def get_user_groups(delta, category=None):
seeds = Util.load_seeds()
log('Finding basic user groups for delta %s and category %s...' %
(delta, category))
(num_users, newsaholics, active_users,
common_users) = basic_groups.group_users(delta, category)
log('Finding precision experts for delta %s and category %s...' %
(delta, category))
experts_p = experts.select_experts_precision(
newsaholics.union(active_users), num_users, delta, .02, category)
log('Finding ground truths...')
gt_rankings = ground_truths.get_gt_rankings(seeds, DataSet.TESTING,
category)
log('Finding target news...')
target_news = ground_truths.find_target_news(gt_rankings, .02)
size_target_news = len(target_news)
log('Finding fscore experts for delta %s and category %s...' %
(delta, category))
experts_f = experts.select_experts_fscore(size_target_news, num_users,
delta, .02, category)
log('Finding ci experts for delta %s and category %s...' %
(delta, category))
experts_ci = experts.select_experts_ci(num_users, delta, .02, category)
experts_all = experts_p.union(experts_f).union(experts_ci)
return experts_all, newsaholics, active_users, common_users
def get_user_groups(delta, category=None):
seeds = Util.load_seeds()
log('Finding basic user groups for delta %s and category %s...' % (delta, category))
(num_users, newsaholics, active_users, common_users) = basic_groups.group_users(delta, category)
log('Finding precision experts for delta %s and category %s...' % (delta, category))
experts_p = experts.select_experts_precision(newsaholics.union(active_users),
num_users, delta, .02, category)
log('Finding ground truths...')
gt_rankings = ground_truths.get_gt_rankings(seeds, DataSet.TESTING, category)
log('Finding target news...')
target_news = ground_truths.find_target_news(gt_rankings, .02)
size_target_news = len(target_news)
log('Finding fscore experts for delta %s and category %s...' % (delta, category))
experts_f = experts.select_experts_fscore(size_target_news, num_users,
delta, .02, category)
log('Finding ci experts for delta %s and category %s...' % (delta, category))
experts_ci = experts.select_experts_ci(num_users, delta, .02, category)
experts_all = experts_p.union(experts_f).union(experts_ci)
return experts_all, newsaholics, active_users, common_users
def run():
"""Contains the main logic for this analysis."""
global _SIZE_TOP_NEWS
FileLog.set_log_dir()
seeds = Util.load_seeds()
for category in _CATEGORIES:
log('Preforming analysis for category: %s' % category)
if category:
_SIZE_TOP_NEWS = .10
else:
_SIZE_TOP_NEWS = .02
gt_rankings = ground_truths.get_gt_rankings(seeds, DataSet.TESTING,
category)
log('Num ground_truth_rankings: %s' % len(gt_rankings))
target_news = ground_truths.find_target_news(gt_rankings, _SIZE_TOP_NEWS)
log('Size target_news: %s' % len(target_news))
# for delta in _DELTAS:
for delta in [4]:
run_params_str = 'd%s_t%s_e%s_%s' % (delta, int(_SIZE_TOP_NEWS * 100),
int(_SIZE_EXPERTS * 100), category)
output_dir = '../graph/CrowdWisdomDef/%s/' % run_params_str
Util.ensure_dir_exist(output_dir)
info_output_dir = '../graph/CrowdWisdomDef/%s/info/' % run_params_str
Util.ensure_dir_exist(info_output_dir)
output_dir = '../graph/CrowdWisdomDef/%s/' % run_params_str
Util.ensure_dir_exist(output_dir)
(num_users, newsaholics,
active_users, common_users) = basic_groups.group_users(delta, category)
log('Num newsaholics: %s' % len(newsaholics))
log('Num active: %s' % len(active_users))
log('Num common: %s' % len(common_users))
common_user_buckets = common_user_groups.group_users(common_users, _NUM_GROUPS)
for i, common_user_bucket in enumerate(common_user_buckets):
print 'Number users in common user bucket %s: %s' % (i, len(common_user_bucket))
experts_precision = experts.select_experts_precision(
newsaholics.union(active_users), num_users, delta, _SIZE_EXPERTS,
category)
experts_fscore = experts.select_experts_fscore(len(target_news),
num_users,
delta, _SIZE_EXPERTS,
category)
experts_ci = experts.select_experts_ci(num_users, delta, _SIZE_EXPERTS,
category)
super_experts = experts.select_super_experts(experts_precision,
experts_fscore,
experts_ci)
log('Num experts (precision): %s' % len(experts_precision))
log('Num experts (fscore): %s' % len(experts_fscore))
log('Num experts (ci): %s' % len(experts_ci))
log('Finding rankings with an %s hour delta.' % delta)
(market_rankings, newsaholic_rankings,
active_rankings,
common_rankings) = basic_groups.get_rankings(delta, seeds, newsaholics,
active_users, category)
(expert_precision_rankings, expert_fscore_rankings,
expert_ci_rankings,
expert_s_rankings) = experts.get_rankings(delta, seeds,
experts_precision,
experts_fscore,
experts_ci,
super_experts,
category)
common_groups_rankings = common_user_groups.get_rankings(delta, seeds,
common_user_buckets,
category)
num_votes_common = 0
for url, count in common_rankings:
num_votes_common += count
log('Num common_rankings: %s' % len(common_rankings))
log('Num common votes: %s' % num_votes_common)
num_votes_expert_precision = 0
for url, count in expert_precision_rankings:
num_votes_expert_precision += count
log('Num expert_precision rankings: %s' % len(expert_precision_rankings))
log('Num expert_precision votes: %s' % num_votes_expert_precision)
num_votes_expert_fscore = 0
for url, count in expert_fscore_rankings:
num_votes_expert_fscore += count
log('Num expert_fscore rankings: %s' % len(expert_fscore_rankings))
log('Num expert_fscore votes: %s' % num_votes_expert_fscore)
num_votes_expert_ci = 0
for url, count in expert_ci_rankings:
num_votes_expert_ci += count
log('Num expert_ci rankings: %s' % len(expert_ci_rankings))
log('Num expert_ci votes: %s' % num_votes_expert_ci)
num_votes_buckets = []
for i, common_group_rankings in enumerate(common_groups_rankings):
num_votes = 0
for url, count in common_group_rankings:
num_votes += count
num_votes_buckets.append(num_votes)
log('Num common rankings (%s buckets): %s' % (i, len(common_group_rankings)))
log('Num expert_ci votes (%s buckets): %s' % (i, num_votes))
with open('%suser_demographics_%s.txt'
% (info_output_dir, run_params_str), 'w') as output_file:
output_file.write('Number of Common Users: %s\n' % len(common_users))
output_file.write('\n');
output_file.write('Number of Precision Experts: %s\n' % len(experts_precision))
output_file.write('Number of F-Score Experts: %s\n' % len(experts_fscore))
output_file.write('Number of CI Experts: %s\n' % len(experts_ci))
output_file.write('Number users per common user bucket: %s\n' %len(common_user_buckets[0]))
output_file.write('Number of Precision and F-Score Experts: %s\n'
% len(experts_precision.intersection(experts_fscore)))
output_file.write('Number of Precision and CI Experts: %s\n'
% len(experts_precision.intersection(experts_ci)))
