summary = query.result.summary()
#query.result.summary()
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)
probs.append((comp_name, state_name, prob))
return (probs[0], probs[1]), summary
if __name__ == '__main__':
fused_bkb = BKB()
fused_bkb.load('/home/public/data/ncats/663Pats6Holdouts/fusion.bkb')
patient_data_file = '/home/public/data/ncats/663Pats6Holdouts/patient_data.pk'
withheld_patients_file = '/home/public/data/ncats/663Pats6Holdouts/withheldPatients.csv'
#fused_bkb.load('/home/public/data/ncats/90PERCENTValidation50Patients10Validation/set1/fusion.bkb')
#patient_data_file = '/home/public/data/ncats/90PERCENTValidation50Patients10Validation/set1/patient_data.pk'
#withheld_patients_file = '/home/public/data/ncats/90PERCENTValidation50Patients10Validation/set1/withheldPatients.csv'
compNames = fused_bkb.getAllComponentNames()
f = open(withheld_patients_file, 'r')
withheldPatientHashes = f.read().split(',')
patientDict = pickle.load(open(patient_data_file, 'rb'))
patientsDynamicEvidence = []
patientIDs = []
count = 0
print(holdoutResults[idx][indexFalse],holdoutResults[idx][indexTrue])
print(probOne, probTwo)
if holdoutResults[idx][indexFalse][2] > holdoutResults[idx][indexTrue][2]:
print("\tfalse -",targetVal, "CORRECT")
numCorrect += 1
else:
print("\tfalse -",targetVal, "WRONG")
numWrong += 1
print("Number correct:", numCorrect)
print("Number wrong:", numWrong)
if __name__ == '__main__':
fused_bkb = BKB()
fused_bkb.load('/home/public/data/ncats/BabelBKBs/collapsedAll/fusion.bkb')
patient_data_file = '/home/public/data/ncats/BabelBKBs/collapsedAll/patient_data.pk'
withheld_patients_file = '/home/public/data/ncats/BabelBKBs/collapsedAll/withheldPatients.csv'
#fused_bkb.load('/home/public/data/ncats/660Pats5PercentHoldout/fusion.bkb')
#patient_data_file = '/home/public/data/ncats/660Pats5PercentHoldout/patient_data.pk'
#withheld_patients_file = '/home/public/data/ncats/660Pats5PercentHoldout/withheldPatients.csv'
compNames = fused_bkb.getAllComponentNames()
f = open(withheld_patients_file, 'r')
withheldPatientHashes = f.read().split(',')
patientDict = pickle.load(open(patient_data_file, 'rb'))
patientsMutationVariantEvidence = []
patientIDs = []
for withheldPatientHash in withheldPatientHashes:
print(probOne, probTwo)
if holdoutResults[idx][indexFalse][2] > holdoutResults[idx][
indexTrue][2]:
print("\tfalse -", targetVal, "CORRECT")
numCorrect += 1
else:
print("\tfalse -", targetVal, "WRONG")
numWrong += 1
print("Number correct:", numCorrect)
print("Number wrong:", numWrong)
if __name__ == '__main__':
fused_bkb = BKB()
fused_bkb.load(
'/home/public/data/ncats/AxleBKBS/660Pats6HoldoutSTAGING/fusion.bkb')
patient_data_file = '/home/public/data/ncats/AxleBKBS/660Pats6HoldoutSTAGING/patient_data.pk'
withheld_patients_file = '/home/public/data/ncats/AxleBKBS/660Pats6HoldoutSTAGING/withheldPatients.csv'
#fused_bkb.load('/home/public/data/ncats/660Pats5PercentHoldout/fusion.bkb')
#patient_data_file = '/home/public/data/ncats/660Pats5PercentHoldout/patient_data.pk'
#withheld_patients_file = '/home/public/data/ncats/660Pats5PercentHoldout/withheldPatients.csv'
compNames = fused_bkb.getAllComponentNames()
f = open(withheld_patients_file, 'r')
withheldPatientHashes = f.read().split(',')
patientDict = pickle.load(open(patient_data_file, 'rb'))
patientsMutationDrugEvidence = []
patientIDs = []
for withheldPatientHash in withheldPatientHashes:
from pybkb import bayesianKnowledgeBase as BKB
from pybkb.core.python_base.reasoning import checkMutex
#!!!!!!!!!! Change to your local bkb-pathway-core directory.
