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merged 3 commits into from
May 19, 2023
Merged

Remove joint_logprob function from tests.logprob.utils #6650

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ccb9cfb
Remove joint_logprob function from tests.logprob.utils
shreyas3156 Apr 4, 2023
ace9892
Move the joint logprob test for subtensors to test_mixture.py
shreyas3156 Apr 4, 2023
1b8bbfd
Solve conflicts in test_basic.py
shreyas3156 Apr 15, 2023
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119 changes: 37 additions & 82 deletions tests/logprob/test_basic.py
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Original file line number Diff line number Diff line change
Expand Up @@ -60,19 +60,19 @@
from pymc.logprob.utils import rvs_to_value_vars, walk_model
from pymc.pytensorf import replace_rvs_by_values
from pymc.testing import assert_no_rvs
from tests.logprob.utils import joint_logprob


def test_joint_logprob_basic():
# A simple check for when `joint_logprob` is the same as `logprob`
def test_factorized_joint_logprob_basic():
# A simple check for when `factorized_joint_logprob` is the same as `logprob`
a = pt.random.uniform(0.0, 1.0)
a.name = "a"
a_value_var = a.clone()

a_logp = joint_logprob({a: a_value_var}, sum=False)
a_logp = factorized_joint_logprob({a: a_value_var})
a_logp_comb = tuple(a_logp.values())[0]
a_logp_exp = logp(a, a_value_var)

assert equal_computations([a_logp], [a_logp_exp])
assert equal_computations([a_logp_comb], [a_logp_exp])

# Let's try a hierarchical model
sigma = pt.random.invgamma(0.5, 0.5)
Expand All @@ -81,7 +81,8 @@ def test_joint_logprob_basic():
sigma_value_var = sigma.clone()
y_value_var = Y.clone()

total_ll = joint_logprob({Y: y_value_var, sigma: sigma_value_var}, sum=False)
total_ll = factorized_joint_logprob({Y: y_value_var, sigma: sigma_value_var})
total_ll_combined = pt.add(*total_ll.values())

# We need to replace the reference to `sigma` in `Y` with its value
# variable
Expand All @@ -92,7 +93,7 @@ def test_joint_logprob_basic():
)
total_ll_exp = logp(sigma, sigma_value_var) + ll_Y

assert equal_computations([total_ll], [total_ll_exp])
assert equal_computations([total_ll_combined], [total_ll_exp])

# Now, make sure we can compute a joint log-probability for a hierarchical
# model with some non-`RandomVariable` nodes
Expand All @@ -105,42 +106,46 @@ def test_joint_logprob_basic():
b_value_var = b.clone()
c_value_var = c.clone()

b_logp = joint_logprob({a: a_value_var, b: b_value_var, c: c_value_var})
b_logp = factorized_joint_logprob({a: a_value_var, b: b_value_var, c: c_value_var})
b_logp_combined = pt.sum([pt.sum(factor) for factor in b_logp.values()])

# There shouldn't be any `RandomVariable`s in the resulting graph
assert_no_rvs(b_logp)
assert_no_rvs(b_logp_combined)

res_ancestors = list(walk_model((b_logp,), walk_past_rvs=True))
res_ancestors = list(walk_model((b_logp_combined,), walk_past_rvs=True))
assert b_value_var in res_ancestors
assert c_value_var in res_ancestors
assert a_value_var in res_ancestors


def test_joint_logprob_multi_obs():
def test_factorized_joint_logprob_multi_obs():
a = pt.random.uniform(0.0, 1.0)
b = pt.random.normal(0.0, 1.0)

a_val = a.clone()
b_val = b.clone()

logp_res = joint_logprob({a: a_val, b: b_val}, sum=False)
logp_res = factorized_joint_logprob({a: a_val, b: b_val})
logp_res_combined = pt.add(*logp_res.values())
logp_exp = logp(a, a_val) + logp(b, b_val)

assert equal_computations([logp_res], [logp_exp])
assert equal_computations([logp_res_combined], [logp_exp])

x = pt.random.normal(0, 1)
y = pt.random.normal(x, 1)

x_val = x.clone()
y_val = y.clone()

logp_res = joint_logprob({x: x_val, y: y_val})
exp_logp = joint_logprob({x: x_val, y: y_val})
logp_res = factorized_joint_logprob({x: x_val, y: y_val})
exp_logp = factorized_joint_logprob({x: x_val, y: y_val})
logp_res_comb = pt.sum([pt.sum(factor) for factor in logp_res.values()])
exp_logp_comb = pt.sum([pt.sum(factor) for factor in exp_logp.values()])

