def get_input_data(input_dir):
file_names = (
'alternatives.xml',
'criteria.xml',
'interactions.xml',
'method_parameters.xml',
'performance_table.xml',
'weights.xml',
)
trees = get_trees(input_dir, file_names)
alternatives = px.getAlternativesID(trees['alternatives'])
criteria = px.getCriteriaID(trees['criteria'])
pref_directions = px.getCriteriaPreferenceDirections(
trees['criteria'], criteria)
thresholds = px.getConstantThresholds(trees['criteria'], criteria)
weights = px.getCriterionValue(trees['weights'], criteria)
performances = px.getPerformanceTable(trees['performance_table'], 1, 1)
interactions = get_criteria_interactions(trees['interactions'], criteria)
check_net_balance(interactions, weights)
z_function = px.getParameterByName(trees['method_parameters'],
'z_function')
ret = {
'alternatives': alternatives,
'criteria': criteria,
'interactions': interactions,
'performances': performances,
'pref_directions': pref_directions,
'thresholds': thresholds,
'weights': weights,
'z_function': z_function,
}
return ret
def get_input_data(input_dir):
file_names = (
'alternatives.xml',
'criteria.xml',
'interactions.xml',
'method_parameters.xml',
'performance_table.xml',
'weights.xml',
)
trees = get_trees(input_dir, file_names)
alternatives = px.getAlternativesID(trees['alternatives'])
criteria = px.getCriteriaID(trees['criteria'])
pref_directions = px.getCriteriaPreferenceDirections(trees['criteria'], criteria)
thresholds = px.getConstantThresholds(trees['criteria'], criteria)
weights = px.getCriterionValue(trees['weights'], criteria)
performances = px.getPerformanceTable(trees['performance_table'], 1, 1)
interactions = get_criteria_interactions(trees['interactions'], criteria)
check_net_balance(interactions, weights)
z_function = px.getParameterByName(trees['method_parameters'], 'z_function')
ret = {
'alternatives': alternatives,
'criteria': criteria,
'interactions': interactions,
'performances': performances,
'pref_directions': pref_directions,
'thresholds': thresholds,
'weights': weights,
'z_function': z_function,
}
return ret
def get_pref_directions(*args, **kwargs):
criteria = px.getCriteriaID(trees['criteria'])
pref_directions = px.getCriteriaPreferenceDirections(
trees['criteria'],
criteria,
)
return pref_directions # dict
def get_input_data(input_dir):
file_names = (
'alternatives.xml',
'categoriesProfiles.xml',
'criteria.xml',
'performanceTable.xml',
'profilesPerformanceTable.xml',
'weights.xml',
)
trees = get_trees(input_dir, file_names)
alternatives = px.getAlternativesID(trees['alternatives'])
criteria = px.getCriteriaID(trees['criteria'])
pref_directions = px.getCriteriaPreferenceDirections(trees['criteria'], criteria)
thresholds = px.getConstantThresholds(trees['criteria'], criteria)
weights = px.getCriterionValue(trees['weights'], criteria)
performances = px.getPerformanceTable(trees['performanceTable'], None, None)
categories_profiles = get_categories_profiles_central(trees['categoriesProfiles'])
profiles_performance_table = px.getPerformanceTable(trees['profilesPerformanceTable'],
None, None)
ret = {
'alternatives': alternatives,
'categories_profiles': categories_profiles,
'criteria': criteria,
'performances': performances,
'pref_directions': pref_directions,
'profiles_performance_table': profiles_performance_table,
'thresholds': thresholds,
'weights': weights,
}
return ret
def parse_xmcda_files(in_weights, in_hierarchy, in_concorlevel, in_criteria, in_alternatives, in_performances):
xml_crit = px.parseValidate(in_criteria)
xml_alt = px.parseValidate(in_alternatives)
xml_pt = px.parseValidate(in_performances)
xml_weight = px.parseValidate(in_weights)
xml_hierarchy = px.parseValidate(in_hierarchy)
xml_concordance = px.parseValidate(in_concorlevel)
if xml_crit == None:
raise ValueError, ["Invalid criteria file"]
if xml_alt == None:
raise ValueError, ["Invalid alternative file"]
if xml_pt == None:
raise ValueError, ["Invalid performance table file"]
if xml_weight == None:
raise ValueError, ["Invalid weight file"]
if xml_hierarchy == None:
raise ValueError, ["Invalid assignment file"]
if xml_concordance == None:
raise ValueError, ["Invalid concordance file"]
try:
alternatives_ids = px.getAlternativesID(xml_alt)
criteria_ids = px.getCriteriaID(xml_crit)
performance_table = px.getPerformanceTable(xml_pt, alternatives_ids, criteria_ids)
criteriaWeight = px.getCriterionValue(xml_weight, criteria_ids, 'Importance')
preferenceDirections = px.getCriteriaPreferenceDirections(xml_crit, criteria_ids)
hierarchyArray = get_hierarchy_array(xml_hierarchy)
criteria_thresholds = px.getConstantThresholds(xml_crit, criteria_ids)
concordanceCutLev = get_criterion_concordance_cutting_level_value(xml_concordance, 'Concordance')
except:
raise ValueError, ["Failed to parse one or more file"]
return
return alternatives_ids, criteria_ids, performance_table, criteriaWeight, preferenceDirections, hierarchyArray, criteria_thresholds, concordanceCutLev
def get_input_data(input_dir):
file_names = (
'alternatives.xml',
'categoriesProfiles.xml',
'criteria.xml',
'performanceTable.xml',
'profilesPerformanceTable.xml',
)
trees = get_trees(input_dir, file_names)
alternatives = px.getAlternativesID(trees['alternatives'])
criteria = px.getCriteriaID(trees['criteria'])
pref_directions = px.getCriteriaPreferenceDirections(trees['criteria'], criteria)
thresholds = px.getConstantThresholds(trees['criteria'], criteria)
performances = px.getPerformanceTable(trees['performanceTable'], None, None)
categories_profiles = get_categories_profiles_central(trees['categoriesProfiles'])
profiles_performance_table = px.getPerformanceTable(
trees['profilesPerformanceTable'], None, None
)
ret = {
'alternatives': alternatives,
'categories_profiles': categories_profiles,
'criteria': criteria,
'performances': performances,
'pref_directions': pref_directions,
'profiles_performance_table': profiles_performance_table,
'thresholds': thresholds,
}
return ret
def get_input_data(input_dir):
file_names = (
'alternatives.xml',
'performanceTable.xml',
'categoriesProfiles.xml',
'criteria.xml',
'profilesPerformanceTable.xml',
)
trees = get_trees(input_dir, file_names)
alternatives = px.getAlternativesID(trees['alternatives'])
criteria = px.getCriteriaID(trees['criteria'])
pref_directions = px.getCriteriaPreferenceDirections(trees['criteria'], criteria)
thresholds = px.getConstantThresholds(trees['criteria'], criteria)
performances = px.getPerformanceTable(trees['performanceTable'], None, None)
profiles_performance_table = px.getPerformanceTable(trees['profilesPerformanceTable'], None, None)
cp_tree = trees['categoriesProfiles']
# we need only categories profiles' names
categories_profiles = [p for p in cp_tree.xpath('//categoriesProfiles//alternativeID/text()')]
ret = {
'alternatives': alternatives,
'categories_profiles': categories_profiles,
'criteria': criteria,
'performances': performances,
'pref_directions': pref_directions,
'profiles_performance_table': profiles_performance_table,
'thresholds': thresholds,
}
return ret
def get_pref_directions(*args, **kwargs):
criteria = px.getCriteriaID(trees['criteria'])
pref_directions = px.getCriteriaPreferenceDirections(
trees['criteria'],
