def test002(self):
c = generate_criteria(3)
cat = generate_categories(3)
cps = generate_categories_profiles(cat)
bp1 = AlternativePerformances('b1',
{'c1': 0.75, 'c2': 0.75, 'c3': 0.75})
bp2 = AlternativePerformances('b2',
{'c1': 0.25, 'c2': 0.25, 'c3': 0.25})
bpt = PerformanceTable([bp1, bp2])
cv1 = CriterionValue('c1', 0.2)
cv2 = CriterionValue('c2', 0.2)
cv3 = CriterionValue('c3', 0.2)
cv12 = CriterionValue(CriteriaSet(['c1', 'c2']), -0.1)
cv23 = CriterionValue(CriteriaSet(['c2', 'c3']), 0.2)
cv13 = CriterionValue(CriteriaSet(['c1', 'c3']), 0.3)
cvs = CriteriaValues([cv1, cv2, cv3, cv12, cv23, cv13])
lbda = 0.6
model = MRSort(c, cvs, bpt, lbda, cps)
a = generate_alternatives(10000)
pt = generate_random_performance_table(a, model.criteria)
aa = model.get_assignments(pt)
model2 = MRSort(c, None, bpt, None, cps)
lp = LpMRSortMobius(model2, pt, aa)
obj = lp.solve()
aa2 = model2.get_assignments(pt)
self.assertEqual(obj, 0)
self.assertEqual(aa, aa2)
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 test003(self):
random.seed(2)
c = generate_criteria(10)
capacities = generate_random_capacities(c)
mobius = capacities_to_mobius(c, capacities)
self.assertAlmostEqual(mobius.sum(), 1)
def test003(self):
random.seed(2)
c = generate_criteria(10)
capacities = generate_random_capacities(c)
mobius = capacities_to_mobius(c, capacities)
self.assertAlmostEqual(mobius.sum(), 1)
def test005(self):
random.seed(4)
c = generate_criteria(10)
capacities = generate_random_capacities(c, 2)
mobius = capacities_to_mobius(c, capacities)
self.assertAlmostEqual(mobius.sum(), 1)
self.assertTrue(capacities_are_monotone(c, capacities))
self.assertAlmostEqual(capacities.max(), 1)
def test005(self):
random.seed(4)
c = generate_criteria(10)
capacities = generate_random_capacities(c, 2)
mobius = capacities_to_mobius(c, capacities)
self.assertAlmostEqual(mobius.sum(), 1)
self.assertTrue(capacities_are_monotone(c, capacities))
self.assertAlmostEqual(capacities.max(), 1)
def test001(self):
random.seed(4)
c = generate_criteria(3)
capacities = generate_random_capacities(c)
mobius = capacities_to_mobius(c, capacities)
capacities2 = mobius_to_capacities(c, mobius)
mobius_truncate(mobius, 2)
self.assertEqual(capacities, capacities2)
def test001(self):
random.seed(4)
c = generate_criteria(3)
capacities = generate_random_capacities(c)
mobius = capacities_to_mobius(c, capacities)
capacities2 = mobius_to_capacities(c, mobius)
mobius_truncate(mobius, 2)
self.assertEqual(capacities, capacities2)
def test003_auc_no_errors(self):
random.seed(3)
crits = generate_criteria(5)
model = generate_random_mrsort_model(len(crits), 3)
alts = generate_alternatives(1000)
pt = generate_random_performance_table(alts, crits)
aa = model.get_assignments(pt)
auc = model.auc(aa, pt)
self.assertEqual(auc, 1)
def test003_auc_no_errors(self):
random.seed(3)
crits = generate_criteria(5)
model = generate_random_mrsort_model(len(crits), 3)
alts = generate_alternatives(1000)
pt = generate_random_performance_table(alts, crits)
aa = model.get_assignments(pt)
auc = model.auc(aa, pt)
self.assertEqual(auc, 1)
def test004(self):
random.seed(3)
c = generate_criteria(10)
capacities = generate_random_capacities(c)
mobius = capacities_to_mobius(c, capacities)
mobius_truncate(mobius, 2)
mobius.normalize_sum_to_unity()
capacities2 = mobius_to_capacities(c, mobius)
self.assertAlmostEqual(mobius.sum(), 1)
