def setUp(self):
self.k_x = FloatKrigingSurrogate()
self.k_y = FloatKrigingSurrogate()
self.k_x.train([(1, 2), (2, 3)], [2, 4])
self.k_y.train([(1, 2), (2, 3)], [4, 7])
self.a = set_as_top(MMTest())
def setUp(self):
self.k_x = FloatKrigingSurrogate()
self.k_y = FloatKrigingSurrogate()
self.k_x.train([(1, 2), (2, 3)], [2, 4])
self.k_y.train([(1, 2), (2, 3)], [4, 7])
self.a = set_as_top(MMTest())
def test_assy_promotion(self):
class Trig(Component):
x = Float(0,iotype="in",units="rad")
f_x_sin = Float(0.0,iotype="out")
f_x_cos = Float(0.0,iotype="out")
def execute(self):
self.f_x_sin = .5*sin(self.x)
self.f_x_cos = .5*cos(self.x)
class TrigAsmb(Assembly):
x = Float(0,iotype="in",units="rad")
def configure(self):
self.add('trig1', Trig())
self.add('trig2', Trig())
self.connect('x', 'trig1.x')
self.connect('trig1.f_x_sin', 'trig2.x')
self.create_passthrough('trig2.f_x_sin')
self.create_passthrough('trig2.f_x_cos')
self.driver.workflow.add(['trig1', 'trig2'])
top = set_as_top(Assembly())
top.add("trig_meta_model", MetaModel())
top.trig_meta_model.model = TrigAsmb()
top.trig_meta_model.surrogates['f_x_sin'] = FloatKrigingSurrogate()
top.trig_meta_model.surrogates['f_x_cos'] = FloatKrigingSurrogate()
# Will error if vars don't promote correctly
top.run()
def test_in_and_out_tree_multiple_surrogates(self):
self.a.mm.model = InandOutTree()
self.a.mm.surrogates['outs.x'] = FloatKrigingSurrogate()
self.a.mm.surrogates['outs.y'] = KrigingSurrogate()
self._run_sweep(self.a, 'ins.a', 'ins.b', 'outs.x', 'outs.y')
def test_in_tree_only_multiple_surrogates(self):
self.a.mm.model = InTreeOnly()
self.a.mm.surrogates['x'] = FloatKrigingSurrogate()
self.a.mm.surrogates['y'] = KrigingSurrogate()
self._run_sweep(self.a, 'ins.a', 'ins.b', 'x', 'y')
class TestMetaModelWithVtree(unittest.TestCase):
def setUp(self):
self.k_x = FloatKrigingSurrogate()
self.k_y = FloatKrigingSurrogate()
self.k_x.train([(1, 2), (2, 3)], [2, 4])
self.k_y.train([(1, 2), (2, 3)], [4, 7])
self.a = set_as_top(MMTest())
def tearDown(self):
self.a = None
def _run_sweep(self, asmb, a_name, b_name, x_name, y_name):
in_arrays = (type(a_name) == tuple)
def set_ab(a, b):
if in_arrays:
asmb.mm.set(a_name[0], a, (a_name[1], ))
asmb.mm.set(b_name[0], b, (b_name[1], ))
else:
asmb.mm.set(a_name, a)
asmb.mm.set(b_name, b)
asmb.mm.train_next = True
set_ab(1, 2)
asmb.run()
x = asmb.mm.get(x_name)
y = asmb.mm.get(y_name)
self.assertEqual(x, 2)
if (isinstance(y, UncertainDistribution)):
self.assertEqual(y.getvalue(), 4)
else:
self.assertEqual(y, 4)
asmb.mm.train_next = True
set_ab(2, 3)
asmb.run()
x = asmb.mm.get(x_name)
y = asmb.mm.get(y_name)
self.assertEqual(x, 4)
if (isinstance(y, UncertainDistribution)):
self.assertEqual(y.getvalue(), 7)
else:
self.assertEqual(y, 7)
#predictions
set_ab(1, 2)
asmb.run()
x = asmb.mm.get(x_name)
y = asmb.mm.get(y_name)
self.assertEqual(x, self.k_x.predict((1, 2)))
if (isinstance(y, UncertainDistribution)):
self.assertEqual(y.getvalue(), self.k_y.predict((1, 2)))
else:
self.assertEqual(y, self.k_y.predict((1, 2)))
set_ab(1.5, 2.5)
asmb.run()
x = asmb.mm.get(x_name)
y = asmb.mm.get(y_name)
self.assertEqual(x, self.k_x.predict((1.5, 2.5)))
if (isinstance(y, UncertainDistribution)):
self.assertEqual(y.getvalue(), self.k_y.predict((1.5, 2.5)))
else:
self.assertEqual(y, self.k_y.predict((1.5, 2.5)))
set_ab(2, 3)
asmb.run()
x = asmb.mm.get(x_name)
y = asmb.mm.get(y_name)
