def confusionmatrix(places):
""" Show the matrix of confusion between LMs by KL-divergence
"""
lmtwt1 = dict()
lmtwt2 = dict()
for pid in places:
cur = CONN_POOL.get_cur(GEOTWEET)
cur.execute('select text from sample' \
' where place_id = \'{0}\' order by rand() limit {1}'.format(pid, 200))
text = [row['text'] for row in cur]
lmtwt1[pid] = lmfromtext(text[:80])
lmtwt2[pid] = lmfromtext(text[81:160])
confmat = list()
for lm_i in places:
confmat.append(
[kl_divergence(lmtwt1[lm_i], lmtwt2[lm_j]) for lm_j in places])
selfavg = sum([confmat[i][i] for i in range(len(places))])
mutavg = sum([sum(confmat[i]) for i in range(len(places))]) - selfavg
selfavg /= float(len(places))
mutavg /= float(len(places) * len(places) - len(places))
print selfavg, mutavg
plt.imshow(np.array(confmat), cmap=cm.gray, interpolation='nearest')
plt.yticks(range(len(places)), \
['{0}: {1}'.format(place_name(places[i]), i) for i in range(len(places))])
plt.xticks(range(len(places)))
plt.subplots_adjust(left=0.4)
plt.colorbar(shrink=0.66)
plt.savefig('sf_confm.eps')
plt.show()
def confusionmatrix(places):
""" Show the matrix of confusion between LMs by KL-divergence
"""
lmtwt1 = dict()
lmtwt2 = dict()
for pid in places:
cur = CONN_POOL.get_cur(GEOTWEET)
cur.execute('select text from sample' \
' where place_id = \'{0}\' order by rand() limit {1}'.format(pid, 200))
text = [row['text'] for row in cur]
lmtwt1[pid] = lmfromtext(text[:80])
lmtwt2[pid] = lmfromtext(text[81:160])
confmat = list()
for lm_i in places:
confmat.append([kl_divergence(lmtwt1[lm_i], lmtwt2[lm_j]) for lm_j in places])
selfavg = sum([confmat[i][i] for i in range(len(places))])
mutavg = sum([sum(confmat[i]) for i in range(len(places))]) - selfavg
selfavg /= float(len(places))
mutavg /= float(len(places)*len(places) - len(places))
print selfavg, mutavg
plt.imshow(np.array(confmat), cmap = cm.gray, interpolation='nearest')
plt.yticks(range(len(places)), \
['{0}: {1}'.format(place_name(places[i]), i) for i in range(len(places))])
plt.xticks(range(len(places)))
plt.subplots_adjust(left=0.4)
plt.colorbar(shrink=0.66)
plt.savefig('sf_confm.eps')
plt.show()
def kldiff(places):
""" compare the difference of kl-divergence between tweets and web pages
for each place in places
"""
diff = Dataset()
for pid in places:
twt = loadrows(GEOTWEET, ('place_id', 'text'),
('place_id=\'{0}\''.format(pid),), 'sample',
'order by rand() limit {0}'.format(100))
web = loadrows(GEOTWEET, ('place_id', 'web'),
('place_id=\'{0}\''.format(pid),), 'web',
'limit 25')
lmref = lmfromtext(twt['text'][:50])
lmtwt = lmfromtext(twt['text'][51:])
lmweb = lmfromtext(web['web'])
diff.append({'pid': pid, 'twtkld': kl_divergence(lmtwt, lmref),
'webkld': kl_divergence(lmweb, lmref)})
for item in diff:
print '{0} & {1} & {2}'.format(place_name(item['pid']), item['twtkld'], item['webkld'])
def kldiff(places):
""" compare the difference of kl-divergence between tweets and web pages
for each place in places
"""
diff = Dataset()
for pid in places:
twt = loadrows(GEOTWEET, ('place_id', 'text'),
('place_id=\'{0}\''.format(pid), ), 'sample',
'order by rand() limit {0}'.format(100))
web = loadrows(GEOTWEET, ('place_id', 'web'),
('place_id=\'{0}\''.format(pid), ), 'web', 'limit 25')
lmref = lmfromtext(twt['text'][:50])
lmtwt = lmfromtext(twt['text'][51:])
lmweb = lmfromtext(web['web'])
diff.append({
'pid': pid,
'twtkld': kl_divergence(lmtwt, lmref),
'webkld': kl_divergence(lmweb, lmref)
})
for item in diff:
print '{0} & {1} & {2}'.format(place_name(item['pid']), item['twtkld'],
item['webkld'])