如何解决与 data.table 的 fread 相关的 cmdtanr 问题
我有一个与支持 = "cmdstar" 的 brms 采样相关的问题。 我认为问题与 brms 没有直接关系 (因为我可以使用后端“rstan”进行采样)。 似乎采样创建了临时文件,它 然后认为“不存在”或“不可读”。我在 Windows 10 系统上 并怀疑某些路径、访问或编译是问题所在。 有人吗?
一些虚拟数据:
pacman::p_load(brms,tidyverse)
d <- tibble(
x = rnorm(100,mean = 0,sd = 1),y = rnorm(100,sd = 1)
)
还有一个虚拟模型:
bftest = bf(y ~ x)
b <- brm(
formula = bftest,data = d,family = gaussian,prior = c(
prior(normal(0,1),class = b),prior(normal(0,class = Intercept),prior(exponential(1),class = sigma)
),cores = 4,chains = 4,sample_prior = TRUE,backend = "cmdstanr"
)
错误信息:
Error in data.table::fread(cmd = fread_cmd,colClasses = "character",:
File 'C:\Users\95\AppData\Local\Temp\RtmpiSpQ4L\file2fc23b8677' does not exist or is non-readable. getwd()=='C:/Users/95/Documents'
会话信息:
> sessionInfo()
R version 4.0.4 (2021-02-15)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19041)
Matrix products: default
locale:
[1] LC_COLLATE=Danish_Denmark.1252
[2] LC_CTYPE=Danish_Denmark.1252
[3] LC_MONETARY=Danish_Denmark.1252
[4] LC_NUMERIC=C
[5] LC_TIME=Danish_Denmark.1252
attached base packages:
[1] stats graphics Grdevices utils datasets
[6] methods base
other attached packages:
[1] forcats_0.5.0 stringr_1.4.0 dplyr_1.0.2
[4] purrr_0.3.4 readr_1.4.0 tidyr_1.1.2
[7] tibble_3.0.5 ggplot2_3.3.3 tidyverse_1.3.0
[10] brms_2.14.4 Rcpp_1.0.5
loaded via a namespace (and not attached):
[1] minqa_1.2.4 colorspace_2.0-0
[3] ellipsis_0.3.1 ggridges_0.5.3
[5] rsconnect_0.8.16 markdown_1.1
[7] fs_1.5.0 base64enc_0.1-3
[9] rstudioapi_0.13 rstan_2.21.2
[11] DT_0.17 lubridate_1.7.9.2
[13] fansi_0.4.2 mvtnorm_1.1-1
[15] xml2_1.3.2 bridgesampling_1.0-0
[17] codetools_0.2-18 splines_4.0.4
[19] knitr_1.30 shinythemes_1.1.2
[21] bayesplot_1.7.2 projpred_2.0.2
[23] jsonlite_1.7.2 nloptr_1.2.2.2
[25] broom_0.7.3 dbplyr_2.0.0
[27] shiny_1.5.0 httr_1.4.2
[29] compiler_4.0.4 backports_1.2.0
[31] assertthat_0.2.1 Matrix_1.3-2
[33] fastmap_1.0.1 cli_2.2.0
[35] later_1.1.0.1 htmltools_0.5.0
[37] prettyunits_1.1.1 tools_4.0.4
[39] igraph_1.2.6 coda_0.19-4
[41] gtable_0.3.0 glue_1.4.2
[43] reshape2_1.4.4 tinytex_0.28
[45] V8_3.4.0 cellranger_1.1.0
[47] vctrs_0.3.6 nlme_3.1-152
[49] crosstalk_1.1.0.1 xfun_0.20
[51] ps_1.5.0 rvest_0.3.6
[53] lme4_1.1-26 mime_0.9
[55] miniui_0.1.1.1 lifecycle_0.2.0
[57] pacman_0.5.1 gtools_3.8.2
[59] statmod_1.4.35 MASS_7.3-53
[61] zoo_1.8-8 scales_1.1.1
[63] colourpicker_1.1.0 hms_0.5.3
[65] promises_1.1.1 brobdingnag_1.2-6
[67] parallel_4.0.4 inline_0.3.17
[69] shinystan_2.5.0 gamm4_0.2-6
[71] yaml_2.2.1 curl_4.3
[73] gridExtra_2.3 loo_2.4.1
[75] StanHeaders_2.21.0-7 stringi_1.5.3
[77] dygraphs_1.1.1.6 checkmate_2.0.0
[79] boot_1.3-26 pkgbuild_1.2.0
