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Add more tests #49

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merged 4 commits into from
Dec 3, 2024
Merged

Add more tests #49

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6363778
Remove manuscript+supplement
dirmeier Aug 18, 2024
0ee2787
Deprecate Distrax where possible
dirmeier Aug 18, 2024
1c8c0de
Deprecate Distrax where possible
dirmeier Aug 18, 2024
bf6920a
Loosen TF pins
dirmeier Sep 24, 2024
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3 changes: 0 additions & 3 deletions Makefile
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Original file line number Diff line number Diff line change
Expand Up @@ -18,6 +18,3 @@ lints:

format:
hatch run test:format

docs:
cd docs && make html
3 changes: 1 addition & 2 deletions examples/slcp-nass_smcabc.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,7 +5,6 @@
"""
import argparse

import distrax
import jax
from jax import numpy as jnp
from jax import random as jr
Expand Down Expand Up @@ -39,7 +38,7 @@ def _unpack_params(ps):
return m0, m1, s0, s1, r

m0, m1, s0, s1, r = _unpack_params(theta)
us = distrax.Normal(0.0, 1.0).sample(
us = tfd.Normal(0.0, 1.0).sample(
seed=us_key, sample_shape=(theta.shape[0], theta.shape[1], 4, 2)
)
xs = jnp.empty_like(us)
Expand Down
20 changes: 10 additions & 10 deletions examples/slcp-snle.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,11 +5,11 @@
"""
import argparse

import distrax
import haiku as hk
import jax
import matplotlib.pyplot as plt
import optax
import surjectors
from jax import numpy as jnp
from jax import random as jr
from jax import scipy as jsp
Expand Down Expand Up @@ -49,7 +49,7 @@ def _unpack_params(ps):
return m0, m1, s0, s1, r

m0, m1, s0, s1, r = _unpack_params(theta)
us = distrax.Normal(0.0, 1.0).sample(
us = tfd.Normal(0.0, 1.0).sample(
seed=us_key, sample_shape=(theta.shape[0], theta.shape[1], 4, 2)
)
xs = jnp.empty_like(us)
Expand All @@ -67,13 +67,13 @@ def likelihood_fn(theta, y):
corr = s1 * s2 * jnp.tanh(theta[4])
cov = jnp.array([[s1**2, corr], [corr, s2**2]])
cov = jsp.linalg.block_diag(*[cov for _ in range(4)])
p = distrax.MultivariateNormalFullCovariance(mu, cov)
p = tfd.MultivariateNormalFullCovariance(mu, cov)
return p.log_prob(y)


def log_density_fn(theta, y):
prior_lp = distrax.Independent(
distrax.Uniform(jnp.full(5, -3.0), jnp.full(5, 3.0)), 1
prior_lp = tfd.Independent(
tfd.Uniform(jnp.full(5, -3.0), jnp.full(5, 3.0)), 1
).log_prob(theta)
likelihood_lp = likelihood_fn(theta, y)

Expand All @@ -84,7 +84,7 @@ def log_density_fn(theta, y):
def make_model(dim, use_surjectors):
def _bijector_fn(params):
means, log_scales = unstack(params, -1)
return distrax.ScalarAffine(means, jnp.exp(log_scales))
return surjectors.ScalarAffine(means, jnp.exp(log_scales))

def _decoder_fn(n_dim):
decoder_net = make_mlp(
Expand All @@ -95,8 +95,8 @@ def _decoder_fn(n_dim):
def _fn(z):
params = decoder_net(z)
mu, log_scale = jnp.split(params, 2, -1)
return distrax.Independent(
distrax.Normal(mu, jnp.exp(log_scale)), 1
return tfd.Independent(
tfd.Normal(mu, jnp.exp(log_scale)), 1
)

return _fn
Expand Down Expand Up @@ -140,8 +140,8 @@ def _flow(method, **kwargs):
layers.append(Permutation(order, 1))
chain = Chain(layers)

base_distribution = distrax.Independent(
distrax.Normal(jnp.zeros(n_dimension), jnp.ones(n_dimension)),
base_distribution = tfd.Independent(
tfd.Normal(jnp.zeros(n_dimension), jnp.ones(n_dimension)),
reinterpreted_batch_ndims=1,
)
td = TransformedDistribution(base_distribution, chain)
Expand Down
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