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# 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.
"""Medical Dialog dataset in english and chinese"""
import copy
import json
import os
import re
import datasets
_CITATION = """\
@article{chen2020meddiag,
title={MedDialog: a large-scale medical dialogue dataset},
author={Chen, Shu and Ju, Zeqian and Dong, Xiangyu and Fang, Hongchao and Wang, Sicheng and Yang, Yue and Zeng, Jiaqi and Zhang, Ruisi and Zhang, Ruoyu and Zhou, Meng and Zhu, Penghui and Xie, Pengtao},
journal={arXiv preprint arXiv:2004.03329},
year={2020}
}
"""
_DESCRIPTION = """\
The MedDialog dataset (English) contains conversations (in English) between doctors and patients.\
It has 0.26 million dialogues. The data is continuously growing and more dialogues will be added. \
The raw dialogues are from healthcaremagic.com and icliniq.com.\
All copyrights of the data belong to healthcaremagic.com and icliniq.com.
"""
_HOMEPAGE = "https://github.com/UCSD-AI4H/Medical-Dialogue-System"
_LICENSE = "Unknown"
# URLS of processed data
_URLS = {
"en": {
"train": "https://drive.google.com/uc?export=download&id=1ria4E6IdTIPsikL4Glm3uy1tFKJKw0W8",
"validation": "https://drive.google.com/uc?export=download&id=1KAZneuwdfEVQQM6euCX4pMDP-9DQpiB5",
"test": "https://drive.google.com/uc?export=download&id=10izqL71kcgnteYsf87Vh6j_mZ8sZM2Rc",
},
"zh": {
"train": "https://drive.google.com/uc?export=download&id=1AaDJoHaiHAwEZwtskRH8oL1UP4FRgmgx",
"validation": "https://drive.google.com/uc?export=download&id=1TvfZCmQqP1kURIfEinOcj5VOPelTuGwI",
"test": "https://drive.google.com/uc?export=download&id=1pmmG95Yl6mMXRXDDSRb9-bYTxOE7ank5",
},
}
class MedicalDialog(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("2.0.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="en", description="The raw dataset of medical dialogs in English.", version=VERSION
),
datasets.BuilderConfig(
name="zh", description="The raw dataset of medical dialogs in Chinese.", version=VERSION
),
datasets.BuilderConfig(
name="processed.en", description="The processed dataset of medical dialogs in English.", version=VERSION
),
datasets.BuilderConfig(
name="processed.zh", description="The processed dataset of medical dialogs in Chinese.", version=VERSION
),
]
@property
def manual_download_instructions(self):
*processed, _ = self.config.name.split(".")
return (
None
if processed
else """\
\n For English:\nYou need to go to https://drive.google.com/drive/folders/1g29ssimdZ6JzTST6Y8g6h-ogUNReBtJD?usp=sharing,\
and manually download the dataset from Google Drive. Once it is completed,
a file named Medical-Dialogue-Dataset-English-<timestamp-info>.zip will appear in your Downloads folder(
or whichever folder your browser chooses to save files to). Unzip the folder to obtain
a folder named "Medical-Dialogue-Dataset-English" several text files.
Now, you can specify the path to this folder for the data_dir argument in the
datasets.load_dataset(...) option.
The <path/to/folder> can e.g. be "/Downloads/Medical-Dialogue-Dataset-English".
The data can then be loaded using the below command:\
`datasets.load_dataset("medical_dialog", name="en", data_dir="/Downloads/Medical-Dialogue-Dataset-English")`.
\n For Chinese:\nFollow the above process. Change the 'name' to 'zh'.The download link is https://drive.google.com/drive/folders/1r09_i8nJ9c1nliXVGXwSqRYqklcHd9e2
**NOTE**
- A caution while downloading from drive. It is better to download single files since creating a zip might not include files <500 MB. This has been observed mutiple times.
- After downloading the files and adding them to the appropriate folder, the path of the folder can be given as input tu the data_dir path.
"""
)
def _info(self):
if self.config.name == "zh":
features = datasets.Features(
{
"file_name": datasets.Value("string"),
"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"),
}
),
}
)
elif self.config.name == "en":
features = datasets.Features(
{
"file_name": datasets.Value("string"),
"dialogue_id": datasets.Value("int32"),
"dialogue_url": datasets.Value("string"),
"dialogue_turns": datasets.Sequence(
{
"speaker": datasets.ClassLabel(names=["Patient", "Doctor"]),
"utterance": datasets.Value("string"),
}
),
}
)
elif self.config.name == "processed.en":
features = datasets.Features(
{
"description": datasets.Value("string"),
"utterances": datasets.Sequence(datasets.Value("string")),
}
)
elif self.config.name == "processed.zh":
features = datasets.Features(
{
"utterances": datasets.Sequence(datasets.Value("string")),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
*processed, lang = self.config.name.split(".")
if processed:
data_dir = dl_manager.download(_URLS[lang])
splits = [datasets.Split.TRAIN, datasets.Split.VALIDATION, datasets.Split.TEST]
return [datasets.SplitGenerator(name=split, gen_kwargs={"filepaths": data_dir[split]}) for split in splits]
else:
path_to_manual_file = os.path.abspath(os.path.expanduser(dl_manager.manual_dir))
if not os.path.exists(path_to_manual_file):
raise FileNotFoundError(
f"{path_to_manual_file} does not exist. Make sure you insert a manual dir via `datasets.load_dataset('medical_dialog', data_dir=...)`. Manual download instructions: {self.manual_download_instructions})"
)
filepaths = [
os.path.join(path_to_manual_file, txt_file_name)
for txt_file_name in sorted(os.listdir(path_to_manual_file))
if txt_file_name.endswith("txt")
]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": filepaths})]
def _generate_examples(self, filepaths):
"""Yields examples. Iterates over each file and give the creates the corresponding features.
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.
"""
*processed, data_lang = self.config.name.split(".")
if processed:
with open(filepaths, encoding="utf-8") as f:
if self.config.name == "processed.en":
data = json.load(f)
for idx, item in enumerate(data):
yield idx, item
elif self.config.name == "processed.zh":
idx = 0
array = ""
for line in f:
if line[0] not in ["[", "]"]:
if line != " ],\n":
array += line
else:
array += "]"
item = json.loads(array)
yield idx, {"utterances": item}
idx += 1
array = ""
else:
id_ = -1
for filepath in filepaths:
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 = f_in.readline().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["file_name"] = filepath
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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