tar_stan_vb: One variational Bayes run per model with multiple outputs

In ropensci/stantargets: Targets for Stan Workflows

One variational Bayes run per model with multiple outputs

Description

Targets to run a Stan model once with variational Bayes and save multiple outputs.

Usage

tar_stan_vb(
  name,
  stan_files,
  data = list(),
  compile = c("original", "copy"),
  quiet = TRUE,
  stdout = NULL,
  stderr = NULL,
  dir = NULL,
  pedantic = FALSE,
  include_paths = NULL,
  cpp_options = list(),
  stanc_options = list(),
  force_recompile = FALSE,
  seed = NULL,
  refresh = NULL,
  init = NULL,
  save_latent_dynamics = FALSE,
  output_dir = NULL,
  algorithm = NULL,
  iter = NULL,
  grad_samples = NULL,
  elbo_samples = NULL,
  eta = NULL,
  adapt_engaged = NULL,
  adapt_iter = NULL,
  tol_rel_obj = NULL,
  eval_elbo = NULL,
  output_samples = NULL,
  sig_figs = NULL,
  variables = NULL,
  variables_fit = NULL,
  summaries = list(),
  summary_args = list(),
  return_draws = TRUE,
  return_summary = TRUE,
  draws = NULL,
  summary = NULL,
  tidy_eval = targets::tar_option_get("tidy_eval"),
  packages = targets::tar_option_get("packages"),
  library = targets::tar_option_get("library"),
  format = "qs",
  format_df = "fst_tbl",
  repository = targets::tar_option_get("repository"),
  error = targets::tar_option_get("error"),
  memory = targets::tar_option_get("memory"),
  garbage_collection = targets::tar_option_get("garbage_collection"),
  deployment = targets::tar_option_get("deployment"),
  priority = targets::tar_option_get("priority"),
  resources = targets::tar_option_get("resources"),
  storage = targets::tar_option_get("storage"),
  retrieval = targets::tar_option_get("retrieval"),
  cue = targets::tar_option_get("cue"),
  description = targets::tar_option_get("description")
)

