def one_test2(self, seed, ncrit, ncat, na):
model = generate_random_mrsort_model(ncrit, ncat, seed)
a = generate_alternatives(na)
pt = generate_random_performance_table(a, model.criteria)
aa = model.pessimist(pt)
model2 = model.copy()
model2.cvs = None
lp_weights = LpMRSortWeights(model2, pt, aa)
bids = model.categories_profiles.get_ordered_profiles()
bpt = generate_random_profiles(bids, model.criteria)
model.bpt = model2.bpt = bpt
aa = model.pessimist(pt)
lp_weights.aa_ori = aa
lp_weights.update_linear_program()
lp_weights.solve()
aa2 = model2.pessimist(pt)
self.assertEqual(aa, aa2)
def test003(self):
model = generate_random_mrsort_model(5, 2, seed=3)
a = generate_alternatives(10)
pt = generate_random_performance_table(a, model.criteria)
aa = model.get_assignments(pt)
self.mcda_object_to_xmcda_file(model.criteria.to_xmcda(),
"criteria.xml")
categories = model.categories_profiles.to_categories()
self.mcda_object_to_xmcda_file(categories.to_xmcda(), "categories.xml")
self.mcda_object_to_xmcda_file(a.to_xmcda(), "alternatives.xml")
self.mcda_object_to_xmcda_file(pt.to_xmcda(), "perfs_table.xml")
self.mcda_object_to_xmcda_file(aa.to_xmcda(), "assign.xml")
self.mcda_object_to_xmcda_file(model.bpt.to_xmcda(),
"profiles_perfs.xml")
mrsort_mip(self.indir, self.outdir)
self.check_output_is_complete()
model2 = self.get_output_model(model.criteria)
aa2 = model2.get_assignments(pt)
self.assertEqual(aa, aa2)
self.assertEqual(model.bpt, model2.bpt)
def one_test2(self, seed, ncrit, ncat, na):
model = generate_random_mrsort_model(ncrit, ncat, seed)
a = generate_alternatives(na)
pt = generate_random_performance_table(a, model.criteria)
aa = model.pessimist(pt)
model2 = model.copy()
model2.cvs = None
lp_weights = LpMRSortWeights(model2, pt, aa)
bids = model.categories_profiles.get_ordered_profiles()
bpt = generate_random_profiles(bids, model.criteria)
model.bpt = model2.bpt = bpt
aa = model.pessimist(pt)
lp_weights.aa_ori = aa
lp_weights.update_linear_program()
lp_weights.solve()
aa2 = model2.pessimist(pt)
self.assertEqual(aa, aa2)
def test003(self):
model = generate_random_mrsort_model(5, 2, seed = 3)
a = generate_alternatives(10)
pt = generate_random_performance_table(a, model.criteria)
aa = model.get_assignments(pt)
self.mcda_object_to_xmcda_file(model.criteria.to_xmcda(),
"criteria.xml")
categories = model.categories_profiles.to_categories()
self.mcda_object_to_xmcda_file(categories.to_xmcda(),
"categories.xml")
self.mcda_object_to_xmcda_file(a.to_xmcda(), "alternatives.xml")
self.mcda_object_to_xmcda_file(pt.to_xmcda(), "perfs_table.xml")
self.mcda_object_to_xmcda_file(aa.to_xmcda(), "assign.xml")
self.mcda_object_to_xmcda_file(model.bpt.to_xmcda(),
"profiles_perfs.xml")
mrsort_mip(self.indir, self.outdir)
self.check_output_is_complete()
model2 = self.get_output_model(model.criteria)
aa2 = model2.get_assignments(pt)
self.assertEqual(aa, aa2)
self.assertEqual(model.bpt, model2.bpt)
def generate_mr_sort_model(self):
ncrit = self.spinbox_criteria.value()
ncat = self.spinbox_categories.value()
self.model = generate_random_mrsort_model(ncrit,
ncat,
worst=self.worst,
best=self.best)
self.categories = self.model.categories_profiles.to_categories()
self.plot_mr_sort_model(self.model, self.layout_original)
def test_heur_mrsort_init_profiles(seed, na, nc, ncat, pcerrors):
# Generate an ELECTRE TRI model and assignment examples
model = generate_random_mrsort_model(nc, ncat, seed)
model2 = model.copy()
model3 = model.copy()
# Generate a first set of alternatives
a = generate_alternatives(na)
pt = generate_random_performance_table(a, model.criteria)
# Compute assignments
aa = model.pessimist(pt)
# Initialize the second model with random generated profiles
b = model.categories_profiles.get_ordered_profiles()
model2.bpt = generate_random_profiles(b, model2.criteria)
# Run the heuristic
cats = model.categories_profiles.to_categories()
pt_sorted = SortedPerformanceTable(pt)
heur = HeurMRSortInitProfiles(model3, pt_sorted, aa)
heur.solve()
# Learn the weights and cut threshold
cps = model.categories_profiles
lp_weights = LpMRSortWeights(model2, pt, aa)
lp_weights.solve()
lp_weights = LpMRSortWeights(model3, pt, aa)
lp_weights.solve()
# Compute the classification accuracy
aa2 = model2.pessimist(pt)
aa3 = model3.pessimist(pt)
ca2 = compute_ca(aa, aa2)
ca3 = compute_ca(aa, aa3)
# Save all infos in test_result class
t = test_result("%s-%d-%d-%d-%g" % (seed, na, nc, ncat, pcerrors))
# Input params
t['seed'] = seed
t['na'] = na
t['nc'] = nc
t['ncat'] = ncat
t['pcerrors'] = pcerrors
# Output params
t['ca_rdom'] = ca2
t['ca_heur'] = ca3
return t
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 test_heur_mrsort_init_profiles(seed, na, nc, ncat, pcerrors):
# Generate an ELECTRE TRI model and assignment examples
model = generate_random_mrsort_model(nc, ncat, seed)
model2 = model.copy()
model3 = model.copy()
# Generate a first set of alternatives
a = generate_alternatives(na)
