def on_button_chooseassign_pressed(self):
items = [c.id for c in self.criteria if c.disabled is True]
if len(items) < 1:
QtGui.QMessageBox.information(None, "Error",
"No assignment column")
return
item, ok = QtGui.QInputDialog.getItem(self,
"Select assignments column",
"Column:", items, 0, False)
if ok is False:
return
try:
cid = item.toString()
except:
cid = str(item)
ncat = len(self.bpt) + 1
pt, aa = PerformanceTable(), AlternativesAssignments()
for ap in self.pt:
perf = int(ap.performances[cid])
if perf > 0 and perf < (ncat + 1):
pt.append(ap)
aa.append(AlternativeAssignment(ap.id, str(perf)))
if len(pt) < 1:
QtGui.QMessageBox.information(None, "Error",
"No assignments examples found")
return
self.a_ref = Alternatives([Alternative(a.id) for a in aa])
self.pt_ref = pt
self.aa_ref = aa
a = Alternatives([Alternative(aid) for aid in pt.keys()])
self.table_refs.reset_table()
self.table_refs.add_criteria(self.criteria)
self.table_refs.add_pt(a, pt, False)
self.__generate_category_colors()
self.table_refs.add_assignments(aa, self.category_colors, True)
self.button_zoom.setEnabled(True)
self.button_show.setEnabled(True)
self.button_infer.setEnabled(True)
def __show_model_learned(self, cv, bpt, lbda, a):
dialog = InferenceDialog(self)
dialog.setModal(True)
self.connect(dialog,
QtCore.SIGNAL("accepted()"),
self.on_model_learned_accepted)
dialog.show()
model = ElectreTri(self.criteria, cv, bpt, lbda, self.cat_profiles)
worst = self.pt.get_worst(self.criteria)
best = self.pt.get_best(self.criteria)
criteria_order = [c.id for c in self.criteria]
graph = QGraphicsSceneEtri(model, worst, best,
self.graph_plot.size(),
criteria_order)
dialog.graph_model.setScene(graph)
dialog.label_lambda.setText("Lambda: %s" % str(lbda))
a_incomp = Alternatives([aref for aref in self.a_ref
if aref.id not in a.keys()])
pt_incomp = PerformanceTable([ap for ap in self.pt_ref
if ap.id in a_incomp.keys()])
pt_comp = PerformanceTable([ap for ap in self.pt_ref
if ap.id in a.keys()])
dialog.table_comp.add_criteria(self.criteria)
dialog.table_comp.add_pt(a, pt_comp)
dialog.table_incomp.add_criteria(self.criteria)
dialog.table_incomp.add_pt(a_incomp, pt_incomp)
def __fill_model_tables(self):
self.layer_loaded = False
self.table_criteria.add_criteria(self.criteria, self.cv)
self.table_prof.add_criteria(self.criteria)
self.table_indiff.add_criteria(self.criteria)
self.table_pref.add_criteria(self.criteria)
self.table_veto.add_criteria(self.criteria)
self.table_prof.add_pt(self.balternatives, self.bpt)
self.label_ncategories.setText("%d" % (len(self.bpt) + 1))
if self.same_pqv_thresholds_for_all_profiles() is True:
self.cbox_samethresholds.setChecked(True)
balternatives = Alternatives([Alternative('b')])
else:
balternatives = self.balternatives
if self.qpt is None and self.ppt is None:
self.cbox_mrsort.setChecked(True)
else:
self.table_indiff.add_pt(balternatives, self.qpt)
self.table_pref.add_pt(balternatives, self.ppt)
if self.vpt is None:
self.cbox_noveto.setChecked(True)
else:
self.table_veto.add_pt(balternatives, self.vpt)
self.spinbox_cutlevel.setValue(self.lbda)
self.__update_graph()
self.layer_loaded = True
def __generate_initial_model(self):
crit_min = {}
crit_max = {}
for altp in self.pt:
for crit in self.criteria:
d = crit.direction
if crit_min.has_key(crit.id) is False:
crit_min[crit.id] = altp.performances[crit.id]
elif crit_min[crit.id]*d > altp(crit.id)*d:
crit_min[crit.id] = altp.performances[crit.id]
if crit_max.has_key(crit.id) is False:
