def runFor(author, adjMatrix, extraData):
print "Running for %s..." % author
mostSimilar, similarityScores = findMostSimilarNodes(adjMatrix, author, extraData, method=getNeighborSimScore)
print 'Most Similar to "%s":' % author
mostSimilarTable = texttable.Texttable()
mostSimilarTable.add_rows([['Author', 'Score']] + [[name, score] for name, score in mostSimilar])
print mostSimilarTable.draw()
def runFor(self, paper, adjMatrix, extraData):
print("Running for %s..." % paper)
# Find the top 10 most similar nodes to some given node
mostSimilar, similarityScores = findMostSimilarNodes(adjMatrix, paper, extraData, method = getNeighborSimScore)
self.output('Most Similar to "%s":' % paper)
mostSimilarTable = texttable.Texttable()
rows = [['Paper', 'Score']]
rows += [[name, score] for name, score in mostSimilar]
mostSimilarTable.add_rows(rows)
self.output(mostSimilarTable.draw())
def runFor(self, author, adjMatrix, extraData, citationCounts, publicationCounts):
print("Running for %s..." % author)
# Find the top 10 most similar nodes to some given node
mostSimilar, similarityScores = findMostSimilarNodes(adjMatrix, author, extraData, method = getNeighborSimScore)
self.output('Most Similar to "%s":' % author)
mostSimilarTable = texttable.Texttable()
rows = [['Author', 'Score', 'Citations', 'Publications']]
rows += [[name, score, citationCounts[name], publicationCounts[name]] for name, score in mostSimilar]
mostSimilarTable.add_rows(rows)
self.output(mostSimilarTable.draw())
# Output all similarity scores
outputPath = os.path.join('../../results', 'authors', 'intermediate', '%s-neighborsim-cpcppa' % author.replace(' ', ''))
cPickle.dump(similarityScores, open(outputPath, 'wb'))
def runFor(self, author, adjTensor, extraData, citationCounts, publicationCounts):
print("Running for %s..." % author)
# Find the top 10 most similar nodes to some given node
mostSimilar, similarityScores = findMostSimilarNodes(
adjTensor, author, extraData, method=getShapeSimScore, alpha=0.0, omit=[0]
)
self.output('Most Similar to "%s":' % author)
mostSimilarTable = texttable.Texttable()
rows = [["Author", "Score", "Citations", "Publications"]]
rows += [[name, score, citationCounts[name], publicationCounts[name]] for name, score in mostSimilar]
mostSimilarTable.add_rows(rows)
self.output(mostSimilarTable.draw())
# Output all similarity scores
outputPath = os.path.join(
"../results", "authors", "intermediate", "%s-shapesim-cppa-relative" % author.replace(" ", "")
)
cPickle.dump(similarityScores, open(outputPath, "wb"))
def runFor(self, conference, adjTensor, extraData, confCitations, confPublications):
print("Running for %s..." % conference)
# Find the top 10 most similar nodes to some given node
mostSimilar, similarityScores = findMostSimilarNodes(
adjTensor, conference, extraData, method=getShapeSimScore, alpha=1.0, omit=[]
)
self.output('Most Similar to "%s":' % conference)
mostSimilarTable = texttable.Texttable()
rows = [['Conference', 'Score', 'Publications', 'Citations', 'Average Citation Per Paper']]
rows += [[name, score, confPublications[name], confCitations[name],
(float(confCitations[name]) / confPublications[name])] for name, score in mostSimilar]
mostSimilarTable.add_rows(rows)
self.output(mostSimilarTable.draw())
# Output all similarity scores
outputPath = os.path.join(
'../results', 'conferences', 'intermediate', '%s-shapesim-tppc' % conference.replace(' ', '')
)
cPickle.dump(similarityScores, open(outputPath, 'wb'))
def runFor(self, author, adjMatrix, extraData, citationCounts,
publicationCounts):
print("Running for %s..." % author)
# Find the top 10 most similar nodes to some given node
mostSimilar, similarityScores = findMostSimilarNodes(
adjMatrix, author, extraData, method=getNeighborSimScore)
self.output('Most Similar to "%s":' % author)
mostSimilarTable = texttable.Texttable()
rows = [['Author', 'Score', 'Citations', 'Publications']]
rows += [[name, score, citationCounts[name], publicationCounts[name]]
for name, score in mostSimilar]
mostSimilarTable.add_rows(rows)
self.output(mostSimilarTable.draw())
# Output all similarity scores
outputPath = os.path.join(
'../../results', 'authors', 'intermediate',
'%s-neighborsim-ppa' % author.replace(' ', ''))
cPickle.dump(similarityScores, open(outputPath, 'wb'))
