def top_compare():
""" Compare top motif and mesostructure
"""
datapath = "data/mesos0825_s0dot2"
movdata = "data/hcl_mesos0825_sample0.2"
bsmap = "data/hcl_mesos0825_bm"
ofname = os.path.join(datapath, "mesos0825_s0dot2_top")
mobgraphs = {}
for person in movement_reader(open(movdata), BaseStationMap(bsmap)):
if person.which_day() != "0825":
continue
nn = len(set(person.locations))
if nn > 20:
continue
if nn not in mobgraphs:
mobgraphs[nn] = {}
mobgraphs[nn][person.id] = person.convert2graph()
new_file = True
for C in range(2, 16):
for kn in range(1, 5):
print C, kn
# Read dist matrix for (group, cluster) users
fileklab = os.path.join(datapath, "mesos0825_s0dot2_c%d_kn%d" % (C, kn))
distmat = []
i = 0
for line in open(fileklab):
if i == 0:
uids = [int(i) for i in line.strip("\r\n").split(",")]
i == 1
continue
distmat.append([float(i) for i in line.strip("\r\n").split(",")])
distmat = np.array(distmat)
distvec = distmat.sum(1) / len(uids)
uids_sorted = [x for (y, x) in sorted(zip(distvec, uids))]
N = len(uids_sorted)
print ("Total users %d: " % N)
mgs = mobgraphs[C]
mesos = Mesos(mgs[uids_sorted[0]], mgs[uids_sorted[1]])
topmesos = mesos.mesos
topmesos_sim = 1 - mesos.struct_dist()
motifs = {}
for i in range(N - 1):
u1 = uids_sorted[i]
u2 = uids_sorted[i + 1]
g1 = mgs[u1]
g2 = mgs[u2]
mesos = Mesos(g1, g2).mesos
found = False
for key in motifs.keys():
if isomorphism.is_isomorphic(key, mesos):
motifs[key].append((mesos, i))
found = True
if found:
break
if not found:
motifs[mesos] = [(mesos, i)]
res = []
for key, value in motifs.items():
res.append((len(value), value[0][0]))
res = sorted(res, key=lambda x: x[0], reverse=True)
topmotif = res[0][1]
topmotif_supp = 1.0 * res[0][0] / N
if new_file:
mode = "wb"
new_file = False
else:
mode = "ab"
ofile = open(ofname, mode)
ofile.write("%d\t%d" % (C, kn))
ofile.write("\t%.3f\t%.3f" % (topmesos_sim, topmotif_supp))
ofile.write("\t%s" % dumps_mobgraph(topmesos))
ofile.write("\t%s" % dumps_mobgraph(topmotif))
ofile.write("\n")
ofile.close()
def gen_mesos(lg1, lg2):
uid1, ts1, grp, g1 = lg1
uid2, ts2, grp, g2 = lg2
mesos = Mesos(g1, g2)
return (grp, mesos.struct_dist(), mesos.mesos, uid1, uid2, ts1, ts2)
def trv_distance():
""" Travel distance for clustered users
"""
datapath = 'data/mesos0825_s0dot2'
movdata = 'data/hcl_mesos0825_sample0.2'
bsmap = 'data/hcl_mesos0825_bm'
ofname = os.path.join(datapath, 'mesos0825_s0dot2_trd')
travdist = {}
mobgraphs = {}
for person in movement_reader(open(movdata), BaseStationMap(bsmap)):
if person.which_day() != '0825':
continue
nn = len(set(person.locations))
if nn > 20:
continue
if nn not in mobgraphs:
mobgraphs[nn] = {}
mobgraphs[nn][person.id] = person.convert2graph()
circle_num = len(person.circles)
edge_freq = np.mean(person.freq.values())
trvd = person.travel_dist()
rg = person.radius_of_gyration()
nloc = len(person.locations)
travdist[person.id] = (rg, trvd, edge_freq, circle_num, nloc)
new_file = True
for C in range(2, 16):
for kn in range(1, 5):
print C, kn
# Read dist matrix for (group, cluster) users
fileklab = os.path.join(datapath,
'mesos0825_s0dot2_c%d_kn%d' % (C, kn))
distmat = []
i = 0
for line in open(fileklab):
if i == 0:
