def friend_user_change(dbname1, dbname2, comname1, comname2):
filter_que = {'level': 1, 'liwc_anal.result.WC':{'$exists':True}}
user2 = iot.get_values_one_field(dbname2, comname2, 'id', filter_que)
fields = ['liwc_anal.result.posemo', 'liwc_anal.result.negemo', 'liwc_anal.result.body', 'liwc_anal.result.ingest']
network1 = gt.load_network(dbname1, 'net')
network2 = gt.load_network(dbname2, 'net')
for field in fields:
print '-----------------%s----------------' %field
user_changes, friends_changes = [], []
for uid in user2:
user_feature_old = iot.get_values_one_field(dbname1, comname1, field, {'id': uid})
user_feature_new = iot.get_values_one_field(dbname2, comname2, field, {'id': uid})
if len(user_feature_old) != len(user_feature_new) and len(user_feature_new) != 1:
print 'User feature value length %d, %d' %(len(user_feature_old), len(user_feature_new))
user_change = np.mean(user_feature_new) - np.mean(user_feature_old)
exist = True
try:
v = network1.vs.find(name=str(uid))
v = network2.vs.find(name=str(uid))
except ValueError:
exist = False
if exist:
friends_old = network1.successors(str(uid))
friends_new = network2.successors(str(uid))
old_friend_ids = [int(network1.vs[v]['name']) for v in friends_old]
new_friend_ids = [int(network2.vs[v]['name']) for v in friends_new]
if len(old_friend_ids) != len(new_friend_ids):
print 'Friend feature value length %d, %d' % (len(old_friend_ids), len(new_friend_ids))
friends_feature_old = iot.get_values_one_field(dbname1, comname1, field, {'id': {'$in': old_friend_ids}})
friends_feature_new = iot.get_values_one_field(dbname2, comname2, field, {'id': {'$in': new_friend_ids}})
friend_change = np.mean(friends_feature_new) - np.mean(friends_feature_old)
friends_changes.append(friend_change)
user_changes.append(user_change)
pltt.correlation(friends_changes, user_changes, r'$\Delta$(F_'+field+')', r'$\Delta$(U_'+field+')', field+'-friend-user.pdf')
def friend_user_change(dbname1, dbname2, comname1, comname2):
filter_que = {'level': 1, 'liwc_anal.result.WC':{'$exists':True}}
user2 = iot.get_values_one_field(dbname2, comname2, 'id', filter_que)
fields = ['liwc_anal.result.posemo', 'liwc_anal.result.negemo', 'liwc_anal.result.body', 'liwc_anal.result.ingest']
network1 = gt.load_network(dbname1, 'net')
network2 = gt.load_network(dbname2, 'net')
for field in fields:
print '-----------------%s----------------' %field
user_changes, friends_changes = [], []
for uid in user2:
user_feature_old = iot.get_values_one_field(dbname1, comname1, field, {'id': uid})
user_feature_new = iot.get_values_one_field(dbname2, comname2, field, {'id': uid})
if len(user_feature_old) != len(user_feature_new) and len(user_feature_new) != 1:
print 'User feature value length %d, %d' %(len(user_feature_old), len(user_feature_new))
user_change = np.mean(user_feature_new) - np.mean(user_feature_old)
exist = True
try:
v = network1.vs.find(name=str(uid))
v = network2.vs.find(name=str(uid))
except ValueError:
exist = False
if exist:
friends_old = network1.successors(str(uid))
friends_new = network2.successors(str(uid))
old_friend_ids = [int(network1.vs[v]['name']) for v in friends_old]
new_friend_ids = [int(network2.vs[v]['name']) for v in friends_new]
if len(old_friend_ids) != len(new_friend_ids):
