-
Notifications
You must be signed in to change notification settings - Fork 84
/
plot_multi_run.py
135 lines (104 loc) · 4.27 KB
/
plot_multi_run.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
from collections import defaultdict
import matplotlib.pyplot as plt
import numpy as np
from config import multi_run_results_file_path, MAX_CASE
print(multi_run_results_file_path)
filename = multi_run_results_file_path
list_case = []
list_tau_fixed = []
list_tau_adaptive = []
list_loss = []
list_acc = []
keys = []
keys_adaptive = []
with open(filename) as f:
for line in f:
l = line.replace('\n', '').split(',')
type = l[0]
simulation = l[1]
case = l[2]
tau_fixed = l[3]
loss = l[4]
accuracy = l[5]
tau_adaptive = l[6]
if (simulation != 'Simulation') and (type != 'centralized'):
list_case.append(int(case))
list_tau_fixed.append(int(tau_fixed))
list_loss.append(float(loss))
list_acc.append(float(accuracy))
keys.append((int(case), int(tau_fixed)))
if tau_fixed == '-1':
list_tau_adaptive.append(float(tau_adaptive))
keys_adaptive.append((int(case), int(tau_fixed)))
if type == 'centralized':
list_case.append(case)
list_tau_fixed.append((tau_fixed))
list_loss.append(float(loss))
list_acc.append(float(accuracy))
keys.append((case, tau_fixed))
list_tau_fixed = list(set(list_tau_fixed))
try:
i = list_tau_fixed.index('nan')
del list_tau_fixed[i]
except: # Exception if no centralized result exists
pass
list_tau_fixed = sorted([i for i in list_tau_fixed])
def avg_over_simulations(keys, values, list_ref):
i = iter(keys)
j = iter(values)
k = list(zip(i, j))
intermediate = defaultdict(list)
d = []
for key, value in k:
intermediate[key].append(value)
for key, value in intermediate.items():
d.append((key, sum(value) / len(value)))
d = dict(d)
# Centralized
centralized = d.get(('None', 'nan'), None)
ncase = list(range(0, MAX_CASE))
case = []
for i in range(0, len(ncase)):
case.append([])
for i in range(0, len(ncase)):
for j in range(0, len(list_ref)):
a = d.get((ncase[i], list_ref[j]), '')
case[i].append(a)
return [centralized, case]
loss_centralized, avg_list_loss = avg_over_simulations(keys, list_loss, list_tau_fixed)
accuracy_centralized, avg_list_acc = avg_over_simulations(keys, list_acc, list_tau_fixed)
_, tauAvg = avg_over_simulations(keys_adaptive, list_tau_adaptive, list_tau_fixed)
N_CASES = 4
color_cases = ['blue', 'green', 'red', 'yellow']
fixed_local_it_indexes = [i for i, x in enumerate(list_tau_fixed) if x > 0]
adapt_local_it_indexes = [i for i, x in enumerate(list_tau_fixed) if x == -1]
adapt_thres_local_it_indexes = [i for i, x in enumerate(list_tau_fixed) if x == -2]
xaxis = [list_tau_fixed[i] for i in fixed_local_it_indexes]
single_point = np.ones(len(xaxis))
if len(adapt_thres_local_it_indexes) == 0:
tauAvgIndex = 0
else:
tauAvgIndex = 1 # because -2 is less than -1
plt.figure(1)
for c in range(0, N_CASES):
plt.semilogx(xaxis, [avg_list_loss[c][i] for i in fixed_local_it_indexes], label='Case' + str(c),
color=color_cases[c])
plt.plot(tauAvg[c][tauAvgIndex], ([avg_list_loss[c][i] for i in adapt_local_it_indexes] * single_point)[0],
marker='o', markersize=8, color=color_cases[c])
if loss_centralized is not None:
plt.semilogx(xaxis, loss_centralized * single_point, '--', label='Centralized case', color='black')
plt.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=2, ncol=2, mode="expand", borderaxespad=0.)
plt.xlabel('Value of \\tau')
plt.ylabel('Loss function Value (on Training Data)')
plt.figure(2)
for c in range(0, N_CASES):
plt.semilogx(xaxis, [avg_list_acc[c][i] for i in fixed_local_it_indexes], label='Case' + str(c),
color=color_cases[c])
plt.plot(tauAvg[c][tauAvgIndex], ([avg_list_acc[c][i] for i in adapt_local_it_indexes] * single_point)[0],
marker='o', markersize=8, color=color_cases[c])
if accuracy_centralized is not None:
plt.semilogx(xaxis, accuracy_centralized * single_point, '--', label='Centralized case', color='black')
plt.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=2, ncol=2, mode="expand", borderaxespad=0.)
plt.xlabel('Value of \\tau')
plt.ylabel('Classification Accuracy (on Testing Data)')
plt.show()