def get_input_data(input_dir):
file_names = (
'alternatives.xml',
'categories.xml',
'categoriesProfiles.xml',
'credibility.xml',
'method_parameters.xml',
)
trees = get_trees(input_dir, file_names)
alternatives = px.getAlternativesID(trees['alternatives'])
categories = px.getCategoriesID(trees['categories'])
categories_rank = px.getCategoriesRank(trees['categories'], categories)
categories_profiles = get_categories_profiles_central(trees['categoriesProfiles'])
credibility = getAlternativesComparisons(trees['credibility'], alternatives,
categories_profiles)
cut_threshold = px.getParameterByName(trees['method_parameters'], 'cut_threshold')
check_cut_threshold(cut_threshold)
ret = {
'alternatives': alternatives,
'categories_rank': categories_rank,
'categories_profiles': categories_profiles,
'credibility': credibility,
'cut_threshold': cut_threshold,
}
return ret
def _sort_profiles (category_profiles, categories_names, rank_tree):
sortedCategories = {}
categories_rank = px.getCategoriesRank(rank_tree, categories_names)
for category in categories_rank:
sortedCategories[categories_rank[category]] = {}
sortedCategories[categories_rank[category]]["classes"] = category_profiles[category]
sortedCategories[categories_rank[category]]["id"] = category
return sortedCategories
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
# Creating lists for error and log messages
errorList = []
logList = []
# If some mandatory input files are missing
if not os.path.isfile (in_dir+"/alternatives.xml") or not os.path.isfile (in_dir+"/categories.xml") or not os.path.isfile (in_dir+"/categoriesProfiles.xml") or not os.path.isfile (in_dir+"/stabilityRelation.xml"):
errorList.append("Some input files are missing")
else :
if os.path.isfile (in_dir+"/sortingMode.xml") :
xmltree_mode = PyXMCDA.parseValidate(in_dir+"/sortingMode.xml")
if xmltree_mode == None :
errorList.append ("sortingMode file cannot be validated.")
else :
mode = PyXMCDA.getParameterByName (xmltree_mode, "sortingMode")
if not (mode == "pessimistic" or mode == "optimistic"):
errorList.append ("Value of parameter sortingMode should be 'pessimistic' or 'optimistic'.")
xmltree_alternatives = PyXMCDA.parseValidate(in_dir+"/alternatives.xml")
xmltree_categories = PyXMCDA.parseValidate(in_dir+"/categories.xml")
xmltree_profiles = PyXMCDA.parseValidate(in_dir+"/categoriesProfiles.xml")
xmltree_altStability = PyXMCDA.parseValidate(in_dir+"/stabilityRelation.xml")
if xmltree_alternatives == None :
errorList.append("The alternatives file cannot be validated.")
if xmltree_categories == None :
errorList.append("The categories file cannot be validated.")
if xmltree_profiles == None :
errorList.append("The categoriesProfiles file cannot be validated.")
if xmltree_altStability == None :
errorList.append("The alternatives comparisons file cannot be validated.")
if not errorList :
alternativesId = PyXMCDA.getAlternativesID(xmltree_alternatives, "ACTIVEREAL")
allalt = PyXMCDA.getAlternativesID(xmltree_alternatives, "ACTIVE")
categoriesId = PyXMCDA.getCategoriesID(xmltree_categories)
categoriesRank = PyXMCDA.getCategoriesRank(xmltree_categories, categoriesId)
altStability = PyXMCDA.getAlternativesComparisons (xmltree_altStability, allalt)
if not alternativesId:
errorList.append("No alternatives found.")
if not categoriesId:
errorList.append("No categories found.")
if not altStability :
errorList.append("No alternatives comparisons found.")
if not errorList :
catPro = PyXMCDA.getCategoriesProfiles(xmltree_profiles, categoriesId)
proCat = PyXMCDA.getProfilesCategories(xmltree_profiles, categoriesId)
profilesId = proCat.keys()
# On retourne la liste pour trier selon les rangs
rankCategories = {}
for i, j in categoriesRank.iteritems():
rankCategories[j] = i
ranks = rankCategories.keys()[:]
ranks.sort()
lowestRank = ranks.pop()
# Un tableau pour conserver les affectations
affectations = {}
if mode == "pessimistic":
# Electre tri pessimistic rule
for alt in alternativesId:
affectations[alt] = []
for rank in ranks:
profile = catPro[rankCategories[rank]]["lower"]
if altStability[alt][profile] >= -1 and altStability[alt][profile] <= 1:
# Surclassement instable, on ajoute les categories sup et inf
if affectations[alt].count(proCat[profile]["lower"]) == 0:
affectations[alt].append(proCat[profile]["lower"])
if affectations[alt].count(proCat[profile]["upper"]) == 0:
affectations[alt].append(proCat[profile]["upper"])
if altStability[alt][profile] > 1:
# Surclassement stable, on ajoute que sup et on arrete
if affectations[alt].count(proCat[profile]["upper"]) == 0:
affectations[alt].append(proCat[profile]["upper"])
break
if affectations[alt] == []:
# Tous les surc stables et negatifs, on force la categorie la plus basse
affectations[alt] = [rankCategories[lowestRank]]
else:
errorList.append("Optimistic rule is not taken into account yet")
if not errorList :
# Creating alternativesAffectations file
fileAffectations = open(out_dir+"/alternativesAffectations.xml",'w')
PyXMCDA.writeHeader(fileAffectations)
# We write some information about the generated file
fileAffectations.write ("\t<projectReference>\n\t\t<title>Stable alternatives affectation</title>\n\t\t<version>"+VERSION+"</version>\n\t\t<author>ws_PyXMCDA suite (TV)</author>\n\t</projectReference>\n\n")
fileAffectations.write("\t<alternativesAffectations>\n")
for alt in alternativesId:
fileAffectations.write("\t\t<alternativeAffectation>\n\t\t\t<alternativeID>"+alt+"</alternativeID>\n\t\t\t<categoriesSet>\n")
for cat in affectations[alt]:
fileAffectations.write("\t\t\t\t<element><categoryID>"+cat+"</categoryID></element>\n")
fileAffectations.write("\t\t\t</categoriesSet>\n\t\t</alternativeAffectation>\n")
fileAffectations.write("\t</alternativesAffectations>\n")
PyXMCDA.writeFooter(fileAffectations)
fileAffectations.close()
# Creating log and error file, messages.xml
fileMessages = open(out_dir+"/messages.xml", 'w')
PyXMCDA.writeHeader (fileMessages)
if not errorList :
logList.append("Execution ok")
PyXMCDA.writeLogMessages (fileMessages, logList)
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 get_categories_rank(*args, **kwargs):
categories = px.getCategoriesID(trees['categories'])
categories_rank = px.getCategoriesRank(trees['categories'], categories)
return categories_rank # dict
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 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
def get_categories_rank(*args, **kwargs):
categories = px.getCategoriesID(trees['categories'])
categories_rank = px.getCategoriesRank(trees['categories'], categories)
return categories_rank # dict