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dag_class.R
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dag_class.R
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#' @importFrom reticulate py_set_attr
#' @importFrom tensorflow dict
#' @importFrom R6 R6Class
# create dag class
dag_class <- R6Class(
"dag_class",
public = list(
mode = "all_forward",
node_list = list(),
target_nodes = list(),
variables_without_free_state = list(),
tf_environment = NA,
tf_graph = NA,
tf_float = NA,
n_cores = 0L,
compile = NA,
trace_names = NULL,
# create a dag from some target nodes
initialize = function(target_greta_arrays,
tf_float = "float32",
compile = FALSE) {
# build the dag
self$build_dag(target_greta_arrays)
# find the nodes we care about
self$target_nodes <- lapply(target_greta_arrays, get_node)
# set up the tf environment, with a graph
self$new_tf_environment()
# store the performance control info
self$tf_float <- tf_float
self$compile <- compile
},
new_tf_environment = function() {
self$tf_environment <- new.env()
self$tf_graph <- tf$Graph()
self$tf_environment$all_forward_data_list <- list()
self$tf_environment$all_sampling_data_list <- list()
self$tf_environment$hybrid_data_list <- list()
},
# execute an expression on this dag's tensorflow graph, with the correct
# float type
on_graph = function(expr) {
# temporarily pass float type info to options, so it can be accessed by
# nodes on definition, without cluncky explicit passing
old_float_type <- options()$greta_tf_float
on.exit(options(greta_tf_float = old_float_type))
options(greta_tf_float = self$tf_float)
with(self$tf_graph$as_default(), expr)
},
# execute an expression in the tensorflow environment
tf_run = function(expr, as_text = FALSE) {
tfe <- self$tf_environment
if (as_text)
tfe$expr <- parse(text = expr)
else
tfe$expr <- substitute(expr)
on.exit(rm("expr", envir = tfe))
self$on_graph(with(tfe, eval(expr)))
},
# sess$run() an expression in the tensorflow environment, with the feed dict
tf_sess_run = function(expr, as_text = FALSE) {
if (!as_text) {
expr <- deparse(substitute(expr))
}
expr <- paste0("sess$run(", expr, ", feed_dict = feed_dict)")
self$tf_run(expr, as_text = TRUE)
},
# return a list of nodes connected to those in the target node list
build_dag = function(greta_array_list) {
target_node_list <- lapply(greta_array_list, get_node)
# loop through the target nodes, recursively registering them to this dag
for (node in target_node_list) {
node$register_family(self)
}
},
# get the TF names for different node types
get_tf_names = function(types = NULL) {
# get tf basenames
names <- self$node_tf_names
if (!is.null(types))
names <- names[which(self$node_types %in% types)]
# prepend mode
if (length(names) > 0) {
names <- paste(self$mode, names, sep = "_")
}
},
# look up the TF name for a single node
tf_name = function(node) {
# get tf basename from node name
name <- self$node_tf_names[node$unique_name]
if (length(name) == 0) {
name <- ""
}
# prepend mode
if (!is.na(name)) {
name <- paste(self$mode, name, sep = "_")
}
name
},
# how to define a node if the sampling mode is hybrid (this is quite knotty,
# so gets its own function)
how_to_define_hybrid = function(node) {
node_type <- node_type(node)
# names of variable nodes not connected to the free state in this dag
stateless_names <- names(self$variables_without_free_state)
# if the node is data, use sampling mode if it has a distribution and
# forward mode if not
if (node_type == "data") {
node_mode <- ifelse(has_distribution(node), "sampling", "forward")
}
# if the node is a variable, use forward mode if it has a free state,
# and sampling mode if not
if (node_type == "variable") {
