def testReadFile(self):
eCsvReader = EgoCsvReader()
#logging.debug(os.getcwd())
dir = PathDefaults.getDataDir()
fileName = dir + "test/TestData.csv"
questionIds = [("Q14", 0), ("Q12", 1) , ("Q2", 0)]
missing = 1
(X, titles) = eCsvReader.readFile(fileName, questionIds, missing)
X2 = numpy.zeros((10, 3))
X2[0, :] = [0.621903386,0.608560354,0.33290608]
X2[1, :] = [0.318548924,0.402390713,0.129956291]
X2[2, :] = [0.956658404,0.344317772,0.680386616]
X2[3, :] = [0.267607668,0.119647983,0.116893619]
X2[4, :] = [0.686589498,0.402390713,0.426789174]
X2[5, :] = [0.373575769,0.025846789,0.797125005]
X2[6, :] = [0.493793948,0.402390713,0.990507109]
X2[7, :] = [0.524534585,0.525169385,0.772917183]
X2[8, :] = [0.339055395,0.402390713,0.684788001]
X2[9, :] = [0.997774183,0.790801992,0.643252009]
self.assertAlmostEquals(numpy.linalg.norm(X-X2),0, places=6)
def checkDistributions():
matFileName = "../../data/EgoAlterTransmissions.mat"
examplesList = ExamplesList.readFromMatFile(matFileName)
numFeatures = examplesList.getDataFieldSize("X", 1)
X = examplesList.getDataField("X")[:, 0:numFeatures/2]
Z = examplesList.getDataField("X")[:, numFeatures/2:numFeatures]
y = examplesList.getDataField("y")
A = Z[y==-1, :]
#Now load directly from the CSV file
#Learn the distribution of the egos
eCsvReader = EgoCsvReader()
egoFileName = "../../data/EgoData.csv"
alterFileName = "../../data/AlterData.csv"
egoQuestionIds = eCsvReader.getEgoQuestionIds()
alterQuestionIds = eCsvReader.getAlterQuestionIds()
(X2, titles) = eCsvReader.readFile(egoFileName, egoQuestionIds)
X2[:, eCsvReader.ageIndex] = eCsvReader.ageToCategories(X2[:, eCsvReader.ageIndex])
(mu, sigma) = Util.computeMeanVar(X)
(mu2, sigma2) = Util.computeMeanVar(X2)
(mu3, sigma3) = Util.computeMeanVar(Z)
(mu4, sigma4) = Util.computeMeanVar(A)
#Seems okay. Next check alters
print(("Mean " + str(mu - mu4)))
print(("Variance " + str(numpy.diag(sigma - sigma4))))
"""
Analysis between the Egos in EgoData.csv and those in EgoAlterTransmissions.mat
reveals that the distributions match closely. The main differences are
in the means and variances in Q44A - D, but this isn't too suprising.
"""
"""
#checkDistributions()
"""
We will read ego and alters data, and check they have the same values.
