def run(self):
pt_sorted = SortedPerformanceTable(self.pt)
meta = MetaMRSortPop3(self.nmodels, self.criteria, self.categories,
pt_sorted, self.aa)
self.mutex.lock()
self.results.append(meta.metas[0].model.copy())
self.fitness.append(meta.metas[0].ca)
self.mutex.unlock()
self.emit(QtCore.SIGNAL('update(int)'), 0)
for i in range(1, self.niter + 1):
if self.is_stopped() is True:
break
model, ca = meta.optimize(self.nmeta)
self.mutex.lock()
self.results.append(model.copy())
self.fitness.append(ca)
self.mutex.unlock()
self.emit(QtCore.SIGNAL('update(int)'), i)
if ca == 1:
break
def test_heur_mrsort_init_profiles(seed, na, nc, ncat, pcerrors):
# Generate an ELECTRE TRI model and assignment examples
model = generate_random_mrsort_model(nc, ncat, seed)
model2 = model.copy()
model3 = model.copy()
# Generate a first set of alternatives
a = generate_alternatives(na)
pt = generate_random_performance_table(a, model.criteria)
# Compute assignments
aa = model.pessimist(pt)
# Initialize the second model with random generated profiles
b = model.categories_profiles.get_ordered_profiles()
model2.bpt = generate_random_profiles(b, model2.criteria)
# Run the heuristic
cats = model.categories_profiles.to_categories()
pt_sorted = SortedPerformanceTable(pt)
heur = HeurMRSortInitProfiles(model3, pt_sorted, aa)
heur.solve()
# Learn the weights and cut threshold
cps = model.categories_profiles
lp_weights = LpMRSortWeights(model2, pt, aa)
lp_weights.solve()
lp_weights = LpMRSortWeights(model3, pt, aa)
lp_weights.solve()
# Compute the classification accuracy
aa2 = model2.pessimist(pt)
aa3 = model3.pessimist(pt)
ca2 = compute_ca(aa, aa2)
ca3 = compute_ca(aa, aa3)
# Save all infos in test_result class
t = test_result("%s-%d-%d-%d-%g" % (seed, na, nc, ncat, pcerrors))
# Input params
t['seed'] = seed
t['na'] = na
t['nc'] = nc
t['ncat'] = ncat
t['pcerrors'] = pcerrors
# Output params
t['ca_rdom'] = ca2
t['ca_heur'] = ca3
return t
def one_test(self, seed, na, nc, ncat, ca_expected):
model = generate_random_mrsort_model(nc, ncat, seed)
a = generate_alternatives(na)
pt = generate_random_performance_table(a, model.criteria)
aa = model.pessimist(pt)
pt_sorted = SortedPerformanceTable(pt)
heur = HeurMRSortInitProfiles(model, pt_sorted, aa)
heur.solve()
aa2 = model.pessimist(pt)
ca = compute_ca(aa, aa2)
self.assertEqual(ca, ca_expected)
def run_meta_mr(pipe, criteria, categories, worst, best, nmodels, niter, nmeta,
pt, aa):
pt_sorted = SortedPerformanceTable(pt)
meta = MetaMRSortPop3(nmodels, criteria, categories, pt_sorted, aa)
ca = meta.metas[0].meta.good / len(aa)
pipe.send([meta.metas[0].model, ca])
for i in range(1, niter + 1):
model, ca = meta.optimize(nmeta)
pipe.send([model, ca])
if ca == 1:
break
pipe.close()
def run_metaheuristic(pipe,
model,
pt,
aa,
algo,
n,
use_heur=False,
worst=None,
best=None):
random.seed(0)
pt_sorted = SortedPerformanceTable(pt)
if use_heur is True:
heur = HeurMRSortInitProfiles(model, pt_sorted, aa)
heur.solve()
else:
model.bpt = generate_random_profiles(model.profiles,
model.criteria,
worst=worst,
best=best)
if algo == "Meta 3":
meta = MetaMRSortProfiles3(model, pt_sorted, aa)
elif algo == "Meta 4":
meta = MetaMRSortProfiles4(model, pt_sorted, aa)
else:
print("Invalid algorithm %s" % algo)
pipe.close()
return
f = compute_ca(aa, meta.aa)
pipe.send([model.copy(), f])