output_file.write('Number of F-Score and CI Experts: %s\n'
% len(experts_fscore.intersection(experts_ci)))
output_file.write('\n');
output_file.write('Number of Users (Total): %s\n'
% (len(newsaholics) + len(active_users)
+ len(common_users)))
output_file.write('\n')
output_file.write('Number of votes by Common Users: %s\n'
% num_votes_common)
output_file.write('\n');
output_file.write('Number of votes by Expert (Precision) Users: %s\n'
% num_votes_expert_precision)
output_file.write('Number of votes by Expert (fscore) Users: %s\n'
% num_votes_expert_fscore)
output_file.write('Number of votes by Expert (ci) Users: %s\n'
% num_votes_expert_ci)
output_file.write('Number of votes per bucket: %s\n' % num_votes_buckets)
output_file.write('\n')
output_file.write('Total Number of Good News: %s\n' % len(target_news))
log('Ground Truth Top 5')
for i in range(min(len(gt_rankings), 5)):
url, count = gt_rankings[i]
log('[%s] %s\t%s' %(i, url.strip(), count))
log('-----------------------------------')
log('Common Top 5')
for i in range(min(len(common_rankings), 5)):
url, count = common_rankings[i]
log('[%s] %s\t%s' %(i, url.strip(), count))
log('-----------------------------------')
log('Expert (Precision) Top 5')
for i in range(min(len(expert_precision_rankings), 5)):
url, count = expert_precision_rankings[i]
log('[%s] %s\t%s' %(i, url.strip(), count))
log('-----------------------------------')
log('Expert (fscore) Top 5')
for i in range(min(len(expert_fscore_rankings), 5)):
url, count = expert_fscore_rankings[i]
log('[%s] %s\t%s' %(i, url.strip(), count))
log('-----------------------------------')
log('Expert (ci) Top 5')
for i in range(min(len(expert_ci_rankings), 5)):
url, count = expert_ci_rankings[i]
log('[%s] %s\t%s' %(i, url.strip(), count))
log('-----------------------------------')
common_precisions, common_recalls = calc_precision_recall(gt_rankings,
common_rankings)
(expert_p_precisions,
expert_p_recalls) = calc_precision_recall(gt_rankings,
expert_precision_rankings)
(expert_f_precisions,
expert_f_recalls) = calc_precision_recall(gt_rankings,
expert_fscore_rankings)
(expert_c_precisions,
expert_c_recalls) = calc_precision_recall(gt_rankings,
expert_ci_rankings)
common_group_ps = []
common_group_rs = []
for common_group_ranking in common_groups_rankings:
common_group_p, common_group_r = calc_precision_recall(gt_rankings,
common_group_ranking)
common_group_ps.append(common_group_p)
common_group_rs.append(common_group_r)
log('Drawing common group model precision-recall graph...')
common_user_groups.draw_precision_recall(common_group_ps, common_group_rs,
expert_p_precisions, expert_p_recalls,
expert_f_precisions, expert_f_recalls,
expert_c_precisions, expert_c_recalls,
run_params_str)
log('Drawing common group model precision graph...')
common_user_groups.draw_precision(common_group_ps, expert_p_precisions,
expert_f_precisions, expert_c_precisions,
run_params_str)
def find_counts(seeds, category=None):
num_0_1 = 0
num_1_4 = 0
num_4_8 = 0
num_after_8 = 0
num_total = 0
log('Finding common users delta 1...')
(num_users_1, newsaholics_1, active_users_1, common_users_1) = basic_groups.group_users(1, category)
log('Finding common users delta 4...')
(num_users_4, newsaholics_4, active_users_4, common_users_4) = basic_groups.group_users(4, category)
log('Finding common users delta 8...')
