criteria_best['avb'] = 0
criteria_best['lvh'] = 0
criteria_best['spo2'] = 100
criteria_best['hypoglycemia'] = 0.92
criteria_best['hyperglycemia'] = 0.92
criteria_best['sys'] = 9
criteria_best['dia'] = 5
xmcda_models_toshow = []
xmcda_models = []
for f in sys.argv[1:]:
if not os.path.isfile(f):
xmcda_models_toshow.append(f)
continue
if is_bz2_file(f) is True:
f = bz2.BZ2File(f)
tree = ElementTree.parse(f)
root = tree.getroot()
xmcda_models = root.findall(".//ElectreTri")
m = MRSort().from_xmcda(xmcda_models[0])
pt_learning = PerformanceTable().from_xmcda(root, 'learning_set')
aa_learning = AlternativesAssignments().from_xmcda(root, 'learning_set')
uniquevalues = pt_learning.get_unique_values()
bname = os.path.basename(os.path.splitext(f.name)[0])
from test_utils import save_to_xmcda
table_ca_learning = []
table_ca_test = []
table_auc_learning = []
table_auc_test = []
cmatrix_learning = {}
cmatrix_test = {}
DATADIR = os.getenv('DATADIR', '%s/pymcda-data' % os.path.expanduser('~'))
directory='%s/test-veto2' % (DATADIR)
for f in sys.argv[1:]:
fname = os.path.splitext(os.path.basename(f))[0]
if is_bz2_file(f) is True:
f = bz2.BZ2File(f)
tree = ElementTree.parse(f)
root = tree.getroot()
m = MRSort().from_xmcda(root, 'learned')
pt_learning = PerformanceTable().from_xmcda(root, 'learning_set')
pt_test = PerformanceTable().from_xmcda(root, 'test_set')
aa_learning = AlternativesAssignments().from_xmcda(root,
'learning_set')
aa_test = AlternativesAssignments().from_xmcda(root,
'test_set')
aa_learning_m2 = m.pessimist(pt_learning)