def run(cur, uid, input_table):
results = list()
imh = database_io.load_individual_mobility_history(cur, uid, input_table)
trajectories = imh['trajectories']
alltraj = merge_trajectories(trajectories)
nbr_points = len(alltraj)
if nbr_points <= 100:
raise Exception
sampling_rate_list = [alltraj[i+1][2] - alltraj[i][2] for i in range(0, len(alltraj)-1)]
avg_sampling_rate = np.mean(sampling_rate_list)
std_sampling_rate = np.std(sampling_rate_list)
med_sampling_rate = np.median(sampling_rate_list)
base_res = [input_table, uid, nbr_points, avg_sampling_rate, std_sampling_rate, med_sampling_rate]
traj_list, user_temporal_thr = segment_trajectories_user_adaptive(alltraj, uid, temporal_thr=60, spatial_thr=50,
max_speed=0.07, gap=60, max_lim=3600 * 48,
window=15, smooth_fun=moving_median, min_size=10,return_cut=True)
eval_res = evaluate(alltraj, traj_list)
results.append(base_res + ['ATS'] + eval_res + [user_temporal_thr])
nbr_traj_adaptive = len(traj_list)
print(len(traj_list))
traj_list = segment_trajectories_random2(alltraj, uid, nbr_traj=nbr_traj_adaptive)
eval_res = evaluate(alltraj, traj_list)
results.append(base_res + ['RND2'] + eval_res + [-1])
print('pippi',len(traj_list) )
traj_list = segment_trajectories(alltraj, uid, temporal_thr=1200, spatial_thr=50, max_speed=0.07)
eval_res = evaluate(alltraj, traj_list)
results.append(base_res + ['FTS_1200'] + eval_res + [1200])
traj_list = segment_trajectories(alltraj, uid, temporal_thr=120, spatial_thr=50, max_speed=0.07)
eval_res = evaluate(alltraj, traj_list)
results.append(base_res + ['FTS_120'] + eval_res + [120])
traj_list = segment_trajectories_random(alltraj, uid)
eval_res = evaluate(alltraj, traj_list)
results.append(base_res + ['RND1'] + eval_res + [-1])
#traj_list = segment_trajectories_random(alltraj, uid, nbr_traj=nbr_traj_adaptive)
#if len(traj_list) > 25:
# eval_res = evaluate(alltraj, traj_list)
# results.append(base_res + ['RND2'] + eval_res + [-1])
return results
def imn_extract(filename,
path,
type_user,
traj_table,
evnt_table,
min_traj_nbr,
min_length,
min_duration,
area,
overwrite=False):
output_filename = path + filename
con = database_io.get_connection()
cur = con.cursor()
#users_list = [100225,101127,100742,100747,100690,100578,1003,100191,100192,100193,321463]
users_list = [100619, 100554]
users_list = sorted(users_list)
nbr_users = len(users_list)
print("user ids before checking :")
print(nbr_users, len(users_list))
if os.path.isfile(output_filename) and not overwrite:
processed_users = list()
fout = gzip.GzipFile(output_filename, 'r')
for row in fout:
customer_obj = json.loads(row)
processed_users.append(customer_obj['uid'])
fout.close()
users_list = [uid for uid in users_list if uid not in processed_users]
print("user ids after checking :")
print(nbr_users, len(users_list))
for i, uid in enumerate(users_list):
if i % 1 == 0:
print(
datetime.datetime.now(),
'%s %s %s [%s/%s] - %.2f' % (traj_table, area, type_user, i,
nbr_users, i / nbr_users * 100.0))
imh = database_io.load_individual_mobility_history(
cur, uid, traj_table, min_length, min_duration)
events = database_io.load_individual_event_history(
cur, uid, evnt_table) if evnt_table is not None else None
#imh['trajectories']=dict(list(islice(imh['trajectories'].items(), 200)))
if len(imh['trajectories']) < min_traj_nbr:
print('len trajectories]) < min_traj_nbr',
len(imh['trajectories']), min_traj_nbr)
continue
main_imh = imh['trajectories']
jan_feb_tid = []
march_april_id = []
for tid, t in imh['trajectories'].items():
start_time = str(t.start_time())
if ('2017-01' in start_time) or ('2017-02' in start_time):
jan_feb_tid.append(tid)
if ('2017-03' in start_time) or ('2017-04' in start_time):
march_april_id.append(tid)
imh['trajectories'] = {x: imh['trajectories'][x] for x in jan_feb_tid}
imn1 = individual_mobility_network.build_imn(imh,
reg_loc=True,
events=events,
verbose=False)
period_imn1 = {"01-02": imn1}