criteria,
)
return pref_directions # dict
def get_input_data(input_dir):
file_names = (
'alternatives.xml',
'performanceTable.xml',
'categoriesProfiles.xml',
'criteria.xml',
'profilesPerformanceTable.xml',
)
trees = get_trees(input_dir, file_names)
alternatives = px.getAlternativesID(trees['alternatives'])
criteria = px.getCriteriaID(trees['criteria'])
pref_directions = px.getCriteriaPreferenceDirections(
trees['criteria'], criteria)
thresholds = px.getConstantThresholds(trees['criteria'], criteria)
performances = px.getPerformanceTable(trees['performanceTable'], None,
None)
profiles_performance_table = px.getPerformanceTable(
trees['profilesPerformanceTable'], None, None)
cp_tree = trees['categoriesProfiles']
# we need only categories profiles' names
categories_profiles = [
p for p in cp_tree.xpath('//categoriesProfiles//alternativeID/text()')
]
ret = {
'alternatives': alternatives,
'categories_profiles': categories_profiles,
'criteria': criteria,
'performances': performances,
'pref_directions': pref_directions,
'profiles_performance_table': profiles_performance_table,
'thresholds': thresholds,
}
return ret
def get_input_data(input_dir):
file_names = (
'alternatives.xml',
'performanceTable.xml',
'categoriesProfiles.xml',
'criteria.xml',
'profilesPerformanceTable.xml',
'weights.xml',
)
trees = get_trees(input_dir, file_names)
alternatives = px.getAlternativesID(trees['alternatives'])
criteria = px.getCriteriaID(trees['criteria'])
pref_directions = px.getCriteriaPreferenceDirections(
trees['criteria'], criteria)
thresholds = px.getConstantThresholds(trees['criteria'], criteria)
weights = px.getCriterionValue(trees['weights'], criteria)
performances = px.getPerformanceTable(trees['performanceTable'], 1, 1)
# we can't assume that categories will be always available as a separate
# input file, therefore it's better to extract them from categoriesProfiles
cp_tree = trees['categoriesProfiles']
categories = list(
set(cp_tree.xpath('//categoriesProfiles//limits//categoryID/text()')))
# since we just need names of categories profiles, it's better to get them like below
# - otherwise, to get 'full' categories profiles, we should use this:
# categories_profiles = px.getCategoriesProfiles(trees['categoriesProfiles'], categories)
categories_profiles = [
p for p in cp_tree.xpath('//categoriesProfiles//alternativeID/text()')
]
# last two args to getPerformanceTable are not used at all anyway...
profiles_performance_table = px.getPerformanceTable(
trees['profilesPerformanceTable'], None, None)
ret = {
'alternatives': alternatives,
'categories_profiles': categories_profiles,
'criteria': criteria,
'performances': performances,
'pref_directions': pref_directions,
'profiles_performance_table': profiles_performance_table,
'thresholds': thresholds,
'weights': weights,
}
return ret
def get_input_data(input_dir):
file_names = (
'alternatives.xml',
'performanceTable.xml',
'categoriesProfiles.xml',
'criteria.xml',
'profilesPerformanceTable.xml',
'weights.xml',
)
trees = get_trees(input_dir, file_names)
alternatives = px.getAlternativesID(trees['alternatives'])
criteria = px.getCriteriaID(trees['criteria'])
pref_directions = px.getCriteriaPreferenceDirections(trees['criteria'], criteria)
thresholds = px.getConstantThresholds(trees['criteria'], criteria)
weights = px.getCriterionValue(trees['weights'], criteria)
performances = px.getPerformanceTable(trees['performanceTable'], 1, 1)
# we can't assume that categories will be always available as a separate
# input file, therefore it's better to extract them from categoriesProfiles
cp_tree = trees['categoriesProfiles']
categories = list(set(cp_tree.xpath('//categoriesProfiles//limits//categoryID/text()')))
# since we just need names of categories profiles, it's better to get them like below
# - otherwise, to get 'full' categories profiles, we should use this:
# categories_profiles = px.getCategoriesProfiles(trees['categoriesProfiles'], categories)
categories_profiles = [p for p in cp_tree.xpath('//categoriesProfiles//alternativeID/text()')]
# last two args to getPerformanceTable are not used at all anyway...
profiles_performance_table = px.getPerformanceTable(trees['profilesPerformanceTable'], None, None)
ret = {
'alternatives': alternatives,
'categories_profiles': categories_profiles,
'criteria': criteria,
'performances': performances,
'pref_directions': pref_directions,
'profiles_performance_table': profiles_performance_table,
'thresholds': thresholds,
'weights': weights,
}
return ret
def get_input_data(input_dir):
file_names = (
'alternatives.xml',
'criteria.xml',
'performanceTable.xml',
)
trees = get_trees(input_dir, file_names)
criteria = px.getCriteriaID(trees['criteria'])
pref_directions = px.getCriteriaPreferenceDirections(
trees['criteria'], criteria)
thresholds = px.getConstantThresholds(trees['criteria'], criteria)
performances = px.getPerformanceTable(trees['performanceTable'], None,
None)
ret = {
'criteria': criteria,
'performances': performances,
'pref_directions': pref_directions,
'thresholds': thresholds,
}
return ret
def parse_xmcda_files(in_dir):
xml_crit = PyXMCDA.parseValidate(in_dir+"/criteria.xml")
xml_alt = PyXMCDA.parseValidate(in_dir+"/alternatives.xml")
xml_pt = PyXMCDA.parseValidate(in_dir+"/perfs_table.xml")
xml_assign = PyXMCDA.parseValidate(in_dir+"/assign.xml")
xml_cat = PyXMCDA.parseValidate(in_dir+"/categories.xml")
if xml_crit == None:
error_list.append("Invalid criteria file")
return
if xml_alt == None:
error_list.append("Invalid alternative file")
return
if xml_pt == None:
error_list.append("Invalid performance table file")
return
if xml_assign == None:
error_list.append("Invalid assignment file")
return
if xml_cat == None:
error_list.append("Invalid categories file")
return
try:
alt_id = PyXMCDA.getAlternativesID(xml_alt)
crit_id = PyXMCDA.getCriteriaID(xml_crit)
pt = PyXMCDA.getPerformanceTable(xml_pt, alt_id, crit_id)
cat_id = PyXMCDA.getCategoriesID(xml_cat)
cat_rank = PyXMCDA.getCategoriesRank(xml_cat, cat_id)
assign = PyXMCDA.getAlternativesAffectations(xml_assign)
pref_dir = PyXMCDA.getCriteriaPreferenceDirections(xml_crit, crit_id)
except:
error_list.append("Failed to parse one or more file")
return
return (alt_id, crit_id, pt, cat_id, cat_rank, assign, pref_dir)
def main(argv=None):
if argv is None:
argv = sys.argv
parser = OptionParser()
parser.add_option("-i", "--in", dest="in_dir")
parser.add_option("-o", "--out", dest="out_dir")
(options, args) = parser.parse_args(argv[1:])
in_dir = options.in_dir
out_dir = options.out_dir
critAverage = {}
critNormalSD = {}
critTriangSD = {}
# Creating a list for error messages
errorList = []
# If some mandatory input files are missing
if not os.path.isfile (in_dir+"/alternatives.xml") or not os.path.isfile (in_dir+"/criteria.xml") :
errorList.append("Some input files are missing")
else :
# We parse all the mandatory input files
xmltree_alternatives = PyXMCDA.parseValidate(in_dir+"/alternatives.xml")
xmltree_criteria = PyXMCDA.parseValidate(in_dir+"/criteria.xml")
# We check if all madatory input files are valid
if xmltree_alternatives == None :
errorList.append("The alternatives file can't be validated.")