for i in range(1, n + 1):
meta.optimize()
f = compute_ca(aa, meta.aa)
pipe.send([model.copy(), f])
if f == 1:
break
pipe.close()
def one_test(self, seed, na, nc, ncat, max_loop, n):
model = generate_random_mrsort_model(nc, ncat, seed)
a = generate_alternatives(na)
pt = generate_random_performance_table(a, model.criteria)
aa = model.pessimist(pt)
model2 = model.copy()
bids = model2.categories_profiles.get_ordered_profiles()
model2.bpt = generate_random_profiles(bids, model.criteria)
pt_sorted = SortedPerformanceTable(pt)
meta = MetaMRSortProfiles4(model2, pt_sorted, aa)
for i in range(1, max_loop + 1):
ca = meta.optimize()
if ca == 1:
break
aa2 = model2.pessimist(pt)
self.assertEqual(i, n)
self.assertEqual(aa, aa2)
model.cv.display(criterion_ids=cids)
print("lambda: %.7s" % model.lbda)
model.vpt.id = "veto"
model.vpt.display(criterion_ids=cids)
model.veto_weights.display(criterion_ids=cids)
print("veto lambda: %.7s" % model.veto_lbda)
print("number of possible coalitions: %d" %
compute_number_of_winning_coalitions(model.cv, model.lbda))
model2 = model.copy()
model2.vpt = generate_random_veto_profiles(model2, worst)
print('Original random profiles')
print('========================')
model2.vpt.display(criterion_ids=cids)
pt_sorted = SortedPerformanceTable(pt)
meta = MetaMRSortVetoProfiles5(model2, pt_sorted, aa)
t1 = time.time()
i = 0
for i in range(0, 101):
f = meta.good / meta.na
print('%d: fitness: %g' % (i, f))
model2.bpt.display(criterion_ids=cids)
if f == 1:
break
f = meta.optimize()
t2 = time.time()
from pymcda.generate import generate_random_profiles
from pymcda.pt_sorted import SortedPerformanceTable
from pymcda.utils import compute_ca
from pymcda.utils import compute_winning_and_loosing_coalitions
from pymcda.utils import display_coalitions
from pymcda.learning.lp_mrsort_weights import LpMRSortWeights
from pymcda.ui.graphic import display_electre_tri_models
model = generate_random_mrsort_model(10, 3, 17)
winning, loosing = compute_winning_and_loosing_coalitions(
model.cv, model.lbda)
print("Number of coalitions: %d" % len(winning))
a = generate_alternatives(1000)
pt = generate_random_performance_table(a, model.criteria)
sorted_pt = SortedPerformanceTable(pt)
aa = model.pessimist(pt)
for cat in model.categories_profiles.get_ordered_categories():
pc = len(aa.get_alternatives_in_category(cat)) / len(aa) * 100
print("Percentage of alternatives in %s: %g %%" % (cat, pc))
# Learn the weights with random generated profiles
for i in range(10):
model2 = model.copy()
b = model.categories_profiles.get_ordered_profiles()
model2.bpt = generate_random_profiles(b, model2.criteria)
lp_weights = LpMRSortWeights(model2, pt, aa)
lp_weights.solve()
def run_test(seed, data, pclearning, nloop, nmodels, nmeta):
random.seed(seed)
global aaa
global allm
global fct_ca
global LOO
# Separate learning data and test data
if LOO:
pt_learning, pt_test = data.pt.split_LOO(seed)
else:
pt_learning, pt_test = data.pt.split(2, [pclearning, 100 - pclearning])
aa_learning = data.aa.get_subset(pt_learning.keys())
aa_test = data.aa.get_subset(pt_test.keys())
#import pdb; pdb.set_trace()
# Initialize a random model
cat_profiles = generate_categories_profiles(data.cats)
worst = data.pt.get_worst(data.c)
best = data.pt.get_best(data.c)
b = generate_alternatives(len(data.cats) - 1, 'b')
bpt = None
cvs = None
