def test_compute_score():
s1 = score.compute_score(cb, 6, 0)
s2 = score.compute_score(cb, 6, 1)
s3 = score.compute_score(cb, 2, 2)
s4 = score.compute_score(cb, 3, 3)
s5 = score.compute_score(cb, 4, 4)
s6 = score.compute_score(cb, 6, 5)
s7 = score.compute_score(cb, 6, 6)
assert s1 == 651
assert s2 == 1202
assert s3 == 653
assert s4 == 704
assert s5 == 1503
assert s6 == 9702
assert s7 == 651
def main():
parser = argparse.ArgumentParser(description="hashcode-2020 solver")
parser.add_argument("input", nargs='+', help="input file")
parser.add_argument("--output",
"-o",
default="tmp.out",
help="output file (ignored if multiple input file)")
args = parser.parse_args()
total_score = 0
multiple = len(args.input) > 1
for input_file_path in args.input:
print(input_file_path)
with open(input_file_path) as input_file:
input_data_set = parse_input_file(input_file)
output_data = solve(input_data_set)
score = compute_score(input_data_set, output_data)
total_score += score
print(f"score: {score:,}")
output_file_path = f"{input_file_path}.out" if multiple else args.output
with open(output_file_path, "w+") as output_file:
write_output_file(output_data, output_file)
if multiple:
print(f"total score: {total_score:,}")
def worker(best_score, best_solution, l, loaded_input, q, output):
"""General
q.get() must be a loaded_input with the method generate_solution(int seed)
"""
# We get the seed that will generate our solution
# STOP stops the worker
for seed in iter(q.get, 'STOP'):
# We calculate a solution from a seed and evaluate its score
solution = loaded_input.generate_solution(seed)
score = compute_score(solution)
# Lock state to prevent race condition
l.acquire()
#print(score)
# If our solution is better we save it in the shared variable
if score > best_score.value:
best_score.value = score
best_solution[0] = solution
# End of atomic operation
l.release()
output.put(True)
# read test topic proportions
test_theta = np.loadtxt('dirlda/test-gamma.dat')
sumtheta = np.sum(test_theta,1)
test_theta = test_theta/sumtheta.reshape(-1,1)
# clustering
group_assgnmt = np.argmax(test_theta,1)
# write group file
np.savetxt(tgroupfile,group_assgnmt.reshape(-1,1),'%d')
# Run M4
seed = np.random.randint(seed0)
cmdtxt = M4Path + '/M4 '+str(seed) + ' test '+ testfile + ' ' + tgroupfile + ' '+str(Gtest)+ ' '+path+'/M4/dirM4/final ' + path + '/M4/dirM4'
os.system(cmdtxt + ' > /dev/null')
# compute score
seed = np.random.randint(seed0)
f1scores = score.compute_score(seed, path, tlblfile, tgroupfile, resfile, anomlist)
fpres = open(resfile,'a')
fpres.write('****** M = %d, T = %d, F1-score = %f\n'%(M,T,np.mean(f1scores)))
fpres.close()
fpres = open('results.txt','a')
fpres.write('############################################\n')
amax = np.argmax(f1scores)
m1 = amax/len(Tlist)
n1 = amax%len(Tlist)
fpres.write('Best F1-score: M = %d, K = %d, F1-score = %f' %(Mlist[m1],Tlist[n1],f1scores[m1,n1]))
fpres.close()
###########################
## Find best solution
###########################
start = time()
solution = solve(loaded_input, seeds, number_cpu)
end = time()
computing_time = end - start
###########################
## Post-processing
###########################
# Improves the solution
solution = improve_solution(solution, loaded_input)
###########################
## Checks solution and writes it out
###########################
# Check if solution is valid
valid = is_valid_solution(solution, loaded_input)
display(solution, loaded_input)
# Compute score and display
score = compute_score(solution) * valid
print_score(score, loaded_input, end - start)
# Writing to output
write_output(args.input, "algo1", score, solution)
# read test topic proportions
test_theta = np.loadtxt('dirlda/test-gamma.dat')
sumtheta = np.sum(test_theta,1)
test_theta = test_theta/sumtheta.reshape(-1,1)
# clustering
group_assgnmt = np.argmax(test_theta,1)
# write group file
np.savetxt(tgroupfile,group_assgnmt.reshape(-1,1),'%d')
# Run M4
seed = np.random.randint(seed0)
cmdtxt = M4Path + '/M4 '+str(seed) + ' test '+ testfile + ' ' + tgroupfile + ' '+str(Gtest)+ ' '+path+'/M4/dirM4/final ' + path + '/M4/dirM4'
os.system(cmdtxt + ' > /dev/null')
# compute score
seed = np.random.randint(seed0)
f1scores = score.compute_score(seed, path, tlblfile, tgroupfile, resfile, anomlist)
fpres = open(resfile,'a')
fpres.write('****** M = %d, T = %d, F1-score = %f\n'%(M,T,np.mean(f1scores)))
fpres.close()
fpres = open('results.txt','a')
fpres.write('############################################\n')
amax = np.argmax(f1scores)
m1 = amax/len(Tlist)
n1 = amax%len(Tlist)
fpres.write('Best F1-score: M = %d, K = %d, F1-score = %f' %(Mlist[m1],Tlist[n1],f1scores[m1,n1]))
fpres.close()
def test_score_b_should_be_easy(self):
score = compute_score(RES_FOLDER + "b_should_be_easy.in",
RES_FOLDER + "b_should_be_easy.out")
self.assertEqual(score.raw_score, 169677)
self.assertEqual(score.bonus_score, 7200)
def test_score_a_example(self):
score = compute_score(RES_FOLDER + "a_example.in",
RES_FOLDER + "a_example.out")
self.assertEqual(score.raw_score, 8)
self.assertEqual(score.bonus_score, 2)
def test_score_b_should_be_easy(self):
(raw_score,
bonus_score) = compute_score(res_folder + "b_should_be_easy.in",
res_folder + "b_should_be_easy.out")
self.assertEqual(raw_score, 169677)
self.assertEqual(bonus_score, 7200)
def test_score_a_example(self):
(raw_score, bonus_score) = compute_score(res_folder + "a_example.in",
res_folder + "a_example.out")
self.assertEqual(raw_score, 8)
self.assertEqual(bonus_score, 2)