self.assertTrue(capacities_are_monotone(c, capacities2))
self.assertAlmostEqual(capacities2.max(), 1)
def test002_auck_all_errors(self):
random.seed(2)
crits = generate_criteria(5)
model = generate_random_mrsort_model(len(crits), 2)
alts = generate_alternatives(1000)
pt = generate_random_performance_table(alts, crits)
aa = model.get_assignments(pt)
aa_err = add_errors_in_assignments(aa, model.categories, 1)
auck = model.auck(aa_err, pt, 1)
self.assertEqual(auck, 0)
def test002_auck_all_errors(self):
random.seed(2)
crits = generate_criteria(5)
model = generate_random_mrsort_model(len(crits), 2)
alts = generate_alternatives(1000)
pt = generate_random_performance_table(alts, crits)
aa = model.get_assignments(pt)
aa_err = add_errors_in_assignments(aa, model.categories, 1)
auck = model.auck(aa_err, pt, 1)
self.assertEqual(auck, 0)
def test004(self):
random.seed(3)
c = generate_criteria(10)
capacities = generate_random_capacities(c)
mobius = capacities_to_mobius(c, capacities)
mobius_truncate(mobius, 2)
mobius.normalize_sum_to_unity()
capacities2 = mobius_to_capacities(c, mobius)
self.assertAlmostEqual(mobius.sum(), 1)
self.assertTrue(capacities_are_monotone(c, capacities2))
self.assertAlmostEqual(capacities2.max(), 1)
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)
def test003(self):
random.seed(0)
crits = generate_criteria(5)
model = generate_random_mrsort_model(len(crits), 2)
alts = generate_alternatives(1000)
pt = generate_random_performance_table(alts, crits)
aa = model.get_assignments(pt)
aa = model.get_assignments(pt)
self.assertEqual(len(aa.get_alternatives_in_categories(['cat1'])), 65)
self.assertEqual(len(aa.get_alternatives_in_categories(['cat2'])), 935)
def test003(self):
random.seed(0)
crits = generate_criteria(5)
model = generate_random_mrsort_model(len(crits), 2)
alts = generate_alternatives(1000)
pt = generate_random_performance_table(alts, crits)
aa = model.get_assignments(pt)
aa = model.get_assignments(pt)
self.assertEqual(len(aa.get_alternatives_in_categories(['cat1'])), 65)
self.assertEqual(len(aa.get_alternatives_in_categories(['cat2'])), 935)
def test001(self):
c = generate_criteria(3)
cat = generate_categories(3)
cps = generate_categories_profiles(cat)
bp1 = AlternativePerformances('b1', {
'c1': 0.75,
'c2': 0.75,
'c3': 0.75
})
bp2 = AlternativePerformances('b2', {
'c1': 0.25,
'c2': 0.25,
'c3': 0.25
})
bpt = PerformanceTable([bp1, bp2])
cv1 = CriterionValue('c1', 0.2)
cv2 = CriterionValue('c2', 0.2)
cv3 = CriterionValue('c3', 0.2)
cv12 = CriterionValue(CriteriaSet(['c1', 'c2']), -0.1)
cv23 = CriterionValue(CriteriaSet(['c2', 'c3']), 0.2)
cv13 = CriterionValue(CriteriaSet(['c1', 'c3']), 0.3)
cvs = CriteriaValues([cv1, cv2, cv3, cv12, cv23, cv13])
lbda = 0.6
model = MRSort(c, cvs, bpt, lbda, cps)
ap1 = AlternativePerformances('a1', {'c1': 0.3, 'c2': 0.3, 'c3': 0.3})
ap2 = AlternativePerformances('a2', {'c1': 0.8, 'c2': 0.8, 'c3': 0.8})
ap3 = AlternativePerformances('a3', {'c1': 0.3, 'c2': 0.3, 'c3': 0.1})
ap4 = AlternativePerformances('a4', {'c1': 0.3, 'c2': 0.1, 'c3': 0.3})
ap5 = AlternativePerformances('a5', {'c1': 0.1, 'c2': 0.3, 'c3': 0.3})
ap6 = AlternativePerformances('a6', {'c1': 0.8, 'c2': 0.8, 'c3': 0.1})
ap7 = AlternativePerformances('a7', {'c1': 0.8, 'c2': 0.1, 'c3': 0.8})
ap8 = AlternativePerformances('a8', {'c1': 0.1, 'c2': 0.8, 'c3': 0.8})
pt = PerformanceTable([ap1, ap2, ap3, ap4, ap5, ap6, ap7, ap8])