(num_users_8, newsaholics_8, active_users_8, common_users_8) = basic_groups.group_users(8, category)
copy_common_users_1 = set(common_users_1)
common_users_1_1 = set()
common_users_1_2 = set()
common_users_1_3 = set()
count = 0
while len(copy_common_users_1) > 0:
if count % 3 == 0:
common_users_1_1.add(copy_common_users_1.pop())
elif count % 3 == 1:
common_users_1_2.add(copy_common_users_1.pop())
elif count % 3 == 2:
common_users_1_3.add(copy_common_users_1.pop())
count += 1
copy_common_users_4 = set(common_users_4)
common_users_4_1 = set()
common_users_4_2 = set()
common_users_4_3 = set()
count = 0
while len(copy_common_users_4) > 0:
if count % 3 == 0:
common_users_4_1.add(copy_common_users_4.pop())
elif count % 3 == 1:
common_users_4_2.add(copy_common_users_4.pop())
elif count % 3 == 2:
common_users_4_3.add(copy_common_users_4.pop())
count += 1
copy_common_users_8 = set(common_users_8)
common_users_8_1 = set()
common_users_8_2 = set()
common_users_8_3 = set()
count = 0
while len(copy_common_users_8) > 0:
if count % 3 == 0:
common_users_8_1.add(copy_common_users_8.pop())
elif count % 3 == 1:
common_users_8_2.add(copy_common_users_8.pop())
elif count % 3 == 2:
common_users_8_3.add(copy_common_users_8.pop())
count += 1
log('Size Common Users 1 (delta 1): %s' % len(common_users_1_1))
log('Size Common Users 2 (delta 1): %s' % len(common_users_1_2))
log('Size Common Users 3 (delta 1): %s' % len(common_users_1_3))
log('Size Common Users 1 (delta 4): %s' % len(common_users_4_1))
log('Size Common Users 2 (delta 4): %s' % len(common_users_4_2))
log('Size Common Users 3 (delta 4): %s' % len(common_users_4_3))
log('Size Common Users 1 (delta 8): %s' % len(common_users_8_1))
log('Size Common Users 2 (delta 8): %s' % len(common_users_8_2))
log('Size Common Users 3 (delta 8): %s' % len(common_users_8_3))
log('Finding precision experts delta 1...')
experts_p_1 = experts.select_experts_precision(newsaholics_1.union(active_users_1),
num_users_1, 1, _SIZE_EXPERTS, category)
log('Finding precision experts delta 1...')
experts_p_4 = experts.select_experts_precision(newsaholics_4.union(active_users_4),
num_users_4, 4, _SIZE_EXPERTS, category)
log('Finding precision experts delta 1...')
experts_p_8 = experts.select_experts_precision(newsaholics_8.union(active_users_8),
num_users_8, 8, _SIZE_EXPERTS, category)
log('Finding ground truths...')
gt_rankings = ground_truths.get_gt_rankings(seeds, DataSet.TESTING, category)
log('Finding target news...')
target_news = ground_truths.find_target_news(gt_rankings, _SIZE_EXPERTS)
size_target_news = len(target_news)
log('Finding fscore experts delta 1...')
experts_f_1 = experts.select_experts_fscore(size_target_news, num_users_1,
1, _SIZE_EXPERTS, category)
log('Finding fscore experts delta 4...')
experts_f_4 = experts.select_experts_fscore(size_target_news, num_users_4,
4, _SIZE_EXPERTS, category)
log('Finding fscore experts delta 8...')
experts_f_8 = experts.select_experts_fscore(size_target_news, num_users_8,
8, _SIZE_EXPERTS, category)
log('Finding ci experts delta 1...')
experts_ci_1 = experts.select_experts_ci(num_users_1, 1, _SIZE_EXPERTS, category)
log('Finding ci experts delta 4...')
experts_ci_4 = experts.select_experts_ci(num_users_4, 4, _SIZE_EXPERTS, category)
log('Finding ci experts delta 8...')
experts_ci_8 = experts.select_experts_ci(num_users_8, 8, _SIZE_EXPERTS, category)
experts_all_1 = experts_p_1.union(experts_f_1).union(experts_ci_1)
experts_all_4 = experts_p_4.union(experts_f_4).union(experts_ci_4)
experts_all_8 = experts_p_8.union(experts_f_8).union(experts_ci_8)
num_0_1_common = 0
num_1_4_common = 0
num_4_8_common = 0
num_cu_1_1 = 0
num_cu_1_2 = 0
num_cu_1_3 = 0
num_cu_4_1 = 0
num_cu_4_2 = 0
num_cu_4_3 = 0
num_cu_8_1 = 0
num_cu_8_2 = 0
num_cu_8_3 = 0
num_0_1_experts_p = 0
num_1_4_experts_p = 0
num_4_8_experts_p = 0
num_0_1_experts_f = 0
num_1_4_experts_f = 0
num_4_8_experts_f = 0
num_0_1_experts_ci = 0
num_1_4_experts_ci = 0
num_4_8_experts_ci = 0
num_0_1_experts_all = 0
num_1_4_experts_all = 0
num_4_8_experts_all = 0
log('Finding counts...')
