def saveStats(args):
i, theta, startDate, endDate, recordStep = args
resultsFileName = outputDir + "SimStats" + str(i) + ".pkl"
try:
with open(resultsFileName) as f: pass
except IOError as e:
featureInds= numpy.ones(targetGraph.vlist.getNumFeatures(), numpy.bool)
featureInds[HIVVertices.dobIndex] = False
featureInds[HIVVertices.infectionTimeIndex] = False
featureInds[HIVVertices.hiddenDegreeIndex] = False
featureInds[HIVVertices.stateIndex] = False
featureInds = numpy.arange(featureInds.shape[0])[featureInds]
matcher = GraphMatch("PATH", alpha=0.5, featureInds=featureInds, useWeightM=False)
graphMetrics = HIVGraphMetrics2(targetGraph, 1.0, matcher, float(endDate))
times, infectedIndices, removedIndices, graph = HIVModelUtils.simulate(thetaArray[i], startDate, endDate, recordStep, M, graphMetrics)
times, vertexArray, removedGraphStats = HIVModelUtils.generateStatistics(graph, startDate, endDate, recordStep)
stats = times, vertexArray, removedGraphStats, graphMetrics.dists, graphMetrics.graphDists, graphMetrics.labelDists
Util.savePickle(stats, resultsFileName)
def profileSimulate(self):
startDate, endDate, recordStep, printStep, M, targetGraph = HIVModelUtils.realSimulationParams()
meanTheta, sigmaTheta = HIVModelUtils.estimatedRealTheta()
meanTheta = numpy.array([337, 1.4319, 0.211, 0.0048, 0.0032, 0.5229, 0.042, 0.0281, 0.0076, 0.0293])
undirected = True
graph = HIVGraph(M, undirected)
logging.info("Created graph: " + str(graph))
alpha = 2
zeroVal = 0.9
p = Util.powerLawProbs(alpha, zeroVal)
hiddenDegSeq = Util.randomChoice(p, graph.getNumVertices())
rates = HIVRates(graph, hiddenDegSeq)
model = HIVEpidemicModel(graph, rates)
model.setT0(startDate)
model.setT(startDate+100)
model.setRecordStep(recordStep)
model.setPrintStep(printStep)
model.setParams(meanTheta)
logging.debug("MeanTheta=" + str(meanTheta))
ProfileUtils.profile('model.simulate()', globals(), locals())
def findDerivative(args):
pertScale, startDate, endDate, recordStep, M, targetGraph, seed = args
numpy.random.seed(seed)
meanTheta, sigmaTheta = HIVModelUtils.toyTheta()
epsilon = 5.0
undirected = True
alpha = 2
zeroVal = 0.9
p = Util.powerLawProbs(alpha, zeroVal)
graph = HIVGraph(M, undirected)
featureInds= numpy.ones(graph.vlist.getNumFeatures(), numpy.bool)
featureInds[HIVVertices.dobIndex] = False
featureInds[HIVVertices.infectionTimeIndex] = False
featureInds[HIVVertices.hiddenDegreeIndex] = False
featureInds[HIVVertices.stateIndex] = False
featureInds = numpy.arange(featureInds.shape[0])[featureInds]
matcher = GraphMatch("PATH", alpha=0.5, featureInds=featureInds, useWeightM=False)
abcParams = HIVABCParameters(meanTheta, sigmaTheta, pertScale)
newTheta = abcParams.perturbationKernel(meanTheta)
undirected = True
graph = HIVGraph(M, undirected)
graphMetrics = HIVGraphMetrics2(targetGraph, epsilon, matcher, float(endDate))
graphMetrics.breakDist = 1.0
hiddenDegSeq = Util.randomChoice(p, graph.getNumVertices())
rates = HIVRates(graph, hiddenDegSeq)
model = HIVEpidemicModel(graph, rates, T=float(endDate), T0=float(startDate), metrics=graphMetrics)
model.setRecordStep(recordStep)
model.setParams(meanTheta)
times, infectedIndices, removedIndices, graph = model.simulate(True)
return abs(0.7 - graphMetrics.distance())/numpy.linalg.norm(newTheta-meanTheta)
from apgl.predictors.ABCSMC import ABCSMC
import logging
import sys
import numpy
import multiprocessing
assert False, "Must run with -O flag"
FORMAT = "%(levelname)s:root:%(process)d:%(message)s"
logging.basicConfig(stream=sys.stdout, level=logging.DEBUG, format=FORMAT)
numpy.set_printoptions(suppress=True, precision=4, linewidth=150)
numpy.seterr(invalid='raise')
resultsDir = PathDefaults.getOutputDir() + "viroscopy/toy/"
startDate, endDate, recordStep, M, targetGraph = HIVModelUtils.toySimulationParams()
epsilonArray = numpy.ones(10)*0.4
logging.debug("Total time of simulation is " + str(endDate-startDate))
posteriorSampleSize = 30
breakDist = 0.5
logging.debug("Posterior sample size " + str(posteriorSampleSize))
def createModel(t):
"""
The parameter t is the particle index.
