-
Notifications
You must be signed in to change notification settings - Fork 5
/
Copy pathsorting_methods.py
202 lines (179 loc) · 9.71 KB
/
sorting_methods.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
193
194
195
196
197
198
199
200
201
202
"""
Examine the pesudo-labelled datasets
1. only sort by prob
2. only sort by sim
3. sort by prob, then sort by sim
4. sort by sim, then sort by prob
5. prob + sim
6. prob x sim
dsc and asc and return the best one.
"""
from exam_stars import test
import numpy as np
def find_best_method(target,tgt_un,labels_proba,tgt_sim,k=100,theta=0.5,sorting="dsc"):
best_acc = 0.0
best_method = ""
pos_star,neg_star,pos_start,pos_end,neg_start,neg_end,pos_proba,neg_proba = just_prob(tgt_un,labels_proba,tgt_sim,k,theta,sorting)
acc = test(target,pos_star,neg_star)
if acc>best_acc:
best_acc = acc
best_method = "just_prob"
best_pos_star,best_neg_star,best_pos_start,best_pos_end,best_neg_start,best_neg_end,\
best_pos_proba,best_neg_proba = pos_star,neg_star,pos_start,pos_end,neg_start,neg_end,pos_proba,neg_proba
pos_star,neg_star,pos_start,pos_end,neg_start,neg_end,pos_proba,neg_proba = just_sim(tgt_un,labels_proba,tgt_sim,k,theta,sorting)
acc = test(target,pos_star,neg_star)
if acc>best_acc:
best_acc = acc
best_method = "just_sim"
best_pos_star,best_neg_star,best_pos_start,best_pos_end,best_neg_start,best_neg_end = pos_star,neg_star,pos_start,pos_end,neg_start,neg_end
pos_star,neg_star,pos_start,pos_end,neg_start,neg_end,pos_proba,neg_proba = prob_sim(tgt_un,labels_proba,tgt_sim,k,theta,sorting)
acc = test(target,pos_star,neg_star)
if acc>best_acc:
best_acc = acc
best_method = "prob_sim"
best_pos_star,best_neg_star,best_pos_start,best_pos_end,best_neg_start,best_neg_end,\
best_pos_proba,best_neg_proba = pos_star,neg_star,pos_start,pos_end,neg_start,neg_end,pos_proba,neg_proba
pos_star,neg_star,pos_start,pos_end,neg_start,neg_end,pos_proba,neg_proba = sim_prob(tgt_un,labels_proba,tgt_sim,k,theta,sorting)
acc = test(target,pos_star,neg_star)
if acc>best_acc:
best_acc = acc
best_method = "sim_prob"
best_pos_star,best_neg_star,best_pos_start,best_pos_end,best_neg_start,best_neg_end,\
best_pos_proba,best_neg_proba = pos_star,neg_star,pos_start,pos_end,neg_start,neg_end,pos_proba,neg_proba
pos_star,neg_star,pos_start,pos_end,neg_start,neg_end,pos_proba,neg_proba = add_prob_sim(tgt_un,labels_proba,tgt_sim,k,theta,sorting)
acc = test(target,pos_star,neg_star)
if acc>best_acc:
best_acc = acc
best_method = "prob+sim"
best_pos_star,best_neg_star,best_pos_start,best_pos_end,best_neg_start,best_neg_end,\
best_pos_proba,best_neg_proba = pos_star,neg_star,pos_start,pos_end,neg_start,neg_end,pos_proba,neg_proba
pos_star,neg_star,pos_start,pos_end,neg_start,neg_end,pos_proba,neg_proba = multi_prob_sim(tgt_un,labels_proba,tgt_sim,k,theta,sorting)
acc = test(target,pos_star,neg_star)
if acc>best_acc:
best_acc = acc
best_method = "prob*sim"
best_pos_star,best_neg_star,best_pos_start,best_pos_end,best_neg_start,best_neg_end,\
best_pos_proba,best_neg_proba = pos_star,neg_star,pos_start,pos_end,neg_start,neg_end,pos_proba,neg_proba
return best_pos_star,best_neg_star,best_pos_start,best_pos_end,best_neg_start,best_neg_end,best_acc,best_method,best_pos_proba,best_neg_proba
# just probs
def just_prob(tgt_un,labels_proba,tgt_sim,k=100,theta=0.5,sort_prob="dsc"):
