def get_alternatives_positive_flows(*args, **kwargs):
alternativesID = px.getAlternativesID(trees['alternatives'])
flows = px.getAlternativeValue(trees['positive_flows'], alternativesID,)
return flows
def get_categories_positive_flows(*args, **kwargs):
profilesID = get_categories()
flows = px.getAlternativeValue(trees['positive_flows'], profilesID,)
return flows
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
