def test001(self):
random.seed(1)
c = generate_criteria(4)
cv1 = CriterionValue('c1', 0.25)
cv2 = CriterionValue('c2', 0.25)
cv3 = CriterionValue('c3', 0.25)
cv4 = CriterionValue('c4', 0.25)
cv = CriteriaValues([cv1, cv2, cv3, cv4])
cat = generate_categories(2)
cps = generate_categories_profiles(cat)
bp = AlternativePerformances('b1', {'c1': 0.5, 'c2': 0.5,
'c3': 0.5, 'c4': 0.5})
bpt = PerformanceTable([bp])
lbda = 0.5
etri = MRSort(c, cv, bpt, 0.5, cps)
a = generate_alternatives(1000)
pt = generate_random_performance_table(a, c)
aas = etri.pessimist(pt)
for aa in aas:
w = 0
perfs = pt[aa.id].performances
for c, val in perfs.items():
if val >= bp.performances[c]:
w += cv[c].value
if aa.category_id == 'cat2':
self.assertLess(w, lbda)
else:
self.assertGreaterEqual(w, lbda)
def test002(self):
random.seed(2)
c = generate_criteria(4)
cv1 = CriterionValue('c1', 0.25)
cv2 = CriterionValue('c2', 0.25)
cv3 = CriterionValue('c3', 0.25)
cv4 = CriterionValue('c4', 0.25)
cv = CriteriaValues([cv1, cv2, cv3, cv4])
cat = generate_categories(3)
cps = generate_categories_profiles(cat)
bp1 = AlternativePerformances('b1', {
'c1': 0.75,
'c2': 0.75,
'c3': 0.75,
'c4': 0.75
})
bp2 = AlternativePerformances('b2', {
'c1': 0.25,
'c2': 0.25,
'c3': 0.25,
'c4': 0.25
})
bpt = PerformanceTable([bp1, bp2])
lbda = 0.5
etri = MRSort(c, cv, bpt, 0.5, cps)
a = generate_alternatives(1000)
pt = generate_random_performance_table(a, c)
aas = etri.pessimist(pt)
for aa in aas:
w1 = w2 = 0
perfs = pt[aa.id].performances
for c, val in perfs.items():
if val >= bp1.performances[c]:
w1 += cv[c].value
if val >= bp2.performances[c]:
w2 += cv[c].value
if aa.category_id == 'cat3':
self.assertLess(w1, lbda)
self.assertLess(w2, lbda)
elif aa.category_id == 'cat2':
self.assertLess(w1, lbda)
self.assertGreaterEqual(w2, lbda)
else:
self.assertGreaterEqual(w1, lbda)
self.assertGreaterEqual(w2, lbda)
fcoalitions = open('%s-wcoalitions.dat' % bname, 'w+')
mwinning = compute_minimal_winning_coalitions(lp.fmins)
for win in mwinning:
win = list(win)
win.sort(key=criteria_order.index)
buf = ""
for c in win:
buf += "%s, " % criteria_names[c]
print('[%s]' % buf[:-2], file=fcoalitions)
fcoalitions.close()
pt_learning = PerformanceTable().from_xmcda(root, 'learning_set')
aa_learning = AlternativesAssignments().from_xmcda(root, 'learning_set')
aa_learned = m.pessimist(pt_learning)
fca = open('%s-ca_learning.dat' % bname, 'w+')
ca = compute_ca(aa_learning, aa_learned)
print("%.4f" % ca, end='', file=fca)
fca.close()
fauc = open('%s-auc_learning.dat' % bname, 'w+')
auc = m.auc(aa_learning, pt_learning)
print("%.4f" % auc, end='', file=fauc)
fauc.close()
ca_learning.append(ca)
auc_learning.append(auc)
pt_test = PerformanceTable().from_xmcda(root, 'test_set')
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):
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 ELECTRE-TRI BM 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 linear program
t1 = time.time()
mip = mip_mrsort(model, pt_learning, aa_learning)
obj = mip.solve()
t_total = time.time() - t1
# CA learning set
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" % (data.name, seed, 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['na_learning'] = len(aa_learning)
t['na_test'] = len(aa_test)
t['obj'] = obj
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 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, na_gen, pcerrors):
# Generate a random ELECTRE TRI BM model
if random_model_type == 'default':
model = generate_random_mrsort_model(nc, ncat, seed)
elif random_model_type == 'choquet':
model = generate_random_mrsort_choquet_model(nc, ncat, 2, 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()
aa_erroned = add_errors_in_assignments_proba(aa_err, model.categories,
pcerrors / 100)
na_err = len(aa_erroned)
# Run the MIP
t1 = time.time()
model2 = MRSort(model.criteria, None, None, None,
model.categories_profiles, None, None, None)
mip = MipMRSort(model2, pt, aa_err)
obj = mip.solve()
ca2_best = obj / na
aa2 = model2.get_assignments(pt)
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.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" %
(seed, na, nc, ncat, na_gen, pcerrors))
model.id = 'initial'
model2.id = 'learned'
pt.id, pt_gen.id = 'learning_set', 'test_set'
aa.id = 'aa'
aa_err.id = 'aa_err'
save_to_xmcda("%s/%s.bz2" % (directory, t.test_name), model, model2, pt,
pt_gen, aa, aa_err)
# Input params
t['seed'] = seed
