def setUp(self):
extra1 = tt.iscalar('extra1')
extra1_ = np.array(0, dtype=extra1.dtype)
extra1.tag.test_value = extra1_
extra1.dshape = tuple()
extra1.dsize = 1
val1 = tt.vector('val1')
val1_ = np.zeros(3, dtype=val1.dtype)
val1.tag.test_value = val1_
val1.dshape = (3,)
val1.dsize = 3
val2 = tt.matrix('val2')
val2_ = np.zeros((2, 3), dtype=val2.dtype)
val2.tag.test_value = val2_
val2.dshape = (2, 3)
val2.dsize = 6
self.val1, self.val1_ = val1, val1_
self.val2, self.val2_ = val2, val2_
self.extra1, self.extra1_ = extra1, extra1_
self.cost = extra1 * val1.sum() + val2.sum()
self.f_grad = ValueGradFunction(
[self.cost], [val1, val2], [extra1], mode='FAST_COMPILE')
def setUp(self):
extra1 = at.iscalar("extra1")
extra1_ = np.array(0, dtype=extra1.dtype)
extra1.dshape = tuple()
extra1.dsize = 1
val1 = at.vector("val1")
val1_ = np.zeros(3, dtype=val1.dtype)
val1.dshape = (3,)
val1.dsize = 3
val2 = at.matrix("val2")
val2_ = np.zeros((2, 3), dtype=val2.dtype)
val2.dshape = (2, 3)
val2.dsize = 6
self.val1, self.val1_ = val1, val1_
self.val2, self.val2_ = val2, val2_
self.extra1, self.extra1_ = extra1, extra1_
self.cost = extra1 * val1.sum() + val2.sum()
self.f_grad = ValueGradFunction(
[self.cost], [val1, val2], {extra1: extra1_}, mode="FAST_COMPILE"
)
def test_no_extra(self):
a = tt.vector('a')
a.tag.test_value = np.zeros(3, dtype=a.dtype)
a.dshape = (3,)
a.dsize = 3
f_grad = ValueGradFunction([a.sum()], [a], [], mode='FAST_COMPILE')
assert f_grad.size == 3
def setUp(self):
extra1 = tt.iscalar('extra1')
extra1_ = np.array(0, dtype=extra1.dtype)
extra1.tag.test_value = extra1_
extra1.dshape = tuple()
extra1.dsize = 1
val1 = tt.vector('val1')
val1_ = np.zeros(3, dtype=val1.dtype)
val1.tag.test_value = val1_
val1.dshape = (3,)
val1.dsize = 3
val2 = tt.matrix('val2')
val2_ = np.zeros((2, 3), dtype=val2.dtype)
val2.tag.test_value = val2_
val2.dshape = (2, 3)
val2.dsize = 6
self.val1, self.val1_ = val1, val1_
self.val2, self.val2_ = val2, val2_
self.extra1, self.extra1_ = extra1, extra1_
self.cost = extra1 * val1.sum() + val2.sum()
self.f_grad = ValueGradFunction(
self.cost, [val1, val2], [extra1], mode='FAST_COMPILE')
def test_invalid_type(self):
a = tt.ivector('a')
a.tag.test_value = np.zeros(3, dtype=a.dtype)
a.dshape = (3,)
a.dsize = 3
with pytest.raises(TypeError) as err:
ValueGradFunction([a.sum()], [a], [], mode='FAST_COMPILE')
err.match('Invalid dtype')
def test_invalid_type(self):
a = at.ivector("a")
a.tag.test_value = np.zeros(3, dtype=a.dtype)
a.dshape = (3,)
a.dsize = 3
with pytest.raises(TypeError) as err:
ValueGradFunction([a.sum()], [a], {}, mode="FAST_COMPILE")
err.match("Invalid dtype")
def test_edge_case(self):
# Edge case discovered in #2948
ndim = 3
with pm.Model() as m:
pm.Lognormal('sigma',
mu=np.zeros(ndim),
tau=np.ones(ndim),
shape=ndim) # variance for the correlation matrix
pm.HalfCauchy('nu', beta=10)
func = ValueGradFunction(m.logpt, m.basic_RVs)
func.set_extra_values(m.test_point)
q = func.dict_to_array(m.test_point)
logp, dlogp = func(q)
assert logp.size == 1
assert dlogp.size == 4
npt.assert_allclose(dlogp, 0., atol=1e-5)
def test_edge_case(self):
# Edge case discovered in #2948
ndim = 3
with pm.Model() as m:
