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'))