t['na'] = na
t['nc'] = nc
t['ncat'] = ncat
t['na_gen'] = na_gen
t['pcerrors'] = pcerrors
# 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['t_total'] = t_total
return t
for i, xmcda in enumerate(xmcda_csets):
result = {}
fmins = CriteriaSets().from_xmcda(xmcda)
result['fmins'] = fmins
result['vector'] = "".join(map(str, sorted([len(fmin)
for fmin in sorted(fmins, key = len)])))
print("\n%d. Fmin: %s" % (i + 1, ', '.join("%s" % f for f in fmins)))
pt, aa = generate_binary_performance_table_and_assignments(criteria, cat,
fmins)
aa.id = 'aa'
a = Alternatives([Alternative(a.id) for a in aa])
model = MRSort(criteria, None, bpt, None, cps)
mip = MipMRSortWeights(model, pt, aa)
obj = mip.solve()
aa2 = model.pessimist(pt)
aa2.id = 'aa_add'
print("MipMRSortWeights: Objective: %d (/%d)" % (obj, len(aa)))
anok = [a.id for a in aa if a.category_id != aa2[a.id].category_id]
print("Alternative not restored: %s" % ','.join("%s" % a for a in anok))
print(model.cv)
print("lambda: %s" % model.lbda)
result['obj_weights'] = obj
mip = MipMRSortMobius(model, pt, aa)
obj = mip.solve()
aa3 = model.pessimist(pt)
aa3.id = 'aa_capa'
print("MipMRSortMobius: Objective: %d (/%d)" % (obj, len(aa)))
anok = [a.id for a in aa if a.category_id != aa3[a.id].category_id]
print("Alternative not restored: %s" % ','.join("%s" % a for a in anok))
def run_test(seed, data, pclearning):
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 ELECTRE-TRI BM 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 linear program
t1 = time.time()
mip = mip_mrsort(model, pt_learning, aa_learning)
obj = mip.solve()
t_total = time.time() - t1
# CA learning set
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" % (data.name, seed, 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['na_learning'] = len(aa_learning)
t['na_test'] = len(aa_test)
t['obj'] = obj
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_mip_mrsort_vc(seed, na, nc, ncat, na_gen, veto_param, pcerrors):
# Generate a random ELECTRE TRI BM model
if vetot == 'binary':
model = generate_random_mrsort_model_with_binary_veto(
nc, ncat, seed, veto_func=veto_func, veto_param=veto_param)
elif vetot == 'coalition':
model = generate_random_mrsort_model_with_coalition_veto(
nc,
ncat,
seed,
veto_weights=indep_veto_weights,
veto_func=veto_func,
veto_param=veto_param)
# Generate a set of alternatives
a = generate_alternatives(na)
pt = generate_random_performance_table(a, model.criteria)
aa = model.pessimist(pt)
nv_m1_learning = sum([model.count_veto_pessimist(ap) for ap in pt])
# Add errors in assignment examples
aa_err = aa.copy()
aa_erroned = add_errors_in_assignments_proba(aa_err, model.categories,
pcerrors / 100)
na_err = len(aa_erroned)
# Run the MIP
t1 = time.time()
model2 = MRSort(model.criteria, None, None, None,
model.categories_profiles, None, None, None)
if algo == MipMRSortVC and vetot == 'binary':
w = {c.id: 1 / len(model.criteria) for c in model.criteria}
w1 = w.keys()[0]
w[w1] += 1 - sum(w.values())
model2.veto_weights = CriteriaValues(
[CriterionValue(c.id, w[c.id]) for c in model.criteria])
model2.veto_lbda = min(w.values())
if algo == MipMRSortVC:
mip = MipMRSortVC(model2, pt, aa, indep_veto_weights)
else:
mip = MipMRSort(model2, pt, aa)
mip.solve()
t_total = time.time() - t1
# Determine the number of erroned alternatives badly assigned
aa2 = model2.pessimist(pt)
nv_m2_learning = sum([model2.count_veto_pessimist(ap) for ap in pt])
cmatrix_learning = compute_confusion_matrix(aa, aa2, model.categories)
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)
nv_m1_gen = sum([model.count_veto_pessimist(ap) for ap in pt_gen])
nv_m2_gen = sum([model2.count_veto_pessimist(ap) for ap in pt_gen])
if len(aa_gen) > 0:
cmatrix_gen = compute_confusion_matrix(aa_gen, aa_gen2,
model.categories)
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-%s-%d" %
(seed, na, nc, ncat, na_gen, veto_param, pcerrors))
model.id = 'initial'
model2.id = 'learned'
a.id, pt.id = 'learning_set', 'learning_set'
aa.id, aa2.id = 'learning_set_m1', 'learning_set_m2'
a_gen.id, pt_gen.id = 'test_set', 'test_set'
aa_gen.id, aa_gen2.id = 'test_set_m1', 'test_set_m2'
save_to_xmcda("%s/%s.bz2" % (directory, t.test_name), model, model2, a,
a_gen, pt, pt_gen, aa, aa2, aa_gen, aa_gen2)
# Input params
t['seed'] = seed
t['na'] = na
t['nc'] = nc
t['ncat'] = ncat
t['na_gen'] = na_gen
t['veto_param'] = veto_param
t['pcerrors'] = pcerrors