Arguments

name	Symbol, base name for the collection of targets. Serves as a prefix for target names.
stan_files	Character vector of Stan model files. If you supply multiple files, each model will run on the one shared dataset generated by the code in data. If you supply an unnamed vector, fs::path_ext_remove(basename(stan_files)) will be used as target name suffixes. If stan_files is a named vector, the suffixed will come from names(stan_files).
data	(multiple options) The data to use for the variables specified in the data block of the Stan program. One of the following: A named list of R objects with the names corresponding to variables declared in the data block of the Stan program. Internally this list is then written to JSON for CmdStan using write_stan_json(). See write_stan_json() for details on the conversions performed on R objects before they are passed to Stan. A path to a data file compatible with CmdStan (JSON or R dump). See the appendices in the CmdStan guide for details on using these formats. NULL or an empty list if the Stan program has no data block.
compile	(logical) Do compilation? The default is TRUE. If FALSE compilation can be done later via the $compile() method.
quiet	(logical) Should the verbose output from CmdStan during compilation be suppressed? The default is TRUE, but if you encounter an error we recommend trying again with quiet=FALSE to see more of the output.
stdout	Character of length 1, file path to write the stdout stream of the model when it runs. Set to NULL to print to the console. Set to R.utils::nullfile() to suppress stdout. Does not apply to messages, warnings, or errors.
stderr	Character of length 1, file path to write the stderr stream of the model when it runs. Set to NULL to print to the console. Set to R.utils::nullfile() to suppress stderr. Does not apply to messages, warnings, or errors.
dir	(string) The path to the directory in which to store the CmdStan executable (or .hpp file if using ⁠$save_hpp_file()⁠). The default is the same location as the Stan program.
pedantic	(logical) Should pedantic mode be turned on? The default is FALSE. Pedantic mode attempts to warn you about potential issues in your Stan program beyond syntax errors. For details see the Pedantic mode chapter in the Stan Reference Manual. Note: to do a pedantic check for a model without compiling it or for a model that is already compiled the $check_syntax() method can be used instead.
include_paths	(character vector) Paths to directories where Stan should look for files specified in ⁠#include⁠ directives in the Stan program.
cpp_options	(list) Any makefile options to be used when compiling the model (STAN_THREADS, STAN_MPI, STAN_OPENCL, etc.). Anything you would otherwise write in the make/local file. For an example of using threading see the Stan case study Reduce Sum: A Minimal Example.
stanc_options	(list) Any Stan-to-C++ transpiler options to be used when compiling the model. See the Examples section below as well as the stanc chapter of the CmdStan Guide for more details on available options: https://mc-stan.org/docs/cmdstan-guide/stanc.html.
force_recompile	(logical) Should the model be recompiled even if was not modified since last compiled. The default is FALSE. Can also be set via a global cmdstanr_force_recompile option.
seed	(positive integer(s)) A seed for the (P)RNG to pass to CmdStan. In the case of multi-chain sampling the single seed will automatically be augmented by the the run (chain) ID so that each chain uses a different seed. The exception is the transformed data block, which defaults to using same seed for all chains so that the same data is generated for all chains if RNG functions are used. The only time seed should be specified as a vector (one element per chain) is if RNG functions are used in transformed data and the goal is to generate different data for each chain.
refresh	(non-negative integer) The number of iterations between printed screen updates. If refresh = 0, only error messages will be printed.
init	(multiple options) The initialization method to use for the variables declared in the parameters block of the Stan program. One of the following: A real number x>0. This initializes all parameters randomly between ⁠[-x,x]⁠ on the unconstrained parameter space.; The number 0. This initializes all parameters to 0; A character vector of paths (one per chain) to JSON or Rdump files containing initial values for all or some parameters. See write_stan_json() to write R objects to JSON files compatible with CmdStan. A list of lists containing initial values for all or some parameters. For MCMC the list should contain a sublist for each chain. For other model fitting methods there should be just one sublist. The sublists should have named elements corresponding to the parameters for which you are specifying initial values. See Examples. A function that returns a single list with names corresponding to the parameters for which you are specifying initial values. The function can take no arguments or a single argument chain_id. For MCMC, if the function has argument chain_id it will be supplied with the chain id (from 1 to number of chains) when called to generate the initial values. See Examples. A CmdStanMCMC, CmdStanMLE, CmdStanVB, CmdStanPathfinder, or CmdStanLaplace fit object. If the fit object's parameters are only a subset of the model parameters then the other parameters will be drawn by Stan's default initialization. The fit object must have at least some parameters that are the same name and dimensions as the current Stan model. For the sample and pathfinder method, if the fit object has fewer draws than the requested number of chains/paths then the inits will be drawn using sampling with replacement. Otherwise sampling without replacement will be used. When a CmdStanPathfinder fit object is used as the init, if . psis_resample was set to FALSE and calculate_lp was set to TRUE (default), then resampling without replacement with Pareto smoothed weights will be used. If psis_resample was set to TRUE or calculate_lp was set to FALSE then sampling without replacement with uniform weights will be used to select the draws. PSIS resampling is used to select the draws for CmdStanVB, and CmdStanLaplace fit objects. A type inheriting from posterior::draws. If the draws object has less samples than the number of requested chains/paths then the inits will be drawn using sampling with replacement. Otherwise sampling without replacement will be used. If the draws object's parameters are only a subset of the model parameters then the other parameters will be drawn by Stan's default initialization. The fit object must have at least some parameters that are the same name and dimensions as the current Stan model.