"""
undirected = True
graph = HIVGraph(M, undirected)
alpha = 2
from exp.viroscopy.model.HIVModelUtils import HIVModelUtils
import matplotlib.pyplot as plt
"""
This is the epidemic model for the HIV spread in cuba. We try to get more bisexual
contacts
"""
assert False, "Must run with -O flag"
logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
numpy.seterr(all='raise')
numpy.random.seed(24)
numpy.set_printoptions(suppress=True, precision=4, linewidth=100)
startDate, endDate, recordStep, M, targetGraph = HIVModelUtils.realSimulationParams()
endDate = startDate + 10000
meanTheta, sigmaTheta = HIVModelUtils.estimatedRealTheta()
meanTheta = numpy.array([ 50, 0.5131, 0.3242, 0.1, 0.0001, 0.0, 325, 0.34, 0.001, 0.1, 0.1, 0.1])
outputDir = PathDefaults.getOutputDir() + "viroscopy/"
undirected = True
graph = HIVGraph(M, undirected)
logging.info("Created graph: " + str(graph))
alpha = 2
zeroVal = 0.9
p = Util.powerLawProbs(alpha, zeroVal)
hiddenDegSeq = Util.randomChoice(p, graph.getNumVertices())
rates = HIVRates(graph, hiddenDegSeq)
from exp.viroscopy.model.HIVEpidemicModel import HIVEpidemicModel
from exp.viroscopy.model.HIVRates import HIVRates
from exp.viroscopy.model.HIVModelUtils import HIVModelUtils
"""
This is the epidemic model for the HIV spread in cuba. We repeat the simulation a number
of times and average the results. The purpose is to test the ABC model selection
by using a known value of theta.