# Ouput params
t['na_err'] = na_err
t['nv_m1_learning'] = nv_m1_learning
t['nv_m2_learning'] = nv_m2_learning
t['nv_m1_gen'] = nv_m1_gen
t['nv_m2_gen'] = nv_m2_gen
t['ca_best'] = ca_best
t['ca_errors'] = ca_errors
t['ca_gen'] = ca_gen
t['ca_gen_err'] = ca_gen_err
t['t_total'] = t_total
for k, v in cmatrix_learning.items():
t['learn_%s_%s' % (k[0], k[1])] = v
for k, v in cmatrix_gen.items():
t['test_%s_%s' % (k[0], k[1])] = v
return t
'c2': 11,
'c3': 7,
'c4': 11,
'c5': 11
})
a45 = AlternativePerformances('a45', {
'c1': 7,
'c2': 7,
'c3': 11,
'c4': 11,
'c5': 11
})
pt = PerformanceTable([eval("a%d" % i) for i in range(1, 46)])
aa = model.pessimist(pt)
print(aa)
nveto = [model.count_veto_pessimist(eval("a%d" % i)) for i in range(1, 46)]
print("Number of veto effects: %d" % sum(nveto))
model2 = MRSort(c, None, None, None, cps, None, None, None)
#model2.veto_lbda = model.veto_lbda
#model2.veto_weights = model.veto_weights
mip = MipMRSortVC(model2, pt, aa)
#mip = MipMRSort(model2, pt, aa)
mip.solve()
print(model2.cv)
print(model2.bpt)
def test001(self):
c = generate_criteria(5)
w1 = CriterionValue('c1', 0.2)
w2 = CriterionValue('c2', 0.2)
w3 = CriterionValue('c3', 0.2)
w4 = CriterionValue('c4', 0.2)
w5 = CriterionValue('c5', 0.2)
w = CriteriaValues([w1, w2, w3, w4, w5])
b1 = AlternativePerformances('b1', {
'c1': 10,
'c2': 10,
'c3': 10,
'c4': 10,
'c5': 10
})
bpt = PerformanceTable([b1])
cat = generate_categories(2)
cps = generate_categories_profiles(cat)
vb1 = AlternativePerformances('b1', {
'c1': 2,
'c2': 2,
'c3': 2,
'c4': 2,
'c5': 2
}, 'b1')
v = PerformanceTable([vb1])
vw = w.copy()
a1 = AlternativePerformances('a1', {
'c1': 9,
'c2': 9,
'c3': 9,
'c4': 9,
'c5': 11
})
a2 = AlternativePerformances('a2', {
'c1': 9,
'c2': 9,
'c3': 9,
'c4': 11,
'c5': 9
})
a3 = AlternativePerformances('a3', {
'c1': 9,
'c2': 9,
'c3': 9,
'c4': 11,
'c5': 11
})
a4 = AlternativePerformances('a4', {
'c1': 9,
'c2': 9,
'c3': 11,
'c4': 9,
'c5': 9
})
a5 = AlternativePerformances('a5', {
'c1': 9,
'c2': 9,
'c3': 11,
'c4': 9,
'c5': 11
})
a6 = AlternativePerformances('a6', {
'c1': 9,
'c2': 9,
'c3': 11,
'c4': 11,
'c5': 9
})
a7 = AlternativePerformances('a7', {
'c1': 9,
'c2': 9,
'c3': 11,
'c4': 11,
'c5': 11
})
a8 = AlternativePerformances('a8', {
'c1': 9,
'c2': 11,
'c3': 9,
'c4': 9,
'c5': 9
})
a9 = AlternativePerformances('a9', {
'c1': 9,
'c2': 11,
'c3': 9,
'c4': 9,
'c5': 11
})
a10 = AlternativePerformances('a10', {
'c1': 9,
'c2': 11,
'c3': 9,
'c4': 11,
'c5': 9
})
a11 = AlternativePerformances('a11', {
'c1': 9,
'c2': 11,
'c3': 9,
'c4': 11,
'c5': 11
})
a12 = AlternativePerformances('a12', {
'c1': 9,
'c2': 11,
'c3': 11,
'c4': 9,
'c5': 9
})
a13 = AlternativePerformances('a13', {
'c1': 9,
'c2': 11,
'c3': 11,
'c4': 9,
'c5': 11
})
a14 = AlternativePerformances('a14', {
'c1': 9,
'c2': 11,
'c3': 11,
'c4': 11,
'c5': 9
})
a15 = AlternativePerformances('a15', {
'c1': 9,
'c2': 11,
'c3': 11,
'c4': 11,
'c5': 11
})
a16 = AlternativePerformances('a16', {
'c1': 11,
'c2': 9,
'c3': 9,
'c4': 9,
'c5': 9
})
a17 = AlternativePerformances('a17', {
'c1': 11,
'c2': 9,
'c3': 9,
'c4': 9,
'c5': 11
})
a18 = AlternativePerformances('a18', {
'c1': 11,
'c2': 9,
'c3': 9,
'c4': 11,
'c5': 9
})
a19 = AlternativePerformances('a19', {
'c1': 11,
'c2': 9,
'c3': 9,
'c4': 11,
'c5': 11
})
a20 = AlternativePerformances('a20', {
'c1': 11,
'c2': 9,
'c3': 11,
'c4': 9,
'c5': 9
})
a21 = AlternativePerformances('a21', {
'c1': 11,
'c2': 9,
'c3': 11,
'c4': 9,
'c5': 11
})
a22 = AlternativePerformances('a22', {
'c1': 11,
'c2': 9,
'c3': 11,
'c4': 11,
'c5': 9
})
a23 = AlternativePerformances('a23', {
'c1': 11,
'c2': 9,
'c3': 11,
'c4': 11,
'c5': 11
})
a24 = AlternativePerformances('a24', {
'c1': 11,
'c2': 11,
'c3': 9,
'c4': 9,
'c5': 9
})
a25 = AlternativePerformances('a25', {
'c1': 11,
'c2': 11,
'c3': 9,
'c4': 9,
'c5': 11
})
a26 = AlternativePerformances('a26', {
'c1': 11,
'c2': 11,
'c3': 9,
'c4': 11,
'c5': 9
})
a27 = AlternativePerformances('a27', {
'c1': 11,
'c2': 11,
'c3': 9,
'c4': 11,
'c5': 11
})
a28 = AlternativePerformances('a28', {
'c1': 11,
'c2': 11,
'c3': 11,
'c4': 9,
'c5': 9
})
a29 = AlternativePerformances('a29', {
'c1': 11,
'c2': 11,
'c3': 11,
'c4': 9,
'c5': 11
})
a30 = AlternativePerformances('a30', {
'c1': 11,
'c2': 11,
'c3': 11,
'c4': 11,
'c5': 9
})
a31 = AlternativePerformances('a31', {
'c1': 11,
'c2': 11,
'c3': 11,
'c4': 11,
'c5': 7
})
a32 = AlternativePerformances('a32', {
'c1': 11,
'c2': 11,
'c3': 11,
'c4': 7,