lbda = None
model = MRSort(data.c, cvs, bpt, lbda, cat_profiles)
# if LOO:
# print(data.c, cvs, bpt, lbda, cat_profiles)
# print(model.categories_profiles.to_categories())
# print(model.categories)
# import pdb; pdb.set_trace()
# Run the metaheuristic
t1 = time.time()
pt_sorted = SortedPerformanceTable(pt_learning)
# Algorithm
meta = meta_mrsort(nmodels, model.criteria,
model.categories_profiles.to_categories(),
pt_sorted, aa_learning,
seed = seed * 100)
# if LOO:
# print(nmodels, model.criteria,
# model.categories_profiles.to_categories(),
# pt_sorted, aa_learning)
#import pdb; pdb.set_trace()
#lp_weights = lp_weights,
#heur_profiles = heur_profiles,
#lp_veto_weights = lp_veto_weights,
#heur_veto_profiles = heur_veto_profiles,
for i in range(0, nloop):
model, ca_learning, all_models = meta.optimize(nmeta, fct_ca)
#import pdb; pdb.set_trace()
if ca_learning == 1:
break
t_total = time.time() - t1
aa_learning2 = model.pessimist(pt_learning)
ca_learning = compute_ca(aa_learning, aa_learning2)
ca_learning_good = compute_ca_good(aa_learning, aa_learning2)
#import pdb; pdb.set_trace()
auc_learning = model.auc(aa_learning, pt_learning)
diff_learning = compute_confusion_matrix(aa_learning, aa_learning2,
model.categories)
# Compute CA of test setting
if len(aa_test) > 0:
aa_test2 = model.pessimist(pt_test)
ca_test = compute_ca(aa_test, aa_test2)
ca_test_good = compute_ca_good(aa_test, aa_test2)
auc_test = model.auc(aa_test, pt_test)
diff_test = compute_confusion_matrix(aa_test, aa_test2,
model.categories)
#import pdb; pdb.set_trace()
else:
ca_test = 0
auc_test = 0
ncat = len(data.cats)
diff_test = OrderedDict([((a, b), 0) for a in model.categories \
for b in model.categories])
# Compute CA of whole set
aa2 = model.pessimist(data.pt)
ca = compute_ca(data.aa, aa2)
ca_good = compute_ca_good(data.aa, aa2)
auc = model.auc(data.aa, data.pt)
diff_all = compute_confusion_matrix(data.aa, aa2, model.categories)
t = test_result("%s-%d-%d-%d-%d-%d" % (data.name, seed, nloop, nmodels,
nmeta, pclearning))
model.id = 'learned'
aa_learning.id, aa_test.id = 'learning_set', 'test_set'
pt_learning.id, pt_test.id = 'learning_set', 'test_set'
save_to_xmcda("%s/%s.bz2" % (directory, t.test_name),
model, aa_learning, aa_test, pt_learning, pt_test)
t['seed'] = seed
t['na'] = len(data.a)
t['nc'] = len(data.c)
t['ncat'] = len(data.cats)
t['pclearning'] = pclearning
t['nloop'] = nloop
t['nmodels'] = nmodels
t['nmeta'] = nmeta
t['na_learning'] = len(aa_learning)
t['na_test'] = len(aa_test)
t['ca_learning'] = ca_learning
t['ca_test'] = ca_test
t['ca_all'] = ca
t['ca_learning_good'] = ca_learning_good
t['ca_test_good'] = ca_test_good
t['ca_all_good'] = ca_good
t['auc_learning'] = auc_learning
t['auc_test'] = auc_test
t['auc_all'] = auc
# import pdb; pdb.set_trace()
aaa[seed]=dict()
aaa[seed]['id'] = seed
aaa[seed]['learning_asgmt_id'] = [i.id for i in aa_learning]
aaa[seed]['learning_asgmt'] = [i.category_id for i in aa_learning]
aaa[seed]['learning_asgmt2'] = [i.category_id for i in aa_learning2]
aaa[seed]['test_asgmt_id'] = [i.id for i in aa_test]
aaa[seed]['test_asgmt'] = [i.category_id for i in aa_test]
aaa[seed]['test_asgmt2'] = [i.category_id for i in aa_test2]
aaa[seed]['criteria'] = [i for i,j in model.criteria.items()]
aaa[seed]['criteria_weights'] = [str(i.value) for i in model.cv.values()]