pm.Lognormal('sigma',
mu=np.zeros(ndim),
tau=np.ones(ndim),
shape=ndim) # variance for the correlation matrix
pm.HalfCauchy('nu', beta=10)
func = ValueGradFunction(m.logpt, m.basic_RVs)
func.set_extra_values(m.test_point)
q = func.dict_to_array(m.test_point)
logp, dlogp = func(q)
assert logp.size == 1
assert dlogp.size == 4
npt.assert_allclose(dlogp, 0., atol=1e-5)
class TestValueGradFunction(unittest.TestCase):
def test_no_extra(self):
a = tt.vector('a')
a.tag.test_value = np.zeros(3, dtype=a.dtype)
a.dshape = (3,)
a.dsize = 3
f_grad = ValueGradFunction([a.sum()], [a], [], mode='FAST_COMPILE')
assert f_grad.size == 3
def test_invalid_type(self):
a = tt.ivector('a')
a.tag.test_value = np.zeros(3, dtype=a.dtype)
a.dshape = (3,)
a.dsize = 3
with pytest.raises(TypeError) as err:
ValueGradFunction([a.sum()], [a], [], mode='FAST_COMPILE')
err.match('Invalid dtype')
def setUp(self):
extra1 = tt.iscalar('extra1')
extra1_ = np.array(0, dtype=extra1.dtype)
extra1.tag.test_value = extra1_
extra1.dshape = tuple()
extra1.dsize = 1
val1 = tt.vector('val1')
val1_ = np.zeros(3, dtype=val1.dtype)
val1.tag.test_value = val1_
val1.dshape = (3,)
val1.dsize = 3
val2 = tt.matrix('val2')
val2_ = np.zeros((2, 3), dtype=val2.dtype)
val2.tag.test_value = val2_
val2.dshape = (2, 3)
val2.dsize = 6
self.val1, self.val1_ = val1, val1_
self.val2, self.val2_ = val2, val2_
self.extra1, self.extra1_ = extra1, extra1_
self.cost = extra1 * val1.sum() + val2.sum()
self.f_grad = ValueGradFunction(
[self.cost], [val1, val2], [extra1], mode='FAST_COMPILE')
def test_extra_not_set(self):
with pytest.raises(ValueError) as err:
self.f_grad.get_extra_values()
err.match('Extra values are not set')
with pytest.raises(ValueError) as err:
self.f_grad(np.zeros(self.f_grad.size, dtype=self.f_grad.dtype))
err.match('Extra values are not set')
def test_grad(self):
self.f_grad.set_extra_values({'extra1': 5})
array = np.ones(self.f_grad.size, dtype=self.f_grad.dtype)
val, grad = self.f_grad(array)
assert val == 21
npt.assert_allclose(grad, [5, 5, 5, 1, 1, 1, 1, 1, 1])
def test_bij(self):
self.f_grad.set_extra_values({'extra1': 5})
array = np.ones(self.f_grad.size, dtype=self.f_grad.dtype)
point = self.f_grad.array_to_dict(array)
assert len(point) == 2
npt.assert_allclose(point['val1'], 1)
npt.assert_allclose(point['val2'], 1)
array2 = self.f_grad.dict_to_array(point)
npt.assert_allclose(array2, array)
point_ = self.f_grad.array_to_full_dict(array)
assert len(point_) == 3
assert point_['extra1'] == 5
def test_edge_case(self):
# Edge case discovered in #2948
ndim = 3
with pm.Model() as m:
pm.Lognormal('sigma',
mu=np.zeros(ndim),
tau=np.ones(ndim),
shape=ndim) # variance for the correlation matrix
pm.HalfCauchy('nu', beta=10)
step = pm.NUTS()
func = step._logp_dlogp_func
func.set_extra_values(m.test_point)
q = func.dict_to_array(m.test_point)
logp, dlogp = func(q)
assert logp.size == 1
assert dlogp.size == 4
npt.assert_allclose(dlogp, 0., atol=1e-5)
def test_tensor_type_conversion(self):
# case described in #3122
X = np.random.binomial(1, 0.5, 10)
X[0] = -1 # masked a single value
X = np.ma.masked_values(X, value=-1)
with pm.Model() as m:
x1 = pm.Uniform('x1', 0., 1.)