#sys.path.append('/home/ghyde/bkb-pathway-provider/core')
sys.path.append(
'/home/cyakaboski/src/python/projects/bkb-pathway-provider/core')
from reasoner import Reasoner
from query import Query
#-- Initalize a BKB
fused_bkb = BKB()
#-- Load in the fused bkb from our datafiles
fused_bkb.load(
'/home/public/data/ncats/AxleBKBS/AxleDemoBKB3-14-20/fusion.bkb')
#-- Here are the associated patient data files
patient_data_file = '/home/public/data/ncats/AxleBKBS/AxleDemoBKB3-14-20/patient_data.pk'
withheld_patients_file = '/home/public/data/ncats/AxleBKBS/AxleDemoBKB3-14-20/withheldPatients.csv'
gene_var_direct_file = '/home/public/data/ncats/AxleBKBS/AxleDemoBKB3-14-20/geneFreqAnal.csv'
#-- Instaniate reasoner
reasoner = Reasoner(fused_bkb=fused_bkb,
gene_var_direct=gene_var_direct_file,
max_new_ev=2)
#-- Set the patient data file
reasoner.set_src_metadata(patient_data_file)
#reasoner.collapsed_bkb.makeGraph()
print(probOne, probTwo)
if holdoutResults[idx][indexFalse][2] > holdoutResults[idx][
indexTrue][2]:
print("\tfalse -", targetVal, "CORRECT")
numCorrect += 1
else:
print("\tfalse -", targetVal, "WRONG")
numWrong += 1
print("Number correct:", numCorrect)
print("Number wrong:", numWrong)
if __name__ == '__main__':
fused_bkb = BKB()
fused_bkb.load('/home/ghyde/bkb-pathway-provider/core/AXLEBKB/fusion.bkb')
patient_data_file = '/home/ghyde/bkb-pathway-provider/core/AXLEBKB/patient_data.pk'
withheld_patients_file = '/home/ghyde/bkb-pathway-provider/core/AXLEBKB/withheldPatients.csv'
freq_file = '/home/ghyde/bkb-pathway-provider/core/AXLEBKB/geneFreqAnal.csv'
maxNewEv = 20
#fused_bkb.load('/home/public/data/ncats/660Pats5PercentHoldout/fusion.bkb')
#patient_data_file = '/home/public/data/ncats/660Pats5PercentHoldout/patient_data.pk'
#withheld_patients_file = '/home/public/data/ncats/660Pats5PercentHoldout/withheldPatients.csv'
compNames = fused_bkb.getAllComponentNames()
f = open(withheld_patients_file, 'r')
withheldPatientHashes = f.read().split(',')
patientDict = pickle.load(open(patient_data_file, 'rb'))
patientsMutationPathEvidence = []
import os
import sys
from pybkb import bayesianKnowledgeBase as BKB
#-- Change to your local data folder
NCATS_DIR = '/home/cyakaboski/src/python/pojects/bkb-pathway-provider'
sys.path.append(os.path.join(NCATS_DIR, 'core'))
from query import Query
from reasoner import Reasoner
if __name__ == '__main__':
bkb = BKB()
bkb.load('/home/public/data/ncats/fusedPatientBKBSmall/fusion.bkb')
reasoner = Reasoner(bkb)
query0 = Query(evidence={
'_sourceFusion_14_02_2020_16:35:04__mut_CACNA1H=':
'[0]_-7750956075882572850'
},
targets=['mut_CACNA1H=', 'mu-STD>=CACNA1H<=mu+STD='],
type='updating')
result = reasoner.analyze_query(query0)
if __name__ == '__main__':
processor = ReactomePathwayProcessor()
processor.processGenePathways(reactome_g2r_file)
processor.processHierarchyPathways(reactome_p2p_file)
processor.processPathwayBKF()
bkf_files, source_names = processor.BKFsToFile('/tmp/')
bkfs = processor.bkfs
fuse(bkfs,
reliabilities=[random.random() for _ in range(len(bkfs))],
source_names=source_names,
file_prefix='react_only-')
fused_bkb = BKB()
fused_bkb.load('/tmp/react_only-fusion.bkb')