aa = model.get_assignments(pt)
self.assertEqual(aa['a1'].category_id, "cat2")
self.assertEqual(aa['a2'].category_id, "cat1")
self.assertEqual(aa['a3'].category_id, "cat3")
self.assertEqual(aa['a4'].category_id, "cat2")
self.assertEqual(aa['a5'].category_id, "cat2")
self.assertEqual(aa['a6'].category_id, "cat3")
self.assertEqual(aa['a7'].category_id, "cat1")
self.assertEqual(aa['a8'].category_id, "cat1")
def test001(self):
c = generate_criteria(3)
cat = generate_categories(3)
cps = generate_categories_profiles(cat)
bp1 = AlternativePerformances('b1', {'c1': 0.75, 'c2': 0.75, 'c3': 0.75})
bp2 = AlternativePerformances('b2', {'c1': 0.25, 'c2': 0.25, 'c3': 0.25})
bpt = PerformanceTable([bp1, bp2])
cv1 = CriterionValue('c1', 0.2)
cv2 = CriterionValue('c2', 0.2)
cv3 = CriterionValue('c3', 0.2)
cv12 = CriterionValue(CriteriaSet(['c1', 'c2']), -0.1)
cv23 = CriterionValue(CriteriaSet(['c2', 'c3']), 0.2)
cv13 = CriterionValue(CriteriaSet(['c1', 'c3']), 0.3)
cvs = CriteriaValues([cv1, cv2, cv3, cv12, cv23, cv13])
lbda = 0.6
model = MRSort(c, cvs, bpt, lbda, cps)
ap1 = AlternativePerformances('a1',
{'c1': 0.3, 'c2': 0.3, 'c3': 0.3})
ap2 = AlternativePerformances('a2',
{'c1': 0.8, 'c2': 0.8, 'c3': 0.8})
ap3 = AlternativePerformances('a3',
{'c1': 0.3, 'c2': 0.3, 'c3': 0.1})
ap4 = AlternativePerformances('a4',
{'c1': 0.3, 'c2': 0.1, 'c3': 0.3})
ap5 = AlternativePerformances('a5',
{'c1': 0.1, 'c2': 0.3, 'c3': 0.3})
ap6 = AlternativePerformances('a6',
{'c1': 0.8, 'c2': 0.8, 'c3': 0.1})
ap7 = AlternativePerformances('a7',
{'c1': 0.8, 'c2': 0.1, 'c3': 0.8})
ap8 = AlternativePerformances('a8',
{'c1': 0.1, 'c2': 0.8, 'c3': 0.8})
pt = PerformanceTable([ap1, ap2, ap3, ap4, ap5, ap6, ap7, ap8])
aa = model.get_assignments(pt)
self.assertEqual(aa['a1'].category_id, "cat2")
self.assertEqual(aa['a2'].category_id, "cat1")
self.assertEqual(aa['a3'].category_id, "cat3")
self.assertEqual(aa['a4'].category_id, "cat2")
self.assertEqual(aa['a5'].category_id, "cat2")
self.assertEqual(aa['a6'].category_id, "cat3")
self.assertEqual(aa['a7'].category_id, "cat1")
self.assertEqual(aa['a8'].category_id, "cat1")
def on_button_generate(self):
seed = self.spinbox_seed.value()
random.seed(seed)
# FIXME
ncrit = self.spinbox_criteria.value()
crit = generate_criteria(ncrit)
self.worst = AlternativePerformances("worst", {c.id: 0 for c in crit})
self.best = AlternativePerformances("best", {c.id: 1 for c in crit})
if self.combobox_type_ori.currentIndex() == COMBO_AVFSORT:
self.generate_avf_sort_model()
else:
self.generate_mr_sort_model()
self.generate_assignments()
def run_test(m, n, lbda_min, lbda_max, filename):
# Create the CSV writer
f = open(filename, 'wb')
writer = csv.writer(f)
# Create a test_results instance
results = test_results()
initialized = False
# Run the algorithm
c = generate_criteria(n)
coalitions = [ i for i in powerset(c.keys()) ]
for i in range(m):
t = one_loop(c, lbda_min, coalitions)
t.test_name = "%d-%d" % (i, n)
t['i'] = i
t['nc'] = n
t['lbda_min'] = lbda_min
t['lbda_max'] = lbda_max
t['n'] = m
if initialized is False:
fields = ['i', 'nc', 'lbda_min', 'lbda_max'] \
+ [pprint_coalition(j) for j in coalitions] \
+ ["%d_criteria" % i for i in range(len(c) + 1)] \
+ ["ncoalitions"]
writer.writerow(fields)
initialized = True
results.append(t)
t.tocsv(writer, fields)
f.flush()
print("%s" % t)
# Perform a summary
writer.writerow(['', ''])