assert equal_computations([logp_res], [exp_logp])
assert equal_computations([logp_res_comb], [exp_logp_comb])


def test_joint_logprob_diff_dims():
def test_factorized_joint_logprob_diff_dims():
M = pt.matrix("M")
x = pt.random.normal(0, 1, size=M.shape[1], name="X")
y = pt.random.normal(M.dot(x), 1, name="Y")
Expand All @@ -150,14 +155,15 @@ def test_joint_logprob_diff_dims():
y_vv = y.clone()
y_vv.name = "y"

logp = joint_logprob({x: x_vv, y: y_vv})
logp = factorized_joint_logprob({x: x_vv, y: y_vv})
logp_combined = pt.sum([pt.sum(factor) for factor in logp.values()])

M_val = np.random.normal(size=(10, 3))
x_val = np.random.normal(size=(3,))
y_val = np.random.normal(size=(10,))

point = {M: M_val, x_vv: x_val, y_vv: y_val}
logp_val = logp.eval(point)
logp_val = logp_combined.eval(point)

exp_logp_val = (
sp.norm.logpdf(x_val, 0, 1).sum() + sp.norm.logpdf(y_val, M_val.dot(x_val), 1).sum()
Expand All @@ -179,60 +185,6 @@ def test_incsubtensor_original_values_output_dict():
assert vv in logp_dict


def test_joint_logprob_subtensor():
"""Make sure we can compute a joint log-probability for ``Y[I]`` where ``Y`` and ``I`` are random variables."""

size = 5

mu_base = np.power(10, np.arange(np.prod(size))).reshape(size)
mu = np.stack([mu_base, -mu_base])
sigma = 0.001
rng = pytensor.shared(np.random.RandomState(232), borrow=True)

A_rv = pt.random.normal(mu, sigma, rng=rng)
A_rv.name = "A"

p = 0.5

I_rv = pt.random.bernoulli(p, size=size, rng=rng)
I_rv.name = "I"

A_idx = A_rv[I_rv, pt.ogrid[A_rv.shape[-1] :]]

assert isinstance(A_idx.owner.op, (Subtensor, AdvancedSubtensor, AdvancedSubtensor1))

A_idx_value_var = A_idx.type()
A_idx_value_var.name = "A_idx_value"

I_value_var = I_rv.type()
I_value_var.name = "I_value"

A_idx_logp = joint_logprob({A_idx: A_idx_value_var, I_rv: I_value_var}, sum=False)

logp_vals_fn = pytensor.function([A_idx_value_var, I_value_var], A_idx_logp)

# The compiled graph should not contain any `RandomVariables`
assert_no_rvs(logp_vals_fn.maker.fgraph.outputs[0])

decimals = 6 if pytensor.config.floatX == "float64" else 4

test_val_rng = np.random.RandomState(3238)

for i in range(10):
bern_sp = sp.bernoulli(p)
I_value = bern_sp.rvs(size=size, random_state=test_val_rng).astype(I_rv.dtype)

norm_sp = sp.norm(mu[I_value, np.ogrid[mu.shape[1] :]], sigma)
A_idx_value = norm_sp.rvs(random_state=test_val_rng).astype(A_idx.dtype)

exp_obs_logps = norm_sp.logpdf(A_idx_value)
exp_obs_logps += bern_sp.logpmf(I_value)

logp_vals = logp_vals_fn(A_idx_value, I_value)

np.testing.assert_almost_equal(logp_vals, exp_obs_logps, decimal=decimals)


def test_persist_inputs():
"""Make sure we don't unnecessarily clone variables."""
x = pt.scalar("x")
Expand All @@ -242,24 +194,27 @@ def test_persist_inputs():
beta_vv = beta_rv.type()
y_vv = Y_rv.clone()

logp = joint_logprob({beta_rv: beta_vv, Y_rv: y_vv})
logp = factorized_joint_logprob({beta_rv: beta_vv, Y_rv: y_vv})
logp_combined = pt.sum([pt.sum(factor) for factor in logp.values()])

assert x in ancestors([logp])
assert x in ancestors([logp_combined])