to_sample <- node$unique_name %in% stateless_names
node_mode <- ifelse(to_sample, "sampling", "forward")
}
# if it's an operation, see if it has a distribution (for lkj and
# wishart) and get mode based on whether the parent has a free state
if (node_type == "operation") {
parent_name <- node$parents[[1]]$unique_name
parent_stateless <- parent_name %in% stateless_names
to_sample <- has_distribution(node) & parent_stateless
node_mode <- ifelse(to_sample, "sampling", "forward")
}
# if the node is a distribution, decide based on its target
if (node_type == "distribution") {
target <- node$target
target_type <- node_type(target)
# if it has no target (e.g. for a mixture distribution), define it in
# sampling mode (so it defines before the things that depend on it)
if (is.null(target)) {
node_mode <- "sampling"
}
# if the target is data, use sampling mode
if (target_type == "data") {
node_mode <- "sampling"
}
# if the target is a variable, use forward mode if it has a free
# state, and sampling mode if not
if (target_type == "variable") {
to_sample <- target$unique_name %in% stateless_names
node_mode <- ifelse(to_sample, "sampling", "forward")
}
# if the target is an operation, see if that operation has a single
# parent that is a variable, and see if that has a free state
if (target_type == "operation") {
target_parent_name <- target$parents[[1]]$unique_name
target_parent_stateless <- target_parent_name %in% stateless_names
node_mode <- ifelse(target_parent_stateless, "sampling", "forward")
}
}
node_mode
},
# how to define the node if we're sampling everything (no free state)
how_to_define_all_sampling = function(node) {
switch(node_type(node),
data = ifelse(has_distribution(node), "sampling", "forward"),
operation = ifelse(has_distribution(node), "sampling", "forward"),
"sampling"
)
},
# tell a node whether to define itself in forward mode (deterministically
# from an existing free state), or in sampling mode (generate a random
# version of itself)
how_to_define = function(node) {
switch(
self$mode,
# if doing inference, everything is push-forward
all_forward = "forward",
# sampling from prior most nodes are in sampling mode
all_sampling = self$how_to_define_all_sampling(node),
# sampling from posterior some nodes defined forward, others sampled
hybrid = self$how_to_define_hybrid(node)
)
},
define_batch_size = function() {
self$tf_run(
batch_size <- tf$compat$v1$placeholder(dtype = tf$int32)
)
},
define_free_state = function(type = c("variable", "placeholder"),
name = "free_state") {
type <- match.arg(type)
tfe <- self$tf_environment
vals <- self$example_parameters(free = TRUE)
vals <- unlist_tf(vals)
if (type == "variable") {
# tf$Variable seems to have trouble assigning values, if created with
# numeric (rather than logical) NAs
vals <- as.logical(vals)
vals <- t(as.matrix(vals))
self$on_graph(free_state <- tf$Variable(initial_value = vals,
dtype = tf_float()))
} else {
shape <- shape(NULL, length(vals))
self$on_graph(free_state <- tf$compat$v1$placeholder(dtype = tf_float(),
shape = shape))
}
assign(name,
free_state,
envir = tfe)
},
# split the overall free state vector into free versions of variables
split_free_state = function() {
tfe <- self$tf_environment
free_state <- get("free_state", envir = tfe)
params <- self$example_parameters(free = TRUE)
lengths <- vapply(params,
function(x) length(x),
FUN.VALUE = 1L)
if (length(lengths) > 1) {
args <- self$on_graph(tf$split(free_state, lengths, axis = 1L))
} else {
args <- list(free_state)
}
names <- paste0(names(params), "_free")
for (i in seq_along(names)) {
assign(names[i], args[[i]], envir = tfe)
