-
Notifications
You must be signed in to change notification settings - Fork 13
/
train_results.py
executable file
·192 lines (159 loc) · 10 KB
/
train_results.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
# -*- coding: utf-8 -*-
"""
@author: sinannasir
"""
import numpy as np
import matplotlib.pyplot as plt
from itertools import cycle
import json
import matplotlib
matplotlib.use('Qt5Agg')
import argparse
def main(scenario):
json_file = scenario['json_file']
json_file_policy = scenario['json_file_policy']
num_sim = scenario['num_sim']
with open ('./config/deployment/'+json_file+'.json','r') as f:
options = json.load(f)
## Kumber of samples
total_samples = options['simulation']['total_samples']
K = options['simulation']['K']
N = options['simulation']['N']
if num_sim == -1:
num_simulations = options['simulation']['num_simulations']
simulation = options['simulation']['simulation_index_start']
else:
num_simulations = 1
simulation = num_sim
# simulation parameters
mobility_params = options['mobility_params']
mobility_params['alpha_angle'] = options['mobility_params']['alpha_angle_rad'] * np.pi #radian/sec
history = 250
mean_p_FP = np.zeros(total_samples)
mean_time_FP = np.zeros(total_samples)
mean_iterations_FP = np.zeros(total_samples)
mean_sum_rate_FP = np.zeros(total_samples)
mean_p_WMMSE = np.zeros(total_samples)
mean_time_WMMSE = np.zeros(total_samples)
mean_iterations_WMMSE = np.zeros(total_samples)
mean_sum_rate_WMMSE = np.zeros(total_samples)
mean_sum_rate_delayed_central = np.zeros(total_samples)
mean_sum_rate_random = np.zeros(total_samples)
mean_sum_rate_max = np.zeros(total_samples)
mean_sum_rate_policy_train_innersims = np.zeros(total_samples)
mean_p_strategy_all_train_innersims = np.zeros(total_samples)
mean_time_optimization_at_each_slot_takes = []
mean_time_calculating_strategy_takes = []
for overal_sims in range(simulation,simulation+num_simulations):
# Get the benchmarks.
file_path = './simulations/sumrate/benchmarks/%s_network%d'%(json_file,overal_sims)
data = np.load(file_path+'.npz')
p_FP = data['arr_0']
time_stats_FP = data['arr_1']
sum_rate_FP = data['arr_2']
p_WMMSE = data['arr_3']
time_stats_WMMSE= data['arr_4']
sum_rate_WMMSE = data['arr_5']
sum_rate_delayed_central = data['arr_6']
sum_rate_random = data['arr_7']
sum_rate_max = data['arr_8']
file_path = './simulations/sumrate/train/%s_%s_network%d.ckpt'%(json_file,json_file_policy,overal_sims)
data = np.load(file_path+'.npz')
# Get the train policy results
sum_rate_policy_train = data['arr_2']
p_strategy_all = data['arr_3']
time_optimization_at_each_slot_takes = data['arr_4']
time_calculating_strategy_takes = data['arr_5']
# Average
mean_p_FP = mean_p_FP + np.sum(p_FP,1)/float(num_simulations)
mean_time_FP = mean_time_FP + time_stats_FP[:,0]/float(num_simulations)
mean_iterations_FP = mean_iterations_FP + time_stats_FP[:,1]/float(num_simulations)
mean_sum_rate_FP = mean_sum_rate_FP + sum_rate_FP/float(num_simulations)
mean_p_WMMSE = mean_p_WMMSE + np.sum(p_WMMSE,1)/float(num_simulations)
mean_time_WMMSE = mean_time_WMMSE + time_stats_WMMSE[:,0]/float(num_simulations)
mean_iterations_WMMSE = mean_iterations_WMMSE + time_stats_WMMSE[:,1]/float(num_simulations)
mean_sum_rate_WMMSE = mean_sum_rate_WMMSE + sum_rate_WMMSE/float(num_simulations)
mean_sum_rate_delayed_central = mean_sum_rate_delayed_central + sum_rate_delayed_central/float(num_simulations)
mean_sum_rate_random = mean_sum_rate_random + sum_rate_random/float(num_simulations)
mean_sum_rate_max = mean_sum_rate_max + sum_rate_max/float(num_simulations)
mean_sum_rate_policy_train_innersims = mean_sum_rate_policy_train_innersims + sum_rate_policy_train/float(num_simulations)
mean_p_strategy_all_train_innersims = mean_p_strategy_all_train_innersims + np.sum(p_strategy_all,1)/float(num_simulations)
mean_time_optimization_at_each_slot_takes.append(time_optimization_at_each_slot_takes)
mean_time_calculating_strategy_takes.append(time_calculating_strategy_takes)
#print('K '+ str(int(N))+' R '+str(R_defined)+ ' r '+str(min_dist) + ' '+file_path[14:18])
#print('Test Sum rate wmmse ' + str(np.mean(mean_sum_rate_WMMSE[total_samples-2500:]/N)))
#print('Test Sum rate optimal ' + str(np.mean(mean_sum_rate[total_samples-2500:]/N)))
#print('Test Sum rate delayed ' + str(np.mean(mean_sum_rate_delayed_central[total_samples-2500:]/N)))
#print('Test Sum rate random ' + str(np.mean(mean_sum_rate_random[total_samples-2500:]/N)))
#print('Test Sum rate max ' + str(np.mean(mean_sum_rate_max[total_samples-2500:]/N)))
#for i in range(len(power_multiplier_allsims)):
# print('Multiplier '+str(power_multiplier_allsims[i])+
# ' Test Sum rate ' +str(np.mean(mean_sum_rate_policy_train_innersims[i,total_samples-2500:]/N)))
lines = ["-","--",':','-.',':','-.']