#g = fused_bkb.makeGraph(show=True, layout='neato')
##-- Select Random Evidence
#num_evidence = 1
#evidence = dict()
#for _ in range(num_evidence):
# rand_rv = random.choice(bkb.components)
# while rand_rv.name in evidence:
# rand_rv = random.choice(bkb.components)
# #-- Get a random state
# rand_state = random.choice(rand_rv.states)
# evidence[rand_rv.name] = rand_state.name
#print('Got Evidence')
results[('Updates', comp_name, state_name)] = [prob]
del query
print('Complete Patient {}.'.format(i))
return self.results_to_dataframe(results), title
def results_to_dataframe(self, results):
print(results)
df = pd.DataFrame(data=results)
df.to_csv('crossValid-results-1000.csv')
return df
if __name__ == '__main__':
fused_bkb = BKB()
fused_bkb.load(
'/home/public/data/ncats/90PERCENTValidationAllPatientsWithDrug/set1/fusion.bkb'
)
#print(fused_bkb.getAllComponentNames())
patient_data_file = '/home/public/data/ncats/90PERCENTValidationAllPatientsWithDrug/set1/patient_data.pk'
test_patient_hashes = read_withheldPatientFile(
'/home/public/data/ncats/90PERCENTValidationAllPatientsWithDrug/set1/withheldPatients.csv'
)
#print(test_patient_hashes)
'''
fused_bkb = BKB()
fused_bkb.load('10pat.bkb')
patient_data_file = 'patient_data.pk'
test_patient_hashes = None
#print(test_patient_hashes)
'''
#demo_evidence_static = [('Drug_Name(s)', '==', 'TAMOXIFEN')]
results[('Updates', comp_name, state_name)] = [prob]
del query
print('Complete Patient {}.'.format(i))
return self.results_to_dataframe(results), title
def results_to_dataframe(self, results):
print(results)
df = pd.DataFrame(data=results)
df.to_csv('crossValid-results-1.csv')
return df
if __name__ == '__main__':
fused_bkb = BKB()
fused_bkb.load(
'/home/public/data/ncats/90PERCENTValidation200Patients40Validation/set1/fusion.bkb'
)
#print(fused_bkb.getAllComponentNames())
patient_data_file = '/home/public/data/ncats/90PERCENTValidation200Patients40Validation/set1/patient_data.pk'
test_patient_hashes = read_withheldPatientFile(
'/home/public/data/ncats/90PERCENTValidation200Patients40Validation/set1/withheldPatients.csv'
)
#print(test_patient_hashes)
'''
fused_bkb = BKB()
fused_bkb.load('10pat.bkb')
patient_data_file = 'patient_data.pk'
test_patient_hashes = None
#print(test_patient_hashes)
demo_evidence = ('Age_Of_Diagnosis', '>=')
demo_evidence_val = 20000
import sys
import random
from pybkb import bayesianKnowledgeBase as BKB
#-- Change to your local data folder
NCATS_DIR = '/home/cyakaboski/src/python/projects/bkb-pathway-provider'
sys.path.append(os.path.join(NCATS_DIR, 'core'))
from query import Query
from reasoner import Reasoner
if __name__ == '__main__':
bkb = BKB()
bkb.load('/home/public/data/ncats/fullBKFs/patientBKFs/fusion.bkb')
g = bkb.makeGraph(show=False, layout='neato')
##-- Select Random Evidence
#num_evidence = 1
#evidence = dict()
#for _ in range(num_evidence):
# rand_rv = random.choice(bkb.components)
# while rand_rv.name in evidence:
# rand_rv = random.choice(bkb.components)
# #-- Get a random state
# rand_state = random.choice(rand_rv.states)
# evidence[rand_rv.name] = rand_state.name
#print('Got Evidence')