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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
"""Reuters 21578"""
from textwrap import dedent
import datasets
_HOMEPAGE = "https://archive.ics.uci.edu/dataset/137/reuters+21578+text+categorization+collection"
_CITATION = """\
@article{APTE94,
author = {Chidanand Apt{\'{e}} and Fred Damerau and Sholom M. Weiss},
title = {Automated Learning of Decision Rules for Text Categorization},
journal = {ACM Transactions on Information Systems},
year = {1994},
note = {To appear.}
}
@inproceedings{APTE94b,
author = {Chidanand Apt{\'{e}} and Fred Damerau and Sholom M. Weiss},
title = {Toward Language Independent Automated Learning of Text Categorization Models},
booktitle = {sigir94},
year = {1994},
note = {To appear.}
}
@inproceedings{HAYES8},
author = {Philip J. Hayes and Peggy M. Anderson and Irene B. Nirenburg and
Linda M. Schmandt},
title = {{TCS}: A Shell for Content-Based Text Categorization},
booktitle = {IEEE Conference on Artificial Intelligence Applications},
year = {1990}
}
@inproceedings{HAYES90b,
author = {Philip J. Hayes and Steven P. Weinstein},
title = {{CONSTRUE/TIS:} A System for Content-Based Indexing of a
Database of News Stories},
booktitle = {Second Annual Conference on Innovative Applications of
Artificial Intelligence},
year = {1990}
}
@incollection{HAYES92 ,
author = {Philip J. Hayes},
title = {Intelligent High-Volume Text Processing using Shallow,
Domain-Specific Techniques},
booktitle = {Text-Based Intelligent Systems},
publisher = {Lawrence Erlbaum},
address = {Hillsdale, NJ},
year = {1992},
editor = {Paul S. Jacobs}
}
@inproceedings{LEWIS91c ,
author = {David D. Lewis},
title = {Evaluating Text Categorization},
booktitle = {Proceedings of Speech and Natural Language Workshop},
year = {1991},
month = {feb},
organization = {Defense Advanced Research Projects Agency},
publisher = {Morgan Kaufmann},
pages = {312--318}
}
@phdthesis{LEWIS91d,
author = {David Dolan Lewis},
title = {Representation and Learning in Information Retrieval},
school = {Computer Science Dept.; Univ. of Massachusetts; Amherst, MA 01003},
year = 1992},
note = {Technical Report 91--93.}
}
@inproceedings{LEWIS91e,
author = {David D. Lewis},
title = {Data Extraction as Text Categorization: An Experiment with
the {MUC-3} Corpus},
booktitle = {Proceedings of the Third Message Understanding Evaluation
and Conference},
year = {1991},
month = {may},
organization = {Defense Advanced Research Projects Agency},
publisher = {Morgan Kaufmann},
address = {Los Altos, CA}
}
@inproceedings{LEWIS92b,
author = {David D. Lewis},
title = {An Evaluation of Phrasal and Clustered Representations on a Text
Categorization Task},
booktitle = {Fifteenth Annual International ACM SIGIR Conference on
Research and Development in Information Retrieval},
year = {1992},
pages = {37--50}
}
@inproceedings{LEWIS92d ,
author = {David D. Lewis and Richard M. Tong},
title = {Text Filtering in {MUC-3} and {MUC-4}},
booktitle = {Proceedings of the Fourth Message Understanding Conference ({MUC-4})},
year = {1992},
month = {jun},
organization = {Defense Advanced Research Projects Agency},
publisher = {Morgan Kaufmann},
address = {Los Altos, CA}
}
@inproceedings{LEWIS92e,
author = {David D. Lewis},
title = {Feature Selection and Feature Extraction for Text Categorization},
booktitle = {Proceedings of Speech and Natural Language Workshop},
year = {1992},
month = {feb} ,
organization = {Defense Advanced Research Projects Agency},
publisher = {Morgan Kaufmann},
pages = {212--217}
}
@inproceedings{LEWIS94b,
author = {David D. Lewis and Marc Ringuette},
title = {A Comparison of Two Learning Algorithms for Text Categorization},
booktitle = {Symposium on Document Analysis and Information Retrieval},
year = {1994},
organization = {ISRI; Univ. of Nevada, Las Vegas},
address = {Las Vegas, NV},
month = {apr},
pages = {81--93}
}
@article{LEWIS94d,
author = {David D. Lewis and Philip J. Hayes},
title = {Guest Editorial},
journal = {ACM Transactions on Information Systems},
year = {1994},
volume = {12},
number = {3},
pages = {231},
month = {jul}
}
@article{SPARCKJONES76,
author = {K. {Sparck Jones} and C. J. {van Rijsbergen}},
title = {Information Retrieval Test Collections},
journal = {Journal of Documentation},
year = {1976},
volume = {32},
number = {1},
pages = {59--75}
}
@book{WEISS91,
author = {Sholom M. Weiss and Casimir A. Kulikowski},
title = {Computer Systems That Learn},
publisher = {Morgan Kaufmann},
year = {1991},
address = {San Mateo, CA}
}
"""
_DESCRIPTION = """\
The Reuters-21578 dataset is one of the most widely used data collections for text
categorization research. It is collected from the Reuters financial newswire service in 1987.
