make laplace agree with GRaF

R/marginalisers.R

@@ -220,7 +220,7 @@ laplace_approximation <- function(tolerance = 1e-6,
       derivs <- function(z) {
         y <- d0(z, reduce = FALSE)
         d1 <- tf$gradients(y, z)[[1]]
-        d2 <- tf$hessians(y, z)[[1]]
+        d2 <- tf$hessians(y, z)[[1]]  # this won't work!
         list(d1, d2)
       }
     }
@@ -237,13 +237,9 @@ laplace_approximation <- function(tolerance = 1e-6,
     # dimension of the MVN distribution
     n <- dim(mu)[[2]]
 
-    # randomly initialise z, and expand to batch (warm starts will need some TF
-    # trickery)
-
     # here z is a *column vector* to simplify later calculations, it needs to be
     # transposed to a row vector before feeding into the likelihood function(s)
-    z_value <- add_first_dim(as_2d_array(rnorm(n)))
-    z <- tf$constant(z_value, dtype = tf_float())
+    z <- tf$identity(mu)
 
     # Newton-Raphson parameters
     tol <- tf$constant(tolerance, tf_float(), shape(1))
@@ -253,11 +249,10 @@ laplace_approximation <- function(tolerance = 1e-6,
 
     # other objects
     a_value <- add_first_dim(as_2d_array(rep(0, n)))
-    a <- tf$constant(a_value, dtype = tf_float())
+    a <- fl(a_value)
     u_value <- add_first_dim(diag(n))
-    u <- tf$constant(u_value, tf_float())
-    eye <- tf$constant(add_first_dim(diag(n)),
-                       dtype = tf_float())
+    u <- fl(u_value)
+    eye <- fl(add_first_dim(diag(n)))
 
     # match batches on everything going into the loop that will have a batch
     # dimension later
@@ -310,11 +305,11 @@ laplace_approximation <- function(tolerance = 1e-6,
         s <- tf$expand_dims(s, 1L)
         s <- tf$expand_dims(s, 2L)
         a_new <- a + s * adiff
-        z_new <- tf$matmul(sigma, a) + mu
+        z_new <- tf$matmul(sigma, a_new) + mu
         psi(a_new, z_new, mu)
       }
 
-      ls_results <- gss(psiline, batch_dim)
+      ls_results <- gss(psiline, batch_dim, upper = 2)
       stepsize <- ls_results$minimum
       stepsize <- tf$expand_dims(stepsize, 1L)
       stepsize <- tf$expand_dims(stepsize, 2L)
@@ -363,11 +358,17 @@ laplace_approximation <- function(tolerance = 1e-6,
     mat1 <- tf$matmul(rw, tf_transpose(rw)) * sigma + eye
     u <- tf$cholesky(mat1)
 
+    # convergence information
+    iter <- out[[7]]
+    converged <- tf$less(iter, maxiter)
+
     # return a list of these things
     list(z = z,
          mu = mu,
          a = a,
-         u = u)
+         u = u,
+         iterations = iter,
+         converged = converged)
 
   }
 
@@ -423,12 +424,23 @@ laplace_approximation <- function(tolerance = 1e-6,
             tf_operation = "get_element",
             operation_args = list("u"))
 
+    iterations <- op("iterations",
+                     parameter_list,
+                     tf_operation = "get_element",
+                     operation_args = list("iterations"))
+
+    converged <- op("converged",
+                    parameter_list,
+                    tf_operation = "get_element",
+                    operation_args = list("converged"))
 
     # pull out the elements
     list(z = z,
          a = a,
          mu = mu,
-         u = u)
+         u = u,
+         iterations = iterations,
+         converged = converged)
 
   }
 
@@ -470,8 +482,10 @@ laplace_approximation <- function(tolerance = 1e-6,
 
   return_list_function <- function(parameters) {
 
-    list(mean = parameters$z - parameters$mu,
-         sigma = chol2symm(parameters$u))
+    list(mean = t(parameters$z),
+         sigma = chol2symm(parameters$u),
+         iterations = parameters$iterations,
+         converged = parameters$converged)
 
   }
 

tests/testthat/test_marginalisation.R

@@ -254,13 +254,16 @@ test_that("laplace approximation converges on correct posterior", {
   out <- marginalise(lik,
                      multivariate_normal(mean, sigma),
                      laplace_approximation(diagonal_hessian = TRUE))
-  theta_mu_est <- t(calculate(out$mean)[[1]])
-  theta_var_est <- calculate(diag(out$sigma))[[1]]
+  res <- calculate(mean =  t(out$mean), diag_sigma = diag(out$sigma))
+  theta_mu_est <- res$mean
+  theta_var_est <- res$diag_sigma
 
   analytic <- cbind(mean = theta_mu, sd = sqrt(theta_var))
   laplace <- cbind(mean = theta_mu_est, sd = sqrt(theta_var_est))
 
   # compare these to within a tolerance
   compare_op(analytic, laplace)
 
+  # modes are right, sds are not!
+
 })