def findPathwayList():
pathways = []
codes = []
# looks for pathways that have gpickles generated originally
for file in os.listdir("gpickles"):
if file.endswith(".gpickle"):
if os.path.isfile('pickles/' + file[:-8] + '_1_local1.pickle'):
codes.append(file[:-8])
else:
print(file[:-8] + ' has no output')
print(codes)
# for each of these pathways, we find the output of the rule determination and scoring procedures and put them together.
for code in codes:
pathVals = []
rules = []
for i in range(1, 6):
[bruteOut1, dev, storeModel, storeModel3, equivalents,
dev2] = pickle.Unpickler(
open('pickles/' + code + '_' + str(i) + '_local1.pickle',
"rb")).load()
model = modelHolder(storeModel3)
pathVals.append(
pickle.Unpickler(
open('pickles/' + code + '_' + str(i) + '_scores1.pickle',
"rb")).load())
rules.append(writeModel(bruteOut1, model))
graph = nx.read_gpickle("gpickles/" + code + ".gpickle")
ImportanceVals = {}
for node in range(len(storeModel[1])):
# ImportanceVals[storeModel[1][node]]=math.log(np.mean([pathVals[i][node] for i in range(5)]),2)
ImportanceVals[storeModel[1][node]] = float(
np.mean(
[math.log(1. + pathVals[i][node], 2) for i in range(5)]))
# add nodes removed during network simplification back in
removedNodes = pickle.Unpickler(
open('pickles/' + code + '_addLaterNodes.pickle', "rb")).load()
doubleRemoveNodes = []
for node in removedNodes:
if node[1] in ImportanceVals:
ImportanceVals[node[0]] = ImportanceVals[node[1]]
else:
doubleRemoveNodes.append(node)
count = 0
while len(doubleRemoveNodes) > 0 and count < 1000:
count = count + 1
tripleRemoveNodes = []
for node in doubleRemoveNodes:
if node[1] in ImportanceVals:
ImportanceVals[node[0]] = ImportanceVals[node[1]]
else:
tripleRemoveNodes.append(node)
doubleRemoveNodes = tripleRemoveNodes
pathways.append([code, ImportanceVals, rules, graph])
if count > 1000:
print('failed to add removed nodes back in:' +
str(doubleRemoveNodes))
return pathways
def findPathwayList():
pathways = []
codes = []
# looks for pathways that have gpickles generated originally
for file in os.listdir("gpickles"):
if file.endswith(".gpickle"):
if os.path.isfile('pickles/' + file[:-8] + '_1_local1.pickle'):
codes.append(file[:-8])
else:
print(file[:-8] + ' has no output')
print(codes)
# for each of these pathways, we find the output of the rule determination and scoring procedures and put them together.
for code in codes:
pathVals = []
rules = []
for i in range(1, 6):
[bruteOut1, dev, storeModel, storeModel3, equivalents,
dev2] = pickle.Unpickler(
open('pickles/' + code + '_' + str(i) + '_local1.pickle',
"rb")).load()
model = modelHolder(storeModel3)
pathVals.append(
pickle.Unpickler(
open('pickles/' + code + '_' + str(i) + '_scores1.pickle',
"rb")).load())
rules.append(writeModel(bruteOut1, model))
print(pathVals)
graph = nx.read_gpickle("gpickles/" + code + ".gpickle")
ImportanceVals = {} # average importance vals over trials
for node in range(len(storeModel[1])):
ImportanceVals[storeModel[1][node]] = float(
np.mean([pathVals[i][node] for i in range(5)]))
# add nodes removed during network simplification back in
print(ImportanceVals)
pathways.append([code, ImportanceVals, rules, graph])
return pathways
# set up parameters of run, model
params=paramClass()
model=modelClass(graph,sampleList, False)
model.updateCpointers()
storeModel=[(model.size), list(model.nodeList), list(model.individualParse), list(model.andNodeList) , list(model.andNodeInvertList), list(model.andLenList), list(model.nodeList), dict(model.nodeDict), list(model.initValueList)]
# put lack of KOs, initial values into correct format
knockoutLists, knockinLists= setupEmptyKOKI(len(sampleList))
newInitValueList=genInitValueList(sampleList,model)
model.initValueList=newInitValueList
# find rules by doing GA then local search
model1, dev, bruteOut =GAsearchModel(model, sampleList, params, knockoutLists, knockinLists, name, boolC) # run GA
bruteOut1, equivalents, dev2 = localSearch(model1, bruteOut, sampleList, params, knockoutLists, knockinLists, boolC) # run local search
# output results
storeModel3=[(model.size), list(model.nodeList), list(model.individualParse), list(model.andNodeList) , list(model.andNodeInvertList), list(model.andLenList), list(model.nodeList), dict(model.nodeDict), list(model.initValueList)]
outputList=[bruteOut1,dev,storeModel, storeModel3, equivalents, dev2]
pickle.dump( outputList, open( name+"_local1.pickle", "wb" ) ) # output rules
# calculate importance scores and output
scores1=calcImportance(bruteOut1,params,model1, sampleList,knockoutLists, knockinLists, boolC)
pickle.dump( scores1, open( name+"_scores1.pickle", "wb" ) )
# write rules
with open(name+"_rules.txt", "w") as text_file:
text_file.write(writeModel(bruteOut1, model1))
print("--- %s seconds ---" % (time.time() - start_time))
'RSV_miic_out', boolC) # run GA
bruteOut1, equivalents, dev2 = localSearch(model1, bruteOut, sampleList,
params, knockoutLists,
knockinLists,
boolC) # run local search
storeModel3 = [(model.size),
list(model.nodeList),
list(model.individualParse),
list(model.andNodeList),
list(model.andNodeInvertList),
list(model.andLenList),
list(model.nodeList),
dict(model.nodeDict),
list(model.initValueList)]
outputList = [bruteOut1, dev, storeModel3, equivalents, dev2]
pickle.dump(outputList, open('RSV_miic_out' + "_local1.pickle",
"wb")) # output rules
# calculate importance scores and output
scores1 = calcImportance(bruteOut1, params, model, sampleList,
knockoutLists, knockinLists, boolC)
pickle.dump(scores1, open('RSV_miic_out' + "_scores1.pickle", "wb"))
ImportanceVals = {}
for node in range(len(storeModel3[1])):
ImportanceVals[storeModel3[1][node]] = float(scores1[node])
# write out rules
modeler = writeModel(bruteOut1, model1)
rules = modeler.replace('not', '~').split('\n')
outputGraphMiic(graph, modeler, ImportanceVals)
print("--- %s seconds ---" % (time.time() - start_time))