def test_reset_y_star_event(self):
ei = MultiObjExpectedImprovement()
ei.target = array([[1, 1, 1]])
ei.current = [
NormalDistribution(mu=1, sigma=1),
NormalDistribution(mu=1, sigma=1),
NormalDistribution(mu=1, sigma=1)
]
ei.execute()
ei.target = array([[2, 2, 2]])
ei.execute()
self.assertEqual(ei.y_star.all(), array([2, 2, 2]).all())
def test_reset_y_star_event(self):
ei = MultiObjExpectedImprovement()
ei.target = array([[1, 1, 1]])
ei.current = [NormalDistribution(mu=1,sigma=1),
NormalDistribution(mu=1,sigma=1),
NormalDistribution(mu=1,sigma=1)]
ei.execute()
ei.target = array([[2, 2, 2]])
ei.execute()
self.assertEqual(ei.y_star.all(), array([2, 2, 2]).all())
def test_ei_2obj(self):
ei = MultiObjExpectedImprovement()
bests = CaseSet()
list_of_cases = [Case(outputs=[("y1",1),("y2",10)]),Case(outputs=[("y1",1),("y2",-10)])]
for case in list_of_cases:
bests.record(case)
ei.best_cases = bests
ei.criteria = ["y1","y2"]
ei.predicted_values = [NormalDistribution(mu=1,sigma=1),NormalDistribution(mu=0,sigma=1)]
ei.calc_switch = "EI"
ei.execute()
self.assertAlmostEqual([5.0],ei.EI,1)
self.assertEqual(0.5,ei.PI,6)
def test_ei_2obj(self):
ei = MultiObjExpectedImprovement()
ei.target = array([[1, 10], [1, -10]])
ei.current = [
NormalDistribution(mu=1, sigma=1),
NormalDistribution(mu=0, sigma=1)
]
ei.calc_switch = "EI"
ei.execute()
self.assertAlmostEqual([5.0], ei.EI, 1)
self.assertEqual(0.5, ei.PI, 6)
def test_ei_nobj(self):
ei = MultiObjExpectedImprovement()
ei.target = array([[1, 1, 1]])
list_of_cases = [Case(outputs=[("y1", 1), ("y2", 1), ("y3", 1)])]
ei.criteria = ['y1', 'y2', 'y3']
ei.current = [
NormalDistribution(mu=1, sigma=1),
NormalDistribution(mu=1, sigma=1),
NormalDistribution(mu=1, sigma=1)
]
ei.execute()
self.assertAlmostEqual(0.875, ei.PI, 1)
def test_ei_2obj(self):
ei = MultiObjExpectedImprovement()
ei.target = array([[1, 10], [1, -10]])
ei.current = [NormalDistribution(mu=1, sigma=1),
NormalDistribution(mu=0, sigma=1)]
ei.calc_switch = "EI"
ei.execute()
self.assertAlmostEqual([5.0], ei.EI,1)
self.assertEqual(0.5, ei.PI,6)
def test_ei_nobj(self):
ei = MultiObjExpectedImprovement(3)
bests = CaseSet()
list_of_cases = [Case(outputs=[("y1",1),("y2",1),("y3",1)])]
for case in list_of_cases:
bests.record(case)
ei.best_cases = bests
ei.criteria = ['y1','y2','y3']
ei.predicted_values = [NormalDistribution(mu=1,sigma=1),
NormalDistribution(mu=1,sigma=1),
NormalDistribution(mu=1,sigma=1)]
ei.execute()
self.assertAlmostEqual(0.875,ei.PI,1)
def test_ei_2obj(self):
ei = MultiObjExpectedImprovement()
bests = CaseSet()
list_of_cases = [Case(outputs=[("y1", 1), ("y2", 10)]), Case(outputs=[("y1", 1), ("y2", -10)])]
for case in list_of_cases:
bests.record(case)
ei.best_cases = bests
ei.criteria = ["y1", "y2"]
ei.predicted_values = [NormalDistribution(mu=1, sigma=1), NormalDistribution(mu=0, sigma=1)]
