def objectiveFunction(proposalAllocation, totalBudget, costCoverageInfo,
optimise, mothers, timestep, agingRateList, agePopSizes,
keyList, data):
import helper as helper
from numpy import array
helper = helper.Helper()
model, constants, params = helper.setupModelConstantsParameters(
'optimisation model', mothers, timestep, agingRateList, agePopSizes,
keyList, data)
if sum(proposalAllocation) == 0:
scaledproposalAllocation = proposalAllocation
else:
scaledproposalAllocation = rescaleAllocation(totalBudget,
proposalAllocation)
# calculate coverage (%)
newCoverages = {}
for i in range(0, len(data.interventionList)):
intervention = data.interventionList[i]
newCoverages[intervention] = costCov.function(
array([scaledproposalAllocation[i]
]), costCoverageInfo[intervention],
targetPopSize[intervention]) / targetPopSize[intervention]
model.updateCoverages(newCoverages)
for t in range(numsteps):
model.moveOneTimeStep()
if optimise == 'deaths':
performanceMeasure = model.getTotalCumulativeDeaths()
if optimise == 'stunting':
performanceMeasure = model.getCumulativeAgingOutStunted()
return performanceMeasure
def objectiveFunction(proposalAllocation, totalBudget, costCoverageInfo, optimise, numModelSteps, dataSpreadsheetName, data):
import helper
import costcov
helper = helper.Helper()
costCov = costcov.Costcov()
model, derived, params = helper.setupModelConstantsParameters(data)
if sum(proposalAllocation) == 0:
scaledproposalAllocation = proposalAllocation
else:
scaledproposalAllocation = rescaleAllocation(totalBudget, proposalAllocation)
# run the model
timestepsPre = 12
for t in range(timestepsPre):
model.moveOneTimeStep()
# update coverages after 1 year
targetPopSize = getTargetPopSizeFromModelInstance(dataSpreadsheetName, helper.keyList, model)
newCoverages = {}
for i in range(0, len(data.interventionList)):
intervention = data.interventionList[i]
newCoverages[intervention] = costCov.function(scaledproposalAllocation[i], costCoverageInfo[intervention], targetPopSize[intervention]) / targetPopSize[intervention]
model.updateCoverages(newCoverages)
for t in range(numModelSteps - timestepsPre):
model.moveOneTimeStep()
if optimise == 'deaths':
performanceMeasure = model.getTotalCumulativeDeaths()
if optimise == 'stunting':
performanceMeasure = model.getCumulativeAgingOutStunted()
return performanceMeasure
def objectiveFunction(proposalAllocation, totalBudget, costCoverageInfo,
optimise, numModelSteps, dataSpreadsheetName, data):
import helper
import costcov
helper = helper.Helper()
costCov = costcov.Costcov()
model, derived, params = helper.setupModelConstantsParameters(data)
if sum(proposalAllocation) == 0:
scaledproposalAllocation = proposalAllocation
else:
scaledproposalAllocation = rescaleAllocation(totalBudget,
proposalAllocation)
# run the model
timestepsPre = 12
for t in range(timestepsPre):
model.moveOneTimeStep()
# update coverages after 1 year
targetPopSize = getTargetPopSizeFromModelInstance(dataSpreadsheetName,
helper.keyList, model)
newCoverages = {}
for i in range(0, len(data.interventionList)):
intervention = data.interventionList[i]
newCoverages[intervention] = costCov.function(
scaledproposalAllocation[i], costCoverageInfo[intervention],
targetPopSize[intervention]) / targetPopSize[intervention]
model.updateCoverages(newCoverages)
for t in range(numModelSteps - timestepsPre):
model.moveOneTimeStep()
if optimise == 'deaths':
performanceMeasure = model.getTotalCumulativeDeaths()
if optimise == 'stunting':
performanceMeasure = model.getCumulativeAgingOutStunted()
return performanceMeasure
def objectiveFunction(proposalAllocation, totalBudget, costCoverageInfo, optimise, mothers, timestep, agingRateList, agePopSizes, keyList, data):
import helper as helper
from numpy import array
helper = helper.Helper()
model, constants, params = helper.setupModelConstantsParameters('optimisation model', mothers, timestep, agingRateList, agePopSizes, keyList, data)
if sum(proposalAllocation) == 0:
scaledproposalAllocation = proposalAllocation
else:
scaledproposalAllocation = rescaleAllocation(totalBudget, proposalAllocation)
# calculate coverage (%)
newCoverages = {}
for i in range(0, len(data.interventionList)):
intervention = data.interventionList[i]
newCoverages[intervention] = costCov.function(array([scaledproposalAllocation[i]]), costCoverageInfo[intervention], targetPopSize[intervention]) / targetPopSize[intervention]
model.updateCoverages(newCoverages)
for t in range(numsteps):
model.moveOneTimeStep()
if optimise == 'deaths':
performanceMeasure = model.getTotalCumulativeDeaths()
if optimise == 'stunting':
performanceMeasure = model.getCumulativeAgingOutStunted()
return performanceMeasure
costCovParams[intervention] = {}
costCovParams[intervention]['unitcost'] = dcp(
inputData.costSaturation[intervention]["unit cost"])
costCovParams[intervention]['saturation'] = dcp(
inputData.costSaturation[intervention]["saturation coverage"])
plotData = []
run = 0
#------------------------------------------------------------------------
# DEFAULT RUN WITH NO CHANGES TO INTERVENTIONS
nametag = "Baseline"
plotcolor = 'grey'
print "\n" + nametag
model, derived, params = helper.setupModelConstantsParameters(inputData)
modelList = []
# Run model
for t in range(numsteps):
model.moveOneTimeStep()
modelList.append(dcp(model))
plotData.append({})
plotData[run]["modelList"] = modelList
plotData[run]["tag"] = nametag
plotData[run]["color"] = plotcolor
run += 1
#------------------------------------------------------------------------
# INTERVENTION
timespan = helper.keyList['timestep'] * float(numsteps)
print "Baseline coverage of: "
for intervention in inputData.interventionList:
print "%s = %g"%(intervention,inputData.coverage[intervention])
plotData = []
run = 0
#------------------------------------------------------------------------
# DEFAULT RUN WITH NO CHANGES TO INTERVENTIONS
nametag = "Baseline"
pickleFilename = '%s_Default_Investment.pkl'%(country)
plotcolor = 'grey'
print "\n"+nametag
model, derived, params = helper.setupModelConstantsParameters(inputData)
# file to dump objects into at each time step
outfile = open(pickleFilename, 'wb')
# Run model
for t in range(numsteps):
model.moveOneTimeStep()
pickle.dump(model, outfile)
outfile.close()
# collect output, make graphs etc.
infile = open(pickleFilename, 'rb')
modelList = []
while 1:
try: