From 9c2643c3d98ee113cb729030b9eb6f978eb6ccba Mon Sep 17 00:00:00 2001 From: jgabry Date: Sat, 29 Jul 2023 19:06:42 -0600 Subject: [PATCH 01/21] initial attempt at laplace method closes #760 --- R/args.R | 67 ++++++++ R/csv.R | 35 +++- R/example.R | 2 +- R/fit.R | 126 +++++++++++--- R/model.R | 180 +++++++++++++++++++- R/run.R | 8 +- man/CmdStanDiagnose.Rd | 1 + man/CmdStanGQ.Rd | 1 + man/CmdStanLaplace.Rd | 72 ++++++++ man/CmdStanMCMC.Rd | 1 + man/CmdStanMLE.Rd | 1 + man/CmdStanModel.Rd | 15 +- man/CmdStanVB.Rd | 1 + man/cmdstan_model.Rd | 15 +- man/cmdstanr-package.Rd | 15 +- man/cmdstanr_example.Rd | 2 +- man/fit-method-lp.Rd | 22 ++- man/fit-method-summary.Rd | 2 +- man/fit-method-time.Rd | 15 +- man/model-method-check_syntax.Rd | 1 + man/model-method-compile.Rd | 1 + man/model-method-diagnose.Rd | 1 + man/model-method-expose_functions.Rd | 1 + man/model-method-format.Rd | 1 + man/model-method-generate-quantities.Rd | 1 + man/model-method-laplace.Rd | 214 ++++++++++++++++++++++++ man/model-method-optimize.Rd | 20 ++- man/model-method-sample.Rd | 16 +- man/model-method-sample_mpi.Rd | 1 + man/model-method-variables.Rd | 1 + man/model-method-variational.Rd | 16 +- man/read_cmdstan_csv.Rd | 3 +- 32 files changed, 765 insertions(+), 93 deletions(-) create mode 100644 man/CmdStanLaplace.Rd create mode 100644 man/model-method-laplace.Rd diff --git a/R/args.R b/R/args.R index 7794510e4..ef994f424 100644 --- a/R/args.R +++ b/R/args.R @@ -14,6 +14,7 @@ #' #' * `SampleArgs`: stores arguments specific to `method=sample`. #' * `OptimizeArgs`: stores arguments specific to `method=optimize`. +#' * `LaplaceArgs`: stores arguments specific to `method=laplace`. #' * `VariationalArgs`: stores arguments specific to `method=variational` #' * `GenerateQuantitiesArgs`: stores arguments specific to `method=generate_quantities` #' * `DiagnoseArgs`: stores arguments specific to `method=diagnose` @@ -427,6 +428,49 @@ OptimizeArgs <- R6::R6Class( ) +# LaplaceArgs ------------------------------------------------------------- + +LaplaceArgs <- R6::R6Class( + "LaplaceArgs", + lock_objects = FALSE, + public = list( + method = "laplace", + initialize = function(mode = NULL, + draws = NULL, + jacobian = TRUE) { + checkmate::assert_r6(mode, classes = "CmdStanMLE") + self$mode_object <- mode # keep the CmdStanMLE for later use (can be returned by CmdStanLaplace$mode()) + self$mode <- self$mode_object$output_files() # mode <- file path to pass to CmdStan + self$jacobian <- jacobian + self$draws <- draws + invisible(self) + }, + validate = function(num_procs) { + validate_laplace_args(self) + invisible(self) + }, + + # Compose arguments to CmdStan command for laplace-specific + # non-default arguments. Works the same way as compose for sampler args, + # but `idx` is ignored (no multiple chains for optimize or variational) + compose = function(idx = NULL, args = NULL) { + .make_arg <- function(arg_name) { + compose_arg(self, arg_name, idx = NULL) + } + new_args <- list( + "method=laplace", + .make_arg("mode"), + .make_arg("draws"), + .make_arg("jacobian") + ) + new_args <- do.call(c, new_args) + c(args, new_args) + } + ) +) + + + # VariationalArgs --------------------------------------------------------- VariationalArgs <- R6::R6Class( @@ -712,6 +756,29 @@ validate_optimize_args <- function(self) { invisible(TRUE) } +#' Validate arguments for laplace +#' @noRd +#' @param self A `LaplaceArgs` object. +#' @return `TRUE` invisibly unless an error is thrown. +validate_laplace_args <- function(self) { + checkmate::assert_file_exists(self$mode, extension = "csv") + checkmate::assert_integerish(self$draws, lower = 1, null.ok = TRUE, len = 1) + if (!is.null(self$draws)) { + self$draws <- as.integer(self$draws) + } + checkmate::assert_flag(self$jacobian, null.ok = FALSE) + if (self$mode_object$metadata()$jacobian != self$jacobian) { + stop( + "'jacobian' argument to optimize and laplace must match!\n", + "laplace was called with jacobian=", self$jacobian, "\n", + "optimize was run with jacobian=", as.logical(self$mode_object$metadata()$jacobian), + call. = FALSE + ) + } + self$jacobian <- as.integer(self$jacobian) + invisible(TRUE) +} + #' Validate arguments for standalone generated quantities #' @noRd #' @param self A `GenerateQuantitiesArgs` object. diff --git a/R/csv.R b/R/csv.R index 5d3824428..86cd14887 100644 --- a/R/csv.R +++ b/R/csv.R @@ -67,7 +67,8 @@ #' #' * `point_estimates`: Point estimates for the model parameters. #' -#' For [variational inference][model-method-variational] the returned list also +#' For [laplace][model-method-laplace] and +#' [variational inference][model-method-variational] the returned list also #' includes the following components: #' #' * `draws`: A [`draws_matrix`][posterior::draws_matrix] (or different format @@ -307,6 +308,11 @@ read_cmdstan_csv <- function(files, repaired_variables <- repaired_variables[repaired_variables != "lp__"] repaired_variables <- gsub("log_p__", "lp__", repaired_variables) repaired_variables <- gsub("log_g__", "lp_approx__", repaired_variables) + } else if (metadata$method == "laplace") { + metadata$variables <- gsub("log_p__", "lp__", metadata$variables) + metadata$variables <- gsub("log_q__", "lp_approx__", metadata$variables) + repaired_variables <- gsub("log_p__", "lp__", repaired_variables) + repaired_variables <- gsub("log_q__", "lp_approx__", repaired_variables) } model_param_dims <- variable_dims(metadata$variables) metadata$stan_variable_sizes <- model_param_dims @@ -385,6 +391,29 @@ read_cmdstan_csv <- function(files, metadata = metadata, draws = variational_draws ) + } else if (metadata$method == "laplace") { + if (is.null(format)) { + format <- "draws_matrix" + } + as_draws_format <- as_draws_format_fun(format) + if (length(draws) == 0) { + laplace_draws <- NULL + } else { + laplace_draws <- do.call(as_draws_format, list(draws[[1]])) + } + if (!is.null(laplace_draws)) { + if ("log_p__" %in% posterior::variables(laplace_draws)) { + laplace_draws <- posterior::rename_variables(laplace_draws, lp__ = "log_p__") + } + if ("log_q__" %in% posterior::variables(laplace_draws)) { + laplace_draws <- posterior::rename_variables(laplace_draws, lp_approx__ = "log_q__") + } + posterior::variables(laplace_draws) <- repaired_variables + } + list( + metadata = metadata, + draws = laplace_draws + ) } else if (metadata$method == "optimize") { if (is.null(format)) { format <- "draws_matrix" @@ -616,7 +645,7 @@ read_csv_metadata <- function(csv_file) { all_names <- strsplit(line, ",")[[1]] if (all(csv_file_info$algorithm != "fixed_param")) { csv_file_info[["sampler_diagnostics"]] <- all_names[endsWith(all_names, "__")] - csv_file_info[["sampler_diagnostics"]] <- csv_file_info[["sampler_diagnostics"]][!(csv_file_info[["sampler_diagnostics"]] %in% c("lp__", "log_p__", "log_g__"))] + csv_file_info[["sampler_diagnostics"]] <- csv_file_info[["sampler_diagnostics"]][!(csv_file_info[["sampler_diagnostics"]] %in% c("lp__", "log_p__", "log_g__", "log_q__"))] csv_file_info[["variables"]] <- all_names[!