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)
def testPurtubationKernel(self):
repetitions = 1000
theta = self.hivAbcParams.sampleParams()
newThetas = numpy.zeros((repetitions, self.numParams))
for i in range(repetitions):
newThetas[i, :] = self.hivAbcParams.perturbationKernel(theta)
self.assertTrue(numpy.linalg.norm(numpy.mean(newThetas, 0) - theta) < 2)
self.assertTrue(numpy.linalg.norm(numpy.std(newThetas, 0) - self.meanTheta/10) < 1)
self.hivAbcParams = HIVABCParameters(theta, theta/20, purtScale=0.1)
for i in range(repetitions):
newThetas[i, :] = self.hivAbcParams.perturbationKernel(theta)
#print(numpy.std(newThetas, 0), self.meanTheta/10)
self.assertTrue(numpy.linalg.norm(numpy.mean(newThetas, 0) - theta) < 2)
def testSampleParams(self):
repetitions = 2000
thetas = numpy.zeros((repetitions, self.numParams))
for i in range(repetitions):
thetas[i, :] = self.hivAbcParams.sampleParams()
#The deviation is half the mean
self.assertTrue(numpy.linalg.norm(numpy.mean(thetas, 0)/self.meanTheta - numpy.ones(self.numParams)) < 2)
self.assertTrue(numpy.linalg.norm(numpy.std(thetas, 0)/self.meanTheta) < 2)
self.hivAbcParams = HIVABCParameters(self.meanTheta, self.meanTheta/10, 0.1)
for i in range(repetitions):
thetas[i, :] = self.hivAbcParams.sampleParams()
#The deviation is half the mean
self.assertTrue(numpy.linalg.norm(numpy.mean(thetas, 0)/self.meanTheta - numpy.ones(self.numParams)) < 2)
self.assertTrue(numpy.linalg.norm(numpy.std(thetas, 0)/self.meanTheta) < 2)
def setUp(self):
numpy.seterr(invalid='raise')
logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
numpy.set_printoptions(suppress=True, precision=4, linewidth=100)
numpy.random.seed(21)
M = 1000
undirected = True
graph = HIVGraph(M, undirected)
alpha = 2
zeroVal = 0.9
p = Util.powerLawProbs(alpha, zeroVal)
hiddenDegSeq = Util.randomChoice(p, graph.getNumVertices())
rates = HIVRates(graph, hiddenDegSeq)
self.numParams = 6
self.graph = graph
self.meanTheta = numpy.array([100, 0.9, 0.05, 0.001, 0.1, 0.005])
self.hivAbcParams = HIVABCParameters(self.meanTheta, self.meanTheta/2)
class HIVABCParametersTest(unittest.TestCase):
def setUp(self):
numpy.seterr(invalid='raise')
logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
numpy.set_printoptions(suppress=True, precision=4, linewidth=100)
numpy.random.seed(21)
M = 1000
undirected = True
graph = HIVGraph(M, undirected)
alpha = 2
zeroVal = 0.9
p = Util.powerLawProbs(alpha, zeroVal)
hiddenDegSeq = Util.randomChoice(p, graph.getNumVertices())
rates = HIVRates(graph, hiddenDegSeq)
self.numParams = 6
self.graph = graph
self.meanTheta = numpy.array([100, 0.9, 0.05, 0.001, 0.1, 0.005])
self.hivAbcParams = HIVABCParameters(self.meanTheta, self.meanTheta/2)
def testGetParamFuncs(self):
self.hivAbcParams.getParamFuncs()
def testSampleParams(self):
repetitions = 2000
thetas = numpy.zeros((repetitions, self.numParams))
for i in range(repetitions):
thetas[i, :] = self.hivAbcParams.sampleParams()
#The deviation is half the mean
self.assertTrue(numpy.linalg.norm(numpy.mean(thetas, 0)/self.meanTheta - numpy.ones(self.numParams)) < 2)
self.assertTrue(numpy.linalg.norm(numpy.std(thetas, 0)/self.meanTheta) < 2)
self.hivAbcParams = HIVABCParameters(self.meanTheta, self.meanTheta/10, 0.1)
for i in range(repetitions):
thetas[i, :] = self.hivAbcParams.sampleParams()
#The deviation is half the mean
self.assertTrue(numpy.linalg.norm(numpy.mean(thetas, 0)/self.meanTheta - numpy.ones(self.numParams)) < 2)
self.assertTrue(numpy.linalg.norm(numpy.std(thetas, 0)/self.meanTheta) < 2)
def testPriorDensity(self):
repetitions = 1000
