def findMinMax(self, model, objectiveName, minValue, targets):
positive = {}
negative = {}
imodel = LinearModel()
imodel.extend(model)
if minValue < 0:
imodel.addColumnLimit(objectiveName,(None,minValue))
else:
imodel.addColumnLimit(objectiveName,(minValue,None))
lp = LinearOptimization()
lp.setModel(model)
for t in targets:
lp.clearObjective()
lp.setObjectiveMap({t: -1})
lp.runSimplex()
fluxes = lp.getPredictionMap()
lowValue = fluxes[t]
negative[t] = lowValue
lp.clearObjective()
lp.setObjectiveMap({t: 1})
lp.runSimplex()
fluxes = lp.getPredictionMap()
highValue = fluxes[t]
positive[t] = highValue
return (positive,negative)
def findBoundaryProduction(originalModel, bounds, combiLimits, objectiveName, productionName):
model = LinearModel()
model.extend(originalModel)
if combiLimits != None:
for (targetValues,limit) in combiLimits:
model = addCombinationLimit(model, targetValues,limit)
#objectiveMap = {objectiveName:-1.0}
objectiveMap = {productionName:1.0}
model.addColumnLimits(bounds)
lp = LPSolver()
fluxMap = lp.run(model,objectiveMap)
objectiveValue = fluxMap[objectiveName]
productionValue = fluxMap[productionName]
return (fluxMap,objectiveValue,productionValue)
def LinearModelVariableBoundarys(originalModel, objectiveName, targets=None, pickleFileName=None, strict=False, minObjectivePercent=None,searchSize=1):
'''
Find min and max boundaries for the dependent variables of a linear model.
Uses limitMap as constraints on model.
Add cumulative boundary persistence as pickle file function.
@param linearModel: linear model to be analyzed
@type linearModel: LinearModel
@param limitMap: map of variable names and lower and upper limits
@type limitMap: {variableName, (float, float)
@param targets: list of variables to perform analysis on
@type targets: list
@return: dictionary of lower and upper limits for each variable
@rtype: {variableName: (lowerlimit, upperlimit)}
'''
linearModel = LinearModel()
linearModel.extend(originalModel)
if pickleFileName != None:
if os.path.isfile(pickleFileName):
print "loading saved flux boundaries"
pFile = open(pickleFileName)
result = pickle.load(pFile)
pFile.close()
return result
originalLimits = linearModel.getColumnLimits()
originalObjectiveLimit = originalLimits[objectiveName]
objectiveVector = {objectiveName:-1.0}
solver = LPSolver()
originalValues = solver.run(model=linearModel, objective=objectiveVector)
originalObjectiveValue = originalValues[objectiveName]
if minObjectivePercent != None:
minObjectiveValue = originalObjectiveValue * minObjectivePercent
linearModel.addColumnLimit(objectiveName,(minObjectiveValue,None))
if targets == None:
targets = linearModel.getColumnNames()
targets.add(objectiveName)
print "Searching flux boundaries for %s > %s" % (objectiveName, minObjectiveValue)
result = {}
targetValues = targetValueCombinations(targets, [-1.0,1.0])
controlSubSets = combinations(targetValues,searchSize)
xcontrolSubSets = set(controlSubSets)
for name in targets:
nameTag = name
# if name not in linearModel.getColumnNames():
# print "target %s not found in model"
# print "boundary discovery not possible"
# continue
#for iTargetValues in xcontrolSubSets:
# name = "testControlValue"
# nameTag = iTargetValues
# linearModel = combinationLimitBuilder(linearModel, iTargetValues, name)
result[name] = (None,None)
negObjective = {name:1.0}
posObjective = {name:-1.0}
solver.clearObjective()
negValues = solver.run(linearModel, objective=negObjective)
negValue = negValues[name]
solver.clearObjective()
posValues = solver.run(linearModel, objective=posObjective)
posValue = posValues[name]
delta = 1e-4
if posValue != 0 or negValue !=0:
pass
if False:
originalValue = originalValues[name]
originalLimit = originalLimits[name]
if not (negValue - delta <= originalValue <= posValue + delta):
print "original value not in boundary"
print "objective value [%s] (%s) %s <= %s <= %s (%s)" % (name, originalLimit[0], negValue, originalValue, posValue, originalLimit[1])
#print "value neg code %s pos code %s" % (nscode, pscode)
(negValue,posValue) = (originalLimit[0],originalLimit[1])
else:
#print "objective value [%s] (%s) %s <= %s <= %s (%s)" % (name, originalLimit[0], negValue, originalValue, posValue, originalLimit[1])
pass
if negValue == None or negValue == float("-inf"):
continue
if posValue == None or posValue == float("-inf"):
continue
result[nameTag] = (negValue,posValue)
print "%s < [%s] < %s " % (negValue,nameTag,posValue)
if pickleFileName != None:
pFile = open(pickleFileName,'w')
pickle.dump(result, pFile)
pFile.close()
solver.clear()
del solver
linearModel.addColumnLimit(objectiveName,originalObjectiveLimit)
#targets.remove(objectiveName)
return result