pt = generate_random_performance_table(a, model.criteria)
# Compute assignments
aa = model.pessimist(pt)
# Initialize the second model with random generated profiles
b = model.categories_profiles.get_ordered_profiles()
model2.bpt = generate_random_profiles(b, model2.criteria)
# Run the heuristic
cats = model.categories_profiles.to_categories()
pt_sorted = SortedPerformanceTable(pt)
heur = HeurMRSortInitProfiles(model3, pt_sorted, aa)
heur.solve()
# Learn the weights and cut threshold
cps = model.categories_profiles
lp_weights = LpMRSortWeights(model2, pt, aa)
lp_weights.solve()
lp_weights = LpMRSortWeights(model3, pt, aa)
lp_weights.solve()
# Compute the classification accuracy
aa2 = model2.pessimist(pt)
aa3 = model3.pessimist(pt)
ca2 = compute_ca(aa, aa2)
ca3 = compute_ca(aa, aa3)
# Save all infos in test_result class
t = test_result("%s-%d-%d-%d-%g" % (seed, na, nc, ncat, pcerrors))
# Input params
t['seed'] = seed
t['na'] = na
t['nc'] = nc
t['ncat'] = ncat
t['pcerrors'] = pcerrors
# Output params
t['ca_rdom'] = ca2
t['ca_heur'] = ca3
return t
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 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 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 one_test(self, seed, ncrit, ncat, na):
model = generate_random_mrsort_model(ncrit, ncat, seed)
a = generate_alternatives(na)
pt = generate_random_performance_table(a, model.criteria)
aa = model.pessimist(pt)
model2 = model.copy()
model2.cvs = None
lp_weights = LpMRSortWeights(model2, pt, aa)
lp_weights.solve()
aa2 = model2.pessimist(pt)
self.assertEqual(aa, aa2)
def one_test(self, seed, ncrit, ncat, na):
model = generate_random_mrsort_model(ncrit, ncat, seed)
a = generate_alternatives(na)
pt = generate_random_performance_table(a, model.criteria)
aa = model.pessimist(pt)
model2 = model.copy()
model2.cvs = None
lp_weights = LpMRSortWeights(model2, pt, aa)
lp_weights.solve()
aa2 = model2.pessimist(pt)
self.assertEqual(aa, aa2)
def one_test(self, seed, na, nc, ncat, ca_expected):
model = generate_random_mrsort_model(nc, ncat, seed)
a = generate_alternatives(na)
pt = generate_random_performance_table(a, model.criteria)
aa = model.pessimist(pt)
pt_sorted = SortedPerformanceTable(pt)
heur = HeurMRSortInitProfiles(model, pt_sorted, aa)
heur.solve()
aa2 = model.pessimist(pt)
ca = compute_ca(aa, aa2)
self.assertEqual(ca, ca_expected)
def one_test(self, seed, na, nc, ncat, ca_expected):
model = generate_random_mrsort_model(nc, ncat, seed)
a = generate_alternatives(na)
pt = generate_random_performance_table(a, model.criteria)
aa = model.pessimist(pt)
pt_sorted = SortedPerformanceTable(pt)
heur = HeurMRSortInitProfiles(model, pt_sorted, aa)
heur.solve()
aa2 = model.pessimist(pt)
ca = compute_ca(aa, aa2)
self.assertEqual(ca, ca_expected)
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 test_heur_mrsort_coalitions(seed, na, nc, ncat, pcexamples, pcerrors):
# Generate an ELECTRE TRI model and assignment examples
model = generate_random_mrsort_model(nc, ncat, seed)
# Generate a first set of alternatives
a = generate_alternatives(na)
pt = generate_random_performance_table(a, model.criteria)
# Compute assignments
aa = model.pessimist(pt)
# Run the heuristic
heur = HeurMRSortCoalitions(model.criteria, model.categories, pt, aa)
coal2 = heur.find_coalitions(int(na * pcexamples))
# Compute the original winning coalitions
winning, loosing = compute_winning_and_loosing_coalitions(
model.cv, model.lbda)
# Compare orignal and computed coalitions
coal_ni = list((set(winning) ^ set(coal2)) & set(winning))
coal_add = list((set(winning) ^ set(coal2)) & set(coal2))
# Save all infos in test_result class
t = test_result("%s-%d-%d-%d-%g-%g" %
(seed, na, nc, ncat, pcexamples, pcerrors))
# Input params
t['seed'] = seed
t['na'] = na
t['nc'] = nc
t['ncat'] = ncat
t['pcexamples'] = pcexamples
t['pcerrors'] = pcerrors
# Output params
t['ncoal'] = len(winning)
t['ncoal_ni'] = len(coal_ni)
t['ncoal_add'] = len(coal_add)
return t
def one_test(self, seed, na, nc, ncat, max_loop, n):
model = generate_random_mrsort_model(nc, ncat, seed)
a = generate_alternatives(na)
pt = generate_random_performance_table(a, model.criteria)
aa = model.pessimist(pt)
model2 = model.copy()
bids = model2.categories_profiles.get_ordered_profiles()
model2.bpt = generate_random_profiles(bids, model.criteria)
pt_sorted = SortedPerformanceTable(pt)
meta = MetaMRSortProfiles4(model2, pt_sorted, aa)
for i in range(1, max_loop + 1):
ca = meta.optimize()
if ca == 1:
break
aa2 = model2.pessimist(pt)
self.assertEqual(i, n)
self.assertEqual(aa, aa2)
def one_test(self, seed, na, nc, ncat, pcerrors):
model = generate_random_mrsort_model(nc, ncat, seed)
a = generate_alternatives(na)
pt = generate_random_performance_table(a, model.criteria)
aa = model.pessimist(pt)
aa_err = aa.copy()
add_errors_in_assignments(aa_err, model.categories, pcerrors / 100)
model2 = model.copy()
model2.bpt = None
model2.cv = None
model2.lbda = None
mip = MipMRSort(model2, pt, aa_err)