x2 = pm.Bernoulli('x2', x1, observed=X)
gf = m.logp_dlogp_function()
assert m['x2_missing'].type == gf._extra_vars_shared['x2_missing'].type
def test_no_extra(self):
a = at.vector("a")
a.tag.test_value = np.zeros(3, dtype=a.dtype)
f_grad = ValueGradFunction([a.sum()], [a], {}, mode="FAST_COMPILE")
assert f_grad._extra_vars == []
class TestValueGradFunction(unittest.TestCase):
def test_no_extra(self):
a = at.vector("a")
a.tag.test_value = np.zeros(3, dtype=a.dtype)
f_grad = ValueGradFunction([a.sum()], [a], {}, mode="FAST_COMPILE")
assert f_grad._extra_vars == []
def test_invalid_type(self):
a = at.ivector("a")
a.tag.test_value = np.zeros(3, dtype=a.dtype)
a.dshape = (3,)
a.dsize = 3
with pytest.raises(TypeError) as err:
ValueGradFunction([a.sum()], [a], {}, mode="FAST_COMPILE")
err.match("Invalid dtype")
def setUp(self):
extra1 = at.iscalar("extra1")
extra1_ = np.array(0, dtype=extra1.dtype)
extra1.dshape = tuple()
extra1.dsize = 1
val1 = at.vector("val1")
val1_ = np.zeros(3, dtype=val1.dtype)
val1.dshape = (3,)
val1.dsize = 3
val2 = at.matrix("val2")
val2_ = np.zeros((2, 3), dtype=val2.dtype)
val2.dshape = (2, 3)
val2.dsize = 6
self.val1, self.val1_ = val1, val1_
self.val2, self.val2_ = val2, val2_
self.extra1, self.extra1_ = extra1, extra1_
self.cost = extra1 * val1.sum() + val2.sum()
self.f_grad = ValueGradFunction(
[self.cost], [val1, val2], {extra1: extra1_}, mode="FAST_COMPILE"
)
def test_extra_not_set(self):
with pytest.raises(ValueError) as err:
self.f_grad.get_extra_values()
err.match("Extra values are not set")
with pytest.raises(ValueError) as err:
size = self.val1_.size + self.val2_.size
self.f_grad(np.zeros(size, dtype=self.f_grad.dtype))
err.match("Extra values are not set")
def test_grad(self):
self.f_grad.set_extra_values({"extra1": 5})
size = self.val1_.size + self.val2_.size
array = RaveledVars(
np.ones(size, dtype=self.f_grad.dtype),
(
("val1", self.val1_.shape, self.val1_.dtype),
("val2", self.val2_.shape, self.val2_.dtype),
),
)
val, grad = self.f_grad(array)
assert val == 21
npt.assert_allclose(grad, [5, 5, 5, 1, 1, 1, 1, 1, 1])
@pytest.mark.xfail(reason="Test not refactored for v4")
def test_edge_case(self):
# Edge case discovered in #2948
ndim = 3
with pm.Model() as m:
pm.Lognormal(
"sigma", mu=np.zeros(ndim), tau=np.ones(ndim), shape=ndim
) # variance for the correlation matrix
pm.HalfCauchy("nu", beta=10)
step = pm.NUTS()
func = step._logp_dlogp_func
func.set_extra_values(m.initial_point)
q = func.dict_to_array(m.initial_point)
logp, dlogp = func(q)
assert logp.size == 1
assert dlogp.size == 4
npt.assert_allclose(dlogp, 0.0, atol=1e-5)
def test_missing_data(self):
# Originally from a case described in #3122
X = np.random.binomial(1, 0.5, 10)
X[0] = -1 # masked a single value
X = np.ma.masked_values(X, value=-1)
with pm.Model() as m:
x1 = pm.Uniform("x1", 0.0, 1.0)
x2 = pm.Bernoulli("x2", x1, observed=X)
gf = m.logp_dlogp_function()
gf._extra_are_set = True
assert m["x2_missing"].type == gf._extra_vars_shared["x2_missing"].type
pnt = m.test_point.copy()
del pnt["x2_missing"]