[81] cmdstanr_0.3.0 rlang_0.4.10
[83] pkgconfig_2.0.3 matrixStats_0.57.0
[85] evaluate_0.14 lattice_0.20-41
[87] rstantools_2.1.1 htmlwidgets_1.5.3
[89] processx_3.4.5 tidyselect_1.1.0
[91] plyr_1.8.6 magrittr_2.0.1
[93] R6_2.5.0 generics_0.1.0
[95] DBI_1.1.0 haven_2.3.1
[97] pillar_1.4.7 withr_2.4.0
[99] mgcv_1.8-33 xts_0.12.1
[101] abind_1.4-5 modelr_0.1.8
[103] Crayon_1.3.4 rmarkdown_2.6
[105] readxl_1.3.1 grid_4.0.4
[107] data.table_1.14.0 callr_3.5.1
[109] threejs_0.3.3 reprex_0.3.0
[111] digest_0.6.27 xtable_1.8-4
[113] httpuv_1.5.4 RcppParallel_5.0.2
[115] stats4_4.0.4 munsell_0.5.0
[117] shinyjs_2.0.0
问题的完整跟踪:
Compiling Stan program...
Running mingw32-make.exe \
"C:/Users/95/AppData/Local/Temp/RtmpiSpQ4L/model-2fc356e659e.exe" \
"STANCFLAGS += --name='file2fc33f856a3_model'"
\
--- Translating Stan model to C++ code ---
bin/stanc.exe --name='file2fc33f856a3_model' --o=C:/Users/95/AppData/Local/Temp/RtmpiSpQ4L/model-2fc356e659e.hpp C:/Users/95/AppData/Local/Temp/RtmpiSpQ4L/model-2fc356e659e.stan
-
--- Compiling,linking C++ code ---
USE_MATH_DEFInes -DBOOST_disABLE_ASSERTS -c -Wno-ignored-attributes -x c++ -o C:/Users/95/AppData/Local/Temp/RtmpiSpQ4L/model-2fc356e659e.o C:/Users/95/AppData/Local/Temp/RtmpiSpQ4L/model-2fc356e659e.hpp -Wno-ignored-attributes -I stan/lib/stan_math/lib/tbb_2019_U8/include -O3 -I src -I stan/src -I lib/rapidjson_1.1.0/ -I lib/CLI11-1.9.1/ -I stan/lib/stan_math/ -I stan/lib/stan_math/lib/eigen_3.3.9 -I stan/lib/stan_math/lib/boost_1.72.0 -I stan/lib/stan_math/lib/sundials_5.6.1/include -D_
s_5.6.1/lib/libsundials_idas.a stan/lib/stan_math/lib/sundials_5.6.1/lib/libsundials_kinsol.a stan/lib/stan_math/lib/tbb/tbb.dll -o C:/Users/95/AppData/Local/Temp/RtmpiSpQ4L/model-2fc356e659e.exext -Wno-attributes -Wno-ignored-attributes -I stan/lib/stan_math/lib/tbb_2019_U8/include -O3 -I src -I stan/src -I lib/rapidjson_1.1.0/ -I lib/CLI11-1.9.1/ -I stan/lib/stan_math/ -I stan/lib/stan_math/lib/eigen_3.3.9 -I stan/lib/stan_math/lib/boost_1.72.0 -I stan/lib/stan_math/lib/sundials_5.6.1/include -D_USE_MATH_DEFInes -DBOOST_disABLE_ASSERTS -Wl,-L,"C:/Users/95/Documents/.cmdstanr/cmdstan-2.26.1/stan/lib/stan_math/lib/tbb" -Wl,-rpath,"C:/Users/95/Documents/.cmdstanr/cmdstan-2.26.1/stan/lib/stan_math/lib/tbb" C:/Users/95/AppData/Local/Temp/RtmpiSpQ4L/model-2fc356e659e.o src/cmdstan/main.o -static-libgcc -static-libstdc++ stan/lib/stan_math/lib/sundials_5.6.1/lib/libsundials_nvecserial.a stan/lib/stan_math/lib/sundials_5.6.1/lib/libsundials_cvodes.a stan/lib/stan_math/lib/sundial
rm -f C:/Users/95/AppData/Local/Temp/RtmpiSpQ4L/model-2fc356e659e.o
Start sampling
Running MCMC with 4 parallel chains...