print 'Friend feature value length %d, %d' % (len(old_friend_ids), len(new_friend_ids))
friends_feature_old = iot.get_values_one_field(dbname1, comname1, field, {'id': {'$in': old_friend_ids}})
friends_feature_new = iot.get_values_one_field(dbname2, comname2, field, {'id': {'$in': new_friend_ids}})
friend_change = np.mean(friends_feature_new) - np.mean(friends_feature_old)
friends_changes.append(friend_change)
user_changes.append(user_change)
pltt.correlation(friends_changes, user_changes, r'$\Delta$(F_'+field+')', r'$\Delta$(U_'+field+')', field+'-friend-user.pdf')
def correlation():
df = pd.read_csv('ded.csv')
df = df.dropna(subset=['Oldposemo', 'Newposemo'])
Outdegree = df['NewOutdegree'] - df['OldOutdegree']
Ingest = df['Newnegemo'] - df['Oldnegemo']
# pt.correlation((Outdegree-np.mean(Outdegree))/np.std(Outdegree),
# (Ingest-np.mean(Ingest))/np.std(Ingest), 'Followee', 'Negative Emotion', 'followee-negemo-95.pdf')
pt.correlation(Outdegree, Ingest, 'Out-degree', 'Negative Emotion', 'outd-negemo-95.pdf')
def correlation():
df = pd.read_csv('ded.csv')
df = df.dropna(subset=['Oldposemo', 'Newposemo'])
Outdegree = df['NewOutdegree'] - df['OldOutdegree']
Ingest = df['Newnegemo'] - df['Oldnegemo']
# pt.correlation((Outdegree-np.mean(Outdegree))/np.std(Outdegree),
# (Ingest-np.mean(Ingest))/np.std(Ingest), 'Followee', 'Negative Emotion', 'followee-negemo-95.pdf')
pt.correlation(Outdegree, Ingest, 'Out-degree', 'Negative Emotion',
'outd-negemo-95.pdf')
def coreness_features(g):
"""Correlation of K-core and feature values"""
g = g.as_undirected(mode="collapse")
all_coreness = g.shell_index(mode='ALL')
g.vs['core'] = all_coreness
fields = iot.read_fields()
for field in fields:
gt.add_attribute(g, 'pof', 'fed', 'com', field)
vlist = g.vs.select(pof_ne=-1000000000.0)['core']
flist = g.vs.select(pof_ne=-1000000000.0)['pof']
pt.correlation(vlist, flist, 'K-Core', 'Feature', 'data/corerel/'+field+'.pdf')
def coreness_features(g):
"""Correlation of K-core and feature values"""
g = g.as_undirected(mode="collapse")
all_coreness = g.shell_index(mode='ALL')
g.vs['core'] = all_coreness
fields = iot.read_fields()
for field in fields:
gt.add_attribute(g, 'pof', 'fed', 'com', field)
vlist = g.vs.select(pof_ne=-1000000000.0)['core']
flist = g.vs.select(pof_ne=-1000000000.0)['pof']
pt.correlation(vlist, flist, 'K-Core', 'Feature',
'data/corerel/' + field + '.pdf')
def rank_feature(gc, dbname, comname, db_field_names, directed=True):
g = gt.giant_component(gc, 'WEAK')
g.vs['nt'] = g.degree(type="in")
netatt = g.vs['nt']
# ranks = g.pagerank(weights='weight')
# g.vs['rank'] = ranks
# cor = st.tau_coef(g.degree(type="in"), g.vs['rank'])
# print 'Indegree' + '\t' + str(cor[0]) + '\t' + str(cor[1])
# cor = st.tau_coef(g.degree(type="out"), g.vs['rank'])
# print 'Outdegree' + '\t' + str(cor[0]) + '\t' + str(cor[1])
for db_field_name in db_field_names:
# print 'Processing ' + db_field_name
g = gt.add_attribute(g, 'foi', dbname, comname, db_field_name)
raw_values = np.array(g.vs['foi'])
values = drop_initials(raw_values)
if len(values) > 100:
# maxv, minv = max(values), min(values)
maxv, minv = np.percentile(values,
97.5), np.percentile(values, 2.5)
vs = g.vs(foi_ge=minv, foi_le=maxv)
