class Labels(PreprocessData):
def __init__(self, bus_stop_distance, traffic_light_distance, array_stops,
array_trfl):
super().__init__()
self.bst_dist = bus_stop_distance
self.trfl_dist = traffic_light_distance
self.array_stops = array_stops
self.array_trfl = array_trfl
self.prepro = PreProcess()
def add_bus_stop_label(self, data):
''' this method is used with multiprocessing
item[4] is the velocity'''
chunck = []
for items in tqdm(data):
final_item = []
for item in items:
for stop in self.array_stops:
# dist = self.prepro.distance_in_meters([item[0],item[1]], [stop[4],stop[5]])
dist = self.prepro.distance_in_meters([item[0], item[1]],
[stop[1], stop[2]])
if item[4] < 5 and dist < self.bst_dist:
print('bustop')
item.append('bus_stop')
break
final_item.append(item)
chunck.append(final_item)
return chunck
def add_traffic_light_label(self, data):
chunck = []
for items in tqdm(data):
final_item = []
for item in items:
for stop in self.array_trfl:
# dist = self.prepro.distance_in_meters([item[0],item[1]], [stop[7],stop[8]])
dist = self.prepro.distance_in_meters([item[0], item[1]],
[stop[1], stop[2]])
if item[4] < 5 and dist < self.trfl_dist and item[
10] != 'bus_stop':
item[10] = 'traffic_light'
break
final_item.append(item)
chunck.append(final_item)
return chunck
def add_other_stop_label(self, data):
for items in tqdm(data):
for item in items:
if item[4] < 5 and item[10] == 'in_route':
item[10] = 'other_stop'
def get_false_labels(self, data, label, min_dist):
''' Remove labels other_stop that is between bus or traffic_light'''
count_b, count_a = [], []
for items in tqdm(data):
for idx in range(len(items) - 1):
if idx > 0 and idx < (len(items) - 1):
lat_lng_b = [items[idx - 1][0], items[idx - 1][1]]
lat_lng_a = [items[idx + 1][0], items[idx + 1][1]]
lat_lng_c = [items[idx][0], items[idx][1]]
if items[idx][16]==label and ((items[idx-1][16]==0.0 or items[idx-1][16]==3.0)\
and (items[idx+1][16]==0.0 or items[idx+1][16]==3.0))\
and (self.prepro.distance_in_meters(lat_lng_c, lat_lng_b)<min_dist or self.prepro.distance_in_meters(lat_lng_c, lat_lng_a)<min_dist):
print(
f'before:{items[idx-1][16]}----current:{items[idx][16]}----after:{items[idx+1][16]}'
)
print(
f'before:{self.prepro.distance_in_meters(lat_lng_c, lat_lng_b)}----after:{self.prepro.distance_in_meters(lat_lng_c, lat_lng_a)}'
)
count_b.append(
self.prepro.distance_in_meters(
lat_lng_c, lat_lng_b))
count_a.append(
self.prepro.distance_in_meters(
lat_lng_c, lat_lng_a))
items[idx][16] = -1
class Sentences(PreprocessData):
def __init__(self, list_of_features):
super().__init__()
self.features = list_of_features
self.prepro = PreProcess()
def _has_min_quantity_of_points(self,items):
return len(items) > 10
def is_window(self, delta_time):
return delta_time < 5
def delta_time(self, t1, t2)->float:
##Return time difference between time in seconds
t1 = pd.to_datetime(t1)
t2 = pd.to_datetime(t2)
delta = pd.Timedelta(np.abs(t2-t1))
return delta.seconds
def get_element_by_element(self, _id, data):
row = data[data['id']==_id]
row = row[self.features].values[0]
return row.tolist()
def create_sentences(self, data)->list:
old_matricula = data.iloc[0].matricula_id
old_viaje = data.iloc[0].viaje
old_time = data.iloc[0].instante
old_rota = data.iloc[0].rota
len_sentence = []
partial_list, complete_list = [], []
iterator = 0
for idx in tqdm(data.index):
if self.is_valid_point(data, old_matricula, old_viaje, old_time, old_rota, idx):
partial_list.append(self.get_element_by_element(data.at[idx,'id'], data))
else:
if self._has_min_quantity_of_points(partial_list):
len_sentence.append(len(partial_list))
complete_list.append(partial_list)
partial_list = []
partial_list.append(self.get_element_by_element(data.at[idx,'id'], data))
old_matricula = data.at[idx,'matricula_id']
old_viaje = data.at[idx,'viaje']
old_time = data.at[idx,'instante']
old_rota = data.at[idx,'rota']
iterator +=1
if self._has_min_quantity_of_points(partial_list):
complete_list.append(partial_list)
