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Speedup logcdf tests #4734

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Speedup logcdf tests #4734

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6159d72
Speedup check_logcdf test
ricardoV94 Jun 3, 2021
fe67c98
Speedup check_selfconsistency_discrete_logcdf test
ricardoV94 Jun 3, 2021
7e8e838
Revert reduced test n_samples due to speed issues
ricardoV94 Jun 2, 2021
5de3f2d
Fix float32 Beta and StudentT logcdf failing tests
ricardoV94 Jun 3, 2021
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83 changes: 49 additions & 34 deletions pymc3/tests/test_distributions.py
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Original file line number Diff line number Diff line change
Expand Up @@ -103,7 +103,7 @@
logpt,
logpt_sum,
)
from pymc3.math import kronecker, logsumexp
from pymc3.math import kronecker
from pymc3.model import Deterministic, Model, Point
from pymc3.tests.helpers import select_by_precision
from pymc3.vartypes import continuous_types
Expand Down Expand Up @@ -750,6 +750,10 @@ def check_logcdf(
if not skip_paramdomain_inside_edge_test:
domains = paramdomains.copy()
domains["value"] = domain

model, param_vars = build_model(pymc3_dist, domain, paramdomains)
pymc3_logcdf = model.fastfn(logpt(model["value"], cdf=True))

if decimal is None:
decimal = select_by_precision(float64=6, float32=3)

Expand All @@ -758,17 +762,23 @@ def check_logcdf(
if skip_params_fn(params):
continue
scipy_cdf = scipy_logcdf(**params)

scipy_eval = scipy_logcdf(**params)
value = params.pop("value")
with Model() as m:
dist = pymc3_dist("y", **params)

# Update shared parameter variables in pymc3_logcdf function
for param_name, param_value in params.items():
param_vars[param_name].set_value(param_value)

pymc3_eval = pymc3_logcdf({"value": value})

params["value"] = value # for displaying in err_msg
with aesara.config.change_flags(on_opt_error="raise", mode=Mode("py")):
assert_almost_equal(
logcdf(dist, value).eval(),
scipy_cdf,
decimal=decimal,
err_msg=str(params),
)
assert_almost_equal(
pymc3_eval,
scipy_eval,
decimal=decimal,
err_msg=str(params),
)

valid_value = domain.vals[0]
valid_params = {param: paramdomain.vals[0] for param, paramdomain in paramdomains.items()}
Expand Down Expand Up @@ -848,24 +858,33 @@ def check_selfconsistency_discrete_logcdf(
"""
Check that logcdf of discrete distributions matches sum of logps up to value
"""
# This test only works for scalar random variables
assert distribution.rv_op.ndim_supp == 0

domains = paramdomains.copy()
domains["value"] = domain
if decimal is None:
decimal = select_by_precision(float64=6, float32=3)

model, param_vars = build_model(distribution, domain, paramdomains)
dist_logcdf = model.fastfn(logpt(model["value"], cdf=True))
dist_logp = model.fastfn(logpt(model["value"]))

for pt in product(domains, n_samples=n_samples):
params = dict(pt)
if skip_params_fn(params):
continue
value = params.pop("value")
values = np.arange(domain.lower, value + 1)
dist = distribution.dist(**params)
# This only works for scalar random variables
assert dist.owner.op.ndim_supp == 0
values_dist = change_rv_size(dist, values.shape)

# Update shared parameter variables in logp/logcdf function
for param_name, param_value in params.items():
param_vars[param_name].set_value(param_value)

with aesara.config.change_flags(mode=Mode("py")):
assert_almost_equal(
logcdf(dist, value).eval(),
logsumexp(logpt(values_dist, values), keepdims=False).eval(),
dist_logcdf({"value": value}),
scipy.special.logsumexp([dist_logp({"value": value}) for value in values]),
decimal=decimal,
err_msg=str(pt),
)
Expand Down Expand Up @@ -1118,14 +1137,18 @@ def test_beta(self):
{"alpha": Rplus, "beta": Rplus},
lambda value, alpha, beta: sp.beta.logpdf(value, alpha, beta),
)
self.check_logp(Beta, Unit, {"mu": Unit, "sigma": Rplus}, beta_mu_sigma)
self.check_logp(
Beta,
Unit,
{"mu": Unit, "sigma": Rplus},
beta_mu_sigma,
)
self.check_logcdf(
Beta,
Unit,
{"alpha": Rplus, "beta": Rplus},
lambda value, alpha, beta: sp.beta.logcdf(value, alpha, beta),
n_samples=10,
decimal=select_by_precision(float64=5, float32=3),
decimal=select_by_precision(float64=5, float32=1),
)