obj = mip.solve()
aa2 = model2.pessimist(pt)
ca = compute_ca(aa, aa2)
ca2 = compute_ca(aa_err, aa2)
self.assertEqual(ca2, obj / len(a))
self.assertLessEqual(pcerrors / 100, ca2)
def one_test(self, seed, na, nc, ncat, max_loop, n):
model = generate_random_mrsort_model(nc, ncat, seed)
a = generate_alternatives(na)
pt = generate_random_performance_table(a, model.criteria)
aa = model.pessimist(pt)
model2 = model.copy()
bids = model2.categories_profiles.get_ordered_profiles()
model2.bpt = generate_random_profiles(bids, model.criteria)
pt_sorted = SortedPerformanceTable(pt)
meta = MetaMRSortProfiles4(model2, pt_sorted, aa)
for i in range(1, max_loop + 1):
ca = meta.optimize()
if ca == 1:
break
aa2 = model2.pessimist(pt)
self.assertEqual(i, n)
self.assertEqual(aa, aa2)
def one_test(self, seed, na, nc, ncat, pcerrors):
model = generate_random_mrsort_model(nc, ncat, seed)
a = generate_alternatives(na)
pt = generate_random_performance_table(a, model.criteria)
aa = model.pessimist(pt)
aa_err = aa.copy()
add_errors_in_assignments(aa_err, model.categories, pcerrors / 100)
model2 = model.copy()
model2.bpt = None
model2.cv = None
model2.lbda = None
mip = MipMRSort(model2, pt, aa_err)
obj = mip.solve()
aa2 = model2.pessimist(pt)
ca = compute_ca(aa, aa2)
ca2 = compute_ca(aa_err, aa2)
self.assertEqual(ca2, obj / len(a))
self.assertLessEqual(pcerrors / 100, ca2)
self.model.bpt = bpt
if __name__ == "__main__":
from pymcda.generate import generate_random_mrsort_model
from pymcda.generate import generate_alternatives
from pymcda.generate import generate_random_performance_table
from pymcda.generate import generate_random_profiles
from pymcda.pt_sorted import SortedPerformanceTable
from pymcda.utils import compute_ca
from pymcda.utils import compute_winning_and_loosing_coalitions
from pymcda.utils import display_coalitions
from pymcda.learning.lp_mrsort_weights import LpMRSortWeights
from pymcda.ui.graphic import display_electre_tri_models
model = generate_random_mrsort_model(10, 3, 17)
winning, loosing = compute_winning_and_loosing_coalitions(
model.cv, model.lbda)
print("Number of coalitions: %d" % len(winning))
a = generate_alternatives(1000)
pt = generate_random_performance_table(a, model.criteria)
sorted_pt = SortedPerformanceTable(pt)
aa = model.pessimist(pt)
for cat in model.categories_profiles.get_ordered_categories():
pc = len(aa.get_alternatives_in_category(cat)) / len(aa) * 100
print("Percentage of alternatives in %s: %g %%" % (cat, pc))
# Learn the weights with random generated profiles
self.model.bpt = best_bpt
self.ca = best_ca
aa2 = self.model.pessimist(self.pt_sorted.pt)
return compute_ca(self.aa_ori, aa2)
if __name__ == "__main__":
import time
from pymcda.generate import generate_alternatives
from pymcda.generate import generate_random_performance_table
from pymcda.utils import compute_winning_and_loosing_coalitions
from pymcda.types import AlternativePerformances
from pymcda.ui.graphic import display_electre_tri_models
# Generate a random ELECTRE TRI BM model
model = generate_random_mrsort_model(10, 3, 1)
worst = AlternativePerformances("worst",
{c.id: 0 for c in model.criteria})
best = AlternativePerformances("best",
{c.id: 1 for c in model.criteria})
# Generate a set of alternatives
a = generate_alternatives(1000)
pt = generate_random_performance_table(a, model.criteria)
aa = model.pessimist(pt)
nmeta = 20
nloops = 30
print('Original model')
print('==============')
self.model.veto = vpt
if __name__ == "__main__":
from pymcda.generate import generate_random_mrsort_model
from pymcda.generate import generate_alternatives
from pymcda.generate import generate_random_performance_table
from pymcda.generate import generate_random_profiles
from pymcda.pt_sorted import SortedPerformanceTable
from pymcda.utils import compute_ca
from pymcda.utils import compute_winning_and_loosing_coalitions
from pymcda.utils import display_coalitions
from pymcda.learning.lp_mrsort_weights import LpMRSortWeights
from pymcda.ui.graphic import display_electre_tri_models
model = generate_random_mrsort_model(10, 2, 1)
a = generate_alternatives(1000)
pt = generate_random_performance_table(a, model.criteria)
sorted_pt = SortedPerformanceTable(pt)
aa = model.pessimist(pt)
# Learn the veto weights with profiles generated by the heuristic
model3 = model.copy()
heur = HeurMRSortInitVetoProfiles(model3, sorted_pt, aa)
heur.solve()
print(model3.veto)
def test_meta_electre_tri_global(seed, na, nc, ncat, na_gen, pcerrors,
max_oloops, nmodels, max_loops):
# Generate a random ELECTRE TRI BM model
if random_model_type == 'mrsort':
model = generate_random_mrsort_model(nc, ncat, seed)
elif random_model_type == 'ncs':
model = generate_random_mrsort_choquet_model(nc, ncat, 2, seed)
elif random_model_type == 'mrsortcv':
model = generate_random_mrsort_model_with_coalition_veto2(
nc, ncat, 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()
categories = model.categories_profiles.to_categories()