res = [gf(DictToArrayBijection.map(Point(pnt, model=m))) for i in range(5)]
assert reduce(lambda x, y: np.array_equal(x, y) and y, res) is not False
def test_aesara_switch_broadcast_edge_cases_1(self):
# Tests against two subtle issues related to a previous bug in Theano
# where `tt.switch` would not always broadcast tensors with single
# values https://github.com/pymc-devs/aesara/issues/270
# Known issue 1: https://github.com/pymc-devs/pymc3/issues/4389
data = pm.floatX(np.zeros(10))
with pm.Model() as m:
p = pm.Beta("p", 1, 1)
obs = pm.Bernoulli("obs", p=p, observed=data)
npt.assert_allclose(
logpt_sum(obs).eval({p.tag.value_var: pm.floatX(np.array(0.0))}),
np.log(0.5) * 10,
)
@pytest.mark.xfail(reason="TruncatedNormal not refactored for v4")
def test_aesara_switch_broadcast_edge_cases_2(self):
# Known issue 2: https://github.com/pymc-devs/pymc3/issues/4417
# fmt: off
data = np.array([
1.35202174, -0.83690274, 1.11175166, 1.29000367, 0.21282749,
0.84430966, 0.24841369, 0.81803141, 0.20550244, -0.45016253,
])
# fmt: on
with pm.Model() as m:
mu = pm.Normal("mu", 0, 5)
obs = pm.TruncatedNormal("obs", mu=mu, sigma=1, lower=-1, upper=2, observed=data)
npt.assert_allclose(m.dlogp([mu])({"mu": 0}), 2.499424682024436, rtol=1e-5)
class TestValueGradFunction(unittest.TestCase):
def test_no_extra(self):
a = tt.vector('a')
a.tag.test_value = np.zeros(3, dtype=a.dtype)
a.dshape = (3,)
a.dsize = 3
f_grad = ValueGradFunction(a.sum(), [a], [], mode='FAST_COMPILE')
assert f_grad.size == 3
def test_invalid_type(self):
a = tt.ivector('a')
a.tag.test_value = np.zeros(3, dtype=a.dtype)
a.dshape = (3,)
a.dsize = 3
with pytest.raises(TypeError) as err:
ValueGradFunction(a.sum(), [a], [], mode='FAST_COMPILE')
err.match('Invalid dtype')
def setUp(self):
extra1 = tt.iscalar('extra1')
extra1_ = np.array(0, dtype=extra1.dtype)
extra1.tag.test_value = extra1_
extra1.dshape = tuple()
extra1.dsize = 1
val1 = tt.vector('val1')
val1_ = np.zeros(3, dtype=val1.dtype)
val1.tag.test_value = val1_
val1.dshape = (3,)
val1.dsize = 3
val2 = tt.matrix('val2')
val2_ = np.zeros((2, 3), dtype=val2.dtype)
val2.tag.test_value = val2_
val2.dshape = (2, 3)
val2.dsize = 6
self.val1, self.val1_ = val1, val1_
self.val2, self.val2_ = val2, val2_
self.extra1, self.extra1_ = extra1, extra1_
self.cost = extra1 * val1.sum() + val2.sum()
self.f_grad = ValueGradFunction(
self.cost, [val1, val2], [extra1], mode='FAST_COMPILE')
def test_extra_not_set(self):
with pytest.raises(ValueError) as err:
self.f_grad.get_extra_values()
err.match('Extra values are not set')
with pytest.raises(ValueError) as err:
self.f_grad(np.zeros(self.f_grad.size, dtype=self.f_grad.dtype))
err.match('Extra values are not set')
def test_grad(self):
self.f_grad.set_extra_values({'extra1': 5})
array = np.ones(self.f_grad.size, dtype=self.f_grad.dtype)
val, grad = self.f_grad(array)
assert val == 21
npt.assert_allclose(grad, [5, 5, 5, 1, 1, 1, 1, 1, 1])
def test_bij(self):
self.f_grad.set_extra_values({'extra1': 5})
array = np.ones(self.f_grad.size, dtype=self.f_grad.dtype)
point = self.f_grad.array_to_dict(array)