imh['trajectories'] = {x: main_imh[x] for x in march_april_id}
imn2 = individual_mobility_network.build_imn(imh,
reg_loc=True,
events=events,
verbose=False)
period_imn2 = {"03-04": imn2}
customer_obj = {'uid': uid}
period_imn1.update(period_imn2)
customer_obj.update(period_imn1)
json_str = '%s\n' % json.dumps(clear_tuples4json(customer_obj),
default=agenda_converter)
json_bytes = json_str.encode('utf-8')
with gzip.GzipFile(output_filename, 'a') as fout:
fout.write(json_bytes)
print("done")
cur.close()
con.close()
def imn_extract_for_one_month(filename,
path,
type_user,
traj_table,
evnt_table,
min_traj_nbr,
min_length,
min_duration,
area,
overwrite=False):
output_filename = path + filename
con = database_io.get_connection()
cur = con.cursor()
users_list = find_user_list(cur, traj_table)
nbr_users = len(users_list)
print("user ids before checking :")
print(nbr_users, len(users_list))
if os.path.isfile(output_filename) and not overwrite:
processed_users = list()
fout = gzip.GzipFile(output_filename, 'r')
for row in fout:
customer_obj = json.loads(row)
processed_users.append(customer_obj['uid'])
fout.close()
users_list = [uid for uid in users_list if uid not in processed_users]
print("user ids after checking :")
print(nbr_users, len(users_list))
for i, uid in enumerate(users_list):
try:
if i % 1 == 0:
print(
datetime.datetime.now(), '%s %s %s [%s/%s] - %.2f' %
(traj_table, area, type_user, i, nbr_users,
i / nbr_users * 100.0))
imh = database_io.load_individual_mobility_history(
cur, uid, traj_table, min_length, min_duration)
events = database_io.load_individual_event_history(
cur, uid, evnt_table) if evnt_table is not None else None
if len(imh['trajectories']) < min_traj_nbr:
print('len trajectories]) < min_traj_nbr',
len(imh['trajectories']), min_traj_nbr)
continue
main_imh = imh['trajectories']
jan_tid = []
for tid, t in imh['trajectories'].items():
start_time = str(t.start_time())
if ('2017-01' in start_time):
jan_tid.append(tid)
imh['trajectories'] = {x: imh['trajectories'][x] for x in jan_tid}
imn1 = individual_mobility_network.build_imn(imh,
reg_loc=True,
events=events,
verbose=False)
period_imn1 = {"01": imn1}
customer_obj = {'uid': uid}
customer_obj.update(period_imn1)
json_str = '%s\n' % json.dumps(clear_tuples4json(customer_obj),
default=agenda_converter)
json_bytes = json_str.encode('utf-8')
with gzip.GzipFile(output_filename, 'a') as fout:
fout.write(json_bytes)
except (TypeError):
print("type error")
continue
print("done")
cur.close()
con.close()
def imn_extract_all_year(filename,
path,
type_user,
traj_table,
evnt_table,
min_traj_nbr,
min_length,
min_duration,
area,
overwrite=False):
output_filename = path + filename
con = database_io.get_connection()
cur = con.cursor()
#users_list=find_user_list(cur,traj_table):
#users_list = [100225,101127,100742,100747,100690,100578,1003,100191,100192,100193,318819,100619,100554,100498]
#users_list = [100843,100836,100827,100795,100747,100717,100681,100669,101293,101194,101091]
users_list = [7925]
users_list = sorted(users_list)
nbr_users = len(users_list)
print("user ids before checking :")
print(nbr_users, len(users_list))
if os.path.isfile(output_filename) and not overwrite:
processed_users = list()
fout = gzip.GzipFile(output_filename, 'r')
for row in fout:
customer_obj = json.loads(row)
processed_users.append(customer_obj['uid'])
fout.close()
users_list = [uid for uid in users_list if uid not in processed_users]
print("user ids after checking :")
print(nbr_users, len(users_list))
for i, uid in enumerate(users_list):
try:
if i % 1 == 0:
print(
datetime.datetime.now(), '%s %s %s [%s/%s] - %.2f' %
(traj_table, area, type_user, i, nbr_users,
i / nbr_users * 100.0))
imh = database_io.load_individual_mobility_history(
cur, uid, traj_table, min_length, min_duration)
events = database_io.load_individual_event_history(
cur, uid, evnt_table) if evnt_table is not None else None
#imh['trajectories']=dict(list(islice(imh['trajectories'].items(), 200)))
if len(imh['trajectories']) < min_traj_nbr:
print('len trajectories]) < min_traj_nbr',
len(imh['trajectories']), min_traj_nbr)
continue
imn = individual_mobility_network.build_imn(imh,
reg_loc=True,
events=events,
verbose=False)
customer_obj = {'uid': uid}
customer_obj.update(imn)
json_str = '%s\n' % json.dumps(clear_tuples4json(customer_obj),
default=agenda_converter)
json_bytes = json_str.encode('utf-8')
with gzip.GzipFile(output_filename, 'a') as fout:
fout.write(json_bytes)
except (TypeError):
print("type error")
continue
print("done")
cur.close()
con.close()
def main():
area = sys.argv[1] # 'rome' 'tuscany' 'london'
type_user = sys.argv[2] # 'crash' 'nocrash'
overwrite = int(sys.argv[3])
country = 'uk' if area == 'london' else 'italy'
min_length = 1.0
min_duration = 60.0
print(datetime.datetime.now(), 'Crash Prediction - Train Test Partitioner')
if not overwrite:
print(datetime.datetime.now(), '(restart)')
path = './'
path_imn = path + 'imn_new/'
path_dataset = path + 'dataset/'
path_traintest = path + 'traintest/'
path_quadtree = path + 'quadtree/'
traj_table = 'tak.%s_traj' % country
evnt_table = 'tak.%s_evnt' % country
crash_table = 'tak.%s_crash' % country
if area == 'london' and type_user == 'nocrash':
users_filename = path_dataset + '%s_%s_users_list.csv' % (area, 'all')
users_filename_crash = path_dataset + '%s_%s_users_list.csv' % (
area, 'crash')
else:
users_filename = path_dataset + '%s_%s_users_list.csv' % (area,
type_user)
users_filename_crash = None
users_list = pd.read_csv(users_filename).values[:, 0].tolist()
users_list = sorted(users_list)
if users_filename_crash is not None:
users_list_crash = pd.read_csv(
users_filename_crash).values[:, 0].tolist()
users_list_crash = sorted(users_list_crash)
users_list = [uid for uid in users_list if uid not in users_list_crash]
nbr_users = len(users_list)
print(datetime.datetime.now(), 'Reading quadtree')
quadtree_poi_filename = path_quadtree + '%s_personal_osm_poi_lv17.json.gz' % area
fout = gzip.GzipFile(quadtree_poi_filename, 'r')
quadtree = json.loads(fout.readline())
fout.close()
print(datetime.datetime.now(), 'Reading quadtree features')
quadtree_features_filename = path_quadtree + '%s_quadtree_features.json.gz' % area
fout = gzip.GzipFile(quadtree_features_filename, 'r')
quadtrees_features_str = json.loads(fout.readline())
quadtrees_features = {int(k): v for k, v in quadtrees_features_str.items()}
fout.close()
processed_users = set()
if overwrite:
for index in range(0, 7):
output_filename = path_traintest + '%s_%s_traintest_%s.json.gz' % (
area, type_user, index)
if os.path.exists(output_filename):
os.remove(output_filename)
else:
processed_users = set()
for index in range(0, 7):
output_filename = path_traintest + '%s_%s_traintest_%s.json.gz' % (
area, type_user, index)
if os.path.isfile(output_filename):
fout = gzip.GzipFile(output_filename, 'r')
for row in fout:
customer_obj = json.loads(row)
processed_users.add(customer_obj['uid'])
fout.close()
window = 4
datetime_from = datetime.datetime.strptime('2017-01-01 00:00:00',
'%Y-%m-%d %H:%M:%S')
datetime_to = datetime.datetime.strptime('2018-01-01 00:00:00',
'%Y-%m-%d %H:%M:%S')
print(datetime.datetime.now(), 'Generating month boundaries')
months = pd.date_range(start=datetime_from, end=datetime_to, freq='MS')
boundaries = [[lm, um]
for lm, um in zip(months[:-window], months[window:])]
training_months = list()
test_months = list()
for i in range(len(boundaries) - 1):
training_months.append(boundaries[i])
test_months.append(boundaries[i + 1])
index = 0
tr_data_map = dict()
ts_data_map = dict()
for tr_months, ts_months in zip(training_months, test_months):
tr_data_map[tuple(tr_months)] = index
ts_data_map[tuple(ts_months)] = index
index += 1
print(datetime.datetime.now(), 'Initializing quadtree features')
tr_quadtree_features = dict()
for m in quadtrees_features:
for lu, index in tr_data_map.items():