if xmltree_criteria == None :
errorList.append("The criteria file can't be validated.")
if not errorList :
alternativesId = PyXMCDA.getAlternativesID(xmltree_alternatives)
criteriaId = PyXMCDA.getCriteriaID(xmltree_criteria)
if not alternativesId :
errorList.append("No alternatives found. Is your alternatives file correct ?")
if not criteriaId :
errorList.append("No criteria found. Is your criteria file correct ?")
if not errorList :
# We check if parameters for criteria distribution profile have been provided
if os.path.isfile (in_dir+"/criteriaProfiles.xml") :
xmltree_CritProfile = PyXMCDA.parseValidate(in_dir+"/criteriaProfiles.xml")
if xmltree_CritProfile == None :
errorList.append ("criteriaProfiles file can't be validated.")
else :
critAverage = PyXMCDA.getNamedParametersByName (xmltree_CritProfile, "distributionAverage")
critNormalSD = PyXMCDA.getNamedParametersByName (xmltree_CritProfile, "normalDistributionStandardDeviation")
critTriangSD = PyXMCDA.getNamedParametersByName (xmltree_CritProfile, "triangularDistributionStandardDeviation")
# ...
if not errorList :
# We check if a seed is provided for the random generation
if os.path.isfile (in_dir+"/seed.xml") :
xmltree_seed = PyXMCDA.parseValidate(in_dir+"/seed.xml")
if xmltree_seed == None :
errorList.append ("seed file can't be validated.")
else :
seed = PyXMCDA.getParameterByName (xmltree_seed, "seed")
if not isinstance(seed,int) :
errorList.append ("seed value should be a strictly positive integer")
else :
if seed <= 0 :
errorList.append ("seed should be a strictly positive integer")
else:
# We initialize the random generator
random.seed(seed)
if not errorList :
for i in range(20):
print random.random()
# We recover criteria scale information
criteriaTypes = PyXMCDA.getCriteriaScalesTypes (xmltree_criteria, criteriaId)
criteriaDir = PyXMCDA.getCriteriaPreferenceDirections (xmltree_criteria, criteriaId)
criteriaUB = PyXMCDA.getCriteriaUpperBounds (xmltree_criteria, criteriaId)
criteriaLB = PyXMCDA.getCriteriaLowerBounds (xmltree_criteria, criteriaId)
criteriaRL = PyXMCDA.getCriteriaRankedLabel (xmltree_criteria, criteriaId)
# We add some default lower and upper bounds
for crit in criteriaId :
if not criteriaLB.has_key (crit) or criteriaLB[crit] == None :
if criteriaTypes[crit] == "quantitative" :
criteriaLB[crit] = 0.0
else :
criteriaLB[crit] = 1
if not criteriaUB.has_key (crit) or criteriaUB[crit] == None :
if criteriaTypes[crit] == "quantitative" :
criteriaUB[crit] = 100.0
else :
if criteriaRL.has_key(crit) and criteriaRL[crit] != None :
criteriaUB[crit] = len(criteriaRL[crit])
else :
criteriaUB[crit] = 10
# We construct the performance Tableau
Tab = {}
for crit in criteriaId :
Tab[crit] = {}
if critNormalSD.has_key(crit) :
sd = critNormalSD[crit]
if critAverage.has_key(crit) :
average = critAverage[crit]
else :
average = (criteriaLB[crit] + criteriaUB[crit])/2.0
for alt in alternativesId :
temp = criteriaLB[crit] -1
while temp < criteriaLB[crit] or temp > criteriaUB[crit] :
temp = random.gauss (average,sd)
Tab[crit][alt] = temp
if criteriaTypes[crit] == "qualitative" :
Tab[crit][alt] = int(Tab[crit][alt])
elif critTriangSD.has_key (crit) :
for alt in alternativesId :
# TO BE CHANGED !!!
Tab[crit][alt] = random.randint (criteriaLB[crit], criteriaUB[crit])
else :
for alt in alternativesId :
if criteriaTypes[crit] == "quantitative" :
Tab[crit][alt] = float(random.randint (criteriaLB[crit]*100, criteriaUB[crit]*100))/100.0
else :
Tab[crit][alt] = random.randint (criteriaLB[crit], criteriaUB[crit])
# We construct the performanceTable.xml file
filePerfTable = open(out_dir+"/performanceTable.xml", 'w')
PyXMCDA.writeHeader (filePerfTable)
filePerfTable.write ("<performanceTable>\n")
for alt in alternativesId :
filePerfTable.write ("\t<alternativePerformances>\n\t\t<alternativeID>" + alt + "</alternativeID>\n")
for crit in criteriaId :
if criteriaTypes[crit] == "quantitative" :
filePerfTable.write ("\t\t<performance>\n\t\t\t<criterionID>" + crit + "</criterionID>\n\t\t\t<value><real>"+ str(Tab[crit][alt]) + "</real></value>\n\t\t</performance>\n")
else :
filePerfTable.write ("\t\t<performance>\n\t\t\t<criterionID>" + crit + "</criterionID>\n\t\t\t<value><integer>"+ str(Tab[crit][alt]) + "</integer></value>\n\t\t</performance>\n")
filePerfTable.write ("\t</alternativePerformances>\n")
filePerfTable.write ("</performanceTable>\n")
PyXMCDA.writeFooter(filePerfTable)
filePerfTable.close()
# Creating log and error file, messages.xml
fileMessages = open(out_dir+"/messages.xml", 'w')
PyXMCDA.writeHeader (fileMessages)
if not errorList :
PyXMCDA.writeLogMessages (fileMessages, ["Execution ok"])
else :
PyXMCDA.writeErrorMessages (fileMessages, errorList)
PyXMCDA.writeFooter(fileMessages)
fileMessages.close()
def get_input_data(input_dir, filenames, params, **kwargs):
trees = _get_trees(input_dir, filenames)
d = _create_data_object(params)
for p in params:
if p == 'alternatives':
d.alternatives = px.getAlternativesID(trees['alternatives'])
elif p == 'categories_profiles':
comparison_with = kwargs.get('comparison_with')
if comparison_with is None:
comparison_with = px.getParameterByName(
trees['method_parameters'], 'comparison_with')
d.categories_profiles = _get_categories_profiles(
trees.get('categories_profiles'), comparison_with)
elif p == 'categories_rank':
categories = px.getCategoriesID(trees['categories'])
d.categories_rank = px.getCategoriesRank(trees['categories'],
categories)
elif p == 'comparison_with':
d.comparison_with = px.getParameterByName(
trees['method_parameters'], 'comparison_with')
elif p == 'concordance':
alternatives = px.getAlternativesID(trees['alternatives'])
comparison_with = px.getParameterByName(trees['method_parameters'],
'comparison_with')
if comparison_with in ('boundary_profiles', 'central_profiles'):
categories_profiles = _get_categories_profiles(
trees['categories_profiles'], comparison_with)
d.concordance = _get_alternatives_comparisons(
trees['concordance'], alternatives, categories_profiles)
else:
d.concordance = px.getAlternativesComparisons(
trees['concordance'], alternatives)
elif p == 'credibility':
alternatives = px.getAlternativesID(trees['alternatives'])
comparison_with = kwargs.get('comparison_with')
if not comparison_with:
comparison_with = px.getParameterByName(
trees['method_parameters'], 'comparison_with')
if comparison_with in ('boundary_profiles', 'central_profiles'):
categories_profiles = _get_categories_profiles(
trees['categories_profiles'], comparison_with)
else:
categories_profiles = None
eliminate_cycles_method = px.getParameterByName(
trees.get('method_parameters'), 'eliminate_cycles_method')
tree = trees.get('credibility')
if eliminate_cycles_method == 'cut_weakest' and tree is None:
raise RuntimeError(
"'cut_weakest' option requires credibility as "
"an additional input (apart from outranking).")
