gabrielaltay
commited on
Commit
•
af21eb1
1
Parent(s):
537a276
upload hub_repos/bioid/bioid.py to hub from bigbio repo
Browse files
bioid.py
ADDED
@@ -0,0 +1,372 @@
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1 |
+
# coding=utf-8
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2 |
+
# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
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3 |
+
#
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4 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
5 |
+
# you may not use this file except in compliance with the License.
|
6 |
+
# You may obtain a copy of the License at
|
7 |
+
#
|
8 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
9 |
+
#
|
10 |
+
# Unless required by applicable law or agreed to in writing, software
|
11 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
12 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
13 |
+
# See the License for the specific language governing permissions and
|
14 |
+
# limitations under the License.
|
15 |
+
|
16 |
+
|
17 |
+
import os
|
18 |
+
from typing import Dict, Iterator, List, Tuple
|
19 |
+
|
20 |
+
import bioc
|
21 |
+
import datasets
|
22 |
+
import pandas as pd
|
23 |
+
|
24 |
+
from .bigbiohub import BigBioConfig, Tasks, kb_features
|
25 |
+
|
26 |
+
_LOCAL = False
|
27 |
+
_PUBMED = True
|
28 |
+
_LANGUAGES = ["English"]
|
29 |
+
|
30 |
+
_CITATION = """\
|
31 |
+
@inproceedings{arighi2017bio,
|
32 |
+
title={Bio-ID track overview},
|
33 |
+
author={Arighi, Cecilia and Hirschman, Lynette and Lemberger, Thomas and Bayer, Samuel and Liechti, Robin and Comeau, Donald and Wu, Cathy},
|
34 |
+
booktitle={Proc. BioCreative Workshop},
|
35 |
+
volume={482},
|
36 |
+
pages={376},
|
37 |
+
year={2017}
|
38 |
+
}
|
39 |
+
"""
|
40 |
+
|
41 |
+
_DATASETNAME = "bioid"
|
42 |
+
_DISPLAYNAME = "BIOID"
|
43 |
+
|
44 |
+
_DESCRIPTION = """\
|
45 |
+
The Bio-ID track focuses on entity tagging and ID assignment to selected bioentity types.
|
46 |
+
The task is to annotate text from figure legends with the entity types and IDs for taxon (organism), gene, protein, miRNA, small molecules,
|
47 |
+
cellular components, cell types and cell lines, tissues and organs. The track draws on SourceData annotated figure
|
48 |
+
legends (by panel), in BioC format, and the corresponding full text articles (also BioC format) provided for context.
|
49 |
+
"""
|
50 |
+
|
51 |
+
_HOMEPAGE = "https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vi/track-1/"
|
52 |
+
|
53 |
+
_LICENSE = "UNKNOWN"
|
54 |
+
|
55 |
+
_URLS = {
|
56 |
+
_DATASETNAME: "https://biocreative.bioinformatics.udel.edu/media/store/files/2017/BioIDtraining_2.tar.gz",
|
57 |
+
}
|
58 |
+
|
59 |
+
_SUPPORTED_TASKS = [
|
60 |
+
Tasks.NAMED_ENTITY_RECOGNITION,
|
61 |
+
Tasks.NAMED_ENTITY_DISAMBIGUATION,
|
62 |
+
]
|
63 |
+
|
64 |
+
_SOURCE_VERSION = "2.0.0"
|
65 |
+
|
66 |
+
_BIGBIO_VERSION = "1.0.0"
|
67 |
+
|
68 |
+
|
69 |
+
class BioidDataset(datasets.GeneratorBasedBuilder):
|
70 |
+
"""TODO: Short description of my dataset."""
