Refactor test_gof into a separate file (#1965)

* refactor test_gof in a separate file

* skip MatrixNormal for gof test

test/test_distributions.py

@@ -35,7 +35,6 @@
 from numpyro.distributions.discrete import _to_probs_bernoulli, _to_probs_multinom
 from numpyro.distributions.distribution import DistributionLike
 from numpyro.distributions.flows import InverseAutoregressiveTransform
-from numpyro.distributions.gof import InvalidTest, auto_goodness_of_fit
 from numpyro.distributions.transforms import (
     LowerCholeskyAffine,
     PermuteTransform,
@@ -53,8 +52,6 @@
 )
 from numpyro.nn import AutoregressiveNN
 
-TEST_FAILURE_RATE = 2e-5  # For all goodness-of-fit tests.
-
 
 def my_kron(A, B):
     D = A[..., :, None, :, None] * B[..., None, :, None, :]
@@ -1648,46 +1645,6 @@ def test_cdf_and_icdf(jax_dist, sp_dist, params):
         pytest.skip("cdf/icdf not implemented")
 
 
-@pytest.mark.parametrize("jax_dist, sp_dist, params", CONTINUOUS + DIRECTIONAL)
-def test_gof(jax_dist, sp_dist, params):
-    if "Improper" in jax_dist.__name__:
-        pytest.skip("distribution has improper .log_prob()")
-    if "LKJ" in jax_dist.__name__ or "Wishart" in jax_dist.__name__:
-        pytest.xfail("incorrect submanifold scaling")
-    if jax_dist is dist.EulerMaruyama:
-        d = jax_dist(*params)
-        if d.event_dim > 1:
-            pytest.skip("EulerMaruyama skip test when event shape is non-trivial.")
-    if jax_dist is dist.ZeroSumNormal:
-        pytest.skip("skip gof test for ZeroSumNormal")
-
-    num_samples = 10000
-    if "BetaProportion" in jax_dist.__name__:
-        num_samples = 20000
-    rng_key = random.PRNGKey(0)
-    d = jax_dist(*params)
-    samples = d.sample(key=rng_key, sample_shape=(num_samples,))
-    probs = np.exp(d.log_prob(samples))
-
-    dim = None
-    if jax_dist is dist.ProjectedNormal:
-        dim = samples.shape[-1] - 1
-
-    # Test each batch independently.
-    probs = probs.reshape(num_samples, -1)
-    samples = samples.reshape(probs.shape + d.event_shape)
-    if "Dirichlet" in jax_dist.__name__:
-        # The Dirichlet density is over all but one of the probs.
-        samples = samples[..., :-1]
-    for b in range(probs.shape[1]):
-        try:
-            gof = auto_goodness_of_fit(samples[:, b], probs[:, b], dim=dim)
-        except InvalidTest:
-            pytest.skip("expensive test")
-        else:
-            assert gof > TEST_FAILURE_RATE
-
-
 @pytest.mark.parametrize("jax_dist, sp_dist, params", CONTINUOUS + DISCRETE)
 def test_independent_shape(jax_dist, sp_dist, params):
     d = jax_dist(*params)

test/test_gof.py

@@ -0,0 +1,60 @@
+# Copyright Contributors to the Pyro project.
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np
+import pytest
+from test_distributions import CONTINUOUS, DIRECTIONAL
+
+import jax.random as random
+
+import numpyro.distributions as dist
+from numpyro.distributions.gof import InvalidTest, auto_goodness_of_fit
+
+TEST_FAILURE_RATE = 2e-5  # For all goodness-of-fit tests.
+
+
+@pytest.mark.parametrize("jax_dist, sp_dist, params", CONTINUOUS + DIRECTIONAL)
+def test_gof(jax_dist, sp_dist, params):
+    if "Improper" in jax_dist.__name__:
+        pytest.skip("distribution has improper .log_prob()")
+    if "LKJ" in jax_dist.__name__ or "Wishart" in jax_dist.__name__:
+        pytest.xfail("incorrect submanifold scaling")
+    if jax_dist is dist.EulerMaruyama:
+        d = jax_dist(*params)
+        if d.event_dim > 1:
+            pytest.skip("EulerMaruyama skip test when event shape is non-trivial.")
+    if jax_dist is dist.ZeroSumNormal:
+        pytest.skip("skip gof test for ZeroSumNormal")
+    if jax_dist is dist.MatrixNormal:
+        pytest.skip(
+            "skip gof test for MatrixNormal, likely incorrect submanifold scaling"
+        )
+
+    num_samples = 10000
+    if any(
+        name in jax_dist.__name__
+        for name in ["BetaProportion", "SineBivariateVonMises"]
+    ):
+        num_samples = 20000
+    rng_key = random.PRNGKey(0)
+    d = jax_dist(*params)
+    samples = d.sample(key=rng_key, sample_shape=(num_samples,))
+    probs = np.exp(d.log_prob(samples))
+
+    dim = None
+    if jax_dist is dist.ProjectedNormal:
+        dim = samples.shape[-1] - 1
+
+    # Test each batch independently.
+    probs = probs.reshape(num_samples, -1)
+    samples = samples.reshape(probs.shape + d.event_shape)
+    if "Dirichlet" in jax_dist.__name__:
+        # The Dirichlet density is over all but one of the probs.
+        samples = samples[..., :-1]
+    for b in range(probs.shape[1]):
+        try:
+            gof = auto_goodness_of_fit(samples[:, b], probs[:, b], dim=dim)
+        except InvalidTest:
+            pytest.skip("expensive test")
+        else:
+            assert gof > TEST_FAILURE_RATE