def testCategoricalToIndicator(self):
X = numpy.zeros((5,5))
X[:, 0] = numpy.array([1, 1, 2, 4, 6])
X[:, 1] = numpy.array([2, 1, 2, 4, 6])
X[:, 2] = numpy.array([1, 1, 2, 4, 2])
X[:, 3] = numpy.array([1, 2, 3, 4, 2])
X[:, 4] = numpy.array([1.1, 2.1, 4.5, 6.2, 1.1])
logging.debug(X)
generator = FeatureGenerator()
inds = [0, 1]
X2 = generator.categoricalToIndicator(X, inds)
X3 = numpy.zeros((5, 11))
X3[0, :] = numpy.array([[ 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1.1]])
X3[1, :] = numpy.array([[ 1, 0, 0, 0, 1, 0, 0, 0, 1, 2, 2.1]])
X3[2, :] = numpy.array([[ 0, 1, 0, 0, 0, 1, 0, 0, 2, 3, 4.5]])
X3[3, :] = numpy.array([[ 0, 0, 1, 0, 0, 0, 1, 0, 4, 4, 6.2]])
X3[4, :] = numpy.array([[ 0, 0, 0, 1, 0, 0, 0, 1, 2, 2, 1.1]])
self.assertTrue(numpy.linalg.norm(X3-X2) < 10**-6)
#Test case where no indices given
inds = []
X2 = generator.categoricalToIndicator(X, inds)
self.assertTrue(numpy.linalg.norm(X-X2) < 10**-6)
def readHIVGraph(self, undirected=True, indicators=True):
"""
We will use pacdate5389.csv which contains the data of infection. The undirected
parameter instructs whether to create an undirected graph. If indicators
is true then categorical varibles are turned into collections of indicator
ones.
"""
converters = {1: CsvConverters.dateConv, 3:CsvConverters.dateConv, 5:CsvConverters.detectionConv, 6:CsvConverters.provConv, 8: CsvConverters.dateConv }
converters[9] = CsvConverters.genderConv
converters[10] = CsvConverters.orientConv
converters[11] = CsvConverters.numContactsConv
converters[12] = CsvConverters.numContactsConv
converters[13] = CsvConverters.numContactsConv
def nanProcessor(X):
means = numpy.zeros(X.shape[1])
for i in range(X.shape[1]):
if numpy.sum(numpy.isnan(X[:, i])) > 0:
logging.info("No. missing values in " + str(i) + "th column: " + str(numpy.sum(numpy.isnan(X[:, i]))))
means[i] = numpy.mean(X[:, i][numpy.isnan(X[:, i]) == False])
X[numpy.isnan(X[:, i]), i] = means[i]
return X
idIndex = 0
featureIndices = converters.keys()
multiGraphCsvReader = MultiGraphCsvReader(idIndex, featureIndices, converters, nanProcessor)
dataDir = PathDefaults.getDataDir()
vertexFileName = dataDir + "HIV/alldata.csv"
edgeFileNames = [dataDir + "HIV/grafdet2.csv", dataDir + "HIV/infect2.csv"]
sparseMultiGraph = multiGraphCsvReader.readGraph(vertexFileName, edgeFileNames, undirected, delimiter="\t")
#For learning purposes we will convert categorial variables into a set of
#indicator features
if indicators:
logging.info("Converting categorial features")
vList = sparseMultiGraph.getVertexList()
V = vList.getVertices(list(range(vList.getNumVertices())))
catInds = [2, 3]
generator = FeatureGenerator()
V = generator.categoricalToIndicator(V, catInds)
vList.replaceVertices(V)
logging.info("Created " + str(sparseMultiGraph.getNumVertices()) + " examples with " + str(sparseMultiGraph.getVertexList().getNumFeatures()) + " features")
return sparseMultiGraph
