import os
import numpy as np
from libcity.data.dataset import TrafficStateCPTDataset, TrafficStateGridDataset
from libcity.utils.dataset import timestamp2array, timestamp2vec_origin
[docs]class ACFMDataset(TrafficStateGridDataset, TrafficStateCPTDataset):
def __init__(self, config):
super().__init__(config)
self.external_time = self.config.get('external_time', True)
self.parameters_str = \
self.parameters_str + '_' + str(self.len_closeness) \
+ '_' + str(self.len_period) + '_' + str(self.len_trend) \
+ '_' + str(self.pad_forward_period) + '_' + str(self.pad_back_period) \
+ '_' + str(self.pad_forward_trend) + '_' + str(self.pad_back_trend) \
+ '_' + str(self.interval_period) + '_' + str(self.interval_trend)
self.cache_file_name = os.path.join('./libcity/cache/dataset_cache/',
'grid_based_{}.npz'.format(self.parameters_str))
[docs] def _get_external_array(self, timestamp_list, ext_data=None, previous_ext=False, ext_time=True):
"""
根据时间戳数组，获取对应时间的外部特征
Args:
timestamp_list(list): 时间戳序列
ext_data: 外部数据
previous_ext: 是否是用过去时间段的外部数据，因为对于预测的时间段Y，
一般没有真实的外部数据，所以用前一个时刻的数据，**多步预测则用提前多步的数据**
ext_time: 是否加载时间数据，False则只考虑星期，True则加上小时的信息
Returns:
numpy.ndarray: External data shape is (len(timestamp_list), ext_dim)
"""
data = []
if ext_time:
vecs_timestamp = timestamp2array(
timestamp_list, 24 * 60 * 60 // self.time_intervals) # len(timestamp_list) * dim
else:
vecs_timestamp = timestamp2vec_origin(timestamp_list) # len(timestamp_list) * dim
data.append(vecs_timestamp)
# 外部数据集
if ext_data is not None:
indexs = []
for ts in timestamp_list:
if previous_ext:
ts_index = self.idx_of_ext_timesolts[ts - self.offset_frame]
else:
ts_index = self.idx_of_ext_timesolts[ts]
indexs.append(ts_index)
select_data = ext_data[indexs] # len(timestamp_list) * ext_dim 选出所需要的时间步的数据
data.append(select_data)
if len(data) > 0:
data = np.hstack(data)
else:
data = np.zeros((len(timestamp_list), 0))
return data # (len(timestamp_list), ext_dim)
[docs] def _load_ext_data(self, ts_x, ts_y):
"""
加载对应时间的外部数据(.ext)
Args:
ts_x: 输入数据X对应的时间戳，shape: (num_samples, T_c+T_p+T_t)
ts_y: 输出数据Y对应的时间戳，shape:(num_samples, )
Returns:
tuple: tuple contains:
ext_x(numpy.ndarray): 对应时间的外部数据, shape: (num_samples, T_c+T_p+T_t, ext_dim),
ext_y(numpy.ndarray): 对应时间的外部数据, shape: (num_samples, ext_dim)
"""
# 加载外部数据
if self.load_external and os.path.exists(self.data_path + self.ext_file + '.ext'): # 外部数据集
ext_data = self._load_ext()
ext_data = 1. * (ext_data - ext_data.min()) / (ext_data.max() - ext_data.min())
else:
ext_data = None
ext_x = []
for ts in ts_x:
ext_x.append(self._get_external_array(ts, ext_data, ext_time=self.external_time))
ext_x = np.asarray(ext_x)
# ext_x: (num_samples_plus, T_c+T_p+T_t, ext_dim)
ext_y = self._get_external_array(ts_y, ext_data, previous_ext=True, ext_time=self.external_time)
# ext_y: (num_samples_plus, ext_dim)
return ext_x, ext_y
[docs] def get_data_feature(self):
"""
返回数据集特征，scaler是归一化方法，adj_mx是邻接矩阵，num_nodes是网格的个数，
len_row是网格的行数，len_column是网格的列数，
feature_dim是输入数据的维度，output_dim是模型输出的维度
Returns:
dict: 包含数据集的相关特征的字典
"""
lp = self.len_period * (self.pad_forward_period + self.pad_back_period + 1)
lt = self.len_trend * (self.pad_forward_trend + self.pad_back_trend + 1)
return {"scaler": self.scaler, "adj_mx": self.adj_mx,
"num_nodes": self.num_nodes, "feature_dim": self.feature_dim, "ext_dim": self.ext_dim,
"output_dim": self.output_dim, "len_row": self.len_row, "len_column": self.len_column,
"len_closeness": self.len_closeness, "len_period": lp, "len_trend": lt,
"num_batches": self.num_batches}