if lu[0].month <= m < lu[1].month:
if index not in tr_quadtree_features:
tr_quadtree_features[index] = dict()
for path in quadtrees_features[m]:
if path not in tr_quadtree_features[index]:
tr_quadtree_features[index][path] = {
'nbr_traj_start': 0,
'nbr_traj_stop': 0,
'nbr_traj_move': 0,
'traj_speed_sum': 0,
'traj_speed_count': 0,
'nbr_evnt_A': 0,
'nbr_evnt_B': 0,
'nbr_evnt_C': 0,
'nbr_evnt_Q': 0,
'nbr_evnt_start': 0,
'nbr_evnt_stop': 0,
'speed_A_sum': 0,
'max_acc_A_sum': 0,
'avg_acc_A_sum': 0,
'speed_B_sum': 0,
'max_acc_B_sum': 0,
'avg_acc_B_sum': 0,
'speed_C_sum': 0,
'max_acc_C_sum': 0,
'avg_acc_C_sum': 0,
'speed_Q_sum': 0,
'max_acc_Q_sum': 0,
'avg_acc_Q_sum': 0,
'nbr_crash': 0,
}
for k, v in quadtrees_features[m][path].items():
tr_quadtree_features[index][path][k] += v
ts_quadtree_features = dict()
for m in quadtrees_features:
for lu, index in tr_data_map.items():
if lu[0].month <= m < lu[1].month:
if index not in ts_quadtree_features:
ts_quadtree_features[index] = dict()
for path in quadtrees_features[m]:
if path not in ts_quadtree_features[index]:
ts_quadtree_features[index][path] = {
'nbr_traj_start': 0,
'nbr_traj_stop': 0,
'nbr_traj_move': 0,
'traj_speed_sum': 0,
'traj_speed_count': 0,
'nbr_evnt_A': 0,
'nbr_evnt_B': 0,
'nbr_evnt_C': 0,
'nbr_evnt_Q': 0,
'nbr_evnt_start': 0,
'nbr_evnt_stop': 0,
'speed_A_sum': 0,
'max_acc_A_sum': 0,
'avg_acc_A_sum': 0,
'speed_B_sum': 0,
'max_acc_B_sum': 0,
'avg_acc_B_sum': 0,
'speed_C_sum': 0,
'max_acc_C_sum': 0,
'avg_acc_C_sum': 0,
'speed_Q_sum': 0,
'max_acc_Q_sum': 0,
'avg_acc_Q_sum': 0,
'nbr_crash': 0,
}
for k, v in quadtrees_features[m][path].items():
ts_quadtree_features[index][path][k] += v
print(datetime.datetime.now(), 'Connecting to database')
con = database_io.get_connection()
cur = con.cursor()
count = 0
imn_filedata = gzip.GzipFile(
path_imn + '%s_imn_%s.json.gz' % (area, type_user), 'r')
print(datetime.datetime.now(),
'Calculating features and partitioning dataset')
for row in imn_filedata:
if len(row) <= 1:
print('new file started ;-)')
continue
user_obj = json.loads(row)
uid = user_obj['uid']
count += 1
if uid in processed_users:
continue
if count % 10 == 0:
print(
datetime.datetime.now(),
'train test partition %s %s [%s/%s] - %.2f' %
(area, type_user, count, nbr_users, 100 * count / nbr_users))
imh = database_io.load_individual_mobility_history(
cur, uid, traj_table, min_length, min_duration)
events = database_io.load_individual_event_history(
cur, uid, evnt_table) if evnt_table is not None else None
trajectories = imh['trajectories']
tr_data = dict()
ts_data = dict()
# partitioning imn for train and test
for imn_months in user_obj:
if imn_months == 'uid':
continue
# print(imn_months)
m0 = int(imn_months.split('-')[0])
m1 = int(imn_months.split('-')[1])
for lu, index in tr_data_map.items():
if lu[0].month <= m0 < m1 < lu[1].month:
if index not in tr_data:
tr_data[index] = {
'uid': uid,
'crash': False,
'trajectories': dict(),
'imns': dict(),
'events': dict(),
}
tr_data[index]['imns'][imn_months] = user_obj[imn_months]
for lu, index in ts_data_map.items():
if lu[0].month <= m0 < lu[1].month:
if index not in ts_data:
ts_data[index] = {
'uid': uid,
'crash': False,
'trajectories': dict(),
'imns': dict(),
'events': dict(),
}
ts_data[index]['imns'][imn_months] = user_obj[imn_months]
# partitioning trajectories for train and test
for tid, traj in trajectories.items():
for lu, index in tr_data_map.items():
if lu[0] <= traj.start_time() < lu[1] and index in tr_data:
tr_data[index]['trajectories'][tid] = traj
for lu, index in ts_data_map.items():
if lu[0] <= traj.start_time() < lu[1] and index in ts_data:
ts_data[index]['trajectories'][tid] = traj
# partitioning events for train and test
for eid, evnt in events.items():
# print(evnt)
for lu, index in tr_data_map.items():
if lu[0] <= evnt[0]['date'] < lu[1] and index in tr_data:
tr_data[index]['events'][eid] = evnt[0]
for lu, index in ts_data_map.items():