(all_names %in% csv_file_info[["sampler_diagnostics"]])] } else { csv_file_info[["variables"]] <- all_names[!endsWith(all_names, "__")] @@ -719,7 +748,7 @@ read_csv_metadata <- function(csv_file) { csv_file_info$step_size <- csv_file_info$stepsize csv_file_info$iter_warmup <- csv_file_info$num_warmup csv_file_info$iter_sampling <- csv_file_info$num_samples - if (csv_file_info$method == "variational" || csv_file_info$method == "optimize") { + if (csv_file_info$method %in% c("variational", "optimize", "laplace")) { csv_file_info$threads <- csv_file_info$num_threads } else { csv_file_info$threads_per_chain <- csv_file_info$num_threads diff --git a/R/example.R b/R/example.R index 755f33703..f6831bdb9 100644 --- a/R/example.R +++ b/R/example.R @@ -49,7 +49,7 @@ #' cmdstanr_example <- function(example = c("logistic", "schools", "schools_ncp"), - method = c("sample", "optimize", "variational", "diagnose"), + method = c("sample", "optimize", "laplace", "variational", "diagnose"), ..., quiet = TRUE) { diff --git a/R/fit.R b/R/fit.R index 2709ae0e8..fa04fe2cc 100644 --- a/R/fit.R +++ b/R/fit.R @@ -2,8 +2,9 @@ #' CmdStanFit superclass #' #' @noRd -#' @description CmdStanMCMC, CmdStanMLE, CmdStanVB, CmdStanGQ all share the -#' methods of the superclass CmdStanFit and also have their own unique methods. +#' @description CmdStanMCMC, CmdStanMLE, CmdStanLaplace, CmdStanVB, CmdStanGQ +#' all share the methods of the superclass CmdStanFit and also have their own +#' unique methods. #' CmdStanFit <- R6::R6Class( classname = "CmdStanFit", @@ -693,10 +694,12 @@ CmdStanFit$set("public", name = "constrain_variables", value = constrain_variabl #' @description The `$lp()` method extracts `lp__`, the total log probability #' (`target`) accumulated in the model block of the Stan program. For #' variational inference the log density of the variational approximation to -#' the posterior is also available via the `$lp_approx()` method. +#' the posterior is available via the `$lp_approx()` method. For +#' Laplace approximation the unnormalized density of the approximation to +#' the posterior is available via the `$lp_approx()` method. #' #' See the [Log Probability Increment vs. Sampling -#' Statement](https://mc-stan.org/docs/2_23/reference-manual/sampling-statements-section.html) +#' Statement](https://mc-stan.org/docs/reference-manual/sampling-statements.html) #' section of the Stan Reference Manual for details on when normalizing #' constants are dropped from log probability calculations. #' @@ -707,21 +710,23 @@ CmdStanFit$set("public", name = "constrain_variables", value = constrain_variabl #' evaluated at a posterior draw (which is on the constrained space). `lp__` is #' intended to diagnose sampling efficiency and evaluate approximations. #' -#' `lp_approx__` is the log density of the variational approximation to `lp__` -#' (also on the unconstrained space). It is exposed in the variational method -#' for performing the checks described in Yao et al. (2018) and implemented in -#' the \pkg{loo} package. +#' For variational inference `lp_approx__` is the log density of the variational +#' approximation to `lp__` (also on the unconstrained space). It is exposed in +#' the variational method for performing the checks described in Yao et al. +#' (2018) and implemented in the \pkg{loo} package. +#' +#' For Laplace approximation `lp_approx__` is the the unnormalized density of +#' the Laplace approximation. #' #' @return A numeric vector with length equal to the number of (post-warmup) -#' draws for MCMC and variational inference, and length equal to `1` for -#' optimization. +#' draws or length equal to `1` for optimization. #' #' @references #' Yao, Y., Vehtari, A., Simpson, D., and Gelman, A. (2018). Yes, but did it #' work?: Evaluating variational inference. *Proceedings of the 35th #' International Conference on Machine Learning*, PMLR 80:5581–5590. #' -#' @seealso [`CmdStanMCMC`], [`CmdStanMLE`], [`CmdStanVB`] +#' @seealso [`CmdStanMCMC`], [`CmdStanMLE`], [`CmdStanLaplace`], [`CmdStanVB`] #' #' @examples #' \dontrun{ @@ -742,6 +747,13 @@ lp <- function() { } CmdStanFit$set("public", name = "lp", value = lp) +# will be used by a subset of fit objects below +#' @rdname fit-method-lp +lp_approx <- function() { + as.numeric(self$draws()[, "lp_approx__"]) +} + + #' Compute a summary table of estimates and diagnostics #' #' @name fit-method-summary @@ -769,7 +781,7 @@ CmdStanFit$set("public", name = "lp", value = lp) #' The `$print()` method returns the fitted model object itself (invisibly), #' which is the standard behavior for print methods in \R. #' -#' @seealso [`CmdStanMCMC`], [`CmdStanMLE`], [`CmdStanVB`], [`CmdStanGQ`] +#' @seealso [`CmdStanMCMC`], [`CmdStanMLE`], [`CmdStanLaplace`], [`CmdStanVB`], [`CmdStanGQ`] #' #' @examples #' \dontrun{ @@ -1008,7 +1020,9 @@ CmdStanFit$set("public", name = "data_file", value = data_file) #' @aliases time #' @description Report the run time in seconds. For MCMC additional information #' is provided about the run times of individual chains and the warmup and -#' sampling phases. +#' sampling phases. For Laplace approximation the time only include the time +#' for drawing the approximate sample and does not include the time +#' taken to run the `$optimize()` method. #' #' @return #' A list with elements @@ -1025,11 +1039,16 @@ CmdStanFit$set("public", name = "data_file", value = data_file) #' fit_mcmc <- cmdstanr_example("logistic", method = "sample") #' fit_mcmc$time() #' -#' fit_mle <- cmdstanr_example("logistic", method = "optimize") -#' fit_mle$time() -#' #' fit_vb <- cmdstanr_example("logistic", method = "variational") #' fit_vb$time() +#' +#' fit_mle <- cmdstanr_example("logistic", method = "optimize", jacobian = TRUE) +#' fit_mle$time() +#' +#' # use fit_mle to draw samples from laplace approximation +#' fit_laplace <- cmdstanr_example("logistic", method = "laplace", mode = fit_mle) +#' fit_laplace$time() # just time for drawing sample not for running optimize +#' fit_laplace$time()$total + fit_mle$time()$total # total time #' } #' time <- function() { @@ -1851,6 +1870,76 @@ mle <- function(variables = NULL) { } CmdStanMLE$set("public", name = "mle", value = mle) +# CmdStanLaplace --------------------------------------------------------------- +#' CmdStanLaplace objects +#' +#' @name CmdStanLaplace +#' @family fitted model objects +#' @template seealso-docs +#' +#' @description A `CmdStanLaplace` object is the fitted model object returned by the +#' [`$laplace()`][model-method-laplace] method of a +#' [`CmdStanModel`] object. +#' +#' @section Methods: `CmdStanLaplace` objects have the following associated methods, +#' all of which have their own (linked) documentation pages. +#' +#' ## Extract contents of fitted model object +#' +#' |**Method**|**Description**| +#' |:----------|:---------------| +#' [`$draws()`][fit-method-draws] | Return approximate posterior draws as a [`draws_matrix`][posterior::draws_matrix]. | +#' `$mode()` | Return the mode as a [`CmdStanMLE`] object. | +#' [`$lp()`][fit-method-lp] | Return the total log probability density (`target`) computed in the model block of the Stan program. | +#' [`$lp_approx()`][fit-method-lp] | Return the log density of the approximation to the posterior. | +#' [`$init()`][fit-method-init] | Return user-specified initial values. | +#' [`$metadata()`][fit-method-metadata] | Return a list of metadata gathered from the CmdStan CSV files. | +#' [`$code()`][fit-method-code] | Return Stan code as a character vector. | +#' +#' ## Summarize inferences +#' +#' |**Method**|**Description**| +#' |:----------|:---------------| +#' [`$summary()`][fit-method-summary] | Run [`posterior::summarise_draws()`][posterior::draws_summary]. | +#' +#' ## Save fitted model object and temporary files +#' +#' |**Method**|**Description**| +#' |:----------|:---------------| +#' [`$save_object()`][fit-method-save_object] | Save fitted model object to a file. | +#' [`$save_output_files()`][fit-method-save_output_files] | Save output CSV files to a specified location. | +#' [`$save_data_file()`][fit-method-save_data_file] | Save JSON data file to a specified location. | +#' [`$save_latent_dynamics_files()`][fit-method-save_latent_dynamics_files] | Save diagnostic CSV files to a specified location. | +#' +#' ## Report run times, console output, return codes +#' +#' |**Method**|**Description**| +#' |:----------|:---------------| +#' [`$time()`][fit-method-time] | Report the run time of the Laplace sampling step. | +#' [`$output()`][fit-method-output] | Pretty print the output that was printed to the console. | +#' [`$return_codes()`][fit-method-return_codes] | Return the return codes from the CmdStan runs. | +#' +CmdStanLaplace <- R6::R6Class( + classname = "CmdStanLaplace", + inherit = CmdStanFit, + public = list( + mode = function() self$runset$args$method_args$mode_object + ), + private = list( + # inherits draws_ and metadata_ slots from CmdStanFit + read_csv_ = function(format = getOption("cmdstanr_draws_format", "draws_matrix")) { + if (!length(self$output_files(include_failed = FALSE))) { + stop("Laplace inference failed. Unable to retrieve the draws.", call. = FALSE) + } + csv_contents <- read_cmdstan_csv(self$output_files(), format = format) + private$draws_ <- csv_contents$draws + private$metadata_ <- csv_contents$metadata + invisible(self) + } + ) +) +CmdStanLaplace$set("public", name = "lp_approx", value = lp_approx) + # CmdStanVB --------------------------------------------------------------- #' CmdStanVB objects @@ -1932,11 +2021,6 @@ CmdStanVB <- R6::R6Class( } ) ) - -#' @rdname fit-method-lp -lp_approx <- function() { - as.numeric(self$draws()[, "lp_approx__"]) -} CmdStanVB$set("public", name = "lp_approx", value = lp_approx) diff --git a/R/model.R b/R/model.R index a545e95c1..2d3cf98e6 100644 --- a/R/model.R +++ b/R/model.R @@ -69,6 +69,9 @@ #' # Use 'posterior' package for summaries #' fit_mcmc$summary() #' +#' # Check sampling diagnostics +#' fit_mcmc$diagnostic_summary() +#' #' # Get posterior draws #' draws <- fit_mcmc$draws() #' print(draws) @@ -79,13 +82,8 @@ #' # Plot posterior using bayesplot (ggplot2) #' mcmc_hist(fit_mcmc$draws("theta")) #' -#' # Call CmdStan's diagnose and stansummary utilities -#' fit_mcmc$cmdstan_diagnose() -#' fit_mcmc$cmdstan_summary() -#' #' # For models fit using MCMC, if you like working with RStan's stanfit objects #' # then you can create one with rstan::read_stan_csv() -#' #' # stanfit <- rstan::read_stan_csv(fit_mcmc$output_files()) #' #' @@ -93,13 +91,16 @@ #' # and also demonstrate specifying data as a path to a file instead of a list #' my_data_file <- file.path(cmdstan_path(), "examples/bernoulli/bernoulli.data.json") #' fit_optim <- mod$optimize(data = my_data_file, seed = 123) -#' #' fit_optim$summary() #' +#' # Run 'optimize' again with 'jacobian=TRUE' and then draw from laplace approximation +#' # to the posterior +#' fit_optim <- mod$optimize(data = my_data_file, jacobian = TRUE) +#' fit_laplace <- mod$laplace(data = my_data_file, mode = fit_optim, draws = 2000) +#' fit_laplace$summary() #' #' # Run 'variational' method to approximate the posterior (default is meanfield ADVI) #' fit_vb <- mod$variational(data = stan_data, seed = 123) -#' #' fit_vb$summary() #' #' # Plot approximate posterior using bayesplot @@ -1374,6 +1375,8 @@ CmdStanModel$set("public", name = "sample_mpi", value = sample_mpi) #' yields the maximum a posteriori estimate. See the #' [Maximum Likelihood Estimation](https://mc-stan.org/docs/cmdstan-guide/maximum-likelihood-estimation.html) #' section of the CmdStan User's Guide for more details. +#' For use later with [`$laplace()`][model-method-laplace] the `jacobian` +#' argument should typically be set to `TRUE`. #' @param init_alpha (positive real) The initial step size parameter. #' @param tol_obj (positive real) Convergence tolerance on changes in objective function value. #' @param tol_rel_obj (positive real) Convergence tolerance on relative changes in objective function value. @@ -1456,6 +1459,169 @@ optimize <- function(data = NULL, CmdStanModel$set("public", name = "optimize", value = optimize) +#' Run Stan's laplace algorithm +#' +#' @name model-method-laplace +#' @aliases laplace +#' @family CmdStanModel methods +#' +#' @description The `$laplace()` method of a [`CmdStanModel`] object produces a +#' sample from a normal approximation centered at the mode of a distribution +#' in the unconstrained space. If the mode is a maximum a posteriori (MAP) +#' estimate, the samples provide an estimate of the mean and standard +#' deviation of the posterior distribution. If the mode is a maximum +#' likelihood estimate (MLE), the sample provides an estimate of the standard +#' error of the likelihood. Whether the mode is the MAP or MLE depends on +#' the value of the `jacobian` argument when running optimization. See the +#' [Laplace Sampling](https://mc-stan.org/docs/cmdstan-guide/laplace-sampling.html) +#' section of the CmdStan User's Guide for more details. +#' +#' Any argument left as `NULL` will default to the default value used by the +#' installed version of CmdStan. See the +#' [CmdStan User’s Guide](https://mc-stan.org/docs/cmdstan-guide/) +#' for more details on the default arguments. +#' +#' @template model-common-args +#' @inheritParams model-method-optimize +#' @param mode (multiple options) The mode to center the approximation at. One +#' of the following: +#' * A [`CmdStanMLE`] object from a previous run of [`$optimize()`][model-method-optimize]. +#' * The path to a CmdStan CSV file from running optimization. +#' * `NULL`, in which case [$optimize()][fit-method-optimize] will be run +#' with `jacobian=jacobian` (see the `jacobian` argument below). +#' +#' In all cases the total time reported by [`$time()`][fit-method-time] will be +#' the time of the Laplace sampling step only and does not include the time +#' taken to run the `$optimize()` method. +#' @param opt_args (named list) A named list of optional arguments to pass to +#' [$optimize()][fit-method-optimize] if `mode=NULL`. +#' @param draws (positive integer) The number of draws to take. +#' @param jacobian (logical) Whether or not to enable the Jacobian adjustment +#' for constrained parameters. The default is `TRUE`. See the +#' [Laplace Sampling](https://mc-stan.org/docs/cmdstan-guide/laplace-sampling.html) +#' section of the CmdStan User's Guide for more details. If `mode` is not +#' `NULL` then the value of `jacobian` must match the value used when +#' optimization was originally run. If `mode` is `NULL` then the value of +#' `jacobian` specified here is used when running optimization. +#' +#' @return A [`CmdStanLaplace`] object. +#' +#' @template seealso-docs +#' @examples +#' \dontrun{ +#' file <- file.path(cmdstan_path(), "examples/bernoulli/bernoulli.stan") +#' mod <- cmdstan_model(file) +#' mod$print() +#' +#' stan_data <- list(N = 10, y = c(0,1,0,0,0,0,0,0,0,1)) +#' fit_mode <- mod$optimize(data = stan_data, jacobian = TRUE) +#' fit_laplace <- mod$laplace(data = stan_data, mode = fit_mode) +#' fit_laplace$summary() +#' +#' # if mode isn't specified optimize is run internally first +#' fit_laplace <- mod$laplace(data = stan_data) +#' fit_laplace$summary() +#' +#' # plot approximate posterior +#' bayesplot::mcmc_hist(fit_laplace$draws("theta")) +#' } +#' +#' +laplace <- function(data = NULL, + seed = NULL, + refresh = NULL, + init = NULL, + output_dir = NULL, + output_basename = NULL, + sig_figs = NULL, + threads = NULL, + opencl_ids = NULL, + mode = NULL, + jacobian = TRUE, # different than for optimize! + draws = NULL, + opt_args = NULL) { + if (cmdstan_version() < "2.32") { + stop("This method is only available in cmdstan >= 2.32", call. = FALSE) + } + if (!is.null(mode) && !is.null(opt_args)) { + stop("Cannot specify both 'opt_args' and 'mode' arguments.", call. = FALSE) + } + procs <- CmdStanProcs$new( + num_procs = 1, + show_stdout_messages = (is.null(refresh) || refresh != 0), + threads_per_proc = assert_valid_threads(threads, self$cpp_options()) + ) + model_variables <- NULL + if (is_variables_method_supported(self)) { + model_variables <- self$variables() + } + + if (!is.null(mode)) { + if (inherits(mode, "CmdStanMLE")) { + cmdstan_mode <- mode + } else { + if (!is.character(mode) && length(mode) == 1) { + stop("If not NULL or a CmdStanMLE object then 'mode' must be a path to a CSV file.", call. = FALSE) + } + cmdstan_mode <- as_cmdstan_fit(mode) + } + } else { # mode = NULL, run optimize() + checkmate::assert_list(opt_args, any.missing = FALSE, names = "unique", null.ok = TRUE) + cmdstan_mode <- self$optimize( + data = data, + seed = seed, + refresh = refresh, + init = init, + save_latent_dynamics = FALSE, + output_dir = output_dir, + output_basename = output_basename, + sig_figs = sig_figs, + threads = threads, + opencl_ids = opencl_ids, + jacobian = jacobian, + algorithm = opt_args$algorithm, + init_alpha = opt_args$init_alpha, + iter = opt_args$iter, + tol_obj = opt_args$tol_obj, + tol_rel_obj = opt_args$tol_rel_obj, + tol_grad = opt_args$tol_grad, + tol_rel_grad = opt_args$tol_rel_grad, + tol_param = opt_args$tol_param, + history_size = opt_args$history_size + ) + } + laplace_args <- LaplaceArgs$new( + mode = cmdstan_mode, + draws = draws, + jacobian = jacobian + ) + args <- CmdStanArgs$new( + method_args = laplace_args, + stan_file = self$stan_file(), + stan_code = suppressWarnings(self$code()), + model_methods_env = private$model_methods_env_, + standalone_env = self$functions, + model_name = self$model_name(), + exe_file = self$exe_file(), + proc_ids = 