|
71 |
+
|
72 |
+
SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
|
73 |
+
BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
|
74 |
+
|
75 |
+
BUILDER_CONFIGS = [
|
76 |
+
BigBioConfig(
|
77 |
+
name="bioid_source",
|
78 |
+
version=SOURCE_VERSION,
|
79 |
+
description="bioid source schema",
|
80 |
+
schema="source",
|
81 |
+
subset_id="bioid",
|
82 |
+
),
|
83 |
+
BigBioConfig(
|
84 |
+
name="bioid_bigbio_kb",
|
85 |
+
version=BIGBIO_VERSION,
|
86 |
+
description="bioid BigBio schema",
|
87 |
+
schema="bigbio_kb",
|
88 |
+
subset_id="bioid",
|
89 |
+
),
|
90 |
+
]
|
91 |
+
|
92 |
+
DEFAULT_CONFIG_NAME = "bioid_source"
|
93 |
+
|
94 |
+
ENTITY_TYPES_NOT_NORMALIZED = [
|
95 |
+
"cell",
|
96 |
+
"gene",
|
97 |
+
"molecule",
|
98 |
+
"protein",
|
99 |
+
"subcellular",
|
100 |
+
"tissue",
|
101 |
+
"organism",
|
102 |
+
]
|
103 |
+
|
104 |
+
DB_NAME_TO_ENTITY_TYPE = {
|
105 |
+
"BAO": "assay", # https://www.ebi.ac.uk/ols/ontologies/bao
|
106 |
+
"CHEBI": "chemical",
|
107 |
+
"CL": "cell", # https://www.ebi.ac.uk/ols/ontologies/cl
|
108 |
+
"Corum": "protein", # https://mips.helmholtz-muenchen.de/corum/
|
109 |
+
"GO": "gene", # https://geneontology.org/
|
110 |
+
"PubChem": "chemical",
|
111 |
+
"Rfam": "rna", # https://rfam.org/
|
112 |
+
"Uberon": "anatomy",
|
113 |
+
"Cellosaurus": "cell",
|
114 |
+
"NCBI gene": "gene",
|
115 |
+
"NCBI taxon": "species",
|
116 |
+
"Uniprot": "protein",
|
117 |
+
}
|
118 |
+
|
119 |
+
def _info(self) -> datasets.DatasetInfo:
|
120 |
+
|
121 |
+
# Create the source schema; this schema will keep all keys/information/labels as close to the original dataset as possible.
|
122 |
+
# You can arbitrarily nest lists and dictionaries.
|
123 |
+
# For iterables, use lists over tuples or `datasets.Sequence`
|
124 |
+
if self.config.schema == "source":
|
125 |
+
features = datasets.Features(
|
126 |
+
{
|
127 |
+
"sourcedata_document": datasets.Value("string"),
|
128 |
+
"doi": datasets.Value("string"),
|
129 |
+
"pmc_id": datasets.Value("string"),
|
130 |
+
"figure": datasets.Value("string"),
|
131 |
+
"sourcedata_figure_dir": datasets.Value("string"),
|
132 |
+
"passages": [
|
133 |
+
{
|
134 |
+
"text": datasets.Value("string"),
|
135 |
+
"offset": datasets.Value("int32"),
|
136 |
+
"annotations": [
|
137 |
+
{
|
138 |
+
"thomas_article": datasets.Value("string"),
|
139 |
+
"doi": datasets.Value("string"),
|
140 |
+
"don_article": datasets.Value("int32"),
|
141 |
+
"figure": datasets.Value("string"),
|
142 |
+
"annot id": datasets.Value("int32"),
|
143 |
+
"paper id": datasets.Value("int32"),
|
144 |
+
"first left": datasets.Value("int32"),
|
145 |
+
"last right": datasets.Value("int32"),
|
146 |
+
"length": datasets.Value("int32"),
|
147 |
+
"byte length": datasets.Value("int32"),
|
148 |
+
"left alphanum": datasets.Value("string"),
|
149 |
+
"text": datasets.Value("string"),
|
150 |
+
"right alphanum": datasets.Value("string"),
|
151 |
+
"obj": datasets.Value("string"),
|
152 |
+
"overlap": datasets.Value("string"),
|
153 |
+
"identical span": datasets.Value("string"),
|
154 |
+
"overlap_label_count": datasets.Value("int32"),
|
155 |
+
}
|
156 |
+
],
|
157 |
+
}
|
158 |
+
],
|
159 |
+
}
|
160 |
+
)
|
161 |
+
|
162 |
+
# Choose the appropriate bigbio schema for your task and copy it here. You can find information on the schemas in the CONTRIBUTING guide.
|
163 |
+
# In rare cases you may get a dataset that supports multiple tasks requiring multiple schemas. In that case you can define multiple bigbio configs with a bigbio_[bigbio_schema_name] format.
|
164 |
+
# For example bigbio_kb, bigbio_t2t
|
165 |
+
elif self.config.schema == "bigbio_kb":
|
166 |
+
features = kb_features
|
167 |
+
|
168 |
+
return datasets.DatasetInfo(
|
169 |
+
description=_DESCRIPTION,
|
170 |
+
features=features,
|
171 |
+
homepage=_HOMEPAGE,
|
172 |
+
license=_LICENSE,
|
173 |
+
citation=_CITATION,
|
174 |
+
)
|
175 |
+
|
176 |
+
def _split_generators(self, dl_manager) -> List[datasets.SplitGenerator]:
|
177 |
+
"""Returns SplitGenerators."""