if lu[0] <= evnt[0]['date'] < lu[1] and index in ts_data:
ts_data[index]['events'][eid] = evnt[0]
# get has crash next month
for lu, index in tr_data_map.items():
if index not in tr_data:
continue
query = """SELECT * FROM %s WHERE uid = '%s'
AND date >= TO_TIMESTAMP('%s','YYYY-MM-DD HH24:MI:SS')
AND date < TO_TIMESTAMP('%s','YYYY-MM-DD HH24:MI:SS')""" % (
crash_table, uid, str(
lu[1]), str(lu[1] + relativedelta(months=1)))
cur.execute(query)
rows = cur.fetchall()
has_crash_next_month = len(rows) > 0
tr_data[index]['crash'] = has_crash_next_month
for lu, index in ts_data_map.items():
if index not in ts_data:
continue
query = """SELECT * FROM %s WHERE uid = '%s'
AND date >= TO_TIMESTAMP('%s','YYYY-MM-DD HH24:MI:SS')
AND date < TO_TIMESTAMP('%s','YYYY-MM-DD HH24:MI:SS')""" % (
crash_table, uid, str(
lu[1]), str(lu[1] + relativedelta(months=1)))
cur.execute(query)
rows = cur.fetchall()
has_crash_next_month = len(rows) > 0
ts_data[index]['crash'] = has_crash_next_month
tr_features, ts_features = feature_extractor.extract_features(
uid, tr_data, ts_data, quadtree, tr_quadtree_features,
ts_quadtree_features)
for index in tr_features:
if index in ts_features:
output_filename = path_traintest + '%s_%s_traintest_%s.json.gz' % (
area, type_user, index)
store_obj = {
'uid': uid,
'train': tr_features[index],
'test': ts_features[index]
}
feature_extractor.store_features(output_filename, store_obj)
imn_filedata.close()
def main():
filename = "exp.csv"
if os.path.isfile(filename):
os.remove(filename)
with open(filename, 'a', newline='\n') as file:
writer = csv.writer(file)
writer.writerow([
"initial_threshold", "uid", "len(alltraj)", "M1 len(traj_list)",
"M1 user_temporal_thresholds", "M1 avg_nbr_points",
"M1 time_precision", "M1 dist_coverage", "M1 mobility_f1",
"M2 len(traj_list)", "M2 user_temporal_thresholds",
"M2 avg_nbr_points", "M2 time_precision", "M2 dist_coverage",
"M2 mobility_f1", "len(traj_list_random )",
"avg_nbr_points_random", "time_precision_random",
"dist_coverage_random", "mobility_random_f1",
"len(traj_list_random4 )", "avg_nbr_points_random4",
"time_precision_random4", "dist_coverage_random4",
"mobility_random4_f1"
])
input_table = 'tak.italy_traj'
# con = database_io.get_connection()
# cur = con.cursor()
# users_list = database_io.extract_users_list('tak.italy_traj', cur)
# cur.close()
# con.close()
users_list = [
'100006', '100022', '100026', '10008', '100086', '100087', '100088',
'100090', '100100', '100117'
]
# uid = users_list[0]
# con = database_io.get_connection()
# cur = con.cursor()
# imh = database_io.load_individual_mobility_history(cur, uid, input_table)
# cur.close()
# con.close()
con = database_io.get_connection()
cur = con.cursor()
#users_list = database_io.extract_users_list('tak.italy_traj', cur)
eval_adaptive = list()
eval_fix1200 = list()
eval_random = list()
eval_random2 = list()
traj_number = list()
thresholds = [60, 120, 180, 240]
for t in thresholds:
for uid in users_list:
print(uid, input_table)
imh = database_io.load_individual_mobility_history(
cur, uid, input_table)
trajectories = imh['trajectories']
alltraj = merge_trajectories(trajectories)
#metodo 1: funzione che usa la mediana mobile
traj_list1, user_temporal_thr1 = segment_trajectories_user_adaptive(
alltraj,
uid,
temporal_thr=t,
spatial_thr=50,
max_speed=0.07,
gap=60,
max_lim=3600 * 48,
window=15,
smooth_fun=moving_median,
min_size=10,
return_cut=True)
avg_nbr_points1 = np.mean([len(t) for t in traj_list1])
print('user_temporal_thr', user_temporal_thr1)
print('NT %d - ANP %.2f' % (len(traj_list1), avg_nbr_points1))
time_precision1, dist_coverage1, mobility1_f1 = evalaute_segmentation(
alltraj, traj_list1, print_report=True)
eval_adaptive.append(
(time_precision1, dist_coverage1, mobility1_f1))
#metodo 2: funzione semplice
traj_list2 = segment_trajectories(alltraj,
uid,
temporal_thr=1200,
spatial_thr=50,
max_speed=0.07)