ei.calc_switch = "EI"
ei.execute()
self.assertAlmostEqual([5.0], ei.EI, 1)
self.assertEqual(0.5, ei.PI, 6)
def test_ei_calc_switch(self):
ei = MultiObjExpectedImprovement(3)
bests = CaseSet()
list_of_cases = [Case(outputs=[("y1",1),("y2",1),("y3",1)])]
for case in list_of_cases:
bests.record(case)
ei.best_cases = bests
ei.criteria = ['y1','y2','y3']
ei.predicted_values = [NormalDistribution(mu=1,sigma=1),
NormalDistribution(mu=1,sigma=1),
NormalDistribution(mu=1,sigma=1)]
ei.calc_switch = 'EI'
try:
ei.execute()
except ValueError,err:
self.assertEqual(str(err),': EI calculations not supported'
' for more than 2 objectives')
def test_ei_nobj(self):
ei = MultiObjExpectedImprovement()
ei.target = array([[1, 1, 1]])
list_of_cases = [Case(outputs=[("y1", 1), ("y2", 1), ("y3", 1)])]
ei.criteria = ['y1', 'y2', 'y3']
ei.current = [NormalDistribution(mu=1, sigma=1),
NormalDistribution(mu=1, sigma=1),
NormalDistribution(mu=1, sigma=1)]
ei.execute()
self.assertAlmostEqual(0.875,ei.PI,1)
def test_bad_criteria(self):
ei = MultiObjExpectedImprovement(2)
bests = CaseSet()
list_of_cases = [Case(outputs=[("y1",1),("y2",1)])]
for case in list_of_cases:
bests.record(case)
ei.best_cases = bests
ei.criteria = ['y1','y3']
ei.predicted_values = [NormalDistribution(mu=1,sigma=1),
NormalDistribution(mu=1,sigma=1)]
try:
ei.execute()
except ValueError,err:
self.assertEqual(str(err),": no cases in the provided case_set"
" had output matching the provided criteria, ['y1' 'y3']")
def test_ei_calc_switch(self):
ei = MultiObjExpectedImprovement()
ei.target = array([[1, 1, 1]])
ei.current = [
NormalDistribution(mu=1, sigma=1),
NormalDistribution(mu=1, sigma=1),
NormalDistribution(mu=1, sigma=1)
]
ei.calc_switch = 'EI'
try:
ei.execute()
except ValueError, err:
self.assertEqual(
str(err), ': EI calculations not supported'
' for more than 2 objectives')
def test_ei_calc_switch(self):
ei = MultiObjExpectedImprovement()
ei.target = array([[1, 1, 1]])
ei.current = [NormalDistribution(mu=1, sigma=1),
NormalDistribution(mu=1, sigma=1),
NormalDistribution(mu=1, sigma=1)]
ei.calc_switch = 'EI'
try:
ei.execute()
except ValueError,err:
self.assertEqual(str(err),': EI calculations not supported'
' for more than 2 objectives')
def test_reset_y_star_event(self):
ei = MultiObjExpectedImprovement(3)
bests = CaseSet()
list_of_cases = [Case(outputs=[("y1",1),("y2",1),("y3",1)])]
for case in list_of_cases:
bests.record(case)
ei.best_cases = bests
ei.criteria = ['y1','y2','y3']
ei.predicted_values = [NormalDistribution(mu=1,sigma=1),
NormalDistribution(mu=1,sigma=1),
NormalDistribution(mu=1,sigma=1)]
ei.execute()
bests = CaseSet()
list_of_cases = [Case(outputs=[("y1",2),("y2",2),("y3",2)])]
for case in list_of_cases:
bests.record(case)
ei.best_cases = bests
ei.reset_y_star = True
ei.execute()
self.assertEqual(ei.y_star.all(),array([2,2,2]).all())