print model2.veto_weights
# Display learned model parameters
print('Learned model')
print('=============')
model2.bpt.display(criterion_ids=cids)
model2.cv.display(criterion_ids=cids)
print("lambda: %.7s" % model2.lbda)
# Compute assignment with the learned model
aa2 = model2.pessimist(pt)
# Compute CA
total = len(a)
nok = 0
anok = []
for alt in a:
if aa(alt.id) != aa2(alt.id):
anok.append(alt)
nok += 1
print("Good assignments: %3g %%" % (float(total - nok) / total * 100))
print("Bad assignments : %3g %%" % (float(nok) / total * 100))
if len(anok) > 0:
print("Alternatives wrongly assigned:")
print_pt_and_assignments(anok.keys(), model.criteria.keys(), [aa, aa2],
pt)
# Display models
display_electre_tri_models([model, model2], [worst, worst], [best, best])
cvs.display()
print('Criteria functions:')
cfs.display()
print('Categories values:')
catv.display()
u2 = AVFSort(c, cvs, cfs, catv)
aa2 = u2.get_assignments(pt)
total = len(a)
nok = nok_erroned = 0
anok = []
for alt in a:
if aa(alt.id) != aa2(alt.id):
anok.append(alt)
nok += 1
if alt.id in aa_erroned:
nok_erroned += 1
print("Good assignments : %3g %%" \
% ((total - nok) / total *100))
print("Bad assignments : %3g %%" \
% ((nok) / total *100))
if aa_erroned:
print("Bad in erroned assignments: %3g %%" \
% (nok_erroned / total * 100))
if len(anok) > 0:
print("Alternatives wrongly assigned:")
print_pt_and_assignments(anok.keys(), c.keys(), [aa, aa2], pt)
anok.append(alt.id)
nok += 1
if alt.id in aa_erroned:
nok_erroned += 1
print("Good assignments : %3g %%" \
% (float(total-nok)/total*100))
print("Bad assignments : %3g %%" \
% (float(nok)/total*100))
if aa_erroned:
print("Bad in erroned assignments: %3g %%" \
% (float(nok_erroned)/total*100))
if len(anok) > 0:
print("Alternatives wrongly assigned:")
print_pt_and_assignments(anok, model.criteria.keys(),
[aa, aa_learned], pt)
win1, loose1 = compute_winning_and_loosing_coalitions(model.cv,
model.lbda)
win2, loose2 = compute_winning_and_loosing_coalitions(model2.cv,
model2.lbda)
coali = list(set(win1) & set(win2))
coal1e = list(set(win1) ^ set(coali))
coal2e = list(set(win2) ^ set(coali))
print("Number of coalitions original: %d"
% len(win1))
print("Number of coalitions learned: %d"
% len(win2))
print("Number of common coalitions: %d"
% len(coali))
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))
print(model.cv)
print("lambda: %s" % model.lbda)
result['obj_capa'] = obj
a = Alternatives([Alternative(a.id) for a in aa])
print_pt_and_assignments(a.keys(), criteria.keys(), [aa, aa2, aa3], pt)
results.append(result)
results.sort(key = lambda x: x['vector'])
results.sort(key = lambda x: len(x['vector']))
maxlen = max([len(', '.join("%s" % f for f in result['fmins']))
for result in results])
print("\n%*s obj_weights obj_capa" % (maxlen, "Fmin"))
for result in results:
print("%*s %*s %*s" % (maxlen, ', '.join("%s" % f
for f in sorted(result['fmins'], key = len)),
len('obj_weights'), result['obj_weights'],
len('obj_capa'), result['obj_capa']))
anok.append(alt.id)
nok += 1
if alt.id in aa_erroned:
nok_erroned += 1
print("Good assignments : %3g %%" \
% (float(total-nok)/total*100))
print("Bad assignments : %3g %%" \
% (float(nok)/total*100))
if aa_erroned:
print("Bad in erroned assignments: %3g %%" \
% (float(nok_erroned)/total*100))
if len(anok) > 0:
print("Alternatives wrongly assigned:")
print_pt_and_assignments(anok, model.criteria.keys(), [aa, aa_learned],
pt)
win1, loose1 = compute_winning_and_loosing_coalitions(model.cv, model.lbda)
win2, loose2 = compute_winning_and_loosing_coalitions(
model2.cv, model2.lbda)
coali = list(set(win1) & set(win2))
coal1e = list(set(win1) ^ set(coali))
coal2e = list(set(win2) ^ set(coali))
print("Number of coalitions original: %d" % len(win1))
print("Number of coalitions learned: %d" % len(win2))
print("Number of common coalitions: %d" % len(coali))
print("Coalitions in original and not in learned: %s" %
'; '.join(map(str, coal1e)))
print("Coalitions in learned and not in original: %s" %
'; '.join(map(str, coal2e)))
cvs.display()
print('Criteria functions:')
