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TrafficWheel
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fix bugs

This commit is contained in:
czzhangheng 2025-03-03 16:31:04 +08:00
parent f35bfbf75d
commit 0a9ac1a025
18 changed files with 66 additions and 450 deletions
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2
README.md
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@ -95,5 +95,7 @@ git push origin dev
目前，实测以下模型性能与原报告相比指标偏高：ARIMA、TCN、DCRNN
STGCN在载入图时会有未知warning
以下模型由于没有源码暂未实现：HA、VAR、FC-LSTM、GRU-ED

47
config/HA/Hainan.yaml
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@ -1,47 +0,0 @@
data:
num_nodes: 13
lag: 12
horizon: 12
val_ratio: 0.2
test_ratio: 0.2
tod: false
normalizer: std
column_wise: false
default_graph: true
add_time_in_day: true
add_day_in_week: true
steps_per_day: 288
days_per_week: 7
batch_size: 64
model:
input_dim: 1
output_dim: 1
train:
loss_func: mae
seed: 10
batch_size: 64
epochs: 50
lr_init: 0.003
weight_decay: 0
lr_decay: false
lr_decay_rate: 0.3
lr_decay_step:
- 5
- 20
- 40
- 70
early_stop: true
early_stop_patience: 15
grad_norm: false
max_grad_norm: 5
real_value: true
test:
mae_thresh: null
mape_thresh: 0.0
log:
log_step: 10000
plot: false

48
config/HA/PEMSD7(L).yaml
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@ -1,48 +0,0 @@
data:
add_day_in_week: true
add_time_in_day: true
column_wise: false
days_per_week: 5
default_graph: true
horizon: 12
lag: 12
normalizer: std
num_nodes: 1026
steps_per_day: 288
test_ratio: 0.2
tod: false
val_ratio: 0.2
log:
log_step: 1000
plot: true
model:
cheb_order: 2
embed_dim: 12
input_dim: 1
num_layers: 1
output_dim: 1
rnn_units: 64
use_day: true
use_week: true
test:
mae_thresh: None
mape_thresh: 0.0
train:
batch_size: 12
early_stop: true
early_stop_patience: 10
epochs: 300
grad_norm: false
loss_func: mae
lr_decay: false
lr_decay_rate: 0.1
lr_decay_step:
- '5'
- '20'
- '40'
- '70'
lr_init: 0.0005625
max_grad_norm: 5
real_value: true
seed: 12
weight_decay: 0

48
config/HA/PEMSD7(M).yaml
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@ -1,48 +0,0 @@
data:
add_day_in_week: true
add_time_in_day: true
column_wise: false
days_per_week: 5
default_graph: true
horizon: 12
lag: 12
normalizer: std
num_nodes: 228
steps_per_day: 288
test_ratio: 0.2
tod: false
val_ratio: 0.2
log:
log_step: 1000
plot: true
model:
cheb_order: 2
embed_dim: 8
input_dim: 1
num_layers: 1
output_dim: 1
rnn_units: 64
use_day: true
use_week: true
test:
mae_thresh: None
mape_thresh: 0.0
train:
batch_size: 64
early_stop: true
early_stop_patience: 15
epochs: 300
grad_norm: false
loss_func: mae
lr_decay: false
lr_decay_rate: 0.1
lr_decay_step:
- '5'
- '20'
- '40'
- '70'
lr_init: 0.003
max_grad_norm: 5
real_value: true
seed: 12
weight_decay: 0

