def run_demo_only(self,
demo_target,
demo_evidence_static=list(),
demo_evidence_dynamic=list(),
demo_evidence_val=0):
#-- Set Up Reasoner
reasoner = Reasoner(self.bkb, None)
reasoner.set_src_metadata(self.patient_data_file)
reasoner.cpp_reasoning = False
#-- Initialize Result dict
result = {'X': demo_evidence_val, 'Compute_time': -1}
#-- Setup demographic evidence tuple
if len(demo_evidence_dynamic) > 0:
demo_evidence_dynamic_run = [
tuple(list(demo_evidence_dynamic) + [demo_evidence_val])
]
demo_evidence_run = demo_evidence_static + demo_evidence_dynamic_run
#-- Make query and analyze
query = Query(meta_evidence=demo_evidence_run,
meta_targets=[demo_target],
type='updating')
query = reasoner.analyze_query(copy.deepcopy(query), save_dir=None)
result['Compute_time'] = query.compute_time
#-- Collect Updates and put into results
for update, prob in query.result.updates.items():
comp_idx, state_idx = update
comp_name = query.bkb.getComponentName(comp_idx)
state_name = query.bkb.getComponentINodeName(comp_idx, state_idx)
result[('Updates', comp_name, state_name)] = prob
return result
def run_patients(self,
demo_target,
gene_evidence=False,
demo_evidence=None,
demo_value=0):
reasoner = Reasoner(self.bkb, None)
reasoner.set_src_metadata(self.patient_data_file)
reasoner.cpp_reasoning = False
#-- Comment this out for real thing
#self.test_patient_hashes = [list(reasoner.metadata.keys())[0]]
target_actual = '{} {} {} Actual'.format(demo_target[0],
demo_target[1],
demo_target[2])
results = {
'X':
[demo_value for _ in range(len(self.test_patient_hashes[:10]))],
'Patient': list(),
'Compute_time': list(),
'Length_evid': list(),
target_actual: list()
}
if gene_evidence is False and demo_evidence is None:
raise ValueError('Gene and demo evidence can not be None.')
if demo_evidence is not None:
title = 'Evidence = {} {} X'.format(demo_evidence[0],
demo_evidence[1])
demo_evidence_run = [tuple(list(demo_evidence) + [demo_value])]
for patient_hash in tqdm.tqdm(self.test_patient_hashes[:10]):
results['Patient'].append(patient_hash)
#-- Calculate Truth
prop_targ, op_targ, val_targ = demo_target
op_ = _process_operator(op_targ)
results[target_actual].append(
op_(reasoner.metadata[patient_hash][prop_targ], val_targ))
if gene_evidence:
gene_evidence_patient = {
gene: 'True'
for gene in patient_data[patient_hash]['Patient_Genes']
}
title += ' w/ GeneEvidence'
else:
title += ' w/o GeneEvidence'
gene_evidence_patient = dict()
#-- Make query
query = Query(evidence=gene_evidence_patient,
meta_evidence=demo_evidence_run,
meta_targets=[demo_target],
type='updating')
query = reasoner.analyze_query(copy.deepcopy(query),
save_dir=None)
results['Length_evid'].append(len(query.evidence))
results['Compute_time'].append(query.compute_time)
for update, prob in query.result.updates.items():
comp_idx, state_idx = update
comp_name = query.bkb.getComponentName(comp_idx)
state_name = query.bkb.getComponentINodeName(
comp_idx, state_idx)
try:
results[('Updates', comp_name,
state_name)].append(prob)
except:
results[('Updates', comp_name, state_name)] = [prob]
else:
title = 'No Demographic Evidence w/ Gene Evidence'
for i, patient_hash in enumerate(self.test_patient_hashes[:10]):
results['Patient'].append(patient_hash)
#-- Calculate Truth
prop_targ, op_targ, val_targ = demo_target
op_ = _process_operator(op_targ)
#-- Calculate Truth
results[target_actual].append(
op_(reasoner.metadata[patient_hash][prop_targ], val_targ))
#-- Take intersection between all bkb genes and patient genes
print('Intersecting genes')
genes_patient = set([
'mut_{}='.format(gene) for gene in
reasoner.metadata[patient_hash]['Patient_Genes']
])
bkb_comps = set(self.bkb.getAllComponentNames())
gene_intersect = genes_patient.intersection(bkb_comps)
gene_evidence_patient = {
gene: 'True'
for gene in gene_intersect
}
#print(set(gene_evidence_patient.keys()) - set(self.bkb.getAllComponentNames()))
#-- Make query
#print(gene_evidence_patient)
results['Length_evid'].append(len(gene_evidence_patient))
query = Query(evidence=gene_evidence_patient,
meta_targets=[demo_target],
type='updating',
name='Pat50-10_600_{}'.format(i))
print('Analyzing Query.')
query = reasoner.analyze_query(copy.deepcopy(query),
save_dir=None)
query.getReport()
results['Compute_time'].append(query.compute_time)
for update, prob in query.result.updates.items():
comp_idx, state_idx = update
comp_name = query.bkb.getComponentName(comp_idx)
state_name = query.bkb.getComponentINodeName(
comp_idx, state_idx)
try:
results[('Updates', comp_name,
state_name)].append(prob)
except:
results[('Updates', comp_name, state_name)] = [prob]
del query
print('Complete Patient {}.'.format(i))
return self.results_to_dataframe(results), title
for patient in PATIENTS
}
patient_data_hash = dict()
for patient, dict_ in patient_data.items():
patient_data_hash[hash(patient)] = dict_
print(patient_data)
#-- Denote Demographic evidence.
demo_ev = [('Age', '>=', 50), ('Gender', '==', 'Male')]
demo_tar = [('Survival', '>=', 2)]
reasoner1 = Reasoner(fused_bkb, None)
reasoner1.metadata = patient_data_hash
reasoner1.cpp_reasoning = True
reasoner2 = Reasoner(fused_bkb_wStruct, None)
reasoner2.metadata = patient_data_hash
reasoner2.cpp_reasoning = True
query0 = Query(
evidence={'Gene{}_mutated'.format(i): 'True'
for i in range(NUM_GENES)},
targets=list(),
meta_evidence=demo_ev,
meta_targets=demo_tar,
type='updating')
query01 = reasoner1.analyze_query(copy.deepcopy(query0))
query02 = reasoner2.analyze_query(copy.deepcopy(query0))