crit_max[crit.id] = altp.performances[crit.id]
elif crit_max[crit.id]*d < altp(crit.id)*d:
crit_max[crit.id] = altp.performances[crit.id]
self.cv = CriteriaValues()
b1 = AlternativePerformances('b1', {})
for crit in self.criteria:
b1.performances[crit.id] = (crit_max[crit.id]
- crit_min[crit.id]) / 2
cv = CriterionValue(crit.id, 1)
self.cv.append(cv)
self.balternatives = Alternatives([Alternative('b1', 'b1')])
self.bpt = PerformanceTable([b1])
self.cbox_mrsort.setChecked(True)
self.cbox_noveto.setChecked(True)
# Categories Profiles
self.categories = generate_categories(len(self.bpt) + 1,
prefix = "")
self.cat_profiles = generate_categories_profiles(self.categories)
self.lbda = 0.75
def get_features_ids(self, aids):
provider = self.layer.dataProvider()
self.alternatives = Alternatives([])
self.pt = PerformanceTable([])
features = []
for feat in provider.getFeatures():
featid = str(feat.id())
if featid in aids:
features.append(feat.id())
return features
def get_alternatives_and_pt(self):
provider = self.layer.dataProvider()
self.alternatives = Alternatives([])
self.pt = PerformanceTable([])
for feat in provider.getFeatures():
featid = str(feat.id())
perfs = {}
for criterion in self.criteria:
try:
perfs[criterion.id] = float(feat[criterion.id])
except:
perfs[criterion.id] = feat[criterion.id].toDouble()[0]
self.alternatives.append(Alternative(featid, featid))
self.pt.append(AlternativePerformances(featid, perfs))
def __load_from_xmcda(self, xmcda_file):
tree = ElementTree.parse(xmcda_file)
root = tree.getroot()
# ElementTree.dump(root)
xmcda_formatversion = root.find('.//formatversion')
xmcda_crit = root.find('.//criteria')
xmcda_critval = root.find('.//criteriaValues')
xmcda_b = root.find('.//alternatives')
xmcda_pt = root.findall('.//performanceTable')
xmcda_lbda = root.find('.//methodParameters/parameter/value/real')
# Update criteria direction
criteria = Criteria().from_xmcda(xmcda_crit)
for c in criteria:
if c.id in self.criteria:
self.criteria[c.id].disabled = c.disabled
self.criteria[c.id].direction = c.direction
# Remove criteria values that are not in the vector layer
cvs = CriteriaValues()
cvs.from_xmcda(xmcda_critval)
for cv in cvs:
if cv.id not in self.criteria:
cvs.remove(cv.id)
for c in self.criteria:
# Disable criteria for which there are no weights
if c.id not in cvs:
c.disabled = True
cvs.append(CriterionValue(c.id, 0))
balternatives = Alternatives()
balternatives.from_xmcda(xmcda_b)
bpt = PerformanceTable()
qpt, ppt, vpt = None, None, None
for xmcda in xmcda_pt:
if xmcda.get('id') is None:
bpt.from_xmcda(xmcda)
for bp, c in product(bpt, self.criteria.get_active()):
perfs = bp.performances
if c.id not in perfs or perfs[c.id] is None:
perfs[c.id] = 0
elif xmcda.get('id') == 'q':
qpt = PerformanceTable(id = 'q')
qpt.from_xmcda(xmcda)
elif xmcda.get('id') == 'p':
ppt = PerformanceTable(id = 'p')
ppt.from_xmcda(xmcda)
elif xmcda.get('id') == 'v':
vpt = PerformanceTable(id = 'v')
vpt.from_xmcda(xmcda)
if qpt is not None and len(qpt) == 0 and len(ppt) > 0:
qpt = ppt.copy()
qpt.id = "q"
if ppt is not None and len(ppt) == 0 and len(qpt) > 0:
ppt = qpt.copy()
ppt.id = "p"
lbda = float(xmcda_lbda.text)
self.__check_params_consistency(bpt, qpt, ppt, vpt)
# Everything is fine
self.cv = cvs
self.balternatives = balternatives
self.bpt = bpt
self.qpt = qpt
self.ppt = ppt
self.vpt = vpt
self.lbda = lbda
# Categories Profiles
self.categories = generate_categories(len(self.bpt) + 1,
prefix = "")
self.cat_profiles = generate_categories_profiles(self.categories)
