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/660Pats6Holdout/fusion.bkb')
patient_data_file = '/home/public/data/ncats/660Pats6Holdout/patient_data.pk'
withheld_patients_file = '/home/public/data/ncats/660Pats6Holdout/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 = []
import sys
import os
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)
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/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(',')
AGE_RANGE_YRS = (20, 90)
SURIVAL_YRS = (0, 10)
patient_data = {
patient: {
'Gender': random.choice(GENDER_OPTIONS),
'Age': random.randrange(AGE_RANGE_YRS[0], AGE_RANGE_YRS[1]),
'Survival': random.randrange(SURIVAL_YRS[0], SURIVAL_YRS[1])
}
for patient in PATIENTS
}
bkfs = list()
#-- Make BKB frags
for patient in PATIENTS:
bkf = BKB()
for gene in GENES:
if random.choice([True, False]):
#-- Setup Gene i component.
comp_idx = bkf.addComponent('mut_{}'.format(gene))
stateTrue_idx = bkf.addComponentState(comp_idx, 'True')
bkf.addSNode(
BKB_S_node(init_component_index=comp_idx,
init_state_index=stateTrue_idx,
init_probability=1))
variant_comp_idx = bkf.addComponent('mut-var_{}'.format(gene))
random_variant = random.choice(GENE_VARIANTS)
variant_state_idx = bkf.addComponentState(variant_comp_idx,
random_variant)
priors[gene] = float(count / len(PATIENTS))
#--Pairs Calculate Correlations
counts = dict()
for gene1 in GENES:
for gene2 in GENES:
count = 0
for patient in PATIENTS:
if patient_data[patient]['Genes'][gene1] and patient_data[patient][
'Genes'][gene2]:
count += 1
counts[(gene1, gene2)] = float(count / len(PATIENTS)) / priors[gene2]
#-- Make BKB Patient frags
for patient in PATIENTS:
bkf = BKB()
for gene in patient_data[patient]['Genes']:
#-- Setup Gene i component.
geneComp_idx = bkf.addComponent(gene)
geneStateTrue_idx = bkf.addComponentState(geneComp_idx, 'True')
s_node_prior = BKB_S_node(init_component_index=geneComp_idx,
init_state_index=geneStateTrue_idx,
init_probability=1)
bkf.addSNode(s_node_prior)
patient_bkfs.append(bkf)
#-- Define Sample Pathway
PATHWAYS = ['Pathway{}'.format(i) for i in range(1)]
def processPathwayBKF(self, exhaustiveOr=False):
assert len(self.pathways) > 0, "Have not processed pathways yet"
for pathway in tqdm.tqdm(self.pathways,
desc='Processing pathway BKFs'):
bkf = BKB(name=pathway.pathwayID)
pathwayActiveComp_idx = bkf.addComponent('{}_active='.format(
pathway.pathwayID))
pathwayActiveTrue_idx = bkf.addComponentState(
pathwayActiveComp_idx, 'True')
if exhaustiveOr:
print("not implemented")
else:
geneSelectorComp_idx = bkf.addComponent("Gene_combo=")
for gene in pathway.genes: #gene[0] = geneName, gene[1] = low value gene[2] = high value
geneCombo_idx = bkf.addComponentState(
geneSelectorComp_idx, gene[0])
statConditionComp_idx = bkf.addComponent(
'mu-STD<={}<=mu+STD='.format(gene[0]))
statConditionTrue_idx = bkf.addComponentState(
statConditionComp_idx, 'True')
bkf.addSNode(
BKB_S_node(statConditionComp_idx,
statConditionTrue_idx, 1.0))
bkf.addSNode(
BKB_S_node(
geneSelectorComp_idx, geneCombo_idx, 1.0,
[(statConditionComp_idx, statConditionTrue_idx)]))
bkf.addSNode(
BKB_S_node(pathwayActiveComp_idx,
pathwayActiveTrue_idx, 1.0,
[(geneSelectorComp_idx, geneCombo_idx)]))
self.bkfs.append(bkf)
assert len(self.pathways) > 0, "Have not processed pathways yet"
from pybkb.core.python_base.fusion import fuse
sys.path.append(
'/home/cyakaboski/src/python/projects/bkb-pathway-provider/core')
from reasoner import Reasoner, _constructSNodesByHead
from query import Query
PATIENTS = ['Patient_{}'.format(i) for i in range(10)]
NUM_GENES = 5
bkfs = list()
#-- Make BKB frags
for j, _ in enumerate(PATIENTS):
bkf = BKB()
for i in range(NUM_GENES):
#-- Setup Gene i component.
comp_idx = bkf.addComponent('Gene{}_mutated'.format(i))
stateTrue_idx = bkf.addComponentState(comp_idx, 'True')
if random.choice([True, False]):
bkf.addSNode(
BKB_S_node(init_component_index=comp_idx,
init_state_index=stateTrue_idx,
init_probability=1))
bkfs.append(bkf)
#-- Fuse patients together.
fused_bkb = fuse(bkfs, [1 for _ in range(len(PATIENTS))],
def read_expression_snodes(self, expression_levels_dict, tcga_expression_file, react_gene_file, pathways_file):
#-- Process all gene_reaction s_nodes and assume there is a header with gene names.
