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())