}
},
# define the body of the tensorflow graph in the environment env; without
# defining the free_state, or the densities etc.
define_tf_body = function(target_nodes = self$node_list) {
# if in forward or hybrid mode, split up the free state
if (self$mode %in% c("all_forward", "hybrid")) {
self$split_free_state()
}
# define all nodes in the environment and on the graph
self$on_graph(
lapply(target_nodes, function(x) x$define_tf(self))
)
invisible(NULL)
},
# use core and compilation options to set up a session in this environment
define_tf_session = function() {
tfe <- self$tf_environment
tfe$n_cores <- self$n_cores
# nolint start
self$tf_run(
config <- tf$compat$v1$ConfigProto(
inter_op_parallelism_threads = n_cores,
intra_op_parallelism_threads = n_cores
)
)
if (self$compile) {
self$tf_run(
py_set_attr(
config$graph_options$optimizer_options,
"global_jit_level",
tf$compat$v1$OptimizerOptions$ON_1
)
)
}
# nolint end
# start a session and initialise all variables
self$tf_run(sess <- tf$compat$v1$Session(config = config))
self$tf_run(sess$run(tf$compat$v1$global_variables_initializer()))
},
# define tf graph in environment; either for forward-mode computation from a
# free state variable, or for sampling
define_tf = function(target_nodes = self$node_list) {
# define the free state variable
if (self$mode %in% c("all_forward", "hybrid")) {
self$define_free_state("placeholder")
}
# define the body of the graph (depending on the mode) and the session
self$define_batch_size()
self$define_tf_body(target_nodes = target_nodes)
self$define_tf_session()
},
# define tensor for overall log density and gradients
define_joint_density = function(adjusted = TRUE) {
tfe <- self$tf_environment
# get all distribution nodes that have a target
distribution_nodes <- self$node_list[self$node_types == "distribution"]
target_nodes <- lapply(distribution_nodes, member, "get_tf_target_node()")
has_target <- !vapply(target_nodes, is.null, FUN.VALUE = TRUE)
distribution_nodes <- distribution_nodes[has_target]
target_nodes <- target_nodes[has_target]
# get the densities, evaluated at these targets
densities <- mapply(self$evaluate_density,
distribution_nodes,
target_nodes,
SIMPLIFY = FALSE)
# assign the un-reduced densities, for use in marginalisation
names(densities) <- NULL
assign("component_densities",
densities,
envir = self$tf_environment)
# reduce_sum each of them (skipping the batch dimension)
self$on_graph(summed_densities <- lapply(densities, tf_sum, drop = TRUE))
# sum them together
names(summed_densities) <- NULL
self$on_graph(joint_density <- tf$add_n(summed_densities))
# assign overall density to environment
assign("joint_density",
joint_density,
envir = self$tf_environment)
if (adjusted) {
# get names of adjustment tensors for all variable nodes
adj_names <- paste0(self$get_tf_names(types = "variable"), "_adj")
# get TF density tensors for all distribution
adj <- lapply(adj_names, get, envir = self$tf_environment)
# remove their names and sum them together (accounting for tfp bijectors
# sometimes returning a scalar tensor)
adj <- match_batches(adj)
# remove their names and sum them together
names(adj) <- NULL
self$on_graph(total_adj <- tf$add_n(adj))
# assign overall density to environment
assign("joint_density_adj",
joint_density + total_adj,
envir = self$tf_environment)
}
},
# evaluate the (truncation-corrected) density of a tfp distribution on its
# target tensor
evaluate_density = function(distribution_node, target_node) {
tfe <- self$tf_environment
parameter_nodes <- distribution_node$parameters
# get the tensorflow objects for these
distrib_constructor <- self$get_tf_object(distribution_node)
tf_target <- self$get_tf_object(target_node)
tf_parameter_list <- lapply(parameter_nodes, self$get_tf_object)