with open(react_gene_file, 'r') as csv_file:
reader = csv.reader(csv_file)
rows = [row for row in reader]
#-- Process Gene Names from header:
print(rows[0])
genes = rows[0][1:]
genes = [genes[i][:-4] for i in range(0,len(genes),2)]
print(genes)
for row in rows[1:]:
bkf = BKB(name=row[0] + ' BKF')
reaction_head_name = row[0]
react_component = BKB_component(reaction_head_name)
i_node_true = BKB_I_node('True', react_component)
i_node_false = BKB_I_node('False', react_component)
react_component.addINode(i_node_true)
react_component.addINode(i_node_false)
bkf.addComponent(react_component)
#-- Extract and Make I-nodes
for gene_j, row_i in enumerate(range(1,len(row), 2)):
gene_name = genes[gene_j]
gene_low = row[row_i]
gene_high = row[row_i + 1]
if gene_low != '':
print(gene_low, gene_high)
num_levels = expression_levels_dict[gene_name]
levels = [rag for rag in np.linspace(int(gene_low), int(gene_high), num_levels)]
component_states = ['{} | {}'.format(levels[i], levels[i+1]) for i in range(len(levels)-1)]
component = BKB_component(gene_name + ' ExpressionLv')
for state in component_states:
i_node = BKB_I_node(state, component)
component.addINode(i_node)
bkf.addComponent(component)
for idx in range(bkf.findComponent(component.name).getNumberStates()):
i_node = component.getState(idx)
bkf.addSNode(BKB_S_node(component, i_node, random.random()))
bkf.addSNode(BKB_S_node(react_component, i_node_true, random.random(), [(component, i_node)]))
bkf.addSNode(BKB_S_node(react_component, i_node_false, random.random(), [(component, i_node)]))
self.bkfs.append(bkf)
print('Done')
#-- Process Pathway BKFs
with open(pathways_file, 'r') as csv_file:
reader = csv.reader(csv_file)
bkf = BKB()
for row in reader:
tail = row[0]
head = row[1]
states = ['True', 'False']
if bkf.findComponent(tail) == -1:
component_tail = BKB_component(tail)
for state in states:
component_tail.addINode(BKB_I_node(state, component_tail))
bkf.addComponent(component_tail)
else:
component_tail = bkf.findComponent(tail)
if bkf.findComponent(head) == -1:
component_head = BKB_component(head)
for state in states:
component_head.addINode(BKB_I_node(state, component_head))
bkf.addComponent(component_head)
else:
component_head = bkf.findComponent(head)
for state1 in states:
for state2 in states:
bkf.addSNode(BKB_S_node(component_head,
component_head.findState(state1),
random.random(),
[(component_tail, component_tail.findState(state2))])
)
self.bkfs.append(bkf)
print('Complete')
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
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)
def processPatientBKF(self):
assert len(
self.patients) > 0, "Have not processed Patient and gene data yet."
#print("Forming Patient BKFs...")
allGenes = list()
for pat in tqdm.tqdm(self.patients, desc='Forming patient BKFs'):
bkf = BKB(name=pat.patientID)
variantCompIndices = list()
variantCompNames = list()
variantCompStateIndices = list()
for idx, gene in enumerate(pat.mutatedGenes):
allGenes.append(gene)
# gene
mutGeneComp_idx = bkf.addComponent('mut_{}='.format(gene))
iNodeGeneMut_idx = bkf.addComponentState(
mutGeneComp_idx, 'True')
# form SNode o---->[mut_<genename>=True]
bkf.addSNode(BKB_S_node(mutGeneComp_idx, iNodeGeneMut_idx,
1.0))
if 'var_' + pat.variants[idx] + '=' in variantCompNames:
cIdx = variantCompNames.index('var_{}='.format(
pat.variants[idx]))
#mut_var combo
mutVarComp_idx = bkf.addComponent('mut-var_{}='.format(
pat.mutatedGeneVariants[idx]))
iNodeMutVar_idx = bkf.addComponentState(
mutVarComp_idx, 'True')
#mut_var s-node
bkf.addSNode(
BKB_S_node(mutVarComp_idx, iNodeMutVar_idx, 1.0,
[(mutGeneComp_idx, iNodeGeneMut_idx),
(variantCompIndices[cIdx],
variantCompStateIndices[cIdx])]))
else:
#var
variantComp_idx = bkf.addComponent('var_{}='.format(
pat.variants[idx]))
iNodeVariant_idx = bkf.addComponentState(
variantComp_idx, 'True')
#var s-node
bkf.addSNode(
BKB_S_node(variantComp_idx, iNodeVariant_idx, 1.0))
variantCompIndices.append(variantComp_idx)
variantCompStateIndices.append(iNodeGeneMut_idx)
variantCompNames.append(
bkf.getComponentName(variantComp_idx))
#mut_var combo
mutVarComp_idx = bkf.addComponent('mut-var_{}='.format(
pat.mutatedGeneVariants[idx]))
iNodeMutVar_idx = bkf.addComponentState(
mutVarComp_idx, 'True')
#mut_var s-node
bkf.addSNode(