d.credibility = _get_alternatives_comparisons(
tree, alternatives, categories_profiles=categories_profiles)
elif p == 'criteria':
d.criteria = px.getCriteriaID(trees['criteria'])
elif p == 'cut_threshold':
cut_threshold = px.getParameterByName(trees['method_parameters'],
'cut_threshold')
if cut_threshold is None or not (0 <= float(cut_threshold) <= 1):
raise RuntimeError(
"'cut_threshold' should be in range [0, 1] "
"(most commonly used values are 0.6 or 0.7).")
d.cut_threshold = cut_threshold
# 'cv_crossed' == 'counter-veto crossed'
elif p == 'cv_crossed':
alternatives = px.getAlternativesID(trees['alternatives'])
comparison_with = px.getParameterByName(trees['method_parameters'],
'comparison_with')
if comparison_with in ('boundary_profiles', 'central_profiles'):
categories_profiles = _get_categories_profiles(
trees['categories_profiles'], comparison_with)
else:
categories_profiles = None
d.cv_crossed = _get_alternatives_comparisons(
trees['counter_veto_crossed'],
alternatives,
categories_profiles=categories_profiles,
use_partials=True,
mcda_concept='counterVetoCrossed')
elif p == 'discordance':
alternatives = px.getAlternativesID(trees['alternatives'])
comparison_with = px.getParameterByName(trees['method_parameters'],
'comparison_with')
if kwargs.get('use_partials') is not None:
use_partials = kwargs.get('use_partials')
else:
parameter = px.getParameterByName(trees['method_parameters'],
'use_partials')
use_partials = True if parameter == 'true' else False
if comparison_with in ('boundary_profiles', 'central_profiles'):
categories_profiles = _get_categories_profiles(
trees['categories_profiles'], comparison_with)
else:
categories_profiles = None
d.discordance = _get_alternatives_comparisons(
trees['discordance'],
alternatives,
categories_profiles=categories_profiles,
use_partials=use_partials)
elif p == 'eliminate_cycles_method':
d.eliminate_cycles_method = px.getParameterByName(
trees['method_parameters'], 'eliminate_cycles_method')
elif p == 'interactions':
criteria = px.getCriteriaID(trees['criteria'])
d.interactions = _get_criteria_interactions(
trees['interactions'], criteria)
elif p == 'outranking':
d.outranking = _get_outranking_crisp(trees['outranking'])
elif p == 'performances':
d.performances = px.getPerformanceTable(trees['performance_table'],
None, None)
elif p == 'pref_directions':
criteria = px.getCriteriaID(trees['criteria'])
d.pref_directions = px.getCriteriaPreferenceDirections(
trees['criteria'], criteria)
elif p == 'profiles_performance_table':
if comparison_with in ('boundary_profiles', 'central_profiles'):
tree = trees.get('profiles_performance_table')
if tree is None:
msg = (
"Missing profiles performance table (did you forget "
"to provide 'profiles_performance_table.xml' file?).")
raise RuntimeError(msg)
d.profiles_performance_table = px.getPerformanceTable(
tree, None, None)
else:
d.profiles_performance_table = None
elif p == 'reinforcement_factors':
criteria = px.getCriteriaID(trees['criteria'])
factors = {}
for c in criteria:
rf = px.getCriterionValue(trees['reinforcement_factors'], c,
'reinforcement_factors')
if len(rf) == 0:
continue
if rf.get(c) <= 1:
msg = ("Reinforcement factor for criterion '{}' should be "
"higher than 1.0 (ideally between 1.2 and 1.5).")
raise RuntimeError(msg)
factors.update(rf)
d.reinforcement_factors = factors
elif p == 'thresholds':
criteria = px.getCriteriaID(trees['criteria'])
d.thresholds = _get_thresholds(trees['criteria'])
elif p == 'weights':
criteria = px.getCriteriaID(trees['criteria'])
d.weights = px.getCriterionValue(trees['weights'], criteria)
elif p == 'z_function':
d.z_function = px.getParameterByName(trees['method_parameters'],
'z_function')
elif p == 'with_denominator':
parameter = px.getParameterByName(trees['method_parameters'],
'with_denominator')
d.with_denominator = True if parameter == 'true' else False
elif p == 'only_max_discordance':
parameter = px.getParameterByName(trees['method_parameters'],
'only_max_discordance')
d.only_max_discordance = True if parameter == 'true' else False
elif p == 'use_partials':
parameter = px.getParameterByName(trees['method_parameters'],
'use_partials')
d.use_partials = True if parameter == 'true' else False
elif p == 'use_pre_veto':
parameter = px.getParameterByName(trees['method_parameters'],
'use_pre_veto')
d.use_pre_veto = True if parameter == 'true' else False
else:
raise RuntimeError("Unknown parameter '{}' specified.".format(p))
for param in params:
data = getattr(d, param)
if type(data) in (type(list), type(dict)) and len(data) == 0:
raise RuntimeError(
"No content for '{}' parameter provided.".format(param))
return d
def main(argv=None):
if argv is None:
argv = sys.argv
parser = OptionParser()
parser.add_option("-i", "--in", dest="in_dir")
parser.add_option("-o", "--out", dest="out_dir")
(options, args) = parser.parse_args(argv[1:])
in_dir = options.in_dir
out_dir = options.out_dir
# Creating a list for error messages
errorList = []
# If some mandatory input files are missing
if not os.path.isfile (in_dir+"/alternatives.xml") or not os.path.isfile (in_dir+"/criteria.xml") or not os.path.isfile (in_dir+"/criteriaWeights.xml") or not os.path.isfile (in_dir+"/performanceTable.xml") :
errorList.append("Some input files are missing")
else :
# We parse all the mandatory input files
xmltree_alternatives = PyXMCDA.parseValidate(in_dir+"/alternatives.xml")
xmltree_criteria = PyXMCDA.parseValidate(in_dir+"/criteria.xml")
xmltree_weights = PyXMCDA.parseValidate(in_dir+"/criteriaWeights.xml")
xmltree_perfTable = PyXMCDA.parseValidate(in_dir+"/performanceTable.xml")
# We check if all madatory input files are valid
if xmltree_alternatives == None :
errorList.append("The alternatives file can't be validated.")
if xmltree_criteria == None :
errorList.append("The criteria file can't be validated.")
if xmltree_perfTable == None :
errorList.append("The performance table file can't be validated.")
if xmltree_weights == None :
errorList.append("The criteria weights file can't be validated.")
