def recompute_posterior_fr(alpha: np.ndarray, beta: np.ndarray, K: np.ndarray) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
"""
Recompute the posterior approximation (for the full rank approximation) mean: K alpha, covariance inv(K + beta)
:param alpha: Alpha vector used to parametrize the posterior approximation
:param beta: Beta vector/matrix used to parametrize the posterior approximation
:param K: prior covariance
:return: Tuple containing the mean and cholesky of the covariance, its inverse and derivatives of the KL divergence with respect to beta and alpha
"""
N = K.shape[0]
L = choleskies._flat_to_triang_pure(beta)
assert(L.shape[0]==1)
L = L[0,:,:]
lam_sqrt= np.diag(L)
lam = lam_sqrt**2
# Compute Mean
m = K @ alpha
jitter = 1e-5
dKL_da = m.copy()
Kinv = np.linalg.inv(K+ np.eye(N)*jitter)
L_inv = np.linalg.inv(L)
Sigma = np.empty((alpha.size, alpha.shape[0]))
Lamda_full_rank = np.dot(L, L.T)
dKL_db_triang = -dL_fr(L, 0.5*(np.linalg.inv(Lamda_full_rank) - Kinv), None, None, None)
mat1 = np.linalg.inv(K + Lamda_full_rank)
#Sigma = np.linalg.inv(Kinv + np.linalg.inv(Lamda_full_rank))
Sigma = Lamda_full_rank
# Compute KL
KL = 0.5*(-N + (m.T@Kinv@m) + np.trace(Kinv @ Sigma) - np.log(np.linalg.det(Sigma @ Kinv)))
dKL_db = choleskies._triang_to_flat_pure(dKL_db_triang)
return m, L, L_inv, KL, dKL_db, dKL_da
def df_d(y: List[Tuple[int, float]], yc: List[List[Tuple[int, int]]], m: np.ndarray, L: np.ndarray, L_inv: np.ndarray, K: np.ndarray, sigma2s: np.ndarray, alpha: np.ndarray, beta: np.ndarray, s_to_l: Callable=dL_fr):
"""
Computes the log marginal likelihood and its derivatives with respect to alpha and beta. Works for both mean feald and full rank approximations
:param y: Direct observations in as a list of tuples telling location index (row in X) and observation value.
:param yc: Batch comparisons in a list of lists of tuples. Each batch is a list and tuples tell the comparisons (winner index, loser index)
:param m: mean of the latent values
:param L: Cholesky decomposition of the latent value covariance
:param L_inv: inverse of the cholesky decomposition
:param K: prior covariance
:param sigma2s: noise variance of the observations
:param alpha: Alpha vector used to parametrize the posterior approximation
:param beta: Beta vector/matrix used to parametrize the posterior approximation
:param s_to_l: A function to compute the derivative of log likelihood with respect to beta using the generalized chain rule and when we know the derivative of log likelihood with respect to Sigma
:return: A tuple containing log marginal likelihood, its derivative with respect to alpha and its derivative with respect to beta
"""
Sigma = L @ L.T
dF_dm_full = np.zeros_like(m)
dF_dSigma_full = np.zeros_like(Sigma)
F_full = 0
#log_marginal = 0
d_list = np.random.choice(range(len(yc)), size=len(yc), replace=False)
for batch_idx in d_list:
loc_inds_winners, loc_inds_losers = [yc[batch_idx][k][0] for k in range(len(yc[batch_idx]))], [yc[batch_idx][k][1] for k in range(len(yc[batch_idx]))]
loc_inds_batch = np.sort(np.unique(loc_inds_winners + loc_inds_losers))
# get winners
ind_winners, ind_losers = [np.where(loc_inds_batch == it)[0][0] for it in loc_inds_winners], [np.where(loc_inds_batch == it)[0][0] for it in loc_inds_losers]
# get variational moments
F_batch, dF_dm_batch, dF_dSigma_batch = variational_expectations_ove_full_rank(m[loc_inds_batch], Sigma[np.ix_(loc_inds_batch, loc_inds_batch)], ind_winners, ind_losers, sigma2s[loc_inds_batch])
dF_dm_full[loc_inds_batch] += dF_dm_batch
dF_dSigma_full[np.ix_(loc_inds_batch, loc_inds_batch)] += dF_dSigma_batch