with open('../data/FolkWisdom/time_deltas.tsv') as input_file:
for line in input_file:
# parse line
tokens = line.split('\t')
url = tokens[_TIMEDELTAS_FILE_URL_INDEX]
time_delta = timedelta(seconds=int(tokens[_TIMEDELTAS_FILE_DELTA_INDEX]))
tweet_category = tokens[_TIMEDELTAS_FILE_CATEGORY_INDEX].strip()
user_id = tokens[_TIMEDELTAS_FILE_USER_ID_INDEX]
if url in seeds:
(seed_tweet_id, seed_user_id, seed_time) = seeds[url]
if Util.is_in_testing_set(seed_time) and category_matches(category,
tweet_category):
num_total += 1
if time_delta < timedelta(hours=1):
num_0_1 += 1
if user_id in common_users_1:
num_0_1_common += 1
if user_id in experts_p_1:
num_0_1_experts_p += 1
if user_id in experts_f_1:
num_0_1_experts_f += 1
if user_id in experts_ci_1:
num_0_1_experts_ci += 1
if user_id in experts_all_1:
num_0_1_experts_all += 1
if user_id in common_users_1_1:
num_cu_1_1 += 1
if user_id in common_users_1_2:
num_cu_1_2 += 1
if user_id in common_users_1_3:
num_cu_1_3 += 1
elif time_delta >= timedelta(hours=1) and time_delta < timedelta(hours=4):
num_1_4 += 1
if user_id in common_users_4:
num_1_4_common += 1
if user_id in experts_p_4:
num_1_4_experts_p += 1
if user_id in experts_f_4:
num_1_4_experts_f += 1
if user_id in experts_ci_4:
num_1_4_experts_ci += 1
if user_id in experts_all_4:
num_1_4_experts_all += 1
if user_id in common_users_4_1:
num_cu_4_1 += 1
if user_id in common_users_4_2:
num_cu_4_2 += 1
if user_id in common_users_4_3:
num_cu_4_3 += 1
elif time_delta >= timedelta(hours=4) and time_delta < timedelta(hours=8):
num_4_8 += 1
if user_id in common_users_8:
num_4_8_common += 1
if user_id in experts_p_8:
num_4_8_experts_p += 1
if user_id in experts_f_8:
num_4_8_experts_f += 1
if user_id in experts_ci_8:
num_4_8_experts_ci += 1
if user_id in experts_all_8:
num_4_8_experts_all += 1
if user_id in common_users_8_1:
num_cu_8_1 += 1
if user_id in common_users_8_2:
num_cu_8_2 += 1
if user_id in common_users_8_3:
num_cu_8_3 += 1
else:
num_after_8 += 1
return (num_0_1, num_0_1_common, num_0_1_experts_p, num_0_1_experts_f, num_0_1_experts_ci, num_0_1_experts_all,
num_1_4, num_1_4_common, num_1_4_experts_p, num_1_4_experts_f, num_1_4_experts_ci, num_1_4_experts_all,
num_4_8, num_4_8_common, num_4_8_experts_p, num_4_8_experts_f, num_4_8_experts_ci, num_4_8_experts_all,
num_cu_1_1, num_cu_1_2, num_cu_1_3,
num_cu_4_1, num_cu_4_2, num_cu_4_3,
num_cu_8_1, num_cu_8_2, num_cu_8_3,
num_after_8, num_total)
def get_all_user_groups(delta=4, category=None):
seeds = Util.load_seeds()
