def pipeline(folder, output):
second_folder = folder + '_reducted'
matrices = [
file for file in os.listdir(folder) if file.endswith('.matrix')
]
matrices = sorted(matrices,
key=file2tuple) # for logic traversal of matrices
print(f'total of {len(matrices)} matrices')
matrices = list(filter(filter_low, matrices))
# matrices = list(filter(filter_low, matrices))
# from_matrix = '14_14_11.matrix'
# from_index = dict(map(reversed,enumerate(matrices)))[from_matrix]
# to_matrix = '14_14_20.matrix'
# to_index = dict(map(reversed,enumerate(matrices)))[to_matrix]
# matrices = matrices[from_index:to_index + 1]
print(f'remaining {len(matrices)} matrices')
# print(matrices[0])
# return
for matrix_file in matrices:
print(f'diagnosing matrix {matrix_file}')
# matrix_file = '1_2_1.matrix'
inst, error, initials, _ = readPlanningFile(os.path.join(
folder, matrix_file),
cut=True)
# uncertain_tests = Experiment_Data().POOL.keys()
uncertain_tests = [
test for (test, outcome) in error.items() if outcome == 1
]
alg1_result, alg1_time, _, _ = run_diagnoser(inst, error, 0.1,
uncertain_tests)
inst, error, initials, _ = readPlanningFile(os.path.join(
second_folder, matrix_file),
cut=True)
if len(Experiment_Data().COMPONENTS_NAMES) <= 2:
print('small matrix. continuing')
continue
alg2_result, alg2_time, _, _ = run_reduction_based(
inst, error, 0.1, uncertain_tests)
alg1_diags = list(map(operator.itemgetter(0), alg1_result))
alg2_diags = list(map(operator.itemgetter(0), alg2_result))
if alg1_diags != alg2_diags:
print(f'difference in {matrix_file}:')
print(f'o2d results: {alg1_result}')
print(f'reduction results: {alg2_result}')
print_matrix(error)
break
def pipeline(folder, output):
matrices = [
file for file in os.listdir(folder) if file.endswith('.matrix')
]
matrices = sorted(matrices,
key=file2tuple) # for logic traversal of matrices.
print(f'total of {len(matrices)} matrices')
matrices = list(filter(filter_low, matrices))
# from_matrix = '14_14_11.matrix'
# from_index = dict(map(reversed,enumerate(matrices)))[from_matrix]
# to_matrix = '14_14_20.matrix'
# to_index = dict(map(reversed,enumerate(matrices)))[to_matrix]
# matrices = matrices[from_index:to_index + 1]
print(f'remaining {len(matrices)} matrices')
# print(matrices[0])
# return
with open(output, 'w', newline='') as f:
writer = csv.writer(f)
writer.writerow(
('matrix_name', 'sample', '#components', '#tests',
'#failing_tests', 'error_vector', 'o2d_time', 'o2d_mhs_time',
'o2d_best_cardinality', 'o2d_mhs_best_cardinality',
'o2d_mean_cardinality', 'o2d_mhs_mean_cardinality', 'o2d_output',
'o2d_mhs_output'))
f.flush()
for matrix_file in matrices:
print(f'diagnosing matrix {matrix_file}')
diagnoser, smart_mhs_diagnoser, error, initials, _ = readPlanningFile(
os.path.join(folder, matrix_file))
error_vec = list(
map(operator.itemgetter(1),
sorted(error.items(), key=operator.itemgetter(0))))
num_of_comps = len(Experiment_Data().COMPONENTS_NAMES.keys())
all_tests = Experiment_Data().POOL.keys()
num_of_tests = len(all_tests)
num_failing_tests = error_vec.count(1)
sample_num = int(matrix_file.split('.')[0].split('_')[2])
# if sample_num != 2:
# continue
print(
f'comps: {num_of_comps}, tests: {num_of_tests}, failing: {num_failing_tests}'
)
for results, time, best_diag_card, mean_card in compare_with_smart_mhs(
diagnoser, error, all_tests, smart_mhs_diagnoser):
writer.writerow((matrix_file, sample_num, num_of_comps,
num_of_tests, num_failing_tests, error_vec,
*time, *best_diag_card, *mean_card, *results))
f.flush()
print()
print('\n')
def pipeline(folder, output):