'c5': 11
})
a33 = AlternativePerformances('a33', {
'c1': 11,
'c2': 11,
'c3': 7,
'c4': 11,
'c5': 11
})
a34 = AlternativePerformances('a34', {
'c1': 11,
'c2': 7,
'c3': 11,
'c4': 11,
'c5': 11
})
a35 = AlternativePerformances('a35', {
'c1': 7,
'c2': 11,
'c3': 11,
'c4': 11,
'c5': 11
})
a36 = AlternativePerformances('a36', {
'c1': 11,
'c2': 11,
'c3': 11,
'c4': 7,
'c5': 7
})
a37 = AlternativePerformances('a37', {
'c1': 11,
'c2': 11,
'c3': 7,
'c4': 11,
'c5': 7
})
a38 = AlternativePerformances('a38', {
'c1': 11,
'c2': 7,
'c3': 11,
'c4': 11,
'c5': 7
})
a39 = AlternativePerformances('a39', {
'c1': 7,
'c2': 11,
'c3': 11,
'c4': 11,
'c5': 7
})
a40 = AlternativePerformances('a40', {
'c1': 11,
'c2': 11,
'c3': 7,
'c4': 7,
'c5': 11
})
a41 = AlternativePerformances('a41', {
'c1': 11,
'c2': 7,
'c3': 11,
'c4': 7,
'c5': 11
})
a42 = AlternativePerformances('a42', {
'c1': 7,
'c2': 11,
'c3': 11,
'c4': 7,
'c5': 11
})
a43 = AlternativePerformances('a43', {
'c1': 11,
'c2': 7,
'c3': 7,
'c4': 11,
'c5': 11
})
a44 = AlternativePerformances('a44', {
'c1': 7,
'c2': 11,
'c3': 7,
'c4': 11,
'c5': 11
})
a45 = AlternativePerformances('a45', {
'c1': 7,
'c2': 7,
'c3': 11,
'c4': 11,
'c5': 11
})
pt = PerformanceTable([
a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15,
a16, a17, a18, a19, a20, a21, a22, a23, a24, a25, a26, a27, a28,
a29, a30, a31, a32, a33, a34, a35, a36, a37, a38, a39, a40, a41,
a42, a43, a44, a45
])
ap1 = AlternativeAssignment('a1', 'cat2')
ap2 = AlternativeAssignment('a2', 'cat2')
ap3 = AlternativeAssignment('a3', 'cat2')
ap4 = AlternativeAssignment('a4', 'cat2')
ap5 = AlternativeAssignment('a5', 'cat2')
ap6 = AlternativeAssignment('a6', 'cat2')
ap7 = AlternativeAssignment('a7', 'cat1')
ap8 = AlternativeAssignment('a8', 'cat2')
ap9 = AlternativeAssignment('a9', 'cat2')
ap10 = AlternativeAssignment('a10', 'cat2')
ap11 = AlternativeAssignment('a11', 'cat1')
ap12 = AlternativeAssignment('a12', 'cat2')
ap13 = AlternativeAssignment('a13', 'cat1')
ap14 = AlternativeAssignment('a14', 'cat1')
ap15 = AlternativeAssignment('a15', 'cat1')
ap16 = AlternativeAssignment('a16', 'cat2')
ap17 = AlternativeAssignment('a17', 'cat2')
ap18 = AlternativeAssignment('a18', 'cat2')
ap19 = AlternativeAssignment('a19', 'cat1')
ap20 = AlternativeAssignment('a20', 'cat2')
ap21 = AlternativeAssignment('a21', 'cat1')
ap22 = AlternativeAssignment('a22', 'cat1')
ap23 = AlternativeAssignment('a23', 'cat1')
ap24 = AlternativeAssignment('a24', 'cat2')
ap25 = AlternativeAssignment('a25', 'cat1')
ap26 = AlternativeAssignment('a26', 'cat1')
ap27 = AlternativeAssignment('a27', 'cat1')
ap28 = AlternativeAssignment('a28', 'cat1')
ap29 = AlternativeAssignment('a29', 'cat1')
ap30 = AlternativeAssignment('a30', 'cat1')
ap31 = AlternativeAssignment('a31', 'cat1')
ap32 = AlternativeAssignment('a32', 'cat1')
ap33 = AlternativeAssignment('a33', 'cat1')
ap34 = AlternativeAssignment('a34', 'cat1')
ap35 = AlternativeAssignment('a35', 'cat1')
ap36 = AlternativeAssignment('a36', 'cat2')
ap37 = AlternativeAssignment('a37', 'cat2')
ap38 = AlternativeAssignment('a38', 'cat2')
ap39 = AlternativeAssignment('a39', 'cat2')
ap40 = AlternativeAssignment('a40', 'cat2')
ap41 = AlternativeAssignment('a41', 'cat2')
ap42 = AlternativeAssignment('a42', 'cat2')
ap43 = AlternativeAssignment('a43', 'cat2')
ap44 = AlternativeAssignment('a44', 'cat2')
ap45 = AlternativeAssignment('a45', 'cat2')
aa = AlternativesAssignments([
ap1, ap2, ap3, ap4, ap5, ap6, ap7, ap8, ap9, ap10, ap11, ap12,
ap13, ap14, ap15, ap16, ap17, ap18, ap19, ap20, ap21, ap22, ap23,
ap24, ap25, ap26, ap27, ap28, ap29, ap30, ap31, ap32, ap33, ap34,
ap35, ap36, ap37, ap38, ap39, ap40, ap41, ap42, ap43, ap44, ap45
])
model = MRSort(c, w, bpt, 0.6, cps, v, vw, 0.4)
aa2 = model.pessimist(pt)
ok = compare_assignments(aa, aa2)
self.assertEqual(ok, 1, "One or more alternatives were wrongly "
"assigned")
def test001(self):
c = generate_criteria(5)
w1 = CriterionValue('c1', 0.2)
w2 = CriterionValue('c2', 0.2)
w3 = CriterionValue('c3', 0.2)
w4 = CriterionValue('c4', 0.2)
w5 = CriterionValue('c5', 0.2)
w = CriteriaValues([w1, w2, w3, w4, w5])
b1 = AlternativePerformances('b1', {'c1': 10, 'c2': 10, 'c3': 10, 'c4': 10, 'c5': 10})
bpt = PerformanceTable([b1])
cat = generate_categories(2)
cps = generate_categories_profiles(cat)
vb1 = AlternativePerformances('b1', {'c1': 2, 'c2': 2, 'c3': 2, 'c4': 2, 'c5': 2}, 'b1')
v = PerformanceTable([vb1])
vw = w.copy()
