#user_sns = [line.strip() for line in open(sys.argv[3]).readlines()]
user_sns = ['Neuro_Skeptic']
print 'num users: ', len(user_sns)
of = codecs.open("output_fil.tsv", "w", "utf8")
for i in range(len(user_sns)):
#creates a Twitter User object to fill with information from the API
user = TwitterUser(handles[i], screen_name=user_sns[i])
user.populate_tweets_from_api(json_output_filename=out_dir + user_sns[i] +
".json",
sleep_var=False)
user.populate_followers()
rts = 0
gt = 0
for t in user.tweets:
if t.retweeted is not None:
rts += 1
if t.geocode_info is not None:
gt += 1
of.write(
tab_stringify_newline([
user.screen_name, gt, rts,
len(user.tweets), user.earliest_tweet_time, user.latest_tweet_time,
user.name, user.n_total_tweets, user.creation_date,
user.followers_count, user.following_count
]))
of.close()
for i in range(len(user_sns)):
#creates a Twitter User object to fill with information from the API
user = TwitterUser(handles[i], screen_name=user_sns[i])
user.populate_tweets_from_api(json_output_filename=out_dir+user_sns[i]+".json",
sleep_var=False)
user.populate_followers()
rts = 0
gt = 0
for t in user.tweets:
if t.retweeted is not None:
rts+=1
if t.geocode_info is not None:
gt +=1
of.write(tab_stringify_newline([user.screen_name,
gt,
rts,
len(user.tweets),
user.earliest_tweet_time,
user.latest_tweet_time,
user.name,
user.n_total_tweets,
user.creation_date,
user.followers_count,
user.following_count]))
of.close()
ht_ht_file = codecs.open(
dir1 + "ht_ht_edgefile.tsv", "w", "utf8"
) # hashtag by hashtag network edgelist where co-occurrence in a tweet defines an edge
mention_file = codecs.open(
dir1 + "mention_edgefile.tsv", "w", "utf8"
) # user by user network edgelist of mention ties
user_url_file = codecs.open(
dir1 + "user_url_edgefile.tsv", "w", "utf8"
) # bipartite network edgelist of users and urls
url_mention_file = codecs.open(dir1 + "url_mention_edgefile.tsv", "w", "utf8") #
attribute_file = codecs.open(dir1 + "attribute.tsv", "w", "utf8") # node attribute table
geo_file = codecs.open(dir1 + "geofile.tsv", "w", "utf8") # a list of all geo tagged tweets
lang_file = codecs.open(dir1 + "langfile.tsv", "w", "utf8") # language frequencies at the user level
retweet_file = codecs.open(dir1 + "retweet.tsv", "w", "utf8") # user by user where an edge is a retweet
friend_file.write(tab_stringify_newline(["Source", "Target"]))
user_ht_file.write(tab_stringify_newline(["Source", "Target", "tweetID", "date"]))
user_url_file.write(tab_stringify_newline(["Source", "Target", "tweetID", "date", "urls_tweet"]))
ht_ht_file.write(tab_stringify_newline(["userID", "hashtag_A", "hashtag_B", "tweetID", "date"]))
mention_file.write(tab_stringify_newline(["Source", "Target", "tweetID", "date"]))
url_mention_file.write(tab_stringify_newline(["Source", "Target", "url", "tweetID", "date"]))
geo_file.write(tab_stringify_newline(["userID", "lat", "lon", "date"]))
lang_file.write(tab_stringify_newline(["userID", "lang", "count"]))
retweet_file.write(tab_stringify_newline(["Source", "retweet_sn", "tweetID", "date"]))
attribute_file.write(
tab_stringify_newline(
[
"userID",
"ScreenName",
"followingCount",
"followerCount",
except:
mkdir(dir2)
friend_file = codecs.open(dir1+"friend_edgefile.tsv", "w", "utf8")
user_ht_file = codecs.open(dir1+"user_ht_edgefile.tsv", "w", "utf8")
ht_ht_file = codecs.open(dir1+"ht_ht_edgefile.tsv", 'w', 'utf8')
mention_file = codecs.open(dir1+"mention_edgefile.tsv", "w", "utf8")
user_url_file = codecs.open(dir1+"user_url_edgefile.tsv", "w", "utf8")
url_mention_file=codecs.open(dir1+"url_mention_edgefile.tsv","w","utf8")
attribute_file = codecs.open(dir1+"attribute.tsv", "w", "utf8")
geo_file = codecs.open(dir1+"geofile.tsv", "w", "utf8")
lang_file = codecs.open(dir1+"langfile.tsv", "w", "utf8")
retweet_file=codecs.open(dir1+ "retweet.tsv", "w", "utf8")
friend_file.write(tab_stringify_newline(['Source', 'Target']))
user_ht_file.write(tab_stringify_newline(['Source', 'Target','tweetID', 'date']))
user_url_file.write(tab_stringify_newline(['Source', 'Target','tweetID', 'date','urls_tweet']))
ht_ht_file.write(tab_stringify_newline(['userID','hashtag_A','hashtag_B','tweetID','date']))
mention_file.write(tab_stringify_newline(['Source', 'Target','tweetID', 'date']))