t = results.summary(['nc', 'lbda_min', 'lbda_max', 'n'], [pprint_coalition(j)
for j in coalitions], [], [])
t.tocsv(writer)
writer.writerow(['', ''])
t = results.summary(['nc', 'lbda_min', 'lbda_max', 'n'],
["%d_criteria" % i for i in range(len(c) + 1)] \
+ ['ncoalitions'], ['ncoalitions'],
['ncoalitions'])
t.tocsv(writer)
def generate_model(self):
ncrit = self.spinbox_criteria.value()
ncat = self.spinbox_categories.value()
# FIXME
crit = generate_criteria(ncrit)
self.worst = AlternativePerformances("worst", {c.id: 0 for c in crit})
self.best = AlternativePerformances("best", {c.id: 10 for c in crit})
self.model = generate_random_mrsort_model(ncrit,
ncat,
worst=self.worst,
best=self.best)
self.graph_model = QGraphicsSceneEtri(self.model, self.worst,
self.best,
self.graphicv_original.size())
self.graphicv_original.setScene(self.graph_model)
def generate_model(self):
ncrit = self.spinbox_criteria.value()
ncat = self.spinbox_categories.value()
# FIXME
crit = generate_criteria(ncrit)
self.worst = AlternativePerformances("worst",
{c.id: 0 for c in crit})
self.best = AlternativePerformances("best",
{c.id: 10 for c in crit})
self.model = generate_random_mrsort_model(ncrit, ncat,
worst = self.worst,
best = self.best)
self.graph_model = QGraphicsSceneEtri(self.model,
self.worst, self.best,
self.graphicv_original.size())
self.graphicv_original.setScene(self.graph_model)
def test002(self):
c = generate_criteria(3)
cat = generate_categories(3)
cps = generate_categories_profiles(cat)
bp1 = AlternativePerformances('b1', {
'c1': 0.75,
'c2': 0.75,
'c3': 0.75
})
bp2 = AlternativePerformances('b2', {
'c1': 0.25,
'c2': 0.25,
'c3': 0.25
})
bpt = PerformanceTable([bp1, bp2])
cv1 = CriterionValue('c1', 0.2)
cv2 = CriterionValue('c2', 0.2)
cv3 = CriterionValue('c3', 0.2)
cv12 = CriterionValue(CriteriaSet(['c1', 'c2']), -0.1)
cv23 = CriterionValue(CriteriaSet(['c2', 'c3']), 0.2)
cv13 = CriterionValue(CriteriaSet(['c1', 'c3']), 0.3)
cvs = CriteriaValues([cv1, cv2, cv3, cv12, cv23, cv13])
lbda = 0.6
model = MRSort(c, cvs, bpt, lbda, cps)
a = generate_alternatives(10000)
pt = generate_random_performance_table(a, model.criteria)
aa = model.get_assignments(pt)
model2 = MRSort(c, None, bpt, None, cps)
lp = LpMRSortMobius(model2, pt, aa)
obj = lp.solve()
aa2 = model2.get_assignments(pt)
self.assertEqual(obj, 0)
self.assertEqual(aa, aa2)
def test002(self):
random.seed(1)
c = generate_criteria(10)
capacities = generate_random_capacities(c)
self.assertTrue(capacities_are_monotone(c, capacities))
if __name__ == "__main__":
import random
from pymcda.generate import generate_criteria
from pymcda.generate import generate_random_criteria_weights
from pymcda.generate import generate_categories
from pymcda.generate import generate_categories_profiles
from pymcda.generate import generate_alternatives
from pymcda.generate import generate_random_performance_table
from pymcda.generate import generate_random_profiles
from pymcda.generate import generate_random_plinear_preference_function
random.seed(123)
criteria = generate_criteria(5)
crit_weights = generate_random_criteria_weights(criteria)
categories = generate_categories(5)
cat_profiles = generate_categories_profiles(categories)
a = generate_alternatives(100)
pt = generate_random_performance_table(a, criteria)
ap_best = pt.get_best(criteria)
ap_worst = pt.get_worst(criteria)
b = cat_profiles.get_ordered_profiles()
