def setUp(self):
numVertices = 500
numFeatures = 49
self.means = rand.randn(numFeatures)
self.vars = rand.randn(numFeatures, numFeatures)
self.vars = self.vars + self.vars.T #Make vars symmetric
p1 = 0.1
self.egoGenerator = EgoGenerator()
vList = self.egoGenerator.generateIndicatorVertices(numVertices, self.means, self.vars, p1)
sGraph = SparseGraph(vList)
p2 = 0.1
k = 5
#Create the graph edges according to the small world model
graphGen = SmallWorldGenerator(p2, k)
self.sGraph = graphGen.generate(sGraph)
dataDir = PathDefaults.getDataDir() + "infoDiffusion/"
matFileName = dataDir + "EgoAlterTransmissions1000.mat"
sampleSize = 100
egoAlterExamplesList = ExamplesList.readFromMatFile(matFileName)
egoAlterExamplesList.setDefaultExamplesName("X")
egoAlterExamplesList.setLabelsName("y")
egoAlterExamplesList.randomSubData(sampleSize)
X = egoAlterExamplesList.getDataField("X")
y = egoAlterExamplesList.getDataField("y")
#Now learn using NaiveBayes
self.nb = NaiveBayes()
self.nb.learnModel(X, y)
self.egoSimulator = EgoSimulator(self.sGraph, self.nb)
#Define a classifier which predicts transfer if gender is female
class DummyClassifier(object):
def __init(self):
pass
def classify(self, X):
y = numpy.zeros((X.shape[0]))
for i in range(X.shape[0]):
if X[i, 0] == 0:
y[i] = 1
else:
y[i] = -1
return y
self.dc = DummyClassifier()
class EgoSimulatorTest(unittest.TestCase):
def setUp(self):
numVertices = 500
numFeatures = 49
self.means = rand.randn(numFeatures)
self.vars = rand.randn(numFeatures, numFeatures)
self.vars = self.vars + self.vars.T #Make vars symmetric
p1 = 0.1
self.egoGenerator = EgoGenerator()
vList = self.egoGenerator.generateIndicatorVertices(numVertices, self.means, self.vars, p1)
sGraph = SparseGraph(vList)
p2 = 0.1
k = 5
#Create the graph edges according to the small world model
graphGen = SmallWorldGenerator(p2, k)
self.sGraph = graphGen.generate(sGraph)
dataDir = PathDefaults.getDataDir() + "infoDiffusion/"
matFileName = dataDir + "EgoAlterTransmissions1000.mat"
sampleSize = 100
egoAlterExamplesList = ExamplesList.readFromMatFile(matFileName)
egoAlterExamplesList.setDefaultExamplesName("X")
egoAlterExamplesList.setLabelsName("y")
egoAlterExamplesList.randomSubData(sampleSize)
X = egoAlterExamplesList.getDataField("X")
y = egoAlterExamplesList.getDataField("y")
#Now learn using NaiveBayes
self.nb = NaiveBayes()
self.nb.learnModel(X, y)
self.egoSimulator = EgoSimulator(self.sGraph, self.nb)
#Define a classifier which predicts transfer if gender is female
class DummyClassifier(object):
def __init(self):
pass
def classify(self, X):
y = numpy.zeros((X.shape[0]))
for i in range(X.shape[0]):
if X[i, 0] == 0:
y[i] = 1
else:
y[i] = -1
return y
self.dc = DummyClassifier()
def tearDown(self):
pass
def testAdvanceGraph(self):
totalInfo = EgoUtils.getTotalInformation(self.sGraph)
self.sGraph = self.egoSimulator.advanceGraph()
totalInfo2 = EgoUtils.getTotalInformation(self.sGraph)
#Test that the number of people who know information is the same or more
self.assertTrue(totalInfo2 >= totalInfo)
def testAdvanceGraph2(self):
#Create a simple graph and deterministic classifier
numExamples = 10
numFeatures = 3
#Here, the first element is gender (say) with female = 0, male = 1
vList = VertexList(numExamples, numFeatures)
vList.setVertex(0, numpy.array([0,0,1]))
vList.setVertex(1, numpy.array([1,0,0]))
vList.setVertex(2, numpy.array([1,0,0]))
vList.setVertex(3, numpy.array([1,0,0]))