assert len(point) == 2
npt.assert_allclose(point['val1'], 1)
npt.assert_allclose(point['val2'], 1)
array2 = self.f_grad.dict_to_array(point)
npt.assert_allclose(array2, array)
point_ = self.f_grad.array_to_full_dict(array)
assert len(point_) == 3
assert point_['extra1'] == 5
def test_edge_case(self):
# Edge case discovered in #2948
ndim = 3
with pm.Model() as m:
pm.Lognormal('sigma',
mu=np.zeros(ndim),
tau=np.ones(ndim),
shape=ndim) # variance for the correlation matrix
pm.HalfCauchy('nu', beta=10)
step = pm.NUTS()
func = step._logp_dlogp_func
func.set_extra_values(m.test_point)
q = func.dict_to_array(m.test_point)
logp, dlogp = func(q)
assert logp.size == 1
assert dlogp.size == 4
npt.assert_allclose(dlogp, 0., atol=1e-5)
def test_tensor_type_conversion(self):
# case described in #3122
X = np.random.binomial(1, 0.5, 10)
X[0] = -1 # masked a single value
X = np.ma.masked_values(X, value=-1)
with pm.Model() as m:
x1 = pm.Uniform('x1', 0., 1.)
x2 = pm.Bernoulli('x2', x1, observed=X)
gf = m.logp_dlogp_function()
assert m['x2_missing'].type == gf._extra_vars_shared['x2_missing'].type
class TestValueGradFunction(unittest.TestCase):
def test_no_extra(self):
a = aet.vector("a")
a.tag.test_value = np.zeros(3, dtype=a.dtype)
a.dshape = (3,)
a.dsize = 3
f_grad = ValueGradFunction([a.sum()], [a], [], mode="FAST_COMPILE")
assert f_grad.size == 3
def test_invalid_type(self):
a = aet.ivector("a")
a.tag.test_value = np.zeros(3, dtype=a.dtype)
a.dshape = (3,)
a.dsize = 3
with pytest.raises(TypeError) as err:
ValueGradFunction([a.sum()], [a], [], mode="FAST_COMPILE")
err.match("Invalid dtype")
def setUp(self):
extra1 = aet.iscalar("extra1")
extra1_ = np.array(0, dtype=extra1.dtype)
extra1.tag.test_value = extra1_
extra1.dshape = tuple()
extra1.dsize = 1
val1 = aet.vector("val1")
val1_ = np.zeros(3, dtype=val1.dtype)
val1.tag.test_value = val1_
val1.dshape = (3,)
val1.dsize = 3
val2 = aet.matrix("val2")
val2_ = np.zeros((2, 3), dtype=val2.dtype)
val2.tag.test_value = val2_
val2.dshape = (2, 3)
val2.dsize = 6
self.val1, self.val1_ = val1, val1_
self.val2, self.val2_ = val2, val2_
self.extra1, self.extra1_ = extra1, extra1_
self.cost = extra1 * val1.sum() + val2.sum()
self.f_grad = ValueGradFunction([self.cost], [val1, val2], [extra1], mode="FAST_COMPILE")
def test_extra_not_set(self):
with pytest.raises(ValueError) as err:
self.f_grad.get_extra_values()
err.match("Extra values are not set")
with pytest.raises(ValueError) as err:
self.f_grad(np.zeros(self.f_grad.size, dtype=self.f_grad.dtype))
err.match("Extra values are not set")
def test_grad(self):
self.f_grad.set_extra_values({"extra1": 5})
array = np.ones(self.f_grad.size, dtype=self.f_grad.dtype)
val, grad = self.f_grad(array)
assert val == 21
npt.assert_allclose(grad, [5, 5, 5, 1, 1, 1, 1, 1, 1])
def test_bij(self):
self.f_grad.set_extra_values({"extra1": 5})
array = np.ones(self.f_grad.size, dtype=self.f_grad.dtype)
point = self.f_grad.array_to_dict(array)
assert len(point) == 2
npt.assert_allclose(point["val1"], 1)
npt.assert_allclose(point["val2"], 1)
array2 = self.f_grad.dict_to_array(point)
