None,
):
"""Load an existing text index usin... | 66 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/search.py |
Args:
index_name (`str`):
The `index_name`/identifier of the index. This is the index name that is used to call `get_nearest` or `search`.
es_index_name (`str`):
The name of elasticsearch index to load.
host (`str`, *optional*, defaults to `localhost`)... | 66 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/search.py |
"analysis": {"analyzer": {"stop_standard": {"type": "standard", " stopwords": "_english_"}}},
},
"mappings": {
"properties": {
"text": {
"type": "text",
... | 66 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/search.py |
def drop_index(self, index_name: str):
"""Drop the index with the specified column.
Args:
index_name (`str`):
The `index_name`/identifier of the index.
"""
del self._indexes[index_name]
def search(self, index_name: str, query: Union[str, np.array], k: in... | 66 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/search.py |
Returns:
`(scores, indices)`:
A tuple of `(scores, indices)` where:
- **scores** (`List[List[float]`): the retrieval scores from either FAISS (`IndexFlatL2` by default) or ElasticSearch of the retrieved examples
- **indices** (`List[List[int]]`): the indices o... | 66 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/search.py |
Args:
index_name (`str`):
The `index_name`/identifier of the index.
queries (`Union[List[str], np.ndarray]`):
The queries as a list of strings if `index_name` is a text index or as a numpy array if `index_name` is a vector index.
k (`int`):
... | 66 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/search.py |
def get_nearest_examples(
self, index_name: str, query: Union[str, np.array], k: int = 10, **kwargs
) -> NearestExamplesResults:
"""Find the nearest examples in the dataset to the query.
Args:
index_name (`str`):
The index_name/identifier of the index.
... | 66 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/search.py |
Returns:
`(scores, examples)`:
A tuple of `(scores, examples)` where:
- **scores** (`List[float]`): the retrieval scores from either FAISS (`IndexFlatL2` by default) or ElasticSearch of the retrieved examples
- **examples** (`dict`): the retrieved examples
... | 66 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/search.py |
Args:
index_name (`str`):
The `index_name`/identifier of the index.
queries (`Union[List[str], np.ndarray]`):
The queries as a list of strings if `index_name` is a text index or as a numpy array if `index_name` is a vector index.
k (`int`):
... | 66 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/search.py |
Returns:
`(total_scores, total_examples)`:
A tuple of `(total_scores, total_examples)` where:
- **total_scores** (`List[List[float]`): the retrieval scores from either FAISS (`IndexFlatL2` by default) or ElasticSearch of the retrieved examples per query
- **to... | 66 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/search.py |
class DatasetsError(Exception):
"""Base class for exceptions in this library.""" | 67 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/exceptions.py |
class DefunctDatasetError(DatasetsError):
"""The dataset has been defunct.""" | 68 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/exceptions.py |
class FileNotFoundDatasetsError(DatasetsError, FileNotFoundError):
"""FileNotFoundError raised by this library.""" | 69 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/exceptions.py |
class DataFilesNotFoundError(FileNotFoundDatasetsError):
"""No (supported) data files found.""" | 70 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/exceptions.py |
class DatasetNotFoundError(FileNotFoundDatasetsError):
"""Dataset not found.
Raised when trying to access:
- a missing dataset, or
- a private/gated dataset and the user is not authenticated.
""" | 71 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/exceptions.py |
class DatasetBuildError(DatasetsError):
pass | 72 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/exceptions.py |
class ManualDownloadError(DatasetBuildError):
pass | 73 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/exceptions.py |
class FileFormatError(DatasetBuildError):
pass | 74 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/exceptions.py |
class DatasetGenerationError(DatasetBuildError):
pass | 75 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/exceptions.py |
class DatasetGenerationCastError(DatasetGenerationError):
@classmethod
def from_cast_error(
cls,
cast_error: CastError,
builder_name: str,
gen_kwargs: Dict[str, Any],
token: Optional[Union[bool, str]],
) -> "DatasetGenerationCastError":
explanation_message = (... | 76 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/exceptions.py |
if isinstance(gen_kwarg, str) and gen_kwarg.startswith("hf://"):
resolved_path = HfFileSystem(endpoint=config.HF_ENDPOINT, token=token).resolve_path(gen_kwarg)
gen_kwarg = "hf://" + resolved_path.unresolve()
if "@" + resolved_path.revision in gen_kwarg:
... | 76 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/exceptions.py |
return cls("An error occurred while generating the dataset" + explanation_message + help_message) | 76 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/exceptions.py |
class ChecksumVerificationError(DatasetsError):
"""Error raised during checksums verifications of downloaded files.""" | 77 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/exceptions.py |
class UnexpectedDownloadedFileError(ChecksumVerificationError):
"""Some downloaded files were not expected.""" | 78 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/exceptions.py |
class ExpectedMoreDownloadedFilesError(ChecksumVerificationError):
"""Some files were supposed to be downloaded but were not.""" | 79 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/exceptions.py |
class NonMatchingChecksumError(ChecksumVerificationError):
"""The downloaded file checksum don't match the expected checksum.""" | 80 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/exceptions.py |
class SplitsVerificationError(DatasetsError):
"""Error raised during splits verifications.""" | 81 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/exceptions.py |
class UnexpectedSplitsError(SplitsVerificationError):
"""The expected splits of the downloaded file is missing.""" | 82 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/exceptions.py |
class ExpectedMoreSplitsError(SplitsVerificationError):
"""Some recorded splits are missing.""" | 83 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/exceptions.py |
class NonMatchingSplitsSizesError(SplitsVerificationError):
"""The splits sizes don't match the expected splits sizes.""" | 84 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/exceptions.py |
class SplitInfo:
name: str = dataclasses.field(default="", metadata={"include_in_asdict_even_if_is_default": True})
num_bytes: int = dataclasses.field(default=0, metadata={"include_in_asdict_even_if_is_default": True})
num_examples: int = dataclasses.field(default=0, metadata={"include_in_asdict_even_if_is_... | 85 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/splits.py |
@property
def file_instructions(self):
"""Returns the list of dict(filename, take, skip)."""