sg = g.subgraph(vs)
maxd, mind = np.percentile(netatt,
97.5), np.percentile(netatt, 2.5)
vs = sg.vs(nt_ge=mind, nt_le=maxd)
sg = sg.subgraph(vs)
# cor = st.tau_coef(sg.vs['foi'], sg.vs['nt'])
# print db_field_name + '\t' + str(len(sg.vs)) + '\t' + str(len(sg.es)) + '\t'\
# + str(min(netatt)) + '\t' + str(max(netatt)) + '\t' + str(mind) + '\t'\
# +str(maxd) + '\t' \
# + str(min(values)) + '\t' + str(max(values)) + '\t' + str(minv) + '\t'\
# +str(maxv) + '\t'\
# + str(cor[0]) + '\t' + str(cor[1])
pt.correlation(sg.vs['nt'], sg.vs['foi'], 'Indegree', 'Feature',
'data/' + db_field_name + '.pdf')
def rank_feature(gc, dbname, comname, db_field_names, directed=True):
g = gt.giant_component(gc, "WEAK")
g.vs["nt"] = g.degree(type="in")
netatt = g.vs["nt"]
# ranks = g.pagerank(weights='weight')
# g.vs['rank'] = ranks
# cor = st.tau_coef(g.degree(type="in"), g.vs['rank'])
# print 'Indegree' + '\t' + str(cor[0]) + '\t' + str(cor[1])
# cor = st.tau_coef(g.degree(type="out"), g.vs['rank'])
# print 'Outdegree' + '\t' + str(cor[0]) + '\t' + str(cor[1])
for db_field_name in db_field_names:
# print 'Processing ' + db_field_name
g = gt.add_attribute(g, "foi", dbname, comname, db_field_name)
raw_values = np.array(g.vs["foi"])
values = drop_initials(raw_values)
if len(values) > 100:
# maxv, minv = max(values), min(values)
maxv, minv = np.percentile(values, 97.5), np.percentile(values, 2.5)
vs = g.vs(foi_ge=minv, foi_le=maxv)
sg = g.subgraph(vs)
maxd, mind = np.percentile(netatt, 97.5), np.percentile(netatt, 2.5)
vs = sg.vs(nt_ge=mind, nt_le=maxd)
sg = sg.subgraph(vs)
# cor = st.tau_coef(sg.vs['foi'], sg.vs['nt'])
# print db_field_name + '\t' + str(len(sg.vs)) + '\t' + str(len(sg.es)) + '\t'\
# + str(min(netatt)) + '\t' + str(max(netatt)) + '\t' + str(mind) + '\t'\
# +str(maxd) + '\t' \
# + str(min(values)) + '\t' + str(max(values)) + '\t' + str(minv) + '\t'\
# +str(maxv) + '\t'\
# + str(cor[0]) + '\t' + str(cor[1])
pt.correlation(sg.vs["nt"], sg.vs["foi"], "Indegree", "Feature", "data/" + db_field_name + ".pdf")
def compare_periods(filename, prefix):
# parser = lambda date: pd.datetime.strptime(date, '%Y-%m-%d %H:%M:%S')
# df = pd.read_csv(filename, parse_dates=['user_created_time', 'first_tweet_time', 'last_tweet_time'], date_parser=parser, index_col=[0])
df = pd.read_pickle(filename+'.pick')
# print df.describe()
users = set(df['user_id'])
print 'number of users: %d' %len(users)
print 'All features:'
names = list(df.columns.values)
print names
noi = ['posemo', 'negemo', 'health', 'body', 'ingest', 'death']
# recover = set([u'946529629', u'1682256595', u'1209898242', u'1250864694', u'2600203795', u'1849655365', u'2956069936', u'880912610', u'1590161850', u'2484784148', u'2525172540', u'521655711', u'2252626398', u'741449148', u'2627692395', u'1458224383', u'1054225837', u'2365997659', u'2327948041', u'2747144492', u'867352153', u'536752131', u'2269511532', u'894435654', u'1167549211', u'846477558', u'870618870', u'1078223436', u'1551911419', u'2824684680', u'4818062473', u'899052614', u'623101932', u'2358940014', u'2357260466', u'891608479', u'622140829', u'1268301654', u'178030167', u'1055888702', u'939440118', u'1220107045', u'3082368646', u'1539188581', u'568455627', u'2746904423', u'3396249681', u'1419350881', u'1705683883', u'2844615608', u'1596203167', u'1319345712', u'780824682', u'734717672', u'1284716263', u'799412802', u'1353540547', u'1876143192', u'3173952120', u'925391660', u'3311796872', u'3260610991', u'3073404545', u'1567191541', u'2223419558', u'910747506', u'1273328341', u'286722806', u'1317062646', u'792872552', u'1489055828', u'2258210623', u'816538232', u'1482675907', u'1229839622', u'1443048278', u'1240523563', u'1674059323', u'2255985468', u'1463477515', u'1300098991', u'360698200', u'932996862', u'1367465329', u'2240053074', u'982964593', u'1619392266', u'2800734048', u'2306743449', u'1626162828', u'1407697442', u'1904180432', u'719277854', u'1614628700', u'2221590855', u'2506154331', u'2151402855', u'2225893177', u'436420640', u'1918910587', u'2747064597', u'1666187750', u'538509802', u'1109557784', u'2713821854', u'777200318', u'1284851154', u'273203971', u'1675281260', u'258440340', u'377619516', u'701678826', u'990053641', u'621888786', u'904365414', u'1058088361', u'635889252', u'164480604', u'487728741', u'1872463484', u'3137701603', u'1494402829', u'781608757', u'220130271', u'725002621', u'599620600', u'1265412168', u'783742651', u'605038884', u'2543905130', u'1049258474', u'1347840504', u'2223704572', u'1134725642', u'869214949', u'304458528', u'908823343', u'899386196', u'527952865', u'888376346', u'1581591648', u'2567802096', u'1463659819', u'614174044', u'1252412918', u'215174706', u'1076293386', u'1442393875', u'2288553834', u'1167069050', u'745468003', u'717697231', u'45273301', u'2419593438', u'996552967', u'602433517', u'413364530', u'1230090788', u'569266852', u'809956010', u'2204726011', u'413561706', u'320967613', u'1005786006', u'1047844286', u'503503094', u'1257657252', u'609171099', u'1483847354', u'1216984920', u'1153308649', u'621484126', u'162011268', u'1088021534', u'1276255952', u'1539094772', u'381794593', u'1420982640', u'1423333874', u'543750643', u'618338817', u'292256172', u'1316502037', u'199752409', u'457136874', u'1186389506', u'1549237184', u'1120647768', u'411794527', u'1418985170', u'1262967985', u'2886089921', u'1525816806', u'852164156', u'1720942249', u'1187274426', u'1635081919', u'2844303053', u'1693966087', u'739655448', u'3379953496', u'833937307', u'2840611944', u'967248524', u'1921762350', u'2534293513', u'2492219417', u'565223880', u'928154102', u'1093878319', u'519692975', u'2657362720', u'630629210', u'2276475811', u'557442390', u'390350321', u'1445144167', u'2572789899', u'1350874850', u'2852489594', u'492964360', u'2891295326', u'1126132572', u'722173116', u'385141868', u'3093482777', u'1283108131', u'482340295', u'2244652602', u'1429540550', u'847529941', u'1107595075', u'1242788161', u'1668723068', u'559067952', u'1242026917', u'1729131446', u'593237064', u'1029313632', u'846211285', u'707163436', u'2451953051', u'928462478', u'3340269245', u'1030833176', u'1656199405', u'530839880', u'2323126634', u'1068718476', u'854652230', u'3165477317', u'1023204037', u'358928191', u'886193125', u'1115264185', u'1411792002', u'4614995115', u'764541175', u'4767809181', u'540401600', u'457519100', u'332770861', u'2273960540', u'548884928', u'817254450', u'1496790434', u'2362224704', u'737889739', u'1220844