npt.assert_allclose(array2, array)
point_ = self.f_grad.array_to_full_dict(array)
assert len(point_) == 3
assert point_["extra1"] == 5
def test_edge_case(self):
# Edge case discovered in #2948
ndim = 3
with pm.Model() as m:
pm.Lognormal(
"sigma", mu=np.zeros(ndim), tau=np.ones(ndim), shape=ndim
) # variance for the correlation matrix
pm.HalfCauchy("nu", beta=10)
step = pm.NUTS()
func = step._logp_dlogp_func
func.set_extra_values(m.test_point)
q = func.dict_to_array(m.test_point)
logp, dlogp = func(q)
assert logp.size == 1
assert dlogp.size == 4
npt.assert_allclose(dlogp, 0.0, atol=1e-5)
def test_tensor_type_conversion(self):
# case described in #3122
X = np.random.binomial(1, 0.5, 10)
X[0] = -1 # masked a single value
X = np.ma.masked_values(X, value=-1)
with pm.Model() as m:
x1 = pm.Uniform("x1", 0.0, 1.0)
x2 = pm.Bernoulli("x2", x1, observed=X)
gf = m.logp_dlogp_function()
assert m["x2_missing"].type == gf._extra_vars_shared["x2_missing"].type
def test_aesara_switch_broadcast_edge_cases(self):
# Tests against two subtle issues related to a previous bug in Aesara where aet.switch would not
# always broadcast tensors with single values https://github.com/pymc-devs/aesara/issues/270
# Known issue 1: https://github.com/pymc-devs/pymc3/issues/4389
data = np.zeros(10)
with pm.Model() as m:
p = pm.Beta("p", 1, 1)
obs = pm.Bernoulli("obs", p=p, observed=data)
# Assert logp is correct
npt.assert_allclose(
obs.logp(m.test_point),
np.log(0.5) * 10,
)
# Known issue 2: https://github.com/pymc-devs/pymc3/issues/4417
# fmt: off
data = np.array([
1.35202174, -0.83690274, 1.11175166, 1.29000367, 0.21282749,
0.84430966, 0.24841369, 0.81803141, 0.20550244, -0.45016253,
])
# fmt: on
with pm.Model() as m:
mu = pm.Normal("mu", 0, 5)
obs = pm.TruncatedNormal("obs", mu=mu, sigma=1, lower=-1, upper=2, observed=data)
# Assert dlogp is correct
npt.assert_allclose(m.dlogp([mu])({"mu": 0}), 2.499424682024436, rtol=1e-5)
class TestValueGradFunction(unittest.TestCase):
def test_no_extra(self):
a = tt.vector('a')
a.tag.test_value = np.zeros(3, dtype=a.dtype)
a.dshape = (3,)
a.dsize = 3
f_grad = ValueGradFunction(a.sum(), [a], [], mode='FAST_COMPILE')
assert f_grad.size == 3
def test_invalid_type(self):
a = tt.ivector('a')
a.tag.test_value = np.zeros(3, dtype=a.dtype)
a.dshape = (3,)
a.dsize = 3
with pytest.raises(TypeError) as err:
ValueGradFunction(a.sum(), [a], [], mode='FAST_COMPILE')
err.match('Invalid dtype')
def setUp(self):
extra1 = tt.iscalar('extra1')
extra1_ = np.array(0, dtype=extra1.dtype)
extra1.tag.test_value = extra1_
extra1.dshape = tuple()
extra1.dsize = 1
val1 = tt.vector('val1')
val1_ = np.zeros(3, dtype=val1.dtype)
val1.tag.test_value = val1_
val1.dshape = (3,)
val1.dsize = 3
val2 = tt.matrix('val2')
val2_ = np.zeros((2, 3), dtype=val2.dtype)
val2.tag.test_value = val2_
val2.dshape = (2, 3)
val2.dsize = 6
self.val1, self.val1_ = val1, val1_
self.val2, self.val2_ = val2, val2_
self.extra1, self.extra1_ = extra1, extra1_
self.cost = extra1 * val1.sum() + val2.sum()
self.f_grad = ValueGradFunction(