# `self.dataset_name` is assigned in `SplitDict.add()`.
instructions = make_file_instructions(
name=self.dataset_name,
split_infos=[self],
instruction=str(self.nam... | 85 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/splits.py |
class SubSplitInfo:
"""Wrapper around a sub split info.
This class expose info on the subsplit:
```
ds, info = datasets.load_dataset(..., split='train[75%:]', with_info=True)
info.splits['train[75%:]'].num_examples
```
"""
instructions: FileInstructions
@property
def num_exampl... | 86 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/splits.py |
class SplitBase(metaclass=abc.ABCMeta):
# pylint: disable=line-too-long
"""Abstract base class for Split compositionality.
See the
[guide on splits](../loading#slice-splits)
for more information.
There are three parts to the composition:
1) The splits are composed (defined, merged, spl... | 87 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/splits.py |
```
read_instruction = split.get_read_instruction(self.info.splits)
```
3) The `SplitReadInstruction` is then used in the `tf.data.Dataset` pipeline
to define which files to read and how to skip examples within file.
"""
# pylint: enable=line-too-long
@abc.abstractme... | 87 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/splits.py |
def __ne__(self, other):
"""InEquality: datasets.Split.TRAIN != 'test'."""
return not self.__eq__(other)
def __add__(self, other):
"""Merging: datasets.Split.TRAIN + datasets.Split.TEST."""
return _SplitMerged(self, other)
def subsplit(self, arg=None, k=None, percent=None, weig... | 87 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/splits.py |
Warning: k and weighted will be converted into percent which mean that
values below the percent will be rounded up or down. The final split may be
bigger to deal with remainders. For instance:
```
train, test, valid = split.subsplit(k=3) # 33%, 33%, 34%
s1, s2, s3, s4 = split.s... | 87 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/splits.py |
Args:
arg: If no kwargs are given, `arg` will be interpreted as one of
`k`, `percent`, or `weighted` depending on the type.
For example:
```
split.subsplit(10) # Equivalent to split.subsplit(k=10)
split.subsplit(datasets.percen... | 87 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/splits.py |
Returns:
A subsplit or list of subsplits extracted from this split object.
"""
# Note that the percent kwargs redefine the outer name datasets.percent. This
# is done for consistency (.subsplit(percent=datasets.percent[:40]))
if sum(bool(x) for x in (arg, k, percent, weighted... | 87 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/splits.py |
def assert_slices_coverage(slices):
# Ensure that the expended slices cover all percents.
assert sum((list(range(*s.indices(100))) for s in slices), []) == list(range(100)) | 87 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/splits.py |
if k:
if not 0 < k <= 100:
raise ValueError(f"Subsplit k should be between 0 and 100, got {k}")
shift = 100 // k
slices = [slice(i * shift, (i + 1) * shift) for i in range(k)]
# Round up last element to ensure all elements are taken
slices[-1] ... | 87 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/splits.py |
# Round up last element to ensure all elements are taken
slices[-1] = slice(slices[-1].start, 100)
# Internal check to ensure full coverage
assert_slices_coverage(slices)
return tuple(_SubSplit(self, s) for s in slices)
else:
# Should not be possible
... | 87 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/splits.py |
class PercentSliceMeta(type):
def __getitem__(cls, slice_value):
if not isinstance(slice_value, slice):
raise ValueError(f"datasets.percent should only be called with slice, not {slice_value}")
return slice_value | 88 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/splits.py |
class PercentSlice(metaclass=PercentSliceMeta):
# pylint: disable=line-too-long
"""Syntactic sugar for defining slice subsplits: `datasets.percent[75:-5]`.
See the
[guide on splits](../loading#slice-splits)
for more information.
