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ggnn_evaluation_sh_top72_peak.py
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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
shtop72_index = np.array([
5,
19,
86,
82,
152,
89,
195,
198,
168,
214,
24,
18,
94,
187,
21,
84,
189,
61,
14,
217,
88,
22,
148,
91,
9,
169,
17,
64,
270,
190,
60,
197,
6,
167,
11,
240,
202,
20,
146,
45,
40,
193,
47,
62,
158,
196,
63,
191,
46,
28,
44,
199,
87,
35,
68,
30,
33,
41,
59,
2,
37,
4,
29,
10,
0,
34,
39,
38,
36,
7,
15,
12
])
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 = []
# horizon = dataset['y_{}'.format(dataset_type)].shape[1]
horizon = cfg['model']['horizon']
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)
for horizon_i in range(horizon):
y_truth = scaler.inverse_transform(
dataset['y_{}'.format(dataset_type)][:,
horizon_i,
shtop72_index, :output_dim])
y_pred = scaler.inverse_transform(y_preds[:y_truth.shape[0],
horizon_i,
shtop72_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 + ' 1/4 ' + '-' * 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)