def testListOfStatesWhereFirstPassesSecondFails(self):
"""Simple test showing API with two states. Read first!."""
n_samples = 1000
# state_0 is two scalar chains taken from iid Normal(0, 1). Will pass.
state_0 = rng.randn(n_samples, 2)
# state_1 is three 4-variate chains taken from Normal(0, 1) that have been
# shifted. Since every chain is shifted, they are not the same, and the
# test should fail.
offset = np.array([1., -1., 2.]).reshape(3, 1)
state_1 = rng.randn(n_samples, 3, 4) + offset
rhat = mcmc_diagnostics.potential_scale_reduction(
chains_states=[state_0, state_1], independent_chain_ndims=1)
self.assertIsInstance(rhat, list)
with self.test_session() as sess:
rhat_0_, rhat_1_ = sess.run(rhat)
# r_hat_0 should be close to 1, meaning test is passed.
self.assertAllEqual((), rhat_0_.shape)
self.assertAllClose(1., rhat_0_, rtol=0.02)
# r_hat_1 should be greater than 1.2, meaning test has failed.
self.assertAllEqual((4,), rhat_1_.shape)
self.assertAllEqual(np.ones_like(rhat_1_).astype(bool), rhat_1_ > 1.2)
def check_results(self, state_, independent_chain_shape, should_pass):
sample_ndims = 1
independent_chain_ndims = len(independent_chain_shape)
with self.test_session():
state = array_ops.placeholder_with_default(
input=state_,
shape=state_.shape if self.use_static_shape else None)
rhat = mcmc_diagnostics.potential_scale_reduction(
state, independent_chain_ndims=independent_chain_ndims)
if self.use_static_shape:
self.assertAllEqual(
state_.shape[sample_ndims + independent_chain_ndims:],
rhat.shape)
rhat_ = rhat.eval()
if should_pass:
self.assertAllClose(np.ones_like(rhat_),
rhat_,
atol=0,
rtol=0.02)
else:
self.assertAllEqual(
np.ones_like(rhat_).astype(bool), rhat_ > 1.2)
def check_results(self, state_, independent_chain_shape, should_pass):
sample_ndims = 1
independent_chain_ndims = len(independent_chain_shape)
with self.test_session():
state = array_ops.placeholder_with_default(
input=state_, shape=state_.shape if self.use_static_shape else None)
rhat = mcmc_diagnostics.potential_scale_reduction(
state, independent_chain_ndims=independent_chain_ndims)
if self.use_static_shape:
self.assertAllEqual(
state_.shape[sample_ndims + independent_chain_ndims:], rhat.shape)
rhat_ = rhat.eval()
if should_pass:
self.assertAllClose(np.ones_like(rhat_), rhat_, atol=0, rtol=0.02)
else:
self.assertAllEqual(np.ones_like(rhat_).astype(bool), rhat_ > 1.2)
def testIndependentNdimsLessThanOneRaises(self):
with self.assertRaisesRegexp(ValueError, "independent_chain_ndims"):
mcmc_diagnostics.potential_scale_reduction(
rng.rand(2, 3, 4), independent_chain_ndims=0)