def test_filter_source():
ev1 = Evidence(source_api='bel')
ev2 = Evidence(source_api='biopax')
ev3 = Evidence(source_api='reach')
st1 = Activation(Agent('a'), Agent('b'), evidence=[ev3])
st2 = Activation(Agent('a'), Agent('b'), evidence=[ev1, ev2])
st3 = Activation(Agent('a'), Agent('b'), evidence=[ev1, ev3])
st_out = ac.filter_evidence_source([st1, st2], ['reach'], 'one')
assert len(st_out) == 1
st_out = ac.filter_evidence_source([st1, st2, st3], ['reach'], 'all')
assert (len(st_out) == 2)
st_out = ac.filter_evidence_source([st1, st2, st3], ['bel', 'biopax'],
'one')
assert (len(st_out) == 2)
st_out = ac.filter_evidence_source([st1, st2, st3], ['bel', 'biopax'],
'all')
assert (len(st_out) == 1)
st_out = ac.filter_evidence_source([st1, st2, st3], ['bel', 'biopax'],
'none')
assert (len(st_out) == 1)
def test_filter_source():
ev1 = Evidence(source_api='bel')
ev2 = Evidence(source_api='biopax')
ev3 = Evidence(source_api='reach')
st1 = Activation(Agent('a'), Agent('b'), evidence=[ev3])
st2 = Activation(Agent('a'), Agent('b'), evidence=[ev1, ev2])
st3 = Activation(Agent('a'), Agent('b'), evidence=[ev1, ev3])
st_out = ac.filter_evidence_source([st1, st2], ['reach'], 'one')
assert (len(st_out) == 1)
st_out = ac.filter_evidence_source([st1, st2, st3], ['reach'], 'all')
assert (len(st_out) == 2)
st_out = ac.filter_evidence_source([st1, st2, st3], ['bel', 'biopax'],
'one')
assert (len(st_out) == 2)
st_out = ac.filter_evidence_source([st1, st2, st3], ['bel', 'biopax'],
'all')
assert (len(st_out) == 1)
st_out = ac.filter_evidence_source([st1, st2, st3], ['bel', 'biopax'],
'none')
assert (len(st_out) == 1)
logger.info('Loading original model.')
inc_model_file = os.path.join(model_path, model_name, 'model.pkl')
model = IncrementalModel(inc_model_file)
# Include search genes as prior genes
model.prior_genes = search_genes
stats = {}
logger.info(time.strftime('%c'))
logger.info('Preassembling original model.')
model.preassemble(filters=global_filters)
logger.info(time.strftime('%c'))
# Original statistics
stats['orig_stmts'] = len(model.get_statements())
stats['orig_assembled'] = len(model.assembled_stmts)
db_stmts = ac.filter_evidence_source(model.assembled_stmts,
['biopax', 'bel'],
policy='one')
no_db_stmts = ac.filter_evidence_source(model.assembled_stmts,
['biopax', 'bel'],
policy='none')
no_db_stmts = ac.filter_belief(no_db_stmts, belief_threshold)
orig_stmts = db_stmts + no_db_stmts
stats['orig_final'] = len(orig_stmts)
logger.info('%d final statements' % len(orig_stmts))
# Extend the model with PMIDs
logger.info('----------------')
logger.info(time.strftime('%c'))
logger.info('Extending model.')
stats['new_papers'], stats['new_abstracts'], stats['existing'] = \
extend_model(model_name, model, pmids)