aaa[seed]['profiles_values'] = [str(model.bpt['b1'].performances[i]) for i,j in model.criteria.items()]
aaa[seed]['lambda'] = model.lbda
#[model.bpt['b1'].performances[i] for i,j in model.criteria.items()]
allm[seed]=dict()
allm[seed]['id'] = seed
current_model = 0
allm[seed]['mresults'] = dict()
for all_model in list(all_models)[1:]:
current_model += 1 # skipping the 1rst model already treated
allm[seed]['mresults'][current_model] = ["",""]
aa_learning2_allm = all_model.model.pessimist(pt_learning)
ca_learning_allm = compute_ca(aa_learning, aa_learning2_allm)
ca_learning_good_allm = compute_ca_good(aa_learning, aa_learning2_allm)
auc_learning_allm = all_model.model.auc(aa_learning, pt_learning)
# diff_learning_allm = compute_confusion_matrix(aa_learning, aa_learning2_allm,
# all_model.model.categories)
# Compute CA of test setting
if len(aa_test) > 0:
aa_test2_allm = all_model.model.pessimist(pt_test)
ca_test_allm = compute_ca(aa_test, aa_test2_allm)
ca_test_good_allm = compute_ca_good(aa_test, aa_test2_allm)
auc_test_allm = all_model.model.auc(aa_test, pt_test)
# diff_test_allm = compute_confusion_matrix(aa_test, aa_test2_allm,
# all_model.categories)
else:
ca_test_allm = 0
auc_test_allm = 0
ncat_allm = len(data.cats)
# diff_test_allm = OrderedDict([((a, b), 0) for a in all_model.categories \
# for b in all_model.model.categories])
# Compute CA of whole set
aa2_allm = all_model.model.pessimist(data.pt)
ca_allm = compute_ca(data.aa, aa2_allm)
ca_good_allm = compute_ca_good(data.aa, aa2_allm)
auc_allm = all_model.model.auc(data.aa, data.pt)
#diff_all_allm = compute_confusion_matrix(data.aa, aa2_allm, all_model.model.categories)
allm[seed]['mresults'][current_model][0] = 'na_learning,na_test,ca_learning,ca_test,ca_all,ca_learning_good,ca_test_good,ca_all_good,auc_learning,auc_test,auc_all'
allm[seed]['mresults'][current_model][1] = str(len(aa_learning)) + "," + str(len(aa_test)) + "," + str(ca_learning_allm) + "," + str(ca_test_allm) + "," + str(ca_allm) + "," + str(ca_learning_good_allm) + "," + str(ca_test_good_allm) + "," + str(ca_good_allm) + "," + str(auc_learning_allm) + "," + str(auc_test_allm) + "," + str(auc_allm)
#allm[seed]['mresults'][current_model][1] =
#all_model.model.bpt['b1'].performances
#all_model.model.cv.values()
#import pdb; pdb.set_trace()
# allm[seed][current_model]['na_learning'] = len(aa_learning)
# allm[seed][current_model]['na_test'] = len(na_test)
# allm[seed][current_model]['ca_learning'] = ca_learning_allm
# allm[seed][current_model]['ca_test'] = ca_test_allm
# allm[seed][current_model]['ca_all'] = ca_allm
# allm[seed][current_model]['ca_learning_good'] = ca_learning_good_allm
# allm[seed][current_model]['ca_test_good'] = ca_test_good_allm
# allm[seed][current_model]['ca_all_good'] = ca_good_allm
# allm[seed][current_model]['auc_learning'] = auc_learning_allm
# allm[seed][current_model]['auc_test'] = auc_test_allm
# allm[seed][current_model]['auc_all'] = auc_allm
for k, v in diff_learning.items():
t['learn_%s_%s' % (k[0], k[1])] = v
for k, v in diff_test.items():
t['test_%s_%s' % (k[0], k[1])] = v
for k, v in diff_all.items():
t['all_%s_%s' % (k[0], k[1])] = v
t['t_total'] = t_total
return t
def run_test(seed, data, pclearning, nloop, nmodels, nmeta):
random.seed(seed)
# Separate learning data and test data
pt_learning, pt_test = data.pt.split(2, [pclearning, 100 - pclearning])