aa_erroned = add_errors_in_assignments_proba(aa_err, model.categories,
pcerrors / 100)
na_err = len(aa_erroned)
# Sort the performance table
pt_sorted = SortedPerformanceTable(pt)
meta = algo(nmodels, model.criteria, categories, pt_sorted, aa)
metas_sorted = meta.sort_models()
ca2_iter = [metas_sorted[0].ca] + [1] * (max_loops)
t1 = time.time()
for i in range(0, max_loops):
model2, ca2_best = meta.optimize(max_oloops)
ca2_iter[i + 1] = ca2_best
if ca2_best == 1:
break
nloops = i + 1
t_total = time.time() - t1
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-%d-%d-%d" %
(seed, na, nc, ncat, na_gen, pcerrors, max_loops, nmodels, max_oloops))
model.id = 'initial'
model2.id = 'learned'
pt.id, pt_gen.id = 'learning_set', 'test_set'
save_to_xmcda("%s/%s.bz2" % (directory, t.test_name), model, model2, pt,
pt_gen)
# Input params
t['seed'] = seed
t['na'] = na
t['nc'] = nc
t['ncat'] = ncat
t['na_gen'] = na_gen
t['pcerrors'] = pcerrors
t['max_loops'] = max_loops
t['nmodels'] = nmodels
t['max_oloops'] = max_oloops
# 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['nloops'] = nloops
t['t_total'] = t_total
t['ca2_iter'] = ca2_iter
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
sum([k * self.yp[i] for i, k in enumerate(self.c_yi.values())]))
if __name__ == "__main__":
import time
import random
from pymcda.generate import generate_alternatives
from pymcda.generate import generate_random_performance_table
from pymcda.generate import generate_random_mrsort_model
from pymcda.utils import add_errors_in_assignments
from pymcda.utils import print_pt_and_assignments
from pymcda.utils import compute_winning_and_loosing_coalitions
from pymcda.types import AlternativesAssignments, PerformanceTable
# Original Electre Tri model
model = generate_random_mrsort_model(10, 5, 890)
# Generate random alternatives
a = generate_alternatives(15000)
pt = generate_random_performance_table(a, model.criteria)
errors = 0.0
delta = 0.0001
nlearn = 1.00
# Assign the alternative with the model
aa = model.pessimist(pt)
a_learn = random.sample(a, int(nlearn * len(a)))
aa_learn = AlternativesAssignments([aa[alt.id] for alt in a_learn])
pt_learn = PerformanceTable([pt[alt.id] for alt in a_learn])
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
vpt.append(b1 - vp)
self.model.veto = vpt
if __name__ == "__main__":
from pymcda.generate import generate_random_mrsort_model
from pymcda.generate import generate_alternatives
from pymcda.generate import generate_random_performance_table
from pymcda.generate import generate_random_profiles
from pymcda.pt_sorted import SortedPerformanceTable
from pymcda.utils import compute_ca
from pymcda.utils import compute_winning_and_loosing_coalitions
from pymcda.utils import display_coalitions
from pymcda.learning.lp_mrsort_weights import LpMRSortWeights
from pymcda.ui.graphic import display_electre_tri_models
model = generate_random_mrsort_model(10, 2, 1)
a = generate_alternatives(1000)
pt = generate_random_performance_table(a, model.criteria)
sorted_pt = SortedPerformanceTable(pt)
aa = model.pessimist(pt)
# Learn the veto weights with profiles generated by the heuristic
model3 = model.copy()
heur = HeurMRSortInitVetoProfiles(model3, sorted_pt, aa)
heur.solve()
print(model3.veto)
def test_meta_electre_tri_profiles(seed, na, nc, ncat, na_gen, pcerrors,
max_loops):
# Generate an ELECTRE TRI model and assignment examples
model = generate_random_mrsort_model(nc, ncat, seed)
model2 = model.copy()
# Generate a first set of alternatives
a = generate_alternatives(na)
pt = generate_random_performance_table(a, model.criteria)
aa = model.pessimist(pt)
# Initiate model with random profiles
model2.bpt = generate_random_profiles(model.profiles, model.criteria)
# Add errors in assignment examples
aa_err = aa.copy()
aa_erroned = add_errors_in_assignments(aa_err, model.categories,
pcerrors / 100)
# Sort the performance table
pt_sorted = SortedPerformanceTable(pt)
t1 = time.time()
# Run the algorithm
meta = algo(model2, pt_sorted, aa_err)
ca2_iter = [1] * (max_loops + 1)
aa2 = model2.pessimist(pt)
ca2 = compute_ca(aa_err, aa2)
ca2_best = ca2
best_bpt = model2.bpt.copy()
ca2_iter[0] = ca2
nloops = 0
for k in range(max_loops):
if ca2_best == 1:
break
meta.optimize()
nloops += 1
aa2 = meta.aa
ca2 = compute_ca(aa_err, aa2)
ca2_iter[k + 1] = ca2
if ca2 > ca2_best:
ca2_best = ca2
best_bpt = model2.bpt.copy()
t_total = time.time() - t1
# Determine the number of erroned alternatives badly assigned
model2.bpt = best_bpt
aa2 = model2.pessimist(pt)
ok = ok_errors = ok2_errors = 0
for alt in a:
if aa_err(alt.id) == aa2(alt.id) and alt.id in aa_erroned:
ok2_errors += 1
if aa(alt.id) == aa2(alt.id):
if alt.id in aa_erroned:
ok_errors += 1
ok += 1
total = len(a)
ca_best = ok / total
ca_best_errors = ok_errors / total
ca2_best_errors = ok2_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)
# Save all infos in test_result class
t = test_result("%s-%d-%d-%d-%d-%g-%d" %
(seed, na, nc, ncat, na_gen, pcerrors, max_loops))