self.assertEqual(x, self.k_x.predict((2, 3)))
if (isinstance(y, UncertainDistribution)):
self.assertEqual(y.getvalue(), self.k_y.predict((2, 3)))
else:
self.assertEqual(y, self.k_y.predict((2, 3)))
def test_in_tree_only(self):
self.a.mm.model = InTreeOnly()
self._run_sweep(self.a, 'ins.a', 'ins.b', 'x', 'y')
def test_in_and_out_tree(self):
self.a.mm.model = InandOutTree()
self._run_sweep(self.a, 'ins.a', 'ins.b', 'outs.x', 'outs.y')
def test_in_tree_only_multiple_surrogates(self):
self.a.mm.model = InTreeOnly()
self.a.mm.surrogates['x'] = FloatKrigingSurrogate()
self.a.mm.surrogates['y'] = KrigingSurrogate()
self._run_sweep(self.a, 'ins.a', 'ins.b', 'x', 'y')
def test_in_and_out_tree_multiple_surrogates(self):
self.a.mm.model = InandOutTree()
self.a.mm.surrogates['outs.x'] = FloatKrigingSurrogate()
self.a.mm.surrogates['outs.y'] = KrigingSurrogate()
self._run_sweep(self.a, 'ins.a', 'ins.b', 'outs.x', 'outs.y')
def test_includes_with_vartrees(self):
self.a.mm.default_surrogate = KrigingSurrogate()
self.a.mm.includes = ['ins']
self.a.mm.model = InandOutTree()
self.assertEqual(self.a.mm.surrogate_input_names(), ['ins.a', 'ins.b'])
self.assertEqual(self.a.mm.surrogate_output_names(), [])
# now try changing the includes
self.a.mm.includes = ['ins', 'outs']
self.assertEqual(self.a.mm.surrogate_input_names(), ['ins.a', 'ins.b'])
self.assertEqual(self.a.mm.surrogate_output_names(),
['outs.y', 'outs.x'])
def test_excludes_with_vartrees(self):
self.a.mm.default_surrogate = KrigingSurrogate()
self.a.mm.model = InandOutTree()
self.a.mm.excludes = ['ins', 'outs']
# now try changing the excludes
self.a.mm.excludes = ['outs']
self.assertEqual(self.a.mm.surrogate_input_names(), ['ins.a', 'ins.b'])
self.assertEqual(self.a.mm.surrogate_output_names(), [])
def test_include_exclude_with_vartrees(self):
self.a.mm.model = InandOutTree()
self.a.mm.default_surrogate = KrigingSurrogate()
self.a.mm.includes = ['ins']
self.a.mm.excludes = ['outs']
try:
self.a.mm.run()
except Exception as err:
self.assertEqual(
str(err), 'mm: includes and excludes are mutually exclusive')
else:
self.fail('Expected Exception')
def test_exclude_vartree_leaf(self):
# Should not be allowed in this simplified
# implementation of variable trees in metamodel
self.a.mm.model = InandOutTree()
self.a.mm.default_surrogate = KrigingSurrogate()
try:
self.a.mm.includes = ['ins.a', 'outs.y']
except Exception as err:
self.assertEqual(
str(err),
'mm: Can only include top level variable trees, not leaves')
else:
self.fail('Expected Exception')
def configure(self):
mm = MetaModel()
self.add('mm', mm)
self.mm.default_surrogate = FloatKrigingSurrogate()
self.driver.workflow.add('mm')
class TestMetaModelWithVtree(unittest.TestCase):
def setUp(self):
self.k_x = FloatKrigingSurrogate()
self.k_y = FloatKrigingSurrogate()
self.k_x.train([(1, 2), (2, 3)], [2, 4])
self.k_y.train([(1, 2), (2, 3)], [4, 7])
self.a = set_as_top(MMTest())
def tearDown(self):
self.a = None
def _run_sweep(self, asmb, a_name, b_name, x_name, y_name):
in_arrays = (type(a_name)==tuple)
def set_ab(a, b):
if in_arrays:
asmb.mm.set(a_name[0], a, (a_name[1],))
asmb.mm.set(b_name[0], b, (b_name[1],))
else:
asmb.mm.set(a_name, a)
asmb.mm.set(b_name, b)
asmb.mm.train_next = True
set_ab(1, 2)
asmb.run()
x = asmb.mm.get(x_name)
y = asmb.mm.get(y_name)
self.assertEqual(x, 2)
if ( isinstance(y,UncertainDistribution) ):
self.assertEqual(y.getvalue(), 4)
else:
