Merge pull request #734 from njtierney/tweaking-wishart-fix-from-729

Tweaking wishart fix from 729

Author: njtierney

DESCRIPTION

@@ -45,7 +45,7 @@ Imports:
     R6,
     reticulate (>= 1.19.0),
     rlang,
-    tensorflow (>= 2.8.0),
+    tensorflow (== 2.16.0),
     tools,
     utils,
     whisker,

NEWS.md

@@ -28,51 +28,65 @@ The following optimisers are removed, as they are no longer supported by Tensorf
 
 ## Installation revamp
 
-This release provides a few improvements to installation in greta. It should now provide more information about installation progress, and be more robust. The intention is, it should _just work_, and if it doesn't fail gracefully with some useful advice on problem solving.
+This release provides a few improvements to installation in greta. It should now provide more information about installation progress, and be more robust. The intention is, it should _just work_, and if it doesn't, it should fail gracefully with some useful advice on problem solving.
 
-* Added option to restart R + run `library(greta)` after installation (#523)
-* Added installation deps object, `greta_deps_sepc()` to help simplify specifying package versions (#664)
-* removed `method` and `conda` arguments from `install_greta_deps()` as they 
+* Added option to restart R + run `library(greta)` after installation (#523).
+* Added installation deps object, `greta_deps_sepc()` to help simplify specifying package versions (#664).
+* Removed `method` and `conda` arguments from `install_greta_deps()` as they 
   were not used.
-* removed `manual` argument in `install_greta_deps()`.
-* added default 5 minute timer to installation processes
-* Added `greta_deps_receipt()` to list the current main python packages installed. (#668)
-* Added checking suite to ensure you are using valid versions of TF, TFP, and Python(#666)
+* Removed `manual` argument in `install_greta_deps()`.
+* Added default 5 minute timer to installation processes.
+* Added `greta_deps_receipt()` to list the current main python packages installed (#668).
+* Added checking suite to ensure you are using valid versions of TF, TFP, and Python(#666).
 * Added data `greta_deps_tf_tfp` (#666), which contains valid versions combinations of TF, TFP, and Python.
-* remove `greta_nodes_install/conda_*()` options as #493 makes them defunct.
-* Added option to write to a single logfile with `greta_set_install_logfile()`, and `write_greta_install_log()`, and `open_greta_install_log()` (#493)
-* Added `destroy_greta_deps()` function to remove miniconda and python conda environment
-* Improved `write_greta_install_log()` and `open_greta_install_log()` to use `tools::R_user_dir()` to always write to a file location. `open_greta_install_log()` will open one found from an environment variable or go to the default location. (#703)
+* Remove `greta_nodes_install/conda_*()` options as #493 makes them defunct.
+* Added option to write to a single logfile with `greta_set_install_logfile()`, and `write_greta_install_log()`, and `open_greta_install_log()` (#493).
+* Added `destroy_greta_deps()` function to remove miniconda and python conda environment.
+* Improved `write_greta_install_log()` and `open_greta_install_log()` to use `tools::R_user_dir()` to always write to a file location. `open_greta_install_log()` will open one found from an environment variable or go to the default location (#703).
 
 ## New Print methods
 
-* New print method for `greta_mcmc_list`. This means MCMC output will be shorter and more informative. (#644)
-* greta arrays now have a print method that stops them from printing too many rows into the console. Similar to MCMC print method, you can control the print output with the `n` argument: `print(object, n = <elements to print>)`. (#644)
+* New print method for `greta_mcmc_list`. This means MCMC output will be shorter and more informative (#644).
+* greta arrays now have a print method that stops them from printing too many rows into the console. Similar to MCMC print method, you can control the print output with the `n` argument: `print(object, n = <elements to print>)` (#644).
 
 ## Minor
 
-* `greta_sitrep()` now checks for installations of Python, TF, and TFP
+* `greta_sitrep()` now checks for installations of Python, TF, and TFP.
 * Slice sampler no longer needs precision = "single" to work.
 * greta now depends on R 4.1.0, which was released May 2021, over 3 years ago.
-* export `is.greta_array()` and `is.greta_mcmc_list()`
-* `restart` argument for `install_greta_deps()` and `reinstall_greta_deps()` to automatically restart R (#523)
+* export `is.greta_array()` and `is.greta_mcmc_list()`.
+* `restart` argument for `install_greta_deps()` and `reinstall_greta_deps()` to automatically restart R (#523).
 
 ## Internals
 
 * Internally we are replacing most of the error handling code as separate 
   `check_*` functions.
 * Implemented `cli::cli_abort/warn/inform()` in place of `cli::format_error/warning/message()` + `stop/warning/message(msg, call. = FALSE)` pattern.
 * Uses legacy optimizer internally (Use `tf$keras$optimizers$legacy$METHOD` over `tf$keras$optimizers$METHOD`). No user impact expected.
-* Update photo of Grete Hermann (#598)
-* Use `%||%` internally to replace the pattern: `if (is.null(x)) x <- thing` with `x <- x %||% thing`. (#630)
+* Update photo of Grete Hermann (#598).
+* Use `%||%` internally to replace the pattern: `if (is.null(x)) x <- thing` with `x <- x %||% thing` (#630).
 * Add more explaining variables - replace `if (thing & thing & what == this)` with `if (explanation_of_thing)`.
-* Refactored repeated uses of `vapply` into functions (#377, #658)
+* Refactored repeated uses of `vapply` into functions (#377, #658).
 * Add internal data files `.deps_tf` and `.deps_tfp` to track dependencies of TF and TFP. Related to #666.
 