self.cost, [val1, val2], [extra1], mode='FAST_COMPILE')
def test_extra_not_set(self):
with pytest.raises(ValueError) as err:
self.f_grad.get_extra_values()
err.match('Extra values are not set')
with pytest.raises(ValueError) as err:
self.f_grad(np.zeros(self.f_grad.size, dtype=self.f_grad.dtype))
err.match('Extra values are not set')
def test_grad(self):
self.f_grad.set_extra_values({'extra1': 5})
array = np.ones(self.f_grad.size, dtype=self.f_grad.dtype)
val, grad = self.f_grad(array)
assert val == 21
npt.assert_allclose(grad, [5, 5, 5, 1, 1, 1, 1, 1, 1])
def test_bij(self):
self.f_grad.set_extra_values({'extra1': 5})
array = np.ones(self.f_grad.size, dtype=self.f_grad.dtype)
point = self.f_grad.array_to_dict(array)
assert len(point) == 2
npt.assert_allclose(point['val1'], 1)
npt.assert_allclose(point['val2'], 1)
array2 = self.f_grad.dict_to_array(point)
npt.assert_allclose(array2, array)
point_ = self.f_grad.array_to_full_dict(array)
assert len(point_) == 3
assert point_['extra1'] == 5
def test_edge_case(self):
# Edge case discovered in #2948
ndim = 3
with pm.Model() as m:
pm.Lognormal('sigma',
mu=np.zeros(ndim),
tau=np.ones(ndim),
shape=ndim) # variance for the correlation matrix
pm.HalfCauchy('nu', beta=10)
func = ValueGradFunction(m.logpt, m.basic_RVs)
func.set_extra_values(m.test_point)
q = func.dict_to_array(m.test_point)
logp, dlogp = func(q)
assert logp.size == 1
assert dlogp.size == 4
npt.assert_allclose(dlogp, 0., atol=1e-5)
class TestValueGradFunction(unittest.TestCase):
def test_no_extra(self):
a = tt.vector('a')
a.tag.test_value = np.zeros(3, dtype=a.dtype)
a.dshape = (3, )
a.dsize = 3
f_grad = ValueGradFunction(a.sum(), [a], [], mode='FAST_COMPILE')
assert f_grad.size == 3
def test_invalid_type(self):
a = tt.ivector('a')
a.tag.test_value = np.zeros(3, dtype=a.dtype)
a.dshape = (3, )
a.dsize = 3
with pytest.raises(TypeError) as err:
ValueGradFunction(a.sum(), [a], [], mode='FAST_COMPILE')
err.match('Invalid dtype')
def setUp(self):
extra1 = tt.iscalar('extra1')
extra1_ = np.array(0, dtype=extra1.dtype)
extra1.tag.test_value = extra1_
extra1.dshape = tuple()
extra1.dsize = 1
val1 = tt.vector('val1')
val1_ = np.zeros(3, dtype=val1.dtype)
val1.tag.test_value = val1_
val1.dshape = (3, )
val1.dsize = 3
val2 = tt.matrix('val2')
val2_ = np.zeros((2, 3), dtype=val2.dtype)
val2.tag.test_value = val2_
val2.dshape = (2, 3)
val2.dsize = 6
self.val1, self.val1_ = val1, val1_
self.val2, self.val2_ = val2, val2_
self.extra1, self.extra1_ = extra1, extra1_
self.cost = extra1 * val1.sum() + val2.sum()
self.f_grad = ValueGradFunction(self.cost, [val1, val2], [extra1],
mode='FAST_COMPILE')
def test_extra_not_set(self):
with pytest.raises(ValueError) as err:
self.f_grad.get_extra_values()
err.match('Extra values are not set')
with pytest.raises(ValueError) as err:
self.f_grad(np.zeros(self.f_grad.size, dtype=self.f_grad.dtype))
err.match('Extra values are not set')
def test_grad(self):
self.f_grad.set_extra_values({'extra1': 5})
array = np.ones(self.f_grad.size, dtype=self.f_grad.dtype)
val, grad = self.f_grad(array)
assert val == 21