a1 = AlternativePerformances('a1', {'c1': 9, 'c2': 9, 'c3': 9, 'c4': 9, 'c5': 11})
a2 = AlternativePerformances('a2', {'c1': 9, 'c2': 9, 'c3': 9, 'c4': 11, 'c5': 9})
a3 = AlternativePerformances('a3', {'c1': 9, 'c2': 9, 'c3': 9, 'c4': 11, 'c5': 11})
a4 = AlternativePerformances('a4', {'c1': 9, 'c2': 9, 'c3': 11, 'c4': 9, 'c5': 9})
a5 = AlternativePerformances('a5', {'c1': 9, 'c2': 9, 'c3': 11, 'c4': 9, 'c5': 11})
a6 = AlternativePerformances('a6', {'c1': 9, 'c2': 9, 'c3': 11, 'c4': 11, 'c5': 9})
a7 = AlternativePerformances('a7', {'c1': 9, 'c2': 9, 'c3': 11, 'c4': 11, 'c5': 11})
a8 = AlternativePerformances('a8', {'c1': 9, 'c2': 11, 'c3': 9, 'c4': 9, 'c5': 9})
a9 = AlternativePerformances('a9', {'c1': 9, 'c2': 11, 'c3': 9, 'c4': 9, 'c5': 11})
a10 = AlternativePerformances('a10', {'c1': 9, 'c2': 11, 'c3': 9, 'c4': 11, 'c5': 9})
a11 = AlternativePerformances('a11', {'c1': 9, 'c2': 11, 'c3': 9, 'c4': 11, 'c5': 11})
a12 = AlternativePerformances('a12', {'c1': 9, 'c2': 11, 'c3': 11, 'c4': 9, 'c5': 9})
a13 = AlternativePerformances('a13', {'c1': 9, 'c2': 11, 'c3': 11, 'c4': 9, 'c5': 11})
a14 = AlternativePerformances('a14', {'c1': 9, 'c2': 11, 'c3': 11, 'c4': 11, 'c5': 9})
a15 = AlternativePerformances('a15', {'c1': 9, 'c2': 11, 'c3': 11, 'c4': 11, 'c5': 11})
a16 = AlternativePerformances('a16', {'c1': 11, 'c2': 9, 'c3': 9, 'c4': 9, 'c5': 9})
a17 = AlternativePerformances('a17', {'c1': 11, 'c2': 9, 'c3': 9, 'c4': 9, 'c5': 11})
a18 = AlternativePerformances('a18', {'c1': 11, 'c2': 9, 'c3': 9, 'c4': 11, 'c5': 9})
a19 = AlternativePerformances('a19', {'c1': 11, 'c2': 9, 'c3': 9, 'c4': 11, 'c5': 11})
a20 = AlternativePerformances('a20', {'c1': 11, 'c2': 9, 'c3': 11, 'c4': 9, 'c5': 9})
a21 = AlternativePerformances('a21', {'c1': 11, 'c2': 9, 'c3': 11, 'c4': 9, 'c5': 11})
a22 = AlternativePerformances('a22', {'c1': 11, 'c2': 9, 'c3': 11, 'c4': 11, 'c5': 9})
a23 = AlternativePerformances('a23', {'c1': 11, 'c2': 9, 'c3': 11, 'c4': 11, 'c5': 11})
a24 = AlternativePerformances('a24', {'c1': 11, 'c2': 11, 'c3': 9, 'c4': 9, 'c5': 9})
a25 = AlternativePerformances('a25', {'c1': 11, 'c2': 11, 'c3': 9, 'c4': 9, 'c5': 11})
a26 = AlternativePerformances('a26', {'c1': 11, 'c2': 11, 'c3': 9, 'c4': 11, 'c5': 9})
a27 = AlternativePerformances('a27', {'c1': 11, 'c2': 11, 'c3': 9, 'c4': 11, 'c5': 11})
a28 = AlternativePerformances('a28', {'c1': 11, 'c2': 11, 'c3': 11, 'c4': 9, 'c5': 9})
a29 = AlternativePerformances('a29', {'c1': 11, 'c2': 11, 'c3': 11, 'c4': 9, 'c5': 11})
a30 = AlternativePerformances('a30', {'c1': 11, 'c2': 11, 'c3': 11, 'c4': 11, 'c5': 9})
a31 = AlternativePerformances('a31', {'c1': 11, 'c2': 11, 'c3': 11, 'c4': 11, 'c5': 7})
a32 = AlternativePerformances('a32', {'c1': 11, 'c2': 11, 'c3': 11, 'c4': 7, 'c5': 11})
a33 = AlternativePerformances('a33', {'c1': 11, 'c2': 11, 'c3': 7, 'c4': 11, 'c5': 11})
a34 = AlternativePerformances('a34', {'c1': 11, 'c2': 7, 'c3': 11, 'c4': 11, 'c5': 11})
a35 = AlternativePerformances('a35', {'c1': 7, 'c2': 11, 'c3': 11, 'c4': 11, 'c5': 11})
a36 = AlternativePerformances('a36', {'c1': 11, 'c2': 11, 'c3': 11, 'c4': 7, 'c5': 7})
a37 = AlternativePerformances('a37', {'c1': 11, 'c2': 11, 'c3': 7, 'c4': 11, 'c5': 7})
a38 = AlternativePerformances('a38', {'c1': 11, 'c2': 7, 'c3': 11, 'c4': 11, 'c5': 7})
a39 = AlternativePerformances('a39', {'c1': 7, 'c2': 11, 'c3': 11, 'c4': 11, 'c5': 7})
a40 = AlternativePerformances('a40', {'c1': 11, 'c2': 11, 'c3': 7, 'c4': 7, 'c5': 11})
a41 = AlternativePerformances('a41', {'c1': 11, 'c2': 7, 'c3': 11, 'c4': 7, 'c5': 11})
a42 = AlternativePerformances('a42', {'c1': 7, 'c2': 11, 'c3': 11, 'c4': 7, 'c5': 11})
a43 = AlternativePerformances('a43', {'c1': 11, 'c2': 7, 'c3': 7, 'c4': 11, 'c5': 11})
a44 = AlternativePerformances('a44', {'c1': 7, 'c2': 11, 'c3': 7, 'c4': 11, 'c5': 11})
a45 = AlternativePerformances('a45', {'c1': 7, 'c2': 7, 'c3': 11, 'c4': 11, 'c5': 11})
pt = PerformanceTable([a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11,
a12, a13, a14, a15, a16, a17, a18, a19, a20,
a21, a22, a23, a24, a25, a26, a27, a28, a29,
a30, a31, a32, a33, a34, a35, a36, a37, a38,
a39, a40, a41, a42, a43, a44, a45])
ap1 = AlternativeAssignment('a1', 'cat2')
ap2 = AlternativeAssignment('a2', 'cat2')
ap3 = AlternativeAssignment('a3', 'cat2')
ap4 = AlternativeAssignment('a4', 'cat2')
ap5 = AlternativeAssignment('a5', 'cat2')
ap6 = AlternativeAssignment('a6', 'cat2')