# By default, the valuation domain is fixed to [0,1]
minValDomain = 0
maxValDomain = 1
# If a valuation domain input file has been provided
if os.path.isfile (in_dir+"/valuationDomain.xml") :
xmltree_valuation = PyXMCDA.parseValidate(in_dir+"/valuationDomain.xml")
if xmltree_valuation == None :
errorList.append ("valuationDomain file can't be validated.")
else :
minValDomain = PyXMCDA.getParameterByName (xmltree_valuation, "min", "valuationDomain")
maxValDomain = PyXMCDA.getParameterByName (xmltree_valuation, "max", "valuationDomain")
# We check the validity of the parameters
if not isinstance(minValDomain,float) and not isinstance(minValDomain,int) :
errorList.append ("min value should be an integer or a real")
if not isinstance(maxValDomain,float) and not isinstance(maxValDomain,int) :
errorList.append ("max value should be an integer or a real")
if not errorList :
if minValDomain >= maxValDomain :
errorList.append ("The max value should be strictly greater than the min value")
if not errorList :
alternativesId = PyXMCDA.getAlternativesID(xmltree_alternatives)
criteriaId = PyXMCDA.getCriteriaID(xmltree_criteria)
perfTable = PyXMCDA.getPerformanceTable(xmltree_perfTable, alternativesId, criteriaId)
thresholds = PyXMCDA.getConstantThresholds (xmltree_criteria, criteriaId)
weights = PyXMCDA.getCriterionValue (xmltree_weights, criteriaId)
if not alternativesId :
errorList.append("No alternatives found. Is your alternatives file correct ?")
if not criteriaId :
errorList.append("No criteria found. Is your criteria file correct ?")
if not perfTable :
errorList.append("No performance table found. Is your performance table file correct ?")
if not weights :
errorList.append("No weights found. Is your weights file correct ?")
if not errorList :
# We compute the weight sum (only the weights associated to active criteria)
sumWeights = 0.0
for crit in criteriaId :
try :
sumWeights = sumWeights + weights[crit]
except :
errorList.append("There is no defined weight for criterion "+crit+".")
if not errorList :
# We recover the criteria preference directions
criteriaDir = PyXMCDA.getCriteriaPreferenceDirections (xmltree_criteria, criteriaId)
# We compute the alternative comparisons values
fileAltValues = open(out_dir+"/alternativesComparisons.xml", 'w')
PyXMCDA.writeHeader (fileAltValues)
fileAltValues.write ("\t<alternativesComparisons>\n\t\t<pairs>\n")
ElemOut = PyXMCDA.getRubisElementaryOutranking (alternativesId, criteriaId, perfTable, thresholds)
for alt1 in alternativesId :
for alt2 in alternativesId :
fileAltValues.write("\t\t\t<pair>\n\t\t\t\t<initial><alternativeID>"+alt1+"</alternativeID></initial>\n\t\t\t\t<terminal><alternativeID>"+alt2+"</alternativeID></terminal>\n")
# Verifier s'il manque des valeurs !!! try expect
#fileAltValues.write ("\t\t\t\t<value><NA>not available</NA></value>\n\t\t\t</pair>\n")
sum = 0.0
for crit in criteriaId :
sum += ElemOut[alt1][alt2][crit] * weights[crit]
sum = sum/sumWeights
# La valeur est entre 0 et 1, on la met dans le bon intervalle
sum = (maxValDomain - minValDomain)*sum + minValDomain
fileAltValues.write ("\t\t\t\t<value><real>"+str(sum)+"</real></value>\n\t\t\t</pair>\n")
fileAltValues.write ("\t\t</pairs>\n\t</alternativesComparisons>\n")
PyXMCDA.writeFooter(fileAltValues)
fileAltValues.close()
# Creating log and error file, messages.xml
fileMessages = open(out_dir+"/messages.xml", 'w')
PyXMCDA.writeHeader (fileMessages)
if not errorList :
PyXMCDA.writeLogMessages (fileMessages, ["Execution ok"])
else :
PyXMCDA.writeErrorMessages (fileMessages, errorList)
PyXMCDA.writeFooter(fileMessages)
fileMessages.close()
def get_input_data(input_dir, filenames, params, **kwargs):
trees = _get_trees(input_dir, filenames)
d = _create_data_object(params)
for p in params:
if p == 'alternatives':
d.alternatives = px.getAlternativesID(trees['alternatives'])
elif p == 'categories_profiles':
comparison_with = kwargs.get('comparison_with')
if comparison_with is None:
comparison_with = px.getParameterByName(trees['method_parameters'], 'comparison_with')
d.categories_profiles = _get_categories_profiles(trees.get('categories_profiles'),
comparison_with)
elif p == 'categories_rank':
categories = px.getCategoriesID(trees['categories'])
d.categories_rank = px.getCategoriesRank(trees['categories'], categories)
elif p == 'comparison_with':
d.comparison_with = px.getParameterByName(trees['method_parameters'], 'comparison_with')
elif p == 'concordance':
alternatives = px.getAlternativesID(trees['alternatives'])
comparison_with = px.getParameterByName(trees['method_parameters'], 'comparison_with')
if comparison_with in ('boundary_profiles', 'central_profiles'):
categories_profiles = _get_categories_profiles(trees['categories_profiles'],
comparison_with)
d.concordance = _get_alternatives_comparisons(trees['concordance'], alternatives,
categories_profiles)
else:
d.concordance = px.getAlternativesComparisons(trees['concordance'], alternatives)
elif p == 'credibility':
alternatives = px.getAlternativesID(trees['alternatives'])
comparison_with = kwargs.get('comparison_with')
if not comparison_with:
comparison_with = px.getParameterByName(trees['method_parameters'], 'comparison_with')
if comparison_with in ('boundary_profiles', 'central_profiles'):
categories_profiles = _get_categories_profiles(trees['categories_profiles'],
comparison_with)
else:
categories_profiles = None
eliminate_cycles_method = px.getParameterByName(trees.get('method_parameters'),
'eliminate_cycles_method')
tree = trees.get('credibility')
if eliminate_cycles_method == 'cut_weakest' and tree is None:
raise RuntimeError("'cut_weakest' option requires credibility as "
"an additional input (apart from outranking).")
d.credibility = _get_alternatives_comparisons(tree, alternatives,
categories_profiles=categories_profiles)
elif p == 'criteria':
d.criteria = px.getCriteriaID(trees['criteria'])
elif p == 'cut_threshold':
cut_threshold = px.getParameterByName(trees['method_parameters'], 'cut_threshold')
if cut_threshold is None or not (0 <= float(cut_threshold) <= 1):
raise RuntimeError(
"'cut_threshold' should be in range [0, 1] "
"(most commonly used values are 0.6 or 0.7)."
