class ArnetMinerDatasetTest(unittest.TestCase):
def setUp(self):
numpy.random.seed(22)
numpy.set_printoptions(suppress=True, precision=3)
#logging.basicConfig(stream=sys.stdout, level=logging.ERROR)
self.field = "Database"
self.dataset = ArnetMinerDataset(additionalFields=[self.field])
self.dataset.dataFilename = self.dataset.dataDir + "DBLP-citation-test.txt"
self.dataset.overwrite = True
self.dataset.overwriteModel = True
self.dataset.overwriteVectoriser = True
def testVectoriseDocuments(self):
#Check document is correct as well as authors
self.dataset.vectoriseDocuments()
def testFindSimilarDocuments(self):
field = "Object"
self.dataset = ArnetMinerDataset()
self.dataset.dataFilename = self.dataset.dataDir + "DBLP-citation-test.txt"
#Check document is correct as well as authors
self.dataset.vectoriseDocuments()
relevantExperts = self.dataset.findSimilarDocumentsLSI(field)
self.assertEquals(['Jos\xc3\xa9 A. Blakeley'], relevantExperts)
#Let's test order of ranking on larger dataset
print("Running on 10000 dataset")
dataset = ArnetMinerDataset()
dataset.minDf = 10**-6
dataset.dataFilename = dataset.dataDir + "DBLP-citation-10000.txt"
dataset.vectoriseDocuments()
relevantExperts = dataset.findSimilarDocumentsLSI("Neural Networks")
self.assertEquals(['Christopher M. Bishop', 'Michael I. Jordan', 'Fred L. Kitchens', 'Ai Cheo', 'Cesare Alippi', 'Giovanni Vanini', 'C. C. Taylor', 'David J. Spiegelhalter', 'Donald Michie'], relevantExperts)
def testFindCoauthors(self):
#Check document is correct as well as authors
self.dataset.vectoriseDocuments()
relevantExperts = self.dataset.findSimilarDocumentsLSI(self.field)
self.dataset.coauthorsGraph(self.field, relevantExperts)
def testCoauthorsGraphFromAuthors(self):
releventExperts = set(["Yuri Breitbart", "Hector Garcia-Molina"])
graph, authorIndexer = self.dataset.coauthorsGraphFromAuthors(releventExperts)
self.assertEquals(graph.get_edgelist(), [(0, 1), (0, 2), (0, 4), (1, 2), (1, 3)])
self.assertEquals(graph.es["weight"], [1, 1, 1, 1, 1])
self.assertEquals(graph.es["invWeight"], [1 ,1,1,1,1])
self.assertEquals(len(authorIndexer.getIdDict()), 5)
def testMatchExperts(self):
#TODO:
self.dataset.vectoriseDocuments()
relevantExperts = self.dataset.findSimilarDocumentsLSI("DBMS")
expertsSet = self.dataset.expertsDict[self.field]
expertMatches = self.dataset.matchExperts(relevantExperts, expertsSet)
self.assertEquals(expertMatches, ['Nathan Goodman'])
self.assertEquals(expertsSet, set(['Hector Garcia-Molina', 'Yuri Breitbart', 'Nathan Goodman']))
def testExpertsFromDocSimilarities(self):
self.dataset.authorList = [["Joe Bloggs", "Alfred Nobel"], ["Ian Hislop"], ["Alfred Nobel", "Ian Hislop"]]
similarities = numpy.array([0.4, 0.5, 0.8])
experts = self.dataset.expertsFromDocSimilarities(similarities)
self.assertEquals(experts, ['Ian Hislop', 'Alfred Nobel', 'Joe Bloggs'])
def testFindSimilarDocumentsLDA(self):
self.dataset = ArnetMinerDataset()
self.dataset.dataFilename = self.dataset.dataDir + "DBLP-citation-1000.txt"
self.dataset.overwrite = True
self.dataset.overwriteModel = True
self.dataset.overwriteVectoriser = True
self.dataset.k = 20
#Check document is correct as well as authors
self.dataset.findSimilarDocumentsLDA(self.field)
#Let's test order of ranking on larger dataset
print("Running on 10000 dataset using LDA")
dataset = ArnetMinerDataset()
dataset.minDf = 10**-5
dataset.dataFilename = dataset.dataDir + "DBLP-citation-10000.txt"
dataset.vectoriseDocuments()
relevantExperts = dataset.findSimilarDocumentsLDA("Neural Networks")
#self.assertEquals(['Christopher M. Bishop', 'Michael I. Jordan', 'Fred L. Kitchens', 'Ai Cheo', 'Cesare Alippi', 'Giovanni Vanini', 'C. C. Taylor', 'David J. Spiegelhalter', 'Donald Michie'], relevantExperts)
@unittest.skip("")
def testModelSelectionLSI(self):
self.dataset.dataFilename = self.dataset.dataDir + "DBLP-citation-1000.txt"
self.dataset.overwrite = True
self.dataset.overwriteModel = True
self.dataset.overwriteVectoriser = True
self.dataset.vectoriseDocuments()
self.dataset.modelSelectionLSI()