bpt = generate_random_profiles(b, criteria)
pf = generate_random_plinear_preference_function(criteria, ap_worst,
ap_best)
print(crit_weights)
print(categories)
from pymcda.types import CategoryValue, CategoriesValues
from pymcda.types import Interval
from pymcda.uta import AVFSort
from pymcda.utils import compute_ca
from pymcda.generate import generate_alternatives
from pymcda.generate import generate_categories
from pymcda.generate import generate_criteria
from pymcda.generate import generate_random_criteria_values
from pymcda.generate import generate_random_performance_table
from pymcda.generate import generate_random_criteria_functions
from pymcda.generate import generate_random_categories_values
from pymcda.utils import add_errors_in_assignments
from pymcda.utils import print_pt_and_assignments
# Generate an avfsort model
c = generate_criteria(7, random_direction = True)
cv = generate_random_criteria_values(c, seed = 1)
cv.normalize_sum_to_unity()
cat = generate_categories(3)
cfs = generate_random_criteria_functions(c, nseg_min = 3, nseg_max = 3)
catv = generate_random_categories_values(cat)
u = AVFSort(c, cv, cfs, catv)
# Generate random alternative and compute assignments
a = generate_alternatives(1000)
pt = generate_random_performance_table(a, c)
aa = u.get_assignments(pt)
aa_err = aa.copy()
aa_erroned = add_errors_in_assignments(aa_err, cat.keys(), 0.0)
return self.solve_function()
if __name__ == "__main__":
import random
from pymcda.generate import generate_criteria
from pymcda.generate import generate_categories
from pymcda.generate import generate_categories_profiles
from pymcda.generate import generate_alternatives
from pymcda.generate import generate_random_performance_table
from pymcda.types import AlternativePerformances, PerformanceTable
from pymcda.types import CriterionValue, CriteriaValues, CriteriaSet
from pymcda.electre_tri import MRSort
random.seed(0)
c = generate_criteria(5)
cat = generate_categories(3)
cps = generate_categories_profiles(cat)
bp1 = AlternativePerformances('b1', {'c1': 0.75, 'c2': 0.75, 'c3': 0.75,
'c4': 0.75, 'c5': 0.75})
bp2 = AlternativePerformances('b2', {'c1': 0.25, 'c2': 0.25, 'c3': 0.25,
'c4': 0.25, 'c5': 0.25})
bpt = PerformanceTable([bp1, bp2])
cv1 = CriterionValue('c1', 0.2)
cv2 = CriterionValue('c2', 0.2)
cv3 = CriterionValue('c3', 0.2)
cv4 = CriterionValue('c4', 0.2)
cv5 = CriterionValue('c5', 0.2)
cv12 = CriterionValue(CriteriaSet(['c1', 'c2']), -0.1)
from pymcda.types import CategoryValue, CategoriesValues
from pymcda.types import Interval
from pymcda.uta import AVFSort
from pymcda.utils import compute_ca
from pymcda.generate import generate_alternatives
from pymcda.generate import generate_categories
from pymcda.generate import generate_criteria
from pymcda.generate import generate_random_criteria_values
from pymcda.generate import generate_random_performance_table
from pymcda.generate import generate_random_criteria_functions
from pymcda.generate import generate_random_categories_values
from pymcda.utils import add_errors_in_assignments
from pymcda.utils import print_pt_and_assignments
# Generate an avfsort model
c = generate_criteria(7, random_direction = True)
cv = generate_random_criteria_values(c, seed = 1)
cv.normalize_sum_to_unity()
cat = generate_categories(3)
cfs = generate_random_criteria_functions(c, nseg_min = 3, nseg_max = 3)
