def generateResultsForGeospatialCascades(self):
import optimisation
for region in range(0, self.numRegions):
regionName = self.regionNameList[region]
spreadsheet = self.regionSpreadsheetList[region]
thisOptimisation = optimisation.Optimisation(
spreadsheet, self.numModelSteps)
filename = self.resultsFileStem + regionName + '_cascade_' + self.optimise + '_'
thisOptimisation.performCascadeOptimisation(
self.optimise, self.MCSampleSize, filename, self.cascadeValues)
def performGeospatialOptimisation(self, geoMCSampleSize, MCSampleSize,
filenameStem):
import optimisation
print 'beginning geospatial optimisation..'
optimisedRegionalBudgetList = self.getOptimisedRegionalBudgetList(
geoMCSampleSize)
print 'finished geospatial optimisation'
for region in range(0, self.numRegions):
regionName = self.regionNameList[region]
print 'optimising for individual region ', regionName
filename = filenameStem + '_' + regionName
thisSpreadsheet = self.regionSpreadsheetList[region]
thisOptimisation = optimisation.Optimisation(
thisSpreadsheet, self.numModelSteps)
thisBudget = optimisedRegionalBudgetList[region]
thisOptimisation.performSingleOptimisationForGivenTotalBudget(
self.optimise, MCSampleSize, filename, thisBudget)
def getTotalNationalBudget(self):
import optimisation
import data
regionalBudgets = []
for region in range(0, self.numRegions):
thisSpreadsheet = self.regionSpreadsheetList[region]
thisOptimisation = optimisation.Optimisation(
thisSpreadsheet, self.numModelSteps)
spreadsheetData = data.readSpreadsheet(
thisSpreadsheet, thisOptimisation.helper.keyList)
costCoverageInfo = thisOptimisation.getCostCoverageInfo()
initialTargetPopSize = thisOptimisation.getInitialTargetPopSize()
initialAllocation = getTotalInitialAllocation(
spreadsheetData, costCoverageInfo, initialTargetPopSize)
regionTotalBudget = sum(initialAllocation)
regionalBudgets.append(regionTotalBudget)
nationalTotalBudget = sum(regionalBudgets)
return nationalTotalBudget
def generateAllRegionsBOC(self):
print 'reading files to generate regional BOCs..'
import optimisation
regionalBOCs = {}
regionalBOCs['spending'] = []
regionalBOCs['outcome'] = []
for region in range(0, self.numRegions):
print 'generating BOC for region: ', self.regionNameList[region]
thisSpreadsheet = self.regionSpreadsheetList[region]
thisOptimisation = optimisation.Optimisation(
thisSpreadsheet, self.numModelSteps)
filename = self.resultsFileStem + self.regionNameList[region]
spending, outcome = thisOptimisation.generateBOCVectors(
filename, self.regionNameList, self.cascadeValues,
self.optimise)
regionalBOCs['spending'].append(spending)
regionalBOCs['outcome'].append(outcome)
print 'finished generating regional BOCs from files'
self.regionalBOCs = regionalBOCs
# read the optimal budget allocations from file
filename = '../Results2016Jul/Bangladesh/deaths/v1/Bangladesh_cascade_deaths_v1_1.0.pkl'
infile = open(filename, 'rb')
deathsOptimumAllocation = pickle.load(infile)
infile.close()
filename = '../Results2016Jul/Bangladesh/stunting/v1/Bangladesh_cascade_stunting_v1_1.0.pkl'
infile = open(filename, 'rb')
stuntingOptimumAllocation = pickle.load(infile)
infile.close()
numModelSteps = 180
# NATIONAL
dataSpreadsheetName = '../input_spreadsheets/Bangladesh/InputForCode_Bangladesh.xlsx'
thisOptimisation = optimisation.Optimisation(dataSpreadsheetName,
numModelSteps)
initialAllocation = thisOptimisation.getInitialAllocationDictionary()
# run models and save output
baseline = thisOptimisation.oneModelRunWithOutput(initialAllocation)
optimiseDeaths = thisOptimisation.oneModelRunWithOutput(
deathsOptimumAllocation)
optimiseStunting = thisOptimisation.oneModelRunWithOutput(
stuntingOptimumAllocation)
# GET Y AXIS FOR NUMBER OF DEATHS
numberOfDeaths_baseline = []
numberOfDeaths_optimiseDeaths = []
numberOfDeaths_optimiseStunting = []
numberOfDeaths_baseline.append(baseline[0].getTotalCumulativeDeaths())
numberOfDeaths_optimiseDeaths.append(
# -*- coding: utf-8 -*-
"""
Created on Fri Jun 24 14:28:54 2016
@author: ruth
"""
import os, sys
moduleDir = os.path.join(os.path.dirname(__file__), '..')
sys.path.append(moduleDir)
import optimisation
numModelSteps = 180
MCSampleSize = 25
optimise = 'stunting'
spreadsheet0 = '../input_spreadsheets/Bangladesh/subregionSpreadsheets/Barisal.xlsx'
spreadsheet1 = '../input_spreadsheets/Bangladesh/subregionSpreadsheets/Chittagong.xlsx'
spreadsheet2 = '../input_spreadsheets/Bangladesh/subregionSpreadsheets/Dhaka.xlsx'
spreadsheet3 = '../input_spreadsheets/Bangladesh/subregionSpreadsheets/Khulna.xlsx'
spreadsheet4 = '../input_spreadsheets/Bangladesh/subregionSpreadsheets/Rajshahi.xlsx'
spreadsheet5 = '../input_spreadsheets/Bangladesh/subregionSpreadsheets/Rangpur.xlsx'
spreadsheet6 = '../input_spreadsheets/Bangladesh/subregionSpreadsheets/Sylhet.xlsx'
spreadsheetList = [spreadsheet0, spreadsheet1, spreadsheet2, spreadsheet3, spreadsheet4, spreadsheet5, spreadsheet6]
for i in range(0, len(spreadsheetList)):
spreadsheet = spreadsheetList[i]
thisOptimisation = optimisation.Optimisation(spreadsheet, numModelSteps)
filename = 'Bangladesh_geospatial_stunting_region_'+str(i)
thisOptimisation.performSingleOptimisation(optimise, MCSampleSize, filename)
from plotting import plotallocations
import optimisation
country = 'Bangladesh'
version = 'Results20160718'
root = '../../%s/%s' % (country, version)
print "\n find input here : %s" % root
# NATIONAL
nationalAllocations = {}
# current baseline
numsteps = 180
spreadsheetPath = '../input_spreadsheets/%s/InputForCode_%s.xlsx' % (country,
country)
thisOptimisation = optimisation.Optimisation(spreadsheetPath, numsteps)
nationalAllocations[
'baseline'] = thisOptimisation.getInitialAllocationDictionary()
# optimised
for objective in ['deaths', 'stunting']:
filename = '%s/%s/national/%s_cascade_%s_1.0.pkl' % (root, objective,
country, objective)
infile = open(filename, 'rb')
allocation = pickle.load(infile)
nationalAllocations[objective] = allocation
infile.close()
# plot
plotallocations(nationalAllocations['baseline'],
nationalAllocations[objective])
# GEOSPATIAL
