def func():
core_ind_d = load_obj(CORE_IND_PATH)
lt_d = load_obj(IND_EDGE_PATH) #d[parent] = [child1,child2...]
translate_d = load_obj(TRANS_PATH)
adpt_time_outnei_d = {}
'''
def collector(rtmid):
new_rtmid_d = {}
rtmid_d = core_ind_d[rtmid]
rtTime = rtmid_d.pop('rtTime')
rtUid = rtmid_d.pop('rtUid')
new_rtmid_d['rtTime'] = rtTime
new_rtmid_d['rtUid'] = rtUid
for uid_ind,temp_l in rtmid_d.iteritems():
t_diff = temp_l[1]
out_neis = lt_d[uid_ind]
new_rtmid_d[uid_ind] = [t_diff, out_neis]
return new_rtmid_d
'''
for rtmid in core_ind_d:
new_rtmid_d = {}
rtmid_d = core_ind_d[rtmid]
#rtTime = rtmid_d.pop('rtTime')
#rtUid = rtmid_d.pop('rtUid')
#new_rtmid_d['rtTime'] = rtTime
#new_rtmid_d['rtUid'] = rtUid
for uid_ind,temp_l in rtmid_d.iteritems():
#print type(uid_ind)
t_diff = temp_l[1]
parent = temp_l[0]
if parent not in translate_d:
parent_ind = -10000*random.random()
else:
parent_ind = translate_d[parent]
uid_ind = str(int(uid_ind)).replace('L','')
#Sometimes one node in G does not have any outgoing nei, so I wrote this if.
#Maybe I should have a number of nodes like this ---> cnt receivers.py
#I hope it is not a mistake
if uid_ind in lt_d: #match them up is a problem!
out_neis = lt_d[uid_ind]
new_rtmid_d[uid_ind] = [t_diff,out_neis,parent_ind]
adpt_time_outnei_d[rtmid] = new_rtmid_d
save_obj(adpt_time_outnei_d,OUT_PATH)
def ind_core():
#ind it and calculate time diff together
aggre_d = load_obj(CORE_PATH)
lookup_d = load_obj(LT_PATH)
core_d_ind = {}
good = 0
bad = 0
for rtmid in aggre_d:
if random.random() <= 0.99:
continue
rtmid_d = aggre_d[rtmid]
rtUid = rtmid_d.pop('rtUid')
rtTime = rtmid_d.pop('rtTime')
rtTime = time_trans(rtTime)
core_d_ind[rtmid] = {}
for adopter in rtmid_d:
#here I am worried about what if this node does not appear in the G?
#Maybe I should record the ratio or update G dynamicly because run Louvain is not that slow here using the java package
if adopter in lookup_d:
good += 1
ind = lookup_d[adopter]
t = rtmid_d[adopter][1]
t = time_trans(t)
parent = rtmid_d[adopter][0]
t_diff = rtTime - t
t_diff = t_diff.seconds/60 + 1 #t_diff is in minutes
core_d_ind[rtmid][ind] = [parent, t_diff]
else:
bad += 1
print 'the number of cases that node in G is %d ' %good
print 'the number of bad cases is %d' %bad
save_obj(core_d_ind,CORE_OUT_PATH)
def func():
print 'Choose the size as %d, with lambda equal to %d' %(THRES_SIZE,LAMBDA)
core_d_outnei = load_obj(CORE_PATH)
cm_d = comm_dict()
comm_dist = {}
comm_dist['size'] = THRES_SIZE
comm_dist['lbd'] = LAMBDA
ind = 0
for rtmid in core_d_outnei:
rtmid_d = core_d_outnei[rtmid]
adps = []
final_size = len(rtmid_d.keys())
ind += 1
if final_size< THRES_SIZE:
continue
#tfr_list = [] # find the first THRES_SIZE adopters
tfr_dict = {}
for adp_ in rtmid_d:
t_diff = abs(int(rtmid_d[adp_][0]))+0.01*random.random()
#print 'time from root for this adp %d is %d, t_diif - T_FR = %d' %(int(adp_),t_diff,t_diff-T_FR)
tfr_dict[int(adp_)] = t_diff
tfr_list = tfr_dict.values()
