diff options
| -rw-r--r-- | DESCRIPTION | 4 | ||||
| -rw-r--r-- | NAMESPACE | 1 | ||||
| -rw-r--r-- | R/mkinfit.R | 17 | ||||
| -rw-r--r-- | R/mkinmod.R | 4 | ||||
| -rw-r--r-- | R/mkinpredict.R | 107 | ||||
| -rw-r--r-- | R/nlme.mmkin.R | 14 | ||||
| -rw-r--r-- | R/saem.R | 22 | ||||
| -rw-r--r-- | man/mkinmod.Rd | 4 | ||||
| -rw-r--r-- | man/mkinpredict.Rd | 21 | ||||
| -rw-r--r-- | man/saem.Rd | 10 | ||||
| -rw-r--r-- | tests/testthat/test_dmta.R | 3 | ||||
| -rw-r--r-- | vignettes/mkin.html | 4 | ||||
| -rw-r--r-- | vignettes/web_only/saem_benchmarks.html | 59 |
13 files changed, 203 insertions, 67 deletions
diff --git a/DESCRIPTION b/DESCRIPTION index 50dba006..2a3f231e 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,8 +1,8 @@ Package: mkin Type: Package Title: Kinetic Evaluation of Chemical Degradation Data -Version: 1.2.2 -Date: 2023-01-08 +Version: 1.3.0 +Date: 2023-02-13 Authors@R: c( person("Johannes", "Ranke", role = c("aut", "cre", "cph"), email = "johannes.ranke@jrwb.de", @@ -150,7 +150,6 @@ export(summary_listing) export(tex_listing) export(transform_odeparms) export(which.best) -import(deSolve) import(graphics) import(nlme) importFrom(R6,R6Class) diff --git a/R/mkinfit.R b/R/mkinfit.R index 693778fd..0d9246dd 100644 --- a/R/mkinfit.R +++ b/R/mkinfit.R @@ -500,6 +500,15 @@ mkinfit <- function(mkinmod, observed, } } + # Get native symbol before iterations info for speed + call_lsoda <- getNativeSymbolInfo("call_lsoda", PACKAGE = "deSolve") + if (solution_type == "deSolve" & use_compiled[1] != FALSE) { + mkinmod$diffs_address <- getNativeSymbolInfo("diffs", + PACKAGE = mkinmod$dll_info[["name"]])$address + mkinmod$initpar_address <- getNativeSymbolInfo("initpar", + PACKAGE = mkinmod$dll_info[["name"]])$address + } + # Get the error model and the algorithm for fitting err_mod <- match.arg(error_model) error_model_algorithm = match.arg(error_model_algorithm) @@ -610,7 +619,8 @@ mkinfit <- function(mkinmod, observed, solution_type = solution_type, use_compiled = use_compiled, method.ode = method.ode, - atol = atol, rtol = rtol, ...) + atol = atol, rtol = rtol, + call_lsoda = call_lsoda, ...) observed_index <- cbind(as.character(observed$time), as.character(observed$name)) observed$predicted <- out[observed_index] @@ -892,7 +902,10 @@ mkinfit <- function(mkinmod, observed, fit$calls <- calls fit$time <- fit_time - # We also need the model and a model name for summary and plotting + # We also need the model and a model name for summary and plotting, + # but without address info that will become invalid + mkinmod$diffs_address <- NULL + mkinmod$initpar_address <- NULL fit$mkinmod <- mkinmod fit$mkinmod$name <- mkinmod_name fit$obs_vars <- obs_vars diff --git a/R/mkinmod.R b/R/mkinmod.R index 47307ab7..215dbed6 100644 --- a/R/mkinmod.R +++ b/R/mkinmod.R @@ -45,7 +45,9 @@ #' @param dll_dir Directory where an DLL object, if generated internally by #' [inline::cfunction()], should be saved. The DLL will only be stored in a #' permanent location for use in future sessions, if 'dll_dir' and 'name' -#' are specified. +#' are specified. This is helpful if fit objects are cached e.g. by knitr, +#' as the cache remains functional across sessions if the DLL is stored in +#' a user defined location. #' @param unload If a DLL from the target location in 'dll_dir' is already #' loaded, should that be unloaded first? #' @param overwrite If a file exists at the target DLL location in 'dll_dir', diff --git a/R/mkinpredict.R b/R/mkinpredict.R index 0dc9cf55..11a3b35b 100644 --- a/R/mkinpredict.R +++ b/R/mkinpredict.R @@ -19,26 +19,24 @@ #' @param solution_type The method that should be used for producing the #' predictions. This should generally be "analytical" if there is only one #' observed variable, and usually "deSolve" in the case of several observed -#' variables. The third possibility "eigen" is faster