|
178 |
+
urls = _URLS[_DATASETNAME]
|
179 |
+
data_dir = dl_manager.download_and_extract(urls)
|
180 |
+
|
181 |
+
# Not all datasets have predefined canonical train/val/test splits.
|
182 |
+
# If your dataset has no predefined splits, use datasets.Split.TRAIN for all of the data.
|
183 |
+
|
184 |
+
return [
|
185 |
+
datasets.SplitGenerator(
|
186 |
+
name=datasets.Split.TRAIN,
|
187 |
+
# Whatever you put in gen_kwargs will be passed to _generate_examples
|
188 |
+
gen_kwargs={
|
189 |
+
"data_dir": data_dir,
|
190 |
+
"split": "train",
|
191 |
+
},
|
192 |
+
),
|
193 |
+
]
|
194 |
+
|
195 |
+
def load_annotations(self, path: str) -> Dict[str, Dict]:
|
196 |
+
"""
|
197 |
+
We load annotations from `annotations.csv`
|
198 |
+
becuase the one in the BioC xml files have offsets issues.
|
199 |
+
"""
|
200 |
+
|
201 |
+
df = pd.read_csv(path, sep=",")
|
202 |
+
|
203 |
+
df.fillna(-1, inplace=True)
|
204 |
+
|
205 |
+
annotations: Dict[str, Dict] = {}
|
206 |
+
|
207 |
+
for record in df.to_dict("records"):
|
208 |
+
|
209 |
+
article_id = str(record["don_article"])
|
210 |
+
|
211 |
+
if article_id not in annotations:
|
212 |
+
annotations[article_id] = {}
|
213 |
+
|
214 |
+
figure = record["figure"]
|
215 |
+
|
216 |
+
if figure not in annotations:
|
217 |
+
annotations[article_id][figure] = []
|
218 |
+
|
219 |
+
annotations[article_id][figure].append(record)
|
220 |
+
|
221 |
+
return annotations
|
222 |
+
|
223 |
+
def load_data(self, data_dir: str) -> List[Dict]:
|
224 |
+
"""
|
225 |
+
Compose text from BioC files with annotations from `annotations.csv`.
|
226 |
+
We load annotations from `annotations.csv`
|
227 |
+
becuase the one in the BioC xml files have offsets issues.
|
228 |
+
"""
|
229 |
+
|
230 |
+
text_dir = os.path.join(data_dir, "BioIDtraining_2", "caption_bioc")
|
231 |
+
annotation_file = os.path.join(data_dir, "BioIDtraining_2", "annotations.csv")
|
232 |
+
|
233 |
+
annotations = self.load_annotations(path=annotation_file)
|
234 |
+
|
235 |
+
data = []
|
236 |
+
|
237 |
+
for file_name in os.listdir(text_dir):
|
238 |
+
|
239 |
+
if file_name.startswith(".") or not file_name.endswith(".xml"):
|
240 |
+
continue
|
241 |
+
|
242 |
+
collection = bioc.load(os.path.join(text_dir, file_name))
|
243 |
+
|
244 |
+
for document in collection.documents:
|
245 |
+
|
246 |
+
item = document.infons
|
247 |
+
|
248 |
+
assert (
|
249 |
+
len(document.passages) == 1
|
250 |
+
), "Document contains more than one passage (figure caption). This is not expected!"