"""
# pylint: enable=line-too-long
pass | 89 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/splits.py |
class _SplitMerged(SplitBase):
"""Represent two split descriptors merged together."""
def __init__(self, split1, split2):
self._split1 = split1
self._split2 = split2
def get_read_instruction(self, split_dict):
read_instruction1 = self._split1.get_read_instruction(split_dict)
... | 90 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/splits.py |
class _SubSplit(SplitBase):
"""Represent a sub split of a split descriptor."""
def __init__(self, split, slice_value):
self._split = split
self._slice_value = slice_value
def get_read_instruction(self, split_dict):
return self._split.get_read_instruction(split_dict)[self._slice_val... | 91 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/splits.py |
class NamedSplit(SplitBase):
"""Descriptor corresponding to a named split (train, test, ...).
Example:
Each descriptor can be composed with other using addition or slice:
```py
split = datasets.Split.TRAIN.subsplit(datasets.percent[0:25]) + datasets.Split.TEST
```
... | 92 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/splits.py |
```py
split = (
datasets.Split.TRAIN.subsplit(datasets.percent[:25]) +
datasets.Split.TEST.subsplit(datasets.percent[:50])
)
split = (datasets.Split.TRAIN + datasets.Split.TEST).subsplit(datasets.percent[:50])
```
But this ... | 92 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/splits.py |
def __init__(self, name):
self._name = name
split_names_from_instruction = [split_instruction.split("[")[0] for split_instruction in name.split("+")]
for split_name in split_names_from_instruction:
if not re.match(_split_re, split_name):
raise ValueError(f"Split name ... | 92 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/splits.py |
def __hash__(self):
return hash(self._name)
def get_read_instruction(self, split_dict):
return SplitReadInstruction(split_dict[self._name]) | 92 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/splits.py |
class NamedSplitAll(NamedSplit):
"""Split corresponding to the union of all defined dataset splits."""
def __init__(self):
super().__init__("all")
def __repr__(self):
return "NamedSplitAll()"
def get_read_instruction(self, split_dict):
# Merge all dataset split together
... | 93 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/splits.py |
class Split:
# pylint: disable=line-too-long
"""`Enum` for dataset splits.
Datasets are typically split into different subsets to be used at various
stages of training and evaluation.
- `TRAIN`: the training data.
- `VALIDATION`: the validation data. If present, this is typically used as
... | 94 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/splits.py |
```py
>>> datasets.SplitGenerator(
... name=datasets.Split.TRAIN,
... gen_kwargs={"split_key": "train", "files": dl_manager.download_and extract(url)},
... ),
... datasets.SplitGenerator(
... name=datasets.Split.VALIDATION,
... gen_kwargs={"split_key": "validation", "files": ... | 94 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/splits.py |
class SplitReadInstruction:
"""Object containing the reading instruction for the dataset.
Similarly to `SplitDescriptor` nodes, this object can be composed with itself,
but the resolution happens instantaneously, instead of keeping track of the
tree, such as all instructions are compiled and flattened ... | 95 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/splits.py |
def add(self, sliced_split):
"""Add a SlicedSplitInfo the read instructions."""
# TODO(epot): Check that the number of examples per shard % 100 == 0
# Otherwise the slices value may be unbalanced and not exactly reflect the
# requested slice.
self._splits[sliced_split.split_info.... | 95 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/splits.py |
def __getitem__(self, slice_value):
"""Sub-splits."""
# Will raise an error if a split has already been sliced
split_instruction = SplitReadInstruction()
for v in self._splits.values():
if v.slice_value is not None:
raise ValueError(f"Trying to slice Split {v.... | 95 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/splits.py |
class SplitDict(dict):
"""Split info object."""
def __init__(self, *args, dataset_name=None, **kwargs):
super().__init__(*args, **kwargs)
self.dataset_name = dataset_name
def __getitem__(self, key: Union[SplitBase, str]):
# 1st case: The key exists: `info.splits['train']`
i... | 96 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/splits.py |
def add(self, split_info: SplitInfo):
"""Add the split info."""
if split_info.name in self:
raise ValueError(f"Split {split_info.name} already present")
split_info.dataset_name = self.dataset_name
super().__setitem__(split_info.name, split_info)
@property
def total_n... | 96 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/splits.py |
for split_info in split_infos:
if isinstance(split_info, dict):
split_info = SplitInfo(**split_info)
split_dict.add(split_info)
return split_dict
def to_split_dict(self):
"""Returns a list of SplitInfo protos that we have."""
out = []
for spl... | 96 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/splits.py |
def _to_yaml_list(self) -> list:
out = [asdict(s) for s in self.to_split_dict()]
# we don't need the shard lengths in YAML, since it depends on max_shard_size and num_proc
for split_info_dict in out:
split_info_dict.pop("shard_lengths", None)
# we don't need the dataset_name ... | 96 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/splits.py |
class SplitGenerator:
"""Defines the split information for the generator.
This should be used as returned value of
`GeneratorBasedBuilder._split_generators`.
See `GeneratorBasedBuilder._split_generators` for more info and example
of usage.
Args:
name (`str`):
Name of the `S... | 97 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/splits.py |
Subsets and Splits
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