366', u'2806743010', u'1120611552', u'2647563619', u'1285514569', u'1914026989', u'2375449677', u'1416609986', u'1424235312', u'713504342', u'2172964410', u'1722276122', u'809827358', u'1049261317', u'1470817399', u'579373511', u'593688348', u'2878516074', u'1700498090', u'1463030096', u'1276019382', u'750382572', u'579278907', u'2758869202', u'350604277', u'598829382', u'469090885', u'2808908655', u'3020433891', u'2850499568', u'2234796254', u'1586534125', u'261996598', u'791079109', u'1388295048', u'1385256074', u'1236433207', u'1198185026', u'898649935', u'1945261902'])
# recovery_users = (set(users).intersection(recover))
for name in noi[:2]:
changes = []
# recovery_changes = []
# non_recovery_changes = []
negatives = []
positives = []
active_time = []
for user in users:
uvs = df[(df.user_id == user)].loc[:, ['time_index', name,
'user_created_time',
'last_tweet_time',
'first_tweet_time',
'count']]
# print uvs
if len(uvs) == 2:
# print user
old = uvs.iloc[0][name]
new = uvs.iloc[1][name]
# print period
if old != 0:
change = (new - old)
period0 = (uvs.iloc[0]['last_tweet_time'] - uvs.iloc[0]['first_tweet_time']).days+1
period1 = (uvs.iloc[1]['last_tweet_time'] - uvs.iloc[1]['first_tweet_time']).days+1
activity0 = float(uvs.iloc[0]['count'])/(period0)
activity1 = float(uvs.iloc[1]['count'])/(period1)
# print change
if change > 0:
positives.append(user)
elif change < 0:
negatives.append(user)
changes.append(change)
# if user in recover:
# recovery_changes.append(change)
# else:
# non_recovery_changes.append(change)
active_time.append(activity1-activity0)
print '----------------------'+name+'---------------'
print 'All users changes: ', len(changes)
print 'Mean of changes: %.3f' %np.mean(changes)
# print 'Recovery users changes: ', len(recovery_changes)
# print 'Non-Recovery users changes: ',len(non_recovery_changes)
# print 'Value-Increased Recovery users:'
# print len(set(positives).intersection(recover))
# # print positives[:10]
# print 'Value-Decreased Recovery susers:'
# print len(set(negatives).intersection(recover))
# # print negatives[:10]
# sns.distplot(changes, label='All user '+str(len(changes)), hist=False)
# sns.distplot(recovery_changes, label='Recovery user '+str(len(recovery_changes)), hist=False)
# sns.distplot(non_recovery_changes, label='Non-Recovery user '+str(len(non_recovery_changes)), hist=False)
# plt.xlabel(r'$\Delta$('+name+')')
# plt.ylabel(r'p($\Delta$)')
# plt.legend()
# plt.savefig(name+'change-recovery.pdf')
# plt.clf()
pltt.correlation(changes, active_time, r'$\Delta$('+name+')', r'$\Delta$(Activity)', prefix+name+'activity-change.pdf')
def compare_periods(filename, prefix):
# parser = lambda date: pd.datetime.strptime(date, '%Y-%m-%d %H:%M:%S')
# df = pd.read_csv(filename, parse_dates=['user_created_time', 'first_tweet_time', 'last_tweet_time'], date_parser=parser, index_col=[0])
df = pd.read_pickle(filename + '.pick')
# print df.describe()
users = set(df['user_id'])
print 'number of users: %d' % len(users)
print 'All features:'
names = list(df.columns.values)
print names
noi = ['posemo', 'negemo', 'health', 'body', 'ingest', 'death']
