gabrielaltay
commited on
Commit
•
6cc5ae2
1
Parent(s):
b0150a2
upload hubscripts/chemprot_hub.py to hub from bigbio repo
Browse files- chemprot.py +446 -0
chemprot.py
ADDED
@@ -0,0 +1,446 @@
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1 |
+
# coding=utf-8
|
2 |
+
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
|
3 |
+
#
|
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 |
+
The BioCreative VI Chemical-Protein interaction dataset identifies entities of
|
17 |
+
chemicals and proteins and their likely relation to one other. Compounds are
|
18 |
+
generally agonists (activators) or antagonists (inhibitors) of proteins. The
|
19 |
+
script loads dataset in bigbio schema (using knowledgebase schema: schemas/kb)
|
20 |
+
AND/OR source (default) schema
|
21 |
+
"""
|
22 |
+
import os
|
23 |
+
from typing import Dict, Tuple
|
24 |
+
|
25 |
+
import datasets
|
26 |
+
|
27 |
+
from .bigbiohub import kb_features
|
28 |
+
from .bigbiohub import BigBioConfig
|
29 |
+
from .bigbiohub import Tasks
|
30 |
+
|
31 |
+
_LANGUAGES = ['English']
|
32 |
+
_PUBMED = True
|
33 |
+
_LOCAL = False
|
34 |
+
_CITATION = """\
|
35 |
+
@article{DBLP:journals/biodb/LiSJSWLDMWL16,
|
36 |
+
author = {Krallinger, M., Rabal, O., Lourenço, A.},
|
37 |
+
title = {Overview of the BioCreative VI chemical-protein interaction Track},
|
38 |
+
journal = {Proceedings of the BioCreative VI Workshop,},
|
39 |
+
volume = {141-146},
|
40 |
+
year = {2017},
|
41 |
+
url = {https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vi/track-5/},
|
42 |
+
doi = {},
|
43 |
+
biburl = {},
|
44 |
+
bibsource = {}
|
45 |
+
}
|
46 |
+
"""
|
47 |
+
_DESCRIPTION = """\
|
48 |
+
The BioCreative VI Chemical-Protein interaction dataset identifies entities of
|
49 |
+
chemicals and proteins and their likely relation to one other. Compounds are
|
50 |
+
generally agonists (activators) or antagonists (inhibitors) of proteins.
|
51 |
+
"""
|
52 |
+
|
53 |
+
_DATASETNAME = "chemprot"
|
54 |
+
_DISPLAYNAME = "ChemProt"
|
55 |
+
|
56 |
+
_HOMEPAGE = "https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vi/track-5/"
|
57 |
+
|
58 |
+
_LICENSE = 'Public Domain Mark 1.0'
|
59 |
+
|
60 |
+
_URLs = {
|
61 |
+
"source": "https://biocreative.bioinformatics.udel.edu/media/store/files/2017/ChemProt_Corpus.zip",
|
62 |
+
"bigbio_kb": "https://biocreative.bioinformatics.udel.edu/media/store/files/2017/ChemProt_Corpus.zip",
|
63 |
+
}
|
64 |
+
|
65 |
+
_SUPPORTED_TASKS = [Tasks.RELATION_EXTRACTION, Tasks.NAMED_ENTITY_RECOGNITION]
|
66 |
+
_SOURCE_VERSION = "1.0.0"
|
67 |
+
_BIGBIO_VERSION = "1.0.0"
|
68 |
+
|
69 |
+
|
70 |
+
# Chemprot specific variables
|
71 |
+
# NOTE: There are 3 examples (2 in dev, 1 in training) with CPR:0
|
72 |
+
_GROUP_LABELS = {
|
73 |
+
"CPR:0": "Undefined",
|
74 |
+
"CPR:1": "Part_of",
|
75 |
+
"CPR:2": "Regulator",
|
76 |
+
"CPR:3": "Upregulator",
|
77 |
+
"CPR:4": "Downregulator",
|
78 |
+
"CPR:5": "Agonist",
|
79 |
+
"CPR:6": "Antagonist",
|
80 |
+
"CPR:7": "Modulator",
|
81 |
+
"CPR:8": "Cofactor",
|
82 |
+
"CPR:9": "Substrate",
|
83 |
+
"CPR:10": "Not",
|
84 |
+
}
|
85 |
+
|
86 |
+
|
87 |
+
class ChemprotDataset(datasets.GeneratorBasedBuilder):
|
88 |
+
"""BioCreative VI Chemical-Protein Interaction Task."""
