def test_unequal_training_outputs(self):
meta = MetaModel()
meta.add_param('x', 0.)
meta.add_param('y', 0.)
meta.add_output('f', 0.)
meta.default_surrogate = FloatKrigingSurrogate()
prob = Problem(Group())
prob.root.add('meta', meta)
prob.setup(check=False)
prob['meta.train:x'] = [1.0, 1.0, 1.0, 1.0]
prob['meta.train:y'] = [1.0, 2.0, 3.0, 4.0]
prob['meta.train:f'] = [1.0, 1.0]
prob['meta.x'] = 1.0
prob['meta.y'] = 1.0
with self.assertRaises(RuntimeError) as cm:
prob.run()
expected = "MetaModel: Each variable must have the same number" \
" of training points. Expected 4 but found" \
" 2 points for 'f'."
self.assertEqual(str(cm.exception), expected)
def test_sin_metamodel(self):
# create a MetaModel for Sin and add it to a Problem
sin_mm = MetaModel()
sin_mm.add_param('x', 0.)
sin_mm.add_output('f_x', 0.)
prob = Problem(Group())
prob.root.add('sin_mm', sin_mm)
# check that missing surrogate is detected in check_setup
stream = cStringIO()
prob.setup(out_stream=stream)
msg = ("No default surrogate model is defined and the "
"following outputs do not have a surrogate model:\n"
"['f_x']\n"
"Either specify a default_surrogate, or specify a "
"surrogate model for all outputs.")
self.assertTrue(msg in stream.getvalue())
# check that output with no specified surrogate gets the default
sin_mm.default_surrogate = FloatKrigingSurrogate()
prob.setup(check=False)
surrogate = prob.root.unknowns.metadata('sin_mm.f_x').get('surrogate')
self.assertTrue(isinstance(surrogate, FloatKrigingSurrogate),
'sin_mm.f_x should get the default surrogate')
# train the surrogate and check predicted value
prob['sin_mm.train:x'] = np.linspace(0, 10, 200)
prob['sin_mm.train:f_x'] = .5 * np.sin(prob['sin_mm.train:x'])
prob['sin_mm.x'] = 2.22
prob.run()
self.assertAlmostEqual(prob['sin_mm.f_x'],
.5 * np.sin(prob['sin_mm.x']),
places=5)
def test_array_outputs(self):
meta = MetaModel()
meta.add_param('x', np.zeros((2, 2)))
meta.add_output('y', np.zeros(2, ))
meta.default_surrogate = FloatKrigingSurrogate()
prob = Problem(Group())
prob.root.add('meta', meta)
prob.setup(check=False)
prob['meta.train:x'] = [[[1.0, 1.0], [1.0, 1.0]],
[[2.0, 1.0], [1.0, 1.0]],
[[1.0, 2.0], [1.0, 1.0]],
[[1.0, 1.0], [2.0, 1.0]],
[[1.0, 1.0], [1.0, 2.0]]]
prob['meta.train:y'] = [[3.0, 1.0], [2.0, 4.0], [1.0, 7.0],
[6.0, -3.0], [-2.0, 3.0]]
prob['meta.x'] = [[1.0, 2.0], [1.0, 1.0]]
prob.run()
assert_rel_error(self, prob['meta.y'], np.array([1.0, 7.0]), .00001)
def test_vector_inputs(self):
meta = MetaModel()
meta.add_param('x', np.zeros(4))
meta.add_output('y1', 0.)
meta.add_output('y2', 0.)
meta.default_surrogate = FloatKrigingSurrogate()
prob = Problem(Group())
prob.root.add('meta', meta)
prob.setup(check=False)
prob['meta.train:x'] = [[1.0, 1.0, 1.0, 1.0], [2.0, 1.0, 1.0, 1.0],
[1.0, 2.0, 1.0, 1.0], [1.0, 1.0, 2.0, 1.0],
[1.0, 1.0, 1.0, 2.0]]
prob['meta.train:y1'] = [3.0, 2.0, 1.0, 6.0, -2.0]
prob['meta.train:y2'] = [1.0, 4.0, 7.0, -3.0, 3.0]
prob['meta.x'] = [1.0, 2.0, 1.0, 1.0]
prob.run()
assert_rel_error(self, prob['meta.y1'], 1.0, .00001)
assert_rel_error(self, prob['meta.y2'], 7.0, .00001)
def test_derivatives(self):
meta = MetaModel()
meta.add_param('x', 0.)
meta.add_output('f', 0.)
meta.default_surrogate = FloatKrigingSurrogate()
prob = Problem(Group())
prob.root.add('meta', meta, promotes=['x'])
prob.root.add('p', ParamComp('x', 0.), promotes=['x'])
prob.setup(check=False)
prob['meta.train:x'] = [0., .25, .5, .75, 1.]
prob['meta.train:f'] = [1., .75, .5, .25, 0.]
prob['x'] = 0.125
prob.run()
stream = cStringIO()
prob.check_partial_derivatives(out_stream=stream)
abs_errors = findall('Absolute Error \(.+\) : (.+)', stream.getvalue())
self.assertTrue(len(abs_errors) > 0)
for match in abs_errors:
abs_error = float(match)
self.assertTrue(abs_error < 1e-6)
def test_basics(self):
# create a metamodel component
mm = MetaModel()
mm.add_param('x1', 0.)
mm.add_param('x2', 0.)
mm.add_output('y1', 0.)
mm.add_output('y2', 0., surrogate=FloatKrigingSurrogate())
mm.default_surrogate = ResponseSurface()
# add metamodel to a problem
prob = Problem(root=Group())
prob.root.add('mm', mm)
prob.setup(check=False)
# check that surrogates were properly assigned
surrogate = prob.root.unknowns.metadata('mm.y1').get('surrogate')
self.assertTrue(isinstance(surrogate, ResponseSurface))
surrogate = prob.root.unknowns.metadata('mm.y2').get('surrogate')
self.assertTrue(isinstance(surrogate, FloatKrigingSurrogate))
# populate training data
prob['mm.train:x1'] = [1.0, 2.0, 3.0]
prob['mm.train:x2'] = [1.0, 3.0, 4.0]
prob['mm.train:y1'] = [3.0, 2.0, 1.0]
prob['mm.train:y2'] = [1.0, 4.0, 7.0]
# run problem for provided data point and check prediction
prob['mm.x1'] = 2.0
prob['mm.x2'] = 3.0
self.assertTrue(mm.train) # training will occur before 1st run
prob.run()
assert_rel_error(self, prob['mm.y1'], 2.0, .00001)
assert_rel_error(self, prob['mm.y2'], 4.0, .00001)
# run problem for interpolated data point and check prediction
prob['mm.x1'] = 2.5
prob['mm.x2'] = 3.5
self.assertFalse(mm.train) # training will not occur before 2nd run
prob.run()
assert_rel_error(self, prob['mm.y1'], 1.5934, .001)
# change default surrogate, re-setup and check that metamodel re-trains
mm.default_surrogate = FloatKrigingSurrogate()
prob.setup(check=False)
surrogate = prob.root.unknowns.metadata('mm.y1').get('surrogate')
self.assertTrue(isinstance(surrogate, FloatKrigingSurrogate))
self.assertTrue(mm.train) # training will occur after re-setup
mm.warm_restart = True # use existing training data
prob['mm.x1'] = 2.5
prob['mm.x2'] = 3.5
prob.run()
assert_rel_error(self, prob['mm.y1'], 1.4609, .001)