#Find all of the decays in information transmission
egoTestFileName = dataDir + "EgoInfoTest.csv"
alterTestFileName = dataDir + "AltersInfoTest.csv"
decayGraph = eCsvReader.findInfoDecayGraph(egoTestFileName, alterTestFileName, egoIndicesR, alterIndices, egoIndicesNR, alterIndicesNR, egoFileName, alterFileName, missing)
logging.info("Size of decay graph: " + str(decayGraph.getNumVertices()))
logging.info("Number of edges: " + str(decayGraph.getNumEdges()))
#Now write the decays to a simplegraph file
decayFileName = dataDir + "EgoAlterDecays.dat"
decayGraph.save(decayFileName)
logging.info("Wrote decays to file " + decayFileName)
#Now write out transmissions
sampleSize = 1000
indices = Util.sampleWithoutReplacement(sampleSize, examplesList.getNumExamples())
examplesListSample = examplesList.getSubExamplesList(indices)
outputFileName1 = dataDir + "EgoAlterTransmissions"
outputFileName2 = dataDir + "EgoAlterTransmissions1000"
examplesList.writeToMatFile(outputFileName1)
examplesListSample.writeToMatFile(outputFileName2)
#Let's also write out the csv file for analysis
X = examplesList.getDataField(examplesList.getDefaultExamplesName())
y = examplesList.getDataField(examplesList.getLabelsName())
X = numpy.c_[X, y]
def generateNonReceivers(self, egoArray, numContactsIndices, homophileIndexPairs, receiverCounts):
"""
Generate a series of non receivers from egoArray based on homophility information.
egoArray is the array of all egos
Inputs
------
numContactsIndices - a list of indices of the number of various contacts (friends, family etc.)
homophileIndexPairs - a list of pairs. The first is the index of the homophility and the second
is the index of the variable.
receiverCounts - the number of receivers for each ego
Outputs
-------
contactsArray - the array of non-receivers
egoIndices - the corresponding 1D array of ego indices
"""
(numEgos, numEgoFeatures) = (egoArray.shape[0], egoArray.shape[1])
egoIndices = numpy.zeros(0, numpy.int32) #Store the index of each ego to each contact
alterIndices = numpy.zeros(0, numpy.int32) #Store the index of each alter to each contact
contactsArray = numpy.zeros((0, numEgoFeatures))
logging.info("Generating non-receivers for " + str(numEgos) + " egos")
#Assume number of contacts above 9 is just 10 (final category)
for i in range(0, numEgos):
Util.printIteration(i, self.printIterationStep, numEgos)
totalContacts = 0
for j in range(0, len(numContactsIndices)):
totalContacts = totalContacts + int(egoArray[i, numContactsIndices[j]]*2)
totalContacts = max(0, totalContacts - receiverCounts[i])
#Get a sample of indices for similar people (remove the current person)
homophileIndices = numpy.array([], numpy.int)
for j in range(0, len(homophileIndexPairs)):
if egoArray[i, homophileIndexPairs[j][0]] == 1:
if j==0:
homophileIndices = numpy.setdiff1d(numpy.array(list(range(0, numEgos))), numpy.array([i]))
subset = numpy.nonzero(egoArray[:, homophileIndexPairs[j][1]] == egoArray[i, homophileIndexPairs[j][1]])[0]
homophileIndices = numpy.intersect1d(homophileIndices, subset)
nonHomophileIndices = numpy.setdiff1d(numpy.array(list(range(0, numEgos))), numpy.array([i]))
nonHomophileIndices = numpy.setdiff1d(nonHomophileIndices, homophileIndices)
numHomophileContacts = min(int(round(self.p * totalContacts)), homophileIndices.shape[0])
numNonHomophilesContacts = min(totalContacts-numHomophileContacts, nonHomophileIndices.shape[0])
tempContacts = numpy.zeros((numHomophileContacts+numNonHomophilesContacts, numEgoFeatures))
#Add homophiles
perm = Util.sampleWithoutReplacement(numHomophileContacts, homophileIndices.shape[0])
tempContacts[0:numHomophileContacts, :] = egoArray[homophileIndices[perm], :]
alterIndices = numpy.r_[alterIndices, homophileIndices[perm]]
#Add non homophiles
perm = Util.sampleWithoutReplacement(numNonHomophilesContacts, nonHomophileIndices.shape[0])
tempContacts[numHomophileContacts:numHomophileContacts+numNonHomophilesContacts, :] = egoArray[nonHomophileIndices[perm], :]
alterIndices = numpy.r_[alterIndices, nonHomophileIndices[perm]]
tempEgoIndices = numpy.ones(numHomophileContacts+numNonHomophilesContacts) * i
contactsArray = numpy.r_[contactsArray, tempContacts]
egoIndices = numpy.r_[egoIndices, tempEgoIndices]
logging.info("Done - generated " + str(egoIndices.shape[0]) + " non-receivers")
return contactsArray, egoIndices, alterIndices
