-
Notifications
You must be signed in to change notification settings - Fork 0
/
plotResult.py
75 lines (62 loc) · 1.7 KB
/
plotResult.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
import re
def loadData():
inp = open("results/logs_LNSM_2.dat")
data = []
while True:
line = inp.readline()
line = line.strip()
if line == "":
break
method = line
values = inp.readline().strip().split(" ")
print(values)
AUC = {}
AUC['mean'] = values[0]
AUC['error'] = values[1]
AUPR = {}
AUPR['mean'] = values[2]
AUPR['error'] = values[3]
it = {}
it['name'] = method
it['AUC'] = AUC
it['AUPR'] = AUPR
data.append(it)
return data
def plotBar(data,metric):
import matplotlib
matplotlib.rcParams.update({'font.size': 16})
#matplotlib.rc('xtick', labelsize=20)
#matplotlib.rc('ytick', labelsize=20)
import matplotlib.pyplot as plt
MethodList = []
Means = []
Errors = []
for it in data:
MethodList.append(it['name'])
me = it[metric]
Means.append(float(me['mean']))
Errors.append(float(me['error']))
import math
import numpy as np
v = np.asarray(Errors,dtype=float)
v *= math.sqrt(5)
print (v)
#plt.scatter(x=Methods,y=values,c=c,s=3**2)
#for i in xrange(len(Methods)):
# plt.bar([Methods[i],Methods[i]],[values[i]+stds[i],values[i]-stds[i]],c=c[i])
plt.bar(MethodList,Means,width=0.3,yerr=Errors)
plt.xlabel('Methods')
plt.ylabel(metric)
#plt.title(title)
plt.grid(True,alpha=0.2,axis='y')
#if ylims is not None:
# plt.ylim(ylims)
plt.tight_layout()
plt.savefig('./figs/%s.eps'%(metric))
plt.show()
def plot():
data = loadData()
plotBar(data,"AUC")
plotBar(data,"AUPR")
if __name__ == "__main__":
plot()