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# coding=utf-8
# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
#
# 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.
# Lint as: python3
"""Web of science"""
from __future__ import absolute_import, division, print_function
import os
import datasets
_CITATION = """\
@inproceedings{kowsari2017HDLTex,
title={HDLTex: Hierarchical Deep Learning for Text Classification},
author={Kowsari, Kamran and Brown, Donald E and Heidarysafa, Mojtaba and Jafari Meimandi, Kiana and and Gerber, Matthew S and Barnes, Laura E},
booktitle={Machine Learning and Applications (ICMLA), 2017 16th IEEE International Conference on},
year={2017},
organization={IEEE}
}
"""
_DESCRIPTION = """\
The Web Of Science (WOS) dataset is a collection of data of published papers
available from the Web of Science. WOS has been released in three versions: WOS-46985, WOS-11967 and WOS-5736. WOS-46985 is the
full dataset. WOS-11967 and WOS-5736 are two subsets of WOS-46985.
"""
_DATA_URL = (
"https://data.mendeley.com/datasets/9rw3vkcfy4/6/files/c9ea673d-5542-44c0-ab7b-f1311f7d61df/WebOfScience.zip?dl=1"
)
class WebOfScienceConfig(datasets.BuilderConfig):
"""BuilderConfig for WebOfScience."""
def __init__(self, **kwargs):
"""BuilderConfig for WebOfScience.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(WebOfScienceConfig, self).__init__(version=datasets.Version("6.0.0", ""), **kwargs)
class WebOfScience(datasets.GeneratorBasedBuilder):
"""Web of Science"""
BUILDER_CONFIGS = [
WebOfScienceConfig(
name="WOS5736",
description="""Web of Science Dataset WOS-5736: This dataset contains 5,736 documents with 11 categories which include 3 parents categories.""",
),
WebOfScienceConfig(
name="WOS11967",
description="""Web of Science Dataset WOS-11967: This dataset contains 11,967 documents with 35 categories which include 7 parents categories.""",
),
WebOfScienceConfig(
name="WOS46985",
description="""Web of Science Dataset WOS-46985: This dataset contains 46,985 documents with 134 categories which include 7 parents categories.""",
),
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION + self.config.description,
features=datasets.Features(
{
"input_data": datasets.Value("string"),
"label": datasets.Value("int32"),
"label_level_1": datasets.Value("int32"),
"label_level_2": datasets.Value("int32"),
}
),
# No default supervised_keys (as we have to pass both premise
# and hypothesis as input).
supervised_keys=None,
homepage="https://data.mendeley.com/datasets/9rw3vkcfy4/6",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
# dl_manager is a datasets.download.DownloadManager that can be used to
dl_path = dl_manager.download_and_extract(_DATA_URL)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"input_file": os.path.join(dl_path, self.config.name, "X.txt"),
"label_file": os.path.join(dl_path, self.config.name, "Y.txt"),
"label_level_1_file": os.path.join(dl_path, self.config.name, "YL1.txt"),
"label_level_2_file": os.path.join(dl_path, self.config.name, "YL2.txt"),
},
)
]
def _generate_examples(self, input_file, label_file, label_level_1_file, label_level_2_file):
"""Yields examples."""
with open(input_file, encoding="utf-8") as f:
input_data = f.readlines()
with open(label_file, encoding="utf-8") as f:
label_data = f.readlines()
with open(label_level_1_file, encoding="utf-8") as f:
label_level_1_data = f.readlines()
with open(label_level_2_file, encoding="utf-8") as f:
label_level_2_data = f.readlines()
for i in range(len(input_data)):
yield i, {
"input_data": input_data[i],
"label": label_data[i],
"label_level_1": label_level_1_data[i],
"label_level_2": label_level_2_data[i],
}
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