aa_learning = data.aa.get_subset(pt_learning.keys())
aa_test = data.aa.get_subset(pt_test.keys())
# Initialize a random model
cat_profiles = generate_categories_profiles(data.cats)
worst = data.pt.get_worst(data.c)
best = data.pt.get_best(data.c)
b = generate_alternatives(len(data.cats) - 1, 'b')
bpt = None
cvs = None
lbda = None
model = MRSort(data.c, cvs, bpt, lbda, cat_profiles)
# Run the metaheuristic
t1 = time.time()
pt_sorted = SortedPerformanceTable(pt_learning)
# Algorithm
meta = meta_mrsort(nmodels,
model.criteria,
model.categories_profiles.to_categories(),
pt_sorted,
aa_learning,
seed=seed * 100)
#lp_weights = lp_weights,
#heur_profiles = heur_profiles,
#lp_veto_weights = lp_veto_weights,
#heur_veto_profiles = heur_veto_profiles,
for i in range(0, nloop):
model, ca_learning = meta.optimize(nmeta)
if ca_learning == 1:
break
t_total = time.time() - t1
aa_learning2 = model.pessimist(pt_learning)
ca_learning = compute_ca(aa_learning, aa_learning2)
auc_learning = model.auc(aa_learning, pt_learning)
diff_learning = compute_confusion_matrix(aa_learning, aa_learning2,
model.categories)
# Compute CA of test setting
if len(aa_test) > 0:
aa_test2 = model.pessimist(pt_test)
ca_test = compute_ca(aa_test, aa_test2)
auc_test = model.auc(aa_test, pt_test)
diff_test = compute_confusion_matrix(aa_test, aa_test2,
model.categories)
else:
ca_test = 0
auc_test = 0
ncat = len(data.cats)
diff_test = OrderedDict([((a, b), 0) for a in model.categories \
for b in model.categories])
# Compute CA of whole set
aa2 = model.pessimist(data.pt)
ca = compute_ca(data.aa, aa2)
auc = model.auc(data.aa, data.pt)
diff_all = compute_confusion_matrix(data.aa, aa2, model.categories)
t = test_result("%s-%d-%d-%d-%d-%d" %
(data.name, seed, nloop, nmodels, nmeta, pclearning))
model.id = 'learned'
aa_learning.id, aa_test.id = 'learning_set', 'test_set'
pt_learning.id, pt_test.id = 'learning_set', 'test_set'
save_to_xmcda("%s/%s.bz2" % (directory, t.test_name), model, aa_learning,
aa_test, pt_learning, pt_test)
t['seed'] = seed
t['na'] = len(data.a)
t['nc'] = len(data.c)
t['ncat'] = len(data.cats)
t['pclearning'] = pclearning
t['nloop'] = nloop
t['nmodels'] = nmodels
t['nmeta'] = nmeta
t['na_learning'] = len(aa_learning)
t['na_test'] = len(aa_test)
t['ca_learning'] = ca_learning
t['ca_test'] = ca_test
t['ca_all'] = ca
t['auc_learning'] = auc_learning
t['auc_test'] = auc_test
t['auc_all'] = auc
for k, v in diff_learning.items():
t['learn_%s_%s' % (k[0], k[1])] = v
for k, v in diff_test.items():
t['test_%s_%s' % (k[0], k[1])] = v
for k, v in diff_all.items():
t['all_%s_%s' % (k[0], k[1])] = v
t['t_total'] = t_total
return t
def test_meta_electre_tri_global(seed, na, nc, ncat, ns, na_gen, pcerrors,
max_oloops, nmodels, max_loops):
# Generate a random UTADIS model
model = generate_random_avfsort_model(nc, ncat, ns, ns, seed)
cats = model.cat_values.get_ordered_categories()
# Generate a set of alternatives
a = generate_alternatives(na)
pt = generate_random_performance_table(a, model.criteria)
aa = model.get_assignments(pt)
# Add errors in assignment examples
aa_err = aa.copy()
aa_erroned = add_errors_in_assignments(aa_err, cats, pcerrors / 100)
# Sort the performance table
pt_sorted = SortedPerformanceTable(pt)
t1 = time.time()
# Perform at max oloops on the set of metas
meta = MetaMRSortPop3(nmodels, model.criteria,