48
config/HA/PEMSD7.yaml
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@ -1,48 +0,0 @@
data:
add_day_in_week: true
add_time_in_day: true
column_wise: false
days_per_week: 7
default_graph: true
horizon: 12
lag: 12
normalizer: std
num_nodes: 883
steps_per_day: 288
test_ratio: 0.2
tod: false
val_ratio: 0.2
log:
log_step: 3000
plot: false
model:
cheb_order: 2
embed_dim: 12
input_dim: 1
num_layers: 1
output_dim: 1
rnn_units: 64
use_day: true
use_week: true
test:
mae_thresh: None
mape_thresh: 0.0
train:
batch_size: 16
early_stop: true
early_stop_patience: 10
epochs: 200
grad_norm: false
loss_func: mae
lr_decay: false
lr_decay_rate: 0.3
lr_decay_step:
- '5'
- '20'
- '40'
- '70'
lr_init: 0.00075
max_grad_norm: 5
real_value: true
seed: 10
weight_decay: 0

40
config/HA/PEMSD8.yaml
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@ -1,40 +0,0 @@
data:
add_day_in_week: true
add_time_in_day: true
column_wise: false
days_per_week: 7
default_graph: true
horizon: 12
lag: 12
normalizer: std
num_nodes: 170
steps_per_day: 288
test_ratio: 0.2
tod: false
val_ratio: 0.2
log:
log_step: 2000
plot: false
model:
cheb_order: 2
embed_dim: 5
input_dim: 1
num_layers: 1
output_dim: 1
rnn_units: 64
use_day: true
use_week: true
test:
mae_thresh: None
mape_thresh: 0.001
train:
batch_size: 64
early_stop: true
early_stop_patience: 15
epochs: 300
grad_norm: false
loss_func: mae
max_grad_norm: 5
real_value: true
seed: 12
weight_decay: 0

21
config/HA/PEMSD4.yaml → config/NLT/PEMSD3.yaml
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@ -1,5 +1,5 @@
data:
num_nodes: 307
num_nodes: 358
lag: 12
horizon: 12
val_ratio: 0.2
@ -8,20 +8,25 @@ data:
normalizer: std
column_wise: False
default_graph: True
add_time_in_day: True
add_day_in_week: True
add_time_in_day: False
add_day_in_week: False
steps_per_day: 288
days_per_week: 7
model:
input_dim: 1
output_dim: 1
input_window: 12
output_window: 12
embed_dim: 10
rnn_units: 64
num_layers: 1
cheb_order: 2
use_day: True
use_week: True
hidden_size: 64
num_layers: 2
natt_hops: 4
nfc: 256
max_up_len: 80
feature_dim: 1
use_day: False
use_week: False
train:
loss_func: mae

27
config/PDFormer/PEMSD3.yaml → config/NLT/PEMSD7.yaml
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@ -1,5 +1,5 @@
data:
num_nodes: 358
num_nodes: 883
lag: 12
horizon: 12
val_ratio: 0.2
@ -8,26 +8,31 @@ data:
normalizer: std
column_wise: False
default_graph: True
add_time_in_day: True
add_day_in_week: True
add_time_in_day: False
add_day_in_week: False
steps_per_day: 288
days_per_week: 7
model:
input_dim: 1
output_dim: 1
embed_dim: 12
rnn_units: 64
num_layers: 1
cheb_order: 2
use_day: True
input_window: 12
output_window: 12
embed_dim: 10
hidden_size: 64
num_layers: 2
natt_hops: 4
nfc: 256
max_up_len: 80
feature_dim: 1
use_day: False
use_week: False
train:
loss_func: mae
seed: 10
batch_size: 64
epochs: 50
batch_size: 32
epochs: 300
lr_init: 0.003
weight_decay: 0
lr_decay: False
@ -44,5 +49,5 @@ test:
mape_thresh: 0.0
log:
log_step: 10000
log_step: 200
plot: False