"""
_DATA_URL = "data/reuters21578.tar.gz"
class Reuters21578Config(datasets.BuilderConfig):
"""BuilderConfig for reuters-21578."""
def __init__(self, **kwargs):
"""BuilderConfig for Reuters21578.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(Reuters21578Config, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
class Reuters21578(datasets.GeneratorBasedBuilder):
"""Reuters 21578"""
BUILDER_CONFIGS = [
Reuters21578Config(
name="ModHayes",
description=dedent(
"""Training Set (20856 docs): CGISPLIT="TRAINING-SET"
Test Set (722 docs): CGISPLIT="PUBLISHED-TESTSET"
Unused (0 docs)"""
),
),
Reuters21578Config(
name="ModLewis",
description=dedent(
"""Training Set (13,625 docs): LEWISSPLIT="TRAIN"; TOPICS="YES" or "NO"
Test Set (6,188 docs): LEWISSPLIT="TEST"; TOPICS="YES" or "NO"
Unused (1,765): LEWISSPLIT="NOT-USED" or TOPICS="BYPASS"""
),
),
Reuters21578Config(
name="ModApte",
description=dedent(
"""Training Set (9,603 docs): LEWISSPLIT="TRAIN"; TOPICS="YES"
Test Set (3,299 docs): LEWISSPLIT="TEST"; TOPICS="YES"
Unused (8,676 docs): LEWISSPLIT="NOT-USED"; TOPICS="YES" or TOPICS="NO" or TOPICS="BYPASS" """
),
),
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"text": datasets.Value("string"),
"text_type": datasets.Value("string"),
"topics": datasets.Sequence(datasets.Value("string")),
"lewis_split": datasets.Value("string"),
"cgis_split": datasets.Value("string"),
"old_id": datasets.Value("string"),
"new_id": datasets.Value("string"),
"places": datasets.Sequence(datasets.Value("string")),
"people": datasets.Sequence(datasets.Value("string")),
"orgs": datasets.Sequence(datasets.Value("string")),
"exchanges": datasets.Sequence(datasets.Value("string")),
"date": datasets.Value("string"),
"title": datasets.Value("string"),
}
),
# No default supervised_keys (as we have to pass both premise
# and hypothesis as input).
supervised_keys=None,
homepage=_HOMEPAGE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
archive = dl_manager.download(_DATA_URL)
filepaths = ["reut2-" + "%03d" % i + ".sgm" for i in range(22)]
if self.config.name == "ModHayes":
return [
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"filepaths": filepaths,
"split": "PUBLISHED-TESTSET",
"files": dl_manager.iter_archive(archive),
},
),
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepaths": filepaths,
"split": "TRAINING-SET",
"files": dl_manager.iter_archive(archive),
},
),
]
else:
return [
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"filepaths": filepaths, "split": "TEST", "files": dl_manager.iter_archive(archive)},
),
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"filepaths": filepaths, "split": "TRAIN", "files": dl_manager.iter_archive(archive)},
),
datasets.SplitGenerator(
name="unused",
gen_kwargs={
"filepaths": filepaths,
"split": "NOT-USED",
"files": dl_manager.iter_archive(archive),
},
),
]
def _generate_examples(self, filepaths, split, files):
"""This function returns the examples in the raw (text) form."""
