# Copyright 2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""
DBpedia load function
"""
# pylint: disable=C0103
import os
import csv
from typing import Union, Tuple
from mindspore.dataset import GeneratorDataset
from mindnlp.utils.download import cache_file
from mindnlp.dataset.register import load, process
from mindnlp.dataset.process import common_process
from mindnlp.dataset.transforms import BasicTokenizer
from mindnlp.configs import DEFAULT_ROOT
from mindnlp.utils import untar
URL = "https://drive.google.com/uc?export=download&id=0Bz8a_Dbh9QhbQ2Vic1kxMmZZQ1k&confirm=t"
MD5 = "dca7b1ae12b1091090db52aa7ec5ca64"
[docs]class Dbpedia:
"""
DBpedia dataset source
"""
def __init__(self, path) -> None:
self.path: str = path
self._label, self._title_text = [], []
self._load()
def _load(self):
csvfile = open(self.path, "r", encoding="utf-8")
dict_reader = csv.reader(csvfile)
for row in dict_reader:
self._label.append(int(row[0]))
self._title_text.append(f"{row[1]} {row[2]}")
def __getitem__(self, index):
return self._label[index], self._title_text[index]
def __len__(self):
return len(self._label)
[docs]@load.register
def DBpedia(
root: str = DEFAULT_ROOT,
split: Union[Tuple[str], str] = ("train", "test"),
proxies=None,
):
r"""
Load the DBpedia dataset
Args:
root (str): Directory where the datasets are saved.
Default:~/.mindnlp
split (str|Tuple[str]): Split or splits to be returned.
Default:('train', 'test').
proxies (dict): a dict to identify proxies,for example: {"https": "https://127.0.0.1:7890"}.
Returns:
- **datasets_list** (list) -A list of loaded datasets.
If only one type of dataset is specified,such as 'trian',
this dataset is returned instead of a list of datasets.
Examples:
>>> root = "~/.mindnlp"
>>> split = ('train', 'test')
>>> dataset_train,dataset_test = DBpedia(root, split)
>>> train_iter = dataset_train.create_tuple_iterator()
>>> print(next(train_iter))
"""
cache_dir = os.path.join(root, "datasets", "DBpedia")
path_dict = {
"train": "train.csv",
"test": "test.csv",
}
column_names = ["label", "title_text"]
path_list = []
datasets_list = []
path, _ = cache_file(
None,
cache_dir=cache_dir,
url=URL,
md5sum=MD5,
download_file_name="dbpedia_csv.tar.gz",
proxies=proxies,
)
untar(path, cache_dir)
if isinstance(split, str):
path_list.append(os.path.join(cache_dir, "dbpedia_csv", path_dict[split]))
else:
for s in split:
path_list.append(os.path.join(cache_dir, "dbpedia_csv", path_dict[s]))
for path in path_list:
datasets_list.append(
GeneratorDataset(
source=Dbpedia(path), column_names=column_names, shuffle=False
)
)
if len(path_list) == 1:
return datasets_list[0]
return datasets_list
[docs]@process.register
def DBpedia_Process(dataset, column="title_text", tokenizer=BasicTokenizer(), vocab=None):
"""
the process of the DBpedia dataset
Args:
dataset (GeneratorDataset): DBpedia dataset.
column (str): the column needed to be transpormed of the DBpedia dataset.
tokenizer (TextTensorOperation): tokenizer you choose to tokenize the text dataset.
vocab (Vocab): vocabulary object, used to store the mapping of token and index.
Returns:
- **dataset** (MapDataset) - dataset after transforms.
- **Vocab** (Vocab) - vocab created from dataset
Raises:
TypeError: If `input_column` is not a string.
Examples:
>>> from mindnlp.dataset import DBpedia, DBpedia_Process
>>> train_dataset, test_dataset = DBpedia()
>>> column = "title_text"
>>> tokenizer = BasicTokenizer()
>>> train_dataset, vocab = DBpedia_Process(train_dataset, column, tokenizer)
>>> train_dataset = train_dataset.create_tuple_iterator()
>>> print(next(train_dataset))
[Tensor(shape=[], dtype=Int64, value= 1), Tensor(shape=[51], dtype=Int32, value= [ 407,
0, 347, 0, 7760, 774, 7760, 3, 16106, 407, 347, 7760, 950,
10, 5, 99, 88888, 485, 69, 2, 16106, 3996, 3092, 156,
42, 73, 20, 1217, 0, 61, 504, 83, 3, 149, 8463, 10, 156,
2614, 9, 1604, 13, 3267, 1986, 4858, 0, 1730, 485, 1831,
2, 594, 0])]
"""
return common_process(dataset, column, tokenizer, vocab)