model2.bpt.display(criterion_ids=cids)
model2.cv.display(criterion_ids=cids)
print("lambda\t%.7s" % model2.lbda)
if model2.vpt is not None:
model2.vpt.display(criterion_ids=cids)
if model2.veto_weights is not None:
model2.veto_weights.display(criterion_ids=cids)
print("veto_lambda\t%.7s" % model2.veto_lbda)
aa_learned = model2.get_assignments(pt)
total = len(a)
nok = 0
anok = []
for alt in a:
if aa(alt.id) != aa_learned(alt.id):
anok.append(alt)
nok += 1
print("Good assignments: %g %%" % (float(total - nok) / total * 100))
print("Bad assignments : %g %%" % (float(nok) / total * 100))
matrix = compute_confusion_matrix(aa, aa_learned, model.categories)
print_confusion_matrix(matrix, model.categories)
model.id = "original"
model2.id = "learned"
display_electre_tri_models(
[model, model2], [worst, worst], [best, best],
[[ap for ap in model.vpt], [ap for ap in model2.vpt]])
stdev_auc_learning = np.std(np.array(table_auc_learning), axis=0)
stdev_auc_test = np.std(np.array(table_auc_test), axis=0)
nfiles = len(sys.argv) - 1
cmatrix_learning.update(
(x, round(y / float(nfiles), 2)) for x, y in cmatrix_learning.items())
cmatrix_test.update(
(x, round(y / float(nfiles), 2)) for x, y in cmatrix_test.items())
print("CA learning avg: %g +- %g" % (avg_ca_learning, stdev_ca_learning))
print("CA test avg: %g +- %g" % (avg_ca_test, stdev_ca_test))
print("AUC learning avg: %g +- %g" % (avg_auc_learning, stdev_auc_learning))
print("AUC test avg: %g +- %g" % (avg_auc_test, stdev_auc_test))
print("Confusion matrix learning set")
print_confusion_matrix(cmatrix_learning, m.categories)
print("Confusion matrix test set")
print_confusion_matrix(cmatrix_test, m.categories)
cmatrix_learning_total = sum(cmatrix_learning.values())
cmatrix_test_total = sum(cmatrix_test.values())
cmatrix_learning.update((x, round(100 * y / float(cmatrix_learning_total), 2))
for x, y in cmatrix_learning.items())
cmatrix_test.update((x, round(100 * y / float(cmatrix_test_total), 2))
for x, y in cmatrix_test.items())
print("Confusion matrix learning set")
print_confusion_matrix(cmatrix_learning, m.categories)
print("Confusion matrix test set")
print_confusion_matrix(cmatrix_test, m.categories)
model2.cv.display(criterion_ids = cids)
print("lambda\t%.7s" % model2.lbda)
if model2.vpt is not None:
model2.vpt.display(criterion_ids = cids)
if model2.veto_weights is not None:
model2.veto_weights.display(criterion_ids = cids)
print("veto_lambda\t%.7s" % model2.veto_lbda)
aa_learned = model2.get_assignments(pt)
total = len(a)
nok = 0
anok = []
for alt in a:
if aa(alt.id) <> aa_learned(alt.id):
anok.append(alt)
nok += 1
print("Good assignments: %g %%" % (float(total-nok)/total*100))
print("Bad assignments : %g %%" % (float(nok)/total*100))
matrix = compute_confusion_matrix(aa, aa_learned, model.categories)
print_confusion_matrix(matrix, model.categories)
model.id = "original"
model2.id = "learned"
display_electre_tri_models([model, model2],
[worst, worst], [best, best],
[[ap for ap in model.vpt],
[ap for ap in model2.vpt]])
stdev_auc_learning = np.std(np.array(table_auc_learning), axis=0)
stdev_auc_test = np.std(np.array(table_auc_test), axis=0)
nfiles = len(sys.argv) - 1
cmatrix_learning.update((x, round(y / float(nfiles), 2))
for x, y in cmatrix_learning.items())
cmatrix_test.update((x, round(y / float(nfiles), 2))
for x, y in cmatrix_test.items())
print("CA learning avg: %g +- %g" % (avg_ca_learning, stdev_ca_learning))
print("CA test avg: %g +- %g" % (avg_ca_test, stdev_ca_test))
print("AUC learning avg: %g +- %g" % (avg_auc_learning, stdev_auc_learning))
print("AUC test avg: %g +- %g" % (avg_auc_test, stdev_auc_test))
print("Confusion matrix learning set")
print_confusion_matrix(cmatrix_learning, m.categories)
print("Confusion matrix test set")
print_confusion_matrix(cmatrix_test, m.categories)
cmatrix_learning_total = sum(cmatrix_learning.values())
cmatrix_test_total = sum(cmatrix_test.values())
cmatrix_learning.update((x, round(100 * y / float(cmatrix_learning_total), 2))
for x, y in cmatrix_learning.items())
cmatrix_test.update((x, round(100 * y / float(cmatrix_test_total), 2))
for x, y in cmatrix_test.items())
print("Confusion matrix learning set")
print_confusion_matrix(cmatrix_learning, m.categories)
print("Confusion matrix test set")
print_confusion_matrix(cmatrix_test, m.categories)
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)
# display_electre_tri_models([model], [worst], [best])
elif model_type == 'utadis':
model.cfs.display(criterion_ids=cids)
model.cat_values.display()
# display_utadis_model(model.cfs)
print("t: %g" % t_total)
print("CA: %g" % ca)
print("AUC: %g" % auc)
print("Confusion matrix:")
print_confusion_matrix(
compute_confusion_matrix(data.aa, aa2, data.cats.get_ordered_categories()))
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)
# display_electre_tri_models([model], [worst], [best])
elif model_type == 'utadis':
model.cfs.display(criterion_ids = cids)
model.cat_values.display()
# display_utadis_model(model.cfs)
print("t: %g" % t_total)
print("CA: %g" % ca)
print("AUC: %g" % auc)
print("Confusion matrix:")
print_confusion_matrix(compute_confusion_matrix(data.aa, aa2,
data.cats.get_ordered_categories()))