# Input params
t['seed'] = seed
t['na'] = na
t['nc'] = nc
t['ncat'] = ncat
t['na_gen'] = na_gen
t['pcerrors'] = pcerrors
t['max_loops'] = max_loops
# Ouput params
t['ca_best'] = ca_best
t['ca_best_errors'] = ca_best_errors
t['ca2_best'] = ca2_best
t['ca2_best_errors'] = ca2_best_errors
t['ca_gen'] = ca_gen
t['nloops'] = nloops
t['t_total'] = t_total
t['ca2_iter'] = ca2_iter
return t
def test_meta_electre_tri_global(seed, na, nc, ncat, na_gen, pcerrors,
max_oloops, nmodels, max_loops):
# Generate a random ELECTRE TRI BM model
if random_model_type == 'mrsort':
model = generate_random_mrsort_model(nc, ncat, seed)
elif random_model_type == 'ncs':
model = generate_random_mrsort_choquet_model(nc, ncat, 2, seed)
elif random_model_type == 'mrsortcv':
model = generate_random_mrsort_model_with_coalition_veto2(nc, ncat,
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()
categories = model.categories_profiles.to_categories()
aa_erroned = add_errors_in_assignments_proba(aa_err,
model.categories,
pcerrors / 100)
na_err = len(aa_erroned)
# Sort the performance table
pt_sorted = SortedPerformanceTable(pt)
meta = algo(nmodels, model.criteria, categories, pt_sorted, aa)
metas_sorted = meta.sort_models()
ca2_iter = [metas_sorted[0].ca] + [1] * (max_loops)
t1 = time.time()
for i in range(0, max_loops):
model2, ca2_best = meta.optimize(max_oloops)
ca2_iter[i + 1] = ca2_best
if ca2_best == 1:
break
nloops = i + 1
t_total = time.time() - t1
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-%d-%d-%d" % (seed, na, nc, ncat,
na_gen, pcerrors, max_loops, nmodels, max_oloops))
model.id = 'initial'
model2.id = 'learned'
pt.id, pt_gen.id = 'learning_set', 'test_set'
save_to_xmcda("%s/%s.bz2" % (directory, t.test_name),
model, model2, pt, pt_gen)
# Input params
t['seed'] = seed
t['na'] = na
t['nc'] = nc
t['ncat'] = ncat
t['na_gen'] = na_gen
t['pcerrors'] = pcerrors
t['max_loops'] = max_loops
t['nmodels'] = nmodels
t['max_oloops'] = max_oloops
# 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['nloops'] = nloops
t['t_total'] = t_total
t['ca2_iter'] = ca2_iter
return t
def solve(self):
return self.solve_function()
if __name__ == "__main__":
from pymcda.generate import generate_random_mrsort_model
from pymcda.generate import generate_alternatives
from pymcda.generate import generate_random_performance_table
from pymcda.utils import print_pt_and_assignments
from pymcda.ui.graphic import display_electre_tri_models
seed = 12
ncrit = 5
ncat = 3
# Generate a random ELECTRE TRI BM model
model = generate_random_mrsort_model(ncrit, ncat, seed)
# Display model parameters
print('Original model')
print('==============')
cids = model.criteria.keys()
cids.sort()
model.bpt.display(criterion_ids = cids)
model.cv.display(criterion_ids = cids)
print("lambda: %.7s" % model.lbda)
# Generate a set of alternatives
a = generate_alternatives(100)
pt = generate_random_performance_table(a, model.criteria)
worst = pt.get_worst(model.criteria)
def test001(self):
model = generate_random_mrsort_model(5, 3)
mxmcda = model.to_xmcda()
model2 = MRSort().from_xmcda(mxmcda)
self.assertEqual(model, model2)
def test_lp_avfsort(seed, na, nc, ncat, ns, na_gen, pcerrors):
# Generate a random ELECTRE TRI model and assignment examples
model = generate_random_mrsort_model(nc, ncat, seed)
# Generate a first 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(aa_err, model.categories,
pcerrors / 100)
gi_worst = AlternativePerformances('worst',
{c.id: 0
for c in model.criteria})
gi_best = AlternativePerformances('best',
{c.id: 1
for c in model.criteria})
css = CriteriaValues([])
for c in model.criteria:
cs = CriterionValue(c.id, ns)
css.append(cs)
# Run linear program
t1 = time.time()
lp = LpAVFSort(model.criteria, css,
model.categories_profiles.to_categories(), gi_worst,
gi_best)
t2 = time.time()
obj, cv_l, cfs_l, catv_l = lp.solve(aa_err, pt)
t3 = time.time()
model2 = AVFSort(model.criteria, cv_l, cfs_l, catv_l)
# Compute new assignment and classification accuracy
aa2 = model2.get_assignments(pt)
ok = ok_errors = ok2 = ok2_errors = 0
for alt in a:
if aa_err(alt.id) == aa2(alt.id):
ok2 += 1
if alt.id in aa_erroned:
ok2_errors += 1
if aa(alt.id) == aa2(alt.id):
ok += 1
if alt.id in aa_erroned:
ok_errors += 1
total = len(a)
ca2 = ok2 / total
ca2_errors = ok2_errors / total
ca = ok / total
ca_errors = ok_errors / total
# Perform the generalization
a_gen = generate_alternatives(na_gen)
pt_gen = generate_random_performance_table(a_gen, model.criteria)
aa = model.pessimist(pt_gen)
aa2 = model2.get_assignments(pt_gen)
ca_gen = compute_ca(aa, aa2)
# Save all infos in test_result class
t = test_result("%s-%d-%d-%d-%d-%d-%g" %
(seed, na, nc, ncat, ns, na_gen, pcerrors))
# Input params
t['seed'] = seed
t['na'] = na
t['nc'] = nc
t['ncat'] = ncat
t['ns'] = ns
t['na_gen'] = na_gen
t['pcerrors'] = pcerrors
# Output params
t['obj'] = obj
t['ca'] = ca
t['ca_errors'] = ca_errors
t['ca2'] = ca2
t['ca2_errors'] = ca2_errors