25
config/HA/PEMSD3.yaml → config/NLT/PEMSD8.yaml
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@ -1,5 +1,5 @@
data:
num_nodes: 358
num_nodes: 170
lag: 12
horizon: 12
val_ratio: 0.2
@ -8,26 +8,31 @@ data:
normalizer: std
column_wise: False
default_graph: True
add_time_in_day: True
add_day_in_week: True
add_time_in_day: False
add_day_in_week: False
steps_per_day: 288
days_per_week: 7
model:
input_dim: 1
output_dim: 1
embed_dim: 12
rnn_units: 64
num_layers: 1
cheb_order: 2
use_day: True
input_window: 12
output_window: 12
embed_dim: 10
hidden_size: 64
num_layers: 2
natt_hops: 4
nfc: 256
max_up_len: 80
feature_dim: 1
use_day: False
use_week: False
train:
loss_func: mae
seed: 10
batch_size: 64
epochs: 50
epochs: 300
lr_init: 0.003
weight_decay: 0
lr_decay: False
@ -44,5 +49,5 @@ test:
mape_thresh: 0.0
log:
log_step: 10000
log_step: 200
plot: False

71
config/PDFormer/PEMSD4.yaml
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@ -1,71 +0,0 @@
data:
num_nodes: 307
lag: 12
horizon: 12
val_ratio: 0.2
test_ratio: 0.2
tod: False
normalizer: std
column_wise: False
default_graph: True
add_time_in_day: True
add_day_in_week: True
steps_per_day: 288
days_per_week: 7
model:
embed_dim: 64
skip_dim: 256
lape_dim: 8
geo_num_heads: 4
sem_num_heads: 2
t_num_heads: 2
mlp_ratio: 4
qkv_bias: True
drop: 0.
attn_drop: 0.
drop_path: 0.3
s_attn_size: 3
t_attn_size: 3
enc_depth: 6
type_ln: pre
type_short_path: hop
input_dim: 3
output_dim: 1
input_window: 12
output_window: 12
add_time_in_day: True
add_day_in_week: True
world_size: 1
huber_delta: 1
quan_delta: 0.25
far_mask_delta: 5
dtw_delta: 5
use_curriculum_learning: True
step_size: 2500
max_epoch: 200
task_level: 0
train:
loss_func: mae
seed: 10
batch_size: 64
epochs: 300
lr_init: 0.003
weight_decay: 0
lr_decay: False
lr_decay_rate: 0.3
lr_decay_step: "5,20,40,70"
early_stop: True
early_stop_patience: 15
grad_norm: False
max_grad_norm: 5
real_value: True
test:
mae_thresh: null
mape_thresh: 0.0
log:
log_step: 200
plot: False

48
config/PDFormer/PEMSD7.yaml
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@ -1,48 +0,0 @@
data:
add_day_in_week: true
add_time_in_day: true
column_wise: false
days_per_week: 7
default_graph: true
horizon: 12
lag: 12
normalizer: std
num_nodes: 883
steps_per_day: 288
test_ratio: 0.2
tod: false
val_ratio: 0.2
log:
log_step: 3000
plot: false
model:
cheb_order: 2
embed_dim: 12
input_dim: 1
num_layers: 1
output_dim: 1
rnn_units: 64
use_day: true
use_week: true
test:
mae_thresh: None
mape_thresh: 0.0
train:
batch_size: 16
early_stop: true
early_stop_patience: 10
epochs: 200
grad_norm: false
loss_func: mae
lr_decay: false
lr_decay_rate: 0.3
lr_decay_step:
- '5'
- '20'
- '40'
- '70'
lr_init: 0.00075
max_grad_norm: 5
real_value: true
seed: 10
weight_decay: 0

44
config/PDFormer/PEMSD8.yaml
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@ -1,44 +0,0 @@
data:
add_day_in_week: true
add_time_in_day: true
column_wise: false
days_per_week: 7
default_graph: true
horizon: 12
lag: 12
normalizer: std
num_nodes: 170
steps_per_day: 288
test_ratio: 0.2
tod: false
val_ratio: 0.2
log:
log_step: 2000
plot: false
model:
cheb_order: 2
embed_dim: 5
input_dim: 1
num_layers: 1
output_dim: 1
rnn_units: 64
use_day: true
use_week: true
test:
mae_thresh: None
mape_thresh: 0.001
train:
batch_size: 64
early_stop: true
early_stop_patience: 15
epochs: 300
grad_norm: false
loss_func: mae
lr_decay: true
lr_decay_rate: 0.3
lr_decay_step: "5,20,40,70"
lr_init: 0.003
max_grad_norm: 5
real_value: true
seed: 12
weight_decay: 0