Running file2fc33f856a3.exe "id=1" random \
"seed=405964877" data \
"file=C:/Users/95/AppData/Local/Temp/RtmpiSpQ4L/standata-2fc642d12a7.json" \
output \
"file=C:/Users/95/AppData/Local/Temp/RtmpiSpQ4L/file2fc33f856a3-202103111527-1-3b1791.csv" \
"method=sample" "num_samples=1000" "num_warmup=1000" \
"save_warmup=0" "thin=1" "algorithm=hmc" \
"engine=nuts" adapt "engaged=1"
Running file2fc33f856a3.exe "id=2" random \
"seed=1689045397" data \
"file=C:/Users/95/AppData/Local/Temp/RtmpiSpQ4L/standata-2fc642d12a7.json" \
output \
"file=C:/Users/95/AppData/Local/Temp/RtmpiSpQ4L/file2fc33f856a3-202103111527-2-3b1791.csv" \
"method=sample" "num_samples=1000" "num_warmup=1000" \
"save_warmup=0" "thin=1" "algorithm=hmc" \
"engine=nuts" adapt "engaged=1"
Running file2fc33f856a3.exe "id=3" random \
"seed=1064312444" data \
"file=C:/Users/95/AppData/Local/Temp/RtmpiSpQ4L/standata-2fc642d12a7.json" \
output \
"file=C:/Users/95/AppData/Local/Temp/RtmpiSpQ4L/file2fc33f856a3-202103111527-3-3b1791.csv" \
"method=sample" "num_samples=1000" "num_warmup=1000" \
"save_warmup=0" "thin=1" "algorithm=hmc" \
"engine=nuts" adapt "engaged=1"
Running file2fc33f856a3.exe "id=4" random \
"seed=2019191347" data \
"file=C:/Users/95/AppData/Local/Temp/RtmpiSpQ4L/standata-2fc642d12a7.json" \
output \
"file=C:/Users/95/AppData/Local/Temp/RtmpiSpQ4L/file2fc33f856a3-202103111527-4-3b1791.csv" \
"method=sample" "num_samples=1000" "num_warmup=1000" \
"save_warmup=0" "thin=1" "algorithm=hmc" \
"engine=nuts" adapt "engaged=1"
Chain 1 method = sample (Default)
Chain 1 sample
Chain 1 num_samples = 1000 (Default)
Chain 1 num_warmup = 1000 (Default)
Chain 1 save_warmup = 0 (Default)
Chain 1 thin = 1 (Default)
Chain 1 adapt
Chain 1 engaged = 1 (Default)
Chain 1 gamma = 0.050000000000000003 (Default)
Chain 1 delta = 0.80000000000000004 (Default)
Chain 1 kappa = 0.75 (Default)
Chain 1 t0 = 10 (Default)
Chain 1 init_buffer = 75 (Default)
Chain 1 term_buffer = 50 (Default)
Chain 1 window = 25 (Default)
Chain 1 algorithm = hmc (Default)
Chain 1 hmc
Chain 1 engine = nuts (Default)
Chain 1 nuts
Chain 1 max_depth = 10 (Default)
Chain 1 metric = diag_e (Default)
Chain 1 metric_file = (Default)
Chain 1 stepsize = 1 (Default)
Chain 1 stepsize_jitter = 0 (Default)
Chain 1 id = 1
Chain 1 data
Chain 1 file = C:/Users/95/AppData/Local/Temp/RtmpiSpQ4L/standata-2fc642d12a7.json
Chain 1 init = 2 (Default)
Chain 1 random
Chain 1 seed = 405964877
Chain 1 output
Chain 1 file = C:/Users/95/AppData/Local/Temp/RtmpiSpQ4L/file2fc33f856a3-202103111527-1-3b1791.csv
Chain 1 diagnostic_file = (Default)
Chain 1 refresh = 100 (Default)
Chain 1 sig_figs = -1 (Default)
Chain 1 profile_file = profile.csv (Default)
Chain 1 Gradient evaluation took 7.8e-005 seconds
Chain 1 1000 transitions using 10 leapfrog steps per transition would take 0.78 seconds.