npt.assert_allclose(grad, [5, 5, 5, 1, 1, 1, 1, 1, 1])
def test_bij(self):
self.f_grad.set_extra_values({'extra1': 5})
array = np.ones(self.f_grad.size, dtype=self.f_grad.dtype)
point = self.f_grad.array_to_dict(array)
assert len(point) == 2
npt.assert_allclose(point['val1'], 1)
npt.assert_allclose(point['val2'], 1)
array2 = self.f_grad.dict_to_array(point)
npt.assert_allclose(array2, array)
point_ = self.f_grad.array_to_full_dict(array)
assert len(point_) == 3
assert point_['extra1'] == 5
def test_edge_case(self):
# Edge case discovered in #2948
ndim = 3
with pm.Model() as m:
pm.Lognormal('sigma',
mu=np.zeros(ndim),
tau=np.ones(ndim),
shape=ndim) # variance for the correlation matrix
pm.HalfCauchy('nu', beta=10)
func = ValueGradFunction(m.logpt, m.basic_RVs)
func.set_extra_values(m.test_point)
q = func.dict_to_array(m.test_point)
logp, dlogp = func(q)
assert logp.size == 1
assert dlogp.size == 4
npt.assert_allclose(dlogp, 0., atol=1e-5)
class TestValueGradFunction(unittest.TestCase):
def test_no_extra(self):
a = tt.vector('a')
a.tag.test_value = np.zeros(3, dtype=a.dtype)
a.dshape = (3,)
a.dsize = 3
f_grad = ValueGradFunction(a.sum(), [a], [], mode='FAST_COMPILE')
assert f_grad.size == 3
def test_invalid_type(self):
a = tt.ivector('a')
a.tag.test_value = np.zeros(3, dtype=a.dtype)
a.dshape = (3,)
a.dsize = 3
with pytest.raises(TypeError) as err:
ValueGradFunction(a.sum(), [a], [], mode='FAST_COMPILE')
err.match('Invalid dtype')
def setUp(self):
extra1 = tt.iscalar('extra1')
extra1_ = np.array(0, dtype=extra1.dtype)
extra1.tag.test_value = extra1_
extra1.dshape = tuple()
extra1.dsize = 1
val1 = tt.vector('val1')
val1_ = np.zeros(3, dtype=val1.dtype)
val1.tag.test_value = val1_
val1.dshape = (3,)
val1.dsize = 3
val2 = tt.matrix('val2')
val2_ = np.zeros((2, 3), dtype=val2.dtype)
val2.tag.test_value = val2_
val2.dshape = (2, 3)
val2.dsize = 6
self.val1, self.val1_ = val1, val1_
self.val2, self.val2_ = val2, val2_
self.extra1, self.extra1_ = extra1, extra1_
self.cost = extra1 * val1.sum() + val2.sum()
self.f_grad = ValueGradFunction(
self.cost, [val1, val2], [extra1], mode='FAST_COMPILE')
def test_extra_not_set(self):
with pytest.raises(ValueError) as err:
self.f_grad.get_extra_values()
err.match('Extra values are not set')
with pytest.raises(ValueError) as err:
self.f_grad(np.zeros(self.f_grad.size, dtype=self.f_grad.dtype))
err.match('Extra values are not set')
def test_grad(self):
self.f_grad.set_extra_values({'extra1': 5})
array = np.ones(self.f_grad.size, dtype=self.f_grad.dtype)
val, grad = self.f_grad(array)
assert val == 21
npt.assert_allclose(grad, [5, 5, 5, 1, 1, 1, 1, 1, 1])
def test_bij(self):
self.f_grad.set_extra_values({'extra1': 5})
array = np.ones(self.f_grad.size, dtype=self.f_grad.dtype)
point = self.f_grad.array_to_dict(array)
assert len(point) == 2
npt.assert_allclose(point['val1'], 1)
npt.assert_allclose(point['val2'], 1)
array2 = self.f_grad.dict_to_array(point)
npt.assert_allclose(array2, array)
point_ = self.f_grad.array_to_full_dict(array)
assert len(point_) == 3
assert point_['extra1'] == 5