avg_nbr_points2 = np.mean([len(t) for t in traj_list2])
user_temporal_thr2 = 1200
print('NT %d - ANP %.2f' % (len(traj_list2), avg_nbr_points2))
time_precision2, dist_coverage2, mobility2_f1 = evalaute_segmentation(
alltraj, traj_list2, print_report=True)
eval_fix1200.append(
(time_precision2, dist_coverage2, mobility2_f1))
#metodo 3: funzione random
traj_list_random = segment_trajectories_random(alltraj,
uid,
nbr_traj=2000)
avg_nbr_points_random = np.mean([len(t) for t in traj_list_random])
print('NT %d - ANP %.2f' %
(len(traj_list_random), avg_nbr_points_random))
time_precision_random, dist_coverage_random, mobility_random_f1 = evalaute_segmentation(
alltraj, traj_list_random, print_report=True)
eval_random.append((time_precision_random, dist_coverage_random,
mobility_random_f1))
#metodo 4: funzione random con nbr_traj_max dato dal primo metodo
traj_list_random4 = segment_trajectories_random(
alltraj, uid, nbr_traj_min=2, nbr_traj_max=len(traj_list2))
avg_nbr_points_random4 = np.mean(
[len(t) for t in traj_list_random4])
print('NT %d - ANP %.2f' %
(len(traj_list_random4), avg_nbr_points_random4))
time_precision_random4, dist_coverage_random4, mobility_random4_f1 = evalaute_segmentation(
alltraj, traj_list_random4, print_report=True)
eval_random2.append((time_precision_random4, dist_coverage_random4,
mobility_random4_f1))
#riempire file
with open(filename, 'a', newline='\n') as file:
writer = csv.writer(file)
writer.writerow([
t, uid,
len(alltraj),
len(traj_list1), user_temporal_thr1, avg_nbr_points1,
time_precision1, dist_coverage1, mobility1_f1,
len(traj_list2), user_temporal_thr2, avg_nbr_points2,
time_precision2, dist_coverage2, mobility2_f1,
len(traj_list_random), avg_nbr_points_random,
time_precision_random, dist_coverage_random,
mobility_random_f1,
len(traj_list_random4), avg_nbr_points_random4,
time_precision_random4, dist_coverage_random4,
mobility_random4_f1
])
def main():
area = sys.argv[1]
country = 'uk' if area == 'london' else 'italy'
overwrite = True
depth = 16
store_evry = 100
path = './'
path_dataset = path + 'dataset/'
path_quadtree = path + 'quadtree/'
traj_table = 'tak.%s_traj' % country
evnt_table = 'tak.%s_evnt' % country
crash_table = 'tak.%s_crash' % country
users_filename = path_dataset + '%s_all_users_list.csv' % area
quadtree_output_filename = path_quadtree + '%s_quadtree_features.json.gz' % area
quadtrees_features = dict()
datetime_from = datetime.datetime.strptime('2017-01-01 00:00:00',
'%Y-%m-%d %H:%M:%S')
datetime_to = datetime.datetime.strptime('2018-01-01 00:00:00',
'%Y-%m-%d %H:%M:%S')
months = pd.date_range(start=datetime_from, end=datetime_to, freq='MS')
boundaries = [[lm, um] for lm, um in zip(months[:-1], months[1:])]
index = 0
data_map = dict()
for months in boundaries:
data_map[tuple(months)] = index
quadtrees_features[index] = dict()
index += 1
users_list = sorted(pd.read_csv(users_filename).values[:, 0].tolist())
last_processed_user = None
if os.path.isfile(quadtree_output_filename) and not overwrite:
fout = gzip.GzipFile(quadtree_output_filename, 'r')
quadtrees_features_str = json.loads(fout.readline())
quadtrees_features = {
int(k): v
for k, v in quadtrees_features_str.items()
}
last_processed_user = json.loads(fout.readline())
fout.close()
con = database_io.get_connection()
cur = con.cursor()
for i, uid in enumerate(users_list):
if last_processed_user is not None and uid <= last_processed_user:
continue
if i % store_evry == 0:
print(
datetime.datetime.now(),
'%s %s %.2f' % (traj_table, area, i / len(users_list) * 100.0))
trajectories = database_io.load_individual_mobility_history(
cur, uid, traj_table, min_length=1.0,
min_duration=60.0)['trajectories']
events = database_io.load_individual_event_history(
cur, uid, evnt_table)
quadtree_data = dict()
# partitioning trajectories for train and test
for tid, traj in trajectories.items():
for lu, index in data_map.items():
if lu[0] <= traj.start_time() < lu[1]:
if index not in quadtree_data:
