-
Notifications
You must be signed in to change notification settings - Fork 0
/
max_sat_interface.py
210 lines (180 loc) · 7.79 KB
/
max_sat_interface.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
203
204
205
206
207
208
209
210
import subprocess as sbp
SOLVER = "maxhs"
BSTSOL = "-printSoln"
CPULIM = "-cpu-lim=100"
fixed_header = "c\nc comments Weighted Max-SAT\nc\np wcnf "
hard_weight = 1000000000
soft_weight = 1
input_for_max_HS = "temp/max_sat_input"
output_data_set = "data set"
SEP1 = "nv"
SEP12 = "Best Model Found:"
SEP2 = "nc"
CPUTIME = "CPU: "
TEMP = r"../max-sat-tester/temp"
# call_Max_Sat call the MaxSAT solver, in this case maxhs
# with the input file previously generate and the option of
# then it parse the solution and return the model found and the time needed to found it
def call_Max_Sat(n):
# get the output
# BSTSOL requires to output the best solution found in case the solving is stopped before finding the actual optimum
# CPULIM limit the time in finding the optimum solution
output_string = str(
sbp.run([SOLVER, BSTSOL, CPULIM, input_for_max_HS], stdout=sbp.PIPE).stdout)
time_string = output_string.split(CPUTIME)[1]
time = float(time_string.split("\\")[0])
# get the interesting output
if(output_string.__contains__("Best Model Found:")):
output_string = (output_string.split(SEP12))[1]
output_string = (output_string.split(SEP2)[1]).split(SEP2)[0]
output_string = (output_string.split(" \\")[0])
model_string = (output_string[0:len(output_string) - 1])[1:]
else:
output_string = (output_string.split(SEP1)[1]).split(SEP2)[0]
model_string = (output_string[0:len(output_string) - 1])[1:]
# parse the model into integer
model = list(map(int, model_string))
noise = model[n:]
model = model[:n]
model = list(map(lambda x: 1 if x > 0 else 0, model))
return model, noise, time
# function to generate file to be read by Max_HS and file of data_set
# output generate the MaxSAT instance in the compact encoding without XOR
# n is the number of variables
# t is the number of group that has been tested
# y is the output result of the group testing instance
# a is the recovery matrix
# noiseless is a boolean, True if the problem is noiseless, False otherwise
# noise_weight is the weight associated to the noise constraint, it expresses the lambda
# lambda is the trade-off between noisy and faulty, we express only the noise weight as
# the weight associated to the faultiness is fixed to 1
# for optimization purpose the function directly generate the string to be included in the input file
def output(n, t, x, y, a, noiseless, noise_weight):
if noiseless:
m = n
else:
m = n + t
max_HS_input = fixed_header + " " + str(m)
hard_clauses_string = ''
soft_clauses_string = ''
neg = []
nc = 0
# building hard constraint input to max_HS input
if noiseless:
for i in range(t):
if y[i] == 1:
if a[i]:
local_hard = str(hard_weight) + " "
nc = nc + 1
for element in a[i]:
local_hard += str(element) + " "
local_hard += " 0\n"
hard_clauses_string += local_hard
else:
if a[i]:
for element in a[i]:
if element not in neg:
nc = nc + 1
neg.append(element)
local_hard = str(hard_weight) + \
" -" + str(element) + " 0\n"
hard_clauses_string += local_hard
# noisy settings
else:
for i in range(t):
if y[i] == 1:
if a[i]:
local_hard = str(hard_weight) + " "
nc = nc + 1
for element in a[i]:
local_hard += str(element) + " "
local_hard += (str(n + i + 1))
local_hard += " 0\n"
soft_clauses_string += str(noise_weight) + \
" -" + str(n + i + 1) + " 0\n"
hard_clauses_string += local_hard
else:
if a[i]:
for element in a[i]:
nc = nc + 1
local_hard = str(hard_weight) + " -" + \
str(element) + " " + str(n + i + 1) + " 0\n"
hard_clauses_string += local_hard
soft_clauses_string += str(noise_weight) + \
" -" + str(n + i + 1) + " 0\n"
# add soft constraint to ensure minimum number of item faulty to max_HS input
for j in range(1, n + 1):
if j not in neg:
nc += 1
soft_clauses_string += str(soft_weight) + " -" + str(j) + " 0\n"
max_HS_input += " " + str(nc) + " " + str(hard_weight) + "\n"
max_HS_input += hard_clauses_string + soft_clauses_string
with open(input_for_max_HS, "w") as output_file:
output_file.write(max_HS_input)
return
# non compact output is create output a file containing a MaxSAT instance
# in the format of the non-compact XOR enconding
# n is the number of variables
# t is the number of group that has been tested
# y is the output result of the group testing instance
# a is the recovery matrix
# noiseless is a boolean, True if the problem is noiseless, False otherwise
# noise_weight is the weight associated to the noise constraint, it expresses the lambda
# lambda is the trade-off between noisy and faulty, we express only the noise weight as
# the weight associated to the faultiness is fixed to 1
# for optimization purpose the function directly generate the string to be included in the input file
def non_compact_output(n, t, x, y, a, noiseless, noise_weight):
m = n + t
max_HS_input = fixed_header + " " + str(m)
hard_clauses_string = ''
soft_clauses_string = ''
neg = []
nc = 0
# noisy settings
for i in range(t):
if y[i] == 1:
if a[i]:
# first part of XOR constraint
for element in a[i]:
local_hard = str(hard_weight) + " "
nc = nc + 1
local_hard += " -" + str(element) + " "
local_hard += "-" + (str(n + i + 1))
local_hard += " 0\n"
hard_clauses_string += local_hard
# second part of XOR constraint
local_hard = str(hard_weight) + " "
nc = nc + 1
for element in a[i]:
local_hard += str(element) + " "
local_hard += (str(n + i + 1))
local_hard += " 0\n"
hard_clauses_string += local_hard
soft_clauses_string += str(noise_weight) + \
" -" + str(n + i + 1) + " 0\n"
else:
if a[i]:
# first part of XOR constraint
for element in a[i]:
nc = nc + 1
local_hard = str(hard_weight) + " -" + \
str(element) + " " + str(n + i + 1) + " 0\n"
hard_clauses_string += local_hard
# second part of XOR constraint
nc = nc + 1
local_hard = str(hard_weight) + " "
for element in a[i]:
local_hard += str(element) + " "
local_hard += "-" + (str(n + i + 1))
local_hard += " 0\n"
hard_clauses_string += local_hard
# add soft constraint to ensure minimum number of item faulty to max_HS input
for j in range(1, n + 1):
if j not in neg:
nc += 1
soft_clauses_string += str(soft_weight) + " -" + str(j) + " 0\n"
max_HS_input += " " + str(nc) + " " + str(hard_weight) + "\n"
max_HS_input += hard_clauses_string + soft_clauses_string
with open(input_for_max_HS, "w+") as output_file:
output_file.write(max_HS_input)
return