thetas = numpy.zeros((repetitions, self.numParams))
densities = numpy.zeros((repetitions, self.numParams))
for i in range(repetitions):
thetas[i, :] = self.hivAbcParams.sampleParams()
densities[i, :] = self.hivAbcParams.priorDensity(thetas[i, :], True)
for i in range(self.numParams):
inds = numpy.argsort(thetas[:, i])
self.assertAlmostEquals(scipy.integrate.trapz(densities[inds, i], thetas[inds, i]), 1.0, 1)
#Total density must be correct because they are independent
def testPurtubationKernel(self):
repetitions = 1000
theta = self.hivAbcParams.sampleParams()
newThetas = numpy.zeros((repetitions, self.numParams))
for i in range(repetitions):
newThetas[i, :] = self.hivAbcParams.perturbationKernel(theta)
self.assertTrue(numpy.linalg.norm(numpy.mean(newThetas, 0) - theta) < 2)
self.assertTrue(numpy.linalg.norm(numpy.std(newThetas, 0) - self.meanTheta/10) < 1)
self.hivAbcParams = HIVABCParameters(theta, theta/20, purtScale=0.1)
for i in range(repetitions):
newThetas[i, :] = self.hivAbcParams.perturbationKernel(theta)
#print(numpy.std(newThetas, 0), self.meanTheta/10)
self.assertTrue(numpy.linalg.norm(numpy.mean(newThetas, 0) - theta) < 2)
#self.assertTrue(numpy.linalg.norm(numpy.std(newThetas, 0) - self.meanTheta/10) < 1)
def testPurtubationKernelDensity(self):
theta = self.hivAbcParams.sampleParams()
repetitions = 1000
thetas = numpy.zeros((repetitions, self.numParams))
densities = numpy.zeros((repetitions, self.numParams))
for i in range(repetitions):
thetas[i, :] = self.hivAbcParams.perturbationKernel(theta)
densities[i, :] = self.hivAbcParams.perturbationKernelDensity(theta, thetas[i, :], True)
for i in range(self.numParams):
inds = numpy.argsort(thetas[:, i])
self.assertAlmostEquals(scipy.integrate.trapz(densities[inds, i], thetas[inds, i]), 1.0, 1)
def testCreateTruncNormParamm(self):
sigma = 0.5
mu = 0.2
numSamples = 2000
sampleArray = numpy.zeros(numSamples)
for i in range(numSamples):
priorDist, priorDensity = self.hivAbcParams.createTruncNormParam(sigma, mu)
sampleArray[i] = priorDist()
self.assertTrue(sampleArray[i] <= 1.0 and sampleArray[i] >= 0)
#A crude way of checking the mode
hist = numpy.histogram(sampleArray, numpy.array([0.0, 0.15, 0.3, 0.45, 0.6, 0.75, 0.9, 1.0]))
#self.assertTrue(numpy.argmax(hist[0]) == 1)
def testCreateDiscTruncNormParam(self):
sigma = 5.0
mu = 10.0
numSamples = 2000
sampleArray = numpy.zeros(numSamples)
upper = 100
for i in range(numSamples):
priorDist, priorDensity = self.hivAbcParams.createDiscTruncNormParam(sigma, mu, upper)
sampleArray[i] = priorDist()
self.assertTrue(sampleArray[i] <= 100 and sampleArray[i] >= 0)
self.assertAlmostEquals(numpy.min(sampleArray), 0, 1)
self.assertTrue((numpy.mean(sampleArray) - mu)<1)
#print(numpy.max(sampleArray))
def testPickle(self):
output = pickle.dumps(self.hivAbcParams)
newParams = pickle.loads(output)
thetas = numpy.zeros((100, self.numParams))
for i in range(thetas.shape[0]):
thetas[i, :] = newParams.sampleParams()
meanTheta = thetas.mean(0)
self.assertTrue(numpy.linalg.norm(meanTheta - self.hivAbcParams.meanTheta) < 1)
def testCreateGammaParam(self):
sigma = 0.1
mu = 0.2
numSamples = 2000
sampleArray = numpy.zeros(numSamples)
for i in range(numSamples):
priorDist, priorDensity = self.hivAbcParams.createGammaParam(sigma, mu)
sampleArray[i] = priorDist()
self.assertTrue(numpy.linalg.norm(mu - numpy.mean(sampleArray)) < 0.5)
sigma = 0.0001
mu = 0.2
numSamples = 2000
sampleArray = numpy.zeros(numSamples)
for i in range(numSamples):
priorDist, priorDensity = self.hivAbcParams.createGammaParam(sigma, mu)
sampleArray[i] = priorDist()
print(numpy.mean(sampleArray))