model.cat_values.to_categories(), pt_sorted, aa_err)
ca2_iter = [meta.metas[0].ca] + [1] * (max_loops)
nloops = 0
for i in range(0, max_loops):
model2, ca2 = meta.optimize(max_oloops)
ca2_iter[i + 1] = ca2
nloops += 1
if ca2 == 1:
break
t_total = time.time() - t1
# Determine the number of erroned alternatives badly assigned
aa2 = model2.pessimist(pt)
ok_errors = ok2_errors = ok = 0
for alt in a:
if aa(alt.id) == aa2(alt.id):
if alt.id in aa_erroned:
ok_errors += 1
ok += 1
if aa_err(alt.id) == aa2(alt.id) and alt.id in aa_erroned:
ok2_errors += 1
total = len(a)
ca2_errors = ok2_errors / total
ca_best = ok / total
ca_errors = ok_errors / total
# Generate alternatives for the generalization
a_gen = generate_alternatives(na_gen)
pt_gen = generate_random_performance_table(a_gen, model.criteria)
aa_gen = model.get_assignments(pt_gen)
aa_gen2 = model2.pessimist(pt_gen)
ca_gen = compute_ca(aa_gen, aa_gen2)
# Save all infos in test_result class
t = test_result(
"%s-%d-%d-%d-%d-%g-%d-%d-%d" %
(seed, na, nc, ncat, na_gen, pcerrors, max_loops, nmodels, max_oloops))
# Input params
t['seed'] = seed
t['na'] = na
t['nc'] = nc
t['ncat'] = ncat
t['ns'] = ns
t['na_gen'] = na_gen
t['pcerrors'] = pcerrors
t['max_loops'] = max_loops
t['nmodels'] = nmodels
t['max_oloops'] = max_oloops
# Ouput params
t['ca_best'] = ca_best
t['ca_errors'] = ca_errors
t['ca2_best'] = ca2
t['ca2_errors'] = ca2_errors
t['ca_gen'] = ca_gen
t['nloops'] = nloops
t['t_total'] = t_total
t['ca2_iter'] = ca2_iter
return t
def mrsort_meta_inference(indir, outdir):
if indir is None or not os.path.isdir(indir):
log_error("Invalid input directory (%s)" % indir)
return 1
if outdir is None or not os.path.isdir(outdir):
log_error("Invalid output directory (%s)" % outdir)
return 1
model, assignments, pt, params = parse_input_files(indir)
if model is None or assignments is None or pt is None or params is None:
log_error("Error while parsing input files")
write_message_error(outdir + '/messages.xml')
return 1
if 'solver' in params:
solver = params['solver'].value
else:
solver = DEFAULT_SOLVER
if solver not in SOLVERS_LIST:
log_error("Invalid solver selected (%s)" % solver)
write_message_error(outdir + '/messages.xml')
return 1
os.environ["SOLVER"] = solver
if 'nmodels' in params:
nmodels = params['nmodels'].value
else:
log_error("Invalid number of models (nmodels)")
write_message_error(outdir + '/messages.xml')
return 1
if 'niter_meta' in params:
niter_meta = params['niter_meta'].value
else:
log_error("Invalid number of iterations (niter_meta)")
write_message_error(outdir + '/messages.xml')
return 1
if 'niter_heur' in params:
niter_heur = params['niter_heur'].value
else:
log_error("Invalid number of iterations (niter_heur)")
write_message_error(outdir + '/messages.xml')
return 1
try:
pt_sorted = SortedPerformanceTable(pt)
meta = MetaMRSortPop3(nmodels, model.criteria,
model.categories_profiles.to_categories(),
pt_sorted, assignments)
for i in range(niter_meta):
model, ca = meta.optimize(niter_heur)
assignments2 = model.get_assignments(pt)
compat = get_compat_alternatives(assignments, assignments2)
compat = to_alternatives(compat)
msg_solver = "Solver: %s" % solver
msg_ca = "CA: %g" % (len(compat) / len(assignments))
profiles = to_alternatives(model.categories_profiles.keys())