tfr_list.sort()
t_time = tfr_list[THRES_SIZE-1] - tfr_list[0]
avg_t_fr = float(sum(tfr_list[0:(THRES_SIZE-1)]))/THRES_SIZE #average time for one adoption behavior happen, not average exposure time for adopters
for adp_ in tfr_dict:
t_diff = tfr_dict[adp_]
if tfr_list.index(t_diff) < THRES_SIZE:
adps.append(adp_)
#For the size version, we also need a time stamp to judge if the outnei is a NA or FNT
#So I pick the time when the 50th adopter reposted the msg as time stamp
T_FR = tfr_list[THRES_SIZE-1]
#cur_size = len(adps)
set_adps = set(adps)
cur_size = len(set_adps)
if cur_size != THRES_SIZE:
print cur_size
raise 'the size is not correct'
adp_comms,na_comms,fnt_comms = [],[],[]
#NEW VERSION
#Check dup of fnt and na before look up their comms
#this would change the entropy while only decrease the number of comm in fnt while keep the num of comms in na
na_nodes,fnt_nodes = [],[]
#adp_comms = Parallel(n_jobs=4)(delayed(comm_look_up)(adopter,cm_d) for adopter in adps)
for adp in adps:
adp_l = rtmid_d[str(adp)] #no dup in adp for it is key of one dict
#adp = int(adp)
if adp in cm_d:
#print 'in'
adp_comms.append(cm_d[adp])
outneis = list(set(adp_l[1])) # dup exists in adp_l[1], so we can set delta here, need to think twice
#need to delete all adps from this list of outneis
outneis = [x for x in outneis if x not in set_adps]
t_diff = int(adp_l[0])
t_exp = abs(T_FR - t_diff)
#comms = [cm_d[int(outnei)] for outnei in outneis]
if t_exp >= LAMBDA:
#na_comms.extend(comms)
na_nodes.extend(outneis)
else:
#fnt_comms.extend(comms)
fnt_nodes.extend(outneis)
set_na_nodes = set(na_nodes)
na_nodes_ = list(set_na_nodes)
cur_na_size = len(na_nodes_)
fnt_nodes_ = [x for x in set(fnt_nodes) if x not in set_na_nodes]
cur_fnt_size = len(fnt_nodes_)
#na_not_a_nodes = [x for x in set(na_nodes_) if x not in set_adps]
#f_not_a_nodes = [x for x in set(fnt_nodes_) if x not in set_adps]
#f_not_ana_nodes = [x for x in f_not_a_nodes if x not in set(na_nodes_)]
for node in na_nodes_:
comm = cm_d[int(node)]
na_comms.append(comm)
for node in fnt_nodes_:
comm = cm_d[int(node)]
fnt_comms.append(comm) #keep dup of comm here to calculate entropy
set_a_comms = set(adp_comms)
set_f_comms = set(fnt_comms)
set_na_comms = set(na_comms)
na_not_a_comms = [comm for comm in na_comms if comm not in set_a_comms]
f_not_a_comms = [comm for comm in fnt_comms if comm not in set_a_comms]
f_not_ana_comms = [comm for comm in fnt_comms if comm not in set_na_comms and comm not in set_a_comms]
comm_dist[rtmid] = {'adp':adp_comms,'fnt':fnt_comms,'na':na_comms,'fs':final_size,
'cs':cur_size,'cnas':cur_na_size,
'fnts':cur_fnt_size,'avg_time':avg_t_fr,'total_time':t_time,'ind':ind,'NA-A':na_not_a_comms,
'F-A':f_not_a_comms,'F-A-NA':f_not_ana_comms}
print comm_dist[rtmid]
print cur_size
#print
save_obj(comm_dist,OUT_PATH)
def func():
comm_dist = load_obj(PATH)
comm_dist.pop('lbd')
comm_dist.pop('size')
res_cmcnt = {} #adp,fnt or na ---> integer
res_entro = {} #adp,fnt or na ---> float