linecycler = cycle(lines)
history = 100
fig = plt.figure()
t=np.arange(0,total_samples,10)
sum_rate_performance_FP = []
sum_rate_performance_random = []
sum_rate_performance_max = []
sum_rate_performance_delayed_central = []
sum_rate_performance_policy = []
sum_rate_performance_wmmse = []
sum_rate_performance_policy = []
ep_start = 0
for i in range(len(t)):
if t[i] % options['train_episodes']['T_train'] == 0:
ep_start = t[i]
sum_rate_performance_FP.append(np.mean(mean_sum_rate_FP[max(ep_start,t[i]-history):t[i]]))
sum_rate_performance_random.append(np.mean(mean_sum_rate_random[max(ep_start,t[i]-history):t[i]]))
sum_rate_performance_max.append(np.mean(mean_sum_rate_max[max(ep_start,t[i]-history):t[i]]))
sum_rate_performance_delayed_central.append(np.mean(mean_sum_rate_delayed_central[max(ep_start,t[i]-history):t[i]]))
sum_rate_performance_wmmse.append(np.mean(mean_sum_rate_WMMSE[max(ep_start,t[i]-history):t[i]]))
sum_rate_performance_policy.append(np.mean(mean_sum_rate_policy_train_innersims[max(ep_start,t[i]-history):t[i]]))
#plt.figure(figsize=(5,5))
t=np.arange(0,total_samples,10)
plt.plot(t, np.array(sum_rate_performance_wmmse)/float(N), label='WMMSE',linestyle=next(linecycler))
plt.plot(t, np.array(sum_rate_performance_FP)/float(N), label='FP',linestyle=next(linecycler))
plt.plot(t, np.array(sum_rate_performance_delayed_central)/float(N), label='FP w delay',linestyle=next(linecycler))
plt.plot(t, np.array(sum_rate_performance_random)/float(N), label='random',linestyle=next(linecycler))
plt.plot(t, np.array(sum_rate_performance_max)/float(N),'c', label='full-power',linestyle=next(linecycler))
plt.plot(t, np.array(sum_rate_performance_policy)/float(N), label='matched policy',linestyle=next(linecycler))# with Multiplier '+str(power_multiplier_allsims[i]),linestyle=next(linecycler))
plt.xlabel('training iterations')
plt.ylabel('moving average spectral efficiency (bps/Hz) per link')
plt.grid(True)
plt.legend(loc=4)
plt.tight_layout()
plt.savefig('./fig/spectraleff_%s_network_%d'%(json_file,overal_sims)+'.pdf', format='pdf', dpi=1000)
plt.savefig('./fig/spectraleff_%s_network_%d'%(json_file,overal_sims)+'.png', format='png', dpi=1000)
plt.show(block=False)
# Average performance of the last 200 training slots.
history = 200
print('Deployment: %s; policy: %s; K: %d; N: %d'%(json_file,json_file_policy,N,K))
print('Averages for last %d episodes:'%(history))
print('Sum rate per link - policy: %.2f'%(np.mean(mean_sum_rate_policy_train_innersims[total_samples-history:])/float(N)))
print('Sum rate per link - WMMSE: %.2f'%(np.mean(mean_sum_rate_WMMSE[total_samples-history:])/float(N)))
print('Sum rate per link - FP: %.2f'%(np.mean(mean_sum_rate_FP[total_samples-history:])/float(N)))
print('Sum rate per link - FP w delay: %.2f'%(np.mean(mean_sum_rate_delayed_central[total_samples-history:])/float(N)))
print('Sum rate per link - random: %.2f'%(np.mean(mean_sum_rate_random[total_samples-history:])/float(N)))
print('Sum rate per link - full: %.2f'%(np.mean(mean_sum_rate_max[total_samples-history:])/float(N)))
# Average time statistics
print('Average time for a WMMSE run: %.2f ms'%(1000 * np.mean(mean_time_WMMSE)))
print('Average time for an FP run: %.2f ms'%(1000 * np.mean(mean_time_FP)))
print('Average time for a policy agent to determine its action %.2f ms'%(1000 * np.mean(mean_time_calculating_strategy_takes)))
print('Average time for a policy mini-batch train %.2f ms'%(1000 * np.mean(mean_time_optimization_at_each_slot_takes)))
print('Average WMMSE iterations per run: %.2f'%(np.mean(mean_iterations_WMMSE)))
print('Average FP iterations per run: %.2f'%(np.mean(mean_iterations_FP)))
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='give test scenarios.')
parser.add_argument('--json-file', type=str, default='train_K5_N10_shadow10_episode2-5000_travel50000_vmax2_5',
help='json file for the deployment the policies are tested on')
parser.add_argument('--json-file-policy', type=str, default='ddpg200_100_50',
help='json file for the hyperparameters')
parser.add_argument('--num-sim', type=int, default=0,
help='If set to -1, it uses num_simulations of the json file. If set to positive, it runs one simulation with the given id.')
args = parser.parse_args()
test_scenario = {'json_file':args.json_file,
'json_file_policy':args.json_file_policy,
'num_sim':args.num_sim}
main(test_scenario)