xmcda_lbda = lambda_to_xmcda(model.lbda)
write_xmcda_file(outdir + '/lambda.xml', xmcda_lbda)
write_xmcda_file(outdir + '/cat_profiles.xml',
model.categories_profiles.to_xmcda())
write_xmcda_file(outdir + '/crit_weights.xml', model.cv.to_xmcda())
write_xmcda_file(outdir + '/profiles_perfs.xml', model.bpt.to_xmcda())
write_xmcda_file(outdir + '/compatible_alts.xml', compat.to_xmcda())
write_message_ok(outdir + '/messages.xml', [msg_solver, msg_ca])
except:
log_error("Cannot solve problem")
log_error(traceback.format_exc())
write_message_error(outdir + '/messages.xml')
return 0
t1 = time.time()
if algo == 'meta_mrsort':
heur_init_profiles = HeurMRSortInitProfiles
lp_weights = LpMRSortWeights
heur_profiles = MetaMRSortProfiles4
elif algo == 'meta_mrsortc':
heur_init_profiles = HeurMRSortInitProfiles
lp_weights = LpMRSortMobius
heur_profiles = MetaMRSortProfilesChoquet
if algo == 'meta_mrsort' or algo == 'meta_mrsortc':
model_type = 'mrsort'
cat_profiles = generate_categories_profiles(data.cats)
model = MRSort(data.c, None, None, None, cat_profiles)
pt_sorted = SortedPerformanceTable(data.pt)
meta = MetaMRSortPop3(nmodels, model.criteria,
model.categories_profiles.to_categories(), pt_sorted,
data.aa, heur_init_profiles, lp_weights,
heur_profiles)
for i in range(0, nloop):
model, ca_learning = meta.optimize(nmeta)
print(ca_learning)
if ca_learning == 1:
break
elif algo == 'mip_mrsort':
model_type = 'mrsort'
cat_profiles = generate_categories_profiles(data.cats)
model = MRSort(data.c, None, None, None, cat_profiles)
def test_meta_electre_tri_global(seed, na, nc, ncat, na_gen, pcerrors,
max_oloops, nmodels, max_loops):
# Generate a random ELECTRE TRI BM model
if random_model_type == 'mrsort':
model = generate_random_mrsort_model(nc, ncat, seed)
elif random_model_type == 'ncs':
model = generate_random_mrsort_choquet_model(nc, ncat, 2, seed)
elif random_model_type == 'mrsortcv':
model = generate_random_mrsort_model_with_coalition_veto2(
nc, ncat, seed)
# Generate a set of alternatives
a = generate_alternatives(na)
pt = generate_random_performance_table(a, model.criteria)
aa = model.pessimist(pt)
# Add errors in assignment examples
aa_err = aa.copy()
categories = model.categories_profiles.to_categories()
aa_erroned = add_errors_in_assignments_proba(aa_err, model.categories,
pcerrors / 100)
na_err = len(aa_erroned)
# Sort the performance table
pt_sorted = SortedPerformanceTable(pt)
meta = algo(nmodels, model.criteria, categories, pt_sorted, aa)
metas_sorted = meta.sort_models()
ca2_iter = [metas_sorted[0].ca] + [1] * (max_loops)
t1 = time.time()
for i in range(0, max_loops):
model2, ca2_best = meta.optimize(max_oloops)
ca2_iter[i + 1] = ca2_best
if ca2_best == 1:
break
nloops = i + 1
t_total = time.time() - t1
aa2 = model2.pessimist(pt)
ok_errors = ok2_errors = ok = 0
for alt in a:
if aa(alt.id) == aa2(alt.id):
if alt.id in aa_erroned:
ok_errors += 1
ok += 1
if aa_err(alt.id) == aa2(alt.id) and alt.id in aa_erroned:
ok2_errors += 1
total = len(a)
ca2_errors = ok2_errors / total
ca_best = ok / total
ca_errors = ok_errors / total
# Generate alternatives for the generalization
a_gen = generate_alternatives(na_gen)
pt_gen = generate_random_performance_table(a_gen, model.criteria)
aa_gen = model.pessimist(pt_gen)
aa_gen2 = model2.pessimist(pt_gen)
ca_gen = compute_ca(aa_gen, aa_gen2)
aa_gen_err = aa_gen.copy()
