def loss_fn_SBL(params, state, model, X, y, warm_restart=True):
model_state, loss_state = state
variables = {"params": params, **model_state}
(prediction, dt, theta, coeffs), updated_model_state = model.apply(
variables, X, mutable=list(model_state.keys()))
n_samples, n_features = theta.shape
# MSE stuff
tau = precision(y, prediction, 0.0, 0.0)
p_mse, MSE = normal_LL(prediction, y, tau)
# Regression stuff
# we dont want the gradient
beta_prior = (
n_samples / 2,
n_samples / (jax.lax.stop_gradient(tau)),
)
# theta_normed = theta / jnp.linalg.norm(theta, axis=0)
if warm_restart:
prior_init = loss_state["prior_init"]
else:
prior_init = None
p_reg, mn, prior, fwd_metric = SBL(
fixed_point_solver_implicit,
theta,
dt,
prior_init=prior_init,
hyper_prior=((1e-6, 1e-6), beta_prior),
max_iter=1000,
)
Reg = jnp.mean((dt - theta @ mn)**2)
updated_loss_state = {"prior_init": prior}
loss = -(p_mse + p_reg)
metrics = {
"loss": loss,
"p_mse": p_mse,
"mse": MSE,
"p_reg": p_reg,
"reg": Reg,
"bayes_coeffs": mn,
"coeffs": coeffs,
"alpha": prior[:-1],
"beta": prior[-1],
"tau": tau,
"its": fwd_metric[0],
}
return (
loss,
(
(updated_model_state, updated_loss_state),
metrics,
(prediction, dt, theta, mn),
),
)
def loss_fn_SBL(params, state, model, X, y, warm_restart=True):
model_state, loss_state = state
variables = {"params": params, **model_state}
(prediction, dt, theta, coeffs), updated_model_state = model.apply(
variables, X, mutable=list(model_state.keys())
)
n_samples, n_features = theta.shape
prior_params_mse = (0.0, 0.0)
# MSE stuff
tau = precision(y, prediction, *prior_params_mse)
p_mse, MSE = normal_LL(prediction, y, tau)
# Regression stuff
# we dont want the gradient
hyper_prior_params = (
n_samples / 2,
n_samples / (2 * jax.lax.stop_gradient(tau)),
)
theta_normed = theta / jnp.linalg.norm(theta, axis=0)
if (loss_state["prior_init"] is None) or (warm_restart is False):
prior_init = jnp.concatenate(
[jnp.ones((n_features,)), (1.0 / (jnp.var(y) + 1e-7))[jnp.newaxis]], axis=0
)
else:
prior_init = loss_state["prior_init"]
p_reg, mn, prior, fwd_metric = SBL(
theta_normed, dt, prior_init=prior_init, beta_prior=hyper_prior_params, tol=1e-3
)
Reg = jnp.mean((dt - theta_normed @ mn) ** 2)
loss_state["prior_init"] = prior
loss = -(p_mse + p_reg)
metrics = {
"loss": loss,
"p_mse": p_mse,
"mse": MSE,
"p_reg": p_reg,
"reg": Reg,
"bayes_coeffs": mn,
"coeffs": coeffs,
"alpha": prior[:-1],
"beta": prior[-1],
"tau": tau,
"its": fwd_metric[0],
"gap": fwd_metric[1],
}
return (
loss,
((updated_model_state, loss_state), metrics, (prediction, dt, theta, mn)),
)
def loss_fn_SBL(params, state, model, X, y, warm_restart=True):
model_state, loss_state = state
variables = {"params": params, **model_state}
(prediction, dt, theta, coeffs), updated_model_state = model.apply(
variables, X, mutable=list(model_state.keys()))
n_samples, n_features = theta.shape
# MSE stuff
tau = precision(y, prediction, 0.0, 0.0)
p_mse, MSE = normal_LL(prediction, y, tau)
# Regression stuff
# we dont want the gradient
beta_prior = (
n_samples / 2,
n_samples / (jax.lax.stop_gradient(tau)),
)
if warm_restart:
prior_init = loss_state["prior_init"]
else:
prior_init = None
p_reg, mn, prior, fwd_metric = SBL(
theta,
dt,
prior_init=prior_init,
hyper_prior=((1e-6, 1e-6), beta_prior),
tol=1e-4,
max_iter=300,
)
reg = jnp.mean((dt - jnp.dot(theta, coeffs))**2)
BIC_val, (mse, masked_reg), masked_coeffs = BIC(prediction, y, dt, theta,
prior[:-1], 1e4)
updated_loss_state = {"prior_init": prior}
loss = -(p_mse + p_reg)
metrics = {
"loss": loss,
"p_mse": p_mse,
"mse": mse,
"p_reg": p_reg,
"coeffs": coeffs,
"reg": reg,
"masked_reg": masked_reg,
"bayes_coeffs": mn,
"masked_coeffs": masked_coeffs,
"alpha": prior[:-1],
"beta": prior[-1],
"tau": tau,
"its": fwd_metric[0],
"BIC": BIC_val,
}
return (
loss,
(
(updated_model_state, updated_loss_state),
metrics,
(prediction, dt, theta, mn),
),
)