1, + data_file = process_data(data, model_variables), + save_latent_dynamics = FALSE, + seed = seed, + init = init, + refresh = refresh, + output_dir = output_dir, + output_basename = output_basename, + sig_figs = sig_figs, + opencl_ids = assert_valid_opencl(opencl_ids, self$cpp_options()), + model_variables = model_variables + ) + runset <- CmdStanRun$new(args, procs) + runset$run_cmdstan() + CmdStanLaplace$new(runset) +} +CmdStanModel$set("public", name = "laplace", value = laplace) + + #' Run Stan's variational approximation algorithms #' #' @name model-method-variational diff --git a/R/run.R b/R/run.R index 556477e43..32c9fe290 100644 --- a/R/run.R +++ b/R/run.R @@ -229,6 +229,9 @@ CmdStanRun <- R6::R6Class( if (self$method() == "optimize") { stop("Not available for optimize method.", call. = FALSE) } + if (self$method() == "laplace") { + stop("Not available for laplace method.", call. = FALSE) + } if (self$method() == "generate_quantities") { stop("Not available for generate_quantities method.", call. = FALSE) } @@ -259,7 +262,7 @@ CmdStanRun <- R6::R6Class( }, time = function() { - if (self$method() %in% c("optimize", "variational")) { + if (self$method() %in% c("laplace", "optimize", "variational")) { time <- list(total = self$procs$total_time()) } else if (self$method() == "generate_quantities") { chain_time <- data.frame( @@ -511,7 +514,7 @@ CmdStanRun$set("private", name = "run_generate_quantities_", value = .run_genera procs$process_output(id) procs$process_error_output(id) successful_fit <- FALSE - if (self$method() == "optimize") { + if (self$method() %in% "optimize") { # QUESTION: should this include laplace? if (procs$proc_state(id = id) > 3) { successful_fit <- TRUE } @@ -528,6 +531,7 @@ CmdStanRun$set("private", name = "run_generate_quantities_", value = .run_genera procs$report_time() } CmdStanRun$set("private", name = "run_optimize_", value = .run_other) +CmdStanRun$set("private", name = "run_laplace_", value = .run_other) CmdStanRun$set("private", name = "run_variational_", value = .run_other) .run_diagnose <- function() { diff --git a/man/CmdStanDiagnose.Rd b/man/CmdStanDiagnose.Rd index c9dd6c2b9..70930db17 100644 --- a/man/CmdStanDiagnose.Rd +++ b/man/CmdStanDiagnose.Rd @@ -42,6 +42,7 @@ The Stan and CmdStan documentation: Other fitted model objects: \code{\link{CmdStanGQ}}, +\code{\link{CmdStanLaplace}}, \code{\link{CmdStanMCMC}}, \code{\link{CmdStanMLE}}, \code{\link{CmdStanVB}} diff --git a/man/CmdStanGQ.Rd b/man/CmdStanGQ.Rd index 6aa7a3ce8..bbea5ae0e 100644 --- a/man/CmdStanGQ.Rd +++ b/man/CmdStanGQ.Rd @@ -103,6 +103,7 @@ The Stan and CmdStan documentation: Other fitted model objects: \code{\link{CmdStanDiagnose}}, +\code{\link{CmdStanLaplace}}, \code{\link{CmdStanMCMC}}, \code{\link{CmdStanMLE}}, \code{\link{CmdStanVB}} diff --git a/man/CmdStanLaplace.Rd b/man/CmdStanLaplace.Rd new file mode 100644 index 000000000..50d782a63 --- /dev/null +++ b/man/CmdStanLaplace.Rd @@ -0,0 +1,72 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/fit.R +\name{CmdStanLaplace} +\alias{CmdStanLaplace} +\title{CmdStanLaplace objects} +\description{ +A \code{CmdStanLaplace} object is the fitted model object returned by the +\code{\link[=model-method-laplace]{$laplace()}} method of a +\code{\link{CmdStanModel}} object. +} +\section{Methods}{ + \code{CmdStanLaplace} objects have the following associated methods, +all of which have their own (linked) documentation pages. +\subsection{Extract contents of fitted model object}{\tabular{ll}{ + \strong{Method} \tab \strong{Description} \cr + \code{\link[=fit-method-draws]{$draws()}} \tab Return approximate posterior draws as a \code{\link[posterior:draws_matrix]{draws_matrix}}. \cr + \verb{$mode()} \tab Return the mode as a \code{\link{CmdStanMLE}} object. \cr + \code{\link[=fit-method-lp]{$lp()}} \tab Return the total log probability density (\code{target}) computed in the model block of the Stan program. \cr + \code{\link[=fit-method-lp]{$lp_approx()}} \tab Return the log density of the approximation to the posterior. \cr + \code{\link[=fit-method-init]{$init()}} \tab Return user-specified initial values. \cr + \code{\link[=fit-method-metadata]{$metadata()}} \tab Return a list of metadata gathered from the CmdStan CSV files. \cr + \code{\link[=fit-method-code]{$code()}} \tab Return Stan code as a character vector. \cr +} + +} + +\subsection{Summarize inferences}{\tabular{ll}{ + \strong{Method} \tab \strong{Description} \cr + \code{\link[=fit-method-summary]{$summary()}} \tab Run \code{\link[posterior:draws_summary]{posterior::summarise_draws()}}. \cr +} + +} + +\subsection{Save fitted model object and temporary files}{\tabular{ll}{ + \strong{Method} \tab \strong{Description} \cr + \code{\link[=fit-method-save_object]{$save_object()}} \tab Save fitted model object to a file. \cr + \code{\link[=fit-method-save_output_files]{$save_output_files()}} \tab Save output CSV files to a specified location. \cr + \code{\link[=fit-method-save_data_file]{$save_data_file()}} \tab Save JSON data file to a specified location. \cr + \code{\link[=fit-method-save_latent_dynamics_files]{$save_latent_dynamics_files()}} \tab Save diagnostic CSV files to a specified location. \cr +} + +} + +\subsection{Report run times, console output, return codes}{\tabular{ll}{ + \strong{Method} \tab \strong{Description} \cr + \code{\link[=fit-method-time]{$time()}} \tab Report the run time of the Laplace sampling step. \cr + \code{\link[=fit-method-output]{$output()}} \tab Pretty print the output that was printed to the console. \cr + \code{\link[=fit-method-return_codes]{$return_codes()}} \tab Return the return codes from the CmdStan runs. \cr +} + +} +} + +\seealso{ +The CmdStanR website +(\href{https://mc-stan.org/cmdstanr/}{mc-stan.org/cmdstanr}) for online +documentation and tutorials. + +The Stan and CmdStan documentation: +\itemize{ +\item Stan documentation: \href{https://mc-stan.org/users/documentation/}{mc-stan.org/users/documentation} +\item CmdStan User’s Guide: \href{https://mc-stan.org/docs/cmdstan-guide/}{mc-stan.org/docs/cmdstan-guide} +} + +Other fitted model objects: +\code{\link{CmdStanDiagnose}}, +\code{\link{CmdStanGQ}}, +\code{\link{CmdStanMCMC}}, +\code{\link{CmdStanMLE}}, +\code{\link{CmdStanVB}} +} +\concept{fitted model objects} diff --git a/man/CmdStanMCMC.Rd b/man/CmdStanMCMC.Rd index 218ee49c3..c74ff41f2 100644 --- a/man/CmdStanMCMC.Rd +++ b/man/CmdStanMCMC.Rd @@ -87,6 +87,7 @@ The Stan and CmdStan documentation: Other fitted model objects: \code{\link{CmdStanDiagnose}}, \code{\link{CmdStanGQ}}, +\code{\link{CmdStanLaplace}}, \code{\link{CmdStanMLE}}, \code{\link{CmdStanVB}} } diff --git a/man/CmdStanMLE.Rd b/man/CmdStanMLE.Rd index 01acae4d9..141e45aa6 100644 --- a/man/CmdStanMLE.Rd +++ b/man/CmdStanMLE.Rd @@ -77,6 +77,7 @@ The Stan and CmdStan documentation: Other fitted model objects: \code{\link{CmdStanDiagnose}}, \code{\link{CmdStanGQ}}, +\code{\link{CmdStanLaplace}}, \code{\link{CmdStanMCMC}}, \code{\link{CmdStanVB}} } diff --git a/man/CmdStanModel.Rd b/man/CmdStanModel.Rd index 94ec10d61..fe4ca5265 100644 --- a/man/CmdStanModel.Rd +++ b/man/CmdStanModel.Rd @@ -80,6 +80,9 @@ fit_mcmc <- mod$sample( # Use 'posterior' package for summaries fit_mcmc$summary() +# Check sampling diagnostics +fit_mcmc$diagnostic_summary() + # Get posterior draws draws <- fit_mcmc$draws() print(draws) @@ -90,13 +93,8 @@ as_draws_df(draws) # Plot posterior using bayesplot (ggplot2) mcmc_hist(fit_mcmc$draws("theta")) -# Call CmdStan's diagnose and stansummary utilities -fit_mcmc$cmdstan_diagnose() -fit_mcmc$cmdstan_summary() - # For models fit using MCMC, if you like working with RStan's stanfit objects # then you can create one with rstan::read_stan_csv() - # stanfit <- rstan::read_stan_csv(fit_mcmc$output_files()) @@ -104,13 +102,16 @@ fit_mcmc$cmdstan_summary() # and also demonstrate specifying data as a path to a file instead of a list my_data_file <- file.path(cmdstan_path(), "examples/bernoulli/bernoulli.data.json") fit_optim <- mod$optimize(data = my_data_file, seed = 123) - fit_optim$summary() +# Run 'optimize' again with 'jacobian=TRUE' and then draw from laplace approximation +# to the posterior +fit_optim <- mod$optimize(data = my_data_file, jacobian = TRUE) +fit_laplace <- mod$laplace(data = my_data_file, mode = fit_optim, draws = 2000) +fit_laplace$summary() # Run 'variational' method to approximate the posterior (default is meanfield ADVI) fit_vb <- mod$variational(data = stan_data, seed = 123) - fit_vb$summary() # Plot approximate posterior using bayesplot diff --git a/man/CmdStanVB.Rd b/man/CmdStanVB.Rd index 4b4d53ada..9c8cd80b2 100644 --- a/man/CmdStanVB.Rd +++ b/man/CmdStanVB.Rd @@ -80,6 +80,7 @@ The Stan and CmdStan documentation: Other fitted model objects: \code{\link{CmdStanDiagnose}}, \code{\link{CmdStanGQ}}, +\code{\link{CmdStanLaplace}}, \code{\link{CmdStanMCMC}}, \code{\link{CmdStanMLE}} } diff --git a/man/cmdstan_model.Rd b/man/cmdstan_model.Rd index 1f48d1c86..6e9408dd1 100644 --- a/man/cmdstan_model.Rd +++ b/man/cmdstan_model.Rd @@ -74,6 +74,9 @@ fit_mcmc <- mod$sample( # Use 'posterior' package for summaries fit_mcmc$summary() +# Check sampling diagnostics +fit_mcmc$diagnostic_summary() + # Get posterior draws draws <- fit_mcmc$draws() print(draws) @@ -84,13 +87,8 @@ as_draws_df(draws) # Plot posterior using bayesplot (ggplot2) mcmc_hist(fit_mcmc$draws("theta")) -# Call CmdStan's diagnose and stansummary utilities -fit_mcmc$cmdstan_diagnose() -fit_mcmc$cmdstan_summary() - # For models fit using MCMC, if you like working with RStan's stanfit objects # then you can create one with rstan::read_stan_csv() - # stanfit <- rstan::read_stan_csv(fit_mcmc$output_files()) @@ -98,13 +96,16 @@ fit_mcmc$cmdstan_summary() # and also demonstrate specifying data as a path to a file instead of a list my_data_file <- file.path(cmdstan_path(), "examples/bernoulli/bernoulli.data.json") fit_optim <- mod$optimize(data = my_data_file, seed = 123) - fit_optim$summary() +# Run 'optimize' again with 'jacobian=TRUE' and then draw from laplace approximation +# to the posterior +fit_optim <- mod$optimize(data = my_data_file, jacobian = TRUE) +fit_laplace <- mod$laplace(data = my_data_file, mode = fit_optim, draws = 2000) +fit_laplace$summary() # Run 'variational' method to approximate the posterior (default is meanfield ADVI) fit_vb <- mod$variational(data = stan_data, seed = 123) - fit_vb$summary() # Plot approximate posterior using bayesplot diff --git a/man/cmdstanr-package.Rd b/man/cmdstanr-package.Rd index 5a07aec2c..9f2feb180 100644 --- a/man/cmdstanr-package.Rd +++ b/man/cmdstanr-package.Rd @@ -99,6 +99,9 @@ fit_mcmc <- mod$sample( # Use 'posterior' package for summaries fit_mcmc$summary() +# Check sampling diagnostics +fit_mcmc$diagnostic_summary() + # Get posterior draws draws <- fit_mcmc$draws() print(draws) @@ -109,13 +112,8 @@ as_draws_df(draws) # Plot posterior using bayesplot (ggplot2) mcmc_hist(fit_mcmc$draws("theta")) -# Call CmdStan's diagnose and stansummary utilities -fit_mcmc$cmdstan_diagnose() -fit_mcmc$cmdstan_summary() - # For models fit using MCMC, if you like working with RStan's stanfit objects # then you can create one with rstan::read_stan_csv() - # stanfit <- rstan::read_stan_csv(fit_mcmc$output_files()) @@ -123,13 +121,16 @@ fit_mcmc$cmdstan_summary() # and also demonstrate specifying data as a path to a file instead of a list my_data_file <- file.path(cmdstan_path(), "examples/bernoulli/bernoulli.data.json") fit_optim <- mod$optimize(data = my_data_file, seed = 123) - fit_optim$summary() +# Run 'optimize' again with 'jacobian=TRUE' and then draw from laplace approximation +# to the posterior +fit_optim <- mod$optimize(data = my_data_file, jacobian = TRUE) +fit_laplace <- mod$laplace(data = my_data_file, mode = fit_optim, draws = 2000) +fit_laplace$summary() # Run 'variational' method to approximate the posterior (default is meanfield ADVI) fit_vb <- mod$variational(data = stan_data, seed = 123) - fit_vb$summary() # Plot approximate posterior using bayesplot diff --git a/man/cmdstanr_example.Rd b/man/cmdstanr_example.Rd index 32e3b63e1..b390ce734 100644 --- a/man/cmdstanr_example.Rd +++ b/man/cmdstanr_example.Rd @@ -7,7 +7,7 @@ \usage{ cmdstanr_example( example = c("logistic", "schools", "schools_ncp"), - method = c("sample", "optimize", "variational", "diagnose"), + method = c("sample", "optimize", "laplace", "variational", "diagnose"), ..., quiet = TRUE ) diff --git a/man/fit-method-lp.Rd b/man/fit-method-lp.Rd index 4bf3eedb2..cc8b9a002 100644 --- a/man/fit-method-lp.Rd +++ b/man/fit-method-lp.Rd @@ -12,16 +12,17 @@ lp_approx() } \value{ A numeric vector with length equal to the number of (post-warmup) -draws for MCMC and variational inference, and length equal to \code{1} for -optimization. +draws or length equal to \code{1} for optimization. } \description{ The \verb{$lp()} method extracts \code{lp__}, the total log probability (\code{target}) accumulated in the model block of the Stan program. For variational inference the log density of the variational approximation to -the posterior is also available via the \verb{$lp_approx()} method. +the posterior is available via the \verb{$lp_approx()} method. For +Laplace approximation the unnormalized density of the approximation to +the posterior is available via the \verb{$lp_approx()} method. -See the \href{https://mc-stan.org/docs/2_23/reference-manual/sampling-statements-section.html}{Log Probability Increment vs. Sampling Statement} +See the \href{https://mc-stan.org/docs/reference-manual/sampling-statements.html}{Log Probability Increment vs. Sampling Statement} section of the Stan Reference Manual for details on when normalizing constants are dropped from log probability calculations. } @@ -32,10 +33,13 @@ This will in general be different than the unnormalized model log density evaluated at a posterior draw (which is on the constrained space). \code{lp__} is intended to diagnose sampling efficiency and evaluate approximations. -\code{lp_approx__} is the log density of the variational approximation to \code{lp__} -(also on the unconstrained space). It is exposed in the variational method -for performing the checks described in Yao et al. (2018) and implemented in -the \pkg{loo} package. +For variational inference \code{lp_approx__} is the log density of the variational +approximation to \code{lp__} (also on the unconstrained space). It is exposed in +the variational method for performing the checks described in Yao et al. +(2018) and implemented in the \pkg{loo} package. + +For Laplace approximation \code{lp_approx__} is the the unnormalized density of +the Laplace approximation. } \examples{ @@ -57,5 +61,5 @@ work?: Evaluating variational inference. \emph{Proceedings of the 35th International Conference on Machine Learning}, PMLR 80:5581–5590. } \seealso{ -\code{\link{CmdStanMCMC}}, \code{\link{CmdStanMLE}}, \code{\link{CmdStanVB}} +\code{\link{CmdStanMCMC}}, \code{\link{CmdStanMLE}}, \code{\link{CmdStanLaplace}}, \code{\link{CmdStanVB}} } diff --git a/man/fit-method-summary.Rd b/man/fit-method-summary.Rd index 89cc06c9a..787feb2ad 100644 --- a/man/fit-method-summary.Rd +++ b/man/fit-method-summary.Rd @@ -75,5 +75,5 @@ fit$print(c("alpha", "beta"), var2 = ~var(as.vector(.x))) } \seealso{ -\code{\link{CmdStanMCMC}}, \code{\link{CmdStanMLE}}, \code{\link{CmdStanVB}}, \code{\link{CmdStanGQ}} +\code{\link{CmdStanMCMC}}, \code{\link{CmdStanMLE}}, \code{\link{CmdStanLaplace}}, \code{\link{CmdStanVB}}, \code{\link{CmdStanGQ}} } diff --git a/man/fit-method-time.Rd b/man/fit-method-time.Rd index 4ed637d9f..38944fd77 100644 --- a/man/fit-method-time.Rd +++ b/man/fit-method-time.Rd @@ -20,18 +20,25 @@ and \code{"total"}. \description{ Report the run time in seconds. For MCMC additional information is provided about the run times of individual chains and the warmup and -sampling phases. +sampling phases. For Laplace approximation the time only include the time +for drawing the approximate sample and does not include the time +taken to run the \verb{$optimize()} method. } \examples{ \dontrun{ fit_mcmc <- cmdstanr_example("logistic", method = "sample") fit_mcmc$time() -fit_mle <- cmdstanr_example("logistic", method = "optimize") -fit_mle$time() - fit_vb <- cmdstanr_example("logistic", method = "variational") fit_vb$time() + +fit_mle <- cmdstanr_example("logistic", method = "optimize", jacobian = TRUE) +fit_mle$time() + +# use