but not applicable to -#' some models e.g. using FOMC for the parent compound. +#' variables. The third possibility "eigen" is fast in comparison to uncompiled +#' ODE models, but not applicable to some models, e.g. using FOMC for the +#' parent compound. #' @param method.ode The solution method passed via [mkinpredict] to [ode]] in -#' case the solution type is "deSolve". The default "lsoda" is performant, but -#' sometimes fails to converge. +#' case the solution type is "deSolve" and we are not using compiled code. #' @param use_compiled If set to \code{FALSE}, no compiled version of the #' [mkinmod] model is used, even if is present. -#' @param atol Absolute error tolerance, passed to [ode]. Default is 1e-8, -#' lower than in [lsoda]. -#' @param rtol Absolute error tolerance, passed to [ode]. Default is 1e-10, -#' much lower than in [lsoda]. -#' @param maxsteps Maximum number of steps, passed to [ode]. +#' @param atol Absolute error tolerance, passed to the ode solver. +#' @param rtol Absolute error tolerance, passed to the ode solver. +#' @param maxsteps Maximum number of steps, passed to the ode solver. #' @param map_output Boolean to specify if the output should list values for #' the observed variables (default) or for all state variables (if set to #' FALSE). Setting this to FALSE has no effect for analytical solutions, #' as these always return mapped output. #' @param na_stop Should it be an error if [ode] returns NaN values +#' @param call_lsoda The address of the compiled function "call_lsoda" #' @param \dots Further arguments passed to the ode solver in case such a #' solver is used. -#' @import deSolve #' @return A matrix with the numeric solution in wide format #' @author Johannes Ranke #' @examples @@ -116,9 +114,10 @@ mkinpredict.mkinmod <- function(x, outtimes = seq(0, 120, by = 0.1), solution_type = "deSolve", use_compiled = "auto", - method.ode = "lsoda", atol = 1e-8, rtol = 1e-10, maxsteps = 20000, + method.ode = "lsoda", atol = 1e-8, rtol = 1e-10, maxsteps = 20000L, map_output = TRUE, na_stop = TRUE, + call_lsoda = NULL, ...) { @@ -173,20 +172,80 @@ mkinpredict.mkinmod <- function(x, if (solution_type == "deSolve") { if (!is.null(x$cf) & use_compiled[1] != FALSE) { - out <- deSolve::ode( - y = odeini, - times = outtimes, - func = "diffs", - initfunc = "initpar", - dllname = if (is.null(x$dll_info)) inline::getDynLib(x$cf)[["name"]] - else x$dll_info[["name"]], - parms = odeparms[x$parms], # Order matters when using compiled models - method = method.ode, - atol = atol, - rtol = rtol, - maxsteps = maxsteps, - ... +# out <- deSolve::ode( +# y = odeini, +# times = outtimes, +# func = "diffs", +# initfunc = "initpar", +# dllname = x$dll_info[["name"]], +# parms = odeparms[x$parms], # Order matters when using compiled models +# method = method.ode, +# atol = atol, +# rtol = rtol, +# maxsteps = maxsteps, +# ... +# ) +# + # Prepare call to "call_lsoda" + # Simplified code from deSolve::lsoda() adapted to our use case + if (is.null(call_lsoda)) { + call_lsoda <- getNativeSymbolInfo("call_lsoda", PACKAGE = "deSolve") + } + if (is.null(x$diffs_address)) { + x$diffs_address <- getNativeSymbolInfo("diffs", + PACKAGE = x$dll_info[["name"]])$address + x$initpar_address <- getNativeSymbolInfo("initpar", + PACKAGE = x$dll_info[["name"]])$address + } + rwork <- vector("double", 20) + rwork[] <- 0. + rwork[6] <- max(abs(diff(outtimes))) + + iwork <- vector("integer", 20) + iwork[] <- 0 + iwork[6] <- maxsteps + + n <- length(odeini) + lmat <- n^2 + 2 # from deSolve::lsoda(), for Jacobian type full, internal (2) + # hard-coded default values of lsoda(): + maxordn <- 12L + maxords <- 5L + lrn <- 20 + n * (maxordn + 1) + 3 * n # length in case non-stiff method + lrs <- 20 + n * (maxords + 1) + 3 * n + lmat # length in case stiff method + lrw <- max(lrn, lrs) # actual length: max of both + liw <- 20 + n + + on.exit(.C("unlock_solver", PACKAGE = "deSolve")) + + out_raw <- .Call(call_lsoda, + as.double(odeini), # y + as.double(outtimes), # times + x$diffs_address, # derivfunc + as.double(odeparms[x$parms]), # parms + rtol, atol, + NULL, NULL, # rho, tcrit + NULL, # jacfunc + x$initpar_address, # initfunc + NULL, # eventfunc + 0L, # verbose + 1L, # iTask + as.double(rwork), # rWork + as.integer(iwork), # iWork + 2L, # jT full Jacobian calculated internally + 0L, # nOut + as.integer(lrw), # lRw + as.integer(liw), # lIw + 1L, # Solver + NULL, # rootfunc + 0L, as.double(0), 0L, # nRoot, Rpar, Ipar + 0L, # Type + list(fmat = 0L, tmat = 0L, imat = 0L, ModelForc = NULL), # flist + list(), # elist + list(islag = 0L) # elag ) + + out <- t(out_raw) + colnames(out) <- c("time", mod_vars) } else { mkindiff <- function(t, state, parms) { diff --git a/R/nlme.mmkin.R b/R/nlme.mmkin.R index 09cb84b8..e193e5e3 100644 --- a/R/nlme.mmkin.R +++ b/R/nlme.mmkin.R @@ -186,10 +186,24 @@ nlme.mmkin <- function(model, data = "auto", thisCall[["control"]] <- control } + # Provide the address of call_lsoda to the fitting function + call_lsoda <- getNativeSymbolInfo("call_lsoda", PACKAGE = "deSolve") + if (model[[1]]$solution_type == "deSolve" & !is.null(model[[1]]$mkinmod$cf)) { + # The mkinmod stored in the first fit will be used by nlme + model[[1]]$mkinmod$diffs_address <- getNativeSymbolInfo("diffs", + PACKAGE = model[[1]]$mkinmod$dll_info[["name"]])$address + model[[1]]$mkinmod$initpar_address <- getNativeSymbolInfo("initpar", + PACKAGE = model[[1]]$mkinmod$dll_info[["name"]])$address + } + fit_time <- system.time(val <- do.call("nlme.formula", thisCall)) val$time <- fit_time val$mkinmod <- model[[1]]$mkinmod + # Don't return addresses that will become invalid + val$mkinmod$diffs_address <- NULL + val$mkinmod$initpar_address <- NULL + val$data <- thisCall[["data"]] val$mmkin <- model if (is.list(start)) val$mean_dp_start <- start$fixed @@ -120,12 +120,12 @@ utils::globalVariables(c("predicted", "std")) #' summary(f_saem_dfop_sfo, data = TRUE) #' #' # The following takes about 6 minutes -#' #f_saem_dfop_sfo_deSolve <- saem(f_mmkin["DFOP-SFO", ], solution_type = "deSolve", -#' # control = list(nbiter.saemix = c(200, 80), nbdisplay = 10)) +#' f_saem_dfop_sfo_deSolve <- saem(f_mmkin["DFOP-SFO", ], solution_type = "deSolve", +#' nbiter.saemix = c(200, 80)) #' -#' #saemix::compare.saemix(list( -#' # f_saem_dfop_sfo$so, -#' # f_saem_dfop_sfo_deSolve$so)) +#' #anova( +#' # f_saem_dfop_sfo, +#' # f_saem_dfop_sfo_deSolve)) #' #' # If the model supports it, we can also use eigenvalue based solutions, which #' # take a similar amount of time @@ -580,6 +580,15 @@ saemix_model <- function(object, solution_type = "auto", transform_rates <- object[[1]]$transform_rates transform_fractions <- object[[1]]$transform_fractions + # Get native symbol info for speed + call_lsoda <- getNativeSymbolInfo("call_lsoda", PACKAGE = "deSolve") + if (solution_type == "deSolve" & !is.null(mkin_model$cf)) { + mkin_model$diffs_address <- getNativeSymbolInfo("diffs", + PACKAGE = mkin_model$dll_info[["name"]])$address + mkin_model$initpar_address <- getNativeSymbolInfo("initpar", + PACKAGE = mkin_model$dll_info[["name"]])$address + } + # Define the model function model_function <- function(psi, id, xidep) { @@ -613,7 +622,8 @@ saemix_model <- function(object, solution_type = "auto", odeparms = odeparms, odeini = odeini, solution_type = solution_type, outtimes = sort(unique(i_time)), - na_stop = FALSE + na_stop = FALSE, + call_lsoda = call_lsoda ) out_index <- cbind(as.character(i_time), as.character(i_name)) diff --git a/man/mkinmod.Rd b/man/mkinmod.Rd index 65b5de1a..77a4f520 100644 --- a/man/mkinmod.Rd +++ b/man/mkinmod.Rd @@ -58,7 +58,9 @@ applicable to give detailed information about the C function being built.