BKB_S_node(mutVarComp_idx, iNodeMutVar_idx, 1.0,
[(mutGeneComp_idx, iNodeGeneMut_idx),
(variantComp_idx, iNodeVariant_idx)]))
self.bkfs.append(bkf)
def processPathwayBKF(self, exhaustiveOr=False):
assert len(self.pathways) > 0, "Have not processed pathways yet"
for pathway in tqdm.tqdm(self.pathways,
desc='Processing pathway BKFs'):
bkf = BKB(name=pathway.ID)
if "_hier" in pathway.ID:
#head
pathwayParentComp_idx = bkf.addComponent('{}_active='.format(
pathway.head))
pathwayParentTrue_idx = bkf.addComponentState(
pathwayParentComp_idx, 'True')
#tail - should only be 1 for _hier bkfs
pathwayChildComp_idx = bkf.addComponent('{}_active='.format(
pathway.tails[0]))
pathwayChildTrue_idx = bkf.addComponentState(
pathwayChildComp_idx, 'True')
#S-node
bkf.addSNode(
BKB_S_node(pathwayChildComp_idx, pathwayChildTrue_idx,
1.0))
bkf.addSNode(
BKB_S_node(pathwayParentComp_idx, pathwayParentTrue_idx,
1.0,
[(pathwayChildComp_idx, pathwayChildTrue_idx)]))
else:
'''
#head
pathwayHierComp_idx = bkf.addComponent('{}_active='.format(pathway.head))
pathwayHierTrue_idx = bkf.addComponentState(pathwayHierComp_idx, 'True')
#tails
for tail in pathway.tails:
statConditionComp_idx = bkf.addComponent('mu-STD<={}<=mu+STD'.format(tail))
statConditionTrue_idx = bkf.addComponentState(statConditionComp_idx, 'True')
bkf.addSNode(BKB_S_node(statConditionComp_idx, statConditionTrue_idx, 1.0))
bkf.addSNode(BKB_S_node(pathwayHierComp_idx, pathwayHierTrue_idx, 1.0, [(statConditionComp_idx, statConditionTrue_idx)]))
self.bkfs.append(bkf)
'''
#=======
if exhaustiveOr:
print("not implemented")
else:
# pathway
pathwayReactionComp_idx = bkf.addComponent(pathway.head +
"_active=")
pathwayReactionTrue_idx = bkf.addComponentState(
pathwayReactionComp_idx, 'True')
# gene slector
geneSelectorComp_idx = bkf.addComponent("Gene_combo=")
#tails
for tail in pathway.tails:
geneCombo_idx = bkf.addComponentState(
geneSelectorComp_idx, tail)
statConditionComp = BKB_component("mu-STD>=" + tail +
"<=mu+STD=")
statConditionTrue = BKB_I_node('True',
statConditionComp)
statConditionComp_idx = bkf.addComponent("mu-STD>=" +
tail +
"<=mu+STD=")
statConditionTrue_idx = bkf.addComponentState(
statConditionComp_idx, 'True')
bkf.addSNode(
BKB_S_node(statConditionComp_idx,
statConditionTrue_idx, 1.0))
bkf.addSNode(
BKB_S_node(geneSelectorComp_idx, geneCombo_idx,
1.0, [(statConditionComp_idx,
statConditionTrue_idx)]))
bkf.addSNode(
BKB_S_node(
pathwayReactionComp_idx,
pathwayReactionTrue_idx, 1.0,
[(geneSelectorComp_idx, geneCombo_idx)]))
self.bkfs.append(bkf)
import sys
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/660Pats6HoldoutSTAGING/fusion.bkb')
#-- Here are the associated patient data files
patient_data_file = '/home/public/data/ncats/AxleBKBS/660Pats6HoldoutSTAGING/patient_data.pk'
withheld_patients_file = '/home/public/data/ncats/AxleBKBS/660Pats6HoldoutSTAGING/withheldPatients.csv'
#-- Instaniate reasoner
reasoner = Reasoner(fused_bkb=fused_bkb)
#-- Set the patient data file
reasoner.set_src_metadata(patient_data_file)
#reasoner.collapsed_bkb.makeGraph()
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
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)
import os
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
else:
print(patID)
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 = []
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(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/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'))
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)
query = self.reasoner.analyze_query(
copy.deepcopy(query), preprocessed_bkb=self.processed_bkb)
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 = []
import sys
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/BabelBKBs/collapsedAll/fusion.bkb')
#-- Here are the associated patient data files
patient_data_file = '/home/public/data/ncats/BabelBKBs/collapsedAll/patient_data.pk'
withheld_patients_file = '/home/public/data/ncats/BabelBKBS/collapsedAll/withheldPatients.csv'
#-- Instaniate reasoner
reasoner = Reasoner(fused_bkb=fused_bkb)
#-- Set the patient data file
reasoner.set_src_metadata(patient_data_file)
#reasoner.collapsed_bkb.makeGraph()
#-- If you want to see what genetic or demographic evidence is avaliable, uncomment the line below
#print(reasoner.metadata_ranges)