import os
import numpy as np
from libcity.data.dataset.trajectory_encoder.abstract_trajectory_encoder import AbstractTrajectoryEncoder
from libcity.utils import parse_time, cal_basetime, cal_timeoff
parameter_list = ['dataset', 'min_session_len', 'min_sessions', 'traj_encoder', 'cut_method',
'window_size', 'history_type']
[docs]class StrnnEncoder(AbstractTrajectoryEncoder):
def __init__(self, config):
super().__init__(config)
self.uid = 0
self.location2id = {} # 因为原始数据集中的部分 loc id 不会被使用到因此这里需要重新编码一下
self.loc_id = 0
self.tim_max = 0 # 记录最大的时间编码
self.feature_dict = {'current_loc': 'int', 'current_tim': 'int',
'target': 'int', 'target_tim': 'int', 'uid': 'int', 'current_dis': 'float'
}
parameters_str = ''
for key in parameter_list:
if key in self.config:
parameters_str += '_' + str(self.config[key])
self.cache_file_name = os.path.join(
'./libcity/cache/dataset_cache/', 'trajectory_{}.json'.format(parameters_str))
self.geo_coord = {}
path = "./raw_data/{}/{}.geo".format(config['dataset'], config['dataset'])
f_geo = open(path)
lines = f_geo.readlines()
for i, line in enumerate(lines):
if i == 0:
continue
tokens = line.strip().replace("\"", "").replace("[", "").replace("]", "").split(',')
loc_id, loc_longi, loc_lati = int(tokens[0]), eval(tokens[2]), eval(tokens[3])
self.geo_coord[loc_id] = [loc_lati, loc_longi]
f_geo.close()
[docs] def encode(self, uid, trajectories, negative_sample=None):
"""standard encoder use the same method as DeepMove
Recode poi id. Encode timestamp with its hour.
Args:
uid ([type]): same as AbstractTrajectoryEncoder
trajectories ([type]): same as AbstractTrajectoryEncoder
trajectory1 = [
(location ID, timestamp, timezone_offset_in_minutes),
(location ID, timestamp, timezone_offset_in_minutes),
.....
]
"""
# 直接对 uid 进行重编码
uid = self.uid
self.uid += 1
encoded_trajectories = []
for index, traj in enumerate(trajectories):
current_loc = []
current_tim = []
current_longi = []
current_lati = []
current_points = []
start_time = parse_time(traj[0][2])
# 以当天凌晨的时间作为计算 time_off 的基准
base_time = cal_basetime(start_time, True)
for point in traj:
loc = point[4]
now_time = parse_time(point[2])
if loc not in self.location2id:
self.location2id[loc] = self.loc_id
self.loc_id += 1
current_points.append(loc)
current_loc.append(self.location2id[loc])
current_lati.append(self.geo_coord[loc][0])
current_longi.append(self.geo_coord[loc][1])
time_code = int(cal_timeoff(now_time, base_time))
if time_code > self.tim_max:
self.tim_max = time_code
current_tim.append(time_code)
# 完成当前轨迹的编码，下面进行输入的形成
trace = []
target = current_loc[-1]
target_tim = current_tim[-1]
current_loc = current_loc[:-1]
current_tim = current_tim[:-1]
lati = self.geo_coord[current_points[-1]][0]
lati = np.array([lati for i in range(len(current_loc))])
longi = self.geo_coord[current_points[-1]][1]
longi = np.array([longi for i in range(len(current_loc))])
current_dis = euclidean_dist(lati - current_lati[:-1], longi - current_longi[:-1])
trace.append(current_loc)
trace.append(current_tim)
trace.append(target)
trace.append(target_tim)
trace.append(uid)
trace.append(current_dis)
encoded_trajectories.append(trace)
return encoded_trajectories
[docs] def gen_data_feature(self):
loc_pad = self.loc_id
tim_pad = self.tim_max + 1
dis_pad = 0.0
self.pad_item = {
'current_loc': loc_pad,
'current_tim': tim_pad,
'current_dis': dis_pad
}
self.data_feature = {
'loc_size': self.loc_id + 1,
'tim_size': self.tim_max + 2,
'uid_size': self.uid,
'loc_pad': loc_pad,
'tim_pad': tim_pad,
'dis_pad': dis_pad
}
[docs]def euclidean_dist(x, y):
return np.sqrt(np.power(x, 2) + np.power(y, 2)).tolist()