"""
logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
numpy.seterr(all='raise')
numpy.random.seed(24)
numpy.set_printoptions(suppress=True, precision=4, linewidth=100)
startDate, endDate, recordStep, M = HIVModelUtils.toySimulationParams(False)
endDate += HIVModelUtils.toyTestPeriod
numRepetitions = 1
undirected = True
outputDir = PathDefaults.getOutputDir() + "viroscopy/toy/"
theta, sigmaTheta = HIVModelUtils.toyTheta()
graphList = []
for j in range(numRepetitions):
graph = HIVGraph(M, undirected)
logging.debug("Created graph: " + str(graph))
alpha = 2
from apgl.predictors.ABCSMC import ABCSMC
import logging
import sys
import numpy
import multiprocessing
assert False, "Must run with -O flag"
FORMAT = "%(levelname)s:root:%(process)d:%(message)s"
logging.basicConfig(stream=sys.stdout, level=logging.DEBUG, format=FORMAT)
numpy.set_printoptions(suppress=True, precision=4, linewidth=150)
numpy.seterr(invalid='raise')
resultsDir = PathDefaults.getOutputDir() + "viroscopy/real/"
startDate, endDates, numRecordSteps, M, targetGraph = HIVModelUtils.realSimulationParams()
if len(sys.argv) > 1:
numProcesses = int(sys.argv[1])
else:
numProcesses = multiprocessing.cpu_count()
posteriorSampleSize = 20
breakDist = 0.4
logging.debug("Posterior sample size " + str(posteriorSampleSize))
for i, endDate in enumerate(endDates):
logging.debug("="*10 + "Starting new simulation batch" + "="*10)
logging.debug("Total time of simulation is " + str(endDate-startDate))
epsilonArray = numpy.ones(15)*0.4
def testSimulate2(self):
startDate = 0.0
endDate = 100.0
M = 1000
meanTheta, sigmaTheta = HIVModelUtils.estimatedRealTheta()
undirected = True
graph = HIVGraph(M, undirected)
alpha = 2
zeroVal = 0.9
p = Util.powerLawProbs(alpha, zeroVal)
hiddenDegSeq = Util.randomChoice(p, graph.getNumVertices())
meanTheta[4] = 0.1
recordStep = 10
printStep = 10
rates = HIVRates(graph, hiddenDegSeq)
model = HIVEpidemicModel(graph, rates, endDate, startDate)
model.setRecordStep(recordStep)
model.setPrintStep(printStep)
model.setParams(meanTheta)
initialInfected = graph.getInfectedSet()
times, infectedIndices, removedIndices, graph = model.simulate(True)
#Now test the final graph
edges = graph.getAllEdges()
for i, j in edges:
if graph.vlist.V[i, HIVVertices.genderIndex] == graph.vlist.V[j, HIVVertices.genderIndex] and (graph.vlist.V[i, HIVVertices.orientationIndex] != HIVVertices.bi or graph.vlist.V[j, HIVVertices.orientationIndex] != HIVVertices.bi):
self.fail()
finalInfected = graph.getInfectedSet()
finalRemoved = graph.getRemovedSet()
self.assertEquals(numpy.intersect1d(initialInfected, finalRemoved).shape[0], len(initialInfected))
#Test case where there is no contact
meanTheta = numpy.array([100, 0.95, 1, 1, 0, 0, 0, 0, 0, 0, 0], numpy.float)
times, infectedIndices, removedIndices, graph, model = runModel(meanTheta)
self.assertEquals(len(graph.getInfectedSet()), 100)
self.assertEquals(len(graph.getRemovedSet()), 0)
self.assertEquals(graph.getNumEdges(), 0)
heteroContactRate = 0.1
meanTheta = numpy.array([100, 0.95, 1, 1, 0, 0, heteroContactRate, 0, 0, 0, 0], numpy.float)
times, infectedIndices, removedIndices, graph, model = runModel(meanTheta)
self.assertEquals(len(graph.getInfectedSet()), 100)
self.assertEquals(len(graph.getRemovedSet()), 0)
edges = graph.getAllEdges()
for i, j in edges:
self.assertNotEqual(graph.vlist.V[i, HIVVertices.genderIndex], graph.vlist.V[j, HIVVertices.genderIndex])
#Number of conacts = rate*people*time
infectedSet = graph.getInfectedSet()
numHetero = (graph.vlist.V[list(infectedSet), HIVVertices.orientationIndex] == HIVVertices.hetero).sum()