# execute the distribution constructor functions to return a tfp
# distribution object
tfp_distribution <- distrib_constructor(tf_parameter_list, dag = self)
self$tf_evaluate_density(tfp_distribution,
tf_target,
truncation = distribution_node$truncation,
bounds = distribution_node$bounds)
},
tf_evaluate_density = function(tfp_distribution,
tf_target,
truncation = NULL,
bounds = NULL) {
# get the uncorrected log density
ld <- tfp_distribution$log_prob(tf_target)
# if required, calculate the log-adjustment to the truncation term of
# the density function i.e. the density of a distribution, truncated
# between a and b, is the non truncated density, divided by the integral
# of the density function between the truncation bounds. This can be
# calculated from the distribution's CDF
if (!is.null(truncation)) {
lower <- truncation[[1]]
upper <- truncation[[2]]
if (all(lower == bounds[1])) {
# if only upper is constrained, just need the cdf at the upper
offset <- tfp_distribution$log_cdf(fl(upper))
} else if (all(upper == bounds[2])) {
# if only lower is constrained, get the log of the integral above it
offset <- tf$math$log(fl(1) - tfp_distribution$cdf(fl(lower)))
} else {
# if both are constrained, get the log of the integral between them
offset <- tf$math$log(tfp_distribution$cdf(fl(upper)) -
tfp_distribution$cdf(fl(lower)))
}
ld <- ld - offset
}
ld
},
# get the tf object in envir correpsonding to 'node'
get_tf_object = function(node) {
get(self$tf_name(node), envir = self$tf_environment)
},
# return a function to obtain the model log probability from a tensor for
# the free state
generate_log_prob_function = function(which = c("adjusted",
"unadjusted",
"both")) {
which <- match.arg(which)
function(free_state) {
# temporarily define a new environment
tfe_old <- self$tf_environment
on.exit(self$tf_environment <- tfe_old)
tfe <- self$tf_environment <- new.env()
# copy the placeholders over here, so they aren't recreated
data_names <- self$get_tf_names(types = "data")
for (name in data_names)
tfe[[name]] <- tfe_old[[name]]
# copy the batch size over
tfe$batch_size <- tfe_old$batch_size
# put the free state in the environment, and build out the tf graph
tfe$free_state <- free_state
self$define_tf_body()
# define the densities
self$define_joint_density()
objectives <- list(adjusted = tfe$joint_density_adj,
unadjusted = tfe$joint_density)
# return either of the densities, or a list of both
result <- switch(which,
adjusted = objectives$adjusted,
unadjusted = objectives$unadjusted,
both = objectives)
result
}
},
# return the expected parameter format either in free state vector form, or
# list of transformed parameters
example_parameters = function(free = TRUE) {
# find all variable nodes in the graph
nodes <- self$node_list[self$node_types == "variable"]
names(nodes) <- self$get_tf_names(types = "variable")
# get their values in either free of non-free form
if (free) {
parameters <- lapply(nodes, member, "value(free = TRUE)")
} else {
parameters <- lapply(nodes, member, "value()")
}
# remove any of these that don't need a free state here (for calculate())
stateless_names <- vapply(self$variables_without_free_state,
self$tf_name,
FUN.VALUE = character(1))
keep <- !names(parameters) %in% stateless_names
parameters <- parameters[keep]
parameters
},
get_tf_data_list = function() {
data_list_name <- paste0(self$mode, "_data_list")
self$tf_environment[[data_list_name]]
},
set_tf_data_list = function(element_name, value) {
data_list_name <- paste0(self$mode, "_data_list")
self$tf_environment[[data_list_name]][[element_name]] <- value
},
build_feed_dict = function(dict_list = list(),
data_list = self$get_tf_data_list()) {
tfe <- self$tf_environment
# put the list in the environment temporarily
tfe$dict_list <- c(dict_list, data_list)
on.exit(rm("dict_list", envir = tfe))
# roll into a dict in the tf environment
self$tf_run(feed_dict <- do.call(dict, dict_list))
