-
Notifications
You must be signed in to change notification settings - Fork 17
/
draw.py
166 lines (139 loc) · 5.73 KB
/
draw.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
import pandas as pd
import os
from glob import glob
import matplotlib.pyplot as plt
from matplotlib.pyplot import MultipleLocator
from util import *
import argparse
def loss_curve(d):
tr_val = pd.read_csv(f'{d}', index_col=0)
# return tr_val[['train_loss', 'valid_loss']]
return tr_val[['mae']]
def plot_loss_curve(log_dir):
d = loss_curve(log_dir)
x_major_locator = MultipleLocator(1)
# 把x轴的刻度间隔设置为1，并存在变量里
y_major_locator = MultipleLocator(0.5)
# 把y轴的刻度间隔设置为10，并存在变量里
ax = plt.gca()
# ax为两条坐标轴的实例
ax.xaxis.set_major_locator(x_major_locator)
# 把x轴的主刻度设置为1的倍数
ax.yaxis.set_major_locator(y_major_locator)
# 把y轴的主刻度设置为10的倍数
plt.xlim(-0.5, 11.5)
# 把x轴的刻度范围设置为-0.5到11，因为0.5不满一个刻度间隔，所以数字不会显示出来，但是能看到一点空白
plt.ylim(2.1, 4.5)
# 把y轴的刻度范围设置为-5到110，同理，-5不会标出来，但是能看到一点空白
plt.plot(d, label='test_mae', c='blue')
plt.ylabel('Test Loss', fontsize=12)
plt.xlabel('Predict length(/5min)', fontsize=12)
plt.grid(True, which='both')
plt.legend()
plt.show()
# log_dir = './experiment/improved_Wave/test_metrics.csv'
# plot_loss_curve(log_dir)
def train_loss_curve(d):
tr_val = pd.read_csv(f'{d}', index_col=0)
# return tr_val[['train_loss', 'valid_loss']]
return tr_val[['valid_loss']], tr_val[['train_loss']]
def plot_train_loss_curve(log_dir):
d0, d1 = train_loss_curve(log_dir)
plt.plot(d0, label='valid_loss')
plt.plot(d1, label='train_loss')
plt.ylabel('Valid Loss', fontsize=12)
plt.xlabel('Epoch', fontsize=12)
plt.grid(True, which='both')
plt.legend()
plt.show()
# train_log = './experiment/improved_Wave/metrics.csv'
# plot_train_loss_curve(train_log)
def plot_baselines_loss():
# WaveNet_path = './experiment/improved_Wave/metrics.csv'
WaveNet_path = './experiment/draw_LA/GWaveNet_LA/record.csv'
STGCN_path = './experiment/draw_LA/STGCN_LA/record.csv'
STGAT_path = './experiment/draw_LA/STGAT_LA/record.csv'
WaveNet = pd.read_csv(f'{WaveNet_path}', index_col=0)
STGCN = pd.read_csv(f'{STGCN_path}', index_col=0)
STGAT = pd.read_csv(f'{STGAT_path}', index_col=0)
# valid
WaveNet_val = WaveNet[['valid_loss']]
STGCN_val = STGCN[['valid_loss']]
STGAT_val = STGAT[['valid_loss']]
plt.tick_params(labelsize=18)
plt.plot(WaveNet_val, label='WaveNet_val')
plt.plot(STGCN_val, label='STGCN_val')
plt.plot(STGAT_val, label='STGAT_val')
plt.ylabel('Valid Loss', fontsize=24)
plt.xlabel('Epoch', fontsize=24)
plt.grid(True, which='both')
plt.legend(fontsize=18)
plt.savefig('LA_baselines_val_loss.png', dpi=300, bbox_inches='tight', pad_inches=0.1)
plt.show()
# train
WaveNet_train = WaveNet[['train_loss']]
STGCN_train = STGCN[['train_loss']]
STGAT_train = STGAT[['train_loss']]
plt.tick_params(labelsize=18)
plt.plot(WaveNet_train, label='WaveNet_train')
plt.plot(STGCN_train, label='STGCN_train')
plt.plot(STGAT_train, label='STGAT_train')
plt.ylabel('Train Loss', fontsize=24)
plt.xlabel('Epoch', fontsize=24)
plt.grid(True, which='both')
plt.legend(fontsize=18)
plt.savefig('LA_baselines_train_loss.png', dpi=300, bbox_inches='tight', pad_inches=0.1)
plt.show()
plot_baselines_loss()
def plot_comparison_gconv():
no_Gconv_path = './experiment/draw_LA/no_gconv_STGAT_LA/record.csv'
STGAT_path = './experiment/draw_LA/STGAT_LA/record.csv'
no_Gconv = pd.read_csv(f'{no_Gconv_path}', index_col=0)
STGAT = pd.read_csv(f'{STGAT_path}', index_col=0)
no_Gconv_val = no_Gconv[['valid_loss']]
STGAT_val = STGAT[['valid_loss']]
plt.tick_params(labelsize=18)
plt.plot(no_Gconv_val, label='without_Gconv_val')
plt.plot(STGAT_val, label='STGAT_val')
plt.ylabel('Valid Loss', fontsize=24)
plt.xlabel('Epoch', fontsize=24)
plt.grid(True, which='both')
plt.legend(fontsize=18)
plt.savefig('LA_without_Gconv.png', dpi=300, bbox_inches='tight', pad_inches=0.1)
plt.show()
# plot_comparison_gconv()
def print_stats(args):
if '/' not in args.adj_mx:
full_path = 'data/sensor_graph/' + args.adj_mx
else:
full_path = args.adj_mx
A = load_pickle(full_path)
adj_mx = A[2]
degrees = np.sum(adj_mx > 0, 1)
print(degrees)
print("adj:", adj_mx.shape)
print("degrees: ", degrees.shape)
print(f"Minimum Degree: {np.min(degrees)}")
print(f"Maximum Degree: {np.max(degrees)}")
print(f"Average Degree: {np.mean(degrees)}")
plt.imshow(adj_mx, cmap='hot', interpolation='nearest')
# plt.title('Adjacency Matrix Heatmap')
plt.savefig('LA_Matrix_Heatmap.png', dpi=300, bbox_inches='tight', pad_inches=0.1)
plt.show()
# print(degrees == 16)
plt.tick_params(labelsize=18)
plt.hist(degrees, bins=19, density=0, edgecolor="black", alpha=0.7, color='blue')
plt.xticks(range(0, max(degrees) + 2, 2))
# plt.title('LA Degree Distribution')
plt.xlabel('LA Node Degree', fontsize=24)
plt.ylabel('Nodes Number', fontsize=24)
plt.savefig('LA_Degree_Distrib.png', dpi=300, bbox_inches='tight', pad_inches=0.1)
plt.show()
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--adj_mx', type=str, default='adj_mx.pkl',
help='Pickle file in data/sensor_graph containing adjacency matrix')
# parser.add_argument('--adj_mx', type=str, default='adj_mx_bay.pkl',
# help='Pickle file in data/sensor_graph containing adjacency matrix')
args = parser.parse_args()
print_stats(args)