catv = generate_random_categories_values(cat)
u = AVFSort(c, cv, cfs, catv)
# Generate random alternative and compute assignments
a = generate_alternatives(1000)
pt = generate_random_performance_table(a, c)
aa = u.get_assignments(pt)
aa_err = aa.copy()
aa_erroned = add_errors_in_assignments(aa_err, cat.keys(), 0.0)
if __name__ == "__main__":
from pymcda.electre_tri import MRSort
from pymcda.generate import generate_alternatives
from pymcda.generate import generate_categories
from pymcda.generate import generate_categories_profiles
from pymcda.generate import generate_random_performance_table
from pymcda.generate import generate_criteria
from pymcda.types import AlternativeAssignment
from pymcda.types import AlternativesAssignments
from pymcda.utils import print_pt_and_assignments
from pymcda.ui.graphic import display_electre_tri_models
cat = generate_categories(2)
cps = generate_categories_profiles(cat)
c = generate_criteria(4)
# Generate assignment incompatible with an MR-Sort model
ap1 = AlternativePerformances('a1', {'c1': 1, 'c2': 1, 'c3': 0, 'c4': 0})
ap2 = AlternativePerformances('a2', {'c1': 0, 'c2': 0, 'c3': 1, 'c4': 1})
ap3 = AlternativePerformances('a3', {'c1': 1, 'c2': 0, 'c3': 1, 'c4': 0})
ap4 = AlternativePerformances('a4', {'c1': 1, 'c2': 0, 'c3': 0, 'c4': 1})
ap5 = AlternativePerformances('a5', {'c1': 0, 'c2': 1, 'c3': 1, 'c4': 0})
ap6 = AlternativePerformances('a6', {'c1': 0, 'c2': 1, 'c3': 0, 'c4': 1})
pt = PerformanceTable([ap1, ap2, ap3, ap4, ap5, ap6])
aa1 = AlternativeAssignment('a1', 'cat1')
aa2 = AlternativeAssignment('a2', 'cat1')
aa3 = AlternativeAssignment('a3', 'cat2')
aa4 = AlternativeAssignment('a4', 'cat2')
aa5 = AlternativeAssignment('a5', 'cat2')
from pymcda.generate import generate_categories
from pymcda.generate import generate_categories_profiles
from pymcda.utils import print_pt_and_assignments
from pymcda.utils import compute_number_of_winning_coalitions
from pymcda.pt_sorted import SortedPerformanceTable
from pymcda.ui.graphic import display_electre_tri_models
from pymcda.electre_tri import MRSort
from pymcda.types import CriterionValue, CriteriaValues
from pymcda.types import AlternativePerformances, PerformanceTable
from pymcda.types import AlternativeAssignment, AlternativesAssignments
# Generate a random ELECTRE TRI BM model
random.seed(127890123456789)
ncriteria = 5
model = MRSort()
model.criteria = generate_criteria(ncriteria)
model.cv = CriteriaValues([CriterionValue('c%d' % (i + 1), 0.2)
for i in range(ncriteria)])
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
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")
aid = "a%d" % i
ap = AlternativePerformances(aid,
OrderedDict({c: 1 if c in coalition else 0
for c in cids}))
pt.append(ap)
cat = cats[0]
for fmin in fmins:
if fmin.issubset(set(coalition)) is True:
cat = cats[1]
break
aa.append(AlternativeAssignment(aid, cat))
i += 1
return pt, aa
if __name__ == "__main__":
from pymcda.generate import generate_criteria
from pymcda.generate import generate_categories
criteria = generate_criteria(4)
categories = generate_categories(2)
pt, aa = generate_binary_performance_table_and_assignments(criteria,
categories,
fmin1)
print(pt)
print(aa)
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 test002(self):
random.seed(1)
c = generate_criteria(10)
capacities = generate_random_capacities(c)
self.assertTrue(capacities_are_monotone(c, capacities))