},
send_parameters = function(parameters) {
# reshape to a row vector if needed
if (is.null(dim(parameters))) {
parameters <- array(parameters, dim = c(1, length(parameters)))
}
# create a feed dict in the TF environment
parameter_list <- list(free_state = parameters)
# set the batch size to match parameters
self$set_tf_data_list("batch_size", nrow(parameters))
self$build_feed_dict(parameter_list)
},
# get adjusted joint log density across the whole dag
log_density = function() {
res <- cleanly(self$tf_sess_run(joint_density_adj))
if (inherits(res, "error"))
res <- NA
res
},
hessians = function() {
tfe <- self$tf_environment
nodes <- self$target_nodes
# get names and dimensions of target greta arrays
ga_names <- names(nodes)
ga_dims <- lapply(nodes, member, "dim")
# build the hessian tensors if needed
if (!exists("hessian_list", envir = tfe)) {
tf_names <- vapply(nodes, self$tf_name, FUN.VALUE = "")
y <- tfe$joint_density
xs <- lapply(tf_names, get, tfe)
names(xs) <- NULL
tfe$hessian_list <- self$on_graph(tf$hessians(y, xs))
}
# evaluate at the current free state and assign
hessian_list <- self$tf_sess_run(hessian_list)
# reshape from tensor to R dimensions
dims <- lapply(ga_dims, hessian_dims)
hessian_list <- mapply(array, hessian_list, dims, SIMPLIFY = FALSE)
# assign names and return
names(hessian_list) <- ga_names
hessian_list
},
trace_values_batch = function(free_state_batch) {
# update the parameters & build the feed dict
self$send_parameters(free_state_batch)
tfe <- self$tf_environment
target_tf_names <- lapply(self$target_nodes,
self$tf_name)
target_tensors <- lapply(target_tf_names,
get,
envir = tfe)
# evaluate them in the tensorflow environment
trace_list <- tfe$sess$run(target_tensors,
feed_dict = tfe$feed_dict)
trace_list
},
# return the current values of the traced nodes, as a named vector
trace_values = function(free_state,
flatten = TRUE,
trace_batch_size = Inf) {
# get the number of samples to trace
n_samples <- nrow(free_state)
indices <- seq_len(n_samples)
splits <- split(indices, (indices - 1) %/% trace_batch_size)
names(splits) <- NULL
# split the free state up into batches
get_rows <- function(rows, x) x[rows, , drop = FALSE]
free_state_batches <- lapply(splits, get_rows, free_state)
# loop through them
trace_list_batches <- lapply(free_state_batches, self$trace_values_batch)
# loop through each of the elements in the lists, and stack them
stack_elements <- function(name, list) {
elems <- lapply(trace_list_batches, `[[`, name)
do.call(abind::abind, c(elems, list(along = 1)))
}
elements <- seq_along(trace_list_batches[[1]])
trace_list <- lapply(elements, stack_elements, trace_list_batches)
names(trace_list) <- names(trace_list_batches[[1]])
# if they are flattened, e.g. for MCMC tracing
if (flatten) {
# loop through elements flattening these arrays to vectors and giving
# the elements better names
trace_list_flat <- lapply(seq_along(trace_list),
flatten_trace,
trace_list)
out <- do.call(cbind, trace_list_flat)
self$trace_names <- colnames(out)
} else {
out <- trace_list
}
out
},
# for all the nodes in this dag, return a vector of membership to sub-graphs
subgraph_membership = function() {
# convert adjacency matrix into absolute connectedness matrix using matrix
# powers. Inspired by Method 2 here:
# http://raphael.candelier.fr/?blog=Adj2cluster
# convert adjacency to a symmetric, logical matrix
adj <- self$adjacency_matrix
sym <- (adj + t(adj)) > 0
# loop through to build a block diagonal matrix of connected components
# (usually only takes a few iterations)
maxit <- 1000
it <- 0
p <- r <- sym
while (it < maxit) {
p <- p %*% sym
t <- (r + p) > 0
if (any(t != r)) {
r <- t
it <- it + 1
} else {
break ()
}
}
# check we didn't time out
if (it == maxit) {
stop("could not determine the number of independent models ",