def test_kumaraswamy(self):
Expand Down Expand Up @@ -1247,20 +1270,17 @@ def scipy_mu_alpha_logcdf(value, mu, alpha):
Nat,
{"mu": Rplus, "alpha": Rplus},
scipy_mu_alpha_logcdf,
n_samples=5,
)
self.check_logcdf(
NegativeBinomial,
Nat,
{"p": Unit, "n": Rplus},
lambda value, p, n: sp.nbinom.logcdf(value, n, p),
n_samples=5,
)
self.check_selfconsistency_discrete_logcdf(
NegativeBinomial,
Nat,
{"mu": Rplus, "alpha": Rplus},
n_samples=10,
)

@pytest.mark.xfail(reason="Distribution not refactored yet")
Expand Down Expand Up @@ -1319,7 +1339,6 @@ def test_lognormal(self):
Rplus,
{"mu": R, "sigma": Rplusbig},
lambda value, mu, sigma: floatX(sp.lognorm.logpdf(value, sigma, 0, np.exp(mu))),
n_samples=5, # Just testing alternative parametrization
)
self.check_logcdf(
Lognormal,
Expand All @@ -1332,10 +1351,9 @@ def test_lognormal(self):
Rplus,
{"mu": R, "sigma": Rplusbig},
lambda value, mu, sigma: sp.lognorm.logcdf(value, sigma, 0, np.exp(mu)),
n_samples=5, # Just testing alternative parametrization
)

def test_t(self):
def test_studentt_logp(self):
self.check_logp(
StudentT,
R,
Expand All @@ -1347,21 +1365,24 @@ def test_t(self):
R,
{"nu": Rplus, "mu": R, "sigma": Rplus},
lambda value, nu, mu, sigma: sp.t.logpdf(value, nu, mu, sigma),
n_samples=5, # Just testing alternative parametrization
)
self.check_logcdf(
StudentT,
R,
{"nu": Rplus, "mu": R, "lam": Rplus},
lambda value, nu, mu, lam: sp.t.logcdf(value, nu, mu, lam ** -0.5),
n_samples=10, # relies on slow incomplete beta
)

@pytest.mark.xfail(
condition=(aesara.config.floatX == "float32"),
reason="Fails on float32 due to numerical issues",
)
def test_studentt_logcdf(self):
self.check_logcdf(
StudentT,
R,
{"nu": Rplus, "mu": R, "sigma": Rplus},
lambda value, nu, mu, sigma: sp.t.logcdf(value, nu, mu, sigma),
n_samples=5, # Just testing alternative parametrization
)

def test_cauchy(self):
Expand Down Expand Up @@ -1538,13 +1559,11 @@ def test_binomial(self):
Nat,
{"n": NatSmall, "p": Unit},
lambda value, n, p: sp.binom.logcdf(value, n, p),
n_samples=10,
)
self.check_selfconsistency_discrete_logcdf(
Binomial,
Nat,
{"n": NatSmall, "p": Unit},
n_samples=10,
)

@pytest.mark.xfail(reason="checkd tests has not been refactored")
Expand Down Expand Up @@ -1747,14 +1766,12 @@ def logcdf_fn(value, psi, mu, alpha):
Nat,
{"psi": Unit, "mu": Rplusbig, "alpha": Rplusbig},
logcdf_fn,
n_samples=10,
)

self.check_selfconsistency_discrete_logcdf(
ZeroInflatedNegativeBinomial,
Nat,
{"psi": Unit, "mu": Rplusbig, "alpha": Rplusbig},
n_samples=10,
)

@pytest.mark.xfail(reason="Test not refactored yet")
Expand Down Expand Up @@ -1787,14 +1804,12 @@ def logcdf_fn(value, psi, n, p):
Nat,
{"psi": Unit, "n": NatSmall, "p": Unit},
logcdf_fn,
n_samples=10,
)

self.check_selfconsistency_discrete_logcdf(
ZeroInflatedBinomial,
Nat,
{"n": NatSmall, "p": Unit, "psi": Unit},
n_samples=10,
)

@pytest.mark.parametrize("n", [1, 2, 3])
Expand Down