)
d.cut_threshold = cut_threshold
# 'cv_crossed' == 'counter-veto crossed'
elif p == 'cv_crossed':
alternatives = px.getAlternativesID(trees['alternatives'])
comparison_with = px.getParameterByName(trees['method_parameters'], 'comparison_with')
if comparison_with in ('boundary_profiles', 'central_profiles'):
categories_profiles = _get_categories_profiles(trees['categories_profiles'],
comparison_with)
else:
categories_profiles = None
d.cv_crossed = _get_alternatives_comparisons(trees['counter_veto_crossed'],
alternatives,
categories_profiles=categories_profiles,
use_partials=True,
mcda_concept='counterVetoCrossed')
elif p == 'discordance':
alternatives = px.getAlternativesID(trees['alternatives'])
comparison_with = px.getParameterByName(trees['method_parameters'], 'comparison_with')
if kwargs.get('use_partials') is not None:
use_partials = kwargs.get('use_partials')
else:
parameter = px.getParameterByName(trees['method_parameters'], 'use_partials')
use_partials = True if parameter == 'true' else False
if comparison_with in ('boundary_profiles', 'central_profiles'):
categories_profiles = _get_categories_profiles(trees['categories_profiles'],
comparison_with)
else:
categories_profiles = None
d.discordance = _get_alternatives_comparisons(trees['discordance'], alternatives,
categories_profiles=categories_profiles,
use_partials=use_partials)
elif p == 'eliminate_cycles_method':
d.eliminate_cycles_method = px.getParameterByName(trees['method_parameters'],
'eliminate_cycles_method')
elif p == 'interactions':
criteria = px.getCriteriaID(trees['criteria'])
d.interactions = _get_criteria_interactions(trees['interactions'], criteria)
elif p == 'outranking':
d.outranking = _get_outranking_crisp(trees['outranking'])
elif p == 'performances':
d.performances = px.getPerformanceTable(trees['performance_table'], None, None)
elif p == 'pref_directions':
criteria = px.getCriteriaID(trees['criteria'])
d.pref_directions = px.getCriteriaPreferenceDirections(trees['criteria'], criteria)
elif p == 'profiles_performance_table':
if comparison_with in ('boundary_profiles', 'central_profiles'):
tree = trees.get('profiles_performance_table')
if tree is None:
msg = ("Missing profiles performance table (did you forget "
"to provide 'profiles_performance_table.xml' file?).")
raise RuntimeError(msg)
d.profiles_performance_table = px.getPerformanceTable(tree, None, None)
else:
d.profiles_performance_table = None
elif p == 'reinforcement_factors':
criteria = px.getCriteriaID(trees['criteria'])
factors = {}
for c in criteria:
rf = px.getCriterionValue(trees['reinforcement_factors'], c,
'reinforcement_factors')
if len(rf) == 0:
continue
if rf.get(c) <= 1:
msg = ("Reinforcement factor for criterion '{}' should be "
"higher than 1.0 (ideally between 1.2 and 1.5).")
raise RuntimeError(msg)
factors.update(rf)
d.reinforcement_factors = factors
elif p == 'thresholds':
criteria = px.getCriteriaID(trees['criteria'])
d.thresholds = _get_thresholds(trees['criteria'])
elif p == 'weights':
criteria = px.getCriteriaID(trees['criteria'])
d.weights = px.getCriterionValue(trees['weights'], criteria)
elif p == 'z_function':
d.z_function = px.getParameterByName(trees['method_parameters'], 'z_function')
elif p == 'with_denominator':
parameter = px.getParameterByName(trees['method_parameters'], 'with_denominator')
d.with_denominator = True if parameter == 'true' else False
elif p == 'only_max_discordance':
parameter = px.getParameterByName(trees['method_parameters'], 'only_max_discordance')
d.only_max_discordance = True if parameter == 'true' else False
elif p == 'use_partials':
parameter = px.getParameterByName(trees['method_parameters'], 'use_partials')
d.use_partials = True if parameter == 'true' else False
elif p == 'use_pre_veto':
parameter = px.getParameterByName(trees['method_parameters'], 'use_pre_veto')
d.use_pre_veto = True if parameter == 'true' else False
else:
raise RuntimeError("Unknown parameter '{}' specified.".format(p))
for param in params:
data = getattr(d, param)
if type(data) in (type(list), type(dict)) and len(data) == 0:
raise RuntimeError("No content for '{}' parameter provided."
.format(param))
return d
def main(argv=None):
if argv is None:
argv = sys.argv
parser = OptionParser()
parser.add_option("-i", "--in", dest="in_dir")
parser.add_option("-o", "--out", dest="out_dir")
(options, args) = parser.parse_args(argv[1:])
in_dir = options.in_dir
out_dir = options.out_dir
# Creating a list for error messages
errorList = []
# If some mandatory input files are missing
if (
not os.path.isfile(in_dir + "/alternatives.xml")
or not os.path.isfile(in_dir + "/criteria.xml")
or not os.path.isfile(in_dir + "/criteriaWeights.xml")
or not os.path.isfile(in_dir + "/performanceTable.xml")
):
errorList.append("Some input files are missing")
else:
# We parse all the mandatory input files
xmltree_alternatives = PyXMCDA.parseValidate(in_dir + "/alternatives.xml")
xmltree_criteria = PyXMCDA.parseValidate(in_dir + "/criteria.xml")
xmltree_weights = PyXMCDA.parseValidate(in_dir + "/criteriaWeights.xml")
xmltree_perfTable = PyXMCDA.parseValidate(in_dir + "/performanceTable.xml")
# We check if all madatory input files are valid
if xmltree_alternatives == None:
errorList.append("The alternatives file can't be validated.")
if xmltree_criteria == None:
errorList.append("The criteria file can't be validated.")
if xmltree_perfTable == None:
errorList.append("The performance table file can't be validated.")
if xmltree_weights == None:
errorList.append("The criteria weights file can't be validated.")
# By default, the valuation domain is fixed to [0,1]
minValDomain = 0
maxValDomain = 1
# If a valuation domain input file has been provided
if os.path.isfile(in_dir + "/valuationDomain.xml"):
xmltree_valuation = PyXMCDA.parseValidate(in_dir + "/valuationDomain.xml")
if xmltree_valuation == None:
errorList.append("valuationDomain file can't be validated.")
else:
minValDomain = PyXMCDA.getParameterByName(xmltree_valuation, "min", "valuationDomain")
maxValDomain = PyXMCDA.getParameterByName(xmltree_valuation, "max", "valuationDomain")
# We check the validity of the parameters
if not isinstance(minValDomain, float) and not isinstance(minValDomain, int):
errorList.append("min value should be an integer or a real")
if not isinstance(maxValDomain, float) and not isinstance(maxValDomain, int):
errorList.append("max value should be an integer or a real")
if not errorList:
if minValDomain >= maxValDomain:
errorList.append("The max value should be strictly greater than the min value")
if not errorList:
alternativesId = PyXMCDA.getAlternativesID(xmltree_alternatives)
criteriaId = PyXMCDA.getCriteriaID(xmltree_criteria)
perfTable = PyXMCDA.getPerformanceTable(xmltree_perfTable, alternativesId, criteriaId)
thresholds = PyXMCDA.getConstantThresholds(xmltree_criteria, criteriaId)
weights = PyXMCDA.getCriterionValue(xmltree_weights, criteriaId)
if not alternativesId:
errorList.append("No alternatives found. Is your alternatives file correct ?")
if not criteriaId:
errorList.append("No criteria found. Is your criteria file correct ?")
if not perfTable:
errorList.append("No performance table found. Is your performance table file correct ?")
if not weights:
errorList.append("No weights found. Is your weights file correct ?")
if not errorList:
# We compute the weight sum (only the weights associated to active criteria)
sumWeights = 0.0
for crit in criteriaId:
try:
sumWeights = sumWeights + weights[crit]
except:
errorList.append("There is no defined weight for criterion " + crit + ".")