"in a reasonable amount of time",
call. = FALSE)
}
# find the cluster IDs
n <- nrow(r)
neighbours <- lapply(seq_len(n), function(i) which(r[i, ]))
cluster_names <- vapply(neighbours, paste, collapse = "_", FUN.VALUE = "")
cluster_id <- match(cluster_names, unique(cluster_names))
# name them
names(cluster_id) <- rownames(adj)
cluster_id
},
# get the tfp distribution object for a distribution node
get_tfp_distribution = function(distrib_node) {
# build the tfp distribution object for the distribution, and use it
# to get the tensor for the sample
distrib_constructor <- self$get_tf_object(distrib_node)
parameter_nodes <- distrib_node$parameters
tf_parameter_list <- lapply(parameter_nodes, self$get_tf_object)
# execute the distribution constructor functions to return a tfp
# distribution object
tfp_distribution <- distrib_constructor(tf_parameter_list, dag = self)
},
# try to draw a random sample from a distribution node
draw_sample = function(distribution_node) {
tfp_distribution <- self$get_tfp_distribution(distribution_node)
sample <- tfp_distribution$sample
if (is.null(sample)) {
stop("sampling is not yet implemented for ",
distribution_node$distribution_name,
" distributions",
call. = FALSE)
}
truncation <- distribution_node$truncation
if (is.null(truncation)) {
# if we're not dealing with truncation, sample directly
tensor <- sample(seed = get_seed())
} else {
# if we're dealing with truncation (therefore univariate and continuous)
# sample a random uniform (tensor), and pass through the truncated
# quantile (inverse cdf) function
cdf <- tfp_distribution$cdf
quantile <- tfp_distribution$quantile
if (is.null(cdf) | is.null(quantile)) {
stop("sampling is not yet implemented for truncated ",
distribution_node$distribution_name,
" distributions",
call. = FALSE)
}
# generate a random uniform sample of the correct shape and transform
# through truncated inverse CDF to get draws on truncated scale
u <- tf_randu(distribution_node$dim, self)
lower <- cdf(fl(truncation[1]))
upper <- cdf(fl(truncation[2]))
range <- upper - lower
tensor <- quantile(lower + u * range)
}
tensor
}
),
active = list(
node_types = function(value) {
vapply(self$node_list, node_type, FUN.VALUE = "")
},
# create human-readable base names for TF tensors. these will actually be
# defined prepended with "all_forward_" or "all_sampling" or "hybrid_
node_tf_names = function(value) {
types <- self$node_types
for (type in c("variable", "data", "operation", "distribution")) {
idx <- which(types == type)
types[idx] <- paste(type, seq_along(idx), sep = "_")
}
types
},
adjacency_matrix = function(value) {
# make dag matrix
n_node <- length(self$node_list)
node_names <- names(self$node_list)
node_types <- self$node_types
dag_mat <- matrix(0, nrow = n_node, ncol = n_node)
rownames(dag_mat) <- colnames(dag_mat) <- node_names
children <- lapply(self$node_list,
member,
"child_names()")
parents <- lapply(self$node_list,
member,
"parent_names(recursive = FALSE)")
# for distribution nodes, remove target nodes from parents, and put them
# in children to send the arrow in the opposite direction when plotting
distribs <- which(node_types == "distribution")
for (i in distribs) {
own_name <- node_names[i]
target_name <- self$node_list[[i]]$target$unique_name
if (!is.null(target_name)) {
# switch the target from child to parent of the distribution
parents[[i]] <- parents[[i]][parents[[i]] != target_name]
children[[i]] <- c(children[[i]], target_name)
# switch the distribution from parent to child of the target
idx <- match(target_name, node_names)
children[[idx]] <- children[[idx]][children[[idx]] != own_name]
parents[[idx]] <- c(parents[[idx]], own_name)
}
}
# parents in the lower left, children in the upper right
for (i in seq_len(n_node)) {
dag_mat[i, children[[i]]] <- 1
dag_mat[parents[[i]], i] <- 1
}
dag_mat
}
)
)