# tgt_sim is not used!
pos_list = [(x,y[1],z) for x,y,z in zip(tgt_un,labels_proba,tgt_sim) if y[1]>theta]
neg_list = [(x,y[0],z) for x,y,z in zip(tgt_un,labels_proba,tgt_sim) if y[0]>theta]
pos_list = sort_by_index(pos_list,index=1,sorting=sort_prob)
neg_list = sort_by_index(neg_list,index=1,sorting=sort_prob)
pos_star,neg_star,pos_proba,neg_proba = starize(pos_list,neg_list,k/2,index=1,sort_prob=sort_prob)
if len(neg_star) > 0 and len(neg_star) >0:
pos_start,pos_end = pos_list[0][1],pos_list[len(pos_star)-1][1]
neg_start,neg_end = neg_list[0][1],neg_list[len(neg_star)-1][1]
else:
pos_start,pos_end = -1,-1
neg_start,neg_end = -1,-1
return pos_star,neg_star,pos_start,pos_end,neg_start,neg_end,pos_proba,neg_proba
# just labels
def just_sim(tgt_un,labels_proba,tgt_sim,k=100,theta=0.5,sort_prob="dsc"):
# labels_proba is not used
pos_list = [(x,y[1],z) for x,y,z in zip(tgt_un,labels_proba,tgt_sim) if y[1]>theta]
neg_list = [(x,y[0],z) for x,y,z in zip(tgt_un,labels_proba,tgt_sim) if y[0]>theta]
pos_list = sort_by_index(pos_list,index=2)
neg_list = sort_by_index(neg_list,index=2)
pos_star,neg_star,pos_proba,neg_proba = starize(pos_list,neg_list,k/2,index=2)
if len(neg_star) > 0 and len(pos_star) >0:
pos_start,pos_end = pos_list[0][1],pos_list[len(pos_star)-1][1]
neg_start,neg_end = neg_list[0][1],neg_list[len(neg_star)-1][1]
else:
pos_start,pos_end = -1,-1
neg_start,neg_end = -1,-1
return pos_star,neg_star,pos_start,pos_end,neg_start,neg_end,pos_proba,neg_proba
def prob_sim(tgt_un,labels_proba,tgt_sim,k=100,theta=0.5,sort_prob="dsc"):
pos_list = [(x,y[1],z) for x,y,z in zip(tgt_un,labels_proba,tgt_sim) if y[1]>theta]
neg_list = [(x,y[0],z) for x,y,z in zip(tgt_un,labels_proba,tgt_sim) if y[0]>theta]
num = min(min(k,len(pos_list)),len(neg_list))
pos_list = sort_by_index(pos_list,index=1,sorting=sort_prob)[:num]
neg_list = sort_by_index(neg_list,index=1,sorting=sort_prob)[:num]
pos_list = sort_by_index(pos_list,index=2)
neg_list = sort_by_index(neg_list,index=2)
pos_star,neg_star,pos_proba,neg_proba = starize(pos_list,neg_list,k/2,index=2,sort_prob=sort_prob)
if len(neg_star) > 0 and len(pos_star) >0:
pos_start,pos_end = pos_list[0][1],pos_list[len(pos_star)-1][1]
neg_start,neg_end = neg_list[0][1],neg_list[len(neg_star)-1][1]
else:
pos_start,pos_end = -1,-1
neg_start,neg_end = -1,-1
return pos_star,neg_star,pos_start,pos_end,neg_start,neg_end,pos_proba,neg_proba
# tgt_sim first
def sim_prob(tgt_un,labels_proba,tgt_sim,k=100,theta=0.5,sort_prob="dsc"):
sum_dict = [(x,y,z) for x,y,z in zip(tgt_un,labels_proba,tgt_sim)]
num = min(k*2,len(sum_dict))
sum_dict = sort_by_index(sum_dict,index=2)[:num]
pos_list = [(x,y[1],z) for x,y,z in sum_dict if y[1] >theta]
neg_list = [(x,y[0],z) for x,y,z in sum_dict if y[0] >theta]
pos_list = sort_by_index(pos_list,index=1,sorting=sort_prob)
neg_list = sort_by_index(neg_list,index=1,sorting=sort_prob)
pos_star,neg_star,pos_proba,neg_proba = starize(pos_list,neg_list,k/2,index=1,sort_prob=sort_prob)
if len(neg_star) > 0 and len(pos_star) >0:
