def test_constraints_testmodel(self):
self.testmodel['.*rbf'].constrain_negative()
self.assertListEqual(
self.testmodel.constraints[
transformations.NegativeLogexp()].tolist(), [0, 1])
self.testmodel['.*lengthscale'].constrain_bounded(0, 1)
self.assertListEqual(
self.testmodel.constraints[
transformations.NegativeLogexp()].tolist(), [1])
self.assertListEqual(
self.testmodel.constraints[transformations.Logistic(0,
1)].tolist(),
[0])
self.testmodel[''].unconstrain_negative()
self.assertListEqual(
self.testmodel.constraints[
transformations.NegativeLogexp()].tolist(), [])
self.assertListEqual(
self.testmodel.constraints[transformations.Logistic(0,
1)].tolist(),
[0])
self.testmodel['.*lengthscale'].unconstrain_bounded(0, 1)
self.assertListEqual(
self.testmodel.constraints[transformations.Logistic(0,
1)].tolist(),
[])
def test_constraints_set_direct(self):
self.testmodel['.*rbf'].constrain_negative()
self.testmodel['.*lengthscale'].constrain_bounded(0, 1)
self.testmodel['.*variance'].fix()
self.assertListEqual(
self.testmodel.constraints[transformations.__fixed__].tolist(),
[1, 2])
self.assertListEqual(
self.testmodel.constraints[transformations.Logistic(0,
1)].tolist(),
[0])
self.assertListEqual(
self.testmodel.constraints[
transformations.NegativeLogexp()].tolist(), [1])
cache_constraints = self.testmodel.constraints.copy()
self.testmodel.unconstrain()
self.testmodel.likelihood.fix()
self.assertListEqual(self.testmodel._fixes_.tolist(), [
transformations.UNFIXED, transformations.UNFIXED,
transformations.FIXED
])
self.assertListEqual(
self.testmodel.constraints[transformations.__fixed__].tolist(),
[2])
self.assertListEqual(
self.testmodel.constraints[transformations.Logistic(0,
1)].tolist(),
[])
self.assertListEqual(
self.testmodel.constraints[
transformations.NegativeLogexp()].tolist(), [])
self.testmodel.constraints = cache_constraints
self.assertListEqual(
self.testmodel.constraints[transformations.__fixed__].tolist(),
[1, 2])
self.assertListEqual(
self.testmodel.constraints[transformations.Logistic(0,
1)].tolist(),
[0])
self.assertListEqual(
self.testmodel.constraints[
transformations.NegativeLogexp()].tolist(), [1])
self.assertListEqual(self.testmodel._fixes_.tolist(), [
transformations.UNFIXED, transformations.FIXED,
transformations.FIXED
])
self.assertIs(self.testmodel.constraints,
self.testmodel.likelihood.constraints._param_index_ops)
self.assertIs(self.testmodel.constraints,
self.testmodel.kern.constraints._param_index_ops)
def test_constraints_in_init(self):
class Test(Parameterized):
def __init__(self, name=None, parameters=[], *a, **kw):
super(Test, self).__init__(name=name)
self.x = Param('x', np.random.uniform(0,1,(3,4)))
self.x[0].constrain_bounded(0,1)
self.link_parameter(self.x)
self.x[1].fix()
t = Test()
c = {transformations.Logistic(0,1): np.array([0, 1, 2, 3]), 'fixed': np.array([4, 5, 6, 7])}
np.testing.assert_equal(t.x.constraints[transformations.Logistic(0,1)], c[transformations.Logistic(0,1)])
np.testing.assert_equal(t.x.constraints['fixed'], c['fixed'])
def test_fix_constrain(self):
# save the constraints as they where:
before_constraints = dict(self.testmodel.constraints.items())
# fix
self.testmodel.fix()
test_constraints = dict(self.testmodel.constraints.items())
# make sure fixes are in place:
np.testing.assert_array_equal(
test_constraints[transformations.__fixed__], [0, 1, 2])
# make sure, the constraints still exist
for k in before_constraints:
np.testing.assert_array_equal(before_constraints[k],
test_constraints[k])
# override fix and previous constraint:
self.testmodel.likelihood.constrain_bounded(0, 1)
# lik not fixed anymore
np.testing.assert_array_equal(
self.testmodel.constraints[transformations.__fixed__], [0, 1])
# previous constraints still in place:
np.testing.assert_array_equal(
self.testmodel.constraints[transformations.Logexp()], [0, 1])
# lik bounded
np.testing.assert_array_equal(
self.testmodel.constraints[transformations.Logistic(0, 1)], [2])
def setUp(self):
class P(Parameterized):
def __init__(self, name, **kwargs):
super(P, self).__init__(name=name)
for k, val in kwargs.items():
self.__setattr__(k, val)
self.link_parameter(self.__getattribute__(k))
def parameters_changed(self):
self.gradient[:] = 1.