Chain 1 Adjust your expectations accordingly!
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Chain 2 method = sample (Default)
Chain 2 sample
Chain 2 num_samples = 1000 (Default)
Chain 2 num_warmup = 1000 (Default)
Chain 2 save_warmup = 0 (Default)
Chain 2 thin = 1 (Default)
Chain 2 adapt
Chain 2 engaged = 1 (Default)
Chain 2 gamma = 0.050000000000000003 (Default)
Chain 2 delta = 0.80000000000000004 (Default)
Chain 2 kappa = 0.75 (Default)
Chain 2 t0 = 10 (Default)
Chain 2 init_buffer = 75 (Default)
Chain 2 term_buffer = 50 (Default)
Chain 2 window = 25 (Default)
Chain 2 algorithm = hmc (Default)
Chain 2 hmc
Chain 2 engine = nuts (Default)
Chain 2 nuts
Chain 2 max_depth = 10 (Default)
Chain 2 metric = diag_e (Default)
Chain 2 metric_file = (Default)
Chain 2 stepsize = 1 (Default)
Chain 2 stepsize_jitter = 0 (Default)
Chain 2 id = 2
Chain 2 data
Chain 2 file = C:/Users/95/AppData/Local/Temp/RtmpiSpQ4L/standata-2fc642d12a7.json
Chain 2 init = 2 (Default)
Chain 2 random
Chain 2 seed = 1689045397
Chain 2 output
Chain 2 file = C:/Users/95/AppData/Local/Temp/RtmpiSpQ4L/file2fc33f856a3-202103111527-2-3b1791.csv
Chain 2 diagnostic_file = (Default)
Chain 2 refresh = 100 (Default)
Chain 2 sig_figs = -1 (Default)
Chain 2 profile_file = profile.csv (Default)
Chain 2 Gradient evaluation took 3.3e-005 seconds
Chain 2 1000 transitions using 10 leapfrog steps per transition would take 0.33 seconds.
Chain 2 Adjust your expectations accordingly!
Chain 2 Iteration: 1 / 2000 [ 0%] (Warmup)
Chain 2 Iteration: 100 / 2000 [ 5%] (Warmup)
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Chain 3 method = sample (Default)
Chain 3 sample
Chain 3 num_samples = 1000 (Default)
Chain 3 num_warmup = 1000 (Default)
Chain 3 save_warmup = 0 (Default)
Chain 3 thin = 1 (Default)
Chain 3 adapt
Chain 3 engaged = 1 (Default)
Chain 3 gamma = 0.050000000000000003 (Default)
Chain 3 delta = 0.80000000000000004 (Default)
Chain 3 kappa = 0.75 (Default)
Chain 3 t0 = 10 (Default)
Chain 3 init_buffer = 75 (Default)
Chain 3 term_buffer = 50 (Default)
Chain 3 window = 25 (Default)
Chain 3 algorithm = hmc (Default)
Chain 3 hmc
Chain 3 engine = nuts (Default)
Chain 3 nuts
Chain 3 max_depth = 10 (Default)
Chain 3 metric = diag_e (Default)
Chain 3 metric_file = (Default)
Chain 3 stepsize = 1 (Default)
Chain 3 stepsize_jitter = 0 (Default)
Chain 3 id = 3
Chain 3 data
Chain 3 file = C:/Users/95/AppData/Local/Temp/RtmpiSpQ4L/standata-2fc642d12a7.json
Chain 3 init = 2 (Default)
Chain 3 random
Chain 3 seed = 1064312444
Chain 3 output
Chain 3 file = C:/Users/95/AppData/Local/Temp/RtmpiSpQ4L/file2fc33f856a3-202103111527-3-3b1791.csv
Chain 3 diagnostic_file = (Default)
Chain 3 refresh = 100 (Default)
Chain 3 sig_figs = -1 (Default)
Chain 3 profile_file = profile.csv (Default)
Chain 3 Gradient evaluation took 3.1e-005 seconds
Chain 3 1000 transitions using 10 leapfrog steps per transition would take 0.31 seconds.
