def test():
authorities = get_authorities()
algo_ii.populate_iks_dict()
report = open( 'report.txt', 'wb' )
data = open('papers30000.csv', 'r')
reader = csv.reader(data, delimiter=',', quotechar='\"')
total_hits = 0
count = 0
total_citations = 0
for row in reader:
index = row[1]
count += 1
citations = row[6].split(';')
keywords = row[9].split(';')
del keywords[-1]
total_citations += len(citations)
recommendations = algo_ii.predict_citations( keywords, authorities )
hits = 0
for citation in citations:
if recommendations.has_key(citation):
hits += 1
total_hits += hits
print( index + " accuracy: " + str(hits) + "/" + str(len(citations)) + "\n" )
report.write(index + " accuracy: " + str(hits) + "/" + str(len(citations)) + "\n")
if (count % 50) == 0:
print( "temp is:" )
print( total_hits )
print( "out of " )
print( total_citations )
print( "achieved accuracy:" )
print( total_hits/float(total_citations) )
report.close()
data.close()
def _specific_test( papers, authorities, test_ids ):
for pid in test_ids:
print( 'ID = '+ pid )
pid = int(pid)
abstract = list( papers[papers['INDEX'] == pid]['ABSTRACT'] )[0]
print( abstract[:45] +'...' )
keywords = list( papers[papers['INDEX'] == pid]['KEYWORDS'] )[0].split(';')
del keywords[-1]
recommendations = algo_ii.predict_citations( keywords, authorities )
citations = list( papers[papers['INDEX'] == pid]['REF_ID'] )[0].split(';')
hits = 0
for citation in citations:
if recommendations.has_key(citation):
hits += 1
print( 'Recommendations for '+ str(pid) +':' )
print( '> '+ str( sorted( recommendations.keys() ) ) )
print( 'Actuals:\n> '+ str( sorted(citations) ) )
print( 'Hits = '+ str(hits) +'\n' )