uids = [int(i) for i in line.strip('\r\n').split(',')]
i == 1
continue
distmat.append(
[float(i) for i in line.strip('\r\n').split(',')])
distmat = np.array(distmat)
distvec = distmat.sum(1) / len(uids)
uids_sorted = [x for (y, x) in sorted(zip(distvec, uids))]
N = len(uids_sorted)
print('Total users %d: ' % N)
mgs = mobgraphs[C]
mesos = Mesos(mgs[uids_sorted[0]], mgs[uids_sorted[1]])
topmesos = mesos.mesos
eigndist = np.sum(
[e[2]['weight'] for e in topmesos.edges(data=True)])
if new_file:
mode = 'wb'
new_file = False
else:
mode = 'ab'
ofile = open(ofname, mode)
if mode == 'wb':
ofile.write(
'uid,group,clust,eigndist,rg,trvd,efreq,circlenum,nloc,dist,selfdist\n'
)
for i in range(0, len(uids)):
uid = uids_sorted[i]
dist = np.sum(distmat[i]) / len(uids)
selfdist = distmat[i][i]
rg, trvd, edge_freq, circle_num, nloc = travdist[uid]
ofile.write('%d,%d,%d,%.3f,%.3f,%.3f,%.3f,%d,%d,%.3f,%.3f' % \
(uid, C, kn, eigndist, rg, trvd, edge_freq, circle_num, nloc, dist, selfdist))
ofile.write('\n')
ofile.close()
def top_compare():
""" Compare top motif and mesostructure
"""
datapath = 'data/mesos0825_s0dot2'
movdata = 'data/hcl_mesos0825_sample0.2'
bsmap = 'data/hcl_mesos0825_bm'
ofname = os.path.join(datapath, 'mesos0825_s0dot2_top')
mobgraphs = {}
for person in movement_reader(open(movdata), BaseStationMap(bsmap)):
if person.which_day() != '0825':
continue
nn = len(set(person.locations))
if nn > 20:
continue
if nn not in mobgraphs:
mobgraphs[nn] = {}
mobgraphs[nn][person.id] = person.convert2graph()
new_file = True
for C in range(2, 16):
for kn in range(1, 5):
print C, kn
# Read dist matrix for (group, cluster) users
fileklab = os.path.join(datapath,
'mesos0825_s0dot2_c%d_kn%d' % (C, kn))
distmat = []
i = 0
for line in open(fileklab):
if i == 0:
uids = [int(i) for i in line.strip('\r\n').split(',')]
i == 1
continue
distmat.append(
[float(i) for i in line.strip('\r\n').split(',')])
distmat = np.array(distmat)
distvec = distmat.sum(1) / len(uids)
uids_sorted = [x for (y, x) in sorted(zip(distvec, uids))]
N = len(uids_sorted)
print('Total users %d: ' % N)
mgs = mobgraphs[C]
mesos = Mesos(mgs[uids_sorted[0]], mgs[uids_sorted[1]])
topmesos = mesos.mesos
topmesos_sim = 1 - mesos.struct_dist()
motifs = {}
for i in range(N - 1):
u1 = uids_sorted[i]
u2 = uids_sorted[i + 1]
g1 = mgs[u1]
g2 = mgs[u2]
mesos = Mesos(g1, g2).mesos
found = False
for key in motifs.keys():
if isomorphism.is_isomorphic(key, mesos):
motifs[key].append((mesos, i))
found = True
if found:
break
if not found:
motifs[mesos] = [(mesos, i)]
res = []
for key, value in motifs.items():
res.append((len(value), value[0][0]))
res = sorted(res, key=lambda x: x[0], reverse=True)
topmotif = res[0][1]
topmotif_supp = 1.0 * res[0][0] / N
if new_file:
mode = 'wb'
new_file = False
else:
mode = 'ab'
ofile = open(ofname, mode)
ofile.write('%d\t%d' % (C, kn))
ofile.write('\t%.3f\t%.3f' % (topmesos_sim, topmotif_supp))
ofile.write('\t%s' % dumps_mobgraph(topmesos))
ofile.write('\t%s' % dumps_mobgraph(topmotif))
ofile.write('\n')
ofile.close()
def gen_mesos(lg1, lg2):
uid1, ts1, grp, g1 = lg1
uid2, ts2, grp, g2 = lg2
mesos = Mesos(g1, g2)
return (grp, mesos.struct_dist(), mesos.mesos, uid1, uid2, ts1, ts2)