|
89 |
+
|
90 |
+
SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
|
91 |
+
BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
|
92 |
+
|
93 |
+
BUILDER_CONFIGS = [
|
94 |
+
BigBioConfig(
|
95 |
+
name="chemprot_full_source",
|
96 |
+
version=SOURCE_VERSION,
|
97 |
+
description="chemprot source schema",
|
98 |
+
schema="source",
|
99 |
+
subset_id="chemprot_full",
|
100 |
+
),
|
101 |
+
BigBioConfig(
|
102 |
+
name="chemprot_shared_task_eval_source",
|
103 |
+
version=SOURCE_VERSION,
|
104 |
+
description="chemprot source schema with only the relation types that were used in the shared task evaluation",
|
105 |
+
schema="source",
|
106 |
+
subset_id="chemprot_shared_task_eval",
|
107 |
+
),
|
108 |
+
BigBioConfig(
|
109 |
+
name="chemprot_bigbio_kb",
|
110 |
+
version=BIGBIO_VERSION,
|
111 |
+
description="chemprot BigBio schema",
|
112 |
+
schema="bigbio_kb",
|
113 |
+
subset_id="chemprot",
|
114 |
+
),
|
115 |
+
]
|
116 |
+
|
117 |
+
DEFAULT_CONFIG_NAME = "chemprot_full_source"
|
118 |
+
|
119 |
+
def _info(self):
|
120 |
+
|
121 |
+
if self.config.schema == "source":
|
122 |
+
features = datasets.Features(
|
123 |
+
{
|
124 |
+
"pmid": datasets.Value("string"),
|
125 |
+
"text": datasets.Value("string"),
|
126 |
+
"entities": datasets.Sequence(
|
127 |
+
{
|
128 |
+
"id": datasets.Value("string"),
|
129 |
+
"type": datasets.Value("string"),
|
130 |
+
"text": datasets.Value("string"),
|
131 |
+
"offsets": datasets.Sequence(datasets.Value("int64")),
|
132 |
+
}
|
133 |
+
),
|
134 |
+
"relations": datasets.Sequence(
|
135 |
+
{
|
136 |
+
"type": datasets.Value("string"),
|
137 |
+
"arg1": datasets.Value("string"),
|
138 |
+
"arg2": datasets.Value("string"),
|
139 |
+
}
|
140 |
+
),
|
141 |
+
}
|
142 |
+
)
|
143 |
+
|
144 |
+
elif self.config.schema == "bigbio_kb":
|
145 |
+
features = kb_features
|
146 |
+
|
147 |
+
return datasets.DatasetInfo(
|
148 |
+
description=_DESCRIPTION,
|
149 |
+
features=features,
|
150 |
+
homepage=_HOMEPAGE,
|
151 |
+
license=str(_LICENSE),
|
152 |
+
citation=_CITATION,
|
153 |
+
)
|
154 |
+
|
155 |
+
def _split_generators(self, dl_manager):
|
156 |
+
"""Returns SplitGenerators."""