# Make sure we don't clone value variables when they're graphs.
y_vv_2 = y_vv * 2
logp_2 = joint_logprob({beta_rv: beta_vv, Y_rv: y_vv_2})
logp_2 = factorized_joint_logprob({beta_rv: beta_vv, Y_rv: y_vv_2})
logp_2_combined = pt.sum([pt.sum(factor) for factor in logp_2.values()])

assert y_vv in ancestors([logp_2])
assert y_vv_2 in ancestors([logp_2])
assert y_vv in ancestors([logp_2_combined])
assert y_vv_2 in ancestors([logp_2_combined])

# Even when they are random
y_vv = pt.random.normal(name="y_vv2")
y_vv_2 = y_vv * 2
logp_2 = joint_logprob({beta_rv: beta_vv, Y_rv: y_vv_2})
logp_2 = factorized_joint_logprob({beta_rv: beta_vv, Y_rv: y_vv_2})
logp_2_combined = pt.sum([pt.sum(factor) for factor in logp_2.values()])

assert y_vv in ancestors([logp_2])
assert y_vv_2 in ancestors([logp_2])
assert y_vv in ancestors([logp_2_combined])
assert y_vv_2 in ancestors([logp_2_combined])


def test_warn_random_found_factorized_joint_logprob():
Expand All @@ -284,7 +239,7 @@ def test_multiple_rvs_to_same_value_raises():

msg = "More than one logprob factor was assigned to the value var x"
with pytest.raises(ValueError, match=msg):
joint_logprob({x_rv1: x, x_rv2: x})
factorized_joint_logprob({x_rv1: x, x_rv2: x})


def test_joint_logp_basic():
Expand Down
62 changes: 35 additions & 27 deletions tests/logprob/test_censoring.py
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Original file line number Diff line number Diff line change
Expand Up @@ -41,10 +41,10 @@
import scipy as sp
import scipy.stats as st

from pymc import logp
from pymc.logprob import factorized_joint_logprob
from pymc.logprob.transforms import LogTransform, TransformValuesRewrite
from pymc.testing import assert_no_rvs
from tests.logprob.utils import joint_logprob


@pytensor.config.change_flags(compute_test_value="raise")
Expand All @@ -55,10 +55,10 @@ def test_continuous_rv_clip():
cens_x_vv = cens_x_rv.clone()
cens_x_vv.tag.test_value = 0

logp = joint_logprob({cens_x_rv: cens_x_vv})
assert_no_rvs(logp)
logprob = pt.sum(logp(cens_x_rv, cens_x_vv))
assert_no_rvs(logprob)

logp_fn = pytensor.function([cens_x_vv], logp)
logp_fn = pytensor.function([cens_x_vv], logprob)
ref_scipy = st.norm(0.5, 1)

assert logp_fn(-3) == -np.inf
Expand All @@ -75,10 +75,10 @@ def test_discrete_rv_clip():

cens_x_vv = cens_x_rv.clone()

logp = joint_logprob({cens_x_rv: cens_x_vv})
assert_no_rvs(logp)
logprob = pt.sum(logp(cens_x_rv, cens_x_vv))
assert_no_rvs(logprob)

logp_fn = pytensor.function([cens_x_vv], logp)
logp_fn = pytensor.function([cens_x_vv], logprob)
ref_scipy = st.poisson(2)

assert logp_fn(0) == -np.inf
Expand All @@ -97,8 +97,8 @@ def test_one_sided_clip():
lb_cens_x_vv = lb_cens_x_rv.clone()
ub_cens_x_vv = ub_cens_x_rv.clone()

lb_logp = joint_logprob({lb_cens_x_rv: lb_cens_x_vv})
ub_logp = joint_logprob({ub_cens_x_rv: ub_cens_x_vv})
lb_logp = pt.sum(logp(lb_cens_x_rv, lb_cens_x_vv))
ub_logp = pt.sum(logp(ub_cens_x_rv, ub_cens_x_vv))
assert_no_rvs(lb_logp)
assert_no_rvs(ub_logp)