save_latent_dynamics	(logical) Should auxiliary diagnostic information about the latent dynamics be written to temporary diagnostic CSV files? This argument replaces CmdStan's diagnostic_file argument and the content written to CSV is controlled by the user's CmdStan installation and not CmdStanR (for some algorithms no content may be written). The default is FALSE, which is appropriate for almost every use case. To save the temporary files created when save_latent_dynamics=TRUE see the $save_latent_dynamics_files() method.
output_dir	(string) A path to a directory where CmdStan should write its output CSV files. For MCMC there will be one file per chain; for other methods there will be a single file. For interactive use this can typically be left at NULL (temporary directory) since CmdStanR makes the CmdStan output (posterior draws and diagnostics) available in R via methods of the fitted model objects. This can be set for an entire R session using options(cmdstanr_output_dir). The behavior of output_dir is as follows: If NULL (the default), then the CSV files are written to a temporary directory and only saved permanently if the user calls one of the ⁠$save_*⁠ methods of the fitted model object (e.g., $save_output_files()). These temporary files are removed when the fitted model object is garbage collected (manually or automatically). If a path, then the files are created in output_dir with names corresponding to the defaults used by ⁠$save_output_files()⁠.
algorithm	(string) The algorithm. Either "meanfield" or "fullrank".
iter	(positive integer) The maximum number of iterations.
grad_samples	(positive integer) The number of samples for Monte Carlo estimate of gradients.
elbo_samples	(positive integer) The number of samples for Monte Carlo estimate of ELBO (objective function).
eta	(positive real) The step size weighting parameter for adaptive step size sequence.
adapt_engaged	(logical) Do warmup adaptation? The default is TRUE. If a precomputed inverse metric is specified via the inv_metric argument (or metric_file) then, if adapt_engaged=TRUE, Stan will use the provided inverse metric just as an initial guess during adaptation. To turn off adaptation when using a precomputed inverse metric set adapt_engaged=FALSE.
adapt_iter	(positive integer) The maximum number of adaptation iterations.
tol_rel_obj	(positive real) Convergence tolerance on the relative norm of the objective.
eval_elbo	(positive integer) Evaluate ELBO every Nth iteration.
output_samples	(positive integer) Use draws argument instead. output_samples will be deprecated in the future.
sig_figs	(positive integer) The number of significant figures used when storing the output values. By default, CmdStan represent the output values with 6 significant figures. The upper limit for sig_figs is 18. Increasing this value will result in larger output CSV files and thus an increased usage of disk space.
variables	(character vector) The variables to include.
variables_fit	Character vector of variables to include in the big CmdStanFit object returned by the model fit target. The variables argument, by contrast, is for the "draws" target only. The "draws" target can only access the variables in the CmdStanFit target. Control the variables in each with the variables and variables_fit arguments.
summaries	Optional list of summary functions passed to ... in posterior::summarize_draws() through ⁠$summary()⁠ on the CmdStanFit object.
summary_args	Optional list of summary function arguments passed to .args in posterior::summarize_draws() through ⁠$summary()⁠ on the CmdStanFit object.
return_draws	Logical, whether to create a target for posterior draws. Saves posterior::as_draws_df(fit$draws()) to a compressed tibble. Convenient, but duplicates storage.
return_summary	Logical, whether to create a target for fit$summary().
draws	Deprecated on 2022-07-22. Use return_draws instead.
summary	Deprecated on 2022-07-22. Use return_summary instead.
tidy_eval	Logical, whether to enable tidy evaluation when interpreting command and pattern. If TRUE, you can use the "bang-bang" operator ⁠!!⁠ to programmatically insert the values of global objects.
packages	Character vector of packages to load right before the target runs or the output data is reloaded for downstream targets. Use tar_option_set() to set packages globally for all subsequent targets you define.
library	Character vector of library paths to try when loading packages.
format	Character of length 1, storage format of the data frame of posterior summaries. We recommend efficient data frame formats such as "feather" or "aws_parquet". For more on storage formats, see the help file of targets::tar_target().
format_df	Character of length 1, storage format of the data frame targets such as posterior draws. We recommend efficient data frame formats such as "feather" or "aws_parquet". For more on storage formats, see the help file of targets::tar_target().