} \item{dll_dir}{Directory where an DLL object, if generated internally by \code{\link[inline:cfunction]{inline::cfunction()}}, should be saved. The DLL will only be stored in a permanent location for use in future sessions, if 'dll_dir' and 'name' -are specified.} +are specified. This is helpful if fit objects are cached e.g. by knitr, +as the cache remains functional across sessions if the DLL is stored in +a user defined location.} \item{unload}{If a DLL from the target location in 'dll_dir' is already loaded, should that be unloaded first?} diff --git a/man/mkinpredict.Rd b/man/mkinpredict.Rd index 0797f259..d93c0753 100644 --- a/man/mkinpredict.Rd +++ b/man/mkinpredict.Rd @@ -18,9 +18,10 @@ mkinpredict(x, odeparms, odeini, outtimes, ...) method.ode = "lsoda", atol = 1e-08, rtol = 1e-10, - maxsteps = 20000, + maxsteps = 20000L, map_output = TRUE, na_stop = TRUE, + call_lsoda = NULL, ... ) @@ -60,23 +61,21 @@ solver is used.} \item{solution_type}{The method that should be used for producing the predictions. This should generally be "analytical" if there is only one observed variable, and usually "deSolve" in the case of several observed -variables. The third possibility "eigen" is faster but not applicable to -some models e.g. using FOMC for the parent compound.} +variables. The third possibility "eigen" is fast in comparison to uncompiled +ODE models, but not applicable to some models, e.g. using FOMC for the +parent compound.} \item{use_compiled}{If set to \code{FALSE}, no compiled version of the \link{mkinmod} model is used, even if is present.} \item{method.ode}{The solution method passed via \link{mkinpredict} to \link{ode}] in -case the solution type is "deSolve". The default "lsoda" is performant, but -sometimes fails to converge.} +case the solution type is "deSolve" and we are not using compiled code.} -\item{atol}{Absolute error tolerance, passed to \link{ode}. Default is 1e-8, -lower than in \link{lsoda}.} +\item{atol}{Absolute error tolerance, passed to the ode solver.} -\item{rtol}{Absolute error tolerance, passed to \link{ode}. Default is 1e-10, -much lower than in \link{lsoda}.} +\item{rtol}{Absolute error tolerance, passed to the ode solver.} -\item{maxsteps}{Maximum number of steps, passed to \link{ode}.} +\item{maxsteps}{Maximum number of steps, passed to the ode solver.} \item{map_output}{Boolean to specify if the output should list values for the observed variables (default) or for all state variables (if set to @@ -84,6 +83,8 @@ FALSE). Setting this to FALSE has no effect for analytical solutions, as these always return mapped output.} \item{na_stop}{Should it be an error if \link{ode} returns NaN values} + +\item{call_lsoda}{The address of the compiled function "call_lsoda"} } \value{ A matrix with the numeric solution in wide format diff --git a/man/saem.Rd b/man/saem.Rd index 984d341b..89647ff4 100644 --- a/man/saem.Rd +++ b/man/saem.Rd @@ -206,12 +206,12 @@ plot(f_saem_dfop_sfo) summary(f_saem_dfop_sfo, data = TRUE) # The following takes about 6 minutes -#f_saem_dfop_sfo_deSolve <- saem(f_mmkin["DFOP-SFO", ], solution_type = "deSolve", -# control = list(nbiter.saemix = c(200, 80), nbdisplay = 10)) +f_saem_dfop_sfo_deSolve <- saem(f_mmkin["DFOP-SFO", ], solution_type = "deSolve", + nbiter.saemix = c(200, 80)) -#saemix::compare.saemix(list( -# f_saem_dfop_sfo$so, -# f_saem_dfop_sfo_deSolve$so)) +#anova( +# f_saem_dfop_sfo, +# f_saem_dfop_sfo_deSolve)) # If the model supports it, we can also use eigenvalue based solutions, which # take a similar amount of time diff --git a/tests/testthat/test_dmta.R b/tests/testthat/test_dmta.R index c44cdac8..825c6e80 100644 --- a/tests/testthat/test_dmta.R +++ b/tests/testthat/test_dmta.R @@ -85,7 +85,8 @@ sfo_sfo3p <- mkinmod( ) dmta_sfo_sfo3p_tc <- mmkin(list("SFO-SFO3+" = sfo_sfo3p), - dmta_ds, error_model = "tc", quiet = TRUE, cores = n_cores) + dmta_ds, error_model = "tc", quiet = TRUE, + cores = n_cores) test_that("Different backends get consistent results for SFO-SFO3+, dimethenamid data", { diff --git a/vignettes/mkin.html b/vignettes/mkin.html index 9d777e47..ec3bf5da 100644 --- a/vignettes/mkin.html +++ b/vignettes/mkin.html @@ -1614,7 +1614,7 @@ div.tocify { <h1 class="title toc-ignore">Introduction to mkin</h1> <h4 class="author">Johannes Ranke</h4> -<h4 class="date">Last change 15 February 2021 (rebuilt 2023-01-05)</h4> +<h4 class="date">Last change 15 February 2021 (rebuilt 2023-02-13)</h4> </div> @@ -1661,7 +1661,7 @@ f_SFO_SFO_SFO <- mkinfit(m_SFO_SFO_SFO, d_SFO_SFO_SFO_err[[1]], quiet = TRUE) # Plot the results separately for parent and metabolites plot_sep(f_SFO_SFO_SFO, lpos = c("topright", "bottomright", "bottomright"))</code></pre> -<p><img 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" 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" /><!-- --></p> </div> <div id="background" class="section level1"> <h1>Background</h1> diff --git a/vignettes/web_only/saem_benchmarks.html b/vignettes/web_only/saem_benchmarks.html index 4875bb1b..714dc1ff 100644 --- a/vignettes/web_only/saem_benchmarks.html +++ b/vignettes/web_only/saem_benchmarks.html @@ -1599,7 +1599,7 @@ div.tocify { <h1 class="title toc-ignore">Benchmark timings for saem.mmkin</h1> <h4 class="author">Johannes Ranke</h4> -<h4 class="date">Last change 14 November 2022 (rebuilt 2022-11-14)</h4> +<h4 class="date">Last change 14 November 2022 (rebuilt 2022-11-15)</h4> </div> @@ -1781,10 +1781,20 @@ t11 <- system.time(sforb_sfo3_plus_const <- saem(three_met_sep_tc["SF <td align="left">Linux</td> <td align="left">1.2.0</td> <td align="left">3.2</td> -<td align="right">2.996</td> -<td align="right">5.207</td> -<td align="right">5.317</td> -<td align="right">5.171</td> +<td align="right">2.110</td> +<td align="right">4.632</td> +<td align="right">4.264</td> +<td align="right">4.930</td> +</tr> +<tr class="even"> +<td align="left">Ryzen 7 1700</td> +<td align="left">Linux</td> +<td align="left">1.3.0</td> +<td align="left">3.2</td> +<td align="right">2.394</td> +<td align="right">4.748</td> +<td align="right">4.883</td> +<td align="right">4.937</td> </tr> </tbody> </table> @@ -1808,10 +1818,20 @@ t11 <- system.time(sforb_sfo3_plus_const <- saem(three_met_sep_tc["SF <td align="left">Linux</td> <td align="left">1.2.0</td> <td align="left">3.2</td> -<td align="right">5.671</td> -<td align="right">7.696</td> -<td align="right">8.166</td> -<td align="right">8.168</td> +<td align="right">5.602</td> +<td align="right">7.373</td> +<td align="right">7.815</td> +<td align="right">7.831</td> +</tr> +<tr class="even"> +<td align="left">Ryzen 7 1700</td> +<td align="left">Linux</td> +<td align="left">1.3.0</td> +<td align="left">3.2</td> +<td align="right">5.622</td> +<td align="right">7.445</td> +<td align="right">8.297</td> +<td align="right">7.740</td> </tr> </tbody> </table> @@ -1836,8 +1856,16 @@ t11 <- system.time(sforb_sfo3_plus_const <- saem(three_met_sep_tc["SF <td align="left">Linux</td> <td align="left">1.2.0</td> <td align="left">3.2</td> -<td align="right">24.883</td> -<td align="right">818.157</td> +<td align="right">24.014</td> +<td align="right">749.699</td> +</tr> +<tr class="even"> +<td align="left">Ryzen 7 1700</td> +<td align="left">Linux</td> +<td align="left">1.3.0</td> +<td align="left">3.2</td> +<td align="right">24.480</td> +<td align="right">519.087</td> </tr> </tbody> </table> @@ -1861,7 +1889,14 @@ t11 <- system.time(sforb_sfo3_plus_const <- saem(three_met_sep_tc["SF <td align="left">Linux</td> <td align="left">1.2.0</td> <td align="left">3.2</td> -<td align="right">1355.036</td> +<td align="right">1249.834</td> +</tr> +<tr class="even"> +<td align="left">Ryzen 7 1700</td> +<td align="left">Linux</td> +<td align="left">1.3.0</td> +<td align="left">3.2</td> +<td align="right">944.471</td> </tr> </tbody> </table> |