self.assertTrue(abs(numHetero*endDate*heteroContactRate- model.getNumContacts()) < 100)
heteroContactRate = 0.01
meanTheta = numpy.array([100, 0.95, 1, 1, 0, 0, heteroContactRate, 0, 0, 0, 0], numpy.float)
times, infectedIndices, removedIndices, graph, model = runModel(meanTheta)
infectedSet = graph.getInfectedSet()
numHetero = (graph.vlist.V[list(infectedSet), HIVVertices.orientationIndex] == HIVVertices.hetero).sum()
self.assertAlmostEqual(numHetero*endDate*heteroContactRate/100, model.getNumContacts()/100.0, 0)
import matplotlib.pyplot as plt
from apgl.graph.GraphStatistics import GraphStatistics
from apgl.util.PathDefaults import PathDefaults
from apgl.util.Util import Util
from apgl.util.Latex import Latex
from apgl.predictors.ABCSMC import loadThetaArray
from exp.viroscopy.model.HIVModelUtils import HIVModelUtils
from exp.viroscopy.model.HIVVertices import HIVVertices
from exp.sandbox.GraphMatch import GraphMatch
from exp.viroscopy.model.HIVGraphMetrics2 import HIVGraphMetrics2
assert False, "Must run with -O flag"
logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
numpy.set_printoptions(suppress=True, precision=4, linewidth=150)
startDate, endDates, numRecordSteps, M, targetGraph = HIVModelUtils.realSimulationParams()
plotStyles = ['k-', 'kx-', 'k+-', 'k.-', 'k*-']
saveResults = False
graphStats = GraphStatistics()
N = 8
t = 0
maxT = 20
#We plot some stats for the ideal simulated epidemic
#and those epidemics found using ABC.
def saveStats(args):
i, theta, startDate, endDate, recordStep = args
resultsFileName = outputDir + "SimStats" + str(i) + ".pkl"
from apgl.predictors.ABCSMC import loadThetaArray
from exp.viroscopy.model.HIVModelUtils import HIVModelUtils
from exp.viroscopy.model.HIVVertices import HIVVertices
from exp.sandbox.GraphMatch import GraphMatch
from exp.viroscopy.model.HIVGraphMetrics2 import HIVGraphMetrics2
assert False, "Must run with -O flag"
logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
numpy.set_printoptions(suppress=True, precision=4, linewidth=150)
plotStyles = ['k-', 'kx-', 'k+-', 'k.-', 'k*-']
resultsDir = PathDefaults.getOutputDir() + "viroscopy/toy/theta/"
outputDir = resultsDir + "stats/"
startDate, endDate, recordStep, M, targetGraph = HIVModelUtils.toySimulationParams()
endDate += HIVModelUtils.toyTestPeriod
saveResults = False
graphStats = GraphStatistics()
N = 16
t = 0
maxT = 10
realTheta, sigmaTheta = HIVModelUtils.toyTheta()
minVal = 10
for i in range(maxT):
thetaArray, distArray = loadThetaArray(N, resultsDir, i)
graph = HIVGraph(M, undirected)
graphMetrics = HIVGraphMetrics2(targetGraph, epsilon, matcher, float(endDate))
graphMetrics.breakDist = 1.0
hiddenDegSeq = Util.randomChoice(p, graph.getNumVertices())
rates = HIVRates(graph, hiddenDegSeq)
model = HIVEpidemicModel(graph, rates, T=float(endDate), T0=float(startDate), metrics=graphMetrics)
model.setRecordStep(recordStep)
model.setParams(meanTheta)
times, infectedIndices, removedIndices, graph = model.simulate(True)
return abs(0.7 - graphMetrics.distance())/numpy.linalg.norm(newTheta-meanTheta)
startDate, endDate, recordStep, M, targetGraph = HIVModelUtils.toySimulationParams()
numReps = 8
pertScales = [0.01, 0.05, 0.1, 0.2]
derivatives = numpy.zeros((numReps, len(pertScales)))
for j, pertScale in enumerate(pertScales):
logging.debug(pertScale)
paramList = []
for i in range(numReps):
paramList.append((pertScale, startDate, endDate, recordStep, M, targetGraph, i))
pool = multiprocessing.Pool(multiprocessing.cpu_count())
resultIterator = pool.map(findDerivative, paramList)
#resultIterator = map(findDerivative, paramList)
pool.terminate()
