def test_candidate_generation(self):
umls_fixture = UmlsKnowledgeBase(
"tests/fixtures/umls_test_fixture.json")
with tempfile.TemporaryDirectory() as dir_name:
umls_concept_aliases, tfidf_vectorizer, ann_index = create_tfidf_ann_index(
dir_name, umls_fixture)
candidate_generator = CandidateGenerator(ann_index, tfidf_vectorizer,
umls_concept_aliases,
umls_fixture)
results = candidate_generator(['(131)I-Macroaggregated Albumin'], 10)
canonical_ids = [x.concept_id for x in results[0]]
assert canonical_ids == ['C0000005', 'C0000102', 'C0000084']
# The mention was an exact match, so should have a distance of zero to a concept:
assert results[0][0] == MentionCandidate(
concept_id='C0000005',
aliases=['(131)I-Macroaggregated Albumin'],
similarities=[1.0])
# Test we don't crash with zero vectors
results = candidate_generator(['ZZZZ'], 10)
assert results == [[]]
def test_empty_list(self):
umls_fixture = UmlsKnowledgeBase("tests/fixtures/umls_test_fixture.json")
with tempfile.TemporaryDirectory() as dir_name:
umls_concept_aliases, tfidf_vectorizer, ann_index = create_tfidf_ann_index(dir_name, umls_fixture)
candidate_generator = CandidateGenerator(ann_index, tfidf_vectorizer, umls_concept_aliases, umls_fixture)
results = candidate_generator([], 10)
assert results == []
def setUp(self):
super().setUp()
self.nlp = spacy.load("en_core_web_sm")
umls_fixture = UmlsKnowledgeBase("tests/fixtures/umls_test_fixture.json", "tests/fixtures/test_umls_tree.tsv")
with tempfile.TemporaryDirectory() as dir_name:
umls_concept_aliases, tfidf_vectorizer, ann_index = create_tfidf_ann_index(dir_name, umls_fixture)
candidate_generator = CandidateGenerator(ann_index, tfidf_vectorizer, umls_concept_aliases, umls_fixture)
self.linker = UmlsEntityLinker(candidate_generator, filter_for_definitions=False)
def test_create_index(self):
umls_fixture = UmlsKnowledgeBase("tests/fixtures/umls_test_fixture.json")
with tempfile.TemporaryDirectory() as dir_name:
umls_concept_aliases, tfidf_vectorizer, ann_index = create_tfidf_ann_index(dir_name, umls_fixture)
assert len(umls_concept_aliases) == 93
assert len(ann_index) == 93 # Number of deduplicated aliases + canonical ids
tfidf_params = tfidf_vectorizer.get_params()
assert tfidf_params["analyzer"] == "char_wb"
assert tfidf_params["min_df"] == 10
assert tfidf_params["ngram_range"] == (3, 3)
def __init__(self,
ann_index: FloatIndex = None,
tfidf_vectorizer: TfidfVectorizer = None,
ann_concept_aliases_list: List[str] = None,
umls: UmlsKnowledgeBase = None,
verbose: bool = False,
ef_search: int = 200) -> None:
self.ann_index = ann_index or load_approximate_nearest_neighbours_index(ef_search=ef_search)
self.vectorizer = tfidf_vectorizer or joblib.load(cached_path(DEFAULT_PATHS["tfidf_vectorizer"]))
self.ann_concept_aliases_list = ann_concept_aliases_list or \
json.load(open(cached_path(DEFAULT_PATHS["concept_aliases_list"])))
self.umls = umls or UmlsKnowledgeBase()
self.verbose = verbose
def init_umls_nlp_linker():
base_dir = ''
tfidf_path = base_dir + 'tfidf_vectors_sparse.npz'
ann_path = base_dir + 'nmslib_index.bin'
ann_index = load_approximate_nearest_neighbours_index(
tfidf_vectors_path=tfidf_path, ann_index_path=ann_path)
vec = joblib.load(cached_path(base_dir + 'tfidf_vectorizer.joblib'))
ann_concept = json.load(
open(cached_path(base_dir + 'concept_aliases.json')))
umlsknowlegebase = UmlsKnowledgeBase(
file_path=base_dir + 'umls_2017_aa_cat0129.json',
types_file_path=base_dir + 'umls_semantic_type_tree.tsv')
cg = CandidateGenerator(ann_index=ann_index,
tfidf_vectorizer=vec,
ann_concept_aliases_list=ann_concept,
umls=umlsknowlegebase)
linker = UmlsEntityLinker(candidate_generator=cg,
max_entities_per_mention=1)
nlp.add_pipe(linker)
return linker
def create_tfidf_ann_index(
out_path: str,
umls: UmlsKnowledgeBase = None
) -> Tuple[List[str], TfidfVectorizer, FloatIndex]:
"""
Build tfidf vectorizer and ann index.
Warning: Running this function on the whole of UMLS requires ~ 200GB of RAM ...
TODO: Make this not take 200GB of RAM.
Parameters
----------
out_path: str, required.
The path where the various model pieces will be saved.
umls : UmlsKnowledgeBase, optional.
The umls kb items to generate the index and vectors for.