res_ol = {} #a_f,a_na,f_na ---> integer
nonviral = {}
viral = {}
keys = ['ind','num_cms_a','num_cms_f','num_cms_na', 'ent_a','ent_f','ent_na', 'ol_af','ol_ana','ol_fna','cs','fs','cnas','fnts','avg_time','total_time']
for x in keys:
nonviral[x],viral[x] = [],[]
for rtmid in comm_dist:
dist_d = comm_dist[rtmid]
ind = dist_d['ind']
total_t = dist_d['total_time']
#print type(dist_d['fs'])
#print int(str(dist_d['fs']))
final_size = dist_d['fs']
cs = dist_d['cs']
avg_t = dist_d['avg_time']
cnas = dist_d['cnas']
fnts = dist_d['fnts']
comms_a = dist_d['adp']
comms_f = dist_d['fnt']
comms_na = dist_d['na']
#number of comms
num_cms_a = len(set(comms_a))
num_cms_f = len(set(comms_f))
num_cms_na = len(set(comms_na))
#num_cms = [num_cms_a,num_cms_f,num_cms_na]
#entropy
ent_a = entro(comms_a)
ent_f = entro(comms_f)
ent_na = entro(comms_na)
#ent = [ent_a,ent_f,ent_na]
#overlap
ol_af = overlap(comms_a,comms_f)
ol_ana = overlap(comms_a,comms_na)
ol_fna = overlap(comms_f,comms_na)
#ol = [ol_af,ol_ana,ol_fna]
my_data = [ind,num_cms_a,num_cms_f,num_cms_na,ent_a,ent_f,ent_na,ol_af,ol_ana,ol_fna,cs,final_size,cnas,fnts,avg_t,total_t]
if final_size >= T:
for i in xrange(0,len(keys)):
viral[keys[i]].append(my_data[i])
else:
for j in xrange(0,len(keys)):
nonviral[keys[j]].append(my_data[j])
save_obj(viral,'C:\weibodataset\\for_box_plot_size_new\\'+F_NAME+'v.pkl')
save_obj(nonviral,'C:\weibodataset\\for_box_plot_size_new\\'+F_NAME+'nv.pkl')
def func():
print 'Choose the time as %d, with lambda equal to %d' %(T_FR,LAMBDA)
core_d_outnei = load_obj(CORE_PATH)
cm_d = comm_dict()
comm_dist = {}
comm_dist['tfr'] = T_FR
comm_dist['lbd'] = LAMBDA
for rtmid in core_d_outnei:
rtmid_d = core_d_outnei[rtmid]
adps = []
final_size = len(rtmid_d.keys())
if final_size< 5:
continue
for adp_ in rtmid_d:
t_diff = abs(int(rtmid_d[adp_][0]))
#print 'time from root for this adp %d is %d, t_diif - T_FR = %d' %(int(adp_),t_diff,t_diff-T_FR)
if t_diff <= T_FR:
#adopted already
adps.append(int(adp_))
cur_size = len(adps)
set_adps = set(adps)
adp_comms,na_comms,fnt_comms = [],[],[]
#NEW VERSION
#Check dup of fnt and na before look up their comms
#this would change the entropy while only decrease the number of comm in fnt while keep the num of comms in na
na_nodes,fnt_nodes = [],[]
#adp_comms = Parallel(n_jobs=4)(delayed(comm_look_up)(adopter,cm_d) for adopter in adps)
for adp in adps:
adp_l = rtmid_d[str(adp)] #no dup in adp for it is key of one dict
#adp = int(adp)
if adp in cm_d:
#print 'in'
adp_comms.append(cm_d[adp])
outneis = list(set(adp_l[1])) # dup exists in adp_l[1], so we can set delta here, need to think twice
#need to delete all adps from this list of outneis
#TODO eliminate dup of frontier and non-adopters
outneis = [x for x in outneis if x not in set_adps]
t_diff = int(adp_l[0])
t_exp = abs(T_FR - t_diff)
#comms = [cm_d[int(outnei)] for outnei in outneis]
if t_exp >= LAMBDA:
#na_comms.extend(comms)
na_nodes.extend(outneis)
else:
#fnt_comms.extend(comms)
