def test_caseiter_to_caseset(self):
cases = ListCaseIterator(self.caselist[3:])
cs = caseiter_to_caseset(cases)
for case1,case2 in zip(cases, cs):
self.assertTrue(case1 == case2)
cssub = caseiter_to_caseset(cases, ['comp1.b','comp2.b','comp2.c+comp2.d'])
for case1,case2 in zip(cases, cssub):
self.assertTrue(set(case2.keys('in')).issubset(case1.keys('in')))
self.assertTrue(set(case2.keys('out')).issubset(case1.keys('out')))
def test_caseiter_to_caseset(self):
cases = ListCaseIterator(self.caselist[3:])
cs = caseiter_to_caseset(cases)
for case1, case2 in zip(cases, cs):
self.assertTrue(case1 == case2)
cssub = caseiter_to_caseset(cases, ["comp1.b", "comp2.b", "comp2.c+comp2.d"])
for case1, case2 in zip(cases, cssub):
self.assertTrue(set(case2.keys("in")).issubset(case1.keys("in")))
self.assertTrue(set(case2.keys("out")).issubset(case1.keys("out")))
def test_caseiter_to_caseset(self):
cases = ListCaseIterator(self.caselist[3:])
cs = caseiter_to_caseset(cases)
for case1, case2 in zip(cases, cs):
self.assertTrue(case1 == case2)
cssub = caseiter_to_caseset(cases,
['comp1.b', 'comp2.b', 'comp2.c+comp2.d'])
for case1, case2 in zip(cases, cssub):
self.assertTrue(set(case2.keys('in')).issubset(case1.keys('in')))
self.assertTrue(set(case2.keys('out')).issubset(case1.keys('out')))
def execute(self):
"""Finds and removes pareto optimal points in the given case set.
Returns a list of pareto optimal points. Smaller is better for all
criteria.
"""
#convert stuff to caseSets if they are not
case_sets = []
for ci in self.case_sets:
if not isinstance(ci, CaseSet):
case_sets.append(caseiter_to_caseset(ci))
else:
case_sets.append(ci)
y_list = []
if len(case_sets) > 1:
case_set = case_sets[0].union(*case_sets[1:])
else:
case_set = case_sets[0]
criteria_count = len(self.criteria)
try:
# need to transpose the list of outputs
y_list = zip(*[case_set[crit] for crit in self.criteria])
except KeyError:
self.raise_exception(
'no cases provided had all of the outputs '
'matching the provided criteria, %s' % self.criteria,
ValueError)
y_temp = list(y_list)
self.dominated_set = CaseSet()
self.pareto_set = CaseSet() #TODO: need a way to copy casesets
for point1, case in zip(y_list, iter(case_set)):
dominated = False
for point2 in y_temp:
if self._is_dominated(point1, point2):
self.dominated_set.record(case)
y_temp.remove(point1)
dominated = True
break
if not dominated:
self.pareto_set.record(case)
def execute(self):
"""Finds and removes pareto optimal points in the given case set.
Returns a list of pareto optimal points. Smaller is better for all
criteria.
"""
#convert stuff to caseSets if they are not
case_sets = []
for ci in self.case_sets:
if not isinstance(ci,CaseSet):
case_sets.append(caseiter_to_caseset(ci))
else:
case_sets.append(ci)
y_list = []
if len(case_sets) > 1:
case_set = case_sets[0].union(*case_sets[1:])
else:
case_set = case_sets[0]
criteria_count = len(self.criteria)
try:
# need to transpose the list of outputs
y_list = zip(*[case_set[crit] for crit in self.criteria])
except KeyError:
self.raise_exception('no cases provided had all of the outputs '
'matching the provided criteria, %s'%self.criteria, ValueError)
y_temp = list(y_list)
self.dominated_set = CaseSet()
self.pareto_set = CaseSet() #TODO: need a way to copy casesets
for point1, case in zip(y_list, iter(case_set)):
dominated = False
for point2 in y_temp:
if self._is_dominated(point1, point2):
self.dominated_set.record(case)
y_temp.remove(point1)
dominated = True
break
if not dominated:
self.pareto_set.record(case)