# matrices = [a for a in
# ['0aa57f04ede369a4f813bbb86d3eac1ed20b084c.matrix2', '0cc451d5e5cb565eb7311308466f487bc534ebaf.matrix2',
# '19f33e4e0d824e732d07f06a08567c27b3c808f3.matrix2', '1c606c3d96838e595a0664cbafdd60caae34aa0e.matrix2',
# '229151ec41339450e4d4f857bf92ed080d3e2430.matrix', '3905071819a14403d1cdb9437d2e005adf18fc70.matrix',
# '3b46d611b2d595131ce0bce9bdb3209c55391be7.matrix', '3cea4b2af3f9caf6aa72fa56d647c513d320e073.matrix',
# '3f900a7395e31eaa72e0fa2fb43c090e5a8fa4ed.matrix', '48bf241d4149919e0928e39616bee2e3783e2987.matrix',
# '5209cefa81c9c48a34e5472fdcf2a308a4da2589.matrix', '575be16474e8e8246d4bbde6f243fdf38c34ad5b.matrix',
# '68217617c54467c7c6098168e714a2fb6a48847d.matrix', '8da5fb28a764eee26c76a5018c293f224017887b.matrix',
# 'ac2a39e92a71d5f9eb3ca7c6cc789b6341c582a4.matrix', 'ac58807ede6d9a0625b489cdca6fd37bad9cacfe.matrix',
# 'b2f1757bf9ec1632a940b9a2e65a1a022ba54af8.matrix', 'b5906d3f325ca3a1147d5fa68912975e2e6c347e.matrix',
# 'b6f7a8a8be57c9525c59e9f21e958e76cee0dbaf.matrix', 'cbf8e4eb017a99af9a8f24eb8429e8a12b62af8b.matrix',
# 'cf28c89dcf72d27573c478eb91e3b470de060edd.matrix', 'cfff06bead88e2c1bb164285f89503a919e0e27f.matrix',
# 'e28c95ac2ce95852add84bdf3d2d9c00ac98f5de.matrix', 'ec0c4e5508dbd8af83253f7c50f8b728a1003388.matrix',
# 'temp.matrix'] if a.endswith(matrix_file_suffix)]
matrices = [
file for file in [
'19f33e4e0d824e732d07f06a08567c27b3c808f3.matrix2',
'229151ec41339450e4d4f857bf92ed080d3e2430.matrix2',
'3cea4b2af3f9caf6aa72fa56d647c513d320e073.matrix2',
'68217617c54467c7c6098168e714a2fb6a48847d.matrix2',
'ac2a39e92a71d5f9eb3ca7c6cc789b6341c582a4.matrix2',
'ac58807ede6d9a0625b489cdca6fd37bad9cacfe.matrix',
'cf28c89dcf72d27573c478eb91e3b470de060edd.matrix',
'cfff06bead88e2c1bb164285f89503a919e0e27f.matrix'
] if file.endswith('.matrix')
]
# matrices = [file for file in os.listdir(folder) if file.endswith('.matrix')]
# matrices = ['temp.matrix']
print(f'total of {len(matrices)} matrices')
# from_matrix = '14_14_11.matrix'
# from_index = dict(map(reversed,enumerate(matrices)))[from_matrix]
# to_matrix = '14_14_20.matrix'
# to_index = dict(map(reversed,enumerate(matrices)))[to_matrix]
# matrices = matrices[from_index:to_index + 1]
print(f'remaining {len(matrices)} matrices')
# print(matrices)
# return
with open(output, 'w', newline='') as f:
writer = csv.writer(f)
writer.writerow(
('#', 'matrix_name', '#components', '#tests',
'#components_before_cut', '#tests_before_cut', '#failing_tests',
'#uncertain_tests', 'faulty_output_prob', 'error_vector',
'scan_all_obs_time', 'scan_all_diags_time', 'scan_best_obs_time',
'scan_all_obs_best_cardinality',
'scan_all_diags_best_cardinality',
'scan_best_obs_best_cardinality', 'scan_all_obs_mean_cardinality',
'scan_all_diags_mean_cardinality',
'scan_best_obs_mean_cardinality', 'scan_all_obs_output',
'scan_all_diags_output', 'scan_best_obs_output'))
f.flush()
for i, matrix_file in enumerate(matrices):
print(f'diagnosing matrix {matrix_file}')
inst, error, all_tests, (old_component,
old_tests) = readPlanningFile(
os.path.join(folder, matrix_file),
cut=True)
error_vec = list(
map(operator.itemgetter(1),
sorted(error.items(), key=operator.itemgetter(0))))
uncertain_tests = [
test for (test, outcome) in error.items() if outcome == 1
]
num_of_comps = len(Experiment_Data().COMPONENTS_NAMES.keys())
num_of_tests = len(all_tests)
old_num_of_comps = len(old_component)
old_num_of_tests = len(old_tests)
num_failing_tests = error_vec.count(1)
num_uncertain = len(uncertain_tests)
print(