fit_mle to draw samples from laplace approximation +fit_laplace <- cmdstanr_example("logistic", method = "laplace", mode = fit_mle) +fit_laplace$time() # just time for drawing sample not for running optimize +fit_laplace$time()$total + fit_mle$time()$total # total time } } diff --git a/man/model-method-check_syntax.Rd b/man/model-method-check_syntax.Rd index 9193a684d..20d643320 100644 --- a/man/model-method-check_syntax.Rd +++ b/man/model-method-check_syntax.Rd @@ -83,6 +83,7 @@ Other CmdStanModel methods: \code{\link{model-method-expose_functions}}, \code{\link{model-method-format}}, \code{\link{model-method-generate-quantities}}, +\code{\link{model-method-laplace}}, \code{\link{model-method-optimize}}, \code{\link{model-method-sample_mpi}}, \code{\link{model-method-sample}}, diff --git a/man/model-method-compile.Rd b/man/model-method-compile.Rd index b4d7b35f8..481f7bee2 100644 --- a/man/model-method-compile.Rd +++ b/man/model-method-compile.Rd @@ -150,6 +150,7 @@ Other CmdStanModel methods: \code{\link{model-method-expose_functions}}, \code{\link{model-method-format}}, \code{\link{model-method-generate-quantities}}, +\code{\link{model-method-laplace}}, \code{\link{model-method-optimize}}, \code{\link{model-method-sample_mpi}}, \code{\link{model-method-sample}}, diff --git a/man/model-method-diagnose.Rd b/man/model-method-diagnose.Rd index 7f9cde7d4..15e7e21c4 100644 --- a/man/model-method-diagnose.Rd +++ b/man/model-method-diagnose.Rd @@ -125,6 +125,7 @@ Other CmdStanModel methods: \code{\link{model-method-expose_functions}}, \code{\link{model-method-format}}, \code{\link{model-method-generate-quantities}}, +\code{\link{model-method-laplace}}, \code{\link{model-method-optimize}}, \code{\link{model-method-sample_mpi}}, \code{\link{model-method-sample}}, diff --git a/man/model-method-expose_functions.Rd b/man/model-method-expose_functions.Rd index c01aa3c1a..066c7bad3 100644 --- a/man/model-method-expose_functions.Rd +++ b/man/model-method-expose_functions.Rd @@ -74,6 +74,7 @@ Other CmdStanModel methods: \code{\link{model-method-diagnose}}, \code{\link{model-method-format}}, \code{\link{model-method-generate-quantities}}, +\code{\link{model-method-laplace}}, \code{\link{model-method-optimize}}, \code{\link{model-method-sample_mpi}}, \code{\link{model-method-sample}}, diff --git a/man/model-method-format.Rd b/man/model-method-format.Rd index 1d7ed11bd..e3b3f3cfb 100644 --- a/man/model-method-format.Rd +++ b/man/model-method-format.Rd @@ -81,6 +81,7 @@ Other CmdStanModel methods: \code{\link{model-method-diagnose}}, \code{\link{model-method-expose_functions}}, \code{\link{model-method-generate-quantities}}, +\code{\link{model-method-laplace}}, \code{\link{model-method-optimize}}, \code{\link{model-method-sample_mpi}}, \code{\link{model-method-sample}}, diff --git a/man/model-method-generate-quantities.Rd b/man/model-method-generate-quantities.Rd index 5a80bb736..1286f9fe9 100644 --- a/man/model-method-generate-quantities.Rd +++ b/man/model-method-generate-quantities.Rd @@ -174,6 +174,7 @@ Other CmdStanModel methods: \code{\link{model-method-diagnose}}, \code{\link{model-method-expose_functions}}, \code{\link{model-method-format}}, +\code{\link{model-method-laplace}}, \code{\link{model-method-optimize}}, \code{\link{model-method-sample_mpi}}, \code{\link{model-method-sample}}, diff --git a/man/model-method-laplace.Rd b/man/model-method-laplace.Rd new file mode 100644 index 000000000..a79e027b3 --- /dev/null +++ b/man/model-method-laplace.Rd @@ -0,0 +1,214 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/model.R +\name{model-method-laplace} +\alias{model-method-laplace} +\alias{laplace} +\title{Run Stan's laplace algorithm} +\usage{ +laplace( + data = NULL, + seed = NULL, + refresh = NULL, + init = NULL, + output_dir = NULL, + output_basename = NULL, + sig_figs = NULL, + threads = NULL, + opencl_ids = NULL, + mode = NULL, + jacobian = TRUE, + draws = NULL, + opt_args = NULL +) +} +\arguments{ +\item{data}{(multiple options) The data to use for the variables specified in +the data block of the Stan program. One of the following: +\itemize{ +\item 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 \code{\link[=write_stan_json]{write_stan_json()}}. See +\code{\link[=write_stan_json]{write_stan_json()}} for details on the conversions performed on \R objects +before they are passed to Stan. +\item 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. +\item \code{NULL} or an empty list if the Stan program has no data block. +}} + +\item{seed}{(positive integer(s)) A seed for the (P)RNG to pass to CmdStan. +In the case of multi-chain sampling the single \code{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 \code{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 \emph{different} data for each +chain.} + +\item{refresh}{(non-negative integer) The number of iterations between +printed screen updates. If \code{refresh = 0}, only error messages will be +printed.} + +\item{init}{(multiple options) The initialization method to use for the +variables declared in the parameters block of the Stan program. One of +the following: +\itemize{ +\item A real number \code{x>0}. This initializes \emph{all} parameters randomly between +\verb{[-x,x]} on the \emph{unconstrained} parameter space.; +\item The number \code{0}. This initializes \emph{all} parameters to \code{0}; +\item A character vector of paths (one per chain) to JSON or Rdump files +containing initial values for all or some parameters. See +\code{\link[=write_stan_json]{write_stan_json()}} to write \R objects to JSON files compatible with +CmdStan. +\item A list of lists containing initial values for all or some parameters. For +MCMC the list should contain a sublist for each chain. For optimization and +variational inference there should be just one sublist. The sublists should +have named elements corresponding to the parameters for which you are +specifying initial values. See \strong{Examples}. +\item 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 \code{chain_id}. For MCMC, if the function +has argument \code{chain_id} it will be supplied with the chain id (from 1 to +number of chains) when called to generate the initial values. See +\strong{Examples}. +}} + +\item{output_dir}{(string) A path to a directory where CmdStan should write +its output CSV files. For interactive use this can typically be left at +\code{NULL} (temporary directory) since CmdStanR makes the CmdStan output +(posterior draws and diagnostics) available in \R via methods of the fitted +model objects. The behavior of \code{output_dir} is as follows: +\itemize{ +\item If \code{NULL} (the default), then the CSV files are written to a temporary +directory and only saved permanently if the user calls one of the \verb{$save_*} +methods of the fitted model object (e.g., +\code{\link[=fit-method-save_output_files]{$save_output_files()}}). These temporary +files are removed when the fitted model object is +\link[base:gc]{garbage collected} (manually or automatically). +\item If a path, then the files are created in \code{output_dir} with names +corresponding to the defaults used by \verb{$save_output_files()}. +}} + +\item{output_basename}{(string) A string to use as a prefix for the names of +the output CSV files of CmdStan. If \code{NULL} (the default), the basename of +the output CSV files will be comprised from the model name, timestamp, and +5 random characters.} + +\item{sig_figs}{(positive integer) The number of significant figures used +when storing the output values. By default, CmdStan represent the output +values with 6 significant figures. The upper limit for \code{sig_figs} is 18. +Increasing this value will result in larger output CSV files and thus an +increased usage of disk space.} + +\item{threads}{(positive integer) If the model was +\link[=model-method-compile]{compiled} with threading support, the number of +threads to use in parallelized sections (e.g., when +using the Stan functions \code{reduce_sum()} or \code{map_rect()}).} + +\item{opencl_ids}{(integer vector of length 2) The platform and +device IDs of the OpenCL device to use for fitting. The model must +be compiled with \code{cpp_options = list(stan_opencl = TRUE)} for this +argument to have an effect.