-
Notifications
You must be signed in to change notification settings - Fork 30
/
ggnn_evaluation_hz_top20_peak.py
261 lines (231 loc) · 8.6 KB
/
ggnn_evaluation_hz_top20_peak.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
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
import argparse
import yaml
import numpy as np
import torch
import pandas as pd
from lib import utils
from lib import metrics
from ggnn.multigraph import Net
from ggnn_train import _get_log_dir
from lib.utils import collate_wrapper
try:
from yaml import CLoader as Loader
except ImportError:
from yaml import Loader
def read_cfg_file(filename):
with open(filename, 'r') as ymlfile:
cfg = yaml.load(ymlfile, Loader=Loader)
return cfg
hztop20_index = np.array(
[2,
53,
55,
46,
24,
5,
14,
13,
16,
22,
33,
8,
12,
20,
11,
10,
4,
7,
9,
15])
def run_model(model, data_iterator, edge_index, edge_attr, device, output_dim):
"""
return a list of (horizon_i, batch_size, num_nodes, output_dim)
"""
# while evaluation, we need model.eval and torch.no_grad
model.eval()
y_pred_list = []
for _, (x, y, xtime, ytime) in enumerate(data_iterator):
y = y[..., :output_dim]
sequences, y = collate_wrapper(x=x, y=y,
edge_index=edge_index,
edge_attr=edge_attr,
device=device)
# (T, N, num_nodes, num_out_channels)
with torch.no_grad():
y_pred = model(sequences)
y_pred_list.append(y_pred.cpu().numpy())
return y_pred_list
def evaluate(model,
dataset,
dataset_type,
edge_index,
edge_attr,
device,
output_dim,
logger,
detail=True,
cfg=None,
format_result=False):
if detail:
logger.info('Evaluation_{}_Begin:'.format(dataset_type))
scaler = dataset['scaler']
y_preds = run_model(
model,
data_iterator=dataset['{}_loader'.format(dataset_type)].get_iterator(),
edge_index=edge_index,
edge_attr=edge_attr,
device=device,
output_dim=output_dim)
y_preds = np.concatenate(y_preds, axis=0) # concat in batch_size dim.
mae_sum = 0
mape_sum = 0
rmse_sum = 0
mae_list = []
mape_list = []
rmse_list = []
mae_peak_list = []
mape_peak_list = []
rmse_peak_list = []
ytime = dataset['ytime_{}'.format(dataset_type)][:, 0]
ytime_pd = pd.to_datetime(ytime.flatten())
morning_peak = np.logical_and(
(ytime_pd.hour * 60 + ytime_pd.minute) >= 7 * 60 + 30,
(ytime_pd.hour * 60 + ytime_pd.minute) <= 8 * 60 + 30)
evening_peak = np.logical_and(
(ytime_pd.hour * 60 + ytime_pd.minute) >= 17 * 60 + 30,
(ytime_pd.hour * 60 + ytime_pd.minute) <= 18 * 60 + 30)
peak = np.logical_or(morning_peak, evening_peak)
# horizon = dataset['y_{}'.format(dataset_type)].shape[1]
horizon = cfg['model']['horizon']
for horizon_i in range(horizon):
y_truth = scaler.inverse_transform(
dataset['y_{}'.format(dataset_type)][:,
horizon_i,
hztop20_index, :output_dim])
y_pred = scaler.inverse_transform(y_preds[:y_truth.shape[0],
horizon_i,
hztop20_index,
:output_dim])
mae = metrics.masked_mae_np(y_pred, y_truth, null_val=0, mode='dcrnn')
mape = metrics.masked_mape_np(y_pred, y_truth, null_val=0)
rmse = metrics.masked_rmse_np(y_pred, y_truth, null_val=0)
mae_sum += mae
mape_sum += mape
rmse_sum += rmse
mae_list.append(mae)
mape_list.append(mape)
rmse_list.append(rmse)
# Peak
y_truth_peak = scaler.inverse_transform(
dataset['y_{}'.format(dataset_type)][peak,
horizon_i,
:, :output_dim])
y_pred_peak = scaler.inverse_transform(y_preds[:y_truth.shape[0],