if not errorList:
# We recover the criteria preference directions
# criteriaDir = PyXMCDA.getCriteriaPreferenceDirections (xmltree_criteria, criteriaId)
# Plus necessaire
# We compute the alternative comparisons values
fileAltValues = open(out_dir + "/alternativesComparisons.xml", "w")
PyXMCDA.writeHeader(fileAltValues)
# We write some information about the generated file
fileAltValues.write(
"\t<projectReference>\n\t\t<title>Rubis outranking relation</title>\n\t\t<version>"
+ VERSION
+ "</version>\n\t\t<author>ws_PyXMCDA suite (TV)</author>\n\t</projectReference>\n\n"
)
fileAltValues.write("\t<alternativesComparisons>\n\t\t<pairs>\n")
# ATTENTION Solution rustine
# On retourne ici le tableau de perf pour les criteres a minimiser
# Devra etre fait par le getRubisElementaryOutranking
criteriaDir = PyXMCDA.getCriteriaPreferenceDirections(xmltree_criteria, criteriaId)
for crit in criteriaId:
if criteriaDir[crit] == "min":
for alt in alternativesId:
perfTable[alt][crit] = -perfTable[alt][crit]
ElemOut = PyXMCDA.getRubisElementaryOutranking(alternativesId, criteriaId, perfTable, thresholds)
tabVetos = PyXMCDA.getVetos(alternativesId, criteriaId, perfTable, thresholds)
for alt1 in alternativesId:
for alt2 in alternativesId:
fileAltValues.write(
"\t\t\t<pair>\n\t\t\t\t<initial><alternativeID>"
+ alt1
+ "</alternativeID></initial>\n\t\t\t\t<terminal><alternativeID>"
+ alt2
+ "</alternativeID></terminal>\n"
)
# Verifier s'il manque des valeurs !!! try expect
# fileAltValues.write ("\t\t\t\t<value><NA>not available</NA></value>\n\t\t\t</pair>\n")
sum = 0.0
isVeto = 0
isWeakVeto = 0
for crit in criteriaId:
sum += ElemOut[alt1][alt2][crit] * weights[crit]
# On verifie si un veto est leve
if (
tabVetos.has_key(alt1)
and tabVetos[alt1].has_key(alt2)
and tabVetos[alt1][alt2].has_key(crit)
and tabVetos[alt1][alt2][crit] == 1
):
isVeto = 1
# On verifie si un veto faible est leve
if (
tabVetos.has_key(alt1)
and tabVetos[alt1].has_key(alt2)
and tabVetos[alt1][alt2].has_key(crit)
and tabVetos[alt1][alt2][crit] == 0.5
):
isWeakVeto = 1
sum = sum / sumWeights
if isVeto == 1:
# On utilise le veto classique, qui met la valeur minimale
sum = 0.0
elif isWeakVeto == 1 and sum > 0.5:
# Un veto faible est leve
sum = 0.5
# La valeur est entre 0 et 1, on la met dans le bon intervalle
sum = (maxValDomain - minValDomain) * sum + minValDomain
fileAltValues.write("\t\t\t\t<value><real>" + str(sum) + "</real></value>\n\t\t\t</pair>\n")
fileAltValues.write("\t\t</pairs>\n\t</alternativesComparisons>\n")
PyXMCDA.writeFooter(fileAltValues)
fileAltValues.close()
# Creating log and error file, messages.xml
fileMessages = open(out_dir + "/messages.xml", "w")
PyXMCDA.writeHeader(fileMessages)
if not errorList:
PyXMCDA.writeLogMessages(fileMessages, ["Execution ok"])
else:
PyXMCDA.writeErrorMessages(fileMessages, errorList)
PyXMCDA.writeFooter(fileMessages)
fileMessages.close()
def get_input_data(input_dir, filenames, params, **kwargs):
trees = _get_trees(input_dir, filenames)
d = _create_data_object(params)
for p in params:
if p == "alternatives":
d.alternatives = px.getAlternativesID(trees["alternatives"])
elif p == "profiles":
d.profiles = px.getProfilesID(trees["profiles"])
elif p == "categories_profiles":
comparison_with = kwargs.get("comparison_with")
if comparison_with is None:
comparison_with = px.getParameterByName(trees["method_parameters"], "comparison_with")
d.categories_profiles = _get_categories_profiles(trees.get("categories_profiles"), comparison_with)
elif p == "categories_rank":
categories = px.getCategoriesID(trees["categories"])
d.categories_rank = px.getCategoriesRank(trees["categories"], categories)
elif p == "comparison_with":
d.comparison_with = px.getParameterByName(trees["method_parameters"], "comparison_with")
elif p == "concordance":
alternatives = px.getAlternativesID(trees["alternatives"])
comparison_with = kwargs.get("comparison_with")
if trees.has_key("methos_parameters"):
comparison_with = px.getParameterByName(trees["method_parameters"], "comparison_with")
if kwargs.get("use_partials") is not None:
use_partials = kwargs.get("use_partials")
else:
if trees.has_key("methos_parameters"):
parameter = px.getParameterByName(trees["method_parameters"], "use_partials")
use_partials = True if parameter == "true" else False
categories_profiles = None
profiles = None
if comparison_with in ("boundary_profiles", "central_profiles"):
categories_profiles = _get_categories_profiles(trees["categories_profiles"], comparison_with)
if comparison_with == "profiles":
profiles = px.getProfilesID(trees["profiles"])
d.concordance = _get_alternatives_comparisons(
trees["concordance"],
alternatives,
profiles=profiles,
categories_profiles=categories_profiles,
use_partials=use_partials,
)
elif p == "crisp_concordance":
alternatives = px.getAlternativesID(trees["alternatives"])
comparison_with = kwargs.get("comparison_with")
if trees.has_key("methos_parameters"):
comparison_with = px.getParameterByName(trees["method_parameters"], "comparison_with")
if kwargs.get("use_partials") is not None:
use_partials = kwargs.get("use_partials")
else:
if trees.has_key("methos_parameters"):
parameter = px.getParameterByName(trees["method_parameters"], "use_partials")
use_partials = True if parameter == "true" else False
categories_profiles = None
profiles = None
if comparison_with in ("boundary_profiles", "central_profiles"):
categories_profiles = _get_categories_profiles(trees["categories_profiles"], comparison_with)
if comparison_with == "profiles":
profiles = px.getProfilesID(trees["profiles"])
d.concordance = _get_alternatives_comparisons(
trees["concordance"],
alternatives,
profiles=profiles,
categories_profiles=categories_profiles,
use_partials=use_partials,
use_value=False,
)
elif p == "credibility":
alternatives = px.getAlternativesID(trees["alternatives"])
comparison_with = kwargs.get("comparison_with")
if not comparison_with:
comparison_with = px.getParameterByName(trees["method_parameters"], "comparison_with")
if comparison_with in ("boundary_profiles", "central_profiles"):
categories_profiles = _get_categories_profiles(trees["categories_profiles"], comparison_with)
else:
categories_profiles = None
eliminate_cycles_method = px.getParameterByName(trees.get("method_parameters"), "eliminate_cycles_method")
tree = trees.get("credibility")
if eliminate_cycles_method == "cut_weakest" and tree is None:
raise RuntimeError(
"'cut_weakest' option requires credibility as " "an additional input (apart from outranking)."
)
d.credibility = _get_alternatives_comparisons(tree, alternatives, categories_profiles=categories_profiles)
elif p == "criteria":
if trees.has_key("criteria"):
d.criteria = px.getCriteriaID(trees["criteria"])
elif p == "cut_threshold":
cut_threshold = px.getParameterByName(trees["method_parameters"], "cut_threshold")
if cut_threshold is None or not (0 <= float(cut_threshold) <= 1):
raise RuntimeError(
"'cut_threshold' should be in range [0, 1] " "(most commonly used values are 0.6 or 0.7)."