"""
egoFileName = "../../data/EgoData.csv"
alterFileName = "../../data/AlterData.csv"
eCsvReader = EgoCsvReader()
egoQuestionIds = eCsvReader.getEgoQuestionIds()
alterQuestionIds = eCsvReader.getAlterQuestionIds()
missing = 0
(egoX, titles) = eCsvReader.readFile(egoFileName, egoQuestionIds, missing)
egoX[:, eCsvReader.ageIndex] = eCsvReader.ageToCategories(egoX[:, eCsvReader.ageIndex])
(alterX, titles) = eCsvReader.readFile(alterFileName, alterQuestionIds, missing)
alterX[:, eCsvReader.ageIndex] = eCsvReader.ageToCategories(alterX[:, eCsvReader.ageIndex])
numFeatures = egoX.shape[1]
numEgoExamples = egoX.shape[0]
numAlterExamples = alterX.shape[0]
for i in range(0, numFeatures):
(histE, uniqElementsE) = Util.histogram(egoX[:, i])
(histA, uniqElementsA) = Util.histogram(alterX[:, i])
print((str(i) + " " + str(egoQuestionIds[i])))
print(("Ego " + str(uniqElementsE)))
def testReadFiles(self):
p = 0.5
eCsvReader = EgoCsvReader()
eCsvReader.setP(p)
dataDir = PathDefaults.getDataDir() + "infoDiffusion/"
egoFileName = dataDir + "EgoData3.csv"
alterFileName = dataDir + "AlterData10.csv"
examplesList, egoIndicesR, alterIndices, egoIndicesNR, alterIndicesNR = eCsvReader.readFiles(egoFileName, alterFileName)
#logging.debug(examplesList.getDataField("X"))
#Read in the ego and alter arrays
(egoArray, _) = eCsvReader.readFile(egoFileName, eCsvReader.getEgoQuestionIds())
(alterArray, _) = eCsvReader.readFile(alterFileName, eCsvReader.getAlterQuestionIds())
#Make up the correct results
numFeatures = examplesList.getDataFieldSize("X", 1)
numPersonFeatures = numFeatures/2
#Note: no alters in this case
numTransmissons = 6
X2 = numpy.zeros((numTransmissons, numFeatures))
y2 = numpy.zeros((numTransmissons, 1))
X2[0, 0:numPersonFeatures] = egoArray[0, :]
X2[0, numPersonFeatures:numFeatures] = egoArray[1, :]
y2[0, 0] = -1
X2[1, 0:numPersonFeatures] = egoArray[0, :]
X2[1, numPersonFeatures:numFeatures] = egoArray[2, :]
y2[1, 0] = -1
X2[2, 0:numPersonFeatures] = egoArray[1, :]
X2[2, numPersonFeatures:numFeatures] = egoArray[0, :]
y2[2, 0] = -1
X2[3, 0:numPersonFeatures] = egoArray[1, :]
X2[3, numPersonFeatures:numFeatures] = egoArray[2, :]
y2[3, 0] = -1
X2[4, 0:numPersonFeatures] = egoArray[2, :]
X2[4, numPersonFeatures:numFeatures] = egoArray[0, :]
y2[4, 0] = -1
X2[5, 0:numPersonFeatures] = egoArray[2, :]
X2[5, numPersonFeatures:numFeatures] = egoArray[1, :]
y2[5, 0] = -1
self.assertTrue((X2 == examplesList.getDataField("X")).all())
self.assertTrue((y2 == examplesList.getDataField("y")).all())
#Second test
#================
#I modified EgoData3 so that person 2 is the same age as person 1, and
# hence a homophile of 1. She (2) is excluded from the non-receivers, since
#she is a homophile of person 1.
p = 0
eCsvReader = EgoCsvReader()
eCsvReader.setP(p)
examplesList, egoIndicesR, alterIndices, egoIndicesNR, alterIndicesNR = eCsvReader.readFiles(egoFileName, alterFileName)
numTransmissons = 5
X2 = numpy.zeros((numTransmissons, numFeatures))
y2 = numpy.zeros((numTransmissons, 1))
X2[0, 0:numPersonFeatures] = egoArray[0, :]
X2[0, numPersonFeatures:numFeatures] = egoArray[2, :]
y2[0, 0] = -1
X2[1, 0:numPersonFeatures] = egoArray[1, :]
X2[1, numPersonFeatures:numFeatures] = egoArray[0, :]
y2[1, 0] = -1
X2[2, 0:numPersonFeatures] = egoArray[1, :]
X2[2, numPersonFeatures:numFeatures] = egoArray[2, :]
y2[2, 0] = -1
X2[3, 0:numPersonFeatures] = egoArray[2, :]
X2[3, numPersonFeatures:numFeatures] = egoArray[0, :]
y2[3, 0] = -1
X2[4, 0:numPersonFeatures] = egoArray[2, :]
X2[4, numPersonFeatures:numFeatures] = egoArray[1, :]
y2[4, 0] = -1
self.assertTrue((X2 == examplesList.getDataField("X")).all())
self.assertTrue((y2 == examplesList.getDataField("y")).all())