# Set up params appropriately.
data_set = DataSet.TRAINING
months = _TRAINING_SET_MONTHS
if _SWITCHED:
data_set = DataSet.TESTING
months = _TESTING_SET_MONTHS
retweets = set()
if _EXCLUDE_RETWEETS:
retweets = ground_truths.find_retweets(months)
gt_rankings = ground_truths.get_gt_rankings(seeds, data_set, category,
exclude_tweets_within_delta=_EXCLUDE_TWEETS_WITHIN_DELTA,
retweets=retweets)
target_news = ground_truths.find_target_news(gt_rankings, _SIZE_TOP_NEWS)
groups = UserGroups()
(num_users, groups.newsaholics,
groups.active_users,
groups.common_users) = basic_groups.group_users(delta, category)
groups.population = groups.newsaholics.union(groups.active_users).union(groups.common_users)
num_users_eg, groups.even_groups = even_groups.group_users(delta,
_NUM_GROUPS,
_SIZE_OF_GROUP_IN_PERCENT,
category)
groups.precision = experts.select_experts_precision(
groups.newsaholics.union(groups.active_users), num_users, delta,
_SIZE_EXPERTS, category)
groups.fscore = experts.select_experts_fscore(len(target_news),
num_users,
delta, _SIZE_EXPERTS,
category)
groups.ci = experts.select_experts_ci(num_users, delta, _SIZE_EXPERTS,
category)
groups.super_experts = experts.select_super_experts(groups.precision,
groups.fscore,
groups.ci)
groups.ci_hi, groups.ci_li = experts.split_ci_experts_by_followers(groups.ci)
groups.ci_1 = set()
groups.ci_2 = set()
groups.ci_3 = set()
counter = 0
for ci_expert in groups.ci:
if counter % 3 == 0:
groups.ci_1.add(ci_expert)
elif counter % 3 == 1:
groups.ci_2.add(ci_expert)
elif counter % 3 == 2:
groups.ci_3.add(ci_expert)
counter += 1
groups.social_bias, d_num_followers = experts.select_experts_social_bias(num_users,
_SIZE_EXPERTS)
groups.all_experts = experts.select_all_experts(groups.precision,
groups.fscore,
groups.ci)
groups.non_experts = groups.population.difference(groups.all_experts)
sample_size = int(len(groups.non_experts) * _NON_EXPERTS_SAMPLE_SIZE)
sample_size_25 = int(len(groups.non_experts) * 0.05)
sample_size_10 = int(len(groups.non_experts) * 0.10)
sample_size_1 = int(len(groups.non_experts) * 0.02)
groups.non_experts_sampled = set(random.sample(groups.non_experts, sample_size))
groups.non_experts_25 = set(random.sample(groups.non_experts, sample_size_25))
groups.non_experts_10 = set(random.sample(groups.non_experts, sample_size_10))
groups.non_experts_1 = set(random.sample(groups.non_experts, sample_size_1))
return groups, d_num_followers
def run():
Util.ensure_dir_exist(_DATA_DIR)
category = None
seeds = Util.load_seeds() #read twitter data
gt_rankings = ground_truths.get_gt_rankings(seeds, DataSet.TESTING,
category)
log('Num ground_truth_rankings: %s' % len(gt_rankings))
target_news = ground_truths.find_target_news(gt_rankings, _SIZE_TOP_NEWS)
log('Size target_news: %s' % len(target_news))
for delta in _DELTAS:
(num_users, newsaholics, active_users,
common_users) = basic_groups.group_users(delta, category)
population = newsaholics.union(active_users).union(common_users)
log('Num newsaholics: %s' % len(newsaholics))
log('Num active: %s' % len(active_users))
log('Num common: %s' % len(common_users))
log('Num users (population): %s' % len(population))
# -- Get experts --
ExpertGroup.precision = experts.select_experts_precision(