|
157 |
+
my_urls = _URLs[self.config.schema]
|
158 |
+
data_dir = dl_manager.download_and_extract(my_urls)
|
159 |
+
|
160 |
+
# Extract each of the individual folders
|
161 |
+
# NOTE: omitting "extract" call cause it uses a new folder
|
162 |
+
train_path = dl_manager.extract(
|
163 |
+
os.path.join(data_dir, "ChemProt_Corpus/chemprot_training.zip")
|
164 |
+
)
|
165 |
+
test_path = dl_manager.extract(
|
166 |
+
os.path.join(data_dir, "ChemProt_Corpus/chemprot_test_gs.zip")
|
167 |
+
)
|
168 |
+
dev_path = dl_manager.extract(
|
169 |
+
os.path.join(data_dir, "ChemProt_Corpus/chemprot_development.zip")
|
170 |
+
)
|
171 |
+
sample_path = dl_manager.extract(
|
172 |
+
os.path.join(data_dir, "ChemProt_Corpus/chemprot_sample.zip")
|
173 |
+
)
|
174 |
+
|
175 |
+
return [
|
176 |
+
datasets.SplitGenerator(
|
177 |
+
name="sample", # should be a named split : /
|
178 |
+
gen_kwargs={
|
179 |
+
"filepath": os.path.join(sample_path, "chemprot_sample"),
|
180 |
+
"abstract_file": "chemprot_sample_abstracts.tsv",
|
181 |
+
"entity_file": "chemprot_sample_entities.tsv",
|
182 |
+
"relation_file": "chemprot_sample_relations.tsv",
|
183 |
+
"gold_standard_file": "chemprot_sample_gold_standard.tsv",
|
184 |
+
"split": "sample",
|
185 |
+
},
|
186 |
+
),
|
187 |
+
datasets.SplitGenerator(
|
188 |
+
name=datasets.Split.TRAIN,
|
189 |
+
gen_kwargs={
|
190 |
+
"filepath": os.path.join(train_path, "chemprot_training"),
|
191 |
+
"abstract_file": "chemprot_training_abstracts.tsv",
|
192 |
+
"entity_file": "chemprot_training_entities.tsv",
|
193 |
+
"relation_file": "chemprot_training_relations.tsv",
|
194 |
+
"gold_standard_file": "chemprot_training_gold_standard.tsv",
|
195 |
+
"split": "train",
|
196 |
+
},
|
197 |
+
),
|
198 |
+
datasets.SplitGenerator(
|
199 |
+
name=datasets.Split.TEST,
|
200 |
+
gen_kwargs={
|
201 |
+
"filepath": os.path.join(test_path, "chemprot_test_gs"),
|
202 |
+
"abstract_file": "chemprot_test_abstracts_gs.tsv",
|
203 |
+
"entity_file": "chemprot_test_entities_gs.tsv",
|
204 |
+
"relation_file": "chemprot_test_relations_gs.tsv",
|
205 |
+
"gold_standard_file": "chemprot_test_gold_standard.tsv",
|
206 |
+
"split": "test",
|
207 |
+
},
|
208 |
+
),
|
209 |
+
datasets.SplitGenerator(
|
210 |
+
name=datasets.Split.VALIDATION,
|
211 |
+
gen_kwargs={
|
212 |
+
"filepath": os.path.join(dev_path, "chemprot_development"),
|
213 |
+
"abstract_file": "chemprot_development_abstracts.tsv",
|
214 |
+
"entity_file": "chemprot_development_entities.tsv",
|
215 |
+
"relation_file": "chemprot_development_relations.tsv",
|
216 |
+
"gold_standard_file": "chemprot_development_gold_standard.tsv",
|
217 |
+
"split": "dev",
|
218 |
+
},
|
219 |
+
),
|
220 |
+
]
|
221 |
+
|
222 |
+
def _generate_examples(
|
223 |
+
self,
|
224 |
+
filepath,
|
225 |
+
abstract_file,
|
226 |
+
entity_file,
|
227 |
+
relation_file,
|
228 |
+
gold_standard_file,
|
229 |
+
split,
|
230 |
+
):
|
231 |
+
"""Yields examples as (key, example) tuples."""
|
232 |
+
if self.config.schema == "source":
|
233 |
+
abstracts = self._get_abstract(os.path.join(filepath, abstract_file))
|
234 |
+
|
235 |
+
entities, entity_id = self._get_entities(
|
236 |
+
os.path.join(filepath, entity_file)
|
237 |
+
)
|
238 |
+
|
239 |
+
if self.config.subset_id == "chemprot_full":
|
240 |
+
relations = self._get_relations(os.path.join(filepath, relation_file))
|
241 |
+
elif self.config.subset_id == "chemprot_shared_task_eval":
|
242 |
+
relations = self._get_relations_gs(