ap7 = AlternativeAssignment('a7', 'cat1')
ap8 = AlternativeAssignment('a8', 'cat2')
ap9 = AlternativeAssignment('a9', 'cat2')
ap10 = AlternativeAssignment('a10', 'cat2')
ap11 = AlternativeAssignment('a11', 'cat1')
ap12 = AlternativeAssignment('a12', 'cat2')
ap13 = AlternativeAssignment('a13', 'cat1')
ap14 = AlternativeAssignment('a14', 'cat1')
ap15 = AlternativeAssignment('a15', 'cat1')
ap16 = AlternativeAssignment('a16', 'cat2')
ap17 = AlternativeAssignment('a17', 'cat2')
ap18 = AlternativeAssignment('a18', 'cat2')
ap19 = AlternativeAssignment('a19', 'cat1')
ap20 = AlternativeAssignment('a20', 'cat2')
ap21 = AlternativeAssignment('a21', 'cat1')
ap22 = AlternativeAssignment('a22', 'cat1')
ap23 = AlternativeAssignment('a23', 'cat1')
ap24 = AlternativeAssignment('a24', 'cat2')
ap25 = AlternativeAssignment('a25', 'cat1')
ap26 = AlternativeAssignment('a26', 'cat1')
ap27 = AlternativeAssignment('a27', 'cat1')
ap28 = AlternativeAssignment('a28', 'cat1')
ap29 = AlternativeAssignment('a29', 'cat1')
ap30 = AlternativeAssignment('a30', 'cat1')
ap31 = AlternativeAssignment('a31', 'cat1')
ap32 = AlternativeAssignment('a32', 'cat1')
ap33 = AlternativeAssignment('a33', 'cat1')
ap34 = AlternativeAssignment('a34', 'cat1')
ap35 = AlternativeAssignment('a35', 'cat1')
ap36 = AlternativeAssignment('a36', 'cat2')
ap37 = AlternativeAssignment('a37', 'cat2')
ap38 = AlternativeAssignment('a38', 'cat2')
ap39 = AlternativeAssignment('a39', 'cat2')
ap40 = AlternativeAssignment('a40', 'cat2')
ap41 = AlternativeAssignment('a41', 'cat2')
ap42 = AlternativeAssignment('a42', 'cat2')
ap43 = AlternativeAssignment('a43', 'cat2')
ap44 = AlternativeAssignment('a44', 'cat2')
ap45 = AlternativeAssignment('a45', 'cat2')
aa = AlternativesAssignments([ap1, ap2, ap3, ap4, ap5, ap6, ap7, ap8,
ap9, ap10, ap11, ap12, ap13, ap14, ap15,
ap16, ap17, ap18, ap19, ap20, ap21,
ap22, ap23, ap24, ap25, ap26, ap27,
ap28, ap29, ap30, ap31, ap32, ap33,
ap34, ap35, ap36, ap37, ap38, ap39,
ap40, ap41, ap42, ap43, ap44, ap45])
model = MRSort(c, w, bpt, 0.6, cps, v, vw, 0.4)
aa2 = model.pessimist(pt)
ok = compare_assignments(aa, aa2)
self.assertEqual(ok, 1, "One or more alternatives were wrongly "
"assigned")
if is_bz2_file(f) is True:
f = bz2.BZ2File(f)
import pdb
pdb.set_trace()
tree = ElementTree.parse(f)
root = tree.getroot()
m = MRSort().from_xmcda(root, 'learned')
pt_learning = PerformanceTable().from_xmcda(root, 'learning_set')
pt_test = PerformanceTable().from_xmcda(root, 'test_set')
aa_learning = AlternativesAssignments().from_xmcda(root, 'learning_set')
aa_test = AlternativesAssignments().from_xmcda(root, 'test_set')
aa_learning_m2 = m.pessimist(pt_learning)
aa_test_m2 = m.pessimist(pt_test)
# Compute classification accuracy
ca_learning = compute_ca(aa_learning, aa_learning_m2)
ca_test = compute_ca(aa_test, aa_test_m2)
table_ca_learning.append(ca_learning)
table_ca_test.append(ca_test)
# Compute area under the curve
auc_learning = m.auc(aa_learning, pt_learning)
auc_test = m.auc(aa_test, pt_test)
table_auc_learning.append(auc_learning)
table_auc_test.append(auc_test)
pt_learning = PerformanceTable().from_xmcda(root, 'learning_set')
except:
pt_learning = None
try:
pt_test = PerformanceTable().from_xmcda(root, 'test_set')
except:
pt_test = None
aa_learning_m1, aa_learning_m2 = None, None
aa_test_m1, aa_test_m2 = None, None
if root.find("ElectreTri[@id='initial']") is not None:
m1 = MRSort().from_xmcda(root, 'initial')
if pt_learning is not None:
aa_learning_m1 = m1.pessimist(pt_learning)
if pt_test is not None:
aa_test_m1 = m1.pessimist(pt_test)
elif root.find("AVFSort[@id='initial']") is not None:
m1 = AVFSort().from_xmcda(root, 'initial')
if pt_learning is not None:
aa_learning_m1 = m1.get_assignments(pt_learning)
if pt_test is not None:
aa_test_m1 = m1.get_assignments(pt_test)
else:
if root.find("alternativesAffectations[@id='learning_set']") is not None:
aa_learning_m1 = AlternativesAssignments().from_xmcda(root,
'learning_set')
if root.find("alternativesAffectations[@id='test_set']") is not None:
aa_test_m1 = AlternativesAssignments().from_xmcda(root, 'test_set')
def test_meta_electre_tri_global(seed, na, nc, ncat, na_gen, pcerrors):
# Generate a random ELECTRE TRI BM model
if random_model_type == 'default':
model = generate_random_mrsort_model(nc, ncat, seed)
elif random_model_type == 'choquet':
model = generate_random_mrsort_choquet_model(nc, ncat, 2, 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()