Expand All @@ -117,10 +117,10 @@ def test_useless_clip():

cens_x_vv = cens_x_rv.clone()

logp = joint_logprob({cens_x_rv: cens_x_vv}, sum=False)
assert_no_rvs(logp)
logprob = logp(cens_x_rv, cens_x_vv)
assert_no_rvs(logprob)

logp_fn = pytensor.function([cens_x_vv], logp)
logp_fn = pytensor.function([cens_x_vv], logprob)
ref_scipy = st.norm(0.5, 1)

np.testing.assert_allclose(logp_fn([-2, 0, 2]), ref_scipy.logpdf([-2, 0, 2]))
Expand All @@ -133,10 +133,12 @@ def test_random_clip():

lb_vv = lb_rv.clone()
cens_x_vv = cens_x_rv.clone()
logp = joint_logprob({cens_x_rv: cens_x_vv, lb_rv: lb_vv}, sum=False)
assert_no_rvs(logp)
logp = factorized_joint_logprob({cens_x_rv: cens_x_vv, lb_rv: lb_vv})
logp_combined = pt.add(*logp.values())

logp_fn = pytensor.function([lb_vv, cens_x_vv], logp)
assert_no_rvs(logp_combined)

logp_fn = pytensor.function([lb_vv, cens_x_vv], logp_combined)
res = logp_fn([0, -1], [-1, -1])
assert res[0] == -np.inf
assert res[1] != -np.inf
Expand All @@ -150,8 +152,10 @@ def test_broadcasted_clip_constant():
lb_vv = lb_rv.clone()
cens_x_vv = cens_x_rv.clone()

logp = joint_logprob({cens_x_rv: cens_x_vv, lb_rv: lb_vv})
assert_no_rvs(logp)
logp = factorized_joint_logprob({cens_x_rv: cens_x_vv, lb_rv: lb_vv})
logp_combined = pt.sum([pt.sum(factor) for factor in logp.values()])

assert_no_rvs(logp_combined)


def test_broadcasted_clip_random():
Expand All @@ -162,8 +166,10 @@ def test_broadcasted_clip_random():
lb_vv = lb_rv.clone()
cens_x_vv = cens_x_rv.clone()

logp = joint_logprob({cens_x_rv: cens_x_vv, lb_rv: lb_vv})
assert_no_rvs(logp)
logp = factorized_joint_logprob({cens_x_rv: cens_x_vv, lb_rv: lb_vv})
logp_combined = pt.sum([pt.sum(factor) for factor in logp.values()])

assert_no_rvs(logp_combined)


def test_fail_base_and_clip_have_values():
Expand Down Expand Up @@ -199,10 +205,11 @@ def test_deterministic_clipping():

x_vv = x_rv.clone()
y_vv = y_rv.clone()
logp = joint_logprob({x_rv: x_vv, y_rv: y_vv})
assert_no_rvs(logp)
logp = factorized_joint_logprob({x_rv: x_vv, y_rv: y_vv})
logp_combined = pt.sum([pt.sum(factor) for factor in logp.values()])
assert_no_rvs(logp_combined)

logp_fn = pytensor.function([x_vv, y_vv], logp)
logp_fn = pytensor.function([x_vv, y_vv], logp_combined)
assert np.isclose(
logp_fn(-1, 1),
st.norm(0, 1).logpdf(-1) + st.norm(0, 1).logpdf(1),
Expand All @@ -216,10 +223,11 @@ def test_clip_transform():
cens_x_vv = cens_x_rv.clone()

transform = TransformValuesRewrite({cens_x_vv: LogTransform()})
logp = joint_logprob({cens_x_rv: cens_x_vv}, extra_rewrites=transform)
logp = factorized_joint_logprob({cens_x_rv: cens_x_vv}, extra_rewrites=transform)
logp_combined = pt.sum([pt.sum(factor) for factor in logp.values()])

cens_x_vv_testval = -1
obs_logp = logp.eval({cens_x_vv: cens_x_vv_testval})
obs_logp = logp_combined.eval({cens_x_vv: cens_x_vv_testval})
exp_logp = sp.stats.norm(0.5, 1).logpdf(np.exp(cens_x_vv_testval)) + cens_x_vv_testval

assert np.isclose(obs_logp, exp_logp)
Expand All @@ -236,8 +244,8 @@ def test_rounding(rounding_op):
xr.name = "xr"

xr_vv = xr.clone()
logp = joint_logprob({xr: xr_vv}, sum=False)
assert logp is not None
logprob = logp(xr, xr_vv)
assert logprob is not None

x_sp = st.norm(loc, scale)
if rounding_op == pt.round:
Expand All @@ -250,6 +258,6 @@ def test_rounding(rounding_op):
raise NotImplementedError()

assert np.allclose(
logp.eval({xr_vv: test_value}),
logprob.eval({xr_vv: test_value}),
expected_logp,
)
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