pos_start,pos_end = pos_list[0][1],pos_list[len(pos_star)-1][1]
neg_start,neg_end = neg_list[0][1],neg_list[len(neg_star)-1][1]
else:
pos_start,pos_end = -1,-1
neg_start,neg_end = -1,-1
return pos_star,neg_star,pos_start,pos_end,neg_start,neg_end,pos_proba,neg_proba
# prob+sim
def add_prob_sim(tgt_un,labels_proba,tgt_sim,k=100,theta=0.5,sort_prob="dsc"):
pos_list = [(x,y[1]+z,0) for x,y,z in zip(tgt_un,labels_proba,tgt_sim) if y[1]>theta]
neg_list = [(x,y[0]+z,0) for x,y,z in zip(tgt_un,labels_proba,tgt_sim) if y[0]>theta]
pos_list = sort_by_index(pos_list,index=1,sorting=sort_prob)
neg_list = sort_by_index(neg_list,index=1,sorting=sort_prob)
pos_star,neg_star,pos_proba,neg_proba = starize(pos_list,neg_list,k/2,index=1,sort_prob=sort_prob)
if len(neg_star) > 0 and len(pos_star) >0:
pos_start,pos_end = pos_list[0][1],pos_list[len(pos_star)-1][1]
neg_start,neg_end = neg_list[0][1],neg_list[len(neg_star)-1][1]
else:
pos_start,pos_end = -1,-1
neg_start,neg_end = -1,-1
return pos_star,neg_star,pos_start,pos_end,neg_start,neg_end,pos_proba,neg_proba
# prob*sim
def multi_prob_sim(tgt_un,labels_proba,tgt_sim,k=100,theta=0.5,sort_prob="dsc"):
pos_list = [(x,(y[1])*z,0) for x,y,z in zip(tgt_un,labels_proba,tgt_sim) if y[1]>theta]
neg_list = [(x,(y[0])*z,0) for x,y,z in zip(tgt_un,labels_proba,tgt_sim) if y[0]>theta]
pos_list = sort_by_index(pos_list,index=1,sorting=sort_prob)
neg_list = sort_by_index(neg_list,index=1,sorting=sort_prob)
pos_star,neg_star,pos_proba,neg_proba = starize(pos_list,neg_list,k/2,index=1,sort_prob=sort_prob)
if len(neg_star) > 0 and len(pos_star) >0:
pos_start,pos_end = pos_list[0][1],pos_list[len(pos_star)-1][1]
neg_start,neg_end = neg_list[0][1],neg_list[len(neg_star)-1][1]
else:
pos_start,pos_end = -1,-1
neg_start,neg_end = -1,-1
return pos_star,neg_star,pos_start,pos_end,neg_start,neg_end,pos_proba,neg_proba
# 1 = probs
# 2 = sim
def sort_by_index(list_to_sort,index=1,sorting='dsc'):
if sorting == "dsc":
list_to_sort.sort(lambda x,y: -1 if x[index]>y[index] else 1)
else: #asc
list_to_sort.sort(lambda x,y: -1 if x[index]<y[index] else 1)
return list_to_sort
# return pseudo labelled positive and negative datasets
# also give the corresponding prediction confidence
def starize(pos_list,neg_list,k,index,sort_prob="dsc"):
temp = min(len(pos_list),len(neg_list))
if k > temp and temp != 0:
k = temp
if temp == 0:
print "empty!"
return [],[],[],[]
pos_star = [x for x,y,z in pos_list[:k]]
neg_star = [x for x,y,z in neg_list[:k]]
pos_proba,neg_proba = compute_normalized_weights(pos_list[:k],neg_list[:k],index,sort_prob)
# print pos_proba
return pos_star,neg_star,pos_proba,neg_proba
def compute_normalized_weights(pos_list,neg_list,index,sort_prob="dsc"):
pos_proba = [l[index] for l in pos_list]
neg_proba = [l[index] for l in neg_list]
if sort_prob == "dsc":
return normalize(pos_proba),normalize(neg_proba)
else:
return normalize([1.0-x for x in pos_proba]),normalize([1.0-x for x in neg_proba])
def normalize(a):
return (1.0/sum(a))*np.array(a)