newsaholics.union(active_users), num_users, delta, _SIZE_EXPERTS,
category)
ExpertGroup.fscore = experts.select_experts_fscore(
len(target_news), num_users, delta, _SIZE_EXPERTS, category)
ExpertGroup.ci = experts.select_experts_ci(num_users, delta,
_SIZE_EXPERTS, category)
ExpertGroup.union = experts.select_all_experts(ExpertGroup.precision,
ExpertGroup.fscore,
ExpertGroup.ci)
log('Num experts (precision): %s' % len(ExpertGroup.precision))
log('Num experts (fscore): %s' % len(ExpertGroup.fscore))
log('Num experts (ci): %s' % len(ExpertGroup.ci))
log('Num all experts: %s' % len(ExpertGroup.union))
non_experts = population.difference(ExpertGroup.union)
log('Num non_experts: %s' % len(non_experts))
# other_users = population.difference(all_experts).difference(common_users)
# -- counting --
total_num_tweets = 0
hour_to_num_tweets = {}
with open('../data/FolkWisdom/time_deltas.tsv') as in_file:
for line in in_file:
tokens = line.split('\t')
time_delta_in_sec = int(tokens[_TIMEDELTAS_FILE_DELTA_INDEX])
url = tokens[_TIMEDELTAS_FILE_URL_INDEX].strip()
user_id = tokens[_TIMEDELTAS_FILE_USER_ID_INDEX]
if time_delta_in_sec > 0 and url in target_news:
current_hour = time_delta_in_sec / _NUM_SEC_PER_HOUR
total_num_tweets += 1
if current_hour not in hour_to_num_tweets:
hour_to_num_tweets[current_hour] = GroupCount()
gcount = hour_to_num_tweets[current_hour]
gcount.population += 1
if user_id in ExpertGroup.union:
gcount.union += 1
if user_id in ExpertGroup.precision:
gcount.precision += 1
if user_id in ExpertGroup.fscore:
gcount.fscore += 1
if user_id in ExpertGroup.ci:
gcount.ci += 1
else:
gcount.non_experts += 1
if user_id in common_users:
gcount.common += 1
# print >> sys.stderr, 'Error, a user in expert union but not belongs to any expert group'
# elif user_id in common_users:
# gcount.common += 1
# else :
# gcount.other += 1
# if user_id in non_experts:
# gcount.non_experts += 1
gcount = GroupCount()
with open(_DATA_DIR + 'hour_thresholds_%s.tsv' % delta,
'w') as out_file:
for hour in hour_to_num_tweets.keys():
gc = hour_to_num_tweets[hour]
gcount.add(gc)
percentage = (gcount.population /
float(total_num_tweets)) * 100.0
percentage_common = (gcount.common /
float(total_num_tweets)) * 100.0
percentage_other = (gcount.other /
float(total_num_tweets)) * 100.0
percentage_experts = (gcount.union /
float(total_num_tweets)) * 100.0
percentage_non_experts = (gcount.non_experts /
float(total_num_tweets)) * 100.0
out_file.write(
'%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\n' %
(hour, percentage, percentage_non_experts,
percentage_experts, percentage_common,
(gcount.precision / float(total_num_tweets)) * 100.0,
(gcount.fscore / float(total_num_tweets)) * 100.0,
(gcount.ci / float(total_num_tweets)) * 100.0))
log('hour\tpopulation\tnon_experts\texperts\tcommon\tprecision\tfscore\tci'
)
def run():
Util.ensure_dir_exist(_DATA_DIR)
category = None
seeds = Util.load_seeds() #read twitter data
gt_rankings = ground_truths.get_gt_rankings(seeds, DataSet.TESTING,
category)
log('Num ground_truth_rankings: %s' % len(gt_rankings))
target_news = ground_truths.find_target_news(gt_rankings, _SIZE_TOP_NEWS)
log('Size target_news: %s' % len(target_news))
for delta in _DELTAS:
(num_users, newsaholics,