self.assertEqual(y, 4)
asmb.mm.train_next = True
set_ab(2, 3)
asmb.run()
x = asmb.mm.get(x_name)
y = asmb.mm.get(y_name)
self.assertEqual(x, 4)
if ( isinstance(y,UncertainDistribution) ):
self.assertEqual(y.getvalue(), 7)
else:
self.assertEqual(y, 7)
#predictions
set_ab(1, 2)
asmb.run()
x = asmb.mm.get(x_name)
y = asmb.mm.get(y_name)
self.assertEqual(x, self.k_x.predict((1, 2)))
if ( isinstance(y,UncertainDistribution) ):
self.assertEqual(y.getvalue(), self.k_y.predict((1, 2)))
else:
self.assertEqual(y, self.k_y.predict((1, 2)))
set_ab(1.5, 2.5)
asmb.run()
x = asmb.mm.get(x_name)
y = asmb.mm.get(y_name)
self.assertEqual(x, self.k_x.predict((1.5, 2.5)))
if ( isinstance(y,UncertainDistribution) ):
self.assertEqual(y.getvalue(), self.k_y.predict((1.5, 2.5)))
else:
self.assertEqual(y, self.k_y.predict((1.5, 2.5)))
set_ab(2, 3)
asmb.run()
x = asmb.mm.get(x_name)
y = asmb.mm.get(y_name)
self.assertEqual(x, self.k_x.predict((2, 3)))
if ( isinstance(y,UncertainDistribution) ):
self.assertEqual(y.getvalue(), self.k_y.predict((2, 3)))
else:
self.assertEqual(y, self.k_y.predict((2, 3)))
def test_in_tree_only(self):
self.a.mm.model = InTreeOnly()
self._run_sweep(self.a, 'ins.a', 'ins.b', 'x', 'y')
def test_in_and_out_tree(self):
self.a.mm.model = InandOutTree()
self._run_sweep(self.a, 'ins.a', 'ins.b', 'outs.x', 'outs.y')
def test_in_tree_only_multiple_surrogates(self):
self.a.mm.model = InTreeOnly()
self.a.mm.surrogates['x'] = FloatKrigingSurrogate()
self.a.mm.surrogates['y'] = KrigingSurrogate()
self._run_sweep(self.a, 'ins.a', 'ins.b', 'x', 'y')
def test_in_and_out_tree_multiple_surrogates(self):
self.a.mm.model = InandOutTree()
self.a.mm.surrogates['outs.x'] = FloatKrigingSurrogate()
self.a.mm.surrogates['outs.y'] = KrigingSurrogate()
self._run_sweep(self.a, 'ins.a', 'ins.b', 'outs.x', 'outs.y')
def test_includes_with_vartrees(self):
self.a.mm.default_surrogate = KrigingSurrogate()
self.a.mm.includes = ['ins']
self.a.mm.model = InandOutTree()
self.assertEqual(self.a.mm.surrogate_input_names(), ['ins.a', 'ins.b'])
self.assertEqual(self.a.mm.surrogate_output_names(), [])
# now try changing the includes
self.a.mm.includes = ['ins', 'outs']
self.assertEqual(self.a.mm.surrogate_input_names(), ['ins.a', 'ins.b'])
self.assertEqual(self.a.mm.surrogate_output_names(), ['outs.y', 'outs.x'])
def test_excludes_with_vartrees(self):
self.a.mm.default_surrogate = KrigingSurrogate()
self.a.mm.model = InandOutTree()
self.a.mm.excludes = [ 'ins', 'outs']
# now try changing the excludes
self.a.mm.excludes = ['outs']
self.assertEqual(self.a.mm.surrogate_input_names(), ['ins.a', 'ins.b'])
self.assertEqual(self.a.mm.surrogate_output_names(), [])
def test_include_exclude_with_vartrees(self):
self.a.mm.model = InandOutTree()
self.a.mm.default_surrogate = KrigingSurrogate()
self.a.mm.includes = ['ins']
self.a.mm.excludes = ['outs']
try:
self.a.mm.run()
except Exception as err:
self.assertEqual(str(err),
'mm: includes and excludes are mutually exclusive')
else:
self.fail('Expected Exception')
def test_exclude_vartree_leaf(self):
# Should not be allowed in this simplified
# implementation of variable trees in metamodel
self.a.mm.model = InandOutTree()
self.a.mm.default_surrogate = KrigingSurrogate()
try:
self.a.mm.includes = ['ins.a','outs.y']
except Exception as err:
self.assertEqual(str(err),
'mm: Can only include top level variable trees, not leaves')
else:
self.fail('Expected Exception')