t['ca_gen'] = ca_gen
t['t_total'] = t3 - t1
t['t_const'] = t2 - t1
t['t_solve'] = t3 - t2
return t
def test_meta_electre_tri_profiles(seed, na, nc, ncat, na_gen, pcerrors,
max_loops):
# Generate an ELECTRE TRI model and assignment examples
model = generate_random_mrsort_model(nc, ncat, seed)
model2 = model.copy()
# Generate a first set of alternatives
a = generate_alternatives(na)
pt = generate_random_performance_table(a, model.criteria)
aa = model.pessimist(pt)
# Initiate model with random profiles
model2.bpt = generate_random_profiles(model.profiles, model.criteria)
# Add errors in assignment examples
aa_err = aa.copy()
aa_erroned = add_errors_in_assignments(aa_err, model.categories,
pcerrors / 100)
# Sort the performance table
pt_sorted = SortedPerformanceTable(pt)
t1 = time.time()
# Run the algorithm
meta = algo(model2, pt_sorted, aa_err)
ca2_iter = [1] * (max_loops + 1)
aa2 = model2.pessimist(pt)
ca2 = compute_ca(aa_err, aa2)
ca2_best = ca2
best_bpt = model2.bpt.copy()
ca2_iter[0] = ca2
nloops = 0
for k in range(max_loops):
if ca2_best == 1:
break
meta.optimize()
nloops += 1
aa2 = meta.aa
ca2 = compute_ca(aa_err, aa2)
ca2_iter[k + 1] = ca2
if ca2 > ca2_best:
ca2_best = ca2
best_bpt = model2.bpt.copy()
t_total = time.time() - t1
# Determine the number of erroned alternatives badly assigned
model2.bpt = best_bpt
aa2 = model2.pessimist(pt)
ok = ok_errors = ok2_errors = 0
for alt in a:
if aa_err(alt.id) == aa2(alt.id) and alt.id in aa_erroned:
ok2_errors += 1
if aa(alt.id) == aa2(alt.id):
if alt.id in aa_erroned:
ok_errors += 1
ok += 1
total = len(a)
ca_best = ok / total
ca_best_errors = ok_errors / total
ca2_best_errors = ok2_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)
# Save all infos in test_result class
t = test_result("%s-%d-%d-%d-%d-%g-%d" % (seed, na, nc, ncat, na_gen,
pcerrors, max_loops))
# Input params
t['seed'] = seed
t['na'] = na
t['nc'] = nc
t['ncat'] = ncat
t['na_gen'] = na_gen
t['pcerrors'] = pcerrors
t['max_loops'] = max_loops
# Ouput params
t['ca_best'] = ca_best
t['ca_best_errors'] = ca_best_errors
t['ca2_best'] = ca2_best
t['ca2_best_errors'] = ca2_best_errors
t['ca_gen'] = ca_gen
t['nloops'] = nloops
t['t_total'] = t_total
t['ca2_iter'] = ca2_iter
return t
self.ca = best_ca
return best_ca
if __name__ == "__main__":
import time
import random
from pymcda.generate import generate_alternatives
from pymcda.generate import generate_random_performance_table
from pymcda.generate import generate_random_criteria_weights
from pymcda.utils import compute_winning_and_loosing_coalitions
from pymcda.types import AlternativePerformances
from pymcda.ui.graphic import display_electre_tri_models
# Generate a random ELECTRE TRI BM model
model = generate_random_mrsort_model(7, 2, 5)
worst = AlternativePerformances("worst", {c.id: 0 for c in model.criteria})
best = AlternativePerformances("best", {c.id: 1 for c in model.criteria})
# Add veto
vpt = generate_random_profiles(model.profiles, model.criteria, None, 3,
worst, model.bpt['b1'])
model.veto = PerformanceTable([model.bpt['b1'] - vpt['b1']])
model.veto_weights = generate_random_criteria_weights(model.criteria)
model.veto_lbda = random.random()
# Generate a set of alternatives
a = generate_alternatives(1000)
pt = generate_random_performance_table(a, model.criteria)
aa = model.pessimist(pt)
def test_lp_learning_weights(seed, na, nc, ncat, na_gen, pcerrors):
# Generate an ELECTRE TRI model and assignment examples
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)
model2 = model.copy()
# Generate a first 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(aa_err, model.categories,
pcerrors / 100)
# Run linear program
t1 = time.time()
if random_model_type == 'default':
lp_weights = LpMRSortWeights(model2, pt, aa_err, 0.0001)
else:
lp_weights = LpMRSortMobius(model2, pt, aa_err, 0.0001)
t2 = time.time()
obj = lp_weights.solve()
t3 = time.time()
# Compute new assignment and classification accuracy
aa2 = model2.pessimist(pt)
ok = ok_errors = ok2 = ok2_errors = 0
for alt in a:
if aa_err(alt.id) == aa2(alt.id):
ok2 += 1
if alt.id in aa_erroned:
ok2_errors += 1
if aa(alt.id) == aa2(alt.id):
ok += 1
if alt.id in aa_erroned:
ok_errors += 1
total = len(a)
ca2 = ok2 / total
ca2_errors = ok2_errors / total
ca = ok / total
ca_errors = ok_errors / total
# Perform the generalization
a_gen = generate_alternatives(na_gen)
pt_gen = generate_random_performance_table(a_gen, model.criteria)
aa = model.pessimist(pt_gen)
aa2 = model2.pessimist(pt_gen)
ca_gen = compute_ca(aa, aa2)
# 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
# Output params
t['obj'] = obj
t['ca'] = ca
t['ca_errors'] = ca_errors
t['ca2'] = ca2
t['ca2_errors'] = ca2_errors
t['ca_gen'] = ca_gen
t['t_total'] = t3 - t1
t['t_const'] = t2 - t1
t['t_solve'] = t3 - t2
return t
def test001(self):
model = generate_random_mrsort_model(5, 3)
mxmcda = model.to_xmcda()
model2 = MRSort().from_xmcda(mxmcda)
self.assertEqual(model, model2)