F_full += F_batch
#delta = 1e-5
if len(y) > 0:
ys = np.zeros((len(y),1))
y_inds = np.zeros(len(y), dtype=int)
#dir_list = np.random.choice(range(len(y)), size=len(y), replace=False)
for ind in range(len(y)):
(y_inds[ind], ys[ind,0]) = y[ind] #index in kernel, y value
F_full += -0.5*np.sum( ( (m[y_inds] - ys)**2 + Sigma[y_inds, y_inds].reshape((-1,1)) ) / sigma2s[y_inds].reshape((-1,1)) )
dF_dm_full[y_inds] += (ys - m[y_inds] ) / sigma2s[y_inds].reshape((-1,1))
dF_dSigma_full[y_inds, y_inds] += -0.5 / sigma2s[y_inds].reshape((-1))
alpha_grad = K @ dF_dm_full
beta_grad = s_to_l(L, dF_dSigma_full, alpha, beta, K)
log_marginal = F_full
if beta_grad.shape[1] > 1:
beta_grad = choleskies._triang_to_flat_pure(beta_grad)
return log_marginal, alpha_grad, beta_grad
def __init__(
self,
X: np.ndarray,
y: List[Tuple[int, float]],
yc: List[List[Tuple[int, int]]],
kernel: GPy.kern.Kern,
likelihood: Gaussian,
vi_mode: str = "fr",
name: str = "VIComparisonGP",
max_iters: int = 50,
get_logger: Callable = None,
):
super(VIComparisonGP, self).__init__(name=name)
self.N, self.D = X.shape[0], X.shape[1]
self.output_dim = 1
self.get_logger = get_logger
self.X = X
self.y = y
self.yc = yc
self.max_iters = max_iters
self.vi_mode = vi_mode
self.kern = kernel
self.likelihood = likelihood
self.sigma2s = self.likelihood.variance * np.ones(
(X.shape[0], 1), dtype=int)
jitter = 1e-6
K = self.kern.K(X)
L = np.linalg.cholesky(K + np.identity(K.shape[0]) * jitter)
self.alpha = np.zeros((self.N, 1))
self.beta = np.ones((self.N, 1))
self.posterior = None
# If we are using full rank VI, we initialize it with mean field VI
if self.vi_mode == "FRVI":
self.posterior, _, _, self.alpha, self.beta = vi.vi_comparison(
self.X,
self.y,
self.yc,
self.kern,
self.sigma2s,
self.alpha,
self.beta,
max_iters=50,
method="mf")
self.beta = choleskies._triang_to_flat_pure(
jitchol(self.posterior.covariance)[None, :])
def test_triang_to_flat(self):
A1 = choleskies._triang_to_flat_pure(self.triang)
A2 = choleskies._triang_to_flat_cython(self.triang)
np.testing.assert_allclose(A1, A2)
def test_triang_to_flat(self):
A1 = choleskies._triang_to_flat_pure(self.triang)
A2 = choleskies._triang_to_flat_cython(self.triang)
np.testing.assert_allclose(A1, A2)
old_mean_grad = mean_grad
old_L_grad = L_grad
# s_mean += mean_grads_running_dot_product
# s_log_var += sigma_grads_running_dot_product
criterion1 = mean_grads_running_dot_product < 0
criterion2 = sigma_grads_running_dot_product < 0
criterion3 = np.abs(elbo_prev - elbo) < np.abs(elbo_threshold_swa * elbo_prev)
#print('old mean gradient')
#print(old_mean_grad)
mean_grads_running_dot_product = np.mean(mean_grad*old_mean_grad)
sigma_grads_running_dot_product = np.mean(old_L_grad*L_grad)
print(step_size)
means_vb_clr += step_size * mean_grad_clr
L_vb_clr += 0.5 * step_size * L_grad_clr
betas_vb_clr = choleskies._triang_to_flat_pure(L_vb_clr[None, :])
betas = choleskies._triang_to_flat_pure(L[None, :])
# betas_vb_swa = np.reshape(L_vb_swa@L_vb_swa.T, (-1,1))
params = [means, betas]
params_rms_prop = [means_vb_rms, L_vb_rms.flatten()]
# step_size, params_swa, swa_n = stepsize_linear_adaptive_schedule(params, step_size, step_size_min, step_size_max,
# itt+1, itt_max+1, start_swa_iter, 80, params_swa, swa_n)
swa_weight = 1.
step_size, params_swa, swa_n = stepsize_linear_weight_averaging(params, step_size, step_size_min,
step_size_max,
itt + 1, itt_max + 1, start_swa_iter,
200, params_swa, swa_n, weight=1.1,
pmz='std')
rho, s = rms_prop_gradient(itt + 1, mean_grad_rms, L_grad_rms.flatten(), s_prev, step_size_rms)
s_prev = s