|
243 |
+
os.path.join(filepath, gold_standard_file)
|
244 |
+
)
|
245 |
+
else:
|
246 |
+
raise ValueError(self.config)
|
247 |
+
|
248 |
+
for id_, pmid in enumerate(abstracts.keys()):
|
249 |
+
yield id_, {
|
250 |
+
"pmid": pmid,
|
251 |
+
"text": abstracts[pmid],
|
252 |
+
"entities": entities[pmid],
|
253 |
+
"relations": relations.get(pmid, []),
|
254 |
+
}
|
255 |
+
|
256 |
+
elif self.config.schema == "bigbio_kb":
|
257 |
+
|
258 |
+
abstracts = self._get_abstract(os.path.join(filepath, abstract_file))
|
259 |
+
entities, entity_id = self._get_entities(
|
260 |
+
os.path.join(filepath, entity_file)
|
261 |
+
)
|
262 |
+
relations = self._get_relations(
|
263 |
+
os.path.join(filepath, relation_file), is_mapped=True
|
264 |
+
)
|
265 |
+
|
266 |
+
uid = 0
|
267 |
+
for id_, pmid in enumerate(abstracts.keys()):
|
268 |
+
data = {
|
269 |
+
"id": str(uid),
|
270 |
+
"document_id": str(pmid),
|
271 |
+
"passages": [],
|
272 |
+
"entities": [],
|
273 |
+
"relations": [],
|
274 |
+
"events": [],
|
275 |
+
"coreferences": [],
|
276 |
+
}
|
277 |
+
uid += 1
|
278 |
+
|
279 |
+
data["passages"] = [
|
280 |
+
{
|
281 |
+
"id": str(uid),
|
282 |
+
"type": "title and abstract",
|
283 |
+
"text": [abstracts[pmid]],
|
284 |
+
"offsets": [[0, len(abstracts[pmid])]],
|
285 |
+
}
|
286 |
+
]
|
287 |
+
uid += 1
|
288 |
+
|
289 |
+
entity_to_id = {}
|
290 |
+
for entity in entities[pmid]:
|
291 |
+
_text = entity["text"]
|
292 |
+
entity.update({"text": [_text]})
|
293 |
+
entity_to_id[entity["id"]] = str(uid)
|
294 |
+
entity.update({"id": str(uid)})
|
295 |
+
_offsets = entity["offsets"]
|
296 |
+
entity.update({"offsets": [_offsets]})
|
297 |
+
entity["normalized"] = []
|
298 |
+
data["entities"].append(entity)
|
299 |
+
uid += 1
|
300 |
+
|
301 |
+
for relation in relations.get(pmid, []):
|
302 |
+
relation["arg1_id"] = entity_to_id[relation.pop("arg1")]
|
303 |
+
relation["arg2_id"] = entity_to_id[relation.pop("arg2")]
|
304 |
+
relation.update({"id": str(uid)})
|
305 |
+
relation["normalized"] = []
|
306 |
+
data["relations"].append(relation)
|
307 |
+
uid += 1
|
308 |
+
|
309 |
+
yield id_, data
|
310 |
+
|
311 |
+
@staticmethod
|
312 |
+
def _get_abstract(abs_filename: str) -> Dict[str, str]:
|
313 |
+
"""
|
314 |
+
For each document in PubMed ID (PMID) in the ChemProt abstract data file, return the abstract. Data is tab-separated.
|
315 |
+
|
316 |
+
:param filename: `*_abstracts.tsv from ChemProt
|
317 |
+
|
318 |
+
:returns Dictionary with PMID keys and abstract text as values.
|
319 |
+
"""
|
320 |
+
with open(abs_filename, "r") as f:
|
321 |
+
contents = [i.strip() for i in f.readlines()]
|
322 |
+
|
323 |
+
# PMID is the first column, Abstract is last
|
324 |
+
return {
|
325 |
+
doc.split("\t")[0]: "\n".join(doc.split("\t")[1:]) for doc in contents
|
326 |
+
} # Includes title as line 1
|
327 |
+
|
328 |
+
@staticmethod
|
329 |
+
def _get_entities(ents_filename: str) -> Tuple[Dict[str, str]]:
|
330 |
+
"""
|
331 |
+
For each document in the corpus, return entity annotations per PMID.
|
332 |
+
Each column in the entity file is as follows:
|
333 |
+
(1) PMID
|
334 |
+
(2) Entity Number
|
335 |
+
(3) Entity Type (Chemical, Gene-Y, Gene-N)
|
336 |
+
(4) Start index
|
337 |
+
(5) End index
|
338 |
+
(6) Actual text of entity
|
339 |
+
|
340 |
+
:param ents_filename: `_*entities.tsv` file from ChemProt
|
341 |
+
|
342 |
+
:returns: Dictionary with PMID keys and entity annotations.
