def runTagAffinity(numQuestions):
ld.loadUsers()
ld.loadTags()
ld.loadQuestions(numQuestions, True)
folds = ld.getCVFolds()
(ttas, finalTagCounts) = getTagTermAffinityScores(folds[0][0])
# ttas_file = codecs.open('ttas-50000.txt', 'r', 'utf-8')
# tc_file = codecs.open('tcount-50000.txt', 'r', 'utf-8')
# ttas = json.load(ttas_file)
# finalTagCounts = json.load(tc_file)
# ttas_file.close()
# tc_file.close()
posts_bodies = codecs.open(posts_body_file, 'r', 'utf-8')
(sum5, sum10) = (0.0, 0.0)
counter = 0
for qid, q in folds[0][1].items():
posts_bodies.seek(q.bodyByte)
body = posts_bodies.readline()
(recall5, recall10) = getRecallScores(body, q.tags, ttas, finalTagCounts)
sum5 += recall5
sum10 += recall10
counter += 1
if counter % 100 == 0:
print 'Done %d' % counter
# print 'Q #%d: %f %f' % (qid, recall5, recall10)
return (sum5 / counter, sum10 / counter)
def runTagAffinity(numQuestions):
ld.loadUsers()
ld.loadTags()
ld.loadQuestions(numQuestions, True)
folds = ld.getCVFolds()
(ttas, finalTagCounts) = getTagTermAffinityScores(folds[0][0])
# ttas_file = codecs.open('ttas-50000.txt', 'r', 'utf-8')
# tc_file = codecs.open('tcount-50000.txt', 'r', 'utf-8')
# ttas = json.load(ttas_file)
# finalTagCounts = json.load(tc_file)
# ttas_file.close()
# tc_file.close()
posts_bodies = codecs.open(posts_body_file, 'r', 'utf-8')
(sum5, sum10) = (0.0, 0.0)
counter = 0
for qid, q in folds[0][1].items():
posts_bodies.seek(q.bodyByte)
body = posts_bodies.readline()
(recall5, recall10) = getRecallScores(body, q.tags, ttas,
finalTagCounts)
sum5 += recall5
sum10 += recall10
counter += 1
if counter % 100 == 0:
print 'Done %d' % counter
# print 'Q #%d: %f %f' % (qid, recall5, recall10)
return (sum5 / counter, sum10 / counter)
r10synpop_avg)
print '(%f, %f, %f, %f): r5 = %s, r10 = %s' % (
alpha, beta, gamma, delta, r5_tuple, r10_tuple)
if outfile:
outfile.write(
'%f,%f,%f,%f,%s,%s\n' %
(alpha, beta, gamma, delta, r5_tuple, r10_tuple))
return bestParams5, best_r5_avg, bestParams10, best_r10_avg
## Comment out this entire block if not running from Python shell
ld.loadData(True)
# This function must be run. Be careful if this is commented out.
setQuestionModelModifications(ld.questions)
folds = ld.getCVFolds()
print 'Generating word vectors'
frequentWords, wordToIndex = wordvectors.getFrequentWords(ld.questions)
wordVecs = wordvectors.getWordVectors(ld.questions, wordToIndex)
## End block
counter = 0
recall_test_scores = [0.0, 0.0]
for fold in folds[0:5]:
resetModels()
counter += 1
print 'Starting Fold %d' % counter
trainQuestions = fold[0]
print 'Fold size %d' % len(fold[0])
comTagCombineModelTrain(trainQuestions)
print 'Train complete. Beginning test.'
if r10_avg > best_r10_avg:
best_r10_avg = r10_avg
updateParameters(alpha, beta, gamma, delta, bestParams10)
r5_tuple = (r5_avg, r5pop_avg, r5syn_avg, r5synpop_avg)
r10_tuple = (r10_avg, r10pop_avg, r10syn_avg, r10synpop_avg)
print '(%f, %f, %f, %f): r5 = %s, r10 = %s' % (alpha, beta, gamma, delta, r5_tuple, r10_tuple)
if outfile:
outfile.write('%f,%f,%f,%f,%s,%s\n' % (alpha, beta, gamma, delta, r5_tuple, r10_tuple))
return bestParams5, best_r5_avg, bestParams10, best_r10_avg
## Comment out this entire block if not running from Python shell
ld.loadData(True)
# This function must be run. Be careful if this is commented out.
setQuestionModelModifications(ld.questions)
folds = ld.getCVFolds()
print 'Generating word vectors'
frequentWords, wordToIndex = wordvectors.getFrequentWords(ld.questions)
wordVecs = wordvectors.getWordVectors(ld.questions, wordToIndex)
## End block
counter = 0
recall_test_scores = [0.0, 0.0]
for fold in folds[0:5]:
resetModels()
counter += 1
print 'Starting Fold %d' % counter
trainQuestions = fold[0]
print 'Fold size %d' % len(fold[0])
comTagCombineModelTrain(trainQuestions)
print 'Train complete. Beginning test.'