+- Posterior density checks (#720):
+  - Don't run Geweke on CI as it takes 30 minutes to run.
+  - Add thinning to Geweke tests.
+  - Fix broken geweke tests from TF1-->TF2 change.
+  - Increase the number of effective samples for check_samples for lkj distribution
+  - Add more checks to posterior to run on CI/on each test of greta
+
 ## Bug fixes
 
 * Fix bug where matrix multiply had dimension error before coercing to greta array. (#464)
-* 
+- Fixes for Wishart and LKJ Correlation distributions (#729 #733 #734):
+  - Add bijection density to choleskied distributions.
+  - Note about some issues with LKJ and our normalisation constant for the density.
+  - Removed our custom `forward_log_det_jacobian()` function from `tf_correlation_cholesky_bijector()` (used in `lkj_correlation()`). Previously, it did not work with unknown dimensions, but it now works with them.
+  - Ensure wishart uses sigma_chol in scale_tril
+  - Wishart uses `tf$matmul(chol_draws, chol_draws, adjoint_b = TRUE)` instead of `tf_chol2symm(chol_draws)`.
+  - Test log prob function returns valid numeric numbers.
+  - Addresses issue with log prob returning NaNs--replace `FillTriangular` with `FillScaleTriL` and apply Chaining to first transpose input.
 
 # greta 0.4.5
 

R/checkers.R

@@ -1943,6 +1943,19 @@ check_has_representation <- function(repr,
   }
 }
 
+check_has_anti_representation <- function(repr,
+                                     name,
+                                     error,
+                                     call = rlang::caller_env()){
+  not_anti_represented <- error && is.null(repr)
+  if (not_anti_represented) {
+    cli::cli_abort(
+      message = "{.cls greta_array} has no anti representation {.var {name}}",
+      call = call
+    )
+  }
+}
+
 check_is_greta_array <- function(x,
                                  arg = rlang::caller_arg(x),
                                  call = rlang::caller_env()){

R/greta_array_class.R

@@ -257,6 +257,23 @@ has_representation <- function(x, name) {
   !is.null(repr)
 }
 
+anti_representation <- function(x, name, error = TRUE) {
+  if (is.greta_array(x)) {
+    x_node <- get_node(x)
+  } else {
+    x_node <- x
+  }
+  repr <- x_node$anti_representations[[name]]
+  check_has_anti_representation(repr, name, error)
+  repr
+}
+
+
+has_anti_representation <- function(x, name){
+  repr <- anti_representation(x, name, error = FALSE)
+  !is.null(repr)
+}
+
 # helper function to make a copy of the greta array & tensor
 copy_representation <- function(x, name) {
   repr <- representation(x, name)

R/greta_stash.R

@@ -28,5 +28,6 @@ greta_stash$tf_num_error <- greta_note_msg
 #' greta_notes_tf_error()
 #' }
 greta_notes_tf_num_error <- function() {
-  cat(greta_stash$tf_num_error)
+  # wrap in paste0 to remove list properties
+  cat(paste0(greta_stash$tf_num_error))
 }

R/inference_class.R

@@ -744,6 +744,9 @@ sampler <- R6Class(
       )
 
       # get trace of free state and drop the null dimension
+      if (is.null(batch_results$all_states)){
+        browser()
+      }
       free_state_draws <- as.array(batch_results$all_states)
 
       # if there is one sample at a time, and it's rejected, conversion from

R/node_class.R

@@ -5,7 +5,12 @@ node <- R6Class(
     unique_name = "",
     parents = list(),
     children = list(),
+    # named greta arrays giving different representations of the greta array
+    # represented by this node that have already been calculated, to be used for
+    # computational speedups or numerical stability. E.g. a logarithm or a
+    # cholesky factor
     representations = list(),
+    anti_representations = list(),
     .value = array(NA),
     dim = NA,
     distribution = NULL,
@@ -82,6 +87,19 @@ node <- R6Class(
         parents <- c(parents, list(self$distribution))
       }
 