} + +\item{mode}{(multiple options) The mode to center the approximation at. One +of the following: +\itemize{ +\item A \code{\link{CmdStanMLE}} object from a previous run of \code{\link[=model-method-optimize]{$optimize()}}. +\item The path to a CmdStan CSV file from running optimization. +\item \code{NULL}, in which case \link[=fit-method-optimize]{$optimize()} will be run +with \code{jacobian=jacobian} (see the \code{jacobian} argument below). +} + +In all cases the total time reported by \code{\link[=fit-method-time]{$time()}} will be +the time of the Laplace sampling step only and does not include the time +taken to run the \verb{$optimize()} method.} + +\item{jacobian}{(logical) Whether or not to enable the Jacobian adjustment +for constrained parameters. The default is \code{TRUE}. See the +\href{https://mc-stan.org/docs/cmdstan-guide/laplace-sampling.html}{Laplace Sampling} +section of the CmdStan User's Guide for more details. If \code{mode} is not +\code{NULL} then the value of \code{jacobian} must match the value used when +optimization was originally run. If \code{mode} is \code{NULL} then the value of +\code{jacobian} specified here is used when running optimization.} + +\item{draws}{(positive integer) The number of draws to take.} + +\item{opt_args}{(named list) A named list of optional arguments to pass to +\link[=fit-method-optimize]{$optimize()} if \code{mode=NULL}.} + +\item{save_latent_dynamics}{(logical) Should auxiliary diagnostic information +about the latent dynamics be written to temporary diagnostic CSV files? +This argument replaces CmdStan's \code{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 \code{FALSE}, which is appropriate for almost every use case. To save the +temporary files created when \code{save_latent_dynamics=TRUE} see the +\code{\link[=fit-method-save_latent_dynamics_files]{$save_latent_dynamics_files()}} +method.} +} +\value{ +A \code{\link{CmdStanLaplace}} object. +} +\description{ +The \verb{$laplace()} method of a \code{\link{CmdStanModel}} object produces a +sample from a normal approximation centered at the mode of a distribution +in the unconstrained space. If the mode is a maximum a posteriori (MAP) +estimate, the samples provide an estimate of the mean and standard +deviation of the posterior distribution. If the mode is a maximum +likelihood estimate (MLE), the sample provides an estimate of the standard +error of the likelihood. Whether the mode is the MAP or MLE depends on +the value of the \code{jacobian} argument when running optimization. See the +\href{https://mc-stan.org/docs/cmdstan-guide/laplace-sampling.html}{Laplace Sampling} +section of the CmdStan User's Guide for more details. + +Any argument left as \code{NULL} will default to the default value used by the +installed version of CmdStan. See the +\href{https://mc-stan.org/docs/cmdstan-guide/}{CmdStan User’s Guide} +for more details on the default arguments. +} +\examples{ +\dontrun{ +file <- file.path(cmdstan_path(), "examples/bernoulli/bernoulli.stan") +mod <- cmdstan_model(file) +mod$print() + +stan_data <- list(N = 10, y = c(0,1,0,0,0,0,0,0,0,1)) +fit_mode <- mod$optimize(data = stan_data, jacobian = TRUE) +fit_laplace <- mod$laplace(data = stan_data, mode = fit_mode) +fit_laplace$summary() + +# if mode isn't specified optimize is run internally first +fit_laplace <- mod$laplace(data = stan_data) +fit_laplace$summary() + +# plot approximate posterior +bayesplot::mcmc_hist(fit_laplace$draws("theta")) +} + + +} +\seealso{ +The CmdStanR website +(\href{https://mc-stan.org/cmdstanr/}{mc-stan.org/cmdstanr}) for online +documentation and tutorials. + +The Stan and CmdStan documentation: +\itemize{ +\item Stan documentation: \href{https://mc-stan.org/users/documentation/}{mc-stan.org/users/documentation} +\item CmdStan User’s Guide: \href{https://mc-stan.org/docs/cmdstan-guide/}{mc-stan.org/docs/cmdstan-guide} +} + +Other CmdStanModel methods: +\code{\link{model-method-check_syntax}}, +\code{\link{model-method-compile}}, +\code{\link{model-method-diagnose}}, +\code{\link{model-method-expose_functions}}, +\code{\link{model-method-format}}, +\code{\link{model-method-generate-quantities}}, +\code{\link{model-method-optimize}}, +\code{\link{model-method-sample_mpi}}, +\code{\link{model-method-sample}}, +\code{\link{model-method-variables}}, +\code{\link{model-method-variational}} +} +\concept{CmdStanModel methods} diff --git a/man/model-method-optimize.Rd b/man/model-method-optimize.Rd index e2444673b..7a04d96a9 100644 --- a/man/model-method-optimize.Rd +++ b/man/model-method-optimize.Rd @@ -138,7 +138,9 @@ constrained variables. By default this is \code{FALSE}, meaning optimization yields the (regularized) maximum likelihood estimate. Setting it to \code{TRUE} yields the maximum a posteriori estimate. See the \href{https://mc-stan.org/docs/cmdstan-guide/maximum-likelihood-estimation.html}{Maximum Likelihood Estimation} -section of the CmdStan User's Guide for more details.} +section of the CmdStan User's Guide for more details. +For use later with \code{\link[=model-method-laplace]{$laplace()}} the \code{jacobian} +argument should typically be set to \code{TRUE}.} \item{init_alpha}{(positive real) The initial step size parameter.} @@ -207,6 +209,9 @@ fit_mcmc <- mod$sample( # Use 'posterior' package for summaries fit_mcmc$summary() +# Check sampling diagnostics +fit_mcmc$diagnostic_summary() + # Get posterior draws draws <- fit_mcmc$draws() print(draws) @@ -217,13 +222,8 @@ as_draws_df(draws) # Plot posterior using bayesplot (ggplot2) mcmc_hist(fit_mcmc$draws("theta")) -# Call CmdStan's diagnose and stansummary utilities -fit_mcmc$cmdstan_diagnose() -fit_mcmc$cmdstan_summary() - # For models fit using MCMC, if you like working with RStan's stanfit objects # then you can create one with rstan::read_stan_csv() - # stanfit <- rstan::read_stan_csv(fit_mcmc$output_files()) @@ -231,13 +231,16 @@ fit_mcmc$cmdstan_summary() # and also demonstrate specifying data as a path to a file instead of a list my_data_file <- file.path(cmdstan_path(), "examples/bernoulli/bernoulli.data.json") fit_optim <- mod$optimize(data = my_data_file, seed = 123) - fit_optim$summary() +# Run 'optimize' again with 'jacobian=TRUE' and then draw from laplace approximation +# to the posterior +fit_optim <- mod$optimize(data = my_data_file, jacobian = TRUE) +fit_laplace <- mod$laplace(data = my_data_file, mode = fit_optim, draws = 2000) +fit_laplace$summary() # Run 'variational' method to approximate the posterior (default is meanfield ADVI) fit_vb <- mod$variational(data = stan_data, seed = 123) - fit_vb$summary() # Plot approximate posterior using bayesplot @@ -304,6 +307,7 @@ Other CmdStanModel methods: \code{\link{model-method-expose_functions}}, \code{\link{model-method-format}}, \code{\link{model-method-generate-quantities}}, +\code{\link{model-method-laplace}}, \code{\link{model-method-sample_mpi}}, \code{\link{model-method-sample}}, \code{\link{model-method-variables}}, diff --git a/man/model-method-sample.Rd b/man/model-method-sample.Rd index e0955abb4..1c415f6a6 100644 --- a/man/model-method-sample.Rd +++ b/man/model-method-sample.Rd @@ -318,6 +318,9 @@ fit_mcmc <- mod$sample( # Use 'posterior' package for summaries fit_mcmc$summary() +# Check sampling diagnostics +fit_mcmc$diagnostic_summary() + # Get posterior draws draws <- fit_mcmc$draws() print(draws) @@ -328,13 +331,8 @@ as_draws_df(draws) # Plot posterior using bayesplot (ggplot2) mcmc_hist(fit_mcmc$draws("theta")) -# Call CmdStan's diagnose and stansummary utilities -fit_mcmc$cmdstan_diagnose() -fit_mcmc$cmdstan_summary() - # For models fit using MCMC, if you like working with RStan's stanfit objects # then you can create one with rstan::read_stan_csv() - # stanfit <- rstan::read_stan_csv(fit_mcmc$output_files()) @@ -342,13 +340,16 @@ fit_mcmc$cmdstan_summary() # and also demonstrate specifying data as a path to a file instead of a list my_data_file <- file.path(cmdstan_path(), "examples/bernoulli/bernoulli.data.json") fit_optim <- mod$optimize(data = my_data_file, seed = 123) - fit_optim$summary() +# Run 'optimize' again with 'jacobian=TRUE' and then