vList.setVertex(4, numpy.array([0,0,1]))
vList.setVertex(5, numpy.array([0,0,1]))
vList.setVertex(6, numpy.array([0,0,0]))
vList.setVertex(7, numpy.array([1,0,0]))
vList.setVertex(8, numpy.array([0,0,1]))
vList.setVertex(9, numpy.array([1,0,0]))
sGraph = SparseGraph(vList)
sGraph.addEdge(0, 1, 1)
sGraph.addEdge(0, 2, 1)
sGraph.addEdge(0, 3, 1)
sGraph.addEdge(4, 5, 1)
sGraph.addEdge(4, 6, 1)
sGraph.addEdge(6, 7, 1)
sGraph.addEdge(6, 8, 1)
sGraph.addEdge(6, 9, 1)
simulator = EgoSimulator(sGraph, self.dc)
simulator.advanceGraph()
self.assertEquals(simulator.getNumIterations(), 1)
self.assertEquals(sGraph.getVertex(0)[numFeatures-1], 1)
self.assertEquals(sGraph.getVertex(1)[numFeatures-1], 1)
self.assertEquals(sGraph.getVertex(2)[numFeatures-1], 1)
self.assertEquals(sGraph.getVertex(3)[numFeatures-1], 1)
self.assertEquals(sGraph.getVertex(4)[numFeatures-1], 1)
self.assertEquals(sGraph.getVertex(5)[numFeatures-1], 1)
self.assertEquals(sGraph.getVertex(6)[numFeatures-1], 1)
self.assertEquals(sGraph.getVertex(7)[numFeatures-1], 0)
self.assertEquals(sGraph.getVertex(8)[numFeatures-1], 1)
self.assertEquals(sGraph.getVertex(9)[numFeatures-1], 0)
#Advance again and all egos have information
simulator.advanceGraph()
self.assertEquals(simulator.getNumIterations(), 2)
self.assertEquals(sGraph.getVertex(0)[numFeatures-1], 1)
self.assertEquals(sGraph.getVertex(1)[numFeatures-1], 1)
self.assertEquals(sGraph.getVertex(2)[numFeatures-1], 1)
self.assertEquals(sGraph.getVertex(3)[numFeatures-1], 1)
self.assertEquals(sGraph.getVertex(4)[numFeatures-1], 1)
self.assertEquals(sGraph.getVertex(5)[numFeatures-1], 1)
self.assertEquals(sGraph.getVertex(6)[numFeatures-1], 1)
self.assertEquals(sGraph.getVertex(7)[numFeatures-1], 1)
self.assertEquals(sGraph.getVertex(8)[numFeatures-1], 1)
self.assertEquals(sGraph.getVertex(9)[numFeatures-1], 1)
#Should be no change
simulator.advanceGraph()
self.assertEquals(simulator.getNumIterations(), 3)
#Check the correct alters are added at each step
self.assertTrue((simulator.getAlters(0) == numpy.array([1,2,3,6])).all())
self.assertTrue((simulator.getAlters(1) == numpy.array([7,9])).all())
self.assertTrue((simulator.getAlters(2) == numpy.array([])).all())
#Check that the transmission graph is okay
transGraph = simulator.getTransmissionGraph()
self.assertEquals(transGraph.getNumVertices(), 9)
self.assertEquals(transGraph.getNumEdges(), 7)
self.assertEquals(transGraph.getAllVertexIds(), [0, 1, 2, 3, 4, 6, 7, 8, 9])
for i in transGraph.getAllVertexIds():
self.assertTrue((transGraph.getVertex(i) == sGraph.getVertex(i)).all())
def testFullTransGraph(self):
transGraph = self.egoSimulator.fullTransGraph()
#Create a simple graph and deterministic classifier
numExamples = 10
numFeatures = 3
#Here, the first element is gender (say) with female = 0, male = 1
vList = VertexList(numExamples, numFeatures)
vList.setVertex(0, numpy.array([0,0,1]))
vList.setVertex(1, numpy.array([1,0,0]))
vList.setVertex(2, numpy.array([1,0,0]))
vList.setVertex(3, numpy.array([1,0,0]))
vList.setVertex(4, numpy.array([0,0,1]))
vList.setVertex(5, numpy.array([0,0,1]))
vList.setVertex(6, numpy.array([0,0,0]))
vList.setVertex(7, numpy.array([1,0,0]))
vList.setVertex(8, numpy.array([0,0,1]))
vList.setVertex(9, numpy.array([1,0,0]))
sGraph = SparseGraph(vList)
sGraph.addEdge(0, 1, 1)
sGraph.addEdge(0, 2, 1)
sGraph.addEdge(0, 3, 1)
sGraph.addEdge(4, 5, 1)
sGraph.addEdge(4, 6, 1)
sGraph.addEdge(6, 7, 1)
