if len(set(X)) >= 1 and len(set(Y)) >= 1:
spearmans.append(correlation.spearman_rho_tr(X, Y))
write('\t%s, %s: bootstrapped spearman: %.3f (dev=%.3f)' %
(condition, condition, utils.mean(spearmans), utils.dev(spearmans)))
for ktype in pair_means.keys():
write('question type: %s' % ktype)
for (condition1, consensus1) in pair_means[ktype].items():
for (condition2, consensus2) in pair_means[ktype].items():
if not consensus1 or not consensus2 or condition1 == condition2 or condition1 == 'all' or condition2 == 'all':
continue
write('\tcondition %s, %s' % (condition1, condition2))
common_ids = list(set(consensus1.keys()).intersection(consensus2.keys()))
X = [consensus1[pid] for pid in common_ids]
Y = [consensus2[pid] for pid in common_ids]
diffs = [(abs(x - y), x, y, pid) for (pid, x, y) in zip(common_ids, X, Y)]
diffs.sort()
diffs.reverse()
write('\t\tpearson: %.3f' % (correlation.pearson_rho(X, Y)))
write('\t\tspearman: %.3f' % (correlation.spearman_rho_tr(X, Y)))
for (d, x, y, pid) in diffs[:10]:
pair = s.id_to_phrases(pid)
ratings1 = pair_ratings[ktype][condition1][pid]
ratings2 = pair_ratings[ktype][condition2][pid]
(t, p) = stats.ttest_ind(ratings1, ratings2)
write('\t\t\t%s, %s: %.2f to %.2f (ttest t=%.5f, p=%.5f)' % (pair[0], pair[1], x, y, t, p))
#!/usr/bin/python -O
import sys
import correlation
import utils
survey = utils.Survey()
wordsim = {}
for line in open('dat/general.txt'):
tokens = line.split('\t')
pair = tuple(sorted([t.strip() for t in tokens[:2]]))
wordsim[pair] = float(tokens[2])
for condition in ('all', 'mturk', 'scholar'):
ratings = survey.get_ratings_by_condition('general', condition)
X = []
Y = []
for (pair_id, pair_ratings) in ratings.items():
pair = tuple(sorted(survey.id_to_phrases(pair_id)))
if pair in wordsim and len(pair_ratings) > 10:
X.append(utils.mean([r.response for r in pair_ratings if r.has_response()]))
Y.append(wordsim[pair])
else:
print 'unknown pair:', pair
print condition, correlation.pearson_rho(X, Y), correlation.spearman_rho_tr(X, Y)
utils.dev(spearmans)))
for ktype in pair_means.keys():
write('question type: %s' % ktype)
for (condition1, consensus1) in pair_means[ktype].items():
for (condition2, consensus2) in pair_means[ktype].items():
if not consensus1 or not consensus2 or condition1 == condition2 or condition1 == 'all' or condition2 == 'all':
continue
write('\tcondition %s, %s' % (condition1, condition2))
common_ids = list(
set(consensus1.keys()).intersection(consensus2.keys()))
X = [consensus1[pid] for pid in common_ids]
Y = [consensus2[pid] for pid in common_ids]
diffs = [(abs(x - y), x, y, pid)
for (pid, x, y) in zip(common_ids, X, Y)]
diffs.sort()
diffs.reverse()
write('\t\tpearson: %.3f' % (correlation.pearson_rho(X, Y)))
write('\t\tspearman: %.3f' % (correlation.spearman_rho_tr(X, Y)))
for (d, x, y, pid) in diffs[:10]:
pair = s.id_to_phrases(pid)
ratings1 = pair_ratings[ktype][condition1][pid]
ratings2 = pair_ratings[ktype][condition2][pid]
(t, p) = stats.ttest_ind(ratings1, ratings2)
write('\t\t\t%s, %s: %.2f to %.2f (ttest t=%.5f, p=%.5f)' %
(pair[0], pair[1], x, y, t, p))
write('\tcondition %s, %s' % (condition1, condition2))
pearsons = []
spearmans = []
samples_per_user = 10
for u1 in users1:
for u2 in users2:
if id(u1) == id(u2):
continue
common_pair_ids = list(
set(u1.keys()).intersection(u2.keys()))
if len(common_pair_ids) >= 5:
up = [] # user pearson samples
us = [] # user spearman samples
for i in xrange(samples_per_user):
sample_pair_ids = random.sample(common_pair_ids, 5)
X = [u1[pid] for pid in sample_pair_ids]
Y = [u2[pid] for pid in sample_pair_ids]
if len(set(X)) != 1 and len(set(Y)) != 1:
p = correlation.pearson_rho(X, Y)
s = correlation.spearman_rho_tr(X, Y)
up.append(p)
us.append(s)
if up and us:
pearsons.append(utils.mean(up))
spearmans.append(utils.mean(us))
write('\t\td unique pairs of users: %d' % len(spearmans))
write('\t\tspearman: mean=%.3f, dev=%s' %
(utils.mean(spearmans), utils.dev(spearmans)))
write('\t\tpearson: mean=%.3f, dev=%s' %
(utils.mean(pearsons), utils.dev(pearsons)))
for (condition2, users2) in users[ktype].items():
if not users1 or not users2:
continue
write('\tcondition %s, %s' % (condition1, condition2))
pearsons = []
spearmans = []
samples_per_user = 10
for u1 in users1:
for u2 in users2:
if id(u1) == id(u2):
continue
common_pair_ids = list(set(u1.keys()).intersection(u2.keys()))
if len(common_pair_ids) >= 5:
up = [] # user pearson samples
us = [] # user spearman samples
for i in xrange(samples_per_user):
sample_pair_ids = random.sample(common_pair_ids, 5)
X = [u1[pid] for pid in sample_pair_ids]
Y = [u2[pid] for pid in sample_pair_ids]
if len(set(X)) != 1 and len(set(Y)) != 1:
p = correlation.pearson_rho(X, Y)
s = correlation.spearman_rho_tr(X, Y)
up.append(p)
us.append(s)
if up and us:
pearsons.append(utils.mean(up))
spearmans.append(utils.mean(us))
write('\t\td unique pairs of users: %d' % len(spearmans))
write('\t\tspearman: mean=%.3f, dev=%s' % (utils.mean(spearmans), utils.dev(spearmans)))
write('\t\tpearson: mean=%.3f, dev=%s' % (utils.mean(pearsons), utils.dev(pearsons)))