cfs.display()
print('Categories values:')
catv.display()
u2 = AVFSort(c, cvs, cfs, catv)
aa2 = u2.get_assignments(pt)
total = len(a)
nok = nok_erroned = 0
anok = []
for alt in a:
if aa(alt.id) != aa2(alt.id):
anok.append(alt)
nok += 1
if alt.id in aa_erroned:
nok_erroned += 1
print("Good assignments : %3g %%" \
% ((total - nok) / total *100))
print("Bad assignments : %3g %%" \
% ((nok) / total *100))
if aa_erroned:
print("Bad in erroned assignments: %3g %%" \
% (nok_erroned / total * 100))
if len(anok) > 0:
print("Alternatives wrongly assigned:")
print_pt_and_assignments(anok.keys(), c.keys(), [aa, aa2], pt)
alternative_ids = model2.profiles)
model2.cv.display(criterion_ids=cids)
print("lambda: %.7s" % model2.lbda)
aa2 = model2.pessimist(pt)
total = len(a)
nok = nok_erroned = 0
anok = []
for alt in a:
if aa(alt.id) != aa2(alt.id):
anok.append(alt)
nok += 1
if alt.id in aa_erroned:
nok_erroned += 1
print("Good assignments : %3g %%" \
% (float(total-nok)/total*100))
print("Bad assignments : %3g %%" \
% (float(nok)/total*100))
if aa_erroned:
print("Bad in erroned assignments: %3g %%" \
% (float(nok_erroned)/total*100))
if len(anok) > 0:
print("Alternatives wrongly assigned:")
print_pt_and_assignments(anok.keys(), model.criteria.keys(),
[aa, aa2], pt)
display_electre_tri_models([model, model2],
[worst, worst], [best, best])
if pt_test is not None:
aa_test_m2 = m2.pessimist(pt_test)
elif root.find("AVFSort[@id='learned']") is not None:
m2 = AVFSort().from_xmcda(root, 'learned')
if pt_learning is not None:
aa_learning_m2 = m2.get_assignments(pt_learning)
aids = []
from pymcda.utils import print_pt_and_assignments
for aid in aa_learning_m2.keys():
if aa_learning_m2[aid].category_id != aa_learning_m1[aid].category_id:
aids.append(aid)
else:
aids.append(aid)
au = m2.global_utilities(pt_learning)
print_pt_and_assignments(aids, None, [aa_learning_m1, aa_learning_m2], pt_learning, au)
# for i in range(1, len(pt_learning) + 1):
# aid = "a%d" % i
# uti = m2.global_utility(pt_learning["a%d" % i])
# if aa_learning_m2[aid].category_id != aa_learning_m1[aid].category_id:
# print("%s %g %s %s" % (aid, uti.value, aa_learning_m2[aid].category_id, aa_learning_m1[aid].category_id))
# print_pt_and_assignments(anok, c, [aa_learning_m1, aa_learning_m2], pt_learning)
if pt_test is not None:
aa_test_m2 = m2.get_assignments(pt_test)
def compute_auc_histo(aa):
pass
if aa_learning_m1 is not None:
ca_learning = compute_ca(aa_learning_m1, aa_learning_m2)
auc_learning = m2.auc(aa_learning_m1, pt_learning)
save_to_xmcda("%s/%s-all-%s-%s.bz2" % (DATADIR, algo, data.name, dt), data.aa,
data.pt, model)
aa2 = model.get_assignments(data.pt)
ca = compute_ca(data.aa, aa2)
auc = model.auc(data.aa, data.pt)
anok = []
for a in data.a:
if data.aa[a.id].category_id != aa2[a.id].category_id:
anok.append(a)
if len(anok) > 0:
print("Alternatives wrongly assigned:")
print_pt_and_assignments(anok.keys(), data.c.keys(), [data.aa, aa2],
data.pt)
print("Model parameters:")
cids = model.criteria.keys()
if model_type == 'mrsort':
print(model.bpt)
print(model.cv)
print("lambda: %.7s" % model.lbda)
print("Weights and lambda optimization:")
if algo == 'meta_mrsort':
lp = LpMRSortPostWeights(model.cv, model.lbda)
obj, model.cv, model.lbda = lp.solve()
print(model.cv)
print(model.lbda)
save_to_xmcda("%s/%s-all-%s-%s.bz2" % (DATADIR, algo, data.name, dt),
data.aa, data.pt, model)
aa2 = model.get_assignments(data.pt)
ca = compute_ca(data.aa, aa2)
auc = model.auc(data.aa, data.pt)
anok = []
for a in data.a:
if data.aa[a.id].category_id != aa2[a.id].category_id:
anok.append(a)
if len(anok) > 0:
print("Alternatives wrongly assigned:")
print_pt_and_assignments(anok.keys(), data.c.keys(), [data.aa, aa2], data.pt)
print("Model parameters:")
cids = model.criteria.keys()
if model_type == 'mrsort':
print(model.bpt)
print(model.cv)
print("lambda: %.7s" % model.lbda)
print("Weights and lambda optimization:")
if algo == 'meta_mrsort':
lp = LpMRSortPostWeights(model.cv, model.lbda)
obj, model.cv, model.lbda = lp.solve()
print(model.cv)
print(model.lbda)