coalitions = sorted(coalitions.items(), key = lambda (k, v): v,
reverse = True)
return coalitions
def find_coalitions(self, n, k = 0):
coal_proba = self.compute_coalitions_probabilities(n)
coalitions = []
for c in coal_proba:
if c[1] > k:
coalitions.append(c[0])
return coalitions
if __name__ == "__main__":
m = generate_random_mrsort_model(5, 2, 123)
winning, loosing = compute_winning_and_loosing_coalitions(m.cv, m.lbda)
print("Number of winning coalitions: %d" % len(winning))
print("List of coalitions:")
display_coalitions(winning)
a = generate_alternatives(1000)
pt = generate_random_performance_table(a, m.criteria)
aa = m.pessimist(pt)
heur = HeurMRSortCoalitions(m.criteria, m.categories, pt, aa)
aids = [aid[0] for aid in heur.sorted_extrem ]
display_electre_tri_models([m], [pt.get_worst(m.criteria)],
[pt.get_best(m.criteria)],
return obj
if __name__ == "__main__":
from pymcda.generate import generate_random_mrsort_model
from pymcda.generate import generate_alternatives
from pymcda.generate import generate_random_performance_table
from pymcda.utils import print_pt_and_assignments
from pymcda.ui.graphic import display_electre_tri_models
seed = 12
ncrit = 5
ncat = 3
# Generate a random ELECTRE TRI BM model
model = generate_random_mrsort_model(ncrit, ncat, seed)
# Display model parameters
print('Original model')
print('==============')
cids = model.criteria.keys()
cids.sort()
model.bpt.display(criterion_ids=cids)
model.cv.display(criterion_ids=cids)
print("lambda: %.7s" % model.lbda)
# Generate a set of alternatives
a = generate_alternatives(100)
pt = generate_random_performance_table(a, model.criteria)
worst = pt.get_worst(model.criteria)
if __name__ == "__main__":
import time
from pymcda.generate import generate_random_mrsort_model
from pymcda.generate import generate_alternatives
from pymcda.generate import generate_random_performance_table
from pymcda.generate import generate_random_profiles
from pymcda.types import CriteriaValues, CriterionValue
from pymcda.types import CriteriaSet
from pymcda.utils import print_pt_and_assignments
from pymcda.utils import compute_ca
from pymcda.utils import compute_number_of_winning_coalitions
from pymcda.pt_sorted import SortedPerformanceTable
from pymcda.ui.graphic import display_electre_tri_models
# Generate a random ELECTRE TRI BM model
model = generate_random_mrsort_model(5, 3, 123)
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)
cv13 = CriterionValue(CriteriaSet(['c1', 'c3']), 0.1)
cv14 = CriterionValue(CriteriaSet(['c1', 'c4']), -0.1)
cv15 = CriterionValue(CriteriaSet(['c1', 'c5']), 0.1)
cv23 = CriterionValue(CriteriaSet(['c2', 'c3']), 0.1)
cv24 = CriterionValue(CriteriaSet(['c2', 'c4']), -0.1)
cv25 = CriterionValue(CriteriaSet(['c2', 'c5']), 0.1)
cv34 = CriterionValue(CriteriaSet(['c3', 'c4']), 0.1)
cv35 = CriterionValue(CriteriaSet(['c3', 'c5']), -0.1)
cv45 = CriterionValue(CriteriaSet(['c4', 'c5']), -0.1)
def solve(self):
return self.solve_function()
if __name__ == "__main__":
import time
import random
from pymcda.generate import generate_alternatives
from pymcda.generate import generate_random_performance_table
from pymcda.generate import generate_random_mrsort_model
from pymcda.utils import add_errors_in_assignments
from pymcda.utils import print_pt_and_assignments
from pymcda.utils import compute_winning_and_loosing_coalitions
from pymcda.types import AlternativesAssignments, PerformanceTable
# Original Electre Tri model
model = generate_random_mrsort_model(10, 5, 890)
# Generate random alternatives
a = generate_alternatives(15000)
pt = generate_random_performance_table(a, model.criteria)
errors = 0.0
delta = 0.0001
nlearn = 1.00
# Assign the alternative with the model
aa = model.pessimist(pt)
a_learn = random.sample(a, int(nlearn*len(a)))
aa_learn = AlternativesAssignments([ aa[alt.id] for alt in a_learn ])
pt_learn = PerformanceTable([ pt[alt.id] for alt in a_learn ])
break
self.model.bpt = best_bpt
return best_ca
if __name__ == "__main__":
import time
from pymcda.generate import generate_alternatives
from pymcda.generate import generate_random_performance_table
from pymcda.utils import compute_winning_and_loosing_coalitions
from pymcda.types import AlternativePerformances
from pymcda.electre_tri import ElectreTri
from pymcda.ui.graphic import display_electre_tri_models
# Generate a random ELECTRE TRI BM model
model = generate_random_mrsort_model(10, 3, 123)
worst = AlternativePerformances("worst",
{c.id: 0 for c in model.criteria})
best = AlternativePerformances("best",
{c.id: 1 for c in model.criteria})
# Generate a set of alternatives
a = generate_alternatives(1000)
pt = generate_random_performance_table(a, model.criteria)
aa = model.pessimist(pt)
nmodels = 1
nmeta = 20
nloops = 50
print('Original model')
return self.good / self.na
if __name__ == "__main__":
from pymcda.generate import generate_random_mrsort_model
from pymcda.generate import generate_alternatives
from pymcda.generate import generate_random_performance_table
