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# coding=utf-8
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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.
"""Covid Dialog dataset in English and Chinese"""
import copy
import os
import re
import textwrap
import datasets
# BibTeX citation
_CITATION = """\
@article{ju2020CovidDialog,
title={CovidDialog: Medical Dialogue Datasets about COVID-19},
author={Ju, Zeqian and Chakravorty, Subrato and He, Xuehai and Chen, Shu and Yang, Xingyi and Xie, Pengtao},
journal={ https://github.com/UCSD-AI4H/COVID-Dialogue},
year={2020}
}
"""
# Official description of the dataset
_DESCRIPTION = textwrap.dedent(
"""
COVID-Dialogue-Dataset is amedical dialogue dataset about COVID-19 and other types of pneumonia.
Patients who are concerned that they may be infected by COVID-19 or other pneumonia consult doctors and doctors provide advice.
There are 603 consultations in English and 1393 consultations in Chinese.
"""
)
# Link to an official homepage for the dataset here
_HOMEPAGE = "https://github.com/UCSD-AI4H/COVID-Dialogue"
_LICENSE = ""
_CHINESE_QA = "COVID-Dialogue-Dataset-Chinese.txt"
_ENGLISH_QA = "COVID-Dialogue-Dataset-English.txt"
class CovidQaUcsd(datasets.GeneratorBasedBuilder):
"""Dataset has one file having consulatations purely based on COVID queries"""
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="en", version=VERSION, description="The dataset of medical dialogs related to Covid in English."
),
datasets.BuilderConfig(
name="zh", version=VERSION, description="The dataset of medical dialogs related to Covid in Chinese."
),
]
@property
def manual_download_instructions(self):
return """\
\nBoth the English and Chinese text files are present in https://github.com/UCSD-AI4H/COVID-Dialogue.
It is present as COVID-Dialogue-Dataset-English.txt (for the english dialogues) and COVID-Dialogue-Dataset-Chinese.txt
(for the Chinese Dialog).
To load the dataset, simple pass the folder where the file is saved to the 'data_dir' param in the datasets.load_dataset(...) option.
The data directory can e.g. be "/Downloads/".
The data can then be loaded using the below command:\n
`datasets.load_dataset("covid_qa_ucsd", name="en", data_dir="/Downloads/")`.
Just change the 'name' parameter to 'zh' for Chinese.
TAKE CARE NOT TO CHANGE THE NAME OF THE INPUT FILE
"""
def _info(self):
# This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
if self.config.name == "zh": # For english dialouge data
features = datasets.Features(
{
"dialogue_id": datasets.Value("int32"),
"dialogue_url": datasets.Value("string"),
"dialogue_turns": datasets.Sequence(
{
"speaker": datasets.ClassLabel(names=["病人", "医生"]),
"utterance": datasets.Value("string"),
}
),
}
)
if self.config.name == "en": # For english dialouge data
features = datasets.Features(
{
"dialogue_id": datasets.Value("int32"),
"dialogue_url": datasets.Value("string"),
"dialogue_turns": datasets.Sequence(
{
"speaker": datasets.ClassLabel(names=["Patient", "Doctor"]),
"utterance": datasets.Value("string"),
}
),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
supervised_keys=None,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
if self.config.name == "zh":
path_to_manual_file = os.path.join(os.path.abspath(os.path.expanduser(dl_manager.manual_dir)), _CHINESE_QA)
if self.config.name == "en":
path_to_manual_file = os.path.join(os.path.abspath(os.path.expanduser(dl_manager.manual_dir)), _ENGLISH_QA)
if not os.path.exists(path_to_manual_file):
raise FileNotFoundError(
f"{path_to_manual_file} does not exist. Make sure the file is present in the directory specified in the data_dir specified in the input {dl_manager.manual_dir} `datasets.load_dataset('covid_qa_ucsd', 'en', data_dir=...)`. Manual download instructions: {self.manual_download_instructions})"
)
return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": path_to_manual_file})]
def _generate_examples(self, filepath):
"""Yields examples. Iterates over the file and creates appropriate dialogue data
NOTE:
- The code makes some assumption on the structure of the raw .txt file.