repository	Character of length 1, remote repository for target storage. Choices: "local": file system of the local machine. "aws": Amazon Web Services (AWS) S3 bucket. Can be configured with a non-AWS S3 bucket using the endpoint argument of tar_resources_aws(), but versioning capabilities may be lost in doing so. See the cloud storage section of https://books.ropensci.org/targets/data.html for details for instructions. "gcp": Google Cloud Platform storage bucket. See the cloud storage section of https://books.ropensci.org/targets/data.html for details for instructions. A character string from tar_repository_cas() for content-addressable storage. Note: if repository is not "local" and format is "file" then the target should create a single output file. That output file is uploaded to the cloud and tracked for changes where it exists in the cloud. The local file is deleted after the target runs.
error	Character of length 1, what to do if the target stops and throws an error. Options: "stop": the whole pipeline stops and throws an error. "continue": the whole pipeline keeps going. "null": The errored target continues and returns NULL. The data hash is deliberately wrong so the target is not up to date for the next run of the pipeline. In addition, as of targets version 1.8.0.9011, a value of NULL is given to upstream dependencies with error = "null" if loading fails. "abridge": any currently running targets keep running, but no new targets launch after that. "trim": all currently running targets stay running. A queued target is allowed to start if: It is not downstream of the error, and It is not a sibling branch from the same tar_target() call (if the error happened in a dynamic branch). The idea is to avoid starting any new work that the immediate error impacts. error = "trim" is just like error = "abridge", but it allows potentially healthy regions of the dependency graph to begin running. (Visit https://books.ropensci.org/targets/debugging.html to learn how to debug targets using saved workspaces.)
memory	Character of length 1, memory strategy. Possible values: "auto": new in targets version 1.8.0.9011, memory = "auto" is equivalent to memory = "transient" for dynamic branching (a non-null pattern argument) and memory = "persistent" for targets that do not use dynamic branching. "persistent": the target stays in memory until the end of the pipeline (unless storage is "worker", in which case targets unloads the value from memory right after storing it in order to avoid sending copious data over a network). "transient": the target gets unloaded after every new target completes. Either way, the target gets automatically loaded into memory whenever another target needs the value. For cloud-based dynamic files (e.g. format = "file" with repository = "aws"), the memory option applies to the temporary local copy of the file: "persistent" means it remains until the end of the pipeline and is then deleted, and "transient" means it gets deleted as soon as possible. The former conserves bandwidth, and the latter conserves local storage.
garbage_collection	Logical: TRUE to run base::gc() just before the target runs, FALSE to omit garbage collection. In the case of high-performance computing, gc() runs both locally and on the parallel worker. All this garbage collection is skipped if the actual target is skipped in the pipeline. Non-logical values of garbage_collection are converted to TRUE or FALSE using isTRUE(). In other words, non-logical values are converted FALSE. For example, garbage_collection = 2 is equivalent to garbage_collection = FALSE.
deployment	Character of length 1. If deployment is "main", then the target will run on the central controlling R process. Otherwise, if deployment is "worker" and you set up the pipeline with distributed/parallel computing, then the target runs on a parallel worker. For more on distributed/parallel computing in targets, please visit https://books.ropensci.org/targets/crew.html.
priority	Numeric of length 1 between 0 and 1. Controls which targets get deployed first when multiple competing targets are ready simultaneously. Targets with priorities closer to 1 get dispatched earlier (and polled earlier in tar_make_future()).
resources	Object returned by tar_resources() with optional settings for high-performance computing functionality, alternative data storage formats, and other optional capabilities of targets. See tar_resources() for details.
storage	Character string to control when the output of the target is saved to storage. Only relevant when using targets with parallel workers (https://books.ropensci.org/targets/crew.html). Must be one of the following values: "main": the target's return value is sent back to the host machine and saved/uploaded locally. "worker": the worker saves/uploads the value. "none": targets makes no attempt to save the result of the target to storage in the location where targets expects it to be. Saving to storage is the responsibility of the user. Use with caution.
retrieval	Character string to control when the current target loads its dependencies into memory before running. (Here, a "dependency" is another target upstream that the current one depends on.) Only relevant when using targets with parallel workers (https://books.ropensci.org/targets/crew.html). Must be one of the following values: "main": the target's dependencies are loaded on the host machine and sent to the worker before the target runs. "worker": the worker loads the target's dependencies. "none": targets makes no attempt to load its dependencies. With retrieval = "none", loading dependencies is the responsibility of the user. Use with caution.
cue	An optional object from tar_cue() to customize the rules that decide whether the target is up to date.
description	Character of length 1, a custom free-form human-readable text description of the target. Descriptions appear as target labels in functions like tar_manifest() and tar_visnetwork(), and they let you select subsets of targets for the names argument of functions like tar_make(). For example, tar_manifest(names = tar_described_as(starts_with("survival model"))) lists all the targets whose descriptions start with the character string "survival model".

