-
Notifications
You must be signed in to change notification settings - Fork 18
/
test.py
176 lines (151 loc) · 7.15 KB
/
test.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
import os
import glob
import argparse
import torch
import torch.nn.functional as F
import yaml
import numpy as np
from easydict import EasyDict as edict
from MTGNN import MTGNN
from AGCRN import AGCRN
from wpf_dataset import PGL4WPFDataset, TestPGL4WPFDataset
from metrics import regressor_detailed_scores
from utils import load_model, get_logger, str2bool
from logging import getLogger
def predict(config, train_data):
log = getLogger()
name2id = {
'weekday': 0,
'time': 1,
'Wspd': 2,
'Wdir': 3,
'Etmp': 4,
'Itmp': 5,
'Ndir': 6,
'Pab1': 7,
'Pab2': 8,
'Pab3': 9,
'Prtv': 10,
'Patv': 11
}
select = config.select
select_ind = [name2id[name] for name in select]
with torch.no_grad():
data_mean = torch.FloatTensor(train_data.data_mean).to(config.device) # (1, 134, 1, 1)
data_scale = torch.FloatTensor(train_data.data_scale).to(config.device) # (1, 134, 1, 1)
graph = train_data.graph # (134, 134)
if config.model == 'MTGNN':
model = MTGNN(config=config, adj_mx=graph).to(config.device)
elif config.model == 'AGCRN':
model = AGCRN(config=config, adj_mx=graph).to(config.device)
else:
raise ValueError('Error config.model = {}'.format(config.model))
output_path = config.output_path+config.exp_id+'_'+config.model
load_model(os.path.join(output_path, "model_%d.pt" % config.best), model, log=log)
model.eval()
test_x = sorted(glob.glob(os.path.join("./data", "test_x", "*")))
test_y = sorted(glob.glob(os.path.join("./data", "test_y", "*")))
maes, rmses = [], []
for i, (test_x_f, test_y_f) in enumerate(zip(test_x, test_y)):
test_x_ds = TestPGL4WPFDataset(filename=test_x_f) # (B,N,T,F)
test_y_ds = TestPGL4WPFDataset(filename=test_y_f) # (B,N,T,F)
if config.only_useful:
test_x = torch.FloatTensor(
test_x_ds.get_data()[:, :, -config.input_len:, select_ind]).to(config.device)
test_y = torch.FloatTensor(
test_y_ds.get_data()[:, :, :config.output_len, select_ind]).to(config.device)
else:
test_x = torch.FloatTensor(
test_x_ds.get_data()[:, :, -config.input_len:, :]).to(config.device)
test_y = torch.FloatTensor(
test_y_ds.get_data()[:, :, :config.output_len, :]).to(config.device)
pred_y = model(test_x, None, data_mean, data_scale) # (B,N,T)
pred_y = F.relu(pred_y * data_scale[:, :, :, -1] + data_mean[:, :, :, -1])
pred_y = np.expand_dims(pred_y.cpu().numpy(), -1) # (B,N,T,1)
test_y = test_y[:, :, :, -1:].cpu().numpy() # (B,N,T,F)
pred_y = np.transpose(pred_y, [ # (N,B,T,1)
1,
0,
2,
3,
])
test_y = np.transpose(test_y, [ # (N,B,T,F)
1,
0,
2,
3,
])
test_y_df = test_y_ds.get_raw_df()
_mae, _rmse = regressor_detailed_scores(
pred_y, test_y, test_y_df, config.capacity, config.output_len)
print('\n\tThe {}-th prediction for File {} -- '
'RMSE: {}, MAE: {}, Score: {}'.format(i, test_y_f, _rmse, _mae, (
_rmse + _mae) / 2))
maes.append(_mae)
rmses.append(_rmse)
avg_mae = np.array(maes).mean()
avg_rmse = np.array(rmses).mean()
total_score = (avg_mae + avg_rmse) / 2
print('\n --- Final MAE: {}, RMSE: {} ---'.format(avg_mae, avg_rmse))
print('--- Final Score --- \n\t{}'.format(total_score))
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='main')
parser.add_argument("--conf", type=str, default="./config.yaml")
parser.add_argument("--model", type=str, default="MTGNN")
parser.add_argument("--gpu_id", type=int, default=0)
parser.add_argument("--epoch", type=int, default=30)
parser.add_argument("--input_len", type=int, default=144, help='input data len')
parser.add_argument("--output_len", type=int, default=288, help='output data len')
parser.add_argument("--lr", type=float, default=0.001)
parser.add_argument("--weight_decay", type=float, default=0)
parser.add_argument("--train_days", type=int, default=214)
parser.add_argument("--val_days", type=int, default=16)
parser.add_argument("--batch_size", type=int, default=32)
parser.add_argument("--seed", type=int, default=0)
parser.add_argument("--exp_id", type=str, default='55237')
parser.add_argument("--best", type=int, default=0)
parser.add_argument("--output_path", type=str, default='kfold_dtw_5_data_diff/')
parser.add_argument("--random", type=str2bool, default=False, help='Whether shuffle num_nodes')
parser.add_argument("--enhance", type=str2bool, default=True, help='Whether enhance the time dim')
parser.add_argument("--only_useful", type=str2bool, default=True, help='Whether remove some feature')
parser.add_argument("--var_len", type=int, default=5, help='Dimensionality of input features')
parser.add_argument("--data_diff", type=str2bool, default=False, help='Whether to use data differential features')
parser.add_argument("--add_apt", type=str2bool, default=False, help='Whether to use adaptive matrix')
parser.add_argument("--binary", type=str2bool, default=True, help='Whether to set the adjacency matrix as binary')
parser.add_argument("--graph_type", type=str, default="geo", help='graph type, dtw or geo')
parser.add_argument("--dtw_topk", type=int, default=5, help='M dtw for dtw graph')
parser.add_argument("--weight_adj_epsilon", type=float, default=0.8, help='epsilon for geo graph')
parser.add_argument("--gsteps", type=int, default=1, help='Gradient Accumulation')
parser.add_argument("--loss", type=str, default='FilterHuberLoss')
parser.add_argument("--select", nargs='+', type=str,
default=['weekday', 'time', 'Wspd', 'Etmp', 'Itmp', 'Prtv', 'Patv'])
args = parser.parse_args()
dict_args = vars(args)
config = edict(yaml.load(open(args.conf), Loader=yaml.FullLoader))
config.update(dict_args)
logger = get_logger(config)
logger.info(config)
size = [config.input_len, config.output_len]
train_data = PGL4WPFDataset(
config.data_path,
filename=config.filename,
size=[config.input_len, config.output_len],
flag='train',
total_days=config.total_days,
train_days=config.train_days,
val_days=config.val_days,
test_days=config.test_days,
random=config.random,
only_useful=config.only_useful,
graph_type=config.graph_type,
weight_adj_epsilon=config.weight_adj_epsilon,
dtw_topk=config.dtw_topk,
binary=config.binary,
)
gpu_id = config.gpu_id
if gpu_id != -1:
device = torch.device('cuda:{}'.format(gpu_id))
else:
device = torch.device('cpu')
config['device'] = device
predict(config, train_data) #, valid_data, test_data)