aa_erroned = add_errors_in_assignments_proba(aa_err,
model.categories,
pcerrors / 100)
na_err = len(aa_erroned)
# Run the MIP
t1 = time.time()
model2 = MRSort(model.criteria, None, None, None,
model.categories_profiles, None, None, None)
mip = MipMRSort(model2, pt, aa_err)
obj = mip.solve()
ca2_best = obj / na
aa2 = model2.get_assignments(pt)
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.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" % (seed, na, nc, ncat,
na_gen, pcerrors))
model.id = 'initial'
model2.id = 'learned'
pt.id, pt_gen.id = 'learning_set', 'test_set'
aa.id = 'aa'
aa_err.id = 'aa_err'
save_to_xmcda("%s/%s.bz2" % (directory, t.test_name),
model, model2, pt, pt_gen, aa, aa_err)
# Input params
t['seed'] = seed
t['na'] = na
t['nc'] = nc
t['ncat'] = ncat
t['na_gen'] = na_gen
t['pcerrors'] = pcerrors
# 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['t_total'] = t_total
return t
b1 = AlternativePerformances('b1', {'c%d' % (i + 1): 0.5
for i in range(ncriteria)})
model.bpt = PerformanceTable([b1])
cat = generate_categories(2)
model.categories_profiles = generate_categories_profiles(cat)
model.lbda = 0.6
vb1 = AlternativePerformances('b1', {'c%d' % (i + 1): random.uniform(0,0.4)
for i in range(ncriteria)})
model.veto = PerformanceTable([vb1])
model.veto_weights = model.cv.copy()
model.veto_lbda = 0.4
# Generate a set of alternatives
a = generate_alternatives(1000)
pt = generate_random_performance_table(a, model.criteria)
aa = model.pessimist(pt)
worst = pt.get_worst(model.criteria)
best = b1
print('Original model')
print('==============')
cids = model.criteria.keys()
model.bpt.display(criterion_ids=cids)
model.cv.display(criterion_ids=cids)
print("lambda: %.7s" % model.lbda)
model.vpt.display(criterion_ids=cids)
model.veto_weights.display(criterion_ids=cids)
model2 = model.copy()
vpt = generate_random_profiles(model.profiles, model.criteria, None, 3,
def test_mip_mrsort_vc(seed, na, nc, ncat, na_gen, veto_param, pcerrors):
# Generate a random ELECTRE TRI BM model
if vetot == 'binary':
model = generate_random_mrsort_model_with_binary_veto(nc, ncat,
seed,
veto_func = veto_func,
veto_param = veto_param)
elif vetot == 'coalition':
model = generate_random_mrsort_model_with_coalition_veto(nc, ncat,
seed,
veto_weights = indep_veto_weights,
veto_func = veto_func,
veto_param = veto_param)
# Generate a set of alternatives
a = generate_alternatives(na)
pt = generate_random_performance_table(a, model.criteria)
aa = model.pessimist(pt)
nv_m1_learning = sum([model.count_veto_pessimist(ap) for ap in pt])
# Add errors in assignment examples
aa_err = aa.copy()
aa_erroned = add_errors_in_assignments_proba(aa_err,
model.categories,
pcerrors / 100)
na_err = len(aa_erroned)
# Run the MIP
t1 = time.time()
model2 = MRSort(model.criteria, None, None, None,
model.categories_profiles, None, None, None)
if algo == MipMRSortVC and vetot == 'binary':
w = {c.id: 1 / len(model.criteria) for c in model.criteria}
w1 = w.keys()[0]
w[w1] += 1 - sum(w.values())
model2.veto_weights = CriteriaValues([CriterionValue(c.id,
w[c.id])
for c in model.criteria])
model2.veto_lbda = min(w.values())
if algo == MipMRSortVC:
mip = MipMRSortVC(model2, pt, aa, indep_veto_weights)
else:
mip = MipMRSort(model2, pt, aa)
mip.solve()
t_total = time.time() - t1
# Determine the number of erroned alternatives badly assigned
aa2 = model2.pessimist(pt)
nv_m2_learning = sum([model2.count_veto_pessimist(ap) for ap in pt])
cmatrix_learning = compute_confusion_matrix(aa, aa2, model.categories)
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)
nv_m1_gen = sum([model.count_veto_pessimist(ap) for ap in pt_gen])
nv_m2_gen = sum([model2.count_veto_pessimist(ap) for ap in pt_gen])
if len(aa_gen) > 0:
cmatrix_gen = compute_confusion_matrix(aa_gen, aa_gen2,
model.categories)
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-%s-%d" % (seed, na, nc, ncat,
na_gen, veto_param, pcerrors))
model.id = 'initial'
model2.id = 'learned'
a.id, pt.id = 'learning_set', 'learning_set'
aa.id, aa2.id = 'learning_set_m1', 'learning_set_m2'
a_gen.id, pt_gen.id = 'test_set', 'test_set'
aa_gen.id, aa_gen2.id = 'test_set_m1', 'test_set_m2'
save_to_xmcda("%s/%s.bz2" % (directory, t.test_name),
model, model2, a, a_gen, pt, pt_gen, aa, aa2,
aa_gen, aa_gen2)
# Input params
t['seed'] = seed
t['na'] = na
t['nc'] = nc
t['ncat'] = ncat
t['na_gen'] = na_gen
t['veto_param'] = veto_param