Details

Most of the arguments are passed to the ⁠$compile()⁠, ⁠$variational()⁠, and ⁠$summary()⁠ methods of the CmdStanModel class. If you previously compiled the model in an upstream tar_stan_compile() target, then the model should not recompile.

Value

tar_stan_vb() returns a list of target objects. See the "Target objects" section for background. The target names use the name argument as a prefix, and the individual elements of stan_files appear in the suffixes where applicable. As an example, the specific target objects returned by tar_stan_vb(name = x, stan_files = "y.stan", ...) are as follows.

• x_file_y: reproducibly track the Stan model file. Returns a character vector with paths to the model file and compiled executable.

• x_lines_y: read the Stan model file for safe transport to parallel workers. Omitted if compile = "original". Returns a character vector of lines in the model file.

• x_data: run the R expression in the data argument to produce a Stan dataset for the model. Returns a Stan data list.

• x_vb_y: run variational Bayes on the model and the dataset. Returns a cmdstanr CmdStanVB object with all the results.

• x_draws_y: extract draws from x_vb_y. Omitted if draws = FALSE. Returns a tidy data frame of draws.

• x_summary_y: extract compact summaries from x_vb_y. Returns a tidy data frame of summaries. Omitted if summary = FALSE.

Target objects

Most stantargets functions are target factories, which means they return target objects or lists of target objects. Target objects represent skippable steps of the analysis pipeline as described at https://books.ropensci.org/targets/. Please read the walkthrough at https://books.ropensci.org/targets/walkthrough.html to understand the role of target objects in analysis pipelines.

For developers, https://wlandau.github.io/targetopia/contributing.html#target-factories explains target factories (functions like this one which generate targets) and the design specification at https://books.ropensci.org/targets-design/ details the structure and composition of target objects.

See Also

Other variational Bayes: tar_stan_vb_rep_draws(), tar_stan_vb_rep_summary()

Examples

if (Sys.getenv("TAR_LONG_EXAMPLES") == "true") {
targets::tar_dir({ # tar_dir() runs code from a temporary directory.
targets::tar_script({
library(stantargets)
# Do not use temporary storage for stan files in real projects
# or else your targets will always rerun.
path <- tempfile(pattern = "", fileext = ".stan")
tar_stan_example_file(path = path)
list(
  tar_stan_vb(
    your_model,
    stan_files = path,
    data = tar_stan_example_data(),
    variables = "beta",
    summaries = list(~quantile(.x, probs = c(0.25, 0.75))),
    stdout = R.utils::nullfile(),
    stderr = R.utils::nullfile()
  )
)
}, ask = FALSE)
targets::tar_make()
})
}

ropensci/stantargets documentation built on Feb. 8, 2025, 10:34 p.m.