def get_owlsim_api():
global owlsim_api
if owlsim_api is None:
owlsim_api = OwlSim2Api()
return owlsim_api
def setup_class(self):
patch('ontobio.sim.api.owlsim2.get_owlsim_stats',
return_value=(None, None)).start()
self.ic_store = OwlSim2Api()
self.annot_scorer = AnnotationScorer(self.ic_store)
self.ic_store.statistics = IcStatistic(mean_mean_ic=6.82480,
mean_sum_ic=120.89767,
mean_cls=15.47425,
max_max_ic=16.16108,
max_sum_ic=6746.96160,
individual_count=65309,
mean_max_ic=9.51535)
self.ic_store.category_statistics = {
'ear feature':
IcStatistic(
mean_mean_ic=7.55126,
mean_sum_ic=26.60304,
mean_cls=3.40207,
max_max_ic=16.15508,
max_sum_ic=613.22168,
individual_count=6004,
mean_max_ic=8.48152,
descendants=['pointy ears', 'large ears', 'small ears']),
'skin feature':
IcStatistic(mean_mean_ic=7.55126,
mean_sum_ic=26.60304,
mean_cls=3.40207,
max_max_ic=16.15508,
max_sum_ic=613.22168,
individual_count=6004,
mean_max_ic=8.48152,
descendants=[
'blue skin', 'orange skin',
'increased pigmentation'
]),
}
self.mock_ic_values = {
'pointy ears': 12.0021,
'large ears': 8.2345,
'small ears': 8.1536,
'blue skin': 10.12593,
'orange skin': 15.1592,
'increased pigmentation': 5.5926
}
self.negation_weight = .23
self.category_weight = .33
def setup_class(self):
self.resolve_mock = patch.object(PhenoSimEngine,
'_resolve_nodes_to_phenotypes',
side_effect=mock_resolve_nodes)
self.mock_scigraph = patch(
'ontobio.util.scigraph_util.get_scigraph_nodes',
side_effect=mock_get_scigraph_nodes)
self.owlsim2_api = OwlSim2Api()
self.owlsim2_api.statistics = IcStatistic(mean_mean_ic=6.82480,
mean_sum_ic=120.89767,
mean_cls=15.47425,
max_max_ic=16.16108,
max_sum_ic=6746.96160,
individual_count=65309,
mean_max_ic=9.51535)
self.pheno_sim = PhenoSimEngine(self.owlsim2_api)
self.resolve_mock.start()
self.mock_scigraph.start()
from pathlib import Path
import json
import pytest
from dacite import from_dict, Config
from ontobio.sim.mme import clean_feature_ids, query_owlsim
from ontobio.model.mme.request import MmeRequest, Observed
from ontobio.sim.api.owlsim2 import OwlSim2Api
from ontobio.sim.phenosim_engine import PhenoSimEngine
patient_1 = Path(__file__).parent / 'resources' / 'mme' / 'patient1.json'
owlsim_api = PhenoSimEngine(OwlSim2Api())
@pytest.mark.parametrize("id, clean_id",
[("MIM:1234", "OMIM:1234"), ("SHH", "HGNC:10848"),
("some-term-we-dont-have", "some-term-we-dont-have")]
)
def test_clean_feature_ids(id, clean_id):
test_id = clean_feature_ids(id)
assert clean_id == test_id
def test_mme_query():
with open(patient_1, 'r') as patient_1_json:
patient1 = json.load(patient_1_json)
mme_request = from_dict(MmeRequest,
patient1,
config=Config(cast=[Observed]))
response = query_owlsim(mme_request, owlsim_api, taxon='9606')
from flask_restplus import Resource
from flask import request
from ontobio.sim.annotation_scorer import AnnotationScorer
from ontobio.sim.api.owlsim2 import OwlSim2Api
from biolink.api.restplus import api
from biolink.datamodel.sim_serializers import sufficiency_input, sufficiency_output
annotation_scorer = AnnotationScorer(OwlSim2Api())
score_get = api.parser()
score_get.add_argument('id',
action='append',
help='Phenotype identifier (eg HP:0004935)')
score_get.add_argument('absent_id',
default=[],
action='append',
help='absent phenotype (eg HP:0002828)')
class AnnotationScore(Resource):
@api.expect(sufficiency_input)
@api.marshal_with(sufficiency_output)
def post(self):
"""
Get annotation score
"""
data = request.json
phenotypes = [
feature['id'] for feature in data['features']
if feature['isPresent'] is True
]
from flask_restplus import Resource
from flask import request
from ontobio.sim.api.owlsim2 import OwlSim2Api
from ontobio.sim.phenosim_engine import PhenoSimEngine
from biolink.api.restplus import api
from biolink.datamodel.sim_serializers import sim_result, compare_input
sim_engine = PhenoSimEngine(OwlSim2Api())
def get_compare_parser():
help_msg = 'A phenotype or identifier that is composed of phenotypes (eg disease, gene)'
sim_get_parser = api.parser()
sim_get_parser.add_argument('ref_id',
action='append',
help=help_msg,
default=[])
sim_get_parser.add_argument('query_id',
action='append',
help=help_msg,
default=[])
return sim_get_parser
def get_search_parser():
sim_search_parser = api.parser()
sim_search_parser.add_argument(
'id',
from flask_restplus import Resource, inputs
from flask import request
from ontobio.sim.api.owlsim2 import OwlSim2Api
from ontobio.sim.phenosim_engine import PhenoSimEngine
from ontobio.vocabulary.similarity import SimAlgorithm
from biolink.api.restplus import api
from biolink.api.sim.endpoints.owlsim import get_owlsim_api
from biolink.datamodel.sim_serializers import sim_result, compare_input
metrics = [matcher.value for matcher in OwlSim2Api.matchers()]
# Common args
sim_parser = api.parser()
sim_parser.add_argument(
'is_feature_set',
type=inputs.boolean,
help='set to true if *all* input ids are phenotypic features, else set to false',
default=True
)
sim_parser.add_argument(
'metric', type=str, choices=metrics,
default='phenodigm', help='Metric for computing similarity')
def get_compare_parser():
help_msg = 'A phenotype or identifier that is composed of phenotypes (eg disease, gene)'
sim_get_parser = sim_parser.copy()
sim_get_parser.add_argument(
def setup_class(self):
patch('ontobio.sim.api.owlsim2.get_owlsim_stats',
return_value=(None, None)).start()
self.sim_api = OwlSim2Api()
def setup_class(self):
self.sim_api = OwlSim2Api()
def setup_class(self):
self.owlsim2_api = OwlSim2Api()
self.annot_scorer = AnnotationScorer(self.owlsim2_api)
self.pheno_sim = PhenoSimEngine(self.owlsim2_api)
from flask_restplus import Resource, inputs
from flask import request
from ontobio.sim.api.owlsim2 import OwlSim2Api
from ontobio.sim.phenosim_engine import PhenoSimEngine
from ontobio.vocabulary.similarity import SimAlgorithm
from biolink.api.restplus import api
from biolink.datamodel.sim_serializers import sim_result, compare_input
sim_engine = PhenoSimEngine(OwlSim2Api())
metrics = [matcher.value for matcher in OwlSim2Api.matchers()]
# Common args
sim_parser = api.parser()
sim_parser.add_argument(
'is_feature_set',
type=inputs.boolean,
help=
'set to true if *all* input ids are phenotypic features, else set to false',
default=True)
sim_parser.add_argument('metric',
type=str,
choices=metrics,
default='phenodigm',
help='Metric for computing similarity')
def get_compare_parser():
help_msg = 'A phenotype or identifier that is composed of phenotypes (eg disease, gene)'