quadtree_data[index] = {
'uid': uid,
'crash': None,
'trajectories': dict(),
'events': dict(),
}
quadtree_data[index]['trajectories'][tid] = traj
# partitioning events for train and test
for eid, evnt in events.items():
for lu, index in data_map.items():
if lu[0] <= evnt[0]['date'] < lu[1] and index in quadtree_data:
quadtree_data[index]['events'][eid] = evnt[0]
# get has crash this month
for lu, index in data_map.items():
if index not in quadtree_data:
continue
query = """SELECT lat, lon FROM %s WHERE uid = '%s'
AND date >= TO_TIMESTAMP('%s','YYYY-MM-DD HH24:MI:SS')
AND date < TO_TIMESTAMP('%s','YYYY-MM-DD HH24:MI:SS')""" % (
crash_table, uid, str(lu[0]), str(lu[1]))
cur.execute(query)
rows = cur.fetchall()
if len(rows) > 0:
quadtree_data[index]['crash'] = {
'lat': float(rows[0][0]),
'lon': float(rows[0][1])
}
quadtrees_features = quadtrees_features_extract(
quadtrees_features, quadtree_data, depth)
if i % store_evry == 0:
json_str_quadtree = '%s\n' % json.dumps(quadtrees_features)
json_bytes_quadtree = json_str_quadtree.encode('utf-8')
json_str_lpu = '%s\n' % json.dumps(last_processed_user)
json_bytes_lpu = json_str_lpu.encode('utf-8')
with gzip.GzipFile(quadtree_output_filename, 'w') as fout:
fout.write(json_bytes_quadtree)
fout.write(json_bytes_lpu)
last_processed_user = uid
def imn_extract(filename,
path,
type_user,
traj_table,
evnt_table,
min_traj_nbr,
min_length,
min_duration,
area,
overwrite=False,
users_filename_crash=None):
output_filename = path + '%s_imn_%s.json.gz' % (area, type_user)
con = database_io.get_connection()
cur = con.cursor()
users_list = pd.read_csv(filename).values[:, 0].tolist()
users_list = sorted(users_list)
if users_filename_crash is not None:
users_list_crash = pd.read_csv(
users_filename_crash).values[:, 0].tolist()
users_list_crash = sorted(users_list_crash)
users_list = [uid for uid in users_list if uid not in users_list_crash]
nbr_users = len(users_list)
print(nbr_users, len(users_list))
if os.path.isfile(output_filename) and not overwrite:
processed_users = list()
fout = gzip.GzipFile(output_filename, 'r')
# count = 0
for row in fout:
customer_obj = json.loads(row)
processed_users.append(customer_obj['uid'])
# print(customer_obj['uid'])
# if count == 100:
# break
# count += 1
fout.close()
users_list = [uid for uid in users_list if uid not in processed_users]
print(nbr_users, len(users_list))
# from_perc = 95
# to_perc = 100
for i, uid in enumerate(users_list):
# if not from_perc < i / len(users_list) * 100.0 <= to_perc:
# continue
if i % 1 == 0:
print(
datetime.datetime.now(),
'%s %s %s [%s/%s] - %.2f' % (traj_table, area, type_user, i,
nbr_users, i / nbr_users * 100.0))
# print(datetime.datetime.now(), '%s %s %s %.2f' % (
# traj_table, area, type_user, i / len(users_list) * 100.0), from_perc, to_perc)
imh = database_io.load_individual_mobility_history(
cur, uid, traj_table, min_length, min_duration)
events = database_io.load_individual_event_history(
cur, uid, evnt_table) if evnt_table is not None else None
if len(imh['trajectories']) < min_traj_nbr:
# print('len trajectories]) < min_traj_nbr', len(imh['trajectories']), min_traj_nbr)
continue
# print(len(events))
# print(list(events.keys()))
wimh_dict = dict()
wevents_dict = dict()
for tid, traj in imh['trajectories'].items():
st = traj.start_time()
stk_list = start_time_map(st)
for stk in stk_list:
if stk is None:
continue
if stk not in wimh_dict:
wimh_dict[stk] = {'uid': uid, 'trajectories': dict()}
wevents_dict[stk] = dict()
wimh_dict[stk]['trajectories'][tid] = traj
if tid in events:
wevents_dict[stk][tid] = events[tid]
customer_obj = {'uid': uid}
for stk in wimh_dict:
wimh = wimh_dict[stk]
wevents = wevents_dict[stk]
# print(stk, len(wimh['trajectories']), len(wevents))
if len(wimh['trajectories']) < min_traj_nbr // 12:
continue
imn = individual_mobility_network.build_imn(wimh,
reg_loc=True,
events=wevents,
verbose=False)
customer_obj[stk] = imn
