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import os
from functools import partial
from typing import Union, Tuple
from torchtext._internal.module_utils import is_module_available
from torchtext.data.datasets_utils import (
_wrap_split_argument,
_create_dataset_directory,
)
if is_module_available("torchdata"):
from torchdata.datapipes.iter import FileOpener, IterableWrapper
from torchtext._download_hooks import GDriveReader
URL = "https://drive.google.com/uc?export=download&id=0Bz8a_Dbh9QhbQ2Vic1kxMmZZQ1k"
MD5 = "dca7b1ae12b1091090db52aa7ec5ca64"
NUM_LINES = {
"train": 560000,
"test": 70000,
}
_PATH = "dbpedia_csv.tar.gz"
_EXTRACTED_FILES = {
"train": os.path.join("dbpedia_csv", "train.csv"),
"test": os.path.join("dbpedia_csv", "test.csv"),
}
DATASET_NAME = "DBpedia"
def _filepath_fn(root, _=None):
return os.path.join(root, _PATH)
def _extracted_filepath_fn(root, split, _=None):
return os.path.join(root, _EXTRACTED_FILES[split])
def _filter_fn(split, x):
return _EXTRACTED_FILES[split] in x[0]
def _modify_res(t):
return int(t[0]), " ".join(t[1:])
@_create_dataset_directory(dataset_name=DATASET_NAME)
@_wrap_split_argument(("train", "test"))
def DBpedia(root: str, split: Union[Tuple[str], str]):
"""DBpedia Dataset
.. warning::
using datapipes is still currently subject to a few caveats. if you wish
to use this dataset with shuffling, multi-processing, or distributed
learning, please see :ref:`this note <datapipes_warnings>` for further
instructions.
For additional details refer to https://www.dbpedia.org/resources/latest-core/
Number of lines per split:
- train: 560000
- test: 70000
Args:
root: Directory where the datasets are saved. Default: os.path.expanduser('~/.torchtext/cache')
split: split or splits to be returned. Can be a string or tuple of strings. Default: (`train`, `test`)
:returns: DataPipe that yields tuple of label (1 to 14) and text containing the news title and contents
:rtype: (int, str)
"""
# TODO Remove this after removing conditional dependency
if not is_module_available("torchdata"):
raise ModuleNotFoundError(
"Package `torchdata` not found. Please install following instructions at https://github.com/pytorch/data"
)
url_dp = IterableWrapper([URL])
cache_compressed_dp = url_dp.on_disk_cache(
filepath_fn=partial(_filepath_fn, root),
hash_dict={_filepath_fn(root): MD5},
hash_type="md5",
)
cache_compressed_dp = GDriveReader(cache_compressed_dp).end_caching(mode="wb", same_filepath_fn=True)
cache_decompressed_dp = cache_compressed_dp.on_disk_cache(filepath_fn=partial(_extracted_filepath_fn, root, split))
cache_decompressed_dp = (
FileOpener(cache_decompressed_dp, mode="b").load_from_tar().filter(partial(_filter_fn, split))
)
cache_decompressed_dp = cache_decompressed_dp.end_caching(mode="wb", same_filepath_fn=True)
data_dp = FileOpener(cache_decompressed_dp, encoding="utf-8")
return data_dp.parse_csv().map(fn=_modify_res).shuffle().set_shuffle(False).sharding_filter()
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