|
343 |
+
"""
|
344 |
+
with open(ents_filename, "r") as f:
|
345 |
+
contents = [i.strip() for i in f.readlines()]
|
346 |
+
|
347 |
+
entities = {}
|
348 |
+
entity_id = {}
|
349 |
+
|
350 |
+
for line in contents:
|
351 |
+
|
352 |
+
pmid, idx, label, start_offset, end_offset, name = line.split("\t")
|
353 |
+
|
354 |
+
# Populate entity dictionary
|
355 |
+
if pmid not in entities:
|
356 |
+
entities[pmid] = []
|
357 |
+
|
358 |
+
ann = {
|
359 |
+
"offsets": [int(start_offset), int(end_offset)],
|
360 |
+
"text": name,
|
361 |
+
"type": label,
|
362 |
+
"id": idx,
|
363 |
+
}
|
364 |
+
|
365 |
+
entities[pmid].append(ann)
|
366 |
+
|
367 |
+
# Populate entity mapping
|
368 |
+
entity_id.update({idx: name})
|
369 |
+
|
370 |
+
return entities, entity_id
|
371 |
+
|
372 |
+
@staticmethod
|
373 |
+
def _get_relations(rel_filename: str, is_mapped: bool = False) -> Dict[str, str]:
|
374 |
+
"""For each document in the ChemProt corpus, create an annotation for all relationships.
|
375 |
+
|
376 |
+
:param is_mapped: Whether to convert into NL the relation tags. Default is OFF
|
377 |
+
"""
|
378 |
+
with open(rel_filename, "r") as f:
|
379 |
+
contents = [i.strip() for i in f.readlines()]
|
380 |
+
|
381 |
+
relations = {}
|
382 |
+
|
383 |
+
for line in contents:
|
384 |
+
pmid, label, _, _, arg1, arg2 = line.split("\t")
|
385 |
+
arg1 = arg1.split("Arg1:")[-1]
|
386 |
+
arg2 = arg2.split("Arg2:")[-1]
|
387 |
+
|
388 |
+
if pmid not in relations:
|
389 |
+
relations[pmid] = []
|
390 |
+
|
391 |
+
if is_mapped:
|
392 |
+
label = _GROUP_LABELS[label]
|
393 |
+
|
394 |
+
ann = {
|
395 |
+
"type": label,
|
396 |
+
"arg1": arg1,
|
397 |
+
"arg2": arg2,
|
398 |
+
}
|
399 |
+
|
400 |
+
relations[pmid].append(ann)
|
401 |
+
|
402 |
+
return relations
|
403 |
+
|
404 |
+
@staticmethod
|
405 |
+
def _get_relations_gs(rel_filename: str, is_mapped: bool = False) -> Dict[str, str]:
|
406 |
+
"""
|
407 |
+
For each document in the ChemProt corpus, create an annotation for the gold-standard relationships.
|
408 |
+
|
409 |
+
The columns include:
|
410 |
+
(1) PMID
|
411 |
+
(2) Relationship Label (CPR)
|
412 |
+
(3) Used in shared task
|
413 |
+
(3) Interactor Argument 1 Entity Identifier
|
414 |
+
(4) Interactor Argument 2 Entity Identifier
|
415 |
+
|
416 |
+
Gold standard includes CPRs 3-9. Relationships are always Gene + Protein.
|
417 |
+
Unlike entities, there is no counter, hence once must be made
|
418 |
+
|
419 |
+
:param rel_filename: Gold standard file name
|
420 |
+
:param ent_dict: Entity Identifier to text
|
421 |
+
"""
|
422 |
+
with open(rel_filename, "r") as f:
|
423 |
+
contents = [i.strip() for i in f.readlines()]
|
424 |
+
|
425 |
+
relations = {}
|
426 |
+
|
427 |
+
for line in contents:
|
428 |
+
pmid, label, arg1, arg2 = line.split("\t")
|
429 |
+
arg1 = arg1.split("Arg1:")[-1]
|
430 |
+
arg2 = arg2.split("Arg2:")[-1]
|
431 |
+
|
432 |
+
if pmid not in relations:
|
433 |
+
relations[pmid] = []
|
434 |
+
|
435 |
+
if is_mapped:
|
436 |
+
label = _GROUP_LABELS[label]
|
437 |
+
|
438 |
+
ann = {
|
439 |
+
"type": label,
|
440 |
+
"arg1": arg1,
|
441 |
+
"arg2": arg2,
|
442 |
+
}
|
443 |
+
|
444 |
+
relations[pmid].append(ann)
|
445 |
+
|
446 |
+
return relations
|