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)
vertexWriter = CsvVertexWriter()
for i in range(len(graphTypes)):
graphType = graphTypes[i]
p = ps[i]
k = ks[i]
outputDirectory = PathDefaults.getOutputDir()
baseFileName = outputDirectory + "InfoGraph" + graphType
graph = simulator.generateRandomGraph(egoFileName, alterFileName, numVertices, infoProb, graphType, p, k)
#Notice that the data is preprocessed in the same way as the survey data
egoSimulator = EgoSimulator(graph, classifier, preprocessor)
totalInfo = numpy.zeros(maxIterations+1)
totalInfo[0] = EgoUtils.getTotalInformation(graph)
logging.info("Total number of people with information: " + str(totalInfo[0]))
logging.info("--- Simulation Started ---")
for i in range(0, maxIterations):
logging.info("--- Iteration " + str(i) + " ---")
graph = egoSimulator.advanceGraph()
totalInfo[i+1] = EgoUtils.getTotalInformation(graph)
logging.info("Total number of people with information: " + str(totalInfo[i+1]))
transmissionGraph = egoSimulator.getTransmissionGraph()
pajekWriter.writeToFile(baseFileName, transmissionGraph)
vertexWriter.writeToFile(baseFileName, transmissionGraph)
simpleGraphWriter.writeToFile(baseFileName, transmissionGraph)
logging.info("--- Simulation Finished ---")
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())