# recover = set([u'946529629', u'1682256595', u'1209898242', u'1250864694', u'2600203795', u'1849655365', u'2956069936', u'880912610', u'1590161850', u'2484784148', u'2525172540', u'521655711', u'2252626398', u'741449148', u'2627692395', u'1458224383', u'1054225837', u'2365997659', u'2327948041', u'2747144492', u'867352153', u'536752131', u'2269511532', u'894435654', u'1167549211', u'846477558', u'870618870', u'1078223436', u'1551911419', u'2824684680', u'4818062473', u'899052614', u'623101932', u'2358940014', u'2357260466', u'891608479', u'622140829', u'1268301654', u'178030167', u'1055888702', u'939440118', u'1220107045', u'3082368646', u'1539188581', u'568455627', u'2746904423', u'3396249681', u'1419350881', u'1705683883', u'2844615608', u'1596203167', u'1319345712', u'780824682', u'734717672', u'1284716263', u'799412802', u'1353540547', u'1876143192', u'3173952120', u'925391660', u'3311796872', u'3260610991', u'3073404545', u'1567191541', u'2223419558', u'910747506', u'1273328341', u'286722806', u'1317062646', u'792872552', u'1489055828', u'2258210623', u'816538232', u'1482675907', u'1229839622', u'1443048278', u'1240523563', u'1674059323', u'2255985468', u'1463477515', u'1300098991', u'360698200', u'932996862', u'1367465329', u'2240053074', u'982964593', u'1619392266', u'2800734048', u'2306743449', u'1626162828', u'1407697442', u'1904180432', u'719277854', u'1614628700', u'2221590855', u'2506154331', u'2151402855', u'2225893177', u'436420640', u'1918910587', u'2747064597', u'1666187750', u'538509802', u'1109557784', u'2713821854', u'777200318', u'1284851154', u'273203971', u'1675281260', u'258440340', u'377619516', u'701678826', u'990053641', u'621888786', u'904365414', u'1058088361', u'635889252', u'164480604', u'487728741', u'1872463484', u'3137701603', u'1494402829', u'781608757', u'220130271', u'725002621', u'599620600', u'1265412168', u'783742651', u'605038884', u'2543905130', u'1049258474', u'1347840504', u'2223704572', u'1134725642', u'869214949', u'304458528', u'908823343', u'899386196', u'527952865', u'888376346', u'1581591648', u'2567802096', u'1463659819', u'614174044', u'1252412918', u'215174706', u'1076293386', u'1442393875', u'2288553834', u'1167069050', u'745468003', u'717697231', u'45273301', u'2419593438', u'996552967', u'602433517', u'413364530', u'1230090788', u'569266852', u'809956010', u'2204726011', u'413561706', u'320967613', u'1005786006', u'1047844286', u'503503094', u'1257657252', u'609171099', u'1483847354', u'1216984920', u'1153308649', u'621484126', u'162011268', u'1088021534', u'1276255952', u'1539094772', u'381794593', u'1420982640', u'1423333874', u'543750643', u'618338817', u'292256172', u'1316502037', u'199752409', u'457136874', u'1186389506', u'1549237184', u'1120647768', u'411794527', u'1418985170', u'1262967985', u'2886089921', u'1525816806', u'852164156', u'1720942249', u'1187274426', u'1635081919', u'2844303053', u'1693966087', u'739655448', u'3379953496', u'833937307', u'2840611944', u'967248524', u'1921762350', u'2534293513', u'2492219417', u'565223880', u'928154102', u'1093878319', u'519692975', u'2657362720', u'630629210', u'2276475811', u'557442390', u'390350321', u'1445144167', u'2572789899', u'1350874850', u'2852489594', u'492964360', u'2891295326', u'1126132572', u'722173116', u'385141868', u'3093482777', u'1283108131', u'482340295', u'2244652602', u'1429540550', u'847529941', u'1107595075', u'1242788161', u'1668723068', u'559067952', u'1242026917', u'1729131446', u'593237064', u'1029313632', u'846211285', u'707163436', u'2451953051', u'928462478', u'3340269245', u'1030833176', u'1656199405', u'530839880', u'2323126634', u'1068718476', u'854652230', u'3165477317', u'1023204037', u'358928191', u'886193125', u'1115264185', u'1411792002', u'4614995115', u'764541175', u'4767809181', u'540401600', u'457519100', u'332770861', u'2273960540', u'548884928', u'817254450', u'1496790434', u'2362224704', u'737889739', u'1220844366', u'2806743010', u'1120611552', u'2647563619', u'1285514569', u'1914026989', u'2375449677', u'1416609986', u'1424235312', u'713504342', u'2172964410', u'1722276122', u'809827358', u'1049261317', u'1470817399', u'579373511', u'593688348', u'2878516074', u'1700498090', u'1463030096', u'1276019382', u'750382572', u'579278907', u'2758869202', u'350604277', u'598829382', u'469090885', u'2808908655', u'3020433891', u'2850499568', u'2234796254', u'1586534125', u'261996598', u'791079109', u'1388295048', u'1385256074', u'1236433207', u'1198185026', u'898649935', u'1945261902'])
# recovery_users = (set(users).intersection(recover))
for name in noi[:2]:
changes = []
# recovery_changes = []
# non_recovery_changes = []
negatives = []
positives = []
active_time = []
for user in users:
uvs = df[(df.user_id == user)].loc[:, [
'time_index', name, 'user_created_time', 'last_tweet_time',
'first_tweet_time', 'count'
]]
# print uvs
if len(uvs) == 2:
# print user
old = uvs.iloc[0][name]
new = uvs.iloc[1][name]
# print period
if old != 0:
change = (new - old)
period0 = (uvs.iloc[0]['last_tweet_time'] -
uvs.iloc[0]['first_tweet_time']).days + 1
period1 = (uvs.iloc[1]['last_tweet_time'] -
uvs.iloc[1]['first_tweet_time']).days + 1
activity0 = float(uvs.iloc[0]['count']) / (period0)
activity1 = float(uvs.iloc[1]['count']) / (period1)
# print change
if change > 0:
positives.append(user)
elif change < 0:
negatives.append(user)
changes.append(change)
# if user in recover:
# recovery_changes.append(change)
# else:
# non_recovery_changes.append(change)
active_time.append(activity1 - activity0)
print '----------------------' + name + '---------------'
print 'All users changes: ', len(changes)
print 'Mean of changes: %.3f' % np.mean(changes)
# print 'Recovery users changes: ', len(recovery_changes)
# print 'Non-Recovery users changes: ',len(non_recovery_changes)
# print 'Value-Increased Recovery users:'
# print len(set(positives).intersection(recover))
# # print positives[:10]
# print 'Value-Decreased Recovery susers:'
# print len(set(negatives).intersection(recover))
# # print negatives[:10]
# sns.distplot(changes, label='All user '+str(len(changes)), hist=False)
# sns.distplot(recovery_changes, label='Recovery user '+str(len(recovery_changes)), hist=False)
# sns.distplot(non_recovery_changes, label='Non-Recovery user '+str(len(non_recovery_changes)), hist=False)
# plt.xlabel(r'$\Delta$('+name+')')
# plt.ylabel(r'p($\Delta$)')
# plt.legend()
# plt.savefig(name+'change-recovery.pdf')
# plt.clf()
pltt.correlation(changes, active_time, r'$\Delta$(' + name + ')',
r'$\Delta$(Activity)',
prefix + name + 'activity-change.pdf')
