def test_laplace_approximation_custom_hessian():
def model(x, y):
a = numpyro.sample("a", dist.Normal(0, 10))
b = numpyro.sample("b", dist.Normal(0, 10))
mu = a + b * x
numpyro.sample("y", dist.Normal(mu, 1), obs=y)
x = random.normal(random.PRNGKey(0), (100, ))
y = 1 + 2 * x
guide = AutoLaplaceApproximation(
model, hessian_fn=lambda f, x: jacobian(jacobian(f))(x))
svi = SVI(model, guide, optim.Adam(0.1), Trace_ELBO(), x=x, y=y)
svi_result = svi.run(random.PRNGKey(0), 10000, progress_bar=False)
guide.get_transform(svi_result.params)
def test_laplace_approximation_warning():
def model(x, y):
a = numpyro.sample("a", dist.Normal(0, 10))
b = numpyro.sample("b", dist.Normal(0, 10), sample_shape=(3,))
mu = a + b[0] * x + b[1] * x ** 2 + b[2] * x ** 3
numpyro.sample("y", dist.Normal(mu, 0.001), obs=y)
x = random.normal(random.PRNGKey(0), (3,))
y = 1 + 2 * x + 3 * x ** 2 + 4 * x ** 3
guide = AutoLaplaceApproximation(model)
svi = SVI(model, guide, optim.Adam(0.1), Trace_ELBO(), x=x, y=y)
init_state = svi.init(random.PRNGKey(0))
svi_state = fori_loop(0, 10000, lambda i, val: svi.update(val)[0], init_state)
params = svi.get_params(svi_state)
with pytest.warns(UserWarning, match="Hessian of log posterior"):
guide.sample_posterior(random.PRNGKey(1), params)
d["D"] = d.Divorce.pipe(lambda x: (x - x.mean()) / x.std())
d["M"] = d.Marriage.pipe(lambda x: (x - x.mean()) / x.std())
# Model
def model(M, A, D=None):
a = numpyro.sample("a", dist.Normal(0, 0.2))
bM = numpyro.sample("bM", dist.Normal(0, 0.5))
bA = numpyro.sample("bA", dist.Normal(0, 0.5))
sigma = numpyro.sample("sigma", dist.Exponential(1))
mu = numpyro.deterministic("mu", a + bM * M + bA * A)
numpyro.sample("D", dist.Normal(mu, sigma), obs=D)
m5_3 = AutoLaplaceApproximation(model)
svi = SVI(model,
m5_3,
optim.Adam(1),
Trace_ELBO(),
M=d.M.values,
A=d.A.values,
D=d.D.values)
p5_3, losses = svi.run(random.PRNGKey(0), 1000)
post = m5_3.sample_posterior(random.PRNGKey(1), p5_3, (1000, ))
# Posterior
param_names = {'a', 'bA', 'bM', 'sigma'}
for p in param_names:
print(f'posterior for {p}')
ax=ax)
pml.savefig(f'multicollinear_joint_post_{method}.pdf')
plt.title(method)
plt.show()
sum_blbr = post["bl"] + post["br"]
fig, ax = plt.subplots()
az.plot_kde(sum_blbr, label="sum of bl and br", ax=ax)
plt.title(method)
pml.savefig(f'multicollinear_sum_post_{method}.pdf')
plt.show()
# Laplace fit
m6_1 = AutoLaplaceApproximation(model)
svi = SVI(model,
m6_1,
optim.Adam(0.1),
Trace_ELBO(),
leg_left=df.leg_left.values,
leg_right=df.leg_right.values,
height=df.height.values,
br_positive=False)
p6_1, losses = svi.run(random.PRNGKey(0), 2000)
post_laplace = m6_1.sample_posterior(random.PRNGKey(1), p6_1, (1000, ))
analyze_post(post_laplace, 'laplace')
# MCMC fit
# code from p298 (code 9.28) of rethinking2