t['pcerrors'] = pcerrors
# Ouput params
t['na_err'] = na_err
t['nv_m1_learning'] = nv_m1_learning
t['nv_m2_learning'] = nv_m2_learning
t['nv_m1_gen'] = nv_m1_gen
t['nv_m2_gen'] = nv_m2_gen
t['ca_best'] = ca_best
t['ca_errors'] = ca_errors
t['ca_gen'] = ca_gen
t['ca_gen_err'] = ca_gen_err
t['t_total'] = t_total
for k, v in cmatrix_learning.items():
t['learn_%s_%s' % (k[0], k[1])] = v
for k, v in cmatrix_gen.items():
t['test_%s_%s' % (k[0], k[1])] = v
return t
mwinning = compute_minimal_winning_coalitions(lp.fmins)
for win in mwinning:
win = list(win)
win.sort(key=criteria_order.index)
buf = ""
for c in win:
buf += "%s, " % criteria_names[c]
print('[%s]' % buf[:-2], file=fcoalitions)
fcoalitions.close()
pt_learning = PerformanceTable().from_xmcda(root, 'learning_set')
aa_learning = AlternativesAssignments().from_xmcda(root,
'learning_set')
aa_learned = m.pessimist(pt_learning)
fca = open('%s-ca_learning.dat' % bname, 'w+')
ca = compute_ca(aa_learning, aa_learned)
print("%.4f" % ca, end = '', file=fca)
fca.close()
fauc = open('%s-auc_learning.dat' % bname, 'w+')
auc = m.auc(aa_learning, pt_learning)
print("%.4f" % auc, end = '', file=fauc)
fauc.close()
ca_learning.append(ca)
auc_learning.append(auc)
pt_test = PerformanceTable().from_xmcda(root, 'test_set')
if is_bz2_file(f) is True:
f = bz2.BZ2File(f)
tree = ElementTree.parse(f)
root = tree.getroot()
m = MRSort().from_xmcda(root, 'learned')
pt_learning = PerformanceTable().from_xmcda(root, 'learning_set')
pt_test = PerformanceTable().from_xmcda(root, 'test_set')
aa_learning = AlternativesAssignments().from_xmcda(root,
'learning_set')
aa_test = AlternativesAssignments().from_xmcda(root,
'test_set')
aa_learning_m2 = m.pessimist(pt_learning)
aa_test_m2 = m.pessimist(pt_test)
# # Remove alternatives that cannot be corrected with a veto rule
# aa_learning_m2p = discard_undersorted_alternatives(m.categories,
# aa_learning,
# aa_learning_m2)
# aa_learning_m2p = discard_alternatives_in_category(aa_learning_m2p,
# m.categories[0])
# Run the metaheuristic
meta = MetaMRSortVCPop3(10, m, SortedPerformanceTable(pt_learning),
aa_learning)
nloops = 10
nmeta = 20
for i in range(nloops):
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
fprofiles.close()
fcoalitions = open('%s-wcoalitions.dat' % bname, 'w+')
mwinning = compute_minimal_winning_coalitions(lp.fmins)
for win in mwinning:
win = list(win)
win.sort(key=criteria_order.index)
buf = ""
for c in win:
buf += "%s, " % criteria_names[c]
print('[%s]' % buf[:-2], file=fcoalitions)
fcoalitions.close()
aa_learned = m.pessimist(pt_learning)
fca = open('%s-ca.dat' % bname, 'w+')
ca = compute_ca(aa_learning, aa_learned)
print("%.4f" % ca, end='', file=fca)
fca.close()
fauc = open('%s-auc.dat' % bname, 'w+')
auc = m.auc(aa_learning, pt_learning)
print("%.4f" % auc, end='', file=fauc)
fauc.close()
fmisclassified = open('%s-misclassified.dat' % bname, 'w+')
print("{Alternative} ", file=fmisclassified, end='')
print("{Original assignment} ", file=fmisclassified, end='')
print("{Model assignment}", file=fmisclassified, end='')
a36 = AlternativePerformances('a36', {'c1': 11, 'c2': 11, 'c3': 11, 'c4': 7, 'c5': 7})
a37 = AlternativePerformances('a37', {'c1': 11, 'c2': 11, 'c3': 7, 'c4': 11, 'c5': 7})
a38 = AlternativePerformances('a38', {'c1': 11, 'c2': 7, 'c3': 11, 'c4': 11, 'c5': 7})
a39 = AlternativePerformances('a39', {'c1': 7, 'c2': 11, 'c3': 11, 'c4': 11, 'c5': 7})
a40 = AlternativePerformances('a40', {'c1': 11, 'c2': 11, 'c3': 7, 'c4': 7, 'c5': 11})
a41 = AlternativePerformances('a41', {'c1': 11, 'c2': 7, 'c3': 11, 'c4': 7, 'c5': 11})
a42 = AlternativePerformances('a42', {'c1': 7, 'c2': 11, 'c3': 11, 'c4': 7, 'c5': 11})
a43 = AlternativePerformances('a43', {'c1': 11, 'c2': 7, 'c3': 7, 'c4': 11, 'c5': 11})
a44 = AlternativePerformances('a44', {'c1': 7, 'c2': 11, 'c3': 7, 'c4': 11, 'c5': 11})
a45 = AlternativePerformances('a45', {'c1': 7, 'c2': 7, 'c3': 11, 'c4': 11, 'c5': 11})
pt = PerformanceTable([eval("a%d" % i) for i in range(1, 46)])
aa = model.pessimist(pt)
print(aa)
nveto = [model.count_veto_pessimist(eval("a%d" % i)) for i in range(1, 46)]
print("Number of veto effects: %d" % sum(nveto))
model2 = MRSort(c, None, None, None, cps, None, None, None)
#model2.veto_lbda = model.veto_lbda
#model2.veto_weights = model.veto_weights
mip = MipMRSortVC(model2, pt, aa)
#mip = MipMRSort(model2, pt, aa)
mip.solve()
print(model2.cv)
print(model2.bpt)