2
dataloader/DCRNNdataloader.py
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@ -128,7 +128,7 @@ def load_st_dataset(dataset, sample):
if len(data.shape) == 2:
data = np.expand_dims(data, axis=-1)
print('Load %s Dataset shaped: ' % dataset, data.shape, data.max(), data.min(), data.mean(), np.median(data))
print('加载 %s 数据集中... ' % dataset)
return data[::sample]
def split_data_by_days(data, val_days, test_days, interval=30):

2
dataloader/PeMSDdataloader.py
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@ -128,7 +128,7 @@ def load_st_dataset(dataset, sample):
if len(data.shape) == 2:
data = np.expand_dims(data, axis=-1)
print('Load %s Dataset shaped: ' % dataset, data.shape, data.max(), data.min(), data.mean(), np.median(data))
print('加载 %s 数据集中... ' % dataset)
return data[::sample]
def split_data_by_days(data, val_days, test_days, interval=30):

15
dataloader/cde_loader/cdeDataloader.py
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@ -1,4 +1,3 @@
import model.STGNCDE.controldiffeq
from lib.normalization import normalize_dataset
import numpy as np
import gc
@ -6,6 +5,8 @@ import os
import torch
import h5py
from model.STGNCDE import controldiffeq
def get_dataloader(args, normalizer='std', single=True):
data = load_st_dataset(args['type']) # 加载数据
@ -129,22 +130,22 @@ def load_st_dataset(dataset):
# output B, N, D
match dataset:
case 'PEMSD3':
data_path = os.path.join('./data/PeMS03/PEMS03.npz')
data_path = os.path.join('./data/PEMS03/PEMS03.npz')
data = np.load(data_path)['data'][:, :, 0] # only the first dimension, traffic flow data
case 'PEMSD4':
data_path = os.path.join('./data/PeMS04/PEMS04.npz')
data_path = os.path.join('./data/PEMS04/PEMS04.npz')
data = np.load(data_path)['data'][:, :, 0] # only the first dimension, traffic flow data
case 'PEMSD7':
data_path = os.path.join('./data/PeMS07/PEMS07.npz')
data_path = os.path.join('./data/PEMS07/PEMS07.npz')
data = np.load(data_path)['data'][:, :, 0] # only the first dimension, traffic flow data
case 'PEMSD8':
data_path = os.path.join('./data/PeMS08/PeMS08.npz')
data_path = os.path.join('./data/PEMS08/PEMS08.npz')
data = np.load(data_path)['data'][:, :, 0] # only the first dimension, traffic flow data
case 'PEMSD7(L)':
data_path = os.path.join('./data/PeMS07(L)/PEMS07L.npz')
data_path = os.path.join('./data/PEMS07(L)/PEMS07L.npz')
data = np.load(data_path)['data'][:, :, 0] # only the first dimension, traffic flow data
case 'PEMSD7(M)':
data_path = os.path.join('./data/PeMS07(M)/V_228.csv')
data_path = os.path.join('./data/PEMS07(M)/V_228.csv')
data = np.genfromtxt(data_path, delimiter=',') # Read CSV directly with numpy
case 'METR-LA':
data_path = os.path.join('./data/METR-LA/METR.h5')

