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mgt_lp_comparison.py
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mgt_lp_comparison.py
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#!/usr/bin/env python3
import max_sat_interface as mxs
import group_testing_function as gtf
import numpy as np
import scipy.spatial.distance as sd
import general_lp_interface as lp
from setup import DIR
from setup import OUT_NAME
from setup import EXTENSION
from setup import CMP
from setup import number_of_trials
# Class of object to contain trials information
class Trial:
def __init__(self, var):
self.E = [] # mean error
self.P = [] # probability of success (after number_of_trials)
self.t_e = [] # temporary error
self.t_h = [] # temporary hamming distance
self.lp_E = [] # on number of tests
self.lp_P = []
self.lp_E_C = [] # on number of tests
self.lp_P_C = [] # probability of success (after number_of_trials)
self.lp_t_e = [] # temporary error
self.lp_t_h = [] # temporary hamming distance
self.lp_t_e_c = [] # temporary error
self.lp_t_h_c = [] # temporary hamming distance
self.var = var
def main_comparison_maxhs_lp(n=100, p=0.03, noiseless=True,noise_probability = 0.05, u=0, verbose=False):
if(verbose):
print("Comparative performance of MaxSAT and LP-relaxed encoding while solving the decoding phase of group testing")
print("- number of items: ", n)
print("- probability of defectivity: ", p)
print("- noiseless testing:", noiseless)
if(not noiseless):
print("- probability of noise: ", noise_probability)
print("- index of experiment:", u)
k = round(n * p)
x = gtf.generate_input_k(n, k)
x_s = [1 if i in x else 0 for i in range(1, n + 1)]
T = [1, int(n / (5 * np.log2(n)) + 1)]
# generate T
i = 1
# until we reach the condition of success have more dense trials
while T[i] < k * np.log2(n / k):
if T[i] - T[i - 1] > round(n / 100):
T.append(int(2 * T[i] - T[i - 1] - round(n / 100)))
else:
T.append(int(T[i] + round(n / 100)))
i += 1
# dense trials around k* log2(n/k)
while T[i] < n:
T.append(int(2 * T[i] - T[i - 1] + round(n / 100)))
i += 1
# T = [1, 10, 30]
nw_trials = []
noise_weight = [0.0]
lambda_w = round((np.log((1-noise_probability)/noise_probability)) / (np.log((1-(k/n))/(k/n))), 2)
if not noiseless:
noise_weight = [lambda_w]
for i in noise_weight:
nw_trials.append(Trial(i))
mean_time_lp = []
mean_time_maxhs = []
success_lp = []
success_maxhs = []
tr = nw_trials[0]
# for every t number of tests
if(verbose):
print("\nperforming tests\n")
for t in T:
if(verbose):
print("- number of tests: ", t)
time_lp = []
time_maxhs = []
tr.t_h = []
tr.lp_t_h = []
tr.lp_t_e = []
tr.t_e = []
for i in range(number_of_trials):
a = gtf.generate_pool_matrix(n, k, t)
y = gtf.get_results(t, a, x, noiseless, noise_probability)
# *************MAX_HS*************
mxs.output(n, t, x, y, a, noiseless, tr.var)
r, noise, tm = mxs.call_Max_Sat(n)
# add execution time
time_maxhs.append(tm)
# calculating hamming distance between model result and input x
hamming_distance = sd.hamming(x_s, r)
tr.t_h.append(hamming_distance)
# there's an error?
if hamming_distance > 0:
tr.t_e.append(1)
else:
tr.t_e.append(0)
# *************LP_RELAX*************
r_lp_i, tm = lp.solve(y, a, n, noiseless)
# add execution time
time_lp.append(tm)
# ****CAST****
r_lp = [int(i) for i in r_lp_i[:n]]
# calculating hamming distance between model result and input x
hamming_distance = sd.hamming(x_s, r_lp)
tr.lp_t_h.append(hamming_distance)
# there's an error?
if hamming_distance > 0:
tr.lp_t_e.append(1)
else:
tr.lp_t_e.append(0)
mean_time_lp.append(np.mean(time_lp))
mean_time_maxhs.append(np.mean(time_maxhs))
for tr in nw_trials:
tr.E.append(np.mean(tr.t_h))
tr.P.append(1 - np.mean(tr.t_e))
tr.lp_E.append(np.mean(tr.lp_t_h))
tr.lp_P.append(1 - np.mean(tr.lp_t_e))
X = []
for i in range(len(T)):
X.append(k * np.log2(n / k))
with open(DIR + OUT_NAME + CMP + "-" + str(u) + EXTENSION, "w+") as output_file:
output_string = ''
output_string += str(n) + "\n" + str(k) + "\n" + str(lambda_w) + "\n" + str(noiseless) + "\n"
output_string += str(T) + "\n"
for tr in nw_trials:
output_string += str(tr.E) + "\n" + str(tr.P) + "\n"
output_string += str(mean_time_maxhs) + "\n"
for tr in nw_trials:
output_string += str(tr.lp_E) + "\n" + str(tr.lp_P) + "\n"
output_string += str(mean_time_lp) + "\n"
output_file.write(output_string)