|
251 |
+
|
252 |
+
passage = document.passages[0]
|
253 |
+
|
254 |
+
article_id = document.infons["pmc_id"]
|
255 |
+
figure = document.infons["sourcedata_figure_dir"]
|
256 |
+
|
257 |
+
try:
|
258 |
+
passage.annotations = annotations[article_id][figure]
|
259 |
+
except KeyError:
|
260 |
+
passage.annotations = []
|
261 |
+
|
262 |
+
item["passages"] = [
|
263 |
+
{
|
264 |
+
"text": passage.text,
|
265 |
+
"annotations": passage.annotations,
|
266 |
+
"offset": passage.offset,
|
267 |
+
}
|
268 |
+
]
|
269 |
+
|
270 |
+
data.append(item)
|
271 |
+
|
272 |
+
return data
|
273 |
+
|
274 |
+
def get_entity(self, normalization: str) -> Tuple[str, List[Dict]]:
|
275 |
+
"""
|
276 |
+
Compile normalization information from annotation
|
277 |
+
"""
|
278 |
+
|
279 |
+
db_name_ids = normalization.split(":")
|
280 |
+
|
281 |
+
db_ids = None
|
282 |
+
|
283 |
+
# ids from cellosaurus do not have db name
|
284 |
+
if len(db_name_ids) == 1:
|
285 |
+
db_name = "Cellosaurus"
|
286 |
+
db_ids = db_name_ids[0].split("|")
|
287 |
+
else:
|
288 |
+
# quirk
|
289 |
+
if db_name_ids[0] == "CVCL_6412|CL":
|
290 |
+
db_name = "Cellosaurus"
|
291 |
+
db_ids = ["CVCL_6412"]
|
292 |
+
else:
|
293 |
+
db_name = db_name_ids[0]
|
294 |
+
# db_name hints for entity type: skip if does not provide normalization
|
295 |
+
if db_name not in self.ENTITY_TYPES_NOT_NORMALIZED:
|
296 |
+
# Uberon:UBERON:0001891
|
297 |
+
# NCBI gene:9341
|
298 |
+
db_id_idx = 2 if db_name == "Uberon" else 1
|
299 |
+
db_ids = [i.split(":")[db_id_idx] for i in normalization.split("|")]
|
300 |
+
|
301 |
+
normalized = (
|
302 |
+
[{"db_name": db_name, "db_id": i} for i in db_ids]
|
303 |
+
if db_ids is not None
|
304 |
+
else []
|
305 |
+
)
|
306 |
+
|
307 |
+
# ideally we should have canonical entity types w/ a dedicated enum like `Tasks`
|
308 |
+
|
309 |
+
if db_name in self.ENTITY_TYPES_NOT_NORMALIZED:
|
310 |
+
entity_type = db_name
|
311 |
+
else:
|
312 |
+
entity_type = self.DB_NAME_TO_ENTITY_TYPE[db_name]
|
313 |
+
|
314 |
+
return entity_type, normalized
|
315 |
+
|
316 |
+
def _generate_examples(
|
317 |
+
self, data_dir: str, split: str
|
318 |
+
) -> Iterator[Tuple[int, Dict]]:
|
319 |
+
"""Yields examples as (key, example) tuples."""
|
320 |
+
|
321 |
+
data = self.load_data(data_dir=data_dir)
|
322 |
+
|
323 |
+
if self.config.schema == "source":
|
324 |
+
for uid, document in enumerate(data):
|
325 |
+
yield uid, document
|
326 |
+
|
327 |
+
elif self.config.schema == "bigbio_kb":
|
328 |
+
|
329 |
+
uid = 0 # global unique id
|
330 |
+
|
331 |
+
for document in data:
|
332 |
+
|
333 |
+
kb_document = {
|
334 |
+
"id": uid,
|
335 |
+
"document_id": document["pmc_id"],
|
336 |
+
"passages": [],
|
337 |
+
"entities": [],
|
338 |
+
"relations": [],
|
339 |
+
"events": [],
|
340 |
+
"coreferences": [],
|
341 |
+
}
|
342 |
+
|
343 |
+
uid += 1
|
344 |
+
|
345 |
+
for passage in document["passages"]:
|
346 |
+
kb_document["passages"].append(
|
347 |
+
{
|
348 |
+
"id": uid,
|
349 |
+
"type": "figure_caption",
|
350 |
+
"text": [passage["text"]],
|
351 |
+
"offsets": [[0, len(passage["text"])]],
|
352 |
+
}
|
353 |
+
)
|
354 |
+
uid += 1
|
355 |
+
|
356 |
+
for a in passage["annotations"]:
|
357 |
+
|
358 |
+
entity_type, normalized = self.get_entity(a["obj"])
|
359 |
+
|
360 |
+
kb_document["entities"].append(
|
361 |
+
{
|
362 |
+
"id": uid,
|
363 |
+
"text": [a["text"]],
|
364 |
+
"type": entity_type,
|
365 |
+
"offsets": [[a["first left"], a["last right"]]],
|
366 |
+
"normalized": normalized,
|
367 |
+
}
|
368 |
+
)
|
369 |
+
|
370 |
+
uid += 1
|
371 |
+
|
372 |
+
yield uid, kb_document
|