aa_gen_erroned = add_errors_in_assignments_proba(aa_gen_err,
model.categories,
pcerrors / 100)
aa_gen2 = model2.pessimist(pt_gen)
ca_gen_err = compute_ca(aa_gen_err, aa_gen2)
# Save all infos in test_result class
t = test_result(
"%s-%d-%d-%d-%d-%g-%d-%d-%d" %
(seed, na, nc, ncat, na_gen, pcerrors, max_loops, nmodels, max_oloops))
model.id = 'initial'
model2.id = 'learned'
pt.id, pt_gen.id = 'learning_set', 'test_set'
save_to_xmcda("%s/%s.bz2" % (directory, t.test_name), model, model2, pt,
pt_gen)
# Input params
t['seed'] = seed
t['na'] = na
t['nc'] = nc
t['ncat'] = ncat
t['na_gen'] = na_gen
t['pcerrors'] = pcerrors
t['max_loops'] = max_loops
t['nmodels'] = nmodels
t['max_oloops'] = max_oloops
# Ouput params
t['na_err'] = na_err
t['ca_best'] = ca_best
t['ca_errors'] = ca_errors
t['ca2_best'] = ca2_best
t['ca2_errors'] = ca2_errors
t['ca_gen'] = ca_gen
t['ca_gen_err'] = ca_gen_err
t['nloops'] = nloops
t['t_total'] = t_total
t['ca2_iter'] = ca2_iter
return t
def test_meta_electre_tri_profiles(seed, na, nc, ncat, na_gen, pcerrors,
max_loops):
# Generate an ELECTRE TRI model and assignment examples
model = generate_random_mrsort_model(nc, ncat, seed)
model2 = model.copy()
# Generate a first set of alternatives
a = generate_alternatives(na)
pt = generate_random_performance_table(a, model.criteria)
aa = model.pessimist(pt)
# Initiate model with random profiles
model2.bpt = generate_random_profiles(model.profiles, model.criteria)
# Add errors in assignment examples
aa_err = aa.copy()
aa_erroned = add_errors_in_assignments(aa_err, model.categories,
pcerrors / 100)
# Sort the performance table
pt_sorted = SortedPerformanceTable(pt)
t1 = time.time()
# Run the algorithm
meta = algo(model2, pt_sorted, aa_err)
ca2_iter = [1] * (max_loops + 1)
aa2 = model2.pessimist(pt)
ca2 = compute_ca(aa_err, aa2)
ca2_best = ca2
best_bpt = model2.bpt.copy()
ca2_iter[0] = ca2
nloops = 0
for k in range(max_loops):
if ca2_best == 1:
break
meta.optimize()
nloops += 1
aa2 = meta.aa
ca2 = compute_ca(aa_err, aa2)
ca2_iter[k + 1] = ca2
if ca2 > ca2_best:
ca2_best = ca2
best_bpt = model2.bpt.copy()
t_total = time.time() - t1
# Determine the number of erroned alternatives badly assigned
model2.bpt = best_bpt
aa2 = model2.pessimist(pt)
ok = ok_errors = ok2_errors = 0
for alt in a:
if aa_err(alt.id) == aa2(alt.id) and alt.id in aa_erroned:
ok2_errors += 1
if aa(alt.id) == aa2(alt.id):
if alt.id in aa_erroned:
ok_errors += 1
ok += 1
total = len(a)
ca_best = ok / total
ca_best_errors = ok_errors / total
ca2_best_errors = ok2_errors / total
# Generate alternatives for the generalization
a_gen = generate_alternatives(na_gen)
pt_gen = generate_random_performance_table(a_gen, model.criteria)
aa_gen = model.pessimist(pt_gen)
aa_gen2 = model2.pessimist(pt_gen)
ca_gen = compute_ca(aa_gen, aa_gen2)
# Save all infos in test_result class
t = test_result("%s-%d-%d-%d-%d-%g-%d" %
(seed, na, nc, ncat, na_gen, pcerrors, max_loops))
# Input params
t['seed'] = seed
t['na'] = na
t['nc'] = nc
t['ncat'] = ncat
t['na_gen'] = na_gen
t['pcerrors'] = pcerrors
t['max_loops'] = max_loops
# Ouput params
t['ca_best'] = ca_best
t['ca_best_errors'] = ca_best_errors
t['ca2_best'] = ca2_best
t['ca2_best_errors'] = ca2_best_errors
t['ca_gen'] = ca_gen
t['nloops'] = nloops
t['t_total'] = t_total
t['ca2_iter'] = ca2_iter
return t