10
lib/normalization.py
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@ -120,7 +120,7 @@ def normalize_dataset(data, normalizer, column_wise=False):
maximum = data.max()
scaler = MinMax01Scaler(minimum, maximum)
# data = scaler.transform(data)
print('Normalize the dataset by MinMax01 Normalization')
# print('Normalize the dataset by MinMax01 Normalization')
elif normalizer == 'max11':
if column_wise:
minimum = data.min(axis=0, keepdims=True)
@ -130,7 +130,7 @@ def normalize_dataset(data, normalizer, column_wise=False):
maximum = data.max()
scaler = MinMax11Scaler(minimum, maximum)
# data = scaler.transform(data)
print('Normalize the dataset by MinMax11 Normalization')
# print('Normalize the dataset by MinMax11 Normalization')
elif normalizer == 'std':
if column_wise:
mean = data.mean(axis=0, keepdims=True)
@ -140,15 +140,15 @@ def normalize_dataset(data, normalizer, column_wise=False):
std = data.std()
scaler = StandardScaler(mean, std)
# data = scaler.transform(data)
print('Normalize the dataset by Standard Normalization')
# print('Normalize the dataset by Standard Normalization')
elif normalizer == 'None':
scaler = NScaler()
# data = scaler.transform(data)
print('Does not normalize the dataset')
# print('Does not normalize the dataset')
elif normalizer == 'cmax':
scaler = ColumnMinMaxScaler(data.min(axis=0), data.max(axis=0))
# data = scaler.transform(data)
print('Normalize the dataset by Column Min-Max Normalization')
# print('Normalize the dataset by Column Min-Max Normalization')
else:
raise ValueError(f"Unsupported normalizer type: {normalizer}")
return scaler

8
model/STSGCN/get_adj.py
View File

@ -9,20 +9,20 @@ def get_adj(args):
dataset_path = './data'
match args['num_nodes']:
case 358:
dataset_name = 'PeMS03'
dataset_name = 'PEMS03'
adj_path = os.path.join(dataset_path, dataset_name, 'PEMS03.csv')
id = os.path.join(dataset_path, dataset_name, 'PEMS03.txt')
A = get_adjacency_matrix(adj_path, args['num_nodes'], args['construct_type'], id_filename=id)
case 307:
dataset_name = 'PeMS04'
dataset_name = 'PEMS04'
adj_path = os.path.join(dataset_path, dataset_name, 'PEMS04.csv')
A = get_adjacency_matrix(adj_path, args['num_nodes'], args['construct_type'])
case 883:
dataset_name = 'PeMS07'
dataset_name = 'PEMS07'
adj_path = os.path.join(dataset_path, dataset_name, 'PEMS07.csv')
A = get_adjacency_matrix(adj_path, args['num_nodes'], args['construct_type'])
case 170:
dataset_name = 'PeMS08'
dataset_name = 'PEMS08'
adj_path = os.path.join(dataset_path, dataset_name, 'PEMS08.csv')
A = get_adjacency_matrix(adj_path, args['num_nodes'], args['construct_type'])

10
run.py
View File

@ -68,10 +68,6 @@ def main():
with open(destination_path, 'w') as f:
f.write(config_content)
# 拷贝配置文件到日志文件夹
# destination_path = os.path.join(args['train']['log_dir'], config_filename)
# shutil.copyfile(config_path, destination_path)
# Start training or testing
trainer = select_trainer(model, loss, optimizer, train_loader, val_loader, test_loader, scaler, args,
lr_scheduler, extra_data)
@ -83,12 +79,8 @@ def main():
model.load_state_dict(torch.load(
f"./pre-trained/{args['model']['type']}/{args['data']['type']}.pth",
map_location=args['device'], weights_only=True))
print(f"Loaded saved model on {args['device']}")
# start_time = time.time()
# print(f"Loaded saved model on {args['device']}")
trainer.test(model.to(args['device']), trainer.args, test_loader, scaler, trainer.logger)
# end_time = time.time()
# elapsed_time = end_time - start_time
# print(f"执行时间：{elapsed_time:.4f} 秒")
case _:
raise ValueError(f"Unsupported mode: {args['mode']}")