for path, f in files:
if path in filepaths:
# only the file reut2-017 has one line non UTF-8 encoded so we can ignore it
line = f.readline().decode("utf-8", errors="ignore")
while line:
if line.startswith("<REUTERS"):
lewis_split = ""
cgis_split = ""
old_id = ""
new_id = ""
topics = []
places = []
people = []
orgs = []
exchanges = []
date = ""
title = ""
text = ""
text_type = ""
line = line.split()
lewis_split = line[2].split("=")[1]
cgis_split = line[3].split("=")[1]
old_id = line[4].split("=")[1]
new_id = line[5].split("=")[1][:-1]
has_topic = line[1].split("=")[1]
line = f.readline().decode("utf-8", errors="ignore")
if (
(self.config.name == "ModHayes" and split not in cgis_split)
or (
self.config.name == "ModLewis"
and (
(split not in lewis_split)
or (split == "TRAIN" and has_topic not in ['"YES"', '"NO"'])
or (split == "TEST" and has_topic not in ['"YES"', '"NO"'])
or (split == "NOT-USED" and has_topic not in ['"YES"', '"NO"', '"BYPASS"'])
)
)
or (
self.config.name == "ModApte"
and (
split not in lewis_split
or (split == "TRAIN" and has_topic != '"YES"')
or (split == "TEST" and has_topic != '"YES"')
or (split == "NOT-USED" and has_topic not in ['"YES"', '"NO"', '"BYPASS"'])
)
)
): # skip example that are not in the current split
li = line
while li and not li.startswith("<REUTERS"):
li = f.readline().decode("utf-8", errors="ignore")
if li:
line = li
elif line.startswith("<TOPICS>"):
if line.replace("\n", "") != "<TOPICS></TOPICS>":
line = line.split("<D>")
topics = [topic.replace("</D>", "") for topic in line[1:]]
topics = [topic.replace("</TOPICS>\n", "") for topic in topics]
line = f.readline().decode("utf-8", errors="ignore")
elif line.startswith("<PLACES>"):
if line.replace("\n", "") != "<PLACES></PLACES>":
line = line.split("<D>")
places = [place.replace("</D>", "") for place in line[1:]]
places = [place.replace("</PLACES>\n", "") for place in places]
line = f.readline().decode("utf-8", errors="ignore")
elif line.startswith("<PEOPLE>"):
if line.replace("\n", "") != "<PEOPLE></PEOPLE>":
line = line.split("<D>")
people = [p.replace("</D>", "") for p in line[1:]]
people = [p.replace("</PEOPLE>\n", "") for p in people]
line = f.readline().decode("utf-8", errors="ignore")
elif line.startswith("<ORGS>"):
if line.replace("\n", "") != "<ORGS></ORGS>":
line = line.split("<D>")
orgs = [org.replace("</D>", "") for org in line[1:]]
orgs = [org.replace("</ORGS>\n", "") for org in orgs]
line = f.readline().decode("utf-8", errors="ignore")
elif line.startswith("<EXCHANGES>"):
if line.replace("\n", "") != "<EXCHANGES></EXCHANGES>":
line = line.split("<D>")
exchanges = [ex.replace("</D>", "") for ex in line[1:]]
exchanges = [ex.replace("</EXCHANGES>\n", "") for ex in exchanges]
line = f.readline().decode("utf-8", errors="ignore")
elif line.startswith("<DATE>"):
date = line.replace("\n", "")
date = line[6:-8]
line = f.readline().decode("utf-8", errors="ignore")
elif line.startswith("<TITLE>"):
title = line[7:-9]
line = f.readline().decode("utf-8", errors="ignore")
elif "*<TITLE>" in line:
# These lines start with a variable number of * chars
title = line.split("*<TITLE>")[1][:-1]
line = f.readline().decode("utf-8", errors="ignore")
while "</TITLE>" not in line:
# Convert any \n in TYPE="BRIEF" text to spaces to match other titles
title += " " + line[:-1]
line = f.readline().decode("utf-8", errors="ignore")
elif "<BODY>" in line:
text = line.split("<BODY>")[1]
line = f.readline().decode("utf-8", errors="ignore")
while "</BODY>" not in line:
text += line
line = f.readline().decode("utf-8", errors="ignore")
elif line.startswith('<TEXT TYPE="UNPROC">'):
text_type = '"UNPROC"'
text = line[20:]
line = f.readline().decode("utf-8", errors="ignore")
while "</TEXT>" not in line:
text += line
line = f.readline().decode("utf-8", errors="ignore")
elif line.startswith('<TEXT TYPE="BRIEF">'):
text_type = '"BRIEF"'
line = f.readline().decode("utf-8", errors="ignore")
elif line.startswith("<TEXT>"):
text_type = '"NORM"'
line = f.readline().decode("utf-8", errors="ignore")
elif line.startswith("</REUTERS>"):
yield new_id, {
"lewis_split": lewis_split,
"cgis_split": cgis_split,
"old_id": old_id,
"new_id": new_id,
"topics": topics,
"places": places,
"people": people,
"orgs": orgs,
"exchanges": exchanges,
"date": date,
"title": title,
"text": text,
"text_type": text_type,
}
line = f.readline().decode("utf-8", errors="ignore")
else:
line = f.readline().decode("utf-8", errors="ignore")