)
d.cut_threshold = cut_threshold
# 'cv_crossed' == 'counter-veto crossed'
elif p == "cv_crossed":
alternatives = px.getAlternativesID(trees["alternatives"])
comparison_with = px.getParameterByName(trees["method_parameters"], "comparison_with")
if comparison_with in ("boundary_profiles", "central_profiles"):
categories_profiles = _get_categories_profiles(trees["categories_profiles"], comparison_with)
else:
categories_profiles = None
d.cv_crossed = _get_alternatives_comparisons(
trees["counter_veto_crossed"],
alternatives,
categories_profiles=categories_profiles,
use_partials=True,
mcda_concept="counterVetoCrossed",
)
elif p == "discordance":
alternatives = px.getAlternativesID(trees["alternatives"])
comparison_with = kwargs.get("comparison_with")
if trees.has_key("methos_parameters"):
comparison_with = px.getParameterByName(trees["method_parameters"], "comparison_with")
if kwargs.get("use_partials") is not None:
use_partials = kwargs.get("use_partials")
else:
if trees.has_key("methos_parameters"):
parameter = px.getParameterByName(trees["method_parameters"], "use_partials")
use_partials = True if parameter == "true" else False
categories_profiles = None
profiles = None
if comparison_with in ("boundary_profiles", "central_profiles"):
categories_profiles = _get_categories_profiles(trees["categories_profiles"], comparison_with)
if comparison_with == "profiles":
profiles = px.getProfilesID(trees["profiles"])
d.discordance = _get_alternatives_comparisons(
trees["discordance"],
alternatives,
profiles=profiles,
categories_profiles=categories_profiles,
use_partials=use_partials,
)
elif p == "crisp_discordance":
alternatives = px.getAlternativesID(trees["alternatives"])
comparison_with = kwargs.get("comparison_with")
if trees.has_key("methos_parameters"):
comparison_with = px.getParameterByName(trees["method_parameters"], "comparison_with")
if kwargs.get("use_partials") is not None:
use_partials = kwargs.get("use_partials")
else:
if trees.has_key("methos_parameters"):
parameter = px.getParameterByName(trees["method_parameters"], "use_partials")
use_partials = True if parameter == "true" else False
categories_profiles = None
profiles = None
if comparison_with in ("boundary_profiles", "central_profiles"):
categories_profiles = _get_categories_profiles(trees["categories_profiles"], comparison_with)
if comparison_with == "profiles":
profiles = px.getProfilesID(trees["profiles"])
d.discordance = _get_alternatives_comparisons(
trees["discordance"],
alternatives,
profiles=profiles,
categories_profiles=categories_profiles,
use_partials=use_partials,
use_value=False,
)
elif p == "preorder":
if trees.has_key("preorder"):
alternatives = px.getAlternativesID(trees["alternatives"])
d.preorder = px.getAlternativeValue(trees["preorder"], alternatives, None)
elif p == "downwards":
alternatives = px.getAlternativesID(trees["alternatives"])
d.downwards = px.getAlternativeValue(trees["downwards"], alternatives, None)
elif p == "upwards":
alternatives = px.getAlternativesID(trees["alternatives"])
d.upwards = px.getAlternativeValue(trees["upwards"], alternatives, None)
elif p == "eliminate_cycles_method":
d.eliminate_cycles_method = px.getParameterByName(trees["method_parameters"], "eliminate_cycles_method")
elif p == "interactions":
criteria = px.getCriteriaID(trees["criteria"])
d.interactions = _get_criteria_interactions(trees["interactions"], criteria)
elif p == "outranking":
alternatives = px.getAlternativesID(trees["alternatives"])
outranking = _get_intersection_distillation(trees["outranking"], alternatives)
if outranking == None:
outranking = px.getAlternativesComparisons(trees["outranking"], alternatives)
if outranking == {}:
outranking = _get_outranking(trees["outranking"])
d.outranking = outranking
elif p == "nonoutranking":
if trees.has_key("nonoutranking"):
alternatives = px.getAlternativesID(trees["alternatives"])
nonoutranking = _get_intersection_distillation(trees["nonoutranking"], alternatives)
if nonoutranking == None:
nonoutranking = px.getAlternativesComparisons(trees["nonoutranking"], alternatives)
if nonoutranking == {}:
nonoutranking = _get_outranking(trees["nonoutranking"])
d.nonoutranking = nonoutranking
elif p == "performances":
d.performances = px.getPerformanceTable(trees["performance_table"], None, None)
elif p == "pref_directions":
criteria = px.getCriteriaID(trees["criteria"])
d.pref_directions = px.getCriteriaPreferenceDirections(trees["criteria"], criteria)
elif p == "profiles_performance_table":
if comparison_with in ("boundary_profiles", "central_profiles"):
tree = trees.get("profiles_performance_table")
if tree is None:
msg = (
"Missing profiles performance table (did you forget "
"to provide 'profiles_performance_table.xml' file?)."
)
raise RuntimeError(msg)
d.profiles_performance_table = px.getPerformanceTable(tree, None, None)
else:
d.profiles_performance_table = None
elif p == "reinforcement_factors":
criteria = px.getCriteriaID(trees["criteria"])
factors = {}
for c in criteria:
rf = px.getCriterionValue(trees["reinforcement_factors"], c, "reinforcement_factors")
if len(rf) == 0:
continue
if rf.get(c) <= 1:
msg = (
"Reinforcement factor for criterion '{}' should be "
"higher than 1.0 (ideally between 1.2 and 1.5)."
)
raise RuntimeError(msg)
factors.update(rf)
d.reinforcement_factors = factors
elif p == "thresholds":
criteria = px.getCriteriaID(trees["criteria"])
d.thresholds = _get_thresholds(trees["criteria"])
elif p == "weights":
criteria = px.getCriteriaID(trees["criteria"])
d.weights = px.getCriterionValue(trees["weights"], criteria)
elif p == "z_function":
d.z_function = px.getParameterByName(trees["method_parameters"], "z_function")
elif p == "with_denominator":
parameter = px.getParameterByName(trees["method_parameters"], "with_denominator")
d.with_denominator = True if parameter == "true" else False
elif p == "use_partials":
parameter = px.getParameterByName(trees["method_parameters"], "use_partials")
d.use_partials = True if parameter == "true" else False
elif p == "use_pre_veto":
parameter = px.getParameterByName(trees["method_parameters"], "use_pre_veto")
d.use_pre_veto = True if parameter == "true" else False
elif p == "alpha":
d.alpha = px.getParameterByName(trees["method_parameters"], "alpha")
elif p == "beta":
d.beta = px.getParameterByName(trees["method_parameters"], "beta")
elif p == "s1":
d.s1 = px.getParameterByName(trees["method_parameters"], "s1")
elif p == "s2":
d.s2 = px.getParameterByName(trees["method_parameters"], "s2")
elif p == "crisp_outranking":
d.crisp_outranking = px.getParameterByName(trees["method_parameters"], "crisp_outranking")
elif p == "direction":
d.direction = px.getParameterByName(trees["method_parameters"], "direction")
elif p == "conc_threshold":
d.conc_threshold = px.getParameterByName(trees["method_parameters"], "conc_threshold")
elif p == "disc_threshold":
d.disc_threshold = px.getParameterByName(trees["method_parameters"], "disc_threshold")
elif p == "comprehensive":
d.comprehensive = px.getParameterByName(trees["method_parameters"], "comprehensive")
else:
raise RuntimeError("Unknown parameter '{}' specified.".format(p))
return d