sGraph.addEdge(6, 8, 1)
sGraph.addEdge(6, 9, 1)
simulator = EgoSimulator(sGraph, self.dc)
logging.debug("Writing out full transmission graph")
transGraph = simulator.fullTransGraph()
self.assertEquals(transGraph.isUndirected(), False)
self.assertEquals(transGraph.getNumEdges(), 11)
self.assertEquals(transGraph.getEdge(0,1), 1)
self.assertEquals(transGraph.getEdge(0,2), 1)
self.assertEquals(transGraph.getEdge(0,3), 1)
self.assertEquals(transGraph.getEdge(4,5), 1)
self.assertEquals(transGraph.getEdge(4,6), 1)
self.assertEquals(transGraph.getEdge(5,4), 1)
self.assertEquals(transGraph.getEdge(6,4), 1)
self.assertEquals(transGraph.getEdge(6,7), 1)
self.assertEquals(transGraph.getEdge(6,8), 1)
self.assertEquals(transGraph.getEdge(6,9), 1)
self.assertEquals(transGraph.getEdge(8,6), 1)
self.assertEquals(transGraph.getVertexList(), vList)
def testAdvanceGraph3(self):
"""
This test will learn from a set of ego and alter pairs, then we will make predictions on
the pairs and see the results. The we test if the same results are present in a simulation.
"""
dataDir = PathDefaults.getDataDir() + "infoDiffusion/"
matFileName = dataDir + "EgoAlterTransmissions1000.mat"
examplesList = ExamplesList.readFromMatFile(matFileName)
examplesList.setDefaultExamplesName("X")
examplesList.setLabelsName("y")
logging.debug(("Number of y = +1: " + str(sum(examplesList.getSampledDataField("y") == 1))))
logging.debug(("Number of y = -1: " + str(sum(examplesList.getSampledDataField("y") == -1))))
#Standardise the examples
preprocessor = Standardiser()
X = examplesList.getDataField(examplesList.getDefaultExamplesName())
X = preprocessor.standardiseArray(X)
examplesList.overwriteDataField(examplesList.getDefaultExamplesName(), X)
classifier = MlpySVM(kernel='linear', kp=1, C=32.0)
y = examplesList.getDataField("y")
classifier.learnModel(X, y)
predY = classifier.classify(X)
logging.debug(("Number of y = +1: " + str(sum(examplesList.getSampledDataField("y") == 1))))
logging.debug(("Number of y = -1: " + str(sum(examplesList.getSampledDataField("y") == -1))))
sampledY = examplesList.getSampledDataField(examplesList.getLabelsName()).ravel()
error = mlpy.err(sampledY, predY)
sensitivity = mlpy.sens(sampledY, predY)
specificity = mlpy.spec(sampledY, predY)
errorP = mlpy.errp(sampledY, predY)
errorN = mlpy.errn(sampledY, predY)
logging.debug("--- Classification evaluation ---")
logging.debug(("Error on " + str(examplesList.getNumExamples()) + " examples is " + str(error)))
logging.debug(("Sensitivity (recall = TP/(TP+FN)): " + str(sensitivity)))
logging.debug(("Specificity (TN/TN+FP): " + str(specificity)))
logging.debug(("Error on positives: " + str(errorP)))
logging.debug(("Error on negatives: " + str(errorN)))
sGraph = EgoUtils.graphFromMatFile(matFileName)
#Notice that the data is preprocessed in the same way as the survey data
egoSimulator = EgoSimulator(sGraph, classifier, preprocessor)
totalInfo = EgoUtils.getTotalInformation(sGraph)
logging.debug(("Total number of people with information: " + str(totalInfo)))
self.assertEquals(totalInfo, 1000)
sGraph = egoSimulator.advanceGraph()
totalInfo = EgoUtils.getTotalInformation(sGraph)
logging.debug(("Total number of people with information: " + str(totalInfo)))
self.assertEquals(totalInfo, 1000 + sum(predY == 1))
altersList = egoSimulator.getAlters(0)
predictedAlters = numpy.nonzero(predY == 1)[0]
self.assertTrue((altersList == predictedAlters*2+1).all())
