-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathrun_gl_MAB.py
161 lines (128 loc) · 5.23 KB
/
run_gl_MAB.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
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import networkx as nx
import os
os.chdir('C:/Kaige_Research/Graph Learning/graph_learning_code/')
from collections import Counter
import datetime
from synthetic_data import *
from gl_MAB import GL_MAB
from knn_MAB import KNN_MAB
from cd_MAB import CD_MAB
from linucb_MAB import LINUCB_MAB
from sklearn.metrics.pairwise import rbf_kernel
path='C:/Kaige_Research/Graph Learning/graph_learning_code/results/'
timeRun = datetime.datetime.now().strftime('_%m_%d_%H_%M_%S')
user_num=20
item_num=1000
dimension=5
item_pool_size=5
cluster_num=4
cluster_std=0.1
noise_scale=0.1
gl_alpha=1
gl_beta=0.2
gl_theta=0.01
gl_step_size=0.5
jump_step=10
alpha=0.05
iteration=2000
newpath=path+'GL_user_num_%s_cluster_num_%s'%(user_num, cluster_num)+'/'+str(timeRun)+'/'
if not os.path.exists(newpath):
os.makedirs(newpath)
user_pool=generate_all_random_users(iteration, user_num)
item_pools=generate_all_article_pool(iteration, item_pool_size, item_num)
combination_list=[]
regret_dict={}
learning_error_dict={}
graph_error_dict={}
denoised_signal_dict={}
adj_dict={}
noisy_signal, item_features, true_user_features, true_label=blob_data(user_num, item_num, dimension, cluster_num, cluster_std, noise_scale)
true_adj=rbf_kernel(true_user_features)
np.fill_diagonal(true_adj,0)
gl_mab_1=GL_MAB(user_num, item_num, dimension, item_pool_size, alpha, gl_alpha, gl_beta, gl_theta, gl_step_size, jump_step=jump_step, mode=1, true_user_features=true_user_features, true_graph=true_adj)
gl_cum_regret_1, gl_adj_1, gl_user_f_1, gl_error_1, gl_graph_error_1, gl_denoised_signal_1=gl_mab_1.run(user_pool, item_pools, item_features, noisy_signal, iteration)
gl_mab_2=GL_MAB(user_num, item_num, dimension, item_pool_size, alpha, gl_alpha, gl_beta, gl_theta, gl_step_size, jump_step=jump_step, mode=2, true_user_features=true_user_features, true_graph=true_adj)
gl_cum_regret_2, gl_adj_2, gl_user_f_2, gl_error_2, gl_graph_error_2, gl_denoised_signal_2=gl_mab_2.run(user_pool, item_pools, item_features, noisy_signal, iteration)
gl_mab_3=GL_MAB(user_num, item_num, dimension, item_pool_size, alpha, gl_alpha, gl_beta, gl_theta, gl_step_size, jump_step=jump_step, mode=3, true_user_features=true_user_features, true_graph=true_adj)
gl_cum_regret_3, gl_adj_3, gl_user_f_3, gl_error_3, gl_graph_error_3, gl_denoised_signal_3=gl_mab_3.run(user_pool, item_pools, item_features, noisy_signal, iteration)
plt.plot(gl_cum_regret_1, label='Mode 1')
plt.plot(gl_cum_regret_2, label='Mode 2')
plt.plot(gl_cum_regret_3, label='Mode 3')
plt.legend(loc=2)
plt.title('regret')
plt.show()
plt.plot(gl_error_1, label='Mode 1')
plt.plot(gl_error_2, label='Mode 2')
plt.plot(gl_error_3, label='Mode 3')
plt.legend(loc=2)
plt.title('learning error')
plt.show()
plt.plot(gl_graph_error_1, label='Mode 1')
plt.plot(gl_graph_error_2, label='Mode 2')
plt.plot(gl_graph_error_3, label='Mode 3')
plt.legend(loc=2)
plt.title('graph error')
plt.show()
## save data
key=noise_scale
combination_list.extend([key])
regret_dict[key]=gl_cum_regret
learning_error_dict[key]=gl_error
denoised_signal_dict[key]=gl_denoised_signal
graph_error_dict[key]=gl_graph_error
adj_dict[key]=gl_adj
np.save(newpath+'combination_list'+'.npy', combination_list)
np.save(newpath+'regret_dict'+'.npy', regret_dict)
np.save(newpath+'learning_error_dict'+'.npy', learning_error_dict)
np.save(newpath+'denoised_signal_dict'+'.npy', denoised_signal_dict)
np.save(newpath+'adj_dict'+'.npy', adj_dict)
np.save(newpath+'graph_error_dict'+'.npy', graph_error_dict)
plt.figure()
plt.plot(gl_cum_regret, label='GL')
plt.ylabel('Cum Regret', fontsize=12)
plt.legend(loc=1)
plt.show()
plt.figure()
plt.plot(gl_error, label='GL')
plt.ylabel('Learning Error', fontsize=12)
plt.legend(loc=1)
plt.show()
plt.figure()
plt.plot(gl_graph_error, label='GL')
plt.ylabel('graph Error', fontsize=12)
plt.legend(loc=1)
plt.show()
pos=true_user_features
fig, (ax1, ax2)=plt.subplots(1,2, figsize=(6,3))
ax1.scatter(pos[:,0], pos[:,1], c=noisy_signal[0].tolist(), cmap=plt.cm.jet)
ax2.scatter(pos[:,0], pos[:,1], c=gl_denoised_signal[0].tolist(), cmap=plt.cm.jet)
ax1.axis('off')
ax2.axis('off')
ax1.set_title('Noisy Signal')
ax2.set_title('GL Signal')
plt.tight_layout()
plt.show()
test_item=np.random.normal(size=dimension)
true_payoff=np.dot(true_user_features, test_item)
gl_payoff=np.dot(gl_user_f, test_item)
pos=true_user_features
graph=create_networkx_graph(user_num, true_adj)
edge_color=true_adj[np.triu_indices(user_num,1)]
plt.figure(figsize=(5,5))
nodes=nx.draw_networkx_nodes(graph, pos, node_color=true_payoff, node_size=100, cmap=plt.cm.jet)
edges=nx.draw_networkx_edges(graph, pos, width=1.0, alpha=0.1, edge_color='grey')
plt.axis('off')
plt.title('True Graph', fontsize=12)
plt.show()
pos=true_user_features
graph=create_networkx_graph(user_num, gl_adj)
edge_color=gl_adj[np.triu_indices(user_num,1)]
plt.figure(figsize=(5,5))
nodes=nx.draw_networkx_nodes(graph, pos, node_color=gl_payoff, node_size=100, cmap=plt.cm.jet)
edges=nx.draw_networkx_edges(graph, pos, width=1.0, alpha=0.1, edge_color='grey')
plt.axis('off')
plt.title('GL Graph', fontsize=12)
plt.show()