import os
from libcity.data.dataset.trajectory_encoder.abstract_trajectory_encoder import AbstractTrajectoryEncoder
from libcity.utils import parse_time
parameter_list = ['dataset', 'min_session_len', 'min_sessions', 'traj_encoder', 'cut_method',
'window_size', 'history_type', 'min_checkins', 'max_session_len']
[docs]class StandardTrajectoryEncoder(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 = 47 # 时间编码方式得改变
self.history_type = self.config['history_type']
self.feature_dict = {'history_loc': 'int', 'history_tim': 'int',
'current_loc': 'int', 'current_tim': 'int',
'target': 'int', 'target_tim': 'int', 'uid': 'int'
}
if config['evaluate_method'] == 'sample':
self.feature_dict['neg_loc'] = 'int'
parameter_list.append('neg_samples')
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))
# 对于这种 history 模式没办法做到 batch
if self.history_type == 'cut_off':
# self.config['batch_size'] = 1
self.feature_dict['history_loc'] = 'array of int'
self.feature_dict['history_tim'] = 'array of int'
[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 = []
history_loc = []
history_tim = []
for index, traj in enumerate(trajectories):
current_loc = []
current_tim = []
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_loc.append(self.location2id[loc])
# 采用工作日编码到0-23，休息日编码到24-47
time_code = self._time_encode(now_time)
current_tim.append(time_code)
# 完成当前轨迹的编码，下面进行输入的形成
if index == 0:
# 因为要历史轨迹特征，所以第一条轨迹是不能构成模型输入的
if self.history_type == 'splice':
history_loc += current_loc
history_tim += current_tim
else:
history_loc.append(current_loc)
history_tim.append(current_tim)
continue
# 一条轨迹可以产生多条训练数据，根据第一个点预测第二个点，前两个点预测第三个点....
for i in range(len(current_loc) - 1):
trace = []
target = current_loc[i+1]
target_tim = current_tim[i+1]
trace.append(history_loc.copy())
trace.append(history_tim.copy())
trace.append(current_loc[:i+1])
trace.append(current_tim[:i+1])
trace.append(target)
trace.append(target_tim)
trace.append(uid)
if negative_sample is not None:
neg_loc = []
for neg in negative_sample[index]:
if neg not in self.location2id:
self.location2id[neg] = self.loc_id
self.loc_id += 1
neg_loc.append(self.location2id[neg])
trace.append(neg_loc)
encoded_trajectories.append(trace)
if self.history_type == 'splice':
history_loc += current_loc
history_tim += current_tim
else:
history_loc.append(current_loc)
history_tim.append(current_tim)
return encoded_trajectories
[docs] def gen_data_feature(self):
loc_pad = self.loc_id
tim_pad = self.tim_max + 1
if self.history_type == 'cut_off':
self.pad_item = {
'current_loc': loc_pad,
'current_tim': tim_pad
}
# 这种情况下不对 history_loc history_tim 做补齐
else:
self.pad_item = {
'current_loc': loc_pad,
'history_loc': loc_pad,
'current_tim': tim_pad,
'history_tim': tim_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
}
def _time_encode(self, time):
if time.weekday() in [0, 1, 2, 3, 4]:
return time.hour
else:
return time.hour + 24