draw from laplace approximation +# to the posterior +fit_optim <- mod$optimize(data = my_data_file, jacobian = TRUE) +fit_laplace <- mod$laplace(data = my_data_file, mode = fit_optim, draws = 2000) +fit_laplace$summary() # Run 'variational' method to approximate the posterior (default is meanfield ADVI) fit_vb <- mod$variational(data = stan_data, seed = 123) - fit_vb$summary() # Plot approximate posterior using bayesplot @@ -415,6 +416,7 @@ Other CmdStanModel methods: \code{\link{model-method-expose_functions}}, \code{\link{model-method-format}}, \code{\link{model-method-generate-quantities}}, +\code{\link{model-method-laplace}}, \code{\link{model-method-optimize}}, \code{\link{model-method-sample_mpi}}, \code{\link{model-method-variables}}, diff --git a/man/model-method-sample_mpi.Rd b/man/model-method-sample_mpi.Rd index 89981273f..8c17a4dee 100644 --- a/man/model-method-sample_mpi.Rd +++ b/man/model-method-sample_mpi.Rd @@ -314,6 +314,7 @@ Other CmdStanModel methods: \code{\link{model-method-expose_functions}}, \code{\link{model-method-format}}, \code{\link{model-method-generate-quantities}}, +\code{\link{model-method-laplace}}, \code{\link{model-method-optimize}}, \code{\link{model-method-sample}}, \code{\link{model-method-variables}}, diff --git a/man/model-method-variables.Rd b/man/model-method-variables.Rd index aa609ddda..b5b85f1c2 100644 --- a/man/model-method-variables.Rd +++ b/man/model-method-variables.Rd @@ -43,6 +43,7 @@ Other CmdStanModel methods: \code{\link{model-method-expose_functions}}, \code{\link{model-method-format}}, \code{\link{model-method-generate-quantities}}, +\code{\link{model-method-laplace}}, \code{\link{model-method-optimize}}, \code{\link{model-method-sample_mpi}}, \code{\link{model-method-sample}}, diff --git a/man/model-method-variational.Rd b/man/model-method-variational.Rd index 21c92fe24..4ad40b618 100644 --- a/man/model-method-variational.Rd +++ b/man/model-method-variational.Rd @@ -210,6 +210,9 @@ fit_mcmc <- mod$sample( # Use 'posterior' package for summaries fit_mcmc$summary() +# Check sampling diagnostics +fit_mcmc$diagnostic_summary() + # Get posterior draws draws <- fit_mcmc$draws() print(draws) @@ -220,13 +223,8 @@ as_draws_df(draws) # Plot posterior using bayesplot (ggplot2) mcmc_hist(fit_mcmc$draws("theta")) -# Call CmdStan's diagnose and stansummary utilities -fit_mcmc$cmdstan_diagnose() -fit_mcmc$cmdstan_summary() - # For models fit using MCMC, if you like working with RStan's stanfit objects # then you can create one with rstan::read_stan_csv() - # stanfit <- rstan::read_stan_csv(fit_mcmc$output_files()) @@ -234,13 +232,16 @@ fit_mcmc$cmdstan_summary() # and also demonstrate specifying data as a path to a file instead of a list my_data_file <- file.path(cmdstan_path(), "examples/bernoulli/bernoulli.data.json") fit_optim <- mod$optimize(data = my_data_file, seed = 123) - fit_optim$summary() +# Run 'optimize' again with 'jacobian=TRUE' and then draw from laplace approximation +# to the posterior +fit_optim <- mod$optimize(data = my_data_file, jacobian = TRUE) +fit_laplace <- mod$laplace(data = my_data_file, mode = fit_optim, draws = 2000) +fit_laplace$summary() # Run 'variational' method to approximate the posterior (default is meanfield ADVI) fit_vb <- mod$variational(data = stan_data, seed = 123) - fit_vb$summary() # Plot approximate posterior using bayesplot @@ -307,6 +308,7 @@ Other CmdStanModel methods: \code{\link{model-method-expose_functions}}, \code{\link{model-method-format}}, \code{\link{model-method-generate-quantities}}, +\code{\link{model-method-laplace}}, \code{\link{model-method-optimize}}, \code{\link{model-method-sample_mpi}}, \code{\link{model-method-sample}}, diff --git a/man/read_cmdstan_csv.Rd b/man/read_cmdstan_csv.Rd index a986843e9..e37a1110d 100644 --- a/man/read_cmdstan_csv.Rd +++ b/man/read_cmdstan_csv.Rd @@ -92,7 +92,8 @@ following components: \item \code{point_estimates}: Point estimates for the model parameters. } -For \link[=model-method-variational]{variational inference} the returned list also +For \link[=model-method-laplace]{laplace} and +\link[=model-method-variational]{variational inference} the returned list also includes the following components: \itemize{ \item \code{draws}: A \code{\link[posterior:draws_matrix]{draws_matrix}} (or different format From 0b2a677fcc8c66585d7ffcc2cfa264094a681f18 Mon Sep 17 00:00:00 2001 From: jgabry Date: Sat, 29 Jul 2023 19:19:29 -0600 Subject: [PATCH 02/21] Update model.R --- R/model.R | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/R/model.R b/R/model.R index 2d3cf98e6..8ebaede12 100644 --- a/R/model.R +++ b/R/model.R @@ -1483,6 +1483,7 @@ CmdStanModel$set("public", name = "optimize", value = optimize) #' #' @template model-common-args #' @inheritParams model-method-optimize +#' @param save_latent_dynamics Ignored for this method. #' @param mode (multiple options) The mode to center the approximation at. One #' of the following: #' * A [`CmdStanMLE`] object from a previous run of [`$optimize()`][model-method-optimize]. @@ -1531,15 +1532,16 @@ laplace <- function(data = NULL, seed = NULL, refresh = NULL, init = NULL, + save_latent_dynamics = FALSE, output_dir = NULL, output_basename = NULL, sig_figs = NULL, threads = NULL, opencl_ids = NULL, mode = NULL, + opt_args = NULL, jacobian = TRUE, # different than for optimize! - draws = NULL, - opt_args = NULL) { + draws = NULL) { if (cmdstan_version() < "2.32") { stop("This method is only available in cmdstan >= 2.32", call. = FALSE) } From 9ea8fd2fb6fac8d04fa94402e987bfbd24673722 Mon Sep 17 00:00:00 2001 From: jgabry Date: Sat, 29 Jul 2023 19:19:31 -0600 Subject: [PATCH 03/21] Update model-method-laplace.Rd --- man/model-method-laplace.Rd | 23 ++++++++--------------- 1 file changed, 8 insertions(+), 15 deletions(-) diff --git a/man/model-method-laplace.Rd b/man/model-method-laplace.Rd index a79e027b3..af79d05c8 100644 --- a/man/model-method-laplace.Rd +++ b/man/model-method-laplace.Rd @@ -10,15 +10,16 @@ laplace( seed = NULL, refresh = NULL, init = NULL, + save_latent_dynamics = FALSE, output_dir = NULL, output_basename = NULL, sig_figs = NULL, threads = NULL, opencl_ids = NULL, mode = NULL, + opt_args = NULL, jacobian = TRUE, - draws = NULL, - opt_args = NULL + draws = NULL ) } \arguments{ @@ -73,6 +74,8 @@ number of chains) when called to generate the initial values. See \strong{Examples}. }} +\item{save_latent_dynamics}{Ignored for this method.} + \item{output_dir}{(string) A path to a directory where CmdStan should write its output CSV files. For interactive use this can typically be left at \code{NULL} (temporary directory) since CmdStanR makes the CmdStan output @@ -123,6 +126,9 @@ In all cases the total time reported by \code{\link[=fit-method-time]{$time()}} the time of the Laplace sampling step only and does not include the time taken to run the \verb{$optimize()} method.} +\item{opt_args}{(named list) A named list of optional arguments to pass to +\link[=fit-method-optimize]{$optimize()} if \code{mode=NULL}.} + \item{jacobian}{(logical) Whether or not to enable the Jacobian adjustment for constrained parameters. The default is \code{TRUE}. See the \href{https://mc-stan.org/docs/cmdstan-guide/laplace-sampling.html}{Laplace Sampling} @@ -132,19 +138,6 @@ optimization was originally run. If \code{mode} is \code{NULL} then the value of \code{jacobian} specified here is used when running optimization.} \item{draws}{(positive integer) The number of draws to take.} - -\item{opt_args}{(named list) A named list of optional arguments to pass to -\link[=fit-method-optimize]{$optimize()} if \code{mode=NULL}.} - -\item{save_latent_dynamics}{(logical) Should auxiliary diagnostic information -about the latent dynamics be written to temporary diagnostic CSV files? -This argument replaces CmdStan's \code{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 \code{FALSE}, which is appropriate for almost every use case. To save the -temporary files created when \code{save_latent_dynamics=TRUE} see the -\code{\link[=fit-method-save_latent_dynamics_files]{$save_latent_dynamics_files()}} -method.} } \value{ A \code{\link{CmdStanLaplace}} object. 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