horizon_i, :,
:output_dim])
y_pred_peak = y_pred_peak[peak]
mae_peak = metrics.masked_mae_np(y_pred_peak,
y_truth_peak,
null_val=0,
mode='dcrnn')
mape_peak = metrics.masked_mape_np(y_pred_peak,
y_truth_peak,
null_val=0)
rmse_peak = metrics.masked_rmse_np(y_pred_peak,
y_truth_peak,
null_val=0)
mae_peak_list.append(mae_peak)
mape_peak_list.append(mape_peak)
rmse_peak_list.append(rmse_peak)
msg = "Horizon {:02d}, MAE: {:.2f}, MAPE: {:.4f}, RMSE: {:.2f}"
if detail:
logger.info(msg.format(horizon_i + 1, mae, mape, rmse))
if detail:
logger.info('Evaluation_{}_End:'.format(dataset_type))
if format_result:
print('-' * 40 + ' TOP20 ' + '-' * 40)
for i in range(len(mape_list)):
print('{:.2f}'.format(mae_list[i]))
print('{:.2f}%'.format(mape_list[i] * 100))
print('{:.2f}'.format(rmse_list[i]))
print()
print('-' * 40 + ' Peak ' + '-' * 40)
for i in range(len(mape_list)):
print('{:.2f}'.format(mae_peak_list[i]))
print('{:.2f}%'.format(mape_peak_list[i] * 100))
print('{:.2f}'.format(rmse_peak_list[i]))
print()
else:
return mae_sum / horizon, mape_sum / horizon, rmse_sum / horizon
def main(args):
cfg = read_cfg_file(args.config_filename)
log_dir = _get_log_dir(cfg)
log_level = cfg.get('log_level', 'INFO')
logger = utils.get_logger(log_dir, __name__, 'info.log', level=log_level)
device = torch.device(
'cuda') if torch.cuda.is_available() else torch.device('cpu')
# all edge_index in same dataset is same
# edge_index = adjacency_to_edge_index(adj_mx) # alreay added self-loop
logger.info(cfg)
# batch_size = cfg['data']['batch_size']
# test_batch_size = cfg['data']['test_batch_size']
# edge_index = utils.load_pickle(cfg['data']['edge_index_pkl_filename'])
hz = cfg['data'].get('name', 'nothz') == 'hz'
adj_mx_list = []
graph_pkl_filename = cfg['data']['graph_pkl_filename']
if not isinstance(graph_pkl_filename, list):
graph_pkl_filename = [graph_pkl_filename]
src = []
dst = []
for g in graph_pkl_filename:
if hz:
adj_mx = utils.load_graph_data_hz(g)
else:
_, _, adj_mx = utils.load_graph_data(g)
for i in range(len(adj_mx)):
adj_mx[i, i] = 0
adj_mx_list.append(adj_mx)
adj_mx = np.stack(adj_mx_list, axis=-1)
if cfg['model'].get('norm', False):
print('row normalization')
adj_mx = adj_mx / (adj_mx.sum(axis=0) + 1e-18)
src, dst = adj_mx.sum(axis=-1).nonzero()
edge_index = torch.tensor([src, dst], dtype=torch.long, device=device)
edge_attr = torch.tensor(adj_mx[adj_mx.sum(axis=-1) != 0],
dtype=torch.float,
device=device)
output_dim = cfg['model']['output_dim']
for i in range(adj_mx.shape[-1]):
logger.info(adj_mx[..., i])
# print(adj_mx.shape) (207, 207)
if hz:
dataset = utils.load_dataset_hz(**cfg['data'],
scaler_axis=(0,
1,
2,
3))
else:
dataset = utils.load_dataset(**cfg['data'])
for k, v in dataset.items():
if hasattr(v, 'shape'):
logger.info((k, v.shape))
model = Net(cfg).to(device)
model.load_state_dict(torch.load(cfg['model']['save_path']), strict=False)
evaluate(model=model,
dataset=dataset,
dataset_type='test',
edge_index=edge_index,
edge_attr=edge_attr,
device=device,
output_dim=output_dim,
logger=logger,
cfg=cfg,
format_result=True)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--config_filename',
default=None,
type=str,
help='Configuration filename for restoring the model.')
args = parser.parse_args()
main(args)