active_users, common_users) = basic_groups.group_users(delta, category)
population = newsaholics.union(active_users).union(common_users)
log('Num newsaholics: %s' % len(newsaholics))
log('Num active: %s' % len(active_users))
log('Num common: %s' % len(common_users))
log('Num users (population): %s' % len(population))
# -- Get experts --
ExpertGroup.precision = experts.select_experts_precision(
newsaholics.union(active_users), num_users, delta, _SIZE_EXPERTS,
category)
ExpertGroup.fscore = experts.select_experts_fscore(len(target_news),
num_users,
delta, _SIZE_EXPERTS,
category)
ExpertGroup.ci = experts.select_experts_ci(num_users, delta, _SIZE_EXPERTS,
category)
ExpertGroup.union = experts.select_all_experts(ExpertGroup.precision,
ExpertGroup.fscore,
ExpertGroup.ci)
log('Num experts (precision): %s' % len(ExpertGroup.precision))
log('Num experts (fscore): %s' % len(ExpertGroup.fscore))
log('Num experts (ci): %s' % len(ExpertGroup.ci))
log('Num all experts: %s' % len(ExpertGroup.union))
non_experts = population.difference(ExpertGroup.union)
log('Num non_experts: %s' % len(non_experts))
# other_users = population.difference(all_experts).difference(common_users)
# -- counting --
total_num_tweets = 0
hour_to_num_tweets = {}
with open('../data/FolkWisdom/time_deltas.tsv') as in_file:
for line in in_file:
tokens = line.split('\t')
time_delta_in_sec = int(tokens[_TIMEDELTAS_FILE_DELTA_INDEX])
url = tokens[_TIMEDELTAS_FILE_URL_INDEX].strip()
user_id = tokens[_TIMEDELTAS_FILE_USER_ID_INDEX]
if time_delta_in_sec > 0 and url in target_news:
current_hour = time_delta_in_sec / _NUM_SEC_PER_HOUR
total_num_tweets += 1
if current_hour not in hour_to_num_tweets:
hour_to_num_tweets[current_hour] = GroupCount()
gcount = hour_to_num_tweets[current_hour]
gcount.population += 1
if user_id in ExpertGroup.union:
gcount.union += 1
if user_id in ExpertGroup.precision:
gcount.precision += 1
if user_id in ExpertGroup.fscore:
gcount.fscore += 1
if user_id in ExpertGroup.ci:
gcount.ci += 1
else:
gcount.non_experts += 1
if user_id in common_users:
gcount.common += 1
# print >> sys.stderr, 'Error, a user in expert union but not belongs to any expert group'
# elif user_id in common_users:
# gcount.common += 1
# else :
# gcount.other += 1
# if user_id in non_experts:
# gcount.non_experts += 1
gcount = GroupCount()
with open(_DATA_DIR + 'hour_thresholds_%s.tsv' % delta, 'w') as out_file:
for hour in hour_to_num_tweets.keys():
gc = hour_to_num_tweets[hour]
gcount.add(gc)
percentage = (gcount.population / float(total_num_tweets)) * 100.0
percentage_common = (gcount.common / float(total_num_tweets)) * 100.0
percentage_other = (gcount.other / float(total_num_tweets)) * 100.0
percentage_experts = (gcount.union / float(total_num_tweets)) * 100.0
percentage_non_experts = (gcount.non_experts / float(total_num_tweets)) * 100.0
out_file.write('%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\n' % (hour, percentage,
percentage_non_experts,
percentage_experts,
percentage_common,
(gcount.precision / float(total_num_tweets)) * 100.0,
(gcount.fscore / float(total_num_tweets)) * 100.0,
(gcount.ci / float(total_num_tweets)) * 100.0))
log('hour\tpopulation\tnon_experts\texperts\tcommon\tprecision\tfscore\tci')