len_sentence.append(len(partial_list))
print(iterator)
return complete_list
def is_valid_point(self, data, old_matricula, old_viaje, old_time, old_rota, idx):
return (data.at[idx,'matricula_id'] == old_matricula) and (data.at[idx,'viaje'] == old_viaje) and (self.is_window(self.delta_time(old_time,data.at[idx,'instante']))) and (data.at[idx,'rota'] == old_rota)
def label_encoder(self, data):
for items in tqdm(data):
for item in items:
if item[10]=='bus_stop':
item[10]=0.0
elif item[10]=='in_route':
item[10]=1.0
elif item[10]=='other_stop':
item[10]=2.0
else:
item[10]=3.0
def bearing(self, point1, point2):
lat1 = math.radians(point1[0])
lat2 = math.radians(point2[0])
y = math.sin(math.radians(point2[1]-point1[1]))*math.cos(lat2)
x = math.cos(lat1)*math.sin(lat2)-(math.sin(lat1)*math.cos(lat2)*math.cos(math.radians(point2[1]-point1[1])))
deg = degrees(math.atan2(y,x))
return (deg+360)%360
def acceleration(self, v1,v2,deltaT)->float:
# v1 e v2 devems ser m/s
return np.abs(v1-v2)/deltaT
def velocity(self, deltaT,deltaS)->float:
return deltaS/deltaT
def delta_space(self, s1,s2)->float:
return self.prepro.distance_in_meters(s1,s2)
def get_frmt(self, date):
return '%Y-%m-%d %H:%M:%S.%f' if len(date)>19 else '%Y-%m-%d %H:%M:%S'
def days_of_week(self, t1):
f1 = self.get_frmt(t1)
t1 = datetime.datetime.strptime(t1,f1)
return float(t1.weekday())
def hours_of_day(self, t1):
f1 = self.get_frmt(t1)
t1 = datetime.datetime.strptime(t1,f1)
return float(t1.hour)
def complete_trajectory(self, item, pad):
new_trajectory = list()
diff = abs(pad - len(item))
if len(item) > pad:
new_trajectory = item[:pad]
return new_trajectory
elif len(item) < pad:
new_trajectory =item
new_trajectory.extend([item[len(item)-1]]*diff)
return new_trajectory
return item
def get_time_in_seconds(self, data):
## returns values in seconds
for items in tqdm(data):
for idx, item in enumerate(items):
if type(item[2]) == str:
frmt = self.get_frmt(item[2])
date_2 = datetime.datetime.strptime(item[2],frmt)
item[2] = date_2.timestamp()
return data
def put_statistics_metrics(self, data, window=16):
# It takes windows and calculates statistics
final_list_x_b,final_list_x_a,final_list_x_c,final_list_x_as,final_list_x_bs = list(),list(),list(),list(),list()
final_list_y= list()
final_list_ys = list()
features= [4,5,6,7,11]
basic_features= [0,1,2,4,5,6,7,9,10,11]
basic_features_c = [0,1,2,4,5,6,7,9,10,11,3,12,13,15]
for item in tqdm(data):
for i in range(0,len(item),1):
if i >= window and i+window <= len(item)-1 and item[i][14] != -1 and item[i][14] != 1.0:
before = item[abs(i-window):i][:,features]
after = item[i+1:i+window+1][:,features]
mean_before= np.mean(before,axis=0)
mean_after= np.mean(after,axis=0)
std_before= np.std(before,axis=0)
std_after= np.std(after,axis=0)
min_before= np.min(before,axis=0)
min_after= np.min(after,axis=0)
max_before= np.max(before,axis=0)
max_after= np.max(after,axis=0)
median_before=np.median(before,axis=0)
median_after= np.median(after,axis=0)
before = np.concatenate((mean_before,std_before,min_before,max_before,median_before)).tolist()
after = np.concatenate((mean_after,std_after,min_after,max_after,median_after)).tolist()
final_list_x_b.append(item[abs(i-window):i][:,basic_features])
final_list_x_a.append(item[i+1:i+window+1][:,basic_features])
final_list_x_c.append(item[i,basic_features_c])
final_list_x_bs.append(before)
final_list_x_as.append(after)
final_list_y.append(item[i][14])
final_list_ys.append(np.array((item[abs(i-window):i,14].tolist()+[item[i][14]]+item[i+1:i+window+1,14].tolist())))
return final_list_x_b,final_list_x_a,final_list_x_c,final_list_x_bs,final_list_x_as, final_list_y, final_list_ys
def put_statistics_metrics_with_padding(self, data, window=16):
# It takes windows and calculates statistics
# 16 é flag que informa o ruído
final_list_x_b, final_list_x_a, final_list_x_c, final_list_x_as, final_list_x_bs = list(
), list(), list(), list(), list()
final_list_y = list()
final_list_ys = list()
features = [4, 5, 6, 7, 11]