Chain 3 Adjust your expectations accordingly!
Chain 3 Iteration: 1 / 2000 [ 0%] (Warmup)
Chain 3 Iteration: 100 / 2000 [ 5%] (Warmup)
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Chain 4 method = sample (Default)
Chain 4 sample
Chain 4 num_samples = 1000 (Default)
Chain 4 num_warmup = 1000 (Default)
Chain 4 save_warmup = 0 (Default)
Chain 4 thin = 1 (Default)
Chain 4 adapt
Chain 4 engaged = 1 (Default)
Chain 4 gamma = 0.050000000000000003 (Default)
Chain 4 delta = 0.80000000000000004 (Default)
Chain 4 kappa = 0.75 (Default)
Chain 4 t0 = 10 (Default)
Chain 4 init_buffer = 75 (Default)
Chain 4 term_buffer = 50 (Default)
Chain 4 window = 25 (Default)
Chain 4 algorithm = hmc (Default)
Chain 4 hmc
Chain 4 engine = nuts (Default)
Chain 4 nuts
Chain 4 max_depth = 10 (Default)
Chain 4 metric = diag_e (Default)
Chain 4 metric_file = (Default)
Chain 4 stepsize = 1 (Default)
Chain 4 stepsize_jitter = 0 (Default)
Chain 4 id = 4
Chain 4 data
Chain 4 file = C:/Users/95/AppData/Local/Temp/RtmpiSpQ4L/standata-2fc642d12a7.json
Chain 4 init = 2 (Default)
Chain 4 random
Chain 4 seed = 2019191347
Chain 4 output
Chain 4 file = C:/Users/95/AppData/Local/Temp/RtmpiSpQ4L/file2fc33f856a3-202103111527-4-3b1791.csv
Chain 4 diagnostic_file = (Default)
Chain 4 refresh = 100 (Default)
Chain 4 sig_figs = -1 (Default)
Chain 4 profile_file = profile.csv (Default)
Chain 4 Gradient evaluation took 2.8e-005 seconds
Chain 4 1000 transitions using 10 leapfrog steps per transition would take 0.28 seconds.
Chain 4 Adjust your expectations accordingly!
Chain 4 Iteration: 1 / 2000 [ 0%] (Warmup)
Chain 4 Iteration: 100 / 2000 [ 5%] (Warmup)
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Chain 1 Elapsed Time: 0.047 seconds (warm-up)
Chain 1 0.071 seconds (Sampling)
Chain 1 0.118 seconds (Total)
Chain 1 finished in 0.1 seconds.
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Chain 2 Elapsed Time: 0.046 seconds (warm-up)
Chain 2 0.075 seconds (Sampling)
Chain 2 0.121 seconds (Total)
Chain 2 finished in 0.1 seconds.
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Chain 3 Elapsed Time: 0.032 seconds (warm-up)
Chain 3 0.103 seconds (Sampling)
Chain 3 0.135 seconds (Total)
Chain 3 finished in 0.1 seconds.
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Chain 4 Elapsed Time: 0.049 seconds (warm-up)
Chain 4 0.099 seconds (Sampling)
Chain 4 0.148 seconds (Total)
Chain 4 finished in 0.1 seconds.
All 4 chains finished successfully.
Mean chain execution time: 0.1 seconds.
Total execution time: 0.5 seconds.
Error in data.table::fread(cmd = fread_cmd,:
File 'C:\Users\95\AppData\Local\Temp\RtmpiSpQ4L\file2fc23b8677' does not exist or is non-readable. getwd()=='C:/Users/95/Documents'
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