url_mention_file.write(tab_stringify_newline(['Source', 'Target','url','tweetID', 'date']))
geo_file.write(tab_stringify_newline(['userID', 'lat', 'lon', 'date']))
lang_file.write(tab_stringify_newline(['userID', 'lang', 'count']))
retweet_file.write(tab_stringify_newline(['Source', 'retweet_sn','tweetID', 'date']))
attribute_file.write(tab_stringify_newline(['userID', 'ScreenName', 'followingCount', 'followerCount',
'tweetCount', 'tweetsCollected', 'firstTweet','lastTweet', 'creation_date',
'urlCount', 'mentionCount']))
for file in onlyfiles:
try:
user_url_file = codecs.open(
dir1 + "user_url_edgefile.tsv", "w",
"utf8") # bipartite network edgelist of users and urls
url_mention_file = codecs.open(dir1 + "url_mention_edgefile.tsv", "w",
"utf8") #
attribute_file = codecs.open(dir1 + "attribute.tsv", "w",
"utf8") # node attribute table
geo_file = codecs.open(dir1 + "geofile.tsv", "w",
"utf8") # a list of all geo tagged tweets
lang_file = codecs.open(dir1 + "langfile.tsv", "w",
"utf8") # language frequencies at the user level
retweet_file = codecs.open(
dir1 + "retweet.tsv", "w",
"utf8") # user by user where an edge is a retweet
friend_file.write(tab_stringify_newline(['Source', 'Target']))
user_ht_file.write(
tab_stringify_newline(['Source', 'Target', 'tweetID', 'date']))
user_url_file.write(
tab_stringify_newline(
['Source', 'Target', 'tweetID', 'date', 'urls_tweet']))
ht_ht_file.write(
tab_stringify_newline(
['userID', 'hashtag_A', 'hashtag_B', 'tweetID', 'date']))
mention_file.write(
tab_stringify_newline(['Source', 'Target', 'tweetID', 'date']))
url_mention_file.write(
tab_stringify_newline(['Source', 'Target', 'url', 'tweetID', 'date']))
geo_file.write(tab_stringify_newline(['userID', 'lat', 'lon', 'date']))
lang_file.write(tab_stringify_newline(['userID', 'lang', 'count']))
retweet_file.write(
print "N TO FIND: ", len(user_sns)
#user_ids = [u for u in user_ids]
user_ids = [u.lower() for u in user_sns]
out_fil = io.open(sys.argv[3], "w")
i = 0
j = 0
print len(user_sns)
out_fil.write(
tab_stringify_newline([
"id", 'name', "screen_name", 'url', 'protected', 'location',
'description', "followers_count", "friends_count", "favourites_count",
"created_at", "utc_offset", 'time_zone', "statuses_count", "lang",
"status_created_at", "status_coordinates", "status_lang",
"profile_image_url_https", "verified"
]))
while i < len(user_sns):
j += 1
print j
api_hook = handles[random.randint(0, len(handles) - 1)]
curr_ids = set(user_ids[i:(i + 100)])
user_data = api_hook.get_from_url("users/lookup.json", {
"user_id": ",".join(curr_ids),
"include_entities": "false"
})
"processed_data/50mpaths2", "dictionaries/*/*",
BOOTSTRAPPED_DICTIONARY_LOCATION)
features_from_conll_file, dictionary_data = get_all_features(
CONLL_FILE, all_dictionaries, ark_clusters, sets, names)
# get ids of all random, put in a set, then iterate through
random.seed(0)
all_random_ids = get_test_ids(CONLL_FILE, 0, -1, -1)
random.shuffle(all_random_ids)
output_file = open("results/param_tuning_results.tsv", "w")
for fold in range(5):
output, models, preds = run_all_on_test_ids(
fold,
all_random_ids[(fold * 150):((fold + 1) * 150)],
model,
features_from_conll_file,
dictionary_data,
eval_params=[.4, .45, .5, .55, .6],
cutoff_params=[.0001, .0005, .001],
use_filtered_params=[True, False],
datasets_to_use=['x', 'wv', 'x_wv', 'all_wv', 'x_wv_ls', 'full'],
regularization_params=[.53, .58, .6, .63, .65])
for o in output:
output_file.write(tab_stringify_newline(o))
output_file.close()
def run(self):
print('Worker started')
# do some initialization here
while True:
data = self.queue.get(True)
try:
if data is None:
print('ALL FINISHED!!!!', self.conn_number)
self.out_file.close()
self.user_info_out_file.close()
break
print 'collecting data'
if self.gets_user_id:
user_data = self.api_hook.get_from_url(
"users/lookup.json", {
"user_id": ",".join(data),
"include_entities": "false"
},
do_post=True)
else:
user_data = self.api_hook.get_from_url(
"users/lookup.json", {
"screen_name": ",".join(data),
"include_entities": "false"
},
do_post=True)
user_ret_ids = [str(u['id']) for u in user_data]
print len(data), len(user_ret_ids)
not_there = set.difference(set(data), set(user_ret_ids))