basic_features = [0, 1, 2, 4, 5, 6, 7, 9, 10, 11]
basic_features_c = [0, 1, 2, 4, 5, 6, 7,
8, 9, 10, 11, 3, 12, 13, 15, 16, 17]
for item in tqdm(data):
for i in range(0, len(item), 1):
# and item[i][14] != 1.0:
if i >= window and i+window <= len(item)-1 and item[i][14] != -1:
before = item[abs(i-window):i][:, features]
after = item[i+1:i+window+1][:, features]
mean_before = np.mean(before, axis=0)
mean_after = np.mean(after, axis=0)
std_before = np.std(before, axis=0)
std_after = np.std(after, axis=0)
min_before = np.min(before, axis=0)
min_after = np.min(after, axis=0)
max_before = np.max(before, axis=0)
max_after = np.max(after, axis=0)
median_before = np.median(before, axis=0)
median_after = np.median(after, axis=0)
before = np.concatenate(
(mean_before, std_before, min_before, max_before, median_before)).tolist()
after = np.concatenate(
(mean_after, std_after, min_after, max_after, median_after)).tolist()
final_list_x_b.append(item[abs(i-window):i][:, basic_features])
final_list_x_a.append(item[i+1:i+window+1][:, basic_features])
final_list_x_c.append(item[i, basic_features_c])
final_list_x_bs.append(before)
final_list_x_as.append(after)
final_list_y.append(item[i][14])
final_list_ys.append(np.array(
(item[abs(i-window):i, 14].tolist()+[item[i][14]]+item[i+1:i+window+1, 14].tolist())))
return final_list_x_b, final_list_x_a, final_list_x_c, final_list_x_bs, final_list_x_as, final_list_y, final_list_ys
def add_features(self, data):
for items in tqdm(data):
for idx in range(len(items)):
if len(items[idx]) <=11:
if idx == 0:
items[idx][4] = items[idx][4]/3.6
items[idx].insert(5,0.0)
items[idx].insert(6,0.0)
items[idx].insert(7,0.0)
items[idx].insert(8,0.0)
items[idx].insert(9,self.days_of_week(items[idx][2]))
items[idx].insert(10,self.hours_of_day(items[idx][2]))
else:
v1 = items[idx-1][4]
v2 = items[idx][4]/3.6
p1 = items[idx-1][:2]
p2 = items[idx][:2]
t1 = items[idx-1][2]
t2 = items[idx][2]
time = self.delta_time(t1,t2)
space = self.delta_space(p1,p2)
if time == 0:
time = 0.00000001
#Uso aqui para calcular a nova velocidade e aceleração devido ao ruído espacial
vel = self.velocity(time,space)
acc = self.acceleration(v1,vel,time)
# acc = sentences.acceleration(v1,v2,time)
bear = self.bearing(p1,p2)
#Mudo de v2 para vel por motivo do ruído espacial
items[idx][4] = vel
items[idx].insert(5, acc)
items[idx].insert(6, space)
items[idx].insert(7, np.abs(bear-items[idx-1][7]))
items[idx].insert(8, time)
items[idx].insert(9,self.days_of_week(t2))
items[idx].insert(10,self.hours_of_day(t2))
if items[idx][4]*3.6 > 5 and items[idx][-1] != 1.0:
items[idx][-1] = 1.0
if items[idx][4]*3.6 < 5 and items[idx][-1] == 1.0:
items[idx][-1] = 2.0
def select_features(self,data):
'''
Select only important features, here we remove 13o fearure and add id point and id trajectory. Both ids is useful to rebuild the trajectories
'''
final_list = list()
idx = 0
for i, items in tqdm(enumerate(data)):
list_item = list()
for j, item in enumerate(items):
aux = list()
aux = copy.copy(item[:12])
# aqui
aux.insert(12, item[14])
aux.insert(13, item[15])
aux.insert(14, item[16])
'''adding id in each point of trajectory'''
aux.insert(15, idx)
'''adding id to identify each trajectory'''
aux.insert(16, i)
idx += 1
list_item.append(aux)
final_list.append(list_item)
return final_list
def add_id_noise(self, data, data_with_noise):
'''
Here, we need pass the index from trajectories with noise, ex: set(np.load('models/id_point_trajectory_without_noise_dublin_clean.npy'))
'''
for i, items in tqdm(enumerate(data)):
if i in data_with_noise:
for item in items:
item.append(1)
else:
for item in items:
item.append(0)
def padding(self,pad,data):
'''
Ex: padding(16,np.array(final_list_with_time))
'''
final_list = list()
for items in data:
item_list = list()
item_list.extend([np.zeros_like(items[0]).tolist()]*pad)
for item in items:
item_list.append(item.tolist())
for i in range(pad):
item_list.append(items[len(items)-1].tolist())
final_list.append(item_list)
return final_list