fnt_nodes.extend(outneis)
set_na_nodes = set(na_nodes)
na_nodes_ = list(set_na_nodes)
cur_na_size = len(na_nodes_)
fnt_nodes_ = [x for x in set(fnt_nodes) if x not in set_na_nodes]
cur_fnt_size = len(fnt_nodes_)
for node in na_nodes_:
comm = cm_d[int(node)]
na_comms.append(comm)
for node in fnt_nodes_:
comm = cm_d[int(node)]
fnt_comms.append(comm) #keep dup of comm here to calculate entropy
comm_dist[rtmid] = {'adp':adp_comms,'fnt':fnt_comms,'na':na_comms,'fs':final_size,'cs':cur_size,'cnas':cur_na_size,'fnts':cur_fnt_size}
print comm_dist[rtmid]
#print
save_obj(comm_dist,OUT_PATH)
def func():
comm_dist = load_obj(PATH)
comm_dist.pop('lbd')
comm_dist.pop('tfr')
res_cmcnt = {} #adp,fnt or na ---> integer
res_entro = {} #adp,fnt or na ---> float
res_ol = {} #a_f,a_na,f_na ---> integer
nonviral = {}
viral = {}
keys = ['ind','num_cms_a','num_cms_f','num_cms_na', 'ent_a','ent_f','ent_na', 'ol_af','ol_ana','ol_fna',
'cs','fs','cnas','fnts',
#'avg_time','total_time',
'NA-A','F-A','F-A-NA','intra_comm_ratio','gini_a','gini_f','gini_na','gini_NA-A','gini_F-A','gini_F-A-NA']
for x in keys:
nonviral[x],viral[x] = [],[]
for rtmid in comm_dist:
dist_d = comm_dist[rtmid]
ind = dist_d['ind']
#total_t = dist_d['total_time']
#print type(dist_d['fs'])
#print int(str(dist_d['fs']))
final_size = dist_d['fs']
cs = dist_d['cs']
#avg_t = dist_d['avg_time']
cnas = dist_d['cnas']
fnts = dist_d['fnts']
comms_a = dist_d['adp']
comms_f = dist_d['fnt']
comms_na = dist_d['na']
comms_na_not_a = dist_d['NA-A']
comms_f_not_a = dist_d['F-A']
comms_f_not_ana = dist_d['F-A-NA']
intra_ratio = dist_d['intra_comm_ratio']
#number of comms
num_cms_a = len(set(comms_a))
num_cms_f = len(set(comms_f))
num_cms_na = len(set(comms_na))
#num_cms_na_not_a = len(set(comms_na_not_a))
#num_cms_f_not_a = len(set(comms_f_not_a))
#num_cms_f_not_ana = len(set(comms_f_not_ana))
#num_cms = [num_cms_a,num_cms_f,num_cms_na]
#entropy
ent_a,gini_a = entro_gini(comms_a)
ent_f,gini_f = entro_gini(comms_f)
ent_na,gini_na = entro_gini(comms_na)
ent_na_not_a,gini_na_not_a = entro_gini(comms_na_not_a)
ent_f_not_a,gini_f_not_a = entro_gini(comms_f_not_a)
ent_f_not_ana,gini_f_not_ana = entro_gini(comms_f_not_ana)
#ent = [ent_a,ent_f,ent_na]
#overlap
ol_af = overlap(comms_a,comms_f)
ol_ana = overlap(comms_a,comms_na)
ol_fna = overlap(comms_f,comms_na)
#ol_na_not_a = overlap()
#ol = [ol_af,ol_ana,ol_fna]
my_data = [ind,num_cms_a,num_cms_f,num_cms_na,ent_a,ent_f,ent_na,ol_af,ol_ana,ol_fna,cs,final_size,cnas,fnts,
#avg_t,total_t,
ent_na_not_a,ent_f_not_a,ent_f_not_ana,intra_ratio,gini_a,gini_f,gini_na,gini_na_not_a,gini_f_not_a,gini_f_not_ana]
if final_size >= T:
for i in xrange(0,len(keys)):
viral[keys[i]].append(my_data[i])
else:
for j in xrange(0,len(keys)):
nonviral[keys[j]].append(my_data[j])
save_obj(viral,'E:\SNAM_2015\\for_box_plot_time_comparison_infomap\\'+F_NAME+'v.pkl')
save_obj(nonviral,'E:\SNAM_2015\\for_box_plot_time_comparison_infomap\\'+F_NAME+'nv.pkl')