"""
tfidf_vectorizer_path = f'{out_path}/tfidf_vectorizer.joblib'
ann_index_path = f'{out_path}/nmslib_index.bin'
tfidf_vectors_path = f'{out_path}/tfidf_vectors_sparse.npz'
uml_concept_aliases_path = f'{out_path}/concept_aliases.json'
umls = umls or UmlsKnowledgeBase()
# nmslib hyperparameters (very important)
# guide: https://github.com/nmslib/nmslib/blob/master/python_bindings/parameters.md
# Default values resulted in very low recall.
# set to the maximum recommended value. Improves recall at the expense of longer indexing time.
# TODO: This variable name is so hot because I don't actually know what this parameter does.
m_parameter = 100
# `C` for Construction. Set to the maximum recommended value
# Improves recall at the expense of longer indexing time
construction = 2000
num_threads = 60 # set based on the machine
index_params = {
'M': m_parameter,
'indexThreadQty': num_threads,
'efConstruction': construction,
'post': 0
}
print(
f'No tfidf vectorizer on {tfidf_vectorizer_path} or ann index on {ann_index_path}'
)
umls_concept_aliases = list(umls.alias_to_cuis.keys())
umls_concept_aliases = numpy.array(umls_concept_aliases)
# NOTE: here we are creating the tf-idf vectorizer with float32 type, but we can serialize the
# resulting vectors using float16, meaning they take up half the memory on disk. Unfortunately
# we can't use the float16 format to actually run the vectorizer, because of this bug in sparse
# matrix representations in scipy: https://github.com/scipy/scipy/issues/7408
print(f'Fitting tfidf vectorizer on {len(umls_concept_aliases)} aliases')
tfidf_vectorizer = TfidfVectorizer(analyzer='char_wb',
ngram_range=(3, 3),
min_df=10,
dtype=numpy.float32)
start_time = datetime.datetime.now()
uml_concept_alias_tfidfs = tfidf_vectorizer.fit_transform(
umls_concept_aliases)
print(f'Saving tfidf vectorizer to {tfidf_vectorizer_path}')
joblib.dump(tfidf_vectorizer, tfidf_vectorizer_path)
end_time = datetime.datetime.now()
total_time = (end_time - start_time)
print(
f'Fitting and saving vectorizer took {total_time.total_seconds()} seconds'
)
print(f'Finding empty (all zeros) tfidf vectors')
empty_tfidfs_boolean_flags = numpy.array(
uml_concept_alias_tfidfs.sum(axis=1) != 0).reshape(-1, )
deleted_aliases = umls_concept_aliases[empty_tfidfs_boolean_flags == False] # pylint: disable=singleton-comparison
number_of_non_empty_tfidfs = len(deleted_aliases)
total_number_of_tfidfs = uml_concept_alias_tfidfs.shape[0]
print(
f'Deleting {number_of_non_empty_tfidfs}/{total_number_of_tfidfs} aliases because their tfidf is empty'
)
# remove empty tfidf vectors, otherwise nmslib will crash
umls_concept_aliases = umls_concept_aliases[
empty_tfidfs_boolean_flags].tolist()
uml_concept_alias_tfidfs = uml_concept_alias_tfidfs[
empty_tfidfs_boolean_flags]
print(deleted_aliases)
print(
f'Saving list of concept ids and tfidfs vectors to {uml_concept_aliases_path} and {tfidf_vectors_path}'
)
json.dump(umls_concept_aliases, open(uml_concept_aliases_path, "w"))
scipy.sparse.save_npz(tfidf_vectors_path,
uml_concept_alias_tfidfs.astype(numpy.float16))
print(
f'Fitting ann index on {len(umls_concept_aliases)} aliases (takes 2 hours)'
)
start_time = datetime.datetime.now()
ann_index = nmslib.init(method='hnsw',
space='cosinesimil_sparse',
data_type=nmslib.DataType.SPARSE_VECTOR)
ann_index.addDataPointBatch(uml_concept_alias_tfidfs)
ann_index.createIndex(index_params, print_progress=True)
ann_index.saveIndex(ann_index_path)
end_time = datetime.datetime.now()
elapsed_time = end_time - start_time
print(f'Fitting ann index took {elapsed_time.total_seconds()} seconds')
return umls_concept_aliases, tfidf_vectorizer, ann_index
sci_nlp = spacy.load('en_core_sci_md')
# sci_nlp = spacy.load('en_core_sci_lg')
st21pv_set = set([
'T005', 'T007', 'T017', 'T022', 'T031', 'T033', 'T037', 'T038', 'T058',
'T062', 'T074', 'T082', 'T091', 'T092', 'T097', 'T098', 'T103', 'T168',
'T170', 'T201', 'T204'
])
def cui2ent(cui):
try:
return umls_kb.cui_to_entity[cui]
except KeyError:
return None
@lru_cache(maxsize=None)
def cui2st(cui):
ent = cui2ent(cui)
if ent is None:
return None
else:
return max(ent.types,
key=lambda x: umls_tree.get_node_from_id(x).level)
umls_kb = UmlsKnowledgeBase()
all_cuis = set(umls_kb.cui_to_entity.keys())