print len(not_there)
for u in not_there:
self.out_file.write(tab_stringify_newline([u]))
print 'sleeping'
for user in user_data:
output_data = [
user["id"],
user.get('name'), user["screen_name"],
user.get('url', ''), user['protected'],
user.get('location', ''),
user.get('description', ''), user["followers_count"],
user["friends_count"], user["created_at"],
user.get("utc_offset", ''),
user.get('time_zone',
''), user["statuses_count"], user["lang"]
]
if 'status' in user:
output_data += [
user["status"]["created_at"],
user["status"]["coordinates"]
if user['status']['coordinates'] else '',
user["status"]["lang"]
]
else:
output_data += ['', '', '']
output_data += [
user.get("profile_image_url_https", ""),
user.get("verified", "")
]
output_data = [(x.replace("\r\n", " ").replace(
"\n", " ").replace("\r", " ").replace("\t", " "))
if type(x) is str else x
for x in output_data]
output_data = [(x.replace(u"\r\n", u" ").replace(
u"\n", u" ").replace(u"\r", " ").replace(
u"\t", u" ")) if type(x) is unicode else x
for x in output_data]
to_write = tab_stringify_newline(output_data)
self.user_info_out_file.write(to_write)
sleep(15)
except KeyboardInterrupt as e:
print e
break
except Exception:
print('FAILED:: ', data)
exc_type, exc_value, exc_traceback = sys.exc_info()
print("*** print_tb:")
traceback.print_tb(exc_traceback, limit=50, file=sys.stdout)
print("*** print_exception:")
print('finished collecting data for: ', data)
def run(self):
print('Worker started')
# do some initialization here
while True:
data = self.queue.get(True)
try:
if data is None:
print('ALL FINISHED!!!!', self.conn_number)
self.out_file.close()
self.user_info_out_file.close()
break
print 'collecting data'
user_data = self.api_hook.get_from_url("users/lookup.json",
{"user_id": ",".join(data), "include_entities": "false"})
user_ret_ids = [str(u['id']) for u in user_data]
print len(data),len(user_ret_ids)
not_there = set.difference(set(data),set(user_ret_ids))
print len(not_there)
for u in not_there:
self.out_file.write(tab_stringify_newline([u]))
print 'sleeping'
for user in user_data:
output_data = [user["id"],
user.get('name'),
user["screen_name"],
user.get('url',''),
user['protected'],
user.get('location',''),
user.get('description', ''),
user["followers_count"],
user["friends_count"],
user["created_at"],
user.get("utc_offset",''),
user.get('time_zone',''),
user["statuses_count"],
user["lang"]]
if 'status' in user:
output_data += [user["status"]["created_at"],
user["status"]["coordinates"] if user['status']['coordinates'] else '',
user["status"]["lang"]]
else:
output_data += ['','','']
output_data += [user.get("profile_image_url_https",""),user.get("verified","")]
output_data = [(x.replace("\r\n"," ")
.replace("\n"," ")
.replace("\r"," ")
.replace("\t"," ")) if type(x) is str else x for x in output_data ]
output_data = [(x.replace(u"\r\n",u" ")
.replace(u"\n",u" ")
.replace(u"\r"," ")
.replace(u"\t",u" ")) if type(x) is unicode else x for x in output_data ]
to_write = tab_stringify_newline(output_data)
self.user_info_out_file.write(to_write)
sleep(15)
except Exception:
print('FAILED:: ', data)
exc_type, exc_value, exc_traceback = sys.exc_info()
print("*** print_tb:")
traceback.print_tb(exc_traceback, limit=50, file=sys.stdout)
print("*** print_exception:")
print('finished collecting data for: ', data)
"processed_data/50mpaths2",
"dictionaries/*/*",
BOOTSTRAPPED_DICTIONARY_LOCATION)
features_from_conll_file, dictionary_data = get_all_features(CONLL_FILE,all_dictionaries,ark_clusters,sets,names)
# get ids of all random, put in a set, then iterate through
random.seed(0)
all_random_ids = get_test_ids(CONLL_FILE, 0, -1, -1)
random.shuffle(all_random_ids)
output_file = open("results/param_tuning_results.tsv","w")
for fold in range(5):
output, models, preds = run_all_on_test_ids(fold,
all_random_ids[(fold*150):( (fold+1)*150)],
model,
features_from_conll_file,
dictionary_data,
eval_params = [.4,.45,.5,.55,.6],
cutoff_params=[.0001,.0005,.001],
use_filtered_params=[True,False],
datasets_to_use = ['x','wv','x_wv','all_wv','x_wv_ls','full'],
regularization_params = [.53,.58,.6,.63,.65])
for o in output:
output_file.write(tab_stringify_newline(o))
output_file.close()