self.rbf = Parameterized('rbf')
self.rbf.lengthscale = Param('lengthscale', np.random.uniform(.1, 1), transformations.Logexp())
self.rbf.variance = Param('variance', np.random.uniform(0.1, 0.5), transformations.Logexp())
self.rbf.link_parameters(self.rbf.variance, self.rbf.lengthscale)
self.white = P('white', variance=Param('variance', np.random.uniform(0.1, 0.5), transformations.Logexp()))
self.param = Param('param', np.random.uniform(0,1,(10,5)), transformations.Logistic(0, 1))
self.test1 = Parameterized('test_parameterized')
self.test1.param = self.param
self.test1.kern = Parameterized('add')
self.test1.kern.link_parameters(self.rbf, self.white)
self.test1.link_parameter(self.test1.kern)
self.test1.link_parameter(self.param, 0)
# print self.test1:
#=============================================================================
# test_model. | Value | Constraint | Prior | Tied to
# param | (25L, 2L) | {0.0,1.0} | |
# add.rbf.variance | 1.0 | 0.0,1.0 +ve | |
# add.rbf.lengthscale | 1.0 | 0.0,1.0 +ve | |
# add.white.variance | 1.0 | 0.0,1.0 +ve | |
#=============================================================================
class M(Model):
def __init__(self, name, **kwargs):
super(M, self).__init__(name=name)
for k, val in kwargs.items():
self.__setattr__(k, val)
self.link_parameter(self.__getattribute__(k))
def objective_function(self):
return self._obj
def parameters_changed(self):
self._obj = self.param_array.sum()
self.testmodel = M('testmodel',
kern=P('rbf',
variance=Param('variance', np.random.uniform(0.1, 0.5), transformations.Logexp()),
lengthscale=Param('lengthscale', np.random.uniform(.1, 1, 1), transformations.Logexp())),
likelihood=P('Gaussian_noise',
variance=Param('variance', np.random.uniform(0.1, 0.5), transformations.Logexp()))
)
def test_add_parameter_in_hierarchy(self):
self.test1.kern.rbf.link_parameter(
Param("NEW", np.random.rand(2), transformations.NegativeLogexp()),
1)
self.assertListEqual(
self.test1.constraints[transformations.NegativeLogexp()].tolist(),
list(range(self.param.size + 1, self.param.size + 1 + 2)))
self.assertListEqual(
self.test1.constraints[transformations.Logistic(0, 1)].tolist(),
list(range(self.param.size)))
self.assertListEqual(
self.test1.constraints[transformations.Logexp(0, 1)].tolist(),
np.r_[50, 53:55].tolist())
def setUp(self):
self.rbf = Parameterized('rbf')
self.rbf.lengthscale = Param('lengthscale', np.random.uniform(.1, 1),
transformations.Logexp())
self.rbf.variance = Param('variance', np.random.uniform(0.1, 0.5),
transformations.Logexp())
self.rbf.link_parameters(self.rbf.variance, self.rbf.lengthscale)
self.white = P('white',
variance=Param('variance', np.random.uniform(0.1, 0.5),
transformations.Logexp()))
self.param = Param('param', np.random.uniform(0, 1, (10, 5)),
transformations.Logistic(0, 1))
self.test1 = Parameterized('test_parameterized')
self.test1.param = self.param
self.test1.kern = Parameterized('add')
self.test1.kern.link_parameters(self.rbf, self.white)
self.test1.link_parameter(self.test1.kern)
self.test1.link_parameter(self.param, 0)