+      if (mode == "sampling" & has_representation(self, "cholesky")){
+        # remove cholesky representation node from parents
+        parent_names <- extract_unique_names(parents)
+        antirep_name <- get_node(self$representations$cholesky)$unique_name
+        parent_names_keep <- setdiff(parent_names, antirep_name)
+        parents <- parents[match(parent_names_keep, parent_names)]
+      }
+
+      if (mode == "sampling" & has_anti_representation(self, "chol2symm")){
+        chol2symm_node <- get_node(self$anti_representations$chol2symm)
+        parents <- c(parents, list(chol2symm_node))
+      }
+
       parents
     },
     add_child = function(node) {
@@ -273,6 +291,31 @@ node <- R6Class(
       }
 
       label
+    },
+    make_antirepresentations = function(representations){
+      mapply(
+        FUN = self$make_one_anti_representation,
+        representations,
+        names(representations)
+        )
+    },
+    make_one_anti_representation = function(ga, name){
+      node <- get_node(ga)
+      anti_name <- self$find_anti_name(name)
+      node$anti_representations[[anti_name]] <- as.greta_array(self)
+      node
+    },
+    find_anti_name = function(name){
+      switch(name,
+             cholesky = "chol2symm",
+             chol2symm = "chol",
+             exp = "log",
+             log = "exp",
+             probit = "iprobit",
+             iprobit = "probit",
+             logit = "ilogit",
+             ilogit = "logit"
+      )
     }
   )
 )

R/node_types.R

@@ -84,12 +84,6 @@ operation_node <- R6Class(
     operation_args = NA,
     arguments = list(),
     tf_function_env = NA,
-
-    # named greta arrays giving different representations of the greta array
-    # represented by this node that have already been calculated, to be used for
-    # computational speedups or numerical stability. E.g. a logarithm or a
-    # cholesky factor
-    representations = list(),
     initialize = function(operation,
                           ...,
                           dim = NULL,
@@ -127,6 +121,7 @@ operation_node <- R6Class(
       self$operation <- tf_operation
       self$operation_args <- operation_args
       self$representations <- representations
+      self$make_antirepresentations(representations)
       self$tf_function_env <- tf_function_env
 
       # assign empty value of the right dimension, or the values passed via the
@@ -158,23 +153,23 @@ operation_node <- R6Class(
         # browser()
         tensor <- dag$draw_sample(self$distribution)
 
-        if (has_representation(self, "cholesky")) {
+        # if (has_representation(self, "cholesky")) {
           # browser()
-          cholesky_tensor <- tf_chol(tensor)
-          # cholesky_tf_name <- dag$tf_name(self$representation$cholesky)
-          cholesky_node <- get_node(representation(self, "cholesky"))
-          cholesky_tf_name <- dag$tf_name(cholesky_node)
-          assign(cholesky_tf_name, cholesky_tensor, envir = tfe)
+          # cholesky_tensor <- tf_chol(tensor)
+          # # cholesky_tf_name <- dag$tf_name(self$representation$cholesky)
+          # cholesky_node <- get_node(representation(self, "cholesky"))
+          # cholesky_tf_name <- dag$tf_name(cholesky_node)
+          # assign(cholesky_tf_name, cholesky_tensor, envir = tfe)
           ## TF1/2
           ## This assignment I think is supposed to be passed down to later on
           ## in the script, as `cholesky_tf_name` gets overwritten
           # cholesky_tf_name <- dag$tf_name(self)
           # tf_name <- cholesky_tf_name
           # tensor <- cholesky_tensor
-          cholesky_tensor <- tf_chol(tensor)
-          cholesky_tf_name <- dag$tf_name(self$representation$cholesky)
-          assign(cholesky_tf_name, cholesky_tensor, envir = dag$tf_environment)
-        }
+          # cholesky_tensor <- tf_chol(tensor)
+          # cholesky_tf_name <- dag$tf_name(self$representation$cholesky)
+          # assign(cholesky_tf_name, cholesky_tensor, envir = dag$tf_environment)
+        # }
       }
 
       if (mode == "forward") {
@@ -292,14 +287,29 @@ variable_node <- R6Class(
         distrib_node <- self$distribution
 
         if (is.null(distrib_node)) {
-
-          # if the variable has no distribution create a placeholder instead
-          # (the value must be passed in via values when using simulate)
-          shape <- to_shape(c(1, self$dim))
-          # TF1/2 check
-            # need to change the placeholder approach here.
-            # NT: can we change this to be a tensor of the right shape with 1s?
-          tensor <- tensorflow::as_tensor(1L, shape = shape, dtype = tf_float())
+          # does it have an anti-representation where it is the cholesky?
+            # the antirepresentation of cholesky is chol2symm
+            # if it does, we will take the antirepresentation and get it to `tf` itself
+              # then we need to get the tf_name
+          chol2symm_ga <- self$anti_representations$chol2symm
+          chol2symm_existing <- !is.null(chol2symm_ga)
+          if (chol2symm_existing) {
+            chol2symm_node <- get_node(chol2symm_ga)
+            chol2symm_name <- dag$tf_name(chol2symm_node)
+            chol2symm_tensor <- get(chol2symm_name, envir = dag$tf_environment)
+            tensor <- tf_chol(chol2symm_tensor)
+          }
+
+          #   chol2symm_ga$define_tf(dag)
+          # } else {
+          #
+          # # if the variable has no distribution create a placeholder instead
+          # # (the value must be passed in via values when using simulate)
+          # shape <- to_shape(c(1, self$dim))
+          # # TF1/2 check
+          #   # need to change the placeholder approach here.
+          #   # NT: can we change this to be a tensor of the right shape with 1s?
+          # tensor <- tensorflow::as_tensor(1L, shape = shape, dtype = tf_float())
         } else {
           tensor <- dag$draw_sample(self$distribution)
         }