import os
import math
import pandas as pd
from datetime import datetime
from geopy import distance
from libcity.data.dataset.trajectory_encoder.abstract_trajectory_encoder import AbstractTrajectoryEncoder
from libcity.utils import parse_time, parse_coordinate
parameter_list = ['dataset', 'min_session_len', 'min_sessions', 'traj_encoder', 'cut_method',
'window_size', 'history_type', 'min_checkins', 'max_session_len']
[docs]class HstlstmEncoder(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 = 100.0 # 最大的时间差（单位秒）
self.dis_max = 0.5 # 最大的距离差（单位千米）
self.history_type = self.config['history_type']
self.feature_dict = {'current_loc': 'int', 'tim_interval': 'float',
'dis': 'float', 'target': '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))
self.data_path = './raw_data/{}/'.format(self.config['dataset'])
self.geo = pd.read_csv(os.path.join(self.data_path, '{}.geo'.format(self.config['dataset'])))
[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 = []
tim_interval = []
dis = []
pre_time = None
pre_lon = None
pre_lat = None
for index, point in enumerate(traj):
loc = point[4]
now_time = parse_time(point[2])
lon, lat = parse_coordinate(self.geo.loc[self.geo['geo_id'] == loc].iloc[0]['coordinates'])
if index == 0:
if loc not in self.location2id:
self.location2id[loc] = self.loc_id
self.loc_id += 1
current_loc.append(self.location2id[loc])
tim_interval.append(self.tim_max) # use fixed interval for start point
dis.append(self.dis_max)
else:
if loc not in self.location2id:
self.location2id[loc] = self.loc_id
self.loc_id += 1
current_loc.append(self.location2id[loc])
tim_diff = datetime.timestamp(now_time) - datetime.timestamp(pre_time)
if tim_diff > self.tim_max:
self.tim_max = tim_diff
tim_interval.append(tim_diff)
dis_diff = distance.distance((pre_lat, pre_lon), (lat, lon)).kilometers
if dis_diff > self.dis_max:
self.dis_max = dis_diff
dis.append(dis_diff)
pre_time = now_time
pre_lat = lat
pre_lon = lon
# 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]
trace.append(current_loc[:i+1])
trace.append(tim_interval[:i+1])
trace.append(dis[:i+1])
trace.append(target)
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)
return encoded_trajectories
[docs] def gen_data_feature(self):
loc_pad = self.loc_id
self.pad_item = {
'current_loc': loc_pad,
'tim_interval': 0.0,
'dis': 0.0
}
self.data_feature = {
'loc_size': self.loc_id + 1,
'tim_slot_max': int(math.ceil(self.tim_max)),
'dis_slot_max': int(math.ceil(self.dis_max)),
'uid_size': self.uid,
'loc_pad': loc_pad
}