json_str = '%s\n' % json.dumps(clear_tuples4json(customer_obj),
default=agenda_converter)
json_bytes = json_str.encode('utf-8')
with gzip.GzipFile(output_filename, 'a') as fout:
fout.write(json_bytes)
# with gzip.GzipFile(output_filename.replace('.json.gz', '_%s_%s.json.gz' % (from_perc, to_perc)), 'a') as fout:
# fout.write(json_bytes)
cur.close()
con.close()
def main():
input_table = 'tak.italy_traj'
# con = database_io.get_connection()
# cur = con.cursor()
# users_list = database_io.extract_users_list('tak.italy_traj', cur)
# cur.close()
# con.close()
users_list = [
'100006', '100022', '100026', '10008', '100086', '100087', '100088',
'100090', '100100', '100117'
]
# uid = users_list[0]
# con = database_io.get_connection()
# cur = con.cursor()
# imh = database_io.load_individual_mobility_history(cur, uid, input_table)
# cur.close()
# con.close()
con = database_io.get_connection()
cur = con.cursor()
eval_adaptive = list()
eval_fix1200 = list()
eval_random = list()
for uid in users_list:
print(uid, input_table)
imh = database_io.load_individual_mobility_history(
cur, uid, input_table)
trajectories = imh['trajectories']
alltraj = merge_trajectories(trajectories)
traj_list, user_temporal_thr = segment_trajectories_user_adaptive(
alltraj,
uid,
temporal_thr=60,
spatial_thr=50,
max_speed=0.07,
gap=60,
max_lim=3600 * 48,
window=15,
smooth_fun=moving_median,
min_size=10,
return_cut=True)
avg_nbr_points = np.mean([len(t) for t in traj_list])
print('user_temporal_thr', user_temporal_thr)
print('NT %d - ANP %.2f' % (len(traj_list), avg_nbr_points))
time_precision, dist_coverage, mobility_f1 = evalaute_segmentation(
alltraj, traj_list, print_report=True)
eval_adaptive.append((time_precision, dist_coverage, mobility_f1))
print('------')
traj_list = segment_trajectories(alltraj,
uid,
temporal_thr=1200,
spatial_thr=50,
max_speed=0.07)
avg_nbr_points = np.mean([len(t) for t in traj_list])
print('NT %d - ANP %.2f' % (len(traj_list), avg_nbr_points))
time_precision, dist_coverage, mobility_f1 = evalaute_segmentation(
alltraj, traj_list, print_report=True)
eval_fix1200.append((time_precision, dist_coverage, mobility_f1))
print('------')
traj_list = segment_trajectories(alltraj,
uid,
temporal_thr=120,
spatial_thr=50,
max_speed=0.07)
avg_nbr_points = np.mean([len(t) for t in traj_list])
print('NT %d - ANP %.2f' % (len(traj_list), avg_nbr_points))
time_precision, dist_coverage, mobility_f1 = evalaute_segmentation(
alltraj, traj_list, print_report=True)
print('------')
traj_list = segment_trajectories_random(alltraj, uid, nbr_traj=2000)
avg_nbr_points = np.mean([len(t) for t in traj_list])
print('NT %d - ANP %.2f' % (len(traj_list), avg_nbr_points))
time_precision, dist_coverage, mobility_f1 = evalaute_segmentation(
alltraj, traj_list, print_report=True)
eval_random.append((time_precision, dist_coverage, mobility_f1))
cur.close()
con.close()
print('')
print('ADP - TP: %.3f - DC: %.3f - F1: %.3f' % (np.median(
[v[0] for v in eval_adaptive]), np.median(
[v[1]
for v in eval_adaptive]), np.median([v[2]
for v in eval_adaptive])))
print('FIX - TP: %.3f - DC: %.3f - F1: %.3f' % (np.median(
[v[0] for v in eval_fix1200]), np.median(
[v[1]
for v in eval_fix1200]), np.median([v[2] for v in eval_fix1200])))
print('ADP - TP: %.3f - DC: %.3f - F1: %.3f' % (np.mean(
[v[0] for v in eval_adaptive]), np.mean(
[v[1]
for v in eval_adaptive]), np.mean([v[2] for v in eval_adaptive])))
print('FIX - TP: %.3f - DC: %.3f - F1: %.3f' % (np.mean(
[v[0] for v in eval_fix1200]), np.mean(
[v[1]
for v in eval_fix1200]), np.mean([v[2] for v in eval_fix1200])))
print('ADP - TP: %.3f - DC: %.3f - F1: %.3f' % (np.std(
[v[0] for v in eval_adaptive]), np.std(
[v[1]
for v in eval_adaptive]), np.std([v[2] for v in eval_adaptive])))
print(
'FIX - TP: %.3f - DC: %.3f - F1: %.3f' %
(np.std([v[0]
for v in eval_fix1200]), np.std([v[1] for v in eval_fix1200]),
np.std([v[2] for v in eval_fix1200])))