- There are some checks to separate different id's. Hopefully, should not cause further issues later when more txt files are added.
"""
data_lang = self.config.name
id_ = -1
with open(filepath, encoding="utf-8") as f_in:
# Parameters to just "sectionize" the raw data
last_part = ""
last_dialog = {}
last_list = []
last_user = ""
check_list = []
# These flags are present to have a single function address both chinese and english data
# English data is a little hahazard (i.e. the sentences spans multiple different lines),
# Chinese is compact with one line for doctor and patient.
conv_flag = False
des_flag = False
while True:
line = f_in.readline()
if not line:
break
# Extracting the dialog id
if line[:2] == "id": # Hardcode alert!
# Handling ID references that may come in the description
# These were observed in the Chinese dataset and were not
# followed by numbers
try:
dialogue_id = int(re.findall(r"\d+", line)[0])
except IndexError:
continue
# Extracting the url
if line[:4] == "http": # Hardcode alert!
dialogue_url = line.rstrip()
# Extracting the patient info from description.
if line[:11] == "Description": # Hardcode alert!
last_part = "description"
last_dialog = {}
last_list = []
last_user = ""
last_conv = {"speaker": "", "utterance": ""}
while True:
line = f_in.readline()
if (not line) or (line in ["\n", "\n\r"]):
break
else:
if data_lang == "zh": # Condition in chinese
if line[:5] == "病情描述：": # Hardcode alert!
last_user = "病人"
sen = line[6:].rstrip()
des_flag = True
if data_lang == "en":
last_user = "Patient"
sen = line.rstrip()
des_flag = True
if des_flag:
if sen == "":
continue
if sen in check_list:
last_conv["speaker"] = ""
last_conv["utterance"] = ""
else:
last_conv["speaker"] = last_user
last_conv["utterance"] = sen
check_list.append(sen)
des_flag = False
break
# Extracting the conversation info from dialogue.
elif line[:8] == "Dialogue": # Hardcode alert!
if last_part == "description" and len(last_conv["utterance"]) > 0:
last_part = "dialogue"
if data_lang == "zh":
last_user = "病人"
if data_lang == "en":
last_user = "Patient"
while True:
line = f_in.readline()
if (not line) or (line in ["\n", "\n\r"]):
conv_flag = False
last_user = ""
last_list.append(copy.deepcopy(last_conv))
# To ensure close of conversation, only even number of sentences
# are extracted
last_turn = len(last_list)
if int(last_turn / 2) > 0:
temp = int(last_turn / 2)
id_ += 1
last_dialog["dialogue_id"] = dialogue_id
last_dialog["dialogue_url"] = dialogue_url
last_dialog["dialogue_turns"] = last_list[: temp * 2]
yield id_, last_dialog
break
if data_lang == "zh":
if line[:3] == "病人：" or line[:3] == "医生：": # Hardcode alert!
user = line[:2] # Hardcode alert!
line = f_in.readline()
conv_flag = True
# The elif block is to ensure that multi-line sentences are captured.
# This has been observed only in english.
if data_lang == "en":
if line.strip() == "Patient:" or line.strip() == "Doctor:": # Hardcode alert!
user = line.replace(":", "").rstrip()
line = f_in.readline()
conv_flag = True
elif line[:2] != "id": # Hardcode alert!
conv_flag = True
# Continues till the next ID is parsed
if conv_flag:
sen = line.rstrip()
if sen == "":
continue
if user == last_user:
last_conv["utterance"] = last_conv["utterance"] + sen
else:
last_user = user
last_list.append(copy.deepcopy(last_conv))
last_conv["utterance"] = sen
last_conv["speaker"] = user
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