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R/calculate.R

@@ -158,10 +158,8 @@ calculate <- function(
   trace_batch_size = 100,
   compute_options = cpu_only()
 ) {
-
   # set device to be CPU/GPU for the entire run
   with(tf$device(compute_options), {
-
     # message users about random seeds and GPU usage if they are using GPU
     message_if_using_gpu(compute_options)
 
@@ -200,7 +198,6 @@ calculate <- function(
     # checks and RNG seed setting if we're sampling
     # REFACTOR: check_rng_seed(nim, seed, compute_option)
     if (!is.null(nsim)) {
-
       # check nsim is valid
       nsim <- check_positive_integer(nsim, "nsim")
 
@@ -210,27 +207,25 @@ calculate <- function(
           x = ".Random.seed",
           envir = .GlobalEnv,
           inherits = FALSE
-          )
+        )
         if (no_global_random_seed) {
           runif(1)
         }
 
-
         r_seed <- get(".Random.seed", envir = .GlobalEnv)
         on.exit(assign(".Random.seed", r_seed, envir = .GlobalEnv))
         tensorflow::set_random_seed(
           seed = seed,
           disable_gpu = is_using_cpu(compute_options)
-          )
+        )
       }
 
-      if (is.null(seed)){
+      if (is.null(seed)) {
         tensorflow::set_random_seed(
           seed = get_seed(),
           disable_gpu = is_using_cpu(compute_options)
         )
       }
-
     }
 
     # set precision
@@ -259,7 +254,6 @@ calculate <- function(
     }
 
     if (!is.greta_mcmc_list(result)) {
-
       # if it's not mcmc samples, make sure the results are in the right order
       # (tensorflow order seems to be platform specific?!?)
       order <- match(names(result), names(target))
@@ -278,12 +272,13 @@ calculate <- function(
 
 #' @importFrom coda thin
 #' @importFrom stats start end
-calculate_greta_mcmc_list <- function(target,
-                                      values,
-                                      nsim,
-                                      tf_float,
-                                      trace_batch_size) {
-
+calculate_greta_mcmc_list <- function(
+  target,
+  values,
+  nsim,
+  tf_float,
+  trace_batch_size
+) {
   # assign the free state
   stochastic <- !is.null(nsim)
 
@@ -321,13 +316,11 @@ calculate_greta_mcmc_list <- function(target,
   # if they didn't specify nsim, check we can deterministically compute the
   # targets from the draws
   if (!stochastic) {
-
     # see if the new dag introduces any new variables
     check_dag_introduces_new_variables(dag, mcmc_dag)
 
     # see if any of the targets are stochastic and not sampled in the mcmc
     check_targets_stochastic_and_not_sampled(target, mcmc_dag_variables)
-
   }
 
   dag$target_nodes <- lapply(target, get_node)
@@ -356,19 +349,20 @@ calculate_greta_mcmc_list <- function(target,
     # add the batch size to the data list
     # assign
     # pass these values in as the free state
-    trace <- dag$trace_values(draws,
+    trace <- dag$trace_values(
+      draws,
       trace_batch_size = trace_batch_size,
       flatten = FALSE
     )
 
     # hopefully values is already a list of the correct dimensions...
   } else {
-
     # for deterministic posterior prediction, just trace the target for each
     # chain
 
-    values <- lapply(draws,
-  #double check the trace value part - can pronbanly just do that here
+    values <- lapply(
+      draws,
+      #double check the trace value part - can pronbanly just do that here
       dag$trace_values,
       trace_batch_size = trace_batch_size
     )
@@ -394,7 +388,6 @@ calculate_list <- function(target, values, nsim, tf_float, env) {
   values_exist <- !identical(values, list())
 
   if (values_exist) {
-
     # check the list of values makes sense, and return these and the
     # corresponding greta arrays (looked up by name in environment env)
     values_list <- check_values_list(values, env)
@@ -408,19 +401,16 @@ calculate_list <- function(target, values, nsim, tf_float, env) {
 
   stochastic <- !is.null(nsim)
   if (stochastic) {
-
     check_if_unsampleable_and_unfixed(fixed_greta_arrays, dag)
-
   } else {
-
     # check there are no unspecified variables on which the target depends
     lapply(target, check_dependencies_satisfied, fixed_greta_arrays, dag, env)
   }
 
   # TF1/2 check todo
   # need to wrap this in tf_function I think?
   if (Sys.getenv("GRETA_DEBUG") == "true") {
-  browser()
+    browser()
   }
   values <- calculate_target_tensor_list(
     dag = dag,
@@ -438,12 +428,12 @@ calculate_list <- function(target, values, nsim, tf_float, env) {
   # simultaneously
   # assign("calculate_target_tensor_list", target_tensor_list, envir = tfe)
 
-#   # add values or data not specified by the user
-#   data_list <- dag$get_tf_data_list()
-#   missing <- !names(data_list) %in% names(values)
-#
-#   # send list to tf environment and roll into a dict
-#
+  #   # add values or data not specified by the user
+  #   data_list <- dag$get_tf_data_list()
+  #   missing <- !names(data_list) %in% names(values)
+  #
+  #   # send list to tf environment and roll into a dict
+  #
 }
 
 

R/callbacks.R

@@ -26,7 +26,6 @@ render_progress <- function(reads) {
   reads[is.na(reads)] <- ""
   some_results <- any(nchar(reads) > 0)
   if (some_results) {
-
     # optionally add blanks to put lines at the edges
     if (length(reads) > 1) {
       reads <- c("", reads, "")
@@ -50,24 +49,23 @@ percentages <- function() {
 }
 
 progress_bars <- function() {
-  reads <- lapply(greta_stash$progress_bar_log_files,
+  reads <- lapply(
+    greta_stash$progress_bar_log_files,
     read_progress_log_file,
     skip = 1
   )
   render_progress(reads)
 }
 
 # determine the type of progress information
-set_progress_bar_type <- function(n_chain){
-
-if (bar_width(n_chain) < 42) {
-  progress_callback <- percentages
-} else {
-  progress_callback <- progress_bars
-}
-
-greta_stash$callbacks$parallel_progress <- progress_callback
+set_progress_bar_type <- function(n_chain) {
+  if (bar_width(n_chain) < 42) {
+    progress_callback <- percentages
+  } else {
+    progress_callback <- progress_bars
+  }
 
+  greta_stash$callbacks$parallel_progress <- progress_callback
 }
 
 # register some