f'comps: {num_of_comps}, tests: {num_of_tests}, uncertain: {num_uncertain}, old_comps: {old_num_of_comps}, old_tests: {old_num_of_tests}'
)
if num_of_comps <= 70 or num_of_comps > 100:
print(f'skipping {matrix_file}')
continue
if num_uncertain < 2:
print(f'skipping {matrix_file}')
continue
# continue
for results, time, best_diag_card, mean_card, faulty_output_prob in run_all_diagnosers(
inst, error, uncertain_tests, all_tests):
writer.writerow(
(i, matrix_file, num_of_comps, num_of_tests,
old_num_of_comps, old_num_of_tests, num_failing_tests,
num_uncertain, faulty_output_prob, error_vec, *time,
*best_diag_card, *mean_card, *results))
f.flush()
print('\n\n')
from software.sfl_diagnoser.Diagnoser.diagnoserUtils import readPlanningFile, write_planning_file, write_merged_matrix from software.sfl_diagnoser.Diagnoser.Diagnosis_Results import Diagnosis_Results base = readPlanningFile(r"temp_matrix.txt") base.diagnose() res = Diagnosis_Results(base.diagnoses, base.initial_tests, base.error) print(res.get_metrics_names()) print(res.get_metrics_values())
def test_mcts(f):
instance = diagnoserUtils.readPlanningFile(f)
return main_mcts(instance)
def pipeline(folder, output):
matrices = [
file for file in os.listdir(folder) if file.endswith('.matrix')
]
matrices = sorted(
matrices, key=file2tuple
) # for logic traversal of matrices. also taking only 10 samples instead of 20
print(f'total of {len(matrices)} matrices')
matrices = list(filter(filter_low, matrices))
# from_matrix = '14_14_11.matrix'
# from_index = dict(map(reversed,enumerate(matrices)))[from_matrix]
# to_matrix = '14_14_20.matrix'
# to_index = dict(map(reversed,enumerate(matrices)))[to_matrix]
# matrices = matrices[from_index:to_index + 1]
print(f'remaining {len(matrices)} matrices')
# print(matrices[0])
# return
with open(output, 'w', newline='') as f:
writer = csv.writer(f)
writer.writerow(
('matrix_name', 'sample', '#components', '#tests',
'#failing_tests', '#uncertain_tests', 'faulty_output_prob',
'error_vector', 'scan_all_obs_time', 'scan_all_diags_time',
'scan_best_obs_time', 'scan_all_obs_best_cardinality',
'scan_all_diags_best_cardinality',
'scan_best_obs_best_cardinality', 'scan_all_obs_mean_cardinality',
'scan_all_diags_mean_cardinality',
'scan_best_obs_mean_cardinality', 'scan_all_obs_output',
'scan_all_diags_output', 'scan_best_obs_output'))
f.flush()
for matrix_file in matrices:
print(f'diagnosing matrix {matrix_file}')
inst, error, initials, _ = readPlanningFile(
os.path.join(folder, matrix_file))
error_vec = list(
map(operator.itemgetter(1),
sorted(error.items(), key=operator.itemgetter(0))))
num_of_comps = len(Experiment_Data().COMPONENTS_NAMES.keys())
all_tests = Experiment_Data().POOL.keys()
num_of_tests = len(all_tests)
num_failing_tests = error_vec.count(1)
sample_num = int(matrix_file.split('.')[0].split('_')[2])
print(
f'comps: {num_of_comps}, tests: {num_of_tests}, failing: {num_failing_tests}'
)
# for proportion_uncertain in [0.1, 0.3, 0.5, 0.7, 1]:
for num_uncertain in range(7, num_of_tests + 1):
# num_uncertain = ceil(num_of_tests * proportion_uncertain)
uncertain_tests = sample(all_tests, num_uncertain)
# print(f'time: {time()}')
print(f'uncertain tests: {uncertain_tests}')
for results, time, best_diag_card, mean_card, faulty_output_prob in run_all_diagnosers(
inst, error, uncertain_tests, initials):
writer.writerow(
(matrix_file, sample_num, num_of_comps, num_of_tests,
num_failing_tests, num_uncertain, faulty_output_prob,
error_vec, *time, *best_diag_card, *mean_card,
*results))
f.flush()
print()
print('\n\n\n')