from pymcda.generate import generate_random_profiles
from pymcda.utils import print_pt_and_assignments
from pymcda.utils import add_errors_in_assignments
from pymcda.utils import compute_ca
from pymcda.pt_sorted import SortedPerformanceTable
from pymcda.types import PerformanceTable
from pymcda.ui.graphic import display_electre_tri_models
# Generate a random ELECTRE TRI BM model
model = generate_random_mrsort_model(10, 3, 123)
# 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 = pt.get_best(model.criteria)
errors = 0.0
nlearn = 1.0
model2 = model.copy()
model2.bpt = generate_random_profiles(model.profiles, model.criteria)
def test_lp_learning_weights(seed, na, nc, ncat, na_gen, pcerrors):
# Generate an ELECTRE TRI model and assignment examples
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)
model2 = model.copy()
# Generate a first 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(aa_err, model.categories,
pcerrors / 100)
# Run linear program
t1 = time.time()
if random_model_type == 'default':
lp_weights = LpMRSortWeights(model2, pt, aa_err, 0.0001)
else:
lp_weights = LpMRSortMobius(model2, pt, aa_err, 0.0001)
t2 = time.time()
obj = lp_weights.solve()
t3 = time.time()
# Compute new assignment and classification accuracy
aa2 = model2.pessimist(pt)
ok = ok_errors = ok2 = ok2_errors = 0
for alt in a:
if aa_err(alt.id) == aa2(alt.id):
ok2 += 1
if alt.id in aa_erroned:
ok2_errors += 1
if aa(alt.id) == aa2(alt.id):
ok += 1
if alt.id in aa_erroned:
ok_errors += 1
total = len(a)
ca2 = ok2 / total
ca2_errors = ok2_errors / total
ca = ok / total
ca_errors = ok_errors / total
# Perform the generalization
a_gen = generate_alternatives(na_gen)
pt_gen = generate_random_performance_table(a_gen, model.criteria)
aa = model.pessimist(pt_gen)
aa2 = model2.pessimist(pt_gen)
ca_gen = compute_ca(aa, aa2)
# 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
# Output params
t['obj'] = obj
t['ca'] = ca
t['ca_errors'] = ca_errors
t['ca2'] = ca2
t['ca2_errors'] = ca2_errors
t['ca_gen'] = ca_gen
t['t_total'] = t3 - t1
t['t_const'] = t2 - t1
t['t_solve'] = t3 - t2
return t
def test_lp_avfsort(seed, na, nc, ncat, ns, na_gen, pcerrors):
# Generate a random ELECTRE TRI model and assignment examples
model = generate_random_mrsort_model(nc, ncat, seed)
# Generate a first 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(aa_err, model.categories,
pcerrors / 100)
gi_worst = AlternativePerformances('worst', {c.id: 0
for c in model.criteria})
gi_best = AlternativePerformances('best', {c.id: 1
for c in model.criteria})
css = CriteriaValues([])
for c in model.criteria:
cs = CriterionValue(c.id, ns)
css.append(cs)
# Run linear program
t1 = time.time()
lp = LpAVFSort(model.criteria, css,
model.categories_profiles.to_categories(),
gi_worst, gi_best)
t2 = time.time()
obj, cv_l, cfs_l, catv_l = lp.solve(aa_err, pt)
t3 = time.time()
model2 = AVFSort(model.criteria, cv_l, cfs_l, catv_l)
# Compute new assignment and classification accuracy
aa2 = model2.get_assignments(pt)
ok = ok_errors = ok2 = ok2_errors = 0
for alt in a:
if aa_err(alt.id) == aa2(alt.id):
ok2 += 1
if alt.id in aa_erroned:
ok2_errors += 1
if aa(alt.id) == aa2(alt.id):
ok += 1
if alt.id in aa_erroned:
ok_errors += 1
total = len(a)
ca2 = ok2 / total
ca2_errors = ok2_errors / total
ca = ok / total
ca_errors = ok_errors / total
# Perform the generalization
a_gen = generate_alternatives(na_gen)
pt_gen = generate_random_performance_table(a_gen, model.criteria)
aa = model.pessimist(pt_gen)
aa2 = model2.get_assignments(pt_gen)
ca_gen = compute_ca(aa, aa2)
# Save all infos in test_result class
t = test_result("%s-%d-%d-%d-%d-%d-%g" % (seed, na, nc, ncat, ns,
na_gen, pcerrors))
# Input params
t['seed'] = seed
t['na'] = na
t['nc'] = nc
t['ncat'] = ncat
t['ns'] = ns
t['na_gen'] = na_gen
t['pcerrors'] = pcerrors
# Output params
t['obj'] = obj
t['ca'] = ca
t['ca_errors'] = ca_errors
t['ca2'] = ca2
t['ca2_errors'] = ca2_errors
t['ca_gen'] = ca_gen
t['t_total'] = t3 - t1
t['t_const'] = t2 - t1
t['t_solve'] = t3 - t2
return t
self.model.bpt = best_bpt
return best_ca
if __name__ == "__main__":
import time
from pymcda.generate import generate_alternatives
from pymcda.generate import generate_random_performance_table
from pymcda.utils import compute_winning_and_loosing_coalitions
from pymcda.types import AlternativePerformances
from pymcda.electre_tri import ElectreTri
from pymcda.ui.graphic import display_electre_tri_models
# Generate a random ELECTRE TRI BM model
model = generate_random_mrsort_model(10, 3, 123)
worst = AlternativePerformances("worst", {c.id: 0 for c in model.criteria})
best = AlternativePerformances("best", {c.id: 1 for c in model.criteria})
# Generate a set of alternatives
a = generate_alternatives(1000)
pt = generate_random_performance_table(a, model.criteria)
aa = model.pessimist(pt)
nmodels = 1